diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..736e9a1aeb22111024df53011ba612ae8e36993c --- /dev/null +++ b/LICENSE @@ -0,0 +1,57 @@ +License +Data & Software Copyright License for non-commercial scientific research purposes +Please read carefully the following terms and conditions and any accompanying documentation before you download and/or use the DECO data, models and software, (the "Data & Software"), including DECO baseline models, 3D meshes, MANO/SMPL-X parameters, images, MoCap data, videos, software, and scripts. By downloading and/or using the Data & Software (including downloading, cloning, installing, and any other use of the corresponding GitHub repository), you acknowledge that you have read these terms and conditions, understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not download and/or use the Data & Software. 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For the avoidance of doubt Licensor shall be liable in accordance with the German Product Liability Act in the event of product liability. The foregoing applies also to Licensor’s legal representatives or assistants in performance. Any further liability shall be excluded. +Patent claims generated through the usage of the Data & Software cannot be directed towards the copyright holders. +The Data & Software is provided in the state of development the licensor defines. If modified or extended by Licensee, the Licensor makes no claims about the fitness of the Data & Software and is not responsible for any problems such modifications cause. + +No Maintenance Services +You understand and agree that Licensor is under no obligation to provide either maintenance services, update services, notices of latent defects, or corrections of defects with regard to the Data & Software. Licensor nevertheless reserves the right to update, modify, or discontinue the Data & Software at any time. + +Defects of the Data & Software must be notified in writing to the Licensor with a comprehensible description of the error symptoms. The notification of the defect should enable the reproduction of the error. The Licensee is encouraged to communicate any use, results, modification, or publication. + +Publications using the Data & Software +You acknowledge that the Data & Software is a valuable scientific resource and agree to appropriately reference the following paper in any publication making use of the Data & Software. + +Subjects' Consent: All subjects gave informed written consent to share their data for research purposes. You further agree to delete data or change their use, in case a subject changes or withdraws their consent. + +Citation: + +@InProceedings{Tripathi_2023_ICCV, + author = {Tripathi, Shashank and Chatterjee, Agniv and Passy, Jean-Claude and Yi, Hongwei and Tzionas, Dimitrios and Black, Michael J.}, + title = {DECO: Dense Estimation of 3D Human-Scene Contact In The Wild}, + booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, + month = {October}, + year = {2023}, + pages = {8001-8013} +} + +Commercial licensing opportunities +For commercial uses of the Data & Software, please send emails to ps-license@tue.mpg.de + +This Agreement shall be governed by the laws of the Federal Republic of Germany except for the UN Sales Convention. \ No newline at end of file diff --git a/README.md b/README.md index 4d8966b4042656f7df315e938a7f598d6e3a5129..429ea7fe7f0b5ef29fce3a051e2839cdca2faac0 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,145 @@ ---- -title: DECO -emoji: 🏢 -colorFrom: blue -colorTo: pink -sdk: gradio -sdk_version: 3.47.1 -app_file: app.py -pinned: false ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference +# DECO: Dense Estimation of 3D Human-Scene Contact in the Wild [ICCV 2023 (Oral)] + +> Code repository for the paper: +> [**DECO: Dense Estimation of 3D Human-Scene Contact in the Wild**](https://openaccess.thecvf.com/content/ICCV2023/html/Tripathi_DECO_Dense_Estimation_of_3D_Human-Scene_Contact_In_The_Wild_ICCV_2023_paper.html) +> [Shashank Tripathi](https://sha2nkt.github.io/), [Agniv Chatterjee](https://ac5113.github.io/), [Jean-Claude Passy](https://is.mpg.de/person/jpassy), [Hongwei Yi](https://xyyhw.top/), [Dimitrios Tzionas](https://ps.is.mpg.de/person/dtzionas), [Michael J. Black](https://ps.is.mpg.de/person/black)
+> *IEEE International Conference on Computer Vision (ICCV), 2023* + +[![arXiv](https://img.shields.io/badge/arXiv-2309.15273-00ff00.svg)](https://arxiv.org/abs/2309.15273) [![Website shields.io](https://img.shields.io/website-up-down-green-red/http/shields.io.svg)](https://deco.is.tue.mpg.de/) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)]() [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)]() + +![teaser](assets/teaser.png) + +[[Project Page](https://deco.is.tue.mpg.de)] [[Paper](https://arxiv.org/abs/2309.15273)] [[Video](https://www.youtube.com/watch?v=o7MLobqAFTQ)] [[Poster](https://www.dropbox.com/scl/fi/kvhpfnkvga2pt19ayko8u/ICCV2023_DECO_Poster_v2.pptx?rlkey=ihbf3fi6u9j0ha9x1gfk2cwd0&dl=0)] [[License](https://deco.is.tue.mpg.de/license.html)] [[Contact](mailto:deco@tue.mpg.de)] + +## Installation and Setup +1. First, clone the repo. Then, we recommend creating a clean [conda](https://docs.conda.io/) environment, activating it and installing torch and torchvision, as follows: +```shell +git clone https://github.com/sha2nkt/deco.git +cd deco +conda create -n deco python=3.9 -y +conda activate deco +pip install torch==1.13.0+cu117 torchvision==0.14.0+cu117 --extra-index-url https://download.pytorch.org/whl/cu117 +``` +Please adjust the CUDA version as required. + +2. Install PyTorch3D from source. Users may also refer to [PyTorch3D-install](https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md) for more details. +However, our tests show that installing using ``conda`` sometimes runs into dependency conflicts. +Hence, users may alternatively install Pytorch3D from source following the steps below. +```shell +git clone https://github.com/facebookresearch/pytorch3d.git +cd pytorch3d +pip install . +cd .. +``` + +3. Install the other dependancies and download the required data. +```bash +pip install -r requirements.txt +sh fetch_data.sh +``` + +4. Please download [SMPL](https://smpl.is.tue.mpg.de/) (version 1.1.0) and [SMPL-X](https://smpl-x.is.tue.mpg.de/) (v1.1) files into the data folder. Please rename the SMPL files to ```SMPL_FEMALE.pkl```, ```SMPL_MALE.pkl``` and ```SMPL_NEUTRAL.pkl```. The directory structure for the ```data``` folder has been elaborated below: + +``` +├── preprocess +├── smpl +│ ├── SMPL_FEMALE.pkl +│ ├── SMPL_MALE.pkl +│ ├── SMPL_NEUTRAL.pkl +│ ├── smpl_neutral_geodesic_dist.npy +│ ├── smpl_neutral_tpose.ply +│ ├── smplpix_vertex_colors.npy +├── smplx +│ ├── SMPLX_FEMALE.npz +│ ├── SMPLX_FEMALE.pkl +│ ├── SMPLX_MALE.npz +│ ├── SMPLX_MALE.pkl +│ ├── SMPLX_NEUTRAL.npz +│ ├── SMPLX_NEUTRAL.pkl +│ ├── smplx_neutral_tpose.ply +├── weights +│ ├── pose_hrnet_w32_256x192.pth +├── J_regressor_extra.npy +├── base_dataset.py +├── mixed_dataset.py +├── smpl_partSegmentation_mapping.pkl +├── smpl_vert_segmentation.json +└── smplx_vert_segmentation.json +``` + +## Run demo on images +The following command will run DECO on all images in the specified `--img_src`, and save rendering and colored mesh in `--out_dir`. The `--model_path` flag is used to specify the specific checkpoint being used. Additionally, the base mesh color and the color of predicted contact annotation can be specified using the `--mesh_colour` and `--annot_colour` flags respectively. +```bash +python inference.py \ + --img_src example_images \ + --out_dir demo_out +``` + +## Training and Evaluation + +We release 3 versions of the DECO model: +
    +
  1. DECO-HRNet ( Best performing model )
    2. +
  2. DECO-HRNet w/o context branches
    3. +
  3. DECO-Swin
    4. +
+ +All the checkpoints have been downloaded to ```checkpoints```. +However, please note that versions 2 and 3 have been trained solely on the RICH dataset.
+We recommend using the first DECO version. + +The dataset npz files have been downloaded to ```datasets/Release_Datasets```. Please download the actual DAMON data and place them in ```datasets``` following the instructions given. + +### Evaluation +To run evaluation on the DAMON dataset, please run the following command: + +```bash +python tester.py --cfg configs/cfg_test.yml +``` + +### Training +The config provided (```cfg_train.yml```) is set to train and evaluate on all three datasets: DAMON, RICH and PROX. To change this, please change the value of the key ```TRAINING.DATASETS``` and ```VALIDATION.DATASETS``` in the config (please also change ```TRAINING.DATASET_MIX_PDF``` as required).
+Also, the best checkpoint is stored by default at ```checkpoints/Other_Checkpoints```. +Please run the following command to start training of the DECO model: + +```bash +python train.py --cfg configs/cfg_train.yml +``` + +### Training on custom datasets + +To train on other datasets, please follow these steps: +1. Please create an npz of the dataset, following the structure of the datasets in ```datasets/Release_Datasets``` with the corresponding keys and values. +2. Please create scene segmentation maps, if not available. We have used [Mask2Former](https://github.com/facebookresearch/Mask2Former) in our work. +3. For creating the part segmentation maps, this [sample script](https://github.com/sha2nkt/deco/blob/main/scripts/datascripts/get_part_seg_mask.py) can be referred to. +4. Add the dataset name(s) to ```train.py``` ([these lines](https://github.com/sha2nkt/deco/blob/d5233ecfad1f51b71a50a78c0751420067e82c02/train.py#L83)), ```tester.py``` ([these lines](https://github.com/sha2nkt/deco/blob/d5233ecfad1f51b71a50a78c0751420067e82c02/tester.py#L51)) and ```data/mixed_dataset.py``` ([these lines](https://github.com/sha2nkt/deco/blob/d5233ecfad1f51b71a50a78c0751420067e82c02/data/mixed_dataset.py#L17)), according to the body model being used (SMPL/SMPL-X) +5. Add the path(s) to the dataset npz(s) to ```common/constants.py``` ([these lines](https://github.com/sha2nkt/deco/blob/d5233ecfad1f51b71a50a78c0751420067e82c02/common/constants.py#L19)). +6. Finally, change ```TRAINING.DATASETS``` and ```VALIDATION.DATASETS``` in the config file and you're good to go! + +## Citing +If you find this code useful for your research, please consider citing the following paper: + +```bibtex +@InProceedings{Tripathi_2023_ICCV, + author = {Tripathi, Shashank and Chatterjee, Agniv and Passy, Jean-Claude and Yi, Hongwei and Tzionas, Dimitrios and Black, Michael J.}, + title = {DECO: Dense Estimation of 3D Human-Scene Contact In The Wild}, + booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, + month = {October}, + year = {2023}, + pages = {8001-8013} +} +``` + +### License + +See [LICENSE](LICENSE). + +### Acknowledgments + +We sincerely thank Alpar Cseke for his contributions to DAMON data collection and PHOSA evaluations, Sai K. Dwivedi for facilitating PROX downstream experiments, Xianghui Xie for his generous help with CHORE evaluations, Lea Muller for her help in initiating the contact annotation tool, Chun-Hao P. Huang for RICH discussions and Yixin Chen for details about the HOT paper. We are grateful to Mengqin Xue and Zhenyu Lou for their collaboration in BEHAVE evaluations, Joachim Tesch and Nikos Athanasiou for insightful visualization advice, and Tsvetelina Alexiadis for valuable data collection guidance. Their invaluable contributions enriched this research significantly. We also thank Benjamin Pellkofer for help with the website and IT support. This work was funded by the International Max Planck Research School for Intelligent Systems (IMPRS-IS). + +### Contact + +For technical questions, please create an issue. For other questions, please contact `deco@tue.mpg.de`. + +For commercial licensing, please contact `ps-licensing@tue.mpg.de`. diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..a699bc5b3c2e987102ca93e0ee28d601e0a93d02 --- /dev/null +++ b/app.py @@ -0,0 +1,7 @@ +import gradio as gr + +def greet(name): + return "Hello " + name + "!!" + +iface = gr.Interface(fn=greet, inputs="text", outputs="text") +iface.launch() \ No newline at end of file diff --git a/assets/overview.png b/assets/overview.png new file mode 100644 index 0000000000000000000000000000000000000000..8522c66382a3a1d863dde1d825881e660af6755e Binary files /dev/null and b/assets/overview.png differ diff --git a/assets/teaser.png b/assets/teaser.png new file mode 100644 index 0000000000000000000000000000000000000000..fc1ee55060a95bf940df736cacc351ae559a65bd Binary files /dev/null and b/assets/teaser.png differ diff --git a/common/constants.py b/common/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..265f9f2fe6dc2e20cce8d08e1c77f74d86393c76 --- /dev/null +++ b/common/constants.py @@ -0,0 +1,35 @@ +from os.path import join + +DIST_MATRIX_PATH = 'data/smpl/smpl_neutral_geodesic_dist.npy' +SMPL_MEAN_PARAMS = 'data/smpl_mean_params.npz' +SMPL_MODEL_DIR = 'data/smpl/' +SMPLX_MODEL_DIR = 'data/smplx/' + +N_PARTS = 24 + +# Mean and standard deviation for normalizing input image +IMG_NORM_MEAN = [0.485, 0.456, 0.406] +IMG_NORM_STD = [0.229, 0.224, 0.225] + +# Output folder to save test/train npz files +DATASET_NPZ_PATH = 'datasets/Release_Datasets' +CONTACT_MAPPING_PATH = 'data/conversions' + +# Path to test/train npz files +DATASET_FILES = { + 'train': { + 'damon': join(DATASET_NPZ_PATH, 'damon/hot_dca_trainval.npz'), + 'rich': join(DATASET_NPZ_PATH, 'rich/rich_train_smplx_cropped_bmp.npz'), + 'prox': join(DATASET_NPZ_PATH, 'prox/prox_train_smplx_ds4.npz'), + }, + 'val': { + 'damon': join(DATASET_NPZ_PATH, 'damon/hot_dca_test.npz'), + 'rich': join(DATASET_NPZ_PATH, 'rich/rich_test_smplx_cropped_bmp.npz'), + 'prox': join(DATASET_NPZ_PATH, 'prox/prox_val_smplx_ds4.npz'), + }, + 'test': { + 'damon': join(DATASET_NPZ_PATH, 'damon/hot_dca_test.npz'), + 'rich': join(DATASET_NPZ_PATH, 'rich/rich_test_smplx_cropped_bmp.npz'), + 'prox': join(DATASET_NPZ_PATH, 'prox/prox_val_smplx_ds4.npz'), + }, +} \ No newline at end of file diff --git a/common/renderer_pyrd.py b/common/renderer_pyrd.py new file mode 100644 index 0000000000000000000000000000000000000000..166b175848bee97d9c79fe7c5cb6f880c1b19a1a --- /dev/null +++ b/common/renderer_pyrd.py @@ -0,0 +1,112 @@ +# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. + +# This program is free software; you can redistribute it and/or modify it +# under the terms of the MIT license. + +# This program is distributed in the hope that it will be useful, but WITHOUT ANY +# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +# PARTICULAR PURPOSE. See the MIT License for more details. + +import os +import trimesh +import pyrender +import numpy as np +import colorsys +import cv2 + + +class Renderer(object): + + def __init__(self, focal_length=600, img_w=512, img_h=512, faces=None, + same_mesh_color=False): + os.environ['PYOPENGL_PLATFORM'] = 'egl' + self.renderer = pyrender.OffscreenRenderer(viewport_width=img_w, + viewport_height=img_h, + point_size=1.0) + self.camera_center = [img_w // 2, img_h // 2] + self.focal_length = focal_length + self.faces = faces + self.same_mesh_color = same_mesh_color + + def render_front_view(self, verts, bg_img_rgb=None, bg_color=(0, 0, 0, 0), vertex_colors=None, render_part_seg=False, part_label_bins=None): + # Create a scene for each image and render all meshes + scene = pyrender.Scene(bg_color=bg_color, ambient_light=np.ones(3) * (1 if render_part_seg else 0)) + # Create camera. Camera will always be at [0,0,0] + camera = pyrender.camera.IntrinsicsCamera(fx=self.focal_length, fy=self.focal_length, + cx=self.camera_center[0], cy=self.camera_center[1]) + scene.add(camera, pose=np.eye(4)) + + # Create light source + if not render_part_seg: + light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=3.0) + # for DirectionalLight, only rotation matters + light_pose = trimesh.transformations.rotation_matrix(np.radians(-45), [1, 0, 0]) + scene.add(light, pose=light_pose) + light_pose = trimesh.transformations.rotation_matrix(np.radians(45), [0, 1, 0]) + scene.add(light, pose=light_pose) + + # Need to flip x-axis + rot = trimesh.transformations.rotation_matrix(np.radians(180), [1, 0, 0]) + # multiple person + num_people = len(verts) + + # for every person in the scene + for n in range(num_people): + mesh = trimesh.Trimesh(verts[n], self.faces, process=False) + mesh.apply_transform(rot) + if self.same_mesh_color: + mesh_color = colorsys.hsv_to_rgb(0.6, 0.5, 1.0) + else: + mesh_color = colorsys.hsv_to_rgb(float(n) / num_people, 0.5, 1.0) + material = pyrender.MetallicRoughnessMaterial( + metallicFactor=1.0, + alphaMode='OPAQUE', + baseColorFactor=mesh_color) + + if vertex_colors is not None: + # color individual vertices based on part labels + mesh.visual.vertex_colors = vertex_colors + mesh = pyrender.Mesh.from_trimesh(mesh, material=material, wireframe=False) + scene.add(mesh, 'mesh') + + # Alpha channel was not working previously, need to check again + # Until this is fixed use hack with depth image to get the opacity + color_rgba, depth_map = self.renderer.render(scene, flags=pyrender.RenderFlags.RGBA) + color_rgb = color_rgba[:, :, :3] + + if render_part_seg: + body_parts = color_rgb.copy() + # make single channel + body_parts = body_parts.max(-1) # reduce to single channel + # convert pixel value to bucket indices + # body_parts = torch.bucketize(body_parts, self.part_label_bins, right=True) + body_parts = np.digitize(body_parts, part_label_bins, right=True) + # part labels start from 2 because of the binning scheme. Subtract 1 from all non-zero labels to make label + # go from 1 to 24. 0 is background + # handle background coinciding with hip label = 0 + body_parts = body_parts + 1 + mask = depth_map > 0 + body_parts = body_parts * mask + return body_parts, color_rgb + + if bg_img_rgb is None: + return color_rgb + else: + mask = depth_map > 0 + bg_img_rgb[mask] = color_rgb[mask] + return bg_img_rgb + + def render_side_view(self, verts): + centroid = verts.mean(axis=(0, 1)) # n*6890*3 -> 3 + # make the centroid at the image center (the X and Y coordinates are zeros) + centroid[:2] = 0 + aroundy = cv2.Rodrigues(np.array([0, np.radians(90.), 0]))[0][np.newaxis, ...] # 1*3*3 + pred_vert_arr_side = np.matmul((verts - centroid), aroundy) + centroid + side_view = self.render_front_view(pred_vert_arr_side) + return side_view + + def delete(self): + """ + Need to delete before creating the renderer next time + """ + self.renderer.delete() diff --git a/configs/cfg_test.yml b/configs/cfg_test.yml new file mode 100644 index 0000000000000000000000000000000000000000..1fe3935413eb454a75d6e589b5bad26cab09bdb8 --- /dev/null +++ b/configs/cfg_test.yml @@ -0,0 +1,28 @@ +EXP_NAME: 'damon_hrnet_testing' +PROJECT_NAME: 'DECO_DAMON_Testing' +OUTPUT_DIR: 'deco_results' +CONDOR_DIR: '' +DATASET: + BATCH_SIZE: 16 + NUM_WORKERS: 4 + NORMALIZE_IMAGES: [True] +OPTIMIZER: + TYPE: 'adam' + LR: [5e-5] + NUM_UPDATE_LR: 3 +TRAINING: + ENCODER: 'hrnet' + CONTEXT: [True] + NUM_EPOCHS: 1 + NUM_EARLY_STOP: 10 + SUMMARY_STEPS: 5 + CHECKPOINT_EPOCHS: 5 + DATASETS: ['damon'] + DATASET_MIX_PDF: ['1.'] + DATASET_ROOT_PATH: '' + BEST_MODEL_PATH: './checkpoints/Release_Checkpoint/deco_best.pth' + PAL_LOSS_WEIGHTS: 0.0 +VALIDATION: + SUMMARY_STEPS: 1000 + DATASETS: ['damon'] + MAIN_DATASET: 'damon' \ No newline at end of file diff --git a/configs/cfg_train.yml b/configs/cfg_train.yml new file mode 100644 index 0000000000000000000000000000000000000000..a920077ec4593b16e3180bd430ef2b92ba5301e3 --- /dev/null +++ b/configs/cfg_train.yml @@ -0,0 +1,29 @@ +EXP_NAME: 'demo_train' +PROJECT_NAME: 'DECO_demo_training' +OUTPUT_DIR: 'deco_results' +CONDOR_DIR: '' +DATASET: + BATCH_SIZE: 4 + NUM_WORKERS: 8 + NORMALIZE_IMAGES: [True] +OPTIMIZER: + TYPE: 'adam' + LR: [1e-5] + NUM_UPDATE_LR: 3 +TRAINING: + ENCODER: 'hrnet' + CONTEXT: [True] + NUM_EPOCHS: 100 + NUM_EARLY_STOP: 10 + SUMMARY_STEPS: 5 + CHECKPOINT_EPOCHS: 5 + DATASETS: ['damon', 'rich', 'prox'] + DATASET_MIX_PDF: ['0.4', '0.3', '0.3'] # should sum to 1.0 unless you want to weight by dataset size + DATASET_ROOT_PATH: '' + BEST_MODEL_PATH: './checkpoints/Other_Checkpoints/demo_train.pth' + LOSS_WEIGHTS: 1. + PAL_LOSS_WEIGHTS: 0.01 +VALIDATION: + SUMMARY_STEPS: 5 + DATASETS: ['damon', 'rich', 'prox'] + MAIN_DATASET: 'damon' \ No newline at end of file diff --git a/data/base_dataset.py b/data/base_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..9426b32b1a1e256bca2dcf2be5aee9376e66b151 --- /dev/null +++ b/data/base_dataset.py @@ -0,0 +1,164 @@ +import torch +import cv2 +import numpy as np +from torch.utils.data import Dataset +from torchvision.transforms import Normalize +from common import constants + +def mask_split(img, num_parts): + if not len(img.shape) == 2: + img = img[:, :, 0] + mask = np.zeros((img.shape[0], img.shape[1], num_parts)) + for i in np.unique(img): + mask[:, :, i] = np.where(img == i, 1., 0.) + return np.transpose(mask, (2, 0, 1)) + +class BaseDataset(Dataset): + + def __init__(self, dataset, mode, model_type='smpl', normalize=False): + self.dataset = dataset + self.mode = mode + + print(f'Loading dataset: {constants.DATASET_FILES[mode][dataset]} for mode: {mode}') + + self.data = np.load(constants.DATASET_FILES[mode][dataset], allow_pickle=True) + + self.images = self.data['imgname'] + + # get 3d contact labels, if available + try: + self.contact_labels_3d = self.data['contact_label'] + # make a has_contact_3d numpy array which contains 1 if contact labels are no empty and 0 otherwise + self.has_contact_3d = np.array([1 if len(x) > 0 else 0 for x in self.contact_labels_3d]) + except KeyError: + self.has_contact_3d = np.zeros(len(self.images)) + + # get 2d polygon contact labels, if available + try: + self.polygon_contacts_2d = self.data['polygon_2d_contact'] + self.has_polygon_contact_2d = np.ones(len(self.images)) + except KeyError: + self.has_polygon_contact_2d = np.zeros(len(self.images)) + + # Get camera parameters - only intrinsics for now + try: + self.cam_k = self.data['cam_k'] + except KeyError: + self.cam_k = np.zeros((len(self.images), 3, 3)) + + self.sem_masks = self.data['scene_seg'] + self.part_masks = self.data['part_seg'] + + # Get gt SMPL parameters, if available + try: + self.pose = self.data['pose'].astype(float) + self.betas = self.data['shape'].astype(float) + self.transl = self.data['transl'].astype(float) + if 'has_smpl' in self.data: + self.has_smpl = self.data['has_smpl'] + else: + self.has_smpl = np.ones(len(self.images)) + self.is_smplx = np.ones(len(self.images)) if model_type == 'smplx' else np.zeros(len(self.images)) + except KeyError: + self.has_smpl = np.zeros(len(self.images)) + self.is_smplx = np.zeros(len(self.images)) + + if model_type == 'smpl': + self.n_vertices = 6890 + elif model_type == 'smplx': + self.n_vertices = 10475 + else: + raise NotImplementedError + + self.normalize = normalize + self.normalize_img = Normalize(mean=constants.IMG_NORM_MEAN, std=constants.IMG_NORM_STD) + + def __getitem__(self, index): + item = {} + + # Load image + img_path = self.images[index] + try: + img = cv2.imread(img_path) + img_h, img_w, _ = img.shape + img = cv2.resize(img, (256, 256), cv2.INTER_CUBIC) + img = img.transpose(2, 0, 1) / 255.0 + except: + print('Img: ', img_path) + + img_scale_factor = np.array([256 / img_w, 256 / img_h]) + + # Get SMPL parameters, if available + if self.has_smpl[index]: + pose = self.pose[index].copy() + betas = self.betas[index].copy() + transl = self.transl[index].copy() + else: + pose = np.zeros(72) + betas = np.zeros(10) + transl = np.zeros(3) + + # Load vertex_contact + if self.has_contact_3d[index]: + contact_label_3d = self.contact_labels_3d[index] + else: + contact_label_3d = np.zeros(self.n_vertices) + + sem_mask_path = self.sem_masks[index] + try: + sem_mask = cv2.imread(sem_mask_path) + sem_mask = cv2.resize(sem_mask, (256, 256), cv2.INTER_CUBIC) + sem_mask = mask_split(sem_mask, 133) + except: + print('Scene seg: ', sem_mask_path) + + try: + part_mask_path = self.part_masks[index] + part_mask = cv2.imread(part_mask_path) + part_mask = cv2.resize(part_mask, (256, 256), cv2.INTER_CUBIC) + part_mask = mask_split(part_mask, 26) + except: + print('Part seg: ', part_mask_path) + + try: + if self.has_polygon_contact_2d[index]: + polygon_contact_2d_path = self.polygon_contacts_2d[index] + polygon_contact_2d = cv2.imread(polygon_contact_2d_path) + polygon_contact_2d = cv2.resize(polygon_contact_2d, (256, 256), cv2.INTER_NEAREST) + # binarize the part mask + polygon_contact_2d = np.where(polygon_contact_2d > 0, 1, 0) + else: + polygon_contact_2d = np.zeros((256, 256, 3)) + except: + print('2D polygon contact: ', polygon_contact_2d_path) + + if self.normalize: + img = torch.tensor(img, dtype=torch.float32) + item['img'] = self.normalize_img(img) + else: + item['img'] = torch.tensor(img, dtype=torch.float32) + + if self.is_smplx[index]: + # Add 6 zeros to the end of the pose vector to match with smpl + pose = np.concatenate((pose, np.zeros(6))) + + item['img_path'] = img_path + item['pose'] = torch.tensor(pose, dtype=torch.float32) + item['betas'] = torch.tensor(betas, dtype=torch.float32) + item['transl'] = torch.tensor(transl, dtype=torch.float32) + item['cam_k'] = self.cam_k[index] + item['img_scale_factor'] = torch.tensor(img_scale_factor, dtype=torch.float32) + item['contact_label_3d'] = torch.tensor(contact_label_3d, dtype=torch.float32) + item['sem_mask'] = torch.tensor(sem_mask, dtype=torch.float32) + item['part_mask'] = torch.tensor(part_mask, dtype=torch.float32) + item['polygon_contact_2d'] = torch.tensor(polygon_contact_2d, dtype=torch.float32) + + item['has_smpl'] = self.has_smpl[index] + item['is_smplx'] = self.is_smplx[index] + item['has_contact_3d'] = self.has_contact_3d[index] + item['has_polygon_contact_2d'] = self.has_polygon_contact_2d[index] + + return item + + def __len__(self): + return len(self.images) diff --git a/data/mixed_dataset.py b/data/mixed_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..0d09e73a9afd0a2895ce745ec0f1d22201f03936 --- /dev/null +++ b/data/mixed_dataset.py @@ -0,0 +1,42 @@ +""" +This file contains the definition of different heterogeneous datasets used for training +""" +import torch +import numpy as np + +from .base_dataset import BaseDataset + +class MixedDataset(torch.utils.data.Dataset): + + def __init__(self, ds_list, mode, dataset_mix_pdf, **kwargs): + self.dataset_list = ds_list + print('Training Dataset list: ', self.dataset_list) + self.num_datasets = len(self.dataset_list) + + self.datasets = [] + for ds in self.dataset_list: + if ds in ['rich', 'prox']: + self.datasets.append(BaseDataset(ds, mode, model_type='smplx', **kwargs)) + elif ds in ['damon']: + self.datasets.append(BaseDataset(ds, mode, model_type='smpl', **kwargs)) + else: + raise ValueError('Dataset not supported') + + total_length = sum([len(ds) for ds in self.datasets]) + length_itw = sum([len(ds) for ds in self.datasets]) + self.length = max([len(ds) for ds in self.datasets]) + + # convert list of strings to list of floats + self.partition = [float(i) for i in dataset_mix_pdf] # should sum to 1.0 unless you want to weight by dataset size + assert sum(self.partition) == 1.0, "Dataset Mix PDF must sum to 1.0 unless you want to weight by dataset size" + assert len(self.partition) == self.num_datasets, "Number of partitions must be equal to number of datasets" + self.partition = np.array(self.partition).cumsum() + + def __getitem__(self, index): + p = np.random.rand() + for i in range(self.num_datasets): + if p <= self.partition[i]: + return self.datasets[i][index % len(self.datasets[i])] + + def __len__(self): + return self.length diff --git a/data/preprocess/behave-30fps-error_frames.json b/data/preprocess/behave-30fps-error_frames.json new file mode 100644 index 0000000000000000000000000000000000000000..137db1aadf7e76440f240e7b11f403661ba8c2ce --- /dev/null +++ b/data/preprocess/behave-30fps-error_frames.json @@ -0,0 +1,511 @@ +{ + "error_seqs": [ + "Date01_Sub01_boxsmall_hand", + "Date01_Sub01_boxtiny_hand", + "Date02_Sub02_backpack_back", + "Date02_Sub02_backpack_hand", + "Date02_Sub02_backpack_twohand", + "Date02_Sub02_boxtiny_hand", + "Date02_Sub02_monitor_move2", + "Date02_Sub02_plasticcontainer", + "Date02_Sub02_toolbox", + "Date02_Sub02_toolbox_part2", + "Date02_Sub02_yogamat", + "Date03_Sub03_boxlong", + "Date03_Sub03_plasticcontainer", + "Date03_Sub04_boxsmall", + "Date03_Sub04_boxtiny", + "Date03_Sub04_boxtiny_part2", + "Date03_Sub04_plasticcontainer_lift", + "Date03_Sub04_toolbox", + "Date03_Sub05_boxsmall", + "Date03_Sub05_boxtiny", + "Date04_Sub05_boxlarge", + "Date04_Sub05_boxlong", + "Date04_Sub05_boxtiny", + "Date04_Sub05_stool", + "Date05_Sub06_boxlong", + "Date05_Sub06_boxtiny", + "Date05_Sub06_toolbox", + "Date06_Sub07_boxlong", + "Date06_Sub07_boxtiny", + "Date06_Sub07_stool_sit", + "Date06_Sub07_suitcase_lift", + "Date06_Sub07_toolbox", + "Date06_Sub07_yogamat", + "Date07_Sub04_boxlong", + "Date07_Sub04_boxsmall", + "Date07_Sub04_boxtiny", + "Date07_Sub04_plasticcontainer", + "Date07_Sub04_suitcase_lift", + "Date07_Sub05_suitcase_open", + "Date07_Sub05_tablesmall", + "Date07_Sub08_boxtiny", + "Date07_Sub08_yogamat" + ], + "good_quality": [ + "Date01_Sub01_backpack_back", + "Date01_Sub01_backpack_hand", + "Date01_Sub01_backpack_hug", + "Date01_Sub01_boxlarge_hand", + "Date01_Sub01_boxlong_hand", + "Date01_Sub01_boxmedium_hand", + "Date01_Sub01_chairblack_hand", + "Date01_Sub01_chairblack_lift", + "Date01_Sub01_chairblack_sit", + "Date01_Sub01_chairwood_hand", + "Date01_Sub01_chairwood_lift", + "Date01_Sub01_chairwood_sit", + "Date01_Sub01_monitor_hand", + "Date01_Sub01_monitor_move", + "Date01_Sub01_plasticcontainer", + "Date01_Sub01_stool_move", + "Date01_Sub01_stool_sit", + "Date01_Sub01_suitcase", + "Date01_Sub01_suitcase_lift", + "Date01_Sub01_tablesmall_lean", + "Date01_Sub01_tablesmall_lift", + "Date01_Sub01_tablesmall_move", + "Date01_Sub01_tablesquare_hand", + "Date01_Sub01_tablesquare_lift", + "Date01_Sub01_tablesquare_sit", + "Date01_Sub01_toolbox", + "Date01_Sub01_trashbin", + "Date01_Sub01_yogaball", + "Date01_Sub01_yogaball_play", + "Date01_Sub01_yogamat_hand", + "Date02_Sub02_boxlarge_hand", + "Date02_Sub02_boxlong_hand", + "Date02_Sub02_boxmedium_hand", + "Date02_Sub02_boxsmall_hand", + "Date02_Sub02_chairblack_hand", + "Date02_Sub02_chairblack_lift", + "Date02_Sub02_chairblack_sit", + "Date02_Sub02_chairwood_hand", + "Date02_Sub02_chairwood_sit", + "Date02_Sub02_monitor_hand", + "Date02_Sub02_monitor_move", + "Date02_Sub02_stool_move", + "Date02_Sub02_stool_sit", + "Date02_Sub02_suitcase_ground", + "Date02_Sub02_suitcase_lift", + "Date02_Sub02_tablesmall_lean", + "Date02_Sub02_tablesmall_lift", + "Date02_Sub02_tablesmall_move", + "Date02_Sub02_tablesquare_lift", + "Date02_Sub02_tablesquare_move", + "Date02_Sub02_tablesquare_sit", + "Date02_Sub02_trashbin", + "Date02_Sub02_yogaball_play", + "Date02_Sub02_yogaball_sit", + "Date03_Sub03_backpack_back", + "Date03_Sub03_backpack_hand", + "Date03_Sub03_backpack_hug", + "Date03_Sub03_boxlarge", + "Date03_Sub03_boxmedium", + "Date03_Sub03_boxsmall", + "Date03_Sub03_boxtiny", + "Date03_Sub03_chairblack_hand", + "Date03_Sub03_chairblack_lift", + "Date03_Sub03_chairblack_sit", + "Date03_Sub03_chairblack_sitstand", + "Date03_Sub03_chairwood_hand", + "Date03_Sub03_chairwood_lift", + "Date03_Sub03_chairwood_sit", + "Date03_Sub03_monitor_move", + "Date03_Sub03_stool_lift", + "Date03_Sub03_stool_sit", + "Date03_Sub03_suitcase_lift", + "Date03_Sub03_suitcase_move", + "Date03_Sub03_tablesmall_lean", + "Date03_Sub03_tablesmall_lift", + "Date03_Sub03_tablesmall_move", + "Date03_Sub03_tablesquare_lift", + "Date03_Sub03_tablesquare_move", + "Date03_Sub03_tablesquare_sit", + "Date03_Sub03_toolbox", + "Date03_Sub03_trashbin", + "Date03_Sub03_yogaball_play", + "Date03_Sub03_yogaball_sit", + "Date03_Sub03_yogamat", + "Date03_Sub04_backpack_back", + "Date03_Sub04_backpack_hand", + "Date03_Sub04_backpack_hug", + "Date03_Sub04_boxlarge", + "Date03_Sub04_boxlong", + "Date03_Sub04_boxmedium", + "Date03_Sub04_chairblack_hand", + "Date03_Sub04_chairblack_liftreal", + "Date03_Sub04_chairblack_sit", + "Date03_Sub04_chairwood_hand", + "Date03_Sub04_chairwood_lift", + "Date03_Sub04_chairwood_sit", + "Date03_Sub04_monitor_hand", + "Date03_Sub04_monitor_move", + "Date03_Sub04_stool_move", + "Date03_Sub04_stool_sit", + "Date03_Sub04_suitcase_ground", + "Date03_Sub04_suitcase_lift", + "Date03_Sub04_tablesmall_hand", + "Date03_Sub04_tablesmall_lean", + "Date03_Sub04_tablesmall_lift", + "Date03_Sub04_tablesquare_hand", + "Date03_Sub04_tablesquare_lift", + "Date03_Sub04_tablesquare_sit", + "Date03_Sub04_trashbin", + "Date03_Sub04_yogaball_play", + "Date03_Sub04_yogaball_play2", + "Date03_Sub04_yogaball_sit", + "Date03_Sub04_yogamat", + "Date03_Sub05_backpack", + "Date03_Sub05_boxlarge", + "Date03_Sub05_boxlong", + "Date03_Sub05_boxmedium", + "Date03_Sub05_chairblack", + "Date03_Sub05_chairwood", + "Date03_Sub05_chairwood_part2", + "Date03_Sub05_monitor", + "Date03_Sub05_plasticcontainer", + "Date03_Sub05_stool", + "Date03_Sub05_suitcase", + "Date03_Sub05_tablesmall", + "Date03_Sub05_tablesquare", + "Date03_Sub05_toolbox", + "Date03_Sub05_trashbin", + "Date03_Sub05_yogaball", + "Date03_Sub05_yogamat", + "Date04_Sub05_backpack", + "Date04_Sub05_boxmedium", + "Date04_Sub05_boxsmall", + "Date04_Sub05_chairblack", + "Date04_Sub05_chairwood", + "Date04_Sub05_monitor", + "Date04_Sub05_monitor_part2", + "Date04_Sub05_monitor_sit", + "Date04_Sub05_plasticcontainer", + "Date04_Sub05_suitcase", + "Date04_Sub05_suitcase_open", + "Date04_Sub05_tablesmall", + "Date04_Sub05_tablesquare", + "Date04_Sub05_toolbox", + "Date04_Sub05_trashbin", + "Date04_Sub05_yogaball", + "Date04_Sub05_yogamat", + "Date05_Sub05_backpack", + "Date05_Sub05_chairblack", + "Date05_Sub05_chairwood", + "Date05_Sub05_yogaball", + "Date05_Sub06_backpack_back", + "Date05_Sub06_backpack_hand", + "Date05_Sub06_backpack_twohand", + "Date05_Sub06_boxlarge", + "Date05_Sub06_boxmedium", + "Date05_Sub06_boxsmall", + "Date05_Sub06_chairblack_hand", + "Date05_Sub06_chairblack_lift", + "Date05_Sub06_chairblack_sit", + "Date05_Sub06_chairwood_hand", + "Date05_Sub06_chairwood_lift", + "Date05_Sub06_chairwood_sit", + "Date05_Sub06_monitor_hand", + "Date05_Sub06_monitor_move", + "Date05_Sub06_plasticcontainer", + "Date05_Sub06_stool_lift", + "Date05_Sub06_stool_sit", + "Date05_Sub06_suitcase_hand", + "Date05_Sub06_suitcase_lift", + "Date05_Sub06_tablesmall_hand", + "Date05_Sub06_tablesmall_lean", + "Date05_Sub06_tablesmall_lift", + "Date05_Sub06_tablesquare_lift", + "Date05_Sub06_tablesquare_move", + "Date05_Sub06_tablesquare_sit", + "Date05_Sub06_trashbin", + "Date05_Sub06_yogaball_play", + "Date05_Sub06_yogaball_sit", + "Date05_Sub06_yogamat", + "Date06_Sub07_backpack_back", + "Date06_Sub07_backpack_hand", + "Date06_Sub07_backpack_twohand", + "Date06_Sub07_boxlarge", + "Date06_Sub07_boxmedium", + "Date06_Sub07_boxsmall", + "Date06_Sub07_chairblack_hand", + "Date06_Sub07_chairblack_lift", + "Date06_Sub07_chairblack_sit", + "Date06_Sub07_chairwood_hand", + "Date06_Sub07_chairwood_lift", + "Date06_Sub07_chairwood_sit", + "Date06_Sub07_monitor_move", + "Date06_Sub07_plasticcontainer", + "Date06_Sub07_stool_lift", + "Date06_Sub07_suitcase_move", + "Date06_Sub07_tablesmall_lean", + "Date06_Sub07_tablesmall_lift", + "Date06_Sub07_tablesmall_move", + "Date06_Sub07_tablesquare_lift", + "Date06_Sub07_tablesquare_move", + "Date06_Sub07_tablesquare_sit", + "Date06_Sub07_trashbin", + "Date06_Sub07_yogaball_play", + "Date06_Sub07_yogaball_sit", + "Date07_Sub04_backpack_back", + "Date07_Sub04_backpack_hand", + "Date07_Sub04_backpack_twohand", + "Date07_Sub04_boxlarge", + "Date07_Sub04_boxmedium", + "Date07_Sub04_chairblack_hand", + "Date07_Sub04_chairblack_lift", + "Date07_Sub04_chairblack_sit", + "Date07_Sub04_chairwood_hand", + "Date07_Sub04_chairwood_lift", + "Date07_Sub04_chairwood_sit", + "Date07_Sub04_monitor_hand", + "Date07_Sub04_monitor_move", + "Date07_Sub04_stool_lift", + "Date07_Sub04_stool_sit", + "Date07_Sub04_suitcase_open", + "Date07_Sub04_tablesmall_lean", + "Date07_Sub04_tablesmall_lift", + "Date07_Sub04_tablesmall_move", + "Date07_Sub04_tablesquare_lift", + "Date07_Sub04_tablesquare_move", + "Date07_Sub04_tablesquare_sit", + "Date07_Sub04_toolbox_lift", + "Date07_Sub04_trashbin", + "Date07_Sub04_yogaball_play", + "Date07_Sub04_yogaball_sit", + "Date07_Sub04_yogamat", + "Date07_Sub05_suitcase_lift", + "Date07_Sub05_tablesquare", + "Date07_Sub08_backpack_back", + "Date07_Sub08_backpack_hand", + "Date07_Sub08_backpack_hug", + "Date07_Sub08_boxlarge", + "Date07_Sub08_boxlong", + "Date07_Sub08_boxmedium", + "Date07_Sub08_boxsmall", + "Date07_Sub08_chairblack_hand", + "Date07_Sub08_chairblack_lift", + "Date07_Sub08_chairblack_sit", + "Date07_Sub08_chairwood_hand", + "Date07_Sub08_chairwood_lift", + "Date07_Sub08_chairwood_sit", + "Date07_Sub08_monitor_hand", + "Date07_Sub08_monitor_move", + "Date07_Sub08_plasticcontainer", + "Date07_Sub08_stool", + "Date07_Sub08_suitcase", + "Date07_Sub08_tablesmall", + "Date07_Sub08_tablesquare", + "Date07_Sub08_toolbox", + "Date07_Sub08_trashbin", + "Date07_Sub08_yogaball" + ], + "error_frames": { + "Date01_Sub01_boxsmall_hand": [ + "t0008.467" + ], + "Date01_Sub01_boxtiny_hand": [ + "t0045.000" + ], + "Date02_Sub02_backpack_back": [ + "t0035.200" + ], + "Date02_Sub02_backpack_hand": [ + "t0031.800" + ], + "Date02_Sub02_backpack_twohand": [ + "t0037.033" + ], + "Date02_Sub02_boxtiny_hand": [ + "t0009.800", + "t0010.333", + "t0010.467", + "t0013.100", + "t0013.300" + ], + "Date02_Sub02_monitor_move2": [ + "t0015.167" + ], + "Date02_Sub02_plasticcontainer": [ + "t0022.300" + ], + "Date02_Sub02_toolbox": [ + "t0010.433", + "t0033.000", + "t0040.467" + ], + "Date02_Sub02_toolbox_part2": [ + "t0048.533" + ], + "Date02_Sub02_yogamat": [ + "t0014.300" + ], + "Date03_Sub03_boxlong": [ + "t0024.367" + ], + "Date03_Sub03_plasticcontainer": [ + "t0045.633" + ], + "Date03_Sub04_boxsmall": [ + "t0020.000", + "t0021.000", + "t0024.100", + "t0024.133", + "t0025.000", + "t0025.633", + "t0027.000" + ], + "Date03_Sub04_boxtiny": [ + "t0005.967", + "t0006.967", + "t0007.833", + "t0019.000", + "t0020.400", + "t0020.467" + ], + "Date03_Sub04_boxtiny_part2": [ + "t0021.133" + ], + "Date03_Sub04_plasticcontainer_lift": [ + "t0023.233" + ], + "Date03_Sub04_toolbox": [ + "t0032.000", + "t0034.600", + "t0035.467", + "t0035.633", + "t0036.167", + "t0036.200", + "t0036.967", + "t0041.533" + ], + "Date03_Sub05_boxsmall": [ + "t0006.700", + "t0036.767" + ], + "Date03_Sub05_boxtiny": [ + "t0006.433", + "t0008.100", + "t0009.167", + "t0010.200", + "t0010.433", + "t0011.800", + "t0011.933", + "t0013.400", + "t0038.000", + "t0040.000", + "t0042.433", + "t0045.067" + ], + "Date04_Sub05_boxlarge": [ + "t0038.000", + "t0038.067", + "t0039.000" + ], + "Date04_Sub05_boxlong": [ + "t0031.333" + ], + "Date04_Sub05_boxtiny": [ + "t0030.800", + "t0031.000", + "t0036.233" + ], + "Date04_Sub05_stool": [ + "t0020.000" + ], + "Date05_Sub06_boxlong": [ + "t0038.167" + ], + "Date05_Sub06_boxtiny": [ + "t0016.933", + "t0035.033", + "t0037.000", + "t0043.067", + "t0043.200", + "t0044.967", + "t0045.000", + "t0047.000", + "t0047.200" + ], + "Date05_Sub06_toolbox": [ + "t0011.000", + "t0012.200", + "t0022.000" + ], + "Date06_Sub07_boxlong": [ + "t0042.000" + ], + "Date06_Sub07_boxtiny": [ + "t0004.633", + "t0004.800", + "t0004.867" + ], + "Date06_Sub07_stool_sit": [ + "t0046.000", + "t0046.900" + ], + "Date06_Sub07_suitcase_lift": [ + "t0006.933", + "t0007.000" + ], + "Date06_Sub07_toolbox": [ + "t0007.733", + "t0008.000", + "t0012.000", + "t0013.000", + "t0040.000", + "t0041.000", + "t0044.000", + "t0047.000" + ], + "Date06_Sub07_yogamat": [ + "t0028.000" + ], + "Date07_Sub04_boxlong": [ + "t0012.767" + ], + "Date07_Sub04_boxsmall": [ + "t0046.300" + ], + "Date07_Sub04_boxtiny": [ + "t0008.667", + "t0009.333", + "t0017.367", + "t0038.600", + "t0040.000", + "t0040.067", + "t0040.900", + "t0042.000", + "t0042.100", + "t0042.133", + "t0042.400", + "t0042.800", + "t0042.900" + ], + "Date07_Sub04_plasticcontainer": [ + "t0024.167", + "t0025.633", + "t0025.833" + ], + "Date07_Sub04_suitcase_lift": [ + "t0024.000", + "t0025.000" + ], + "Date07_Sub05_suitcase_open": [ + "t0031.000", + "t0032.000" + ], + "Date07_Sub05_tablesmall": [ + "t0076.000", + "t0117.567" + ], + "Date07_Sub08_boxtiny": [ + "t0017.933" + ], + "Date07_Sub08_yogamat": [ + "t0005.867" + ] +} +} \ No newline at end of file diff --git a/data/preprocess/behave.py b/data/preprocess/behave.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/data/preprocess/behave_test/behave_simple_test.npz.py b/data/preprocess/behave_test/behave_simple_test.npz.py new file mode 100644 index 0000000000000000000000000000000000000000..0e2ca712b068c294704c02528bb5b145b75a86fd --- /dev/null +++ b/data/preprocess/behave_test/behave_simple_test.npz.py @@ -0,0 +1,37 @@ +# load split.json and make an npz with all the folders in the test split +import argparse +import json +import os +import glob +import numpy as np + +BEHAVE_PATH = '/ps/project/datasets/BEHAVE/sequences/' + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', type=str, default=BEHAVE_PATH) + parser.add_argument('--split_file', type=str, default='data/preprocess/behave_test/split.json') + parser.add_argument('--out_file', type=str, default='data/dataset_extras/behave/behave_simple_test.npz') + args = parser.parse_args() + + with open(args.split_file, 'r') as f: + split = json.load(f) + + test_split = split['test'] + + # structs we use + imgnames_ = [] + + data = {} + for seq_name in test_split: + print(seq_name) + seq_dir = os.path.join(args.data_dir, seq_name) + # get recusive images in the seq_dir folder + images = glob.glob(os.path.join(seq_dir, '**/*color.jpg'), recursive=True) + print(len(images)) + images.sort() + imgnames_.extend(images) + + np.savez(args.out_file, imgname=imgnames_,) + print('Saved to ', args.out_file) + diff --git a/data/preprocess/behave_test/split.json b/data/preprocess/behave_test/split.json new file mode 100644 index 0000000000000000000000000000000000000000..68001f8817696f5c8617380c0cfac7a4402c9091 --- /dev/null +++ b/data/preprocess/behave_test/split.json @@ -0,0 +1,327 @@ +{ + "train": [ + "Date01_Sub01_backpack_back", + "Date01_Sub01_backpack_hand", + "Date01_Sub01_backpack_hug", + "Date01_Sub01_basketball", + "Date01_Sub01_boxlarge_hand", + "Date01_Sub01_boxlong_hand", + "Date01_Sub01_boxmedium_hand", + "Date01_Sub01_boxsmall_hand", + "Date01_Sub01_boxtiny_hand", + "Date01_Sub01_chairblack_hand", + "Date01_Sub01_chairblack_lift", + "Date01_Sub01_chairblack_sit", + "Date01_Sub01_chairwood_hand", + "Date01_Sub01_chairwood_lift", + "Date01_Sub01_chairwood_sit", + "Date01_Sub01_keyboard_move", + "Date01_Sub01_keyboard_typing", + "Date01_Sub01_monitor_hand", + "Date01_Sub01_monitor_move", + "Date01_Sub01_plasticcontainer", + "Date01_Sub01_stool_move", + "Date01_Sub01_stool_sit", + "Date01_Sub01_suitcase", + "Date01_Sub01_suitcase_lift", + "Date01_Sub01_tablesmall_lean", + "Date01_Sub01_tablesmall_lift", + "Date01_Sub01_tablesmall_move", + "Date01_Sub01_tablesquare_hand", + "Date01_Sub01_tablesquare_lift", + "Date01_Sub01_tablesquare_sit", + "Date01_Sub01_toolbox", + "Date01_Sub01_trashbin", + "Date01_Sub01_yogaball", + "Date01_Sub01_yogaball_play", + "Date01_Sub01_yogamat_hand", + "Date02_Sub02_backpack_back", + "Date02_Sub02_backpack_hand", + "Date02_Sub02_backpack_twohand", + "Date02_Sub02_basketball", + "Date02_Sub02_boxlarge_hand", + "Date02_Sub02_boxlong_hand", + "Date02_Sub02_boxmedium_hand", + "Date02_Sub02_boxsmall_hand", + "Date02_Sub02_boxtiny_hand", + "Date02_Sub02_chairblack_hand", + "Date02_Sub02_chairblack_lift", + "Date02_Sub02_chairblack_sit", + "Date02_Sub02_chairwood_hand", + "Date02_Sub02_chairwood_sit", + "Date02_Sub02_keyboard_move", + "Date02_Sub02_keyboard_typing", + "Date02_Sub02_monitor_hand", + "Date02_Sub02_monitor_move", + "Date02_Sub02_plasticcontainer", + "Date02_Sub02_stool_move", + "Date02_Sub02_stool_sit", + "Date02_Sub02_suitcase_ground", + "Date02_Sub02_suitcase_lift", + "Date02_Sub02_tablesmall_lean", + "Date02_Sub02_tablesmall_lift", + "Date02_Sub02_tablesmall_move", + "Date02_Sub02_tablesquare_lift", + "Date02_Sub02_tablesquare_move", + "Date02_Sub02_tablesquare_sit", + "Date02_Sub02_toolbox", + "Date02_Sub02_trashbin", + "Date02_Sub02_yogaball_play", + "Date02_Sub02_yogaball_sit", + "Date02_Sub02_yogamat", + "Date04_Sub05_backpack", + "Date04_Sub05_basketball", + "Date04_Sub05_boxlarge", + "Date04_Sub05_boxlong", + "Date04_Sub05_boxmedium", + "Date04_Sub05_boxsmall", + "Date04_Sub05_boxtiny", + "Date04_Sub05_chairblack", + "Date04_Sub05_chairwood", + "Date04_Sub05_keyboard", + "Date04_Sub05_monitor", + "Date04_Sub05_monitor_sit", + "Date04_Sub05_plasticcontainer", + "Date04_Sub05_stool", + "Date04_Sub05_suitcase", + "Date04_Sub05_suitcase_open", + "Date04_Sub05_tablesmall", + "Date04_Sub05_tablesquare", + "Date04_Sub05_toolbox", + "Date04_Sub05_trashbin", + "Date04_Sub05_yogaball", + "Date04_Sub05_yogamat", + "Date05_Sub05_backpack", + "Date05_Sub05_chairblack", + "Date05_Sub05_chairwood", + "Date05_Sub05_yogaball", + "Date05_Sub06_backpack_back", + "Date05_Sub06_backpack_hand", + "Date05_Sub06_backpack_twohand", + "Date05_Sub06_basketball", + "Date05_Sub06_boxlarge", + "Date05_Sub06_boxlong", + "Date05_Sub06_boxmedium", + "Date05_Sub06_boxsmall", + "Date05_Sub06_boxtiny", + "Date05_Sub06_chairblack_hand", + "Date05_Sub06_chairblack_lift", + "Date05_Sub06_chairblack_sit", + "Date05_Sub06_chairwood_hand", + "Date05_Sub06_chairwood_lift", + "Date05_Sub06_chairwood_sit", + "Date05_Sub06_keyboard_hand", + "Date05_Sub06_keyboard_move", + "Date05_Sub06_monitor_hand", + "Date05_Sub06_monitor_move", + "Date05_Sub06_plasticcontainer", + "Date05_Sub06_stool_lift", + "Date05_Sub06_stool_sit", + "Date05_Sub06_suitcase_hand", + "Date05_Sub06_suitcase_lift", + "Date05_Sub06_tablesmall_hand", + "Date05_Sub06_tablesmall_lean", + "Date05_Sub06_tablesmall_lift", + "Date05_Sub06_tablesquare_lift", + "Date05_Sub06_tablesquare_move", + "Date05_Sub06_tablesquare_sit", + "Date05_Sub06_toolbox", + "Date05_Sub06_trashbin", + "Date05_Sub06_yogaball_play", + "Date05_Sub06_yogaball_sit", + "Date05_Sub06_yogamat", + "Date06_Sub07_backpack_back", + "Date06_Sub07_backpack_hand", + "Date06_Sub07_backpack_twohand", + "Date06_Sub07_basketball", + "Date06_Sub07_boxlarge", + "Date06_Sub07_boxlong", + "Date06_Sub07_boxmedium", + "Date06_Sub07_boxsmall", + "Date06_Sub07_boxtiny", + "Date06_Sub07_chairblack_hand", + "Date06_Sub07_chairblack_lift", + "Date06_Sub07_chairblack_sit", + "Date06_Sub07_chairwood_hand", + "Date06_Sub07_chairwood_lift", + "Date06_Sub07_chairwood_sit", + "Date06_Sub07_keyboard_move", + "Date06_Sub07_keyboard_typing", + "Date06_Sub07_monitor_move", + "Date06_Sub07_plasticcontainer", + "Date06_Sub07_stool_lift", + "Date06_Sub07_stool_sit", + "Date06_Sub07_suitcase_lift", + "Date06_Sub07_suitcase_move", + "Date06_Sub07_tablesmall_lean", + "Date06_Sub07_tablesmall_lift", + "Date06_Sub07_tablesmall_move", + "Date06_Sub07_tablesquare_lift", + "Date06_Sub07_tablesquare_move", + "Date06_Sub07_tablesquare_sit", + "Date06_Sub07_toolbox", + "Date06_Sub07_trashbin", + "Date06_Sub07_yogaball_play", + "Date06_Sub07_yogaball_sit", + "Date06_Sub07_yogamat", + "Date07_Sub04_backpack_back", + "Date07_Sub04_backpack_hand", + "Date07_Sub04_backpack_twohand", + "Date07_Sub04_basketball", + "Date07_Sub04_boxlarge", + "Date07_Sub04_boxlong", + "Date07_Sub04_boxmedium", + "Date07_Sub04_boxsmall", + "Date07_Sub04_boxtiny", + "Date07_Sub04_chairblack_hand", + "Date07_Sub04_chairblack_lift", + "Date07_Sub04_chairblack_sit", + "Date07_Sub04_chairwood_hand", + "Date07_Sub04_chairwood_lift", + "Date07_Sub04_chairwood_sit", + "Date07_Sub04_keyboard_move", + "Date07_Sub04_keyboard_typing", + "Date07_Sub04_monitor_hand", + "Date07_Sub04_monitor_move", + "Date07_Sub04_plasticcontainer", + "Date07_Sub04_stool_lift", + "Date07_Sub04_stool_sit", + "Date07_Sub04_suitcase_lift", + "Date07_Sub04_suitcase_open", + "Date07_Sub04_tablesmall_lean", + "Date07_Sub04_tablesmall_lift", + "Date07_Sub04_tablesmall_move", + "Date07_Sub04_tablesquare_lift", + "Date07_Sub04_tablesquare_move", + "Date07_Sub04_tablesquare_sit", + "Date07_Sub04_toolbox_lift", + "Date07_Sub04_trashbin", + "Date07_Sub04_yogaball_play", + "Date07_Sub04_yogaball_sit", + "Date07_Sub04_yogamat", + "Date07_Sub05_suitcase_lift", + "Date07_Sub05_suitcase_open", + "Date07_Sub05_tablesmall", + "Date07_Sub05_tablesquare", + "Date07_Sub08_backpack_back", + "Date07_Sub08_backpack_hand", + "Date07_Sub08_backpack_hug", + "Date07_Sub08_basketball", + "Date07_Sub08_boxlarge", + "Date07_Sub08_boxlong", + "Date07_Sub08_boxmedium", + "Date07_Sub08_boxsmall", + "Date07_Sub08_boxtiny", + "Date07_Sub08_chairblack_hand", + "Date07_Sub08_chairblack_lift", + "Date07_Sub08_chairblack_sit", + "Date07_Sub08_chairwood_hand", + "Date07_Sub08_chairwood_lift", + "Date07_Sub08_chairwood_sit", + "Date07_Sub08_keyboard_move", + "Date07_Sub08_keyboard_typing", + "Date07_Sub08_monitor_hand", + "Date07_Sub08_monitor_move", + "Date07_Sub08_plasticcontainer", + "Date07_Sub08_stool", + "Date07_Sub08_suitcase", + "Date07_Sub08_tablesmall", + "Date07_Sub08_tablesquare", + "Date07_Sub08_toolbox", + "Date07_Sub08_trashbin", + "Date07_Sub08_yogaball", + "Date07_Sub08_yogamat" + ], + "test": [ + "Date03_Sub03_backpack_back", + "Date03_Sub03_backpack_hand", + "Date03_Sub03_backpack_hug", + "Date03_Sub03_basketball", + "Date03_Sub03_boxlarge", + "Date03_Sub03_boxlong", + "Date03_Sub03_boxmedium", + "Date03_Sub03_boxsmall", + "Date03_Sub03_boxtiny", + "Date03_Sub03_chairblack_hand", + "Date03_Sub03_chairblack_lift", + "Date03_Sub03_chairblack_sit", + "Date03_Sub03_chairblack_sitstand", + "Date03_Sub03_chairwood_hand", + "Date03_Sub03_chairwood_lift", + "Date03_Sub03_chairwood_sit", + "Date03_Sub03_keyboard_move", + "Date03_Sub03_keyboard_typing", + "Date03_Sub03_monitor_move", + "Date03_Sub03_plasticcontainer", + "Date03_Sub03_stool_lift", + "Date03_Sub03_stool_sit", + "Date03_Sub03_suitcase_lift", + "Date03_Sub03_suitcase_move", + "Date03_Sub03_tablesmall_lean", + "Date03_Sub03_tablesmall_lift", + "Date03_Sub03_tablesmall_move", + "Date03_Sub03_tablesquare_lift", + "Date03_Sub03_tablesquare_move", + "Date03_Sub03_tablesquare_sit", + "Date03_Sub03_toolbox", + "Date03_Sub03_trashbin", + "Date03_Sub03_yogaball_play", + "Date03_Sub03_yogaball_sit", + "Date03_Sub03_yogamat", + "Date03_Sub04_backpack_back", + "Date03_Sub04_backpack_hand", + "Date03_Sub04_backpack_hug", + "Date03_Sub04_basketball", + "Date03_Sub04_boxlarge", + "Date03_Sub04_boxlong", + "Date03_Sub04_boxmedium", + "Date03_Sub04_boxsmall", + "Date03_Sub04_boxtiny", + "Date03_Sub04_chairblack_hand", + "Date03_Sub04_chairblack_liftreal", + "Date03_Sub04_chairblack_sit", + "Date03_Sub04_chairwood_hand", + "Date03_Sub04_chairwood_lift", + "Date03_Sub04_chairwood_sit", + "Date03_Sub04_keyboard_move", + "Date03_Sub04_keyboard_typing", + "Date03_Sub04_monitor_hand", + "Date03_Sub04_monitor_move", + "Date03_Sub04_plasticcontainer_lift", + "Date03_Sub04_stool_move", + "Date03_Sub04_stool_sit", + "Date03_Sub04_suitcase_ground", + "Date03_Sub04_suitcase_lift", + "Date03_Sub04_tablesmall_hand", + "Date03_Sub04_tablesmall_lean", + "Date03_Sub04_tablesmall_lift", + "Date03_Sub04_tablesquare_hand", + "Date03_Sub04_tablesquare_lift", + "Date03_Sub04_tablesquare_sit", + "Date03_Sub04_toolbox", + "Date03_Sub04_trashbin", + "Date03_Sub04_yogaball_play", + "Date03_Sub04_yogaball_sit", + "Date03_Sub04_yogamat", + "Date03_Sub05_backpack", + "Date03_Sub05_basketball", + "Date03_Sub05_boxlarge", + "Date03_Sub05_boxlong", + "Date03_Sub05_boxmedium", + "Date03_Sub05_boxsmall", + "Date03_Sub05_boxtiny", + "Date03_Sub05_chairblack", + "Date03_Sub05_chairwood", + "Date03_Sub05_keyboard", + "Date03_Sub05_monitor", + "Date03_Sub05_plasticcontainer", + "Date03_Sub05_stool", + "Date03_Sub05_suitcase", + "Date03_Sub05_tablesmall", + "Date03_Sub05_tablesquare", + "Date03_Sub05_toolbox", + "Date03_Sub05_trashbin", + "Date03_Sub05_yogaball", + "Date03_Sub05_yogamat" + ] +} \ No newline at end of file diff --git a/data/preprocess/hot_dca.py b/data/preprocess/hot_dca.py new file mode 100644 index 0000000000000000000000000000000000000000..aa2eaeb176faa93e7346fb27b0f45e1d2a93b6ef --- /dev/null +++ b/data/preprocess/hot_dca.py @@ -0,0 +1,220 @@ +import os +import cv2 +import numpy as np +from tqdm import tqdm +import sys +import imagesize +import argparse +import torch +import pandas as pd +import json + +import monai.metrics as metrics + +HOT_TRAIN_SPLIT = "/ps/scratch/ps_shared/ychen2/4shashank/split/hot_train.odgt" +HOT_VAL_SPLIT = "/ps/scratch/ps_shared/ychen2/4shashank/split/hot_validation.odgt" +HOT_TEST_SPLIT = "/ps/scratch/ps_shared/ychen2/4shashank/split/hot_test.odgt" + +def metric(mask, pred, back=True): + iou = metrics.compute_meaniou(pred, mask, back, False) + iou = iou.mean() + return iou + + +def combine_hot_prox_split(split): + if split == 'train': + with open(HOT_TRAIN_SPLIT, "r") as f: + records = [ + json.loads(line.strip("\n")) for line in f.readlines() + ] + elif split == 'val': + with open(HOT_VAL_SPLIT, "r") as f: + records = [ + json.loads(line.strip("\n")) for line in f.readlines() + ] + elif split == 'test': + with open(HOT_TEST_SPLIT, "r") as f: + records = [ + json.loads(line.strip("\n")) for line in f.readlines() + ] + return records + +def hot_extract(img_dataset_path, smpl_params_path, dca_csv_path, out_dir, split=None, vis_path=None, visualize=False, include_supporting=True): + + n_vertices = 6890 + + # structs we use + imgnames_ = [] + poses_, shapes_, transls_ = [], [], [] + cams_k_ = [] + polygon_2d_contact_ = [] + contact_3d_labels_ = [] + scene_seg_, part_seg_ = [], [] + + img_dir = os.path.join(img_dataset_path, 'images', 'training') + smpl_params = np.load(smpl_params_path) + # smpl_params = np.load(smpl_params_path, allow_pickle=True) + # smpl_params = smpl_params['arr_0'].item() + annotations_dir = img_dir.replace('images', 'annotations') + records = combine_hot_prox_split(split) + + # load dca csv + dca_csv = pd.read_csv(dca_csv_path) + + iou_thresh = 0 + + num_with_3d_contact = 0 + + focal_length_accumulator = [] + for i, record in enumerate(tqdm(records, dynamic_ncols=True)): + imgpath = record['fpath_img'] + imgname = os.path.basename(imgpath) + # save image in temp_images + if visualize: + img = cv2.imread(os.path.join(img_dir, imgname)) + cv2.imwrite(os.path.join(vis_path, os.path.basename(imgname)), img) + + # load image to get the size + img_w, img_h = record["width"], record["height"] + + # get mask anns + polygon_2d_contact_path = os.path.join(annotations_dir, os.path.splitext(imgname)[0] + '.png') + + + # Get 3D contact annotations from DCA mturk csv + dca_row = dca_csv.loc[dca_csv['imgnames'] == imgname] # if no imgnames column, run scripts/datascripts/add_imgname_column_to_deco_csv.py + if len(dca_row) == 0: + contact_3d_labels = [] + continue + else: + num_with_3d_contact += 1 + supporting_object = dca_row['supporting_object'].values[0] + vertices = eval(dca_row['vertices'].values[0]) + contact_3d_list = vertices[os.path.join('hot/training/', imgname)] + # Aggregate values in all keys + contact_3d_idx = [] + for item in contact_3d_list: + # one iteration loop as it is a list of one dict key value + for k, v in item.items(): + if include_supporting: + contact_3d_idx.extend(v) + else: + if k != 'SUPPORTING': + contact_3d_idx.extend(v) + # removed repeated values + contact_3d_idx = list(set(contact_3d_idx)) + contact_3d_labels = np.zeros(n_vertices) # smpl has 6980 vertices + contact_3d_labels[contact_3d_idx] = 1. + + # find indices that match the imname + inds = np.where(smpl_params['imgname'] == os.path.join(img_dir, imgname))[0] + select_inds = [] + ious = [] + for ind in inds: + # part mask + part_path = smpl_params['part_seg'][ind] + # load the part_mask + part_mask = cv2.imread(part_path) + # binarize the part mask + part_mask = np.where(part_mask > 0, 1, 0) + # save part mask + if visualize: + cv2.imwrite(os.path.join(vis_path, os.path.basename(part_path)), part_mask*255) + + # load gt polygon mask + polygon_2d_contact = cv2.imread(polygon_2d_contact_path) + # binarize the gt polygon mask + polygon_2d_contact = np.where(polygon_2d_contact > 0, 1, 0) + + # save gt polygon mask in temp_images + if visualize: + cv2.imwrite(os.path.join(vis_path, os.path.basename(polygon_2d_contact_path)), polygon_2d_contact*255) + + polygon_2d_contact = torch.from_numpy(polygon_2d_contact)[None,:].permute(0,3,1,2) + part_mask = torch.from_numpy(part_mask)[None,:].permute(0,3,1,2) + # compute iou with part mask and gt polygon mask + iou = metric(polygon_2d_contact, part_mask) + if iou > iou_thresh: + ious.append(iou) + select_inds.append(ind) + + # get select_ind with maximum iou + if len(select_inds) > 0: + max_iou_ind = select_inds[np.argmax(ious)] + else: + continue + + # part mask + part_path = smpl_params['part_seg'][max_iou_ind] + + # scene mask + scene_path = smpl_params['scene_seg'][max_iou_ind] + + # get smpl params + pose = smpl_params['pose'][max_iou_ind] + shape = smpl_params['shape'][max_iou_ind] + transl = smpl_params['global_t'][max_iou_ind] + focal_length = smpl_params['focal_l'][max_iou_ind] + camC = np.array([[img_w//2, img_h//2]]) + + # read GT 2D keypoints + K = np.eye(3, dtype=np.float64) + K[0, 0] = focal_length + K[1, 1] = focal_length + K[:2, 2:] = camC.T + + # store data + imgnames_.append(os.path.join(img_dir, imgname)) + polygon_2d_contact_.append(polygon_2d_contact_path) + # we use the heuristic that the 3D contact labeled is for the person with maximum iou with HOT contacts + contact_3d_labels_.append(contact_3d_labels) + scene_seg_.append(scene_path) + part_seg_.append(part_path) + poses_.append(pose.squeeze()) + transls_.append(transl.squeeze()) + shapes_.append(shape.squeeze()) + cams_k_.append(K.tolist()) + focal_length_accumulator.append(focal_length) + + print('Average focal length: ', np.mean(focal_length_accumulator)) + print('Median focal length: ', np.median(focal_length_accumulator)) + print('Std Dev focal length: ', np.std(focal_length_accumulator)) + + # store the data struct + os.makedirs(out_dir, exist_ok=True) + out_file = os.path.join(out_dir, f'hot_dca_supporting_{str(include_supporting)}_{split}.npz') + np.savez(out_file, imgname=imgnames_, + pose=poses_, + transl=transls_, + shape=shapes_, + cam_k=cams_k_, + polygon_2d_contact=polygon_2d_contact_, + contact_label=contact_3d_labels_, + scene_seg=scene_seg_, + part_seg=part_seg_ + ) + print(f'Total number of rows: {len(imgnames_)}') + print('Saved to ', out_file) + print(f'Number of images with 3D contact labels: {num_with_3d_contact}') + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--img_dataset_path', type=str, default='/ps/project/datasets/HOT/Contact_Data/') + parser.add_argument('--smpl_params_path', type=str, default='/ps/scratch/ps_shared/stripathi/deco/4agniv/hot/hot.npz') + parser.add_argument('--dca_csv_path', type=str, default='/ps/scratch/ps_shared/stripathi/deco/4agniv/hot/dca.csv') + parser.add_argument('--out_dir', type=str, default='/is/cluster/work/stripathi/pycharm_remote/dca_contact/data/dataset_extras') + parser.add_argument('--vis_path', type=str, default='/is/cluster/work/stripathi/pycharm_remote/dca_contact/temp_images') + parser.add_argument('--visualize', action='store_true', default=False) + parser.add_argument('--include_supporting', action='store_true', default=False) + parser.add_argument('--split', type=str, default='train') + args = parser.parse_args() + + hot_extract(img_dataset_path=args.img_dataset_path, + smpl_params_path=args.smpl_params_path, + dca_csv_path=args.dca_csv_path, + out_dir=args.out_dir, + vis_path=args.vis_path, + visualize=args.visualize, + split=args.split, + include_supporting=args.include_supporting) + diff --git a/data/preprocess/hot_noprox.py b/data/preprocess/hot_noprox.py new file mode 100644 index 0000000000000000000000000000000000000000..4f757aa5c7029634e50e0eb70a3b863a5e733fc0 --- /dev/null +++ b/data/preprocess/hot_noprox.py @@ -0,0 +1,241 @@ +import os +import cv2 +import numpy as np +from tqdm import tqdm +import sys +import imagesize +import argparse +import torch +import pandas as pd +import json + +import monai.metrics as metrics + +HOT_TRAIN_SPLIT = "/ps/scratch/ps_shared/ychen2/4shashank/split/hot_train.odgt" +HOT_VAL_SPLIT = "/ps/scratch/ps_shared/ychen2/4shashank/split/hot_validation.odgt" +HOT_TEST_SPLIT = "/ps/scratch/ps_shared/ychen2/4shashank/split/hot_test.odgt" + +def metric(mask, pred, back=True): + iou = metrics.compute_meaniou(pred, mask, back, False) + iou = iou.mean() + return iou + + +def combine_hot_prox_split(split): + if split == 'train': + with open(HOT_TRAIN_SPLIT, "r") as f: + records = [ + json.loads(line.strip("\n")) for line in f.readlines() + ] + elif split == 'val': + with open(HOT_VAL_SPLIT, "r") as f: + records = [ + json.loads(line.strip("\n")) for line in f.readlines() + ] + elif split == 'test': + with open(HOT_TEST_SPLIT, "r") as f: + records = [ + json.loads(line.strip("\n")) for line in f.readlines() + ] + elif split == 'trainval': + with open(HOT_TRAIN_SPLIT, "r") as f: + train_records = [ + json.loads(line.strip("\n")) for line in f.readlines() + ] + with open(HOT_VAL_SPLIT, "r") as f: + val_records = [ + json.loads(line.strip("\n")) for line in f.readlines() + ] + records = train_records + val_records + return records + +def hot_extract(img_dataset_path, smpl_params_path, dca_csv_path, out_dir, split=None, vis_path=None, visualize=False, record_idx=None, include_supporting=True): + + n_vertices = 6890 + + # structs we use + imgnames_ = [] + poses_, shapes_, transls_ = [], [], [] + cams_k_ = [] + polygon_2d_contact_ = [] + contact_3d_labels_ = [] + scene_seg_, part_seg_ = [], [] + + img_dir = os.path.join(img_dataset_path, 'images', 'training') + smpl_params = np.load(smpl_params_path) + # smpl_params = np.load(smpl_params_path, allow_pickle=True) + # smpl_params = smpl_params['arr_0'].item() + annotations_dir = img_dir.replace('images', 'annotations') + records = combine_hot_prox_split(split) + # split records list into 4 sublists + if record_idx is not None: + records = np.array_split(records, 4)[record_idx] + + # load dca csv + dca_csv = pd.read_csv(dca_csv_path) + + iou_thresh = 0 + + num_with_3d_contact = 0 + + focal_length_accumulator = [] + for i, record in enumerate(tqdm(records, dynamic_ncols=True)): + imgpath = record['fpath_img'] + imgname = os.path.basename(imgpath) + # save image in temp_images + if visualize: + img = cv2.imread(os.path.join(img_dir, imgname)) + cv2.imwrite(os.path.join(vis_path, os.path.basename(imgname)), img) + + # load image to get the size + img_w, img_h = record["width"], record["height"] + + # get mask anns + polygon_2d_contact_path = os.path.join(annotations_dir, os.path.splitext(imgname)[0] + '.png') + + + # Get 3D contact annotations from DCA mturk csv + dca_row = dca_csv.loc[dca_csv['imgnames'] == imgname] # if no imgnames column, run scripts/datascripts/add_imgname_column_to_deco_csv.py + if len(dca_row) == 0: + contact_3d_labels = [] + else: + num_with_3d_contact += 1 + supporting_object = dca_row['supporting_object'].values[0] + vertices = eval(dca_row['vertices'].values[0]) + contact_3d_list = vertices[os.path.join('hot/training/', imgname)] + # Aggregate values in all keys + contact_3d_idx = [] + for item in contact_3d_list: + # one iteration loop as it is a list of one dict key value + for k, v in item.items(): + if include_supporting: + contact_3d_idx.extend(v) + else: + if k != 'SUPPORTING': + contact_3d_idx.extend(v) + # removed repeated values + contact_3d_idx = list(set(contact_3d_idx)) + contact_3d_labels = np.zeros(n_vertices) # smpl has 6980 vertices + contact_3d_labels[contact_3d_idx] = 1. + + # find indices that match the imname + inds = np.where(smpl_params['imgname'] == os.path.join(img_dir, imgname))[0] + select_inds = [] + ious = [] + for ind in inds: + # part mask + part_path = smpl_params['part_seg'][ind] + # load the part_mask + part_mask = cv2.imread(part_path) + # binarize the part mask + part_mask = np.where(part_mask > 0, 1, 0) + # save part mask + if visualize: + cv2.imwrite(os.path.join(vis_path, os.path.basename(part_path)), part_mask*255) + + # load gt polygon mask + polygon_2d_contact = cv2.imread(polygon_2d_contact_path) + # binarize the gt polygon mask + polygon_2d_contact = np.where(polygon_2d_contact > 0, 1, 0) + + # save gt polygon mask in temp_images + if visualize: + cv2.imwrite(os.path.join(vis_path, os.path.basename(polygon_2d_contact_path)), polygon_2d_contact*255) + + polygon_2d_contact = torch.from_numpy(polygon_2d_contact)[None,:].permute(0,3,1,2) + part_mask = torch.from_numpy(part_mask)[None,:].permute(0,3,1,2) + # compute iou with part mask and gt polygon mask + iou = metric(polygon_2d_contact, part_mask) + if iou > iou_thresh: + ious.append(iou) + select_inds.append(ind) + + # get select_ind with maximum iou + if len(select_inds) > 0: + max_iou_ind = select_inds[np.argmax(ious)] + else: + continue + + for ind in select_inds: + # part mask + part_path = smpl_params['part_seg'][ind] + + # scene mask + scene_path = smpl_params['scene_seg'][ind] + + # get smpl params + pose = smpl_params['pose'][ind] + shape = smpl_params['shape'][ind] + transl = smpl_params['global_t'][ind] + focal_length = smpl_params['focal_l'][ind] + camC = np.array([[img_w//2, img_h//2]]) + + # read GT 2D keypoints + K = np.eye(3, dtype=np.float64) + K[0, 0] = focal_length + K[1, 1] = focal_length + K[:2, 2:] = camC.T + + # store data + imgnames_.append(os.path.join(img_dir, imgname)) + polygon_2d_contact_.append(polygon_2d_contact_path) + # we use the heuristic that the 3D contact labeled is for the person with maximum iou with HOT contacts + if ind == max_iou_ind: + contact_3d_labels_.append(contact_3d_labels) + else: + contact_3d_labels_.append([]) + scene_seg_.append(scene_path) + part_seg_.append(part_path) + poses_.append(pose.squeeze()) + transls_.append(transl.squeeze()) + shapes_.append(shape.squeeze()) + cams_k_.append(K.tolist()) + focal_length_accumulator.append(focal_length) + + print('Average focal length: ', np.mean(focal_length_accumulator)) + print('Median focal length: ', np.median(focal_length_accumulator)) + print('Std Dev focal length: ', np.std(focal_length_accumulator)) + + # store the data struct + os.makedirs(out_dir, exist_ok=True) + if record_idx is not None: + out_file = os.path.join(out_dir, f'hot_noprox_supporting_{str(include_supporting)}_{split}_{record_idx}.npz') + else: + out_file = os.path.join(out_dir, f'hot_noprox_supporting_{str(include_supporting)}_{split}_combined.npz') + np.savez(out_file, imgname=imgnames_, + pose=poses_, + transl=transls_, + shape=shapes_, + cam_k=cams_k_, + polygon_2d_contact=polygon_2d_contact_, + contact_label=contact_3d_labels_, + scene_seg=scene_seg_, + part_seg=part_seg_ + ) + print(f'Total number of rows: {len(imgnames_)}') + print('Saved to ', out_file) + print(f'Number of images with 3D contact labels: {num_with_3d_contact}') + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--img_dataset_path', type=str, default='/ps/project/datasets/HOT/Contact_Data/') + parser.add_argument('--smpl_params_path', type=str, default='/ps/scratch/ps_shared/stripathi/deco/4agniv/hot/hot.npz') + parser.add_argument('--dca_csv_path', type=str, default='/ps/scratch/ps_shared/stripathi/deco/4agniv/hot/dca.csv') + parser.add_argument('--out_dir', type=str, default='/is/cluster/work/stripathi/pycharm_remote/dca_contact/data/dataset_extras') + parser.add_argument('--vis_path', type=str, default='/is/cluster/work/stripathi/pycharm_remote/dca_contact/temp_images') + parser.add_argument('--visualize', action='store_true', default=False) + parser.add_argument('--include_supporting', action='store_true', default=False) + parser.add_argument('--record_idx', type=int, default=None) + parser.add_argument('--split', type=str, default='train') + args = parser.parse_args() + + hot_extract(img_dataset_path=args.img_dataset_path, + smpl_params_path=args.smpl_params_path, + dca_csv_path=args.dca_csv_path, + out_dir=args.out_dir, + vis_path=args.vis_path, + visualize=args.visualize, + split=args.split, + record_idx=args.record_idx, + include_supporting=args.include_supporting) + diff --git a/data/preprocess/hot_prox.py b/data/preprocess/hot_prox.py new file mode 100644 index 0000000000000000000000000000000000000000..ee59566b5f1fb4db1523bef54be0d599d16e8470 --- /dev/null +++ b/data/preprocess/hot_prox.py @@ -0,0 +1,217 @@ +import os +import cv2 +import numpy as np +from tqdm import tqdm +import sys +import imagesize +import argparse +import torch +import pandas as pd +import json + +import monai.metrics as metrics + +PROX_TRAIN_SPLIT = "/ps/scratch/ps_shared/ychen2/4shashank/split/prox_train.odgt" +PROX_VAL_SPLIT = "/ps/scratch/ps_shared/ychen2/4shashank/split/prox_validation.odgt" +PROX_TEST_SPLIT = "/ps/scratch/ps_shared/ychen2/4shashank/split/prox_test.odgt" + +def metric(mask, pred, back=True): + iou = metrics.compute_meaniou(pred, mask, back, False) + iou = iou.mean() + return iou + + +def combine_hot_prox_split(split): + if split == 'train': + with open(PROX_TRAIN_SPLIT, "r") as f: + records = [ + json.loads(line.strip("\n")) for line in f.readlines() + ] + elif split == 'val': + with open(PROX_VAL_SPLIT, "r") as f: + records = [ + json.loads(line.strip("\n")) for line in f.readlines() + ] + elif split == 'test': + with open(PROX_TEST_SPLIT, "r") as f: + records = [ + json.loads(line.strip("\n")) for line in f.readlines() + ] + return records + +def hot_extract(img_dataset_path, smpl_params_path, dca_csv_path, out_dir, split=None, vis_path=None, visualize=False, downsample_factor=4): + + n_vertices = 6890 + + # structs we use + imgnames_ = [] + poses_, shapes_, transls_ = [], [], [] + cams_k_ = [] + polygon_2d_contact_ = [] + contact_3d_labels_ = [] + scene_seg_, part_seg_ = [], [] + + img_dir = os.path.join(img_dataset_path, 'images', 'training') + smpl_params = np.load(smpl_params_path) + # smpl_params = np.load(smpl_params_path, allow_pickle=True) + # smpl_params = smpl_params['arr_0'].item() + annotations_dir = img_dir.replace('images', 'annotations') + records = combine_hot_prox_split(split) + + # load dca csv + dca_csv = pd.read_csv(dca_csv_path) + + iou_thresh = 0 + + num_with_3d_contact = 0 + + focal_length_accumulator = [] + for i, record in enumerate(tqdm(records, dynamic_ncols=True)): + imgpath = record['fpath_img'] + imgname = os.path.basename(imgpath) + # save image in temp_images + if visualize: + img = cv2.imread(os.path.join(img_dir, imgname)) + cv2.imwrite(os.path.join(vis_path, os.path.basename(imgname)), img) + + # load image to get the size + img_w, img_h = record["width"], record["height"] + + # get mask anns + polygon_2d_contact_path = os.path.join(annotations_dir, os.path.splitext(imgname)[0] + '.png') + + + # Get 3D contact annotations from DCA mturk csv + dca_row = dca_csv.loc[dca_csv['imgnames'] == imgname] # if no imgnames column, run scripts/datascripts/add_imgname_column_to_deco_csv.py + if len(dca_row) == 0: + contact_3d_labels = [] + else: + num_with_3d_contact += 1 + supporting_object = dca_row['supporting_object'].values[0] + vertices = eval(dca_row['vertices'].values[0]) + contact_3d_list = vertices[os.path.join('hot/training/', imgname)] + # Aggregate values in all keys + contact_3d_idx = [] + for item in contact_3d_list: + # one iteration loop as it is a list of one dict key value + for k, v in item.items(): + contact_3d_idx.extend(v) + # removed repeated values + contact_3d_idx = list(set(contact_3d_idx)) + contact_3d_labels = np.zeros(n_vertices) # smpl has 6980 vertices + contact_3d_labels[contact_3d_idx] = 1. + + # find indices that match the imname + inds = np.where(smpl_params['imgname'] == os.path.join(img_dir, imgname))[0] + select_inds = [] + ious = [] + for ind in inds: + # part mask + part_path = smpl_params['part_seg'][ind] + # load the part_mask + part_mask = cv2.imread(part_path) + # binarize the part mask + part_mask = np.where(part_mask > 0, 1, 0) + # save part mask + if visualize: + cv2.imwrite(os.path.join(vis_path, os.path.basename(part_path)), part_mask*255) + + # load gt polygon mask + polygon_2d_contact = cv2.imread(polygon_2d_contact_path) + # binarize the gt polygon mask + polygon_2d_contact = np.where(polygon_2d_contact > 0, 1, 0) + + # save gt polygon mask in temp_images + if visualize: + cv2.imwrite(os.path.join(vis_path, os.path.basename(polygon_2d_contact_path)), polygon_2d_contact*255) + + polygon_2d_contact = torch.from_numpy(polygon_2d_contact)[None,:].permute(0,3,1,2) + part_mask = torch.from_numpy(part_mask)[None,:].permute(0,3,1,2) + # compute iou with part mask and gt polygon mask + iou = metric(polygon_2d_contact, part_mask) + if iou > iou_thresh: + ious.append(iou) + select_inds.append(ind) + + # get select_ind with maximum iou + if len(select_inds) > 0: + max_iou_ind = select_inds[np.argmax(ious)] + else: + continue + + for ind in select_inds: + # part mask + part_path = smpl_params['part_seg'][ind] + + # scene mask + scene_path = smpl_params['scene_seg'][ind] + + # get smpl params + pose = smpl_params['pose'][ind] + shape = smpl_params['shape'][ind] + transl = smpl_params['global_t'][ind] + focal_length = smpl_params['focal_l'][ind] + camC = np.array([[img_w//2, img_h//2]]) + + # read GT 2D keypoints + K = np.eye(3, dtype=np.float64) + K[0, 0] = focal_length + K[1, 1] = focal_length + K[:2, 2:] = camC.T + + # store data + imgnames_.append(os.path.join(img_dir, imgname)) + polygon_2d_contact_.append(polygon_2d_contact_path) + # we use the heuristic that the 3D contact labeled is for the person with maximum iou with HOT contacts + if ind == max_iou_ind: + contact_3d_labels_.append(contact_3d_labels) + else: + contact_3d_labels_.append([]) + scene_seg_.append(scene_path) + part_seg_.append(part_path) + poses_.append(pose.squeeze()) + transls_.append(transl.squeeze()) + shapes_.append(shape.squeeze()) + cams_k_.append(K.tolist()) + focal_length_accumulator.append(focal_length) + + print('Average focal length: ', np.mean(focal_length_accumulator)) + print('Median focal length: ', np.median(focal_length_accumulator)) + print('Std Dev focal length: ', np.std(focal_length_accumulator)) + + # store the data struct + os.makedirs(out_dir, exist_ok=True) + out_file = os.path.join(out_dir, f'hot_prox_{split}.npz') + np.savez(out_file, imgname=imgnames_, + pose=poses_, + transl=transls_, + shape=shapes_, + cam_k=cams_k_, + polygon_2d_contact=polygon_2d_contact_, + contact_label=contact_3d_labels_, + scene_seg=scene_seg_, + part_seg=part_seg_ + ) + print(f'Total number of rows: {len(imgnames_)}') + print('Saved to ', out_file) + print(f'Number of images with 3D contact labels: {num_with_3d_contact}') + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--img_dataset_path', type=str, default='/ps/project/datasets/HOT/Contact_Data/') + parser.add_argument('--smpl_params_path', type=str, default='/ps/scratch/ps_shared/stripathi/deco/4agniv/hot/hot.npz') + parser.add_argument('--dca_csv_path', type=str, default='/ps/scratch/ps_shared/stripathi/deco/4agniv/hot/dca.csv') + parser.add_argument('--out_dir', type=str, default='/is/cluster/work/stripathi/pycharm_remote/dca_contact/data/dataset_extras') + parser.add_argument('--vis_path', type=str, default='/is/cluster/work/stripathi/pycharm_remote/dca_contact/temp_images') + parser.add_argument('--visualize', action='store_true', default=False) + parser.add_argument('--split', type=str, default='train') + args = parser.parse_args() + + hot_extract(img_dataset_path=args.img_dataset_path, + smpl_params_path=args.smpl_params_path, + dca_csv_path=args.dca_csv_path, + out_dir=args.out_dir, + vis_path=args.vis_path, + visualize=args.visualize, + split=args.split) + diff --git a/data/preprocess/prepare_damon_behave_split.py b/data/preprocess/prepare_damon_behave_split.py new file mode 100644 index 0000000000000000000000000000000000000000..05f1c547d65720a04b4f78c334bfa0c83b103fa8 --- /dev/null +++ b/data/preprocess/prepare_damon_behave_split.py @@ -0,0 +1,69 @@ +import os.path as osp +import os +import shutil +import json +import argparse +import numpy as np +from PIL import Image +from tqdm import tqdm + +objects = { + "backpack": 24, + "chair": 56, + "keyboard": 66, + "suitcase": 28 +} + +def copy_images_to_behave_format(in_img_dir, in_image_list, in_part_dir, in_seg_dir, out_dir): + """ + Copy images from in_img_dir to out_dir + :param in_img_dir: input directory containing images + :param out_dir: output directory to copy images to + :return: + """ + # read image list + with open(in_image_list, 'r') as fp: + img_list_dict = json.load(fp) + + for k, v in img_list_dict.items(): + out_dir_object = osp.join(out_dir, k) + os.makedirs(out_dir_object, exist_ok=True) + # copy images to out_dir + for img_name in tqdm(v, dynamic_ncols=True): + input_image_path = osp.join(in_img_dir, img_name) + input_part_path = osp.join(in_part_dir, img_name.replace('.jpg', '_0.png')) + input_seg_path = osp.join(in_seg_dir, img_name.replace('.jpg', '.png')) + if not osp.exists(input_part_path) or not osp.exists(input_image_path) or not osp.exists(input_seg_path): + print(f'{input_image_path} or {input_part_path} or {input_seg_path} does not exist') + continue + out_dir_image = osp.join(out_dir_object, img_name) + os.makedirs(out_dir_image, exist_ok=True) + shutil.copy(input_image_path, osp.join(out_dir_image, 'k1.color.jpg')) + + # load body mask + body_mask = Image.open(input_part_path) + # convert all non-zero pixels to 255 + body_mask = np.array(body_mask) + body_mask[body_mask > 0] = 255 + body_mask = Image.fromarray(body_mask) + body_mask.save(osp.join(out_dir_image, 'k1.person_mask.png')) + + # load seg mask + body_mask = Image.open(input_seg_path) + # convert all non-object pixels to 255 + body_mask = np.array(body_mask) + object_num = objects[k] + body_mask[body_mask == object_num] = 255 + body_mask[body_mask != 255] = 0 + body_mask = Image.fromarray(body_mask) + body_mask.save(osp.join(out_dir_image, 'k1.object_rend.png')) + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--in_img_dir', type=str, default='/ps/project/datasets/HOT/Contact_Data/images/training') + parser.add_argument('--in_part_dir', type=str, default='/ps/scratch/ps_shared/stripathi/deco/4agniv/hot/parts/training') + parser.add_argument('--in_seg_dir', type=str, default='/ps/scratch/ps_shared/stripathi/deco/4agniv/hot_behave_split/agniv/masks') + parser.add_argument('--in_image_list', type=str, default='/ps/scratch/ps_shared/stripathi/deco/4agniv/hot_behave_split/imgnames_per_object_dict.json') + parser.add_argument('--out_dir', type=str, default='/ps/scratch/ps_shared/stripathi/deco/4agniv/hot_behave_split/training') + args = parser.parse_args() + copy_images_to_behave_format(args.in_img_dir, args.in_image_list, args.in_part_dir, args.in_seg_dir, args.out_dir) \ No newline at end of file diff --git a/data/preprocess/rich_smplx.py b/data/preprocess/rich_smplx.py new file mode 100644 index 0000000000000000000000000000000000000000..b1b76111ebf1e7e22cb4ee4101f2516be7e5d706 --- /dev/null +++ b/data/preprocess/rich_smplx.py @@ -0,0 +1,222 @@ +import os +os.environ["CDF_LIB"] = "/is/cluster/scratch/stripathi/data/cdf37_1-dist/src/lib" + +import cv2 +import pandas as pd +import json +import glob +import h5py +import torch +import trimesh +import numpy as np +import pickle as pkl +from xml.dom import minidom +import xml.etree.ElementTree as ET +from tqdm import tqdm +from spacepy import pycdf +# from .read_openpose import read_openpose +import sys +sys.path.append('../../') +from models import hmr, SMPL +import config +import constants + +import shutil + +import smplx +import pytorch3d.transforms as p3dt + +from utils.geometry import batch_rodrigues, batch_rot2aa, ea2rm + + +model_type = 'smplx' +model_folder = '/ps/project/common/smplifyx/models/' +body_model_params = dict(model_path=model_folder, + model_type=model_type, + create_global_orient=True, + create_body_pose=True, + create_betas=True, + num_betas=10, + create_left_hand_pose=True, + create_right_hand_pose=True, + create_expression=True, + create_jaw_pose=True, + create_leye_pose=True, + create_reye_pose=True, + create_transl=True, + use_pca=False) +body_model = smplx.create(gender='neutral', **body_model_params).to('cuda') + +def rich_extract(img_dataset_path, out_path, split=None, vis_path=None, visualize=False, downsample_factor=4): + + # structs we use + imgnames_ = [] + poses_, shapes_, transls_ = [], [], [] + cams_k_ = [] + contact_label_ = [] + scene_seg_, part_seg_ = [], [] + + for i, fl in tqdm(enumerate(sorted(os.listdir(os.path.join(img_dataset_path, 'images', split)))), dynamic_ncols=True): + ind = fl.index('cam') + location = fl[:ind-1] + + cam_num = fl[ind:ind+6] + + img = fl[ind+7:-3] + 'jpeg' + + imgname = os.path.join(location, cam_num, img) + + mask_name = fl + sp = mask_name.split('_') + indx = mask_name.index('cam') + st = mask_name[indx-1:indx+7] + mask_name = mask_name.replace(st, '/') + mask_name = mask_name[:-7] + new_p = mask_name.split('/') + mask_name = new_p[0] + '/' + new_p[1] + '/' + sp[1] + '.pkl' + mask_path = os.path.join(img_dataset_path, 'labels', split, mask_name) + df = pd.read_pickle(mask_path) + mask = df['contact'] + + scene_path = os.path.join(img_dataset_path, 'segmentation_masks', split, fl[:-3] + 'png') + + part_path = os.path.join(img_dataset_path, 'parts', split, fl[:-3] + 'png') + + dataset_path = '/ps/project/datasets/RICH' + + ind = fl.index('cam') + frame_id = fl[:ind-1] + location = frame_id.split('_')[0] + + if location == 'LectureHall': + if 'chair' in frame_id: + cam2world_location = location + '_' + 'chair' + else: + cam2world_location = location + '_' + 'yoga' + else: + cam2world_location = location + + img_num = fl.split('_')[-2] + + cam_num = int(fl.split('_')[-1][:2]) + + # get ioi2scan transformation per sequence + ioi2scan_fn = os.path.join(dataset_path, 'website_release/multicam2world', cam2world_location + '_multicam2world.json') + + try: + camera_fn = os.path.join(dataset_path, 'rich_toolkit/data/scan_calibration', location, f'calibration/{cam_num:03d}.xml') + focal_length_x, focal_length_y, camC, camR, camT, _, _, _ = extract_cam_param_xml(camera_fn) + except: + print(f'camera calibration file not found: {camera_fn}') + continue + + # path to smpl params + smplx_param = os.path.join(dataset_path, 'rich_toolkit/data/bodies', split, frame_id, str(img_num), frame_id.split('_')[1] + '.pkl') + + # get smpl parameters + ## body resides in multi-ioi coordidate, where camera 0 is world zero. + with open(smplx_param, 'rb') as f: + body_params = pkl.load(f) + # in ioi coordinates: cam 0 + beta = body_params['betas'] + pose_aa = body_params['body_pose'] + pose_rotmat = p3dt.axis_angle_to_matrix(torch.FloatTensor(pose_aa.reshape(-1,3))).numpy() + + transl = body_params['transl'] + global_orient = body_params['global_orient'] + global_orient = p3dt.axis_angle_to_matrix(torch.FloatTensor(global_orient.reshape(-1,3))).numpy() + + smpl_body_cam0 = body_model(betas=torch.FloatTensor(beta).to('cuda')) # canonical body with shape + vertices_cam0 = smpl_body_cam0.vertices.detach().cpu().numpy().squeeze() + joints_cam0 = smpl_body_cam0.joints.detach().cpu().numpy() + pelvis_cam0 = joints_cam0[:, 0, :] + + # ## rigid transformation between multi-ioi and Leica scan (world) + with open(ioi2scan_fn, 'r') as f: + ioi2scan_dict = json.load(f) + R_ioi2world = np.array(ioi2scan_dict['R']) # Note: R is transposed + t_ioi2world= np.array(ioi2scan_dict['t']).reshape(1, 3) + + # # get SMPL params in camera coordinates + global_orient_cam = np.matmul(np.array(camR), global_orient) + full_pose_rotmat_cam = np.concatenate((global_orient_cam, pose_rotmat), axis=0).squeeze() + theta_cam = batch_rot2aa(torch.FloatTensor(full_pose_rotmat_cam)).reshape(-1, 66).cpu().numpy() + + # read GT 2D keypoints + K = np.eye(3, dtype=np.float64) + K[0, 0] = focal_length_x / downsample_factor + K[1, 1] = focal_length_y / downsample_factor + K[:2, 2:] = camC.T / downsample_factor + + # get camera parameters wrt to scan + R_worldtocam = np.matmul(camR, R_ioi2world) # Note: R_ioi2world is transposed + T_worldtocam = -t_ioi2world + camT + + # store data + imgnames_.append(os.path.join('/ps/project/datasets/RICH_JPG', split, imgname)) + contact_label_.append(mask) + scene_seg_.append(scene_path) + part_seg_.append(part_path) + poses_.append(theta_cam.squeeze()) + transls_.append(transl.squeeze()) + shapes_.append(beta.squeeze()) + cams_k_.append(K.tolist()) + + # store the data struct + if not os.path.isdir(out_path): + os.makedirs(out_path) + out_file = os.path.join(out_path, f'rich_{split}_smplx.npz') + np.savez(out_file, imgname=imgnames_, + pose=poses_, + transl=transls_, + shape=shapes_, + cam_k=cams_k_, + contact_label=contact_label_, + scene_seg=scene_seg_, + part_seg=part_seg_ + ) + print('Saved to ', out_file) + +def rectify_pose(camera_r, body_aa): + body_r = batch_rodrigues(body_aa).reshape(-1,3,3) + final_r = camera_r @ body_r + body_aa = batch_rot2aa(final_r) + return body_aa + + +def extract_cam_param_xml(xml_path: str = '', dtype=float): + import xml.etree.ElementTree as ET + tree = ET.parse(xml_path) + + extrinsics_mat = [float(s) for s in tree.find('./CameraMatrix/data').text.split()] + intrinsics_mat = [float(s) for s in tree.find('./Intrinsics/data').text.split()] + distortion_vec = [float(s) for s in tree.find('./Distortion/data').text.split()] + + focal_length_x = intrinsics_mat[0] + focal_length_y = intrinsics_mat[4] + center = np.array([[intrinsics_mat[2], intrinsics_mat[5]]], dtype=dtype) + + rotation = np.array([[extrinsics_mat[0], extrinsics_mat[1], extrinsics_mat[2]], + [extrinsics_mat[4], extrinsics_mat[5], extrinsics_mat[6]], + [extrinsics_mat[8], extrinsics_mat[9], extrinsics_mat[10]]], dtype=dtype) + + translation = np.array([[extrinsics_mat[3], extrinsics_mat[7], extrinsics_mat[11]]], dtype=dtype) + + # t = -Rc --> c = -R^Tt + cam_center = [-extrinsics_mat[0] * extrinsics_mat[3] - extrinsics_mat[4] * extrinsics_mat[7] - extrinsics_mat[8] * + extrinsics_mat[11], + -extrinsics_mat[1] * extrinsics_mat[3] - extrinsics_mat[5] * extrinsics_mat[7] - extrinsics_mat[9] * + extrinsics_mat[11], + -extrinsics_mat[2] * extrinsics_mat[3] - extrinsics_mat[6] * extrinsics_mat[7] - extrinsics_mat[10] * + extrinsics_mat[11]] + + cam_center = np.array([cam_center], dtype=dtype) + + k1 = np.array([distortion_vec[0]], dtype=dtype) + k2 = np.array([distortion_vec[1]], dtype=dtype) + + return focal_length_x, focal_length_y, center, rotation, translation, cam_center, k1, k2 + +rich_extract(img_dataset_path='/is/cluster/work/achatterjee/rich', out_path='/is/cluster/work/achatterjee/rich/npzs', split='train') +rich_extract(img_dataset_path='/is/cluster/work/achatterjee/rich', out_path='/is/cluster/work/achatterjee/rich/npzs', split='val') +rich_extract(img_dataset_path='/is/cluster/work/achatterjee/rich', out_path='/is/cluster/work/achatterjee/rich/npzs', split='test') diff --git a/data/preprocess/rich_smplx_agniv.py b/data/preprocess/rich_smplx_agniv.py new file mode 100644 index 0000000000000000000000000000000000000000..b1bb3e7776a41070f0e9a95430bbf37d5dd2fbc0 --- /dev/null +++ b/data/preprocess/rich_smplx_agniv.py @@ -0,0 +1,578 @@ +import os +os.environ["CDF_LIB"] = "/is/cluster/scratch/stripathi/data/cdf37_1-dist/src/lib" + +import cv2 +import pandas as pd +# import json +# import glob +# import h5py +import torch +# import trimesh +import numpy as np +import pickle as pkl +# from xml.dom import minidom +# import xml.etree.ElementTree as ET +from tqdm import tqdm +from spacepy import pycdf +# from .read_openpose import read_openpose +import sys +sys.path.append('../../') +# from models import hmr, SMPL +# import config +# import constants +import argparse + +# import shutil + +import smplx +import pytorch3d.transforms as p3dt + +from utils.geometry import batch_rodrigues, batch_rot2aa, ea2rm +# from vis_utils.world_vis import overlay_mesh, vis_smpl_with_ground + + +model_type = 'smplx' +model_folder = '/ps/project/common/smplifyx/models/' +body_model_params = dict(model_path=model_folder, + model_type=model_type, + create_global_orient=True, + create_body_pose=True, + create_betas=True, + num_betas=10, + create_left_hand_pose=True, + create_right_hand_pose=True, + create_expression=True, + create_jaw_pose=True, + create_leye_pose=True, + create_reye_pose=True, + create_transl=True, + use_pca=False) +body_model = smplx.create(gender='neutral', **body_model_params).to('cuda') + +def rich_extract(img_dataset_path, out_path, scene_indx, split=None, vis_path=None, visualize=False, downsample_factor=1): + + # structs we use + imgnames_ = [] + # scales_, centers_, parts_, Ss_, Ss_world_, openposes_ = [], [], [], [], [], [] + poses_, shapes_, transls_ = [], [], [] + # poses_world_, transls_world_, cams_r_, cams_t_ = [], [], [], [] + cams_k_ = [] + # ground_offset_ = [] + # in_bos_label_ = [] + contact_label_ = [] + scene_seg_, part_seg_ = [], [] + + # # seqs in validation set + # if split == 'val': + # seq_list = ['2021-06-15_Multi_IOI_ID_00176_Yoga2', + # '2021-06-15_Multi_IOI_ID_00228_Yoga1', + # '2021-06-15_Multi_IOI_ID_03588_Yoga1', + # '2021-06-15_Multi_IOI_ID_00176_Yoga1'] + + # # seqs in testing set + # if split == 'test': + # seq_list = ['2021-06-15_Multi_IOI_ID_00186_Yoga1', + # '2021-06-15_Multi_IOI_ID_03588_Yoga2', + # 'MultiIOI_201019_ID03581_parkingLot_Calibration06_Settings06_PushUp__2', + # 'Multi-IOI_ID00227_Scene_ParkingLot_Calibration_03_CameraSettings_4_pushup_1'] + + scenes = sorted(os.listdir('/ps/project/datasets/RICH/rich_toolkit/data/images/test')) + # scene = scenes[scene_indx] + scene = 'ParkingLot2_009_impro5' + + for scene_name in [scene]: + # for scene_name in ['LectureHall_003_wipingchairs1']: + out_file = os.path.join(out_path, f'rich_{scene_name}_smplx.npz') + if os.path.exists(out_file): return + print(scene_name) + for i, fl in tqdm(enumerate(sorted(os.listdir(os.path.join(img_dataset_path, 'images', split)))), dynamic_ncols=True): + if not scene_name in fl: continue + + ind = fl.index('cam') + location = fl[:ind-1] + + cam_num = fl[ind:ind+6] + + img = fl[ind+7:-3] + 'jpeg' + + imgname = os.path.join(location, cam_num, img) + + mask_name = fl + sp = mask_name.split('_') + indx = mask_name.index('cam') + st = mask_name[indx-1:indx+7] + mask_name = mask_name.replace(st, '/') + mask_name = mask_name[:-7] + new_p = mask_name.split('/') + mask_name = new_p[0] + '/' + new_p[1] + '/' + sp[1] + '.pkl' + mask_path = os.path.join(img_dataset_path, 'labels', split, mask_name) + df = pd.read_pickle(mask_path) + mask = df['contact'] + + scene_path = os.path.join('/ps/scratch/ps_shared/stripathi/deco/4agniv/rich/seg_masks_new', split, fl[:-3] + 'png') + + part_path = os.path.join('/ps/scratch/ps_shared/stripathi/deco/4agniv/rich/part_masks_new', split, fl[:-3] + 'png') + + dataset_path = '/ps/project/datasets/RICH' + + ind = fl.index('cam') + frame_id = fl[:ind-1] + location = frame_id.split('_')[0] + + if location == 'LectureHall': + if 'chair' in frame_id: + cam2world_location = location + '_' + 'chair' + else: + cam2world_location = location + '_' + 'yoga' + else: + cam2world_location = location + + img_num = fl.split('_')[-2] + + cam_num = int(fl.split('_')[-1][:2]) + + # get ioi2scan transformation per sequence + ioi2scan_fn = os.path.join(dataset_path, 'website_release/multicam2world', cam2world_location + '_multicam2world.json') + + try: + camera_fn = os.path.join(dataset_path, 'rich_toolkit/data/scan_calibration', location, f'calibration/{cam_num:03d}.xml') + focal_length_x, focal_length_y, camC, camR, camT, _, _, _ = extract_cam_param_xml(camera_fn) + except: + print(f'camera calibration file not found: {camera_fn}') + continue + + # print('X: ', focal_length_x) + # print('Y: ', focal_length_y) + + # path to smpl params + smplx_param = os.path.join(dataset_path, 'rich_toolkit/data/bodies', split, frame_id, str(img_num), frame_id.split('_')[1] + '.pkl') + + # # path to GT bounding boxes + # bbox_path = os.path.join(dataset_path, 'preprocessed', split, frame_id, img_num, frame_id.split('_')[1], 'bbox_refine', f'{img_num}_{cam_num:02d}.json') + # # path with 2D openpose keypoints + # openpose_path = os.path.join(dataset_path, 'preprocessed', split, frame_id, img_num, frame_id.split('_')[1], 'keypoints_refine', f'{img_num}_{str(cam_num).zfill(2)}_keypoints.json') + # # path to image crops + # img_path = os.path.join(dataset_path, 'preprocessed', split, frame_id, img_num, frame_id.split('_')[1], 'images_refine', f'{img_num}_{cam_num:02d}.png') + + # # bbox file + # try: + # with open(bbox_path, 'r') as f: + # bbox_dict = json.load(f) + # except: + # print(f'bbox file not found: {bbox_path}') + # continue + + # # read GT bounding box + # x1_ul = bbox_dict['x1'] // downsample_factor + # y1_ul = bbox_dict['y1'] // downsample_factor + # x2_br = bbox_dict['x2'] // downsample_factor + # y2_br = bbox_dict['y2'] // downsample_factor + # bbox = np.array([x1_ul, y1_ul, x2_br, y2_br]) + # center = [(bbox[2]+bbox[0])/2, (bbox[3]+bbox[1])/2] + # scale = 0.9 * max(bbox[2] - bbox[0], bbox[3] - bbox[1]) / 200. + + # get smpl parameters + ## body resides in multi-ioi coordidate, where camera 0 is world zero. + with open(smplx_param, 'rb') as f: + body_params = pkl.load(f) + # in ioi coordinates: cam 0 + beta = body_params['betas'] + pose_aa = body_params['body_pose'] + pose_rotmat = p3dt.axis_angle_to_matrix(torch.FloatTensor(pose_aa.reshape(-1,3))).numpy() + + transl = body_params['transl'] + global_orient = body_params['global_orient'] + global_orient = p3dt.axis_angle_to_matrix(torch.FloatTensor(global_orient.reshape(-1,3))).numpy() + + smpl_body_cam0 = body_model(betas=torch.FloatTensor(beta).to('cuda')) # canonical body with shape + vertices_cam0 = smpl_body_cam0.vertices.detach().cpu().numpy().squeeze() + joints_cam0 = smpl_body_cam0.joints.detach().cpu().numpy() + pelvis_cam0 = joints_cam0[:, 0, :] + + # ## rigid transformation between multi-ioi and Leica scan (world) + # with open(ioi2scan_fn, 'r') as f: + # ioi2scan_dict = json.load(f) + # R_ioi2world = np.array(ioi2scan_dict['R']) # Note: R is transposed + # t_ioi2world= np.array(ioi2scan_dict['t']).reshape(1, 3) + + # # # get SMPL params in world coordinates + # # # import ipdb; ipdb.set_trace() + # global_orient_world = np.matmul(R_ioi2world.T, global_orient) + # transl_world = np.matmul((pelvis_cam0+transl), R_ioi2world) + t_ioi2world - pelvis_cam0 # right multiplication to avoid transpose + # full_pose_rotmat_world = np.concatenate((global_orient_world, pose_rotmat), axis=0).squeeze() + # theta_world = batch_rot2aa(torch.FloatTensor(full_pose_rotmat_world)).reshape(-1, 66).cpu().numpy() + + # smpl_body_world = body_model(betas=torch.FloatTensor(beta).to('cuda'), + # body_pose=torch.FloatTensor(theta_world[:, 3:]).to('cuda'), + # transl=torch.FloatTensor(transl_world).to('cuda'), + # global_orient=torch.FloatTensor(theta_world[:, :3]).to('cuda')) + # vertices_world = smpl_body_world.vertices.detach().cpu().numpy().squeeze() + # joints3d_world = smpl_body_world.joints[:, 25:, :].detach().cpu().numpy().squeeze() + + # # get SMPL params in camera coordinates + global_orient_cam = np.matmul(np.array(camR), global_orient) + transl_cam = np.matmul(camR, (pelvis_cam0 + transl).T).T + camT - pelvis_cam0 + full_pose_rotmat_cam = np.concatenate((global_orient_cam, pose_rotmat), axis=0).squeeze() + theta_cam = batch_rot2aa(torch.FloatTensor(full_pose_rotmat_cam)).reshape(-1, 66).cpu().numpy() + + # read GT 2D keypoints + K = np.eye(3, dtype=np.float64) + K[0, 0] = focal_length_x / downsample_factor + K[1, 1] = focal_length_y / downsample_factor + K[:2, 2:] = camC.T / downsample_factor + + # # get openpose 2D keypoints + # try: + # with open(openpose_path, 'r') as f: + # openpose = json.load(f) + # openpose = np.array(openpose['people'][0]['pose_keypoints_2d']).reshape([-1, 3]) + # except: + # print(f'No openpose !! Missing {openpose_path}') + # continue + + # get camera parameters wrt to scan + # R_worldtocam = np.matmul(camR, R_ioi2world) # Note: R_ioi2world is transposed + # T_worldtocam = -t_ioi2world + camT + + # ground offset + # ground_offset = ground_eq[2] + + # store data + jpg_img_path = os.path.join('/ps/project/datasets/RICH_JPG', split, imgname) + bmp_img_path = jpg_img_path.replace('/ps/project/datasets/RICH_JPG', '/ps/project/datasets/RICH/rich_toolkit/data/images') + bmp_img_path = bmp_img_path.replace('.jpeg', '.bmp') + if not os.path.exists(bmp_img_path): + bmp_img_path = bmp_img_path.replace('.bmp', '.png') + imgnames_.append(bmp_img_path) + contact_label_.append(mask) + scene_seg_.append(scene_path) + part_seg_.append(part_path) + # centers_.append(center) + # scales_.append(scale) + # openposes_.append(openpose) + poses_.append(theta_cam.squeeze()) + transls_.append(transl_cam.squeeze()) + # poses_world_.append(theta_world.squeeze()) + # transls_world_.append(transl_world.squeeze()) + shapes_.append(beta.squeeze()) + # cams_r_.append(R_worldtocam.tolist()) + # # Todo: note that T_worldtocam here is (1,3) whereas in h36m T_worldtocam is (1,3) + # cams_t_.append(T_worldtocam.tolist()) + cams_k_.append(K.tolist()) + # ground_offset_.append(ground_offset) + + # for seq_i in tqdm(seq_list): + # print(f'Processing sequence: {seq_i}') + + # # path with GT bounding boxes + # params_path = os.path.join(dataset_path, seq_i, 'params') + + # # path to metadata for files + # md_path = os.path.join(dataset_path, seq_i, 'data') + + # # glob all folders in params path + # frame_param_paths = sorted(glob.glob(os.path.join(params_path, '*'))) + # frame_param_paths = [p for p in frame_param_paths if '.yaml' not in p] + + # # get ioi2scan transformation per sequence + # ioi2scan_fn = os.path.join(dataset_path, seq_i, 'cam2scan.json') + + # ## ground resides in Leica scan coordinate, which is (roughly) axis aligned. + # # ground_mesh = trimesh.load(os.path.join(dataset_path, seq_i, 'ground_mesh.ply'), process=False) + # # ground_eq = np.mean(ground_mesh.vertices, axis=0) + + # # list all files in the folder + # cam_files = os.listdir(os.path.join(dataset_path, seq_i, f'calibration')) + # cam_list = sorted([int(os.path.splitext(f)[0]) for f in cam_files if '.xml' in f]) + + # # if split == 'val': + # # cam_list = cam_list[1:] # remove first camera in val + # for cam_num in cam_list[:1]: + # camera_fn = os.path.join(dataset_path, seq_i, f'calibration/{cam_num:03d}.xml') + # focal_length_x, focal_length_y, camC, camR, camT, _, _, _ = extract_cam_param_xml(camera_fn) + + # for frame_param_path in tqdm(frame_param_paths): + # frame_id = os.path.basename(frame_param_path) + # frame_num = int(frame_id) + + # # path to smpl params + # try: + # smplx_param = os.path.join(frame_param_path, '00', 'results/000.pkl') + # except: + # import ipdb; ipdb.set_trace() + + # # path to GT bounding boxes + # bbox_path = os.path.join(md_path, frame_id, '00', 'bbox_refine', f'{frame_id}_{cam_num:02d}.json') + # # path with 2D openpose keypoints + # openpose_path = os.path.join(md_path, frame_id, '00', 'keypoints_refine', f'{frame_id}_{str(cam_num).zfill(2)}_keypoints.json') + # # path to image crops + # if downsample_factor == 1: + # img_path = os.path.join(md_path, frame_id, '00', 'images_orig', f'{frame_id}_{cam_num:02d}.png') + # else: + # img_path = os.path.join(md_path, frame_id, '00', 'images_orig_720p', f'{frame_id}_{cam_num:02d}.png') + + # if not os.path.isfile(img_path): + # print(f'image not found: {img_path}') + # continue + # # raise FileNotFoundError + + # # bbox file + # try: + # with open(bbox_path, 'r') as f: + # bbox_dict = json.load(f) + # except: + # print(f'bbox file not found: {bbox_path}') + # continue + # # read GT bounding box + # x1_ul = bbox_dict['x1'] // downsample_factor + # y1_ul = bbox_dict['y1'] // downsample_factor + # x2_br = bbox_dict['x2'] // downsample_factor + # y2_br = bbox_dict['y2'] // downsample_factor + # bbox = np.array([x1_ul, y1_ul, x2_br, y2_br]) + # center = [(bbox[2]+bbox[0])/2, (bbox[3]+bbox[1])/2] + # scale = 0.9 * max(bbox[2] - bbox[0], bbox[3] - bbox[1]) / 200. + + # # get smpl parameters + # ## body resides in multi-ioi coordidate, where camera 0 is world zero. + # with open(smplx_param, 'rb') as f: + # body_params = pkl.load(f) + # # in ioi coordinates: cam 0 + # beta = body_params['betas'] + # pose_aa = body_params['body_pose'] + # pose_rotmat = p3dt.axis_angle_to_matrix(torch.FloatTensor(pose_aa.reshape(-1,3))).numpy() + + # transl = body_params['transl'] + # global_orient = body_params['global_orient'] + # global_orient = p3dt.axis_angle_to_matrix(torch.FloatTensor(global_orient.reshape(-1,3))).numpy() + + # smpl_body_cam0 = body_model(betas=torch.FloatTensor(beta).to('cuda')) # canonical body with shape + # vertices_cam0 = smpl_body_cam0.vertices.detach().cpu().numpy().squeeze() + # joints_cam0 = smpl_body_cam0.joints.detach().cpu().numpy() + # pelvis_cam0 = joints_cam0[:, 0, :] + + + # ## rigid transformation between multi-ioi and Leica scan (world) + # with open(ioi2scan_fn, 'r') as f: + # ioi2scan_dict = json.load(f) + # R_ioi2world = np.array(ioi2scan_dict['R']) # Note: R is transposed + # t_ioi2world= np.array(ioi2scan_dict['t']).reshape(1, 3) + + # # get SMPL params in world coordinates + # # import ipdb; ipdb.set_trace() + # global_orient_world = np.matmul(R_ioi2world.T, global_orient) + # transl_world = np.matmul((pelvis_cam0+transl), R_ioi2world) + t_ioi2world - pelvis_cam0 # right multiplication to avoid transpose + # full_pose_rotmat_world = np.concatenate((global_orient_world, pose_rotmat), axis=0).squeeze() + # theta_world = batch_rot2aa(torch.FloatTensor(full_pose_rotmat_world)).reshape(-1, 66).cpu().numpy() + + # smpl_body_world = body_model(betas=torch.FloatTensor(beta).to('cuda'), + # body_pose=torch.FloatTensor(theta_world[:, 3:]).to('cuda'), + # transl=torch.FloatTensor(transl_world).to('cuda'), + # global_orient=torch.FloatTensor(theta_world[:, :3]).to('cuda')) + # vertices_world = smpl_body_world.vertices.detach().cpu().numpy().squeeze() + # joints3d_world = smpl_body_world.joints[:, 25:, :].detach().cpu().numpy().squeeze() + + # mesh = trimesh.Trimesh(vertices_world, body_model.faces, + # process=False, + # maintain_order=True) + # mesh.export('gt_mesh_world_smplx.obj') + + + + # # smpl_body_world = body_model(betas=torch.FloatTensor(beta).to('cuda'), + # # body_pose=torch.FloatTensor(pose_rotmat[None, ...]).to('cuda'), + # # transl=torch.FloatTensor(transl_world[None, ...]).to('cuda'), + # # global_orient=torch.FloatTensor(global_orient_world[None, ...]).to('cuda'), + # # left_hand_pose=torch.eye(3).reshape(1, 1, 3, 3).expand(1, 15, -1, -1).to('cuda'), + # # right_hand_pose=torch.eye(3).reshape(1, 1, 3, 3).expand(1, 15, -1, -1).to('cuda'), + # # leye_pose= torch.eye(3).reshape(1, 1, 3, 3).expand(batch_size, -1, -1, -1), + # # + # # pose2rot=False) + # # vertices_world = smpl_body_world.vertices.detach().cpu().numpy().squeeze() + # # joints3d_world = smpl_body_world.joints[:, 25:, :].detach().cpu().numpy().squeeze() + + # # mesh = trimesh.Trimesh(vertices_world, body_model.faces, + # # process=False, + # # maintain_order=True) + # # mesh.export('gt_mesh_world_smplx.obj') + + # # get SMPL params in camera coordinates + # global_orient_cam = np.matmul(camR, global_orient) + # transl_cam = np.matmul(camR, (pelvis_cam0 + transl).T).T + camT - pelvis_cam0 + # full_pose_rotmat_cam = np.concatenate((global_orient_cam, pose_rotmat), axis=0).squeeze() + # theta_cam = batch_rot2aa(torch.FloatTensor(full_pose_rotmat_cam)).reshape(-1, 66).cpu().numpy() + # # smpl_body_cam = body_model(betas=torch.FloatTensor(beta).to('cuda'), + # # body_pose=torch.FloatTensor(pose_rotmat).to('cuda'), + # # transl=torch.FloatTensor(transl_cam).to('cuda'), + # # global_orient=torch.FloatTensor(global_orient_cam).to('cuda'), + # # pose2rot=False) + # # vertices_cam = smpl_body_cam.vertices.detach().cpu().numpy().squeeze() + # # joints3d_cam = smpl_body_cam.joints[:, 25:, :].detach().cpu().numpy().squeeze() + # # + # # mesh = trimesh.Trimesh(vertices_cam, body_model.faces, + # # process=False, + # # maintain_order=True) + # # mesh.export('mesh_in_cam0.obj') + + # # read GT 2D keypoints + # K = np.eye(3, dtype=np.float) + # K[0, 0] = focal_length_x / downsample_factor + # K[1, 1] = focal_length_y / downsample_factor + # K[:2, 2:] = camC.T / downsample_factor + # # projected_points = (K @ joints3d_cam.T).T + # # joints2d = projected_points[:, :2] / np.hstack((projected_points[:, 2:], projected_points[:, 2:])) + # # part = np.hstack((joints2d, np.ones((joints2d.shape[0], 1)))) + + # # get openpose 2D keypoints + # try: + # with open(openpose_path, 'r') as f: + # openpose = json.load(f) + # openpose = np.array(openpose['people'][0]['pose_keypoints_2d']).reshape([-1, 3]) + # except: + # print(f'No openpose !! Missing {openpose_path}') + # continue + + # # get camera parameters wrt to scan + # R_worldtocam = np.matmul(camR, R_ioi2world) # Note: R_ioi2world is transposed + # T_worldtocam = -t_ioi2world + camT + + # # ground offset + # # ground_offset = ground_eq[2]/ + + # # # get stability labels: 1: stable, 0: unstable but in contact, -1: unstable and not `in contact + # # in_bos_label, contact_label, contact_mask = vis_smpl_with_ground(theta_world, transl_world, beta, seq_i, + # # vis_path, + # # start_idx=frame_num, + # # sub_sample=1, + # # ground_offset=ground_offset, + # # smpl_batch_size=1, + # # visualize=False) + # # in_bos_label = in_bos_label.detach().cpu().numpy() + # # contact_label = contact_label.detach().cpu().numpy() + # # contact_mask = contact_mask.detach().cpu().numpy() + + # # visualize world smpl on ground plane + # # if visualize: + # # if cam_num == 0: + # # vis_smpl_with_ground(theta_world, transl_world, beta, split+'_'+seq_i, vis_path, + # # start_idx=frame_num, + # # sub_sample=1, + # # ground_offset=ground_offset, + # # smpl_batch_size=1, + # # visualize=True) + + + # # ## visualize projected points + # # img = cv2.imread(img_path) + # # joints2d = joints2d.astype(np.int) + # # img[joints2d[:, 1], joints2d[:, 0], :] = [0, 255, 0] + + # # read GT 3D pose in cam coordinates + # # S24 = joints3d_cam + # # pelvis_cam = (S24[[2], :] + S24[[3], :]) / 2 + # # S24 -= pelvis_cam + # # S24 = np.hstack([S24, np.ones((S24.shape[0], 1))]) + + # # read GT 3D pose in world coordinates + # # S24_world = joints3d_world + # # S24_world = np.hstack([S24_world, np.ones((S24_world.shape[0], 1))]) + + # # store data + # imgnames_.append(img_path) + # centers_.append(center) + # scales_.append(scale) + # # parts_.append(part) + # # Ss_.append(S24) + # # Ss_world_.append(S24_world) + # openposes_.append(openpose) + # poses_.append(theta_cam.squeeze()) + # transls_.append(transl.squeeze()) + # poses_world_.append(theta_world.squeeze()) + # transls_world_.append(transl_world.squeeze()) + # shapes_.append(beta.squeeze()) + # cams_r_.append(R_worldtocam.tolist()) + # # Todo: note that T_worldtocam here is (1,3) whereas in h36m T_worldtocam is (1,3) + # cams_t_.append(T_worldtocam.tolist()) + # cams_k_.append(K.tolist()) + # # in_bos_label_.append(in_bos_label) + # # contact_label_.append(contact_label) + # ground_offset_.append(ground_offset) + + + # store the data struct + if not os.path.isdir(out_path): + os.makedirs(out_path) + out_file = os.path.join(out_path, f'rich_{scene_name}_smplx.npz') + np.savez(out_file, imgname=imgnames_, + # center=centers_, + # scale=scales_, + # part=parts_, + # S=Ss_, + # S_world=Ss_world_, + pose=poses_, + transl=transls_, + shape=shapes_, + # openpose=openposes_, + # pose_world=poses_world_, + # transl_world=transls_world_, + # cam_r=cams_r_, + # cam_t=cams_t_, + cam_k=cams_k_, + # in_bos_label=in_bos_label_, + contact_label=contact_label_, + # ground_offset=ground_offset_ + scene_seg=scene_seg_, + part_seg=part_seg_ + ) + print('Saved to ', out_file) + +def rectify_pose(camera_r, body_aa): + body_r = batch_rodrigues(body_aa).reshape(-1,3,3) + final_r = camera_r @ body_r + body_aa = batch_rot2aa(final_r) + return body_aa + + +def extract_cam_param_xml(xml_path: str = '', dtype=float): + import xml.etree.ElementTree as ET + tree = ET.parse(xml_path) + + extrinsics_mat = [float(s) for s in tree.find('./CameraMatrix/data').text.split()] + intrinsics_mat = [float(s) for s in tree.find('./Intrinsics/data').text.split()] + distortion_vec = [float(s) for s in tree.find('./Distortion/data').text.split()] + + focal_length_x = intrinsics_mat[0] + focal_length_y = intrinsics_mat[4] + center = np.array([[intrinsics_mat[2], intrinsics_mat[5]]], dtype=dtype) + + rotation = np.array([[extrinsics_mat[0], extrinsics_mat[1], extrinsics_mat[2]], + [extrinsics_mat[4], extrinsics_mat[5], extrinsics_mat[6]], + [extrinsics_mat[8], extrinsics_mat[9], extrinsics_mat[10]]], dtype=dtype) + + translation = np.array([[extrinsics_mat[3], extrinsics_mat[7], extrinsics_mat[11]]], dtype=dtype) + + # t = -Rc --> c = -R^Tt + cam_center = [-extrinsics_mat[0] * extrinsics_mat[3] - extrinsics_mat[4] * extrinsics_mat[7] - extrinsics_mat[8] * + extrinsics_mat[11], + -extrinsics_mat[1] * extrinsics_mat[3] - extrinsics_mat[5] * extrinsics_mat[7] - extrinsics_mat[9] * + extrinsics_mat[11], + -extrinsics_mat[2] * extrinsics_mat[3] - extrinsics_mat[6] * extrinsics_mat[7] - extrinsics_mat[10] * + extrinsics_mat[11]] + + cam_center = np.array([cam_center], dtype=dtype) + + k1 = np.array([distortion_vec[0]], dtype=dtype) + k2 = np.array([distortion_vec[1]], dtype=dtype) + + return focal_length_x, focal_length_y, center, rotation, translation, cam_center, k1, k2 + +# rich_extract(img_dataset_path='/is/cluster/work/achatterjee/rich', out_path='/is/cluster/work/achatterjee/rich/npzs', split='train') +# rich_extract(img_dataset_path='/is/cluster/work/achatterjee/rich', out_path='/is/cluster/fast/achatterjee/rich/scene_npzs', split='val') +# rich_extract(img_dataset_path='/is/cluster/work/achatterjee/rich', out_path='/is/cluster/fast/achatterjee/rich/scene_npzs/test', split='test') +# rich_extract(dataset_path='/ps/scratch/ps_shared/stripathi/4yogi/RICH/val/', out_path='/home/achatterjee/rich_ext', split='val') + +if __name__=='__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--index', type=int) + args = parser.parse_args() + + rich_extract(img_dataset_path='/is/cluster/work/achatterjee/rich', out_path='/is/cluster/fast/achatterjee/rich/scene_npzs/test', scene_indx=args.index, split='test') \ No newline at end of file diff --git a/data/preprocess/yoga-82_test/yoga82_simple_test.npz.py b/data/preprocess/yoga-82_test/yoga82_simple_test.npz.py new file mode 100644 index 0000000000000000000000000000000000000000..4776daa8d2c5762007b884bcccf87f449d991d8f --- /dev/null +++ b/data/preprocess/yoga-82_test/yoga82_simple_test.npz.py @@ -0,0 +1,34 @@ +# load split.json and make an npz with all the folders in the test split +import argparse +import json +import os +import glob +import numpy as np + +Yoga_82_PATH = '/is/cluster/work/stripathi/pycharm_remote/yogi/data/Yoga-82/yoga_dataset_images' + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--data_dir', type=str, default=Yoga_82_PATH) + parser.add_argument('--out_file', type=str, default='data/dataset_extras/yoga-82/yoga-82_simple_test_20each.npz') + args = parser.parse_args() + + # structs we use + imgnames_ = [] + + # get all ffolder names in the data_dir + folders = glob.glob(os.path.join(args.data_dir, '*')) + folders.sort() + for folder in folders: + print(folder) + # get all images in the folder + images = glob.glob(os.path.join(folder, '*.jpg'), recursive=True) + print(len(images)) + # only take random 50 images from each folder + images = np.random.choice(images, 20, replace=False) + imgnames_.extend(images) + + + np.savez(args.out_file, imgname=imgnames_,) + print('Saved to ', args.out_file) + diff --git a/example_images/213.jpg b/example_images/213.jpg new file mode 100644 index 0000000000000000000000000000000000000000..23fc4c953cae57a6a81b53e175f54454e1f677ea Binary files /dev/null and b/example_images/213.jpg differ diff --git a/example_images/pexels-photo-15732209.jpeg b/example_images/pexels-photo-15732209.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..f33474dbd5954230790ae2b35216eae3e5d9ba18 Binary files /dev/null and b/example_images/pexels-photo-15732209.jpeg differ diff --git a/example_images/pexels-photo-207569.webp b/example_images/pexels-photo-207569.webp new file mode 100644 index 0000000000000000000000000000000000000000..3615685aa1848462a4131c1008686b3a562a42e3 Binary files /dev/null and b/example_images/pexels-photo-207569.webp differ diff --git a/example_images/pexels-photo-3622517.webp b/example_images/pexels-photo-3622517.webp new file mode 100644 index 0000000000000000000000000000000000000000..6685dca45360cde9954d19bf2950a7544386faca Binary files /dev/null and b/example_images/pexels-photo-3622517.webp differ diff --git a/fetch_data.sh b/fetch_data.sh new file mode 100644 index 0000000000000000000000000000000000000000..d6bc3e4f819356604d08db2b7f4c8ff0e59c34c3 --- /dev/null +++ b/fetch_data.sh @@ -0,0 +1,18 @@ +#!/bin/bash + +# Script that fetches checkpoints and other necessary data for inference + +# Download utility files and other constants +wget https://keeper.mpdl.mpg.de/f/50cf65320b824391854b/?dl=1 --max-redirect=2 --trust-server-names && tar -xvf data.tar.gz && rm -r data.tar.gz + +# Downloading existing checkpoint +mkdir checkpoints +wget https://keeper.mpdl.mpg.de/f/6f2e2258558f46ceb269/?dl=1 --max-redirect=2 --trust-server-names && tar -xvf Release_Checkpoint.tar.gz --directory checkpoints && rm -r Release_Checkpoint.tar.gz + +# Downloading other checkpoint +wget https://keeper.mpdl.mpg.de/f/9cb970221b1e45d185b8/?dl=1 --max-redirect=2 --trust-server-names && tar -xvf Other_Checkpoints.tar.gz --directory checkpoints && rm -r Other_Checkpoints.tar.gz + +# Downloading training datasets +mkdir datasets +wget https://keeper.mpdl.mpg.de/f/81c3ec9997dd440b8db3/?dl=1 --max-redirect=2 --trust-server-names && tar -xvf Release_Datasets.tar.gz --directory datasets && rm -r Release_Datasets.tar.gz + diff --git a/hot_analysis/.ipynb_checkpoints/hico_analysis-checkpoint.ipynb b/hot_analysis/.ipynb_checkpoints/hico_analysis-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..b66fc0bf2a72ab372a5e2563cb09ae7bc1b9e105 --- /dev/null +++ b/hot_analysis/.ipynb_checkpoints/hico_analysis-checkpoint.ipynb @@ -0,0 +1,307 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import pandas as pd \n", + "import ipdb\n", + "import os\n", + "import pickle as pkl\n", + "import os.path as osp\n", + "import numpy as np\n", + "from PIL import Image\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total images found: 9642\n", + "Images after KP filtering: 3895\n", + "['hake_train2015_HICO_train2015_00005476.jpg', 'hake_train2015_HICO_train2015_00008329.jpg', 'hake_train2015_HICO_train2015_00008027.jpg', 'hake_train2015_HICO_train2015_00013408.jpg', 'hake_train2015_HICO_train2015_00010656.jpg']\n" + ] + } + ], + "source": [ + "# Load Agniv VITpose-base hico filtering\n", + "filter_path = './agniv_pose_filter/hico.npy'\n", + "pose_md = np.load(filter_path, allow_pickle=True)\n", + "pose_md = pose_md.item()\n", + "filter_img_names = {}\n", + "\n", + "print(f'Total images found: {len(pose_md)}')\n", + "\n", + "# Filter out images with < 10 visible keypoints \n", + "kp_thresh = 10\n", + "\n", + "for imgname, pose_num in pose_md.items():\n", + " if pose_num > kp_thresh:\n", + " filter_img_names[imgname] = pose_num\n", + " \n", + "print(f'Images after KP filtering: {len(filter_img_names)}')\n", + "\n", + "print(list(filter_img_names.keys())[:5])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# # Load Agniv VITpose-base hot dict\n", + "# filter_path = \"./agniv_pose_filter/hot_dict.pkl\"\n", + "# with open(filter_path, 'rb') as f:\n", + "# pose_md_dict = pkl.load(f)\n", + " \n", + "# hico_dict = {}\n", + "\n", + "# for k, v in pose_md_dict.items():\n", + "# if 'hake' in k:\n", + "# hico_dict[k] = v\n", + " \n", + "# print(f'Total images found: {len(hico_dict)}')\n", + "\n", + "# # Filter out images with < 10 visible keypoints \n", + "# kp_thresh = 10\n", + "\n", + "# filter_img_names = {}\n", + "\n", + "# for imgname, kp_md in hico_dict.items():\n", + "# if kp_md == 0:\n", + "# continue\n", + "# if kp_md[\"num_persons\"] == 1 and kp_md[\"num_kpt\"][0.5][0] > kp_thresh:\n", + "# filter_img_names[imgname] = kp_md[\"num_kpt\"][0.5][0]\n", + " \n", + "# print(f'Images after KP filtering: {len(filter_img_names)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2\n", + "0 1 airplane board\n", + "1 2 airplane direct\n", + "2 3 airplane exit\n", + "3 4 airplane fly\n", + "4 5 airplane inspect\n", + ".. ... ... ...\n", + "595 596 zebra feed\n", + "596 597 zebra hold\n", + "597 598 zebra pet\n", + "598 599 zebra watch\n", + "599 600 zebra no_interaction\n", + "\n", + "[600 rows x 3 columns]\n" + ] + } + ], + "source": [ + "\n", + "hico_annot = json.load(open('/ps/project/datasets/HICO/hico-image-level/hico-training-set-image-level.json', 'rb'))\n", + "hoi_mapping = pd.read_csv('/ps/project/datasets/HICO/hico-image-level/hico_hoi_list.txt', header=None, delim_whitespace=True)\n", + "print(hoi_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final number of images 3154\n" + ] + } + ], + "source": [ + "version = '1'\n", + "out_dir = f'./filtered_data/v_{version}'\n", + "os.makedirs(out_dir, exist_ok=True)\n", + "\n", + "objectwise_img_names = {}\n", + "imgwise_object_names = {}\n", + "img_dir = '/ps/project/datasets/HICO/hico_20150920/images/train2015'\n", + "\n", + "bad_object_names = ['bear', 'bird', 'cat', 'cow', \n", + " 'dog', 'elephant', 'giraffe', 'horse', \n", + " 'mouse', 'person', 'sheep', 'zebra' ]\n", + "bad_action_names = ['buy', 'chase', 'direct', 'greet', 'herd', 'hose',\n", + " 'hug', 'hunt', 'milk', 'no_interaction', 'pet', 'point', 'teach',\n", + " 'watch', 'wave']\n", + "\n", + "for i, (img_name, img_md) in enumerate(hico_annot.items()):\n", + " \n", + " # Apply keypoint number filtering on the images \n", + " full_img_name = 'hake_train2015_' + img_name\n", + " if full_img_name not in filter_img_names.keys():\n", + " continue\n", + " \n", + " # show the image\n", + " if i < 0:\n", + " img = Image.open(osp.join(img_dir,img_name))\n", + " display(img)\n", + " \n", + " obj_names = []\n", + " action_names = []\n", + " kp_num = filter_img_names[full_img_name]\n", + " \n", + " # travel through all hoi in the metadata, save obj_names and action_names for the hois\n", + " for hoi_id in img_md['hoi_id']:\n", + " img_md_row = hoi_mapping.loc[hoi_mapping[0] == hoi_id].iloc[0]\n", + "\n", + " obj_name = img_md_row[1]\n", + " if obj_name in bad_object_names:\n", + " continue\n", + " action_name = img_md_row[2]\n", + " if action_name in bad_action_names:\n", + " continue\n", + " \n", + " obj_names.append(obj_name)\n", + " action_names.append(action_name)\n", + " \n", + " if len(set(obj_names)) == 0 or len(action_names) == 0:\n", + " continue\n", + " \n", + " imgwise_object_names.setdefault(full_img_name,[]).extend(list(set(obj_names)))\n", + " \n", + "# # # Display images with multiple objects\n", + "# if len(set(obj_names)) > 1:\n", + "# print(img_name)\n", + "# print(obj_names)\n", + "# print(action_names)\n", + "# print(f'Number of Kps: {kp_num}')\n", + "# img = Image.open(osp.join(img_dir,img_name))\n", + "# display(img)\n", + " \n", + " for obj_name in set(obj_names):\n", + " objectwise_img_names.setdefault(obj_name,[]).append(full_img_name)\n", + " \n", + "print(f'Final number of images {len(imgwise_object_names)}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Save the imagewise_object dict\n", + "out_path = osp.join(out_dir, 'object_per_image_dict.json')\n", + "with open(out_path, 'w') as fp:\n", + " json.dump(imgwise_object_names, fp)\n", + " print(f'saved at {out_path}')\n", + " \n", + "# # save image_list \n", + "# out_path = osp.join(out_dir, 'hico_imglist_all_140223.txt')\n", + "# with open(out_path, 'w') as f:\n", + "# f.write('\\n'.join(imgwise_object_names.keys()))\n", + "# print(f'saved at {out_path}')\n", + "\n", + "\n", + "# Save the object_wise dict\n", + "out_path = osp.join(out_dir, 'imgnames_per_object_dict.json')\n", + "with open(out_path, 'w') as fp:\n", + " json.dump(objectwise_img_names, fp)\n", + " print(f'saved at {out_path}')\n", + "\n", + " \n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# sort the dictionary \n", + "objectwise_img_names = dict(sorted(objectwise_img_names.items(), key=lambda x: len(x[1]), reverse=True))\n", + "\n", + "# Extract object names and image counts\n", + "obj_names = list(objectwise_img_names.keys())\n", + "img_counts = [len(objectwise_img_names[obj]) for obj in objectwise_img_names]\n", + "print(sum(img_counts))\n", + "\n", + "# Create bar plot\n", + "sns.barplot(x=obj_names, y=img_counts)\n", + "\n", + "# Add x-axis and y-axis labels\n", + "plt.xlabel('Object')\n", + "plt.ylabel('Number of Images')\n", + "\n", + "plt.xticks(rotation=45, ha='right', fontsize=3)\n", + "\n", + "# Save the plot as a high-resolution image file\n", + "out_path = osp.join(out_dir, 'image_per_object_category.png')\n", + "plt.savefig(out_path, dpi=300)\n", + "\n", + "# Show plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/hot_analysis/.ipynb_checkpoints/vcoco_analysis-checkpoint.ipynb b/hot_analysis/.ipynb_checkpoints/vcoco_analysis-checkpoint.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..062ae397e03d68459a841527c122e3af0a510c2d --- /dev/null +++ b/hot_analysis/.ipynb_checkpoints/vcoco_analysis-checkpoint.ipynb @@ -0,0 +1,276 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import pandas as pd \n", + "import ipdb\n", + "import os\n", + "import pickle as pkl\n", + "import os.path as osp\n", + "import numpy as np\n", + "from PIL import Image" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total images found: 5459\n", + "Images after KP filtering: 2386\n", + "['vcoco_000000051899.jpg', 'vcoco_000000093127.jpg', 'vcoco_000000455665.jpg', 'vcoco_000000248389.jpg', 'vcoco_000000403947.jpg']\n" + ] + } + ], + "source": [ + "# Load Agniv VITpose-base hot dict\n", + "filter_path = \"./agniv_pose_filter/hot_dict.pkl\"\n", + "with open(filter_path, 'rb') as f:\n", + " pose_md_dict = pkl.load(f)\n", + " \n", + "vcoco_dict = {}\n", + "\n", + "for k, v in pose_md_dict.items():\n", + " if 'vcoco' in k:\n", + " vcoco_dict[k] = v\n", + " \n", + "print(f'Total images found: {len(vcoco_dict)}')\n", + "\n", + "# Filter out images with < 10 visible keypoints \n", + "kp_thresh = 10\n", + "\n", + "filter_img_names = {}\n", + "\n", + "for imgname, kp_md in vcoco_dict.items():\n", + " if kp_md == 0:\n", + " continue\n", + " if kp_md[\"num_persons\"] == 1 and kp_md[\"num_kpt\"][0.5][0] > kp_thresh:\n", + " filter_img_names[imgname] = kp_md[\"num_kpt\"][0.5][0]\n", + " \n", + "print(f'Images after KP filtering: {len(filter_img_names)}')\n", + "\n", + "print(list(filter_img_names.keys())[:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=1.30s)\n", + "creating index...\n", + "index created!\n", + "Final number of images 2368\n" + ] + } + ], + "source": [ + "from pycocotools.coco import COCO\n", + "version = '1'\n", + "out_dir = f'./filtered_data/v_{version}/vcoco'\n", + "os.makedirs(out_dir, exist_ok=True)\n", + "\n", + "coco_dir = '/is/cluster/work/stripathi/pycharm_remote/v-coco/data'\n", + "coco = COCO(os.path.join(coco_dir, 'instances_vcoco_all_2014.json'))\n", + "\n", + "imgwise_object_names = {}\n", + "img_dir = '/ps/project/datasets/HOT/Contact_Data/images/training/'\n", + "\n", + "bad_object_names = ['bird', 'cat', 'dog', 'horse', 'sheep', 'cow', \n", + " 'elephant', 'bear', 'zebra', 'giraffe']\n", + "\n", + "# # Get all coco objects\n", + "# count = 0\n", + "# for id in range(91):\n", + "# try: \n", + "# print(coco.loadCats(id))\n", + "# count += 1\n", + "# except:\n", + "# print(f'ID:{id} does not exist')\n", + "# continue\n", + "# print(count)\n", + "\n", + "objectwise_img_names = {}\n", + "for img_name in filter_img_names.keys():\n", + " img_id = int(os.path.splitext(img_name.split('_')[-1])[0])\n", + " ann_ids = coco.getAnnIds([img_id])\n", + " anns = coco.loadAnns(ann_ids)\n", + " object_list = []\n", + " for ann in anns:\n", + " object_cat = coco.loadCats(ann[\"category_id\"])\n", + " if len(object_cat) > 1:\n", + " ipdb.set_trace()\n", + " object_name = object_cat[0]['name']\n", + " if object_name in bad_object_names:\n", + " continue\n", + " if object_name != 'person':\n", + " object_list.append(object_name)\n", + " if len(object_list) != 0:\n", + " imgwise_object_names[img_name] = list(set(object_list))\n", + " \n", + " for obj_name in set(object_list):\n", + " objectwise_img_names.setdefault(obj_name,[]).append(img_name)\n", + " \n", + "# # Visualize images \n", + "# print(img_name)\n", + "# print(list(set(object_list)))\n", + "# img = Image.open(osp.join(img_dir,img_name))\n", + "# display(img)\n", + " \n", + " \n", + "print(f'Final number of images {len(imgwise_object_names)}')\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saved at ./filtered_data/v_1/vcoco/object_per_image_dict.json\n", + "saved at ./filtered_data/v_1/vcoco/vcoco_imglist_all_170223.txt\n", + "saved at ./filtered_data/v_1/vcoco/imgnames_per_object_dict.json\n" + ] + } + ], + "source": [ + "# Save the imagewise_object dict\n", + "out_path = osp.join(out_dir, 'object_per_image_dict.json')\n", + "with open(out_path, 'w') as fp:\n", + " json.dump(imgwise_object_names, fp)\n", + " print(f'saved at {out_path}')\n", + " \n", + "# save image_list \n", + "out_path = osp.join(out_dir, 'vcoco_imglist_all_170223.txt')\n", + "with open(out_path, 'w') as f:\n", + " f.write('\\n'.join(imgwise_object_names.keys()))\n", + " print(f'saved at {out_path}')\n", + "\n", + "\n", + "# Save the object_wise dict\n", + "out_path = osp.join(out_dir, 'imgnames_per_object_dict.json')\n", + "with open(out_path, 'w') as fp:\n", + " json.dump(objectwise_img_names, fp)\n", + " print(f'saved at {out_path}')\n", + "\n", + " \n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5072\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# sort the dictionary \n", + "objectwise_img_names = dict(sorted(objectwise_img_names.items(), key=lambda x: len(x[1]), reverse=True))\n", + "\n", + "# Extract object names and image counts\n", + "obj_names = list(objectwise_img_names.keys())\n", + "img_counts = [len(objectwise_img_names[obj]) for obj in objectwise_img_names]\n", + "print(sum(img_counts))\n", + "\n", + "# Create bar plot\n", + "sns.barplot(x=obj_names, y=img_counts)\n", + "\n", + "# Add x-axis and y-axis labels\n", + "plt.xlabel('Object')\n", + "plt.ylabel('Number of Images')\n", + "\n", + "plt.xticks(rotation=45, ha='right', fontsize=3)\n", + "\n", + "# Save the plot as a high-resolution image file\n", + "out_path = osp.join(out_dir, 'image_per_object_category.png')\n", + "plt.savefig(out_path, dpi=300)\n", + "\n", + "# Show plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/hot_analysis/agniv_pose_filter/hico.npy b/hot_analysis/agniv_pose_filter/hico.npy new file mode 100644 index 0000000000000000000000000000000000000000..22e1b844446156ebc85f3a947b14b029daeca594 --- /dev/null +++ b/hot_analysis/agniv_pose_filter/hico.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b01acf6e31dad34eb1e48277e227ea0668f17905f55eae44b640cc1d14a9e41c +size 520245 diff --git a/hot_analysis/agniv_pose_filter/hot.npy b/hot_analysis/agniv_pose_filter/hot.npy new file mode 100644 index 0000000000000000000000000000000000000000..f27cfbdd7dba723f35603ecc8a1c16e903a471bd --- /dev/null +++ b/hot_analysis/agniv_pose_filter/hot.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:64872b84670cb2ec643f1649e1a7926aed950adcb8f6f952916924bc4ab10f37 +size 731363 diff --git a/hot_analysis/agniv_pose_filter/hot_dict.pkl b/hot_analysis/agniv_pose_filter/hot_dict.pkl new file mode 100644 index 0000000000000000000000000000000000000000..2c00811961d9ae6892a2ce4096bf794be6aea4bd --- /dev/null +++ b/hot_analysis/agniv_pose_filter/hot_dict.pkl @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f0c0a6e35bf87e98327189ef8d732aa19b0d1c85c963c1ee348e1e466864845e +size 3679000 diff --git a/hot_analysis/agniv_pose_filter/pq_wnp.npy b/hot_analysis/agniv_pose_filter/pq_wnp.npy new file mode 100644 index 0000000000000000000000000000000000000000..5d331256e6cc8712bc1981be3eb3500684c5453e --- /dev/null +++ b/hot_analysis/agniv_pose_filter/pq_wnp.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7cad59d5aaf077141d60a492c3784d8994d53f4dfd020f9eb4e11b3e430ef65d +size 25433 diff --git a/hot_analysis/agniv_pose_filter/vcoco.npy b/hot_analysis/agniv_pose_filter/vcoco.npy new file mode 100644 index 0000000000000000000000000000000000000000..a4071a491f422eaa179807b793b809eb3901fd8f --- /dev/null +++ b/hot_analysis/agniv_pose_filter/vcoco.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:14cc34940ff4a61cfc952d8c5b98e0e7b1a58392177198fa4589f8b2bcead02a +size 184801 diff --git a/hot_analysis/count_objects_per_img.py b/hot_analysis/count_objects_per_img.py new file mode 100644 index 0000000000000000000000000000000000000000..1bc4f6e997ab887a809449c997ed006a7e1a8d44 --- /dev/null +++ b/hot_analysis/count_objects_per_img.py @@ -0,0 +1,35 @@ +# get average number of objects per image +import os.path as osp +import json +import plotly.express as px +import plotly.io as pio + +version = '1' +dir = '/is/cluster/work/stripathi/pycharm_remote/dca_contact/hot_analysis/' +out_dir_hico = osp.join(dir, f'filtered_data/v_{version}/hico') +out_dir_vcoco = osp.join(dir, f'filtered_data/v_{version}/vcoco') + +imgwise_obj_dict_hico = osp.join(out_dir_hico, 'object_per_image_dict.json') +imgwise_obj_dict_vcoco = osp.join(out_dir_vcoco, 'object_per_image_dict.json') + +with open(imgwise_obj_dict_hico, 'r') as fp: + imgwise_obj_dict_hico = json.load(fp) +with open(imgwise_obj_dict_vcoco, 'r') as fp: + imgwise_obj_dict_vcoco = json.load(fp) + +# combine the dicts +imgwise_obj_dict = imgwise_obj_dict_hico.copy() +imgwise_obj_dict.update(imgwise_obj_dict_vcoco) + +# get average length of object in the object per image key +avg_obj_per_img = sum([len(v) for v in imgwise_obj_dict.values()]) / len(imgwise_obj_dict) +print(f'Average number of objects per image: {avg_obj_per_img}') + +# get average searately for hico and vcoco +avg_obj_per_img_hico = sum([len(v) for v in imgwise_obj_dict_hico.values()]) / len(imgwise_obj_dict_hico) +print(f'Average number of objects per image in hico: {avg_obj_per_img_hico}') + +avg_obj_per_img_vcoco = sum([len(v) for v in imgwise_obj_dict_vcoco.values()]) / len(imgwise_obj_dict_vcoco) +print(f'Average number of objects per image in vcoco: {avg_obj_per_img_vcoco}') + + diff --git a/hot_analysis/create_combined_objectwise_plots.ipynb b/hot_analysis/create_combined_objectwise_plots.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..1ad6034daf2355b4e58b4a8d2e6849c12c7809b8 --- /dev/null +++ b/hot_analysis/create_combined_objectwise_plots.ipynb @@ -0,0 +1,291 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 22, + "id": "1738c3a5", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "import os.path as osp\n", + "import json\n", + "import os\n", + "\n", + "version = '1'\n", + "out_dir_hico = f'./filtered_data/v_{version}/hico'\n", + "out_dir_vcoco = f'./filtered_data/v_{version}/vcoco'\n", + "out_dir_phosa = f'./filtered_data/v_{version}/phosa_split'\n", + "out_dir_behave = f'./filtered_data/v_{version}/behave_split'\n", + "\n", + "objectwise_img_dict_hico = osp.join(out_dir_hico, 'imgnames_per_object_dict.json')\n", + "objectwise_img_dict_vcoco = osp.join(out_dir_vcoco, 'imgnames_per_object_dict.json')\n", + "\n", + "with open(objectwise_img_dict_hico, 'r') as fp:\n", + " objectwise_img_dict_hico = json.load(fp)\n", + "with open(objectwise_img_dict_vcoco, 'r') as fp:\n", + " objectwise_img_dict_vcoco = json.load(fp)\n", + "\n", + "# sort the dictionary \n", + "objectwise_img_names1 = dict(sorted(objectwise_img_dict_hico.items(), key=lambda x: len(x[1]), reverse=True))\n", + "\n", + "# # Extract object names and image counts\n", + "# obj_names1 = list(objectwise_img_names_hico.keys())\n", + "# print(sorted(obj_names1))\n", + "# image_count1 = [len(objectwise_img_names_hico[obj]) for obj in objectwise_img_names_hico]\n", + "# print(sum(image_count1))\n", + "\n", + "# sort the dictionary \n", + "objectwise_img_names2 = dict(sorted(objectwise_img_dict_vcoco.items(), key=lambda x: len(x[1]), reverse=True))\n", + "\n", + "# # Extract object names and image counts\n", + "# obj_names2 = list(objectwise_img_names_vcoco.keys())\n", + "# print(sorted(obj_names2))\n", + "# image_count2 = [len(objectwise_img_names_vcoco[obj]) for obj in objectwise_img_names_vcoco]\n", + "# print(sum(image_count2))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "2d6ad5ed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2119\n", + "saved at ./filtered_data/v_1/phosa_split/imgnames_per_object_dict.json\n" + ] + } + ], + "source": [ + "# Extract image list for object in PHOSA\n", + "def merge_dictionaries(dict1, dict2):\n", + " merged_dict = {}\n", + "\n", + " # Merge values for common keys\n", + " for key in set(dict1.keys()) | set(dict2.keys()):\n", + " merged_dict[key] = list(set(dict1.get(key, [])) | set(dict2.get(key, [])))\n", + "\n", + " return merged_dict\n", + "\n", + "def filter_dictionary(dictionary, keys):\n", + " filtered_dict = {key: dictionary[key] for key in keys if key in dictionary}\n", + " return filtered_dict\n", + "\n", + "phosa_object_names = [\"bat\", \"bench\", \"bicycle\", \"laptop\", \"motorcycle\", \"skateboard\", \"surfboard\", \"tennis\"]\n", + "combined_objectwise_img_names = merge_dictionaries(objectwise_img_names1, objectwise_img_names2)\n", + "phosa_objectwise_img_names = filter_dictionary(combined_objectwise_img_names, phosa_object_names)\n", + "net_img_count = 0\n", + "for v in phosa_objectwise_img_names.values():\n", + " net_img_count += len(v)\n", + "print(net_img_count)\n", + "# Save the object_wise dict\n", + "os.makedirs(out_dir_phosa, exist_ok=True)\n", + "out_path_phosa = osp.join(out_dir_phosa, 'imgnames_per_object_dict.json')\n", + "with open(out_path_phosa, 'w') as fp:\n", + " json.dump(phosa_objectwise_img_names, fp)\n", + " print(f'saved at {out_path_phosa}')\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "3f2a5e62", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saved at ./filtered_data/v_1/behave_split/imgnames_per_object_dict.json\n" + ] + } + ], + "source": [ + "# Extract image list for object in CHORE\n", + "\n", + "behave_object_names_original = [\"backpack\", \"basketball\", \"boxlarge\", \"boxlong\", \"boxmedium\", \"boxsmall\", \"boxtiny\", \"chairblack\", \"chairwood\", \"keyboard\", \"monitor\", \"plasticcontainer\", \"stool\", \"suitcase\", \"tablesmall\", \"tablesquare\", \"toolbox\", \"trashbin\", \"yogaball\", \"yogamat\"]\n", + "behave_object_names_in_hot = [\"backpack\", \"chair\", \"keyboard\", \"suitcase\"]\n", + "\n", + "combined_objectwise_img_names = merge_dictionaries(objectwise_img_names1, objectwise_img_names2)\n", + "behave_objectwise_img_names = filter_dictionary(combined_objectwise_img_names, behave_object_names_in_hot)\n", + "net_img_count = 0\n", + "for v in behave_objectwise_img_names.values():\n", + " net_img_count += len(v)\n", + "\n", + "# Save the object_wise dict\n", + "os.makedirs(out_dir_behave, exist_ok=True)\n", + "out_path_behave = osp.join(out_dir_behave, 'imgnames_per_object_dict.json')\n", + "with open(out_path_behave, 'w') as fp:\n", + " json.dump(behave_objectwise_img_names, fp)\n", + " print(f'saved at {out_path_behave}')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "730e9ac2", + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'seaborn'", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[0;32mIn[3], line 2\u001B[0m\n\u001B[1;32m 1\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mmatplotlib\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mpyplot\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01mplt\u001B[39;00m\n\u001B[0;32m----> 2\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mseaborn\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m \u001B[38;5;21;01msns\u001B[39;00m\n\u001B[1;32m 4\u001B[0m \u001B[38;5;66;03m# sort the dictionary \u001B[39;00m\n\u001B[1;32m 5\u001B[0m objectwise_img_names1 \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mdict\u001B[39m(\u001B[38;5;28msorted\u001B[39m(objectwise_img_names1\u001B[38;5;241m.\u001B[39mitems(), key\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mlambda\u001B[39;00m x: \u001B[38;5;28mlen\u001B[39m(x[\u001B[38;5;241m1\u001B[39m]), reverse\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m))\n", + "\u001B[0;31mModuleNotFoundError\u001B[0m: No module named 'seaborn'" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# sort the dictionary \n", + "objectwise_img_names1 = dict(sorted(objectwise_img_names1.items(), key=lambda x: len(x[1]), reverse=True))\n", + "\n", + "# Extract object names and image counts\n", + "obj_names1 = list(objectwise_img_names1.keys())\n", + "print(obj_names1)\n", + "img_counts1 = [len(objectwise_img_names1[obj]) for obj in objectwise_img_names1]\n", + "print(sum(img_counts1))\n", + "\n", + "# sort the dictionary \n", + "objectwise_img_names2 = dict(sorted(objectwise_img_names2.items(), key=lambda x: len(x[1]), reverse=True))\n", + "\n", + "# Extract object names and image counts\n", + "obj_names2 = list(objectwise_img_names2.keys())\n", + "print(obj_names2)\n", + "img_counts2 = [len(objectwise_img_names2[obj]) for obj in objectwise_img_names2]\n", + "print(sum(img_counts2))\n", + "\n", + "# Create a figure with two subplots\n", + "fig, axs = plt.subplots(nrows=2, sharex=True, figsize=(10, 8))\n", + "\n", + "# Plot the first graph in the first subplot\n", + "sns.barplot(x=obj_names1, y=img_counts1, ax=axs[0])\n", + "\n", + "# Add y-axis label to the first subplot\n", + "axs[0].set_ylabel('Number of Images')\n", + "\n", + "# Set the rotation and alignment of x-tick labels in the first subplot\n", + "axs[0].tick_params(axis='x', rotation=45)\n", + "\n", + "# Plot the second graph in the second subplot\n", + "sns.barplot(x=obj_names2, y=img_counts2, ax=axs[1])\n", + "\n", + "# Add x-axis and y-axis labels to the second subplot\n", + "axs[1].set_xlabel('Object')\n", + "axs[1].set_ylabel('Number of Images')\n", + "\n", + "# Set the rotation and alignment of x-tick labels in the second subplot\n", + "axs[1].tick_params(axis='x', rotation=45)\n", + "\n", + "# Save the plot as a high-resolution image file\n", + "out_path = osp.join(out_dir, 'image_per_object_category.png')\n", + "plt.savefig(out_path, dpi=300)\n", + "\n", + "# Show the plot\n", + "plt.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "23c503dd", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_135310/1442551168.py:22: UserWarning: FixedFormatter should only be used together with FixedLocator\n", + " ax.set_xticklabels(X1)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "\n", + "# Create data\n", + "X1 = ['A', 'B', 'C', 'D', 'E']\n", + "Y1 = [10, 15, 20, 25, 30]\n", + "Y2 = [20, 25, 30, 35, 40]\n", + "\n", + "# Set color scheme and font\n", + "colors = sns.color_palette(\"Blues\", 2)\n", + "\n", + "# Create bar plot\n", + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "ax.bar(X1, Y1, color=colors[0], edgecolor='white', linewidth=1)\n", + "ax.bar(X1, Y2, bottom=Y1, color=colors[1], edgecolor='white', linewidth=1)\n", + "\n", + "# Set axis labels and ticks\n", + "ax.set_xlabel('X1')\n", + "ax.set_ylabel('Y')\n", + "ax.set_xticklabels(X1)\n", + "ax.tick_params(axis='both', which='major', labelsize=14)\n", + "ax.grid(axis='y', alpha=0.4)\n", + "\n", + "# Add legend and title\n", + "ax.legend(['Y1', 'Y2'], loc='upper left')\n", + "ax.set_title('Vertical stacked bar graph of Y1 and Y2')\n", + "\n", + "# Save the graph as PDF or PNG\n", + "plt.savefig('vertical_stacked_bar_graph.png', dpi=300, bbox_inches='tight')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c055ec7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/hot_analysis/create_part_probability_mesh.py b/hot_analysis/create_part_probability_mesh.py new file mode 100644 index 0000000000000000000000000000000000000000..b7603fdf2f5db0227d70596fa925275f0d5c2edc --- /dev/null +++ b/hot_analysis/create_part_probability_mesh.py @@ -0,0 +1,86 @@ +import numpy as np +import os +import json +import trimesh +import seaborn as sns + + +# Load the combined dca train, val and test npzs +dir = '/is/cluster/work/stripathi/pycharm_remote/dca_contact/data/dataset_extras' +trainval_npz = np.load(os.path.join(dir, 'hot_dca_trainval.npz'), allow_pickle=True) +test_npz = np.load(os.path.join(dir, 'hot_dca_test.npz'), allow_pickle=True) + +# combine the two npz +combined_npz = {} +for key in trainval_npz.keys(): + combined_npz[key] = np.concatenate([trainval_npz[key], test_npz[key]], axis=0) + +segmentation_path = 'data/smpl_vert_segmentation.json' +with open(segmentation_path, 'rb') as f: + part_segmentation = json.load(f) + +combine_keys = {'leftFoot': ['leftToeBase'], + 'rightFoot': ['rightToeBase'], + 'leftHand': ['leftHandIndex1'], + 'rightHand': ['rightHandIndex1'], + 'spine': ['spine1', 'spine2'], + 'head': ['neck'],} + +for key in combine_keys: + for subkey in combine_keys[key]: + part_segmentation[key] += part_segmentation[subkey] + del part_segmentation[subkey] + +# reverse the part segmentation +part_segmentation_rev = {} +for part in part_segmentation: + for vert in part_segmentation[part]: + part_segmentation_rev[vert] = part + +# count the number of contact instances per vertex +per_vert_contact_count = np.zeros(6890) +for cls in combined_npz['contact_label']: + per_vert_contact_count += cls + +# calculate the maximum contact count per part +part_contact_max = {} +for part in part_segmentation: + part_contact_max[part] = np.max(per_vert_contact_count[part_segmentation[part]]) + +# calculate the contact probability globally +contact_prob = np.zeros(6890) +for vid in range(6890): + contact_prob[vid] = (per_vert_contact_count[vid] / max(per_vert_contact_count)) ** 0.3 + +# save the contact probability mesh +outdir = "/is/cluster/work/stripathi/pycharm_remote/dca_contact/hot_analysis" + +# load template smpl mesh +mesh = trimesh.load_mesh('data/smpl/smpl_neutral_tpose.ply') +vertex_colors = trimesh.visual.interpolate(contact_prob, 'jet') +# set the vertex colors of the mesh +mesh.visual.vertex_colors = vertex_colors +# save the mesh +out_path = os.path.join(outdir, "contact_probability_mesh.obj") +mesh.export(out_path) + +# # calculate the contact probability per part +# contact_prob = np.zeros(6890) +# for vid in range(6890): +# if 'Hand' in part_segmentation_rev[vid]: +# contact_prob[vid] = (per_vert_contact_count[vid] / part_contact_max[part_segmentation_rev[vid]]) ** 0.4 if 'Hand' not in part_segmentation_rev[vid] else (per_vert_contact_count[vid] / part_contact_max[part_segmentation_rev[vid]]) ** 0.8 +# +# # save the contact probability mesh +# outdir = "/is/cluster/work/stripathi/pycharm_remote/dca_contact/hot_analysis" +# +# # load template smpl mesh +# mesh = trimesh.load_mesh('data/smpl/smpl_neutral_tpose.ply') +# vertex_colors = trimesh.visual.interpolate(contact_prob, 'jet') +# # set the vertex colors of the mesh +# mesh.visual.vertex_colors = vertex_colors +# # save the mesh +# out_path = os.path.join(outdir, "contact_probability_mesh_part.obj") +# mesh.export(out_path) + + + diff --git a/hot_analysis/damon_qc_stats/compute_accuracy_iou_damon.py b/hot_analysis/damon_qc_stats/compute_accuracy_iou_damon.py new file mode 100644 index 0000000000000000000000000000000000000000..e4e45e469419042fbf3a89a3b4166f9c3eb9b2a0 --- /dev/null +++ b/hot_analysis/damon_qc_stats/compute_accuracy_iou_damon.py @@ -0,0 +1,59 @@ +# load amt csv, go through each line in vertices, combine the vertices for each object label and then compute the iou with GT from RICH and PROX +import pandas as pd +import numpy as np + +# load csv +csv_path = './quality_assurance_accuracy.csv' +df = pd.read_csv(csv_path) + +# load gt npz +gt_path = './qa_accuracy_gt_contact_combined.npz' +gt = np.load(gt_path) + +def compute_iou(pred_verts, gt_verts): + if len(pred_verts) != 0: + intersect = list(set(pred_verts) & set(gt_verts)) + iou = len(intersect) / (len(pred_verts) + len(gt_verts) - len(intersect)) + else: + iou = 0 + return iou + +all_ious = [] +# for loop each row in df +for index, row in df.iterrows(): + combined_annotation_ids = [] + imgname = [] + # get vertices + annotation_dict = eval(row['vertices']) + worker_id = row['WorkerId'] + # single for loop in the dictionary + for im, anno in annotation_dict.items(): + imgname.append(im) + for ann in anno: + # single for loop in the dict + for k, v in ann.items(): + combined_annotation_ids.extend(v) + # remove repeated values + combined_annotation_ids = list(set(combined_annotation_ids)) + + assert len(imgname) == 1 + imgname = imgname[0] + + # get gt for the imgname + gt_ids = gt[imgname] + if 'prox' in imgname: + continue + + # compute iou + iou = compute_iou(combined_annotation_ids, gt_ids) + print('worker id: ', worker_id, 'imgname: ', imgname, 'iou: ', iou) + all_ious.append(iou) + +# compute mean iou +mean_iou = np.mean(all_ious) +print('mean iou: ', mean_iou) + + + + + diff --git a/hot_analysis/damon_qc_stats/compute_fleiss_kappa_damon.py b/hot_analysis/damon_qc_stats/compute_fleiss_kappa_damon.py new file mode 100644 index 0000000000000000000000000000000000000000..ffe8849cc8e84fd6c3fcf71cb6a1da8f55b65a64 --- /dev/null +++ b/hot_analysis/damon_qc_stats/compute_fleiss_kappa_damon.py @@ -0,0 +1,111 @@ +import numpy as np +import pandas as pd +from scipy.stats import chi2 + + +def correct_supporting(vertices): + # Copy vertices from objects to supporting since the dropdown option was missing in the QA app + def add_supporting(verts, object_name): + temp_supporting_vids = [] + supporting_vid = -1 + for id, v_i in enumerate(verts): + # single key dict + for k, v in v_i.items(): + if k == object_name: + temp_supporting_vids = v + if k == 'SUPPORTING': + supporting_vid = id + if supporting_vid != -1: + # append to supporting + verts[supporting_vid]['SUPPORTING'] += temp_supporting_vids + return verts + + # correct supporting contacts + for i, vert in enumerate(vertices): + for k, v in vert.items(): + if k == 'hot/training/hake_train2015_HICO_train2015_00000019.jpg': + # copy bicycle contacts to supporting + v = add_supporting(v, 'BICYCLE') + if k == 'hot/training/hake_train2015_HICO_train2015_00000020.jpg': + # copy skateboard contacts to supporting + v = add_supporting(v, 'SKATEBOARD') + if k == 'hot/training/hake_train2015_HICO_train2015_00000942': + # copy bench contacts to supporting + v = add_supporting(v, 'BENCH') + + # combine all vert_ids into a single list no matter the object + v = {ki: vi for d in v for ki, vi in d.items()} + v = [vi for k, vi in v.items()] + v = [item for sublist in v for item in sublist] + v = list(set(v)) + # binarize the list to a numpy array + v_np = np.zeros(6890) + v_np[v] = 1 + vert[k] = v_np + vertices[i] = vert + return vertices + +def fleiss_kappa_per_img(vertices): + """ + Compute Fleiss' kappa per imagename + Parameters + ---------- + vertices : list of np arrays where each array is of shape (6890,) and 1 indicates a vertex is selected + """ + n = len(vertices) # number of raters + N = 6890 # number of images + k = 2 # number of categories + + # compute the observed agreement + M = np.zeros((N, k)) + + for i in range(k): + M[:, i] = np.sum(vertices == i, axis=0) + + assert np.sum(M) == N * n + + # compute the expected agreement + p = np.sum(M, axis=0) / (N * n) + P = (np.sum(M * M, axis=1) - n) / (n * (n - 1)) + Pbar = np.mean(P) + PbarE = np.sum(p * p) + + # compute Fleiss' kappa + kappa = (Pbar - PbarE) / (1 - PbarE) + return kappa + +def fleiss_kappa(data): + """ + Compute Fleiss' kappa per imagename + Parameters + ---------- + data : list of dicts where keys are imgnames + """ + imgnames = sorted(data[0].keys()) + kappas = [] + for img in imgnames: + kappa_data = [] + for d in data: + kappa_data.append(d[img]) + kappa_data = np.array(kappa_data) + kappa_img = fleiss_kappa_per_img(kappa_data) + print(f'Fleiss\' Kappa for {img}: {kappa_img}') + kappas.append(kappa_img) + + # computer mean kappa + kappa = np.mean(kappas) + return kappa + + +# Load the combined qa csv file +csv_file = 'quality_assurance_fleiss.csv' +df = pd.read_csv(csv_file) + +vertices = df['vertices'].values +vertices = [eval(v) for v in vertices] + +vertices = correct_supporting(vertices) + +kappa = fleiss_kappa(vertices) + +print('Fleiss\' Kappa:', kappa) diff --git a/hot_analysis/damon_qc_stats/qa_accuracy_gt_contact_combined.npz b/hot_analysis/damon_qc_stats/qa_accuracy_gt_contact_combined.npz new file mode 100644 index 0000000000000000000000000000000000000000..51ccd40130d0439924c9dde0e312ebdf566da525 --- /dev/null +++ b/hot_analysis/damon_qc_stats/qa_accuracy_gt_contact_combined.npz @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b34393328dae058aa12245f547ee954f48508bf0c4ed7ecbe40a674245ba5717 +size 456742 diff --git a/hot_analysis/damon_qc_stats/quality_assurance_accuracy.csv 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Thank you!,"{'hot/training/hake_train2015_HICO_train2015_00000302.jpg': [{'BOOK': [5815, 5817, 5795, 5816, 5813, 5789, 5814, 5794, 5786, 5812, 5782, 5811, 5810, 5800, 5791, 5788, 5799, 5779, 5781, 5797, 5801, 5785, 5771, 5780, 5805, 5809, 5775, 5808, 5770, 5776, 5798, 5790, 5796, 5787, 5774, 5769, 5766, 5784, 5773, 5807, 5777, 5768, 5764, 5806, 5804, 5765, 5767, 5783, 5793, 5772, 5792, 5762, 5763, 5802, 5803, 5760, 5778, 5759, 5758, 5761, 5742, 5694, 5679, 5756, 5693, 5706, 5734, 5733, 5488, 5489, 5741, 5757, 5606, 5678, 5499, 5676, 5490, 5619, 5620, 5677, 5491, 5680, 5743, 5647, 5618, 5617, 5648, 5627, 5607, 5744, 5642, 5498, 5500, 5681, 5585, 5591, 5590, 5624, 5501, 5623, 5626, 5630, 5628, 5724, 5588, 5589, 5621, 5584, 5622, 5528, 5891, 5890, 5896, 5905, 5878, 5906, 5899, 5881, 5895, 5907, 5888, 5904, 5927, 5898, 5913, 5912, 5926, 5929, 5880, 5879, 5901, 5925, 5887, 5928, 5924, 5885, 5894, 5897, 5909, 5883, 5911, 5923, 5922, 5900, 5886, 5884, 5882, 5889, 5917, 5908, 5921, 5893, 5892, 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In picture 6, the skateboard supports the right leg.","{'hot/training/hake_train2015_HICO_train2015_00000302.jpg': [{'BOOK': [2232, 2030, 2027, 2280, 2029, 2158, 2233, 2146, 2028, 2281, 2342, 2341, 2245, 2302, 2299, 2343, 2332, 2345, 2331, 2348, 2344, 2349, 2350, 2356, 2352, 2351, 2355, 2354, 2329, 2330, 2333, 2328, 2334, 2327, 2325, 2324, 2318, 2326, 2319, 2441, 2442, 2432, 2411, 2410, 2453, 2436, 2455, 2433, 2454, 2399, 2398, 2401, 2397, 2374, 2357, 2404, 2360, 2414, 2403, 2359, 2377, 2457, 2456, 2439, 2358, 2364, 2373, 2363, 2386, 2463, 2440, 2462, 2467, 2437, 2443, 2468, 2438, 2461, 2466, 2464, 2450, 2435, 2465, 2434, 2444, 2445, 2429, 2452, 2483, 2470, 2484, 2469, 2482, 2471, 2487, 2472, 2486, 2514, 2503, 2515, 2507, 2502, 2525, 2565, 2508, 2568, 2564, 2566, 2553, 2571, 2567, 2550, 2574, 2551, 2547, 2570, 2573, 2578, 2548, 2554, 2579, 2544, 2549, 2577, 2572, 2575, 2546, 2576, 2545, 2555, 2625, 2642, 2626, 2682, 2681, 2683, 2670, 2685, 2684, 2669, 2667, 2688, 2691, 2664, 2668, 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I like it. Thanks for again giving the task for me.,"{'hot/training/hake_train2015_HICO_train2015_00000302.jpg': [{'BOOK': [5792, 5793, 5804, 5802, 5760, 5803, 5790, 5787, 5783, 5759, 5806, 5693, 5488, 5606, 5491, 5489, 5490, 5706, 5499, 5607, 5648, 5642, 5500, 5647, 5618, 5741, 5619, 5694, 5763, 5742, 5762, 5679, 5812, 5817, 5794, 5811, 5791, 5816, 5789, 5815, 5805, 5813, 5810, 5809, 5788, 5795, 5786, 5814, 5808, 5799, 5785, 5782, 5800, 5779, 5781, 5797, 5734, 5758, 5733, 5757, 5767, 5784, 5744, 5778, 5768, 5585, 5623, 5626, 5584, 5617, 5590, 5586, 5624, 5589, 5621, 5591, 5588, 5622, 5625, 5583, 5504, 5601, 5630, 5627, 5620, 5678, 5677, 5680, 5681, 5676, 5666, 5743, 5761, 5528, 5628, 5860, 5863, 5858, 5872, 5871, 5862, 5859, 5834, 5914, 5818, 5903, 5870, 5893, 5823, 5865, 5915, 5819, 5902, 5867, 5835, 5821, 5869, 5822, 5855, 5820, 5882, 5895, 5898, 5878, 5896, 5881, 5894, 5901, 5891, 5897, 5899, 5904, 5880, 5890, 5879, 5906, 5927, 5900, 5924, 5929, 5888, 5905, 5883, 5885, 5907, 5928, 5917, 5925, 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6689, 6687, 6679, 6711, 6690, 6680, 6715, 6638, 3221, 3220, 3222, 3254, 3301, 3260, 3261]}]}" diff --git a/hot_analysis/damon_qc_stats/successful_qualifications_fleiss.csv b/hot_analysis/damon_qc_stats/successful_qualifications_fleiss.csv new file mode 100644 index 0000000000000000000000000000000000000000..6165295014fa22b9b145a95c4b8b25e4a2c62d49 --- /dev/null +++ b/hot_analysis/damon_qc_stats/successful_qualifications_fleiss.csv @@ -0,0 +1,15 @@ +AcceptTime,AssignmentId,AutoApprovalTime,HITId,SubmitTime,WorkerId,feedback,vertices +2023-02-13 19:56:24+01:00,3ON104KXQKX03PVF3H1DQYJJUJV4WE,2023-03-15 21:24:38+01:00,30UZJB2POHCILQZXH04J29I5VY935F,2023-02-13 21:24:38+01:00,A1HD422M3W0CYY,"Hello initially thank you for the opportunity and I look forward to your positive feedback.I would also like to say that in image '8' I ended up forgetting to delimit the elbow of the person holding the apple and in image '10' I delimited the support with the object, I tried to go back, but it was not possible. If my mistakes are serious, I ask for a new opportunity to live up to expectations.Once again, thank you very much and I hope I can contribute to more projects, even more.","{'hot/training/hake_train2015_HICO_train2015_00000302.jpg': [{'BOOK': [2332, 2331, 2343, 2341, 2299, 2342, 2322, 2329, 2326, 2345, 2298, 2302, 2233, 2344, 2348, 2351, 2330, 2347, 2355, 2350, 2327, 2333, 2356, 2328, 2346, 2337, 2354, 2352, 2349, 2325, 2334, 2353, 2338, 2324, 2336, 2340, 2318, 2301, 2232, 2281, 2323, 2305, 2319, 2320, 2339, 2321, 2314, 2310, 2309, 2313, 2466, 2467, 2444, 2464, 2438, 2468, 2465, 2435, 2443, 2445, 2463, 2462, 2452, 2461, 2450, 2429, 2437, 2440, 2448, 2451, 2446, 2434, 2430, 2426, 2449, 2459, 2456, 2460, 2439, 2417, 2436, 2455, 2457, 2458, 2442, 2454, 2414, 2441, 2453, 2413, 2397, 2432, 2411, 2410, 2433, 2422, 2409, 2398, 2577, 2578, 2555, 2549, 2575, 2554, 2579, 2546, 2576, 2574, 2556, 2551, 2573, 2548, 2572, 2563, 2561, 2545, 2559, 2528, 2562, 2537, 2538, 2558, 2560, 2567, 2571, 2570, 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But there was no option to like shown in video. It simply moved next object. In above drop down list also it's not allowing me to select anything.,"{'hot/training/hake_train2015_HICO_train2015_00000302.jpg': [{'BOOK': [6197, 6200, 6192, 6207, 6196, 6228, 6179, 6205, 6190, 6230, 6199, 6182, 6226, 6206, 6227, 6202, 6180, 6181, 6229, 6198, 6195, 6225, 6183, 6194, 6184, 6201, 6204, 6218, 6172, 6171, 6203, 6173, 6165, 6163, 6215, 6161, 5575, 6164, 5710, 5576, 5574, 5709, 5791, 5794, 5788, 5812, 5789, 5817, 5790, 5785, 5805, 5786, 5811, 5816, 5815, 5787, 5771, 5813, 5779, 5810, 5809, 5795, 5802, 5760, 5693, 5803, 5792, 5694, 5759, 5793, 5706, 5763, 5804, 5734, 5488, 5489, 5606, 5499, 5607, 5491, 5642, 5490, 5648, 5647, 5618, 5626, 5586, 5623, 5625, 5719, 5624, 5622, 5621, 5590, 5741, 5619, 5742, 5783, 5806, 5507, 5579, 5504, 5589, 5583, 5506, 5601, 5535, 5580, 5502, 5454, 5505, 5840, 5842, 5841, 5723, 5830, 5683, 5820, 5838, 5821, 5819, 5818, 5848, 5837, 5839, 5835, 5849, 5834, 5824, 5847, 5858, 5860, 5859, 5914, 5872, 5863, 5865, 5915, 5871, 5902, 5903, 5875, 5916, 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Hope I did well!,"{'hot/training/hake_train2015_HICO_train2015_00000302.jpg': [{'BOOK': [5790, 5787, 5805, 5791, 5788, 5784, 5792, 5794, 5804, 5812, 5785, 5789, 5809, 5817, 5786, 5783, 5766, 5679, 5676, 5742, 5762, 5763, 5761, 5678, 5677, 5743, 5807, 5806, 5803, 5620, 5680, 5619, 5741, 5617, 5627, 5666, 5618, 5647, 5490, 5591, 5648, 5590, 5630, 5724, 5588, 5589, 5621, 5583, 5601, 5624, 5622, 5504, 5600, 5535, 5602, 5505, 5506, 5502, 5503, 5580, 5507, 5454, 5453, 5450, 5452, 5728, 5473, 5727, 5576, 6162, 5470, 6166, 6216, 6161, 6176, 6160, 6215, 6217, 6219, 6220, 6175, 6222, 6218, 6221, 6224, 6225, 6211, 6229, 6223, 6202, 6230, 6205, 6200, 6228, 6199, 6226, 6201, 6198, 6196, 6197, 5444, 5445, 5603, 5511, 5683, 5732, 5750, 5841, 5832, 5840, 5842, 5833, 5820, 5838, 5821, 5819, 5818, 5848, 5837, 5839, 5835, 5849, 5834, 5823, 5858, 5864, 5860, 5914, 5872, 5915, 5902, 5903, 5859, 5871, 5875, 5916, 5893, 5897, 5894, 5900, 5917, 5882, 5924, 5928, 5923, 5870, 5836, 5843, 5844, 5518, 5517, 5708, 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6683, 6713, 6717, 6626, 6625, 6624, 6623, 6681]}]}" +2023-02-16 19:27:20+01:00,3DZQRBDBSLGMECV2JYMTJ4OSBDUS38,2023-03-18 21:12:37+01:00,378G7J1SJLYF87UE2L8OB0ZF6OXEW0,2023-02-16 21:12:37+01:00,A2JDYN6QM8M5UN,"It's too long for the pay. I could have been a little more precise but that would have brought me below $5/per hour, which I consider unacceptable. I'm sure I would get used to the task and be able to go faster and more accurately the more I do it though.","{'hot/training/hake_train2015_HICO_train2015_00000302.jpg': [{'BOOK': [5599, 5598, 5736, 5519, 5516, 5593, 5732, 5601, 5583, 5602, 5600, 5589, 5621, 5588, 5590, 5624, 5591, 5618, 5647, 5617, 5619, 5620, 5741, 5490, 5489, 5491, 5488, 5694, 5803, 5693, 5742, 5802, 5763, 5804, 5792, 5793, 5760, 5806, 5809, 5808, 5805, 5811, 5807, 5812, 5798, 5810, 5799, 5816, 5813, 5797, 5817, 5814, 5815, 5794, 5791, 5790, 5762, 5903, 5893, 5902, 5872, 5871, 5914, 5897, 5882, 5894, 5870, 5915, 5858, 5859, 5860, 5819, 5864, 5818, 5820, 5838, 5821, 5848, 5837, 5849, 5841, 5832, 5840, 5833, 5750, 5865, 5836, 5517, 5518, 5707, 5952, 5950, 5942, 5708, 5755, 5592, 5650, 5946, 5947, 5953, 5949, 5948, 5966, 5961, 5963, 5964, 5975, 5931, 5932, 5951, 5959, 5957, 5958, 5933, 5945, 5936, 5944, 5935, 5930, 5941, 5968, 5970, 5969, 6025, 5983, 6026, 5986, 6027, 6013, 5976, 6014, 6011, 6004, 5982, 5934, 5846, 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osp\n", + "import numpy as np\n", + "from PIL import Image\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total images found: 9642\n", + "Images after KP filtering: 3895\n", + "['hake_train2015_HICO_train2015_00005476.jpg', 'hake_train2015_HICO_train2015_00008329.jpg', 'hake_train2015_HICO_train2015_00008027.jpg', 'hake_train2015_HICO_train2015_00013408.jpg', 'hake_train2015_HICO_train2015_00010656.jpg']\n" + ] + } + ], + "source": [ + "# Load Agniv VITpose-base hico filtering\n", + "filter_path = './agniv_pose_filter/hico.npy'\n", + "pose_md = np.load(filter_path, allow_pickle=True)\n", + "pose_md = pose_md.item()\n", + "filter_img_names = {}\n", + "\n", + "print(f'Total images found: {len(pose_md)}')\n", + "\n", + "# Filter out images with < 10 visible keypoints \n", + "kp_thresh = 10\n", + "\n", + "for imgname, pose_num in pose_md.items():\n", + " if pose_num > kp_thresh:\n", + " filter_img_names[imgname] = pose_num\n", + " \n", + "print(f'Images after KP filtering: {len(filter_img_names)}')\n", + "\n", + "print(list(filter_img_names.keys())[:5])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# # Load Agniv VITpose-base hot dict\n", + "# filter_path = \"./agniv_pose_filter/hot_dict.pkl\"\n", + "# with open(filter_path, 'rb') as f:\n", + "# pose_md_dict = pkl.load(f)\n", + " \n", + "# hico_dict = {}\n", + "\n", + "# for k, v in pose_md_dict.items():\n", + "# if 'hake' in k:\n", + "# hico_dict[k] = v\n", + " \n", + "# print(f'Total images found: {len(hico_dict)}')\n", + "\n", + "# # Filter out images with < 10 visible keypoints \n", + "# kp_thresh = 10\n", + "\n", + "# filter_img_names = {}\n", + "\n", + "# for imgname, kp_md in hico_dict.items():\n", + "# if kp_md == 0:\n", + "# continue\n", + "# if kp_md[\"num_persons\"] == 1 and kp_md[\"num_kpt\"][0.5][0] > kp_thresh:\n", + "# filter_img_names[imgname] = kp_md[\"num_kpt\"][0.5][0]\n", + " \n", + "# print(f'Images after KP filtering: {len(filter_img_names)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 0 1 2\n", + "0 1 airplane board\n", + "1 2 airplane direct\n", + "2 3 airplane exit\n", + "3 4 airplane fly\n", + "4 5 airplane inspect\n", + ".. ... ... ...\n", + "595 596 zebra feed\n", + "596 597 zebra hold\n", + "597 598 zebra pet\n", + "598 599 zebra watch\n", + "599 600 zebra no_interaction\n", + "\n", + "[600 rows x 3 columns]\n" + ] + } + ], + "source": [ + "\n", + "hico_annot = json.load(open('/ps/project/datasets/HICO/hico-image-level/hico-training-set-image-level.json', 'rb'))\n", + "hoi_mapping = pd.read_csv('/ps/project/datasets/HICO/hico-image-level/hico_hoi_list.txt', header=None, delim_whitespace=True)\n", + "print(hoi_mapping)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Final number of images 3154\n" + ] + } + ], + "source": [ + "version = '1'\n", + "out_dir = f'./filtered_data/v_{version}'\n", + "os.makedirs(out_dir, exist_ok=True)\n", + "\n", + "objectwise_img_names = {}\n", + "imgwise_object_names = {}\n", + "img_dir = '/ps/project/datasets/HICO/hico_20150920/images/train2015'\n", + "\n", + "bad_object_names = ['bear', 'bird', 'cat', 'cow', \n", + " 'dog', 'elephant', 'giraffe', 'horse', \n", + " 'mouse', 'person', 'sheep', 'zebra' ]\n", + "bad_action_names = ['buy', 'chase', 'direct', 'greet', 'herd', 'hose',\n", + " 'hug', 'hunt', 'milk', 'no_interaction', 'pet', 'point', 'teach',\n", + " 'watch', 'wave']\n", + "\n", + "for i, (img_name, img_md) in enumerate(hico_annot.items()):\n", + " \n", + " # Apply keypoint number filtering on the images \n", + " full_img_name = 'hake_train2015_' + img_name\n", + " if full_img_name not in filter_img_names.keys():\n", + " continue\n", + " \n", + " # show the image\n", + " if i < 0:\n", + " img = Image.open(osp.join(img_dir,img_name))\n", + " display(img)\n", + " \n", + " obj_names = []\n", + " action_names = []\n", + " kp_num = filter_img_names[full_img_name]\n", + " \n", + " # travel through all hoi in the metadata, save obj_names and action_names for the hois\n", + " for hoi_id in img_md['hoi_id']:\n", + " img_md_row = hoi_mapping.loc[hoi_mapping[0] == hoi_id].iloc[0]\n", + "\n", + " obj_name = img_md_row[1]\n", + " if obj_name in bad_object_names:\n", + " continue\n", + " action_name = img_md_row[2]\n", + " if action_name in bad_action_names:\n", + " continue\n", + " \n", + " obj_names.append(obj_name)\n", + " action_names.append(action_name)\n", + " \n", + " if len(set(obj_names)) == 0 or len(action_names) == 0:\n", + " continue\n", + " \n", + " imgwise_object_names.setdefault(full_img_name,[]).extend(list(set(obj_names)))\n", + " \n", + "# # # Display images with multiple objects\n", + "# if len(set(obj_names)) > 1:\n", + "# print(img_name)\n", + "# print(obj_names)\n", + "# print(action_names)\n", + "# print(f'Number of Kps: {kp_num}')\n", + "# img = Image.open(osp.join(img_dir,img_name))\n", + "# display(img)\n", + " \n", + " for obj_name in set(obj_names):\n", + " objectwise_img_names.setdefault(obj_name,[]).append(full_img_name)\n", + " \n", + "print(f'Final number of images {len(imgwise_object_names)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saved at ./filtered_data/v_1/object_per_image_dict.json\n", + "saved at ./filtered_data/v_1/imgnames_per_object_dict.json\n" + ] + } + ], + "source": [ + "# Save the imagewise_object dict\n", + "out_path = osp.join(out_dir, 'object_per_image_dict.json')\n", + "with open(out_path, 'w') as fp:\n", + " json.dump(imgwise_object_names, fp)\n", + " print(f'saved at {out_path}')\n", + " \n", + "# # save image_list \n", + "# out_path = osp.join(out_dir, 'hico_imglist_all_140223.txt')\n", + "# with open(out_path, 'w') as f:\n", + "# f.write('\\n'.join(imgwise_object_names.keys()))\n", + "# print(f'saved at {out_path}')\n", + "\n", + "\n", + "# Save the object_wise dict\n", + "out_path = osp.join(out_dir, 'imgnames_per_object_dict.json')\n", + "with open(out_path, 'w') as fp:\n", + " json.dump(objectwise_img_names, fp)\n", + " print(f'saved at {out_path}')\n", + "\n", + " \n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/stripathi/anaconda3/envs/cliff/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n", + " warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3189\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# sort the dictionary \n", + "objectwise_img_names = dict(sorted(objectwise_img_names.items(), key=lambda x: len(x[1]), reverse=True))\n", + "\n", + "# Extract object names and image counts\n", + "obj_names = list(objectwise_img_names.keys())\n", + "img_counts = [len(objectwise_img_names[obj]) for obj in objectwise_img_names]\n", + "print(sum(img_counts))\n", + "\n", + "# Create bar plot\n", + "sns.barplot(x=obj_names, y=img_counts)\n", + "\n", + "# Add x-axis and y-axis labels\n", + "plt.xlabel('Object')\n", + "plt.ylabel('Number of Images')\n", + "\n", + "plt.xticks(rotation=45, ha='right', fontsize=3)\n", + "\n", + "# Save the plot as a high-resolution image file\n", + "out_path = osp.join(out_dir, 'image_per_object_category.png')\n", + "plt.savefig(out_path, dpi=300)\n", + "\n", + "# Show plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/hot_analysis/image_per_object_category.png b/hot_analysis/image_per_object_category.png new file mode 100644 index 0000000000000000000000000000000000000000..0bb710b59680b06c341e2801d1a3c42036050fdd Binary files /dev/null and b/hot_analysis/image_per_object_category.png differ diff --git a/hot_analysis/jc_graphs/documents-export-2023-03-07/image_per_object_category.csv b/hot_analysis/jc_graphs/documents-export-2023-03-07/image_per_object_category.csv new file mode 100644 index 0000000000000000000000000000000000000000..cdc909b3ef600ed2c66399a540754d9c9bb437da --- /dev/null +++ b/hot_analysis/jc_graphs/documents-export-2023-03-07/image_per_object_category.csv @@ -0,0 +1,70 @@ +,count +surfboard,452 +chair,287 +snowboard,280 +laptop,237 +cell phone,224 +sports ball,208 +dining table,184 +skateboard,172 +tennis racket,172 +book,171 +frisbee,160 +cup,155 +car,138 +bed,129 +bottle,123 +bench,119 +couch,113 +handbag,113 +backpack,106 +knife,89 +bowl,86 +skis,85 +baseball glove,75 +potted plant,69 +tv,67 +mouse,59 +pizza,57 +suitcase,54 +fork,52 +keyboard,50 +tie,47 +spoon,46 +remote,44 +cake,43 +truck,40 +clock,39 +sandwich,35 +bicycle,34 +umbrella,33 +wine glass,31 +boat,28 +donut,28 +oven,27 +teddy bear,26 +sink,25 +refrigerator,23 +scissors,22 +vase,21 +baseball bat,18 +kite,16 +banana,16 +hot dog,16 +traffic light,16 +motorcycle,14 +microwave,14 +apple,12 +fire hydrant,10 +carrot,9 +bus,8 +broccoli,8 +orange,8 +toilet,7 +stop sign,6 +hair drier,6 +train,4 +airplane,4 +parking meter,4 +toothbrush,3 +toaster,1 diff --git a/hot_analysis/jc_graphs/documents-export-2023-03-07/object_nest_piechart_data.csv b/hot_analysis/jc_graphs/documents-export-2023-03-07/object_nest_piechart_data.csv new file mode 100644 index 0000000000000000000000000000000000000000..1f6373901b0db81f37e917c3337a1048c86f3863 --- /dev/null +++ b/hot_analysis/jc_graphs/documents-export-2023-03-07/object_nest_piechart_data.csv @@ -0,0 +1,77 @@ +,character,parent,value +0,transportation,objects,0 +1,accessories,objects,0 +2,furniture,objects,0 +3,everyday objects,objects,0 +4,sports equipment,objects,0 +5,food items,objects,0 +6,kitchen appliances,objects,0 +7,motorcycle,transportation,14 +8,bicycle,transportation,129 +9,boat,transportation,33 +10,car,transportation,106 +11,truck,transportation,31 +12,bus,transportation,34 +13,train,transportation,172 +14,airplane,transportation,85 +15,backpack,accessories,280 +16,tie,accessories,171 +17,handbag,accessories,16 +18,baseball glove,accessories,43 +19,bench,furniture,113 +20,chair,furniture,452 +21,couch,furniture,75 +22,bed,furniture,123 +23,toilet,furniture,287 +24,dining table,furniture,160 +25,book,everyday objects,6 +26,umbrella,everyday objects,28 +27,cell phone,everyday objects,54 +28,laptop,everyday objects,44 +29,kite,everyday objects,16 +30,suitcase,everyday objects,119 +31,bottle,everyday objects,89 +32,remote,everyday objects,46 +33,toothbrush,everyday objects,67 +34,teddy bear,everyday objects,237 +35,scissors,everyday objects,224 +36,keyboard,everyday objects,172 +37,hair drier,everyday objects,138 +38,traffic light,everyday objects,27 +39,fire hydrant,everyday objects,208 +40,stop sign,everyday objects,12 +41,tv,everyday objects,18 +42,vase,everyday objects,26 +43,parking meter,everyday objects,113 +44,clock,everyday objects,9 +45,potted plant,everyday objects,155 +46,mouse,everyday objects,8 +47,frisbee,sports equipment,22 +48,sports ball,sports equipment,28 +49,tennis racket,sports equipment,50 +50,baseball bat,sports equipment,47 +51,skateboard,sports equipment,4 +52,snowboard,sports equipment,16 +53,skis,sports equipment,57 +54,surfboard,sports equipment,184 +55,banana,food items,6 +56,cake,food items,3 +57,apple,food items,4 +58,carrot,food items,16 +59,pizza,food items,40 +60,donut,food items,21 +61,hot dog,food items,35 +62,sandwich,food items,8 +63,broccoli,food items,7 +64,orange,food items,25 +65,knife,kitchen appliances,4 +66,spoon,kitchen appliances,52 +67,cup,kitchen appliances,86 +68,wine glass,kitchen appliances,23 +69,oven,kitchen appliances,10 +70,fork,kitchen appliances,39 +71,bowl,kitchen appliances,69 +72,refrigerator,kitchen appliances,8 +73,toaster,kitchen appliances,59 +74,sink,kitchen appliances,14 +75,microwave,kitchen appliances,1 diff --git a/hot_analysis/jc_graphs/documents-export-2023-03-07/partwise_contact_graph.csv b/hot_analysis/jc_graphs/documents-export-2023-03-07/partwise_contact_graph.csv new file mode 100644 index 0000000000000000000000000000000000000000..b479fab571be1fd04e914e103105b77d7930b010 --- /dev/null +++ b/hot_analysis/jc_graphs/documents-export-2023-03-07/partwise_contact_graph.csv @@ -0,0 +1,18 @@ +,count +leftFoot,8109 +rightFoot,7928 +rightHand,5085 +leftHand,3949 +spine,3694 +hips,2346 +leftUpLeg,1870 +rightUpLeg,1847 +rightForeArm,736 +rightLeg,700 +head,692 +rightShoulder,670 +leftShoulder,667 +leftForeArm,635 +leftLeg,591 +rightArm,571 +leftArm,535 diff --git a/hot_analysis/jc_graphs/plot_image_per_object.py b/hot_analysis/jc_graphs/plot_image_per_object.py new file mode 100644 index 0000000000000000000000000000000000000000..6c237b5fcc0d8fdddc9621e98266d14f1b868dd5 --- /dev/null +++ b/hot_analysis/jc_graphs/plot_image_per_object.py @@ -0,0 +1,147 @@ +import csv +from pathlib import Path + +import matplotlib.pyplot as plt +from matplotlib import gridspec +import numpy as np +import seaborn as sns + +# Input/output files +CSV_FOLDER = Path(__file__).parent / "documents-export-2023-03-07" +IMAGE_PER_OBJECT_FILE = CSV_FOLDER / "image_per_object_category.csv" +PIE_CHART_DATA_FILE = CSV_FOLDER / "object_nest_piechart_data.csv" +OUTPUT = 'output.pdf' + +# Some configuration +FONTSIZE = 20 +FONTSIZE_PIE = 25 +COLOR_RANGE = (0.15, 0.85) # to avoid having exterme values +CUTOFF = 30 # min number of counts for an object to be considered +PIE_INNER_RADIUS = 0.3 # value used for the nested pie chart +plt.rcParams['grid.color'] = (0.5, 0.5, 0.5, 0.1) # low grid opacity + +# Categories and associated objects +categorization_unordered = { + "transportation": [ + 'motorcycle', 'bicycle', 'boat', 'car', 'truck', 'bus', 'train', 'airplane',], + "accessories": [ + 'backpack', 'tie', 'handbag', 'baseball glove'], + "furniture": [ + 'bench', 'chair', 'couch', 'bed', 'toilet', 'dining table'], + 'everyday objects': [ + 'book', 'umbrella', 'cell phone', 'laptop', 'kite', 'suitcase', 'bottle', 'remote', + 'toothbrush', 'teddy bear', 'scissors', 'keyboard', 'hair drier', 'traffic light', + 'fire hydrant', 'stop sign', 'tv', 'vase', 'parking meter', 'clock', 'potted plant', + 'mouse'], + 'sports equipment': [ + 'frisbee', 'sports ball', 'tennis racket', 'baseball bat', + 'skateboard', 'snowboard', 'skis', 'surfboard'], + 'food items': [ + 'banana', 'cake', 'apple', 'carrot', 'pizza', 'donut', 'hot dog', + 'sandwich', 'broccoli', 'orange'], + 'kitchen appliances': [ + 'knife', 'spoon', 'cup', 'wine glass', 'oven', 'fork', 'bowl', + 'refrigerator', 'toaster', 'sink', 'microwave']} + +# Colors for each category +cat_colors = { + "transportation": plt.cm.spring, + "accessories": plt.cm.Greys, + "furniture": plt.cm.Purples, + "everyday objects": plt.cm.Reds, + "sports equipment": plt.cm.Blues, + "food items": plt.cm.Wistia, + "kitchen appliances": plt.cm.summer} + +# Read CSV files +objects = {} +with open(IMAGE_PER_OBJECT_FILE, newline='', encoding="utf-8") as csvfile: + reader = csv.DictReader(csvfile) + for row in reader: + objects[row['']] = {} + objects[row['']]['count'] = int(row['count']) + +# In each caterory, order object by count in descending order +categorization = { + cat: sorted(val, key=lambda _: objects[_]["count"]) + for cat, val in categorization_unordered.items()} + +# Some sanity checks :) +assert list(categorization.keys()) == list(categorization_unordered.keys()) +assert list(categorization.keys()) == list(cat_colors.keys()) +for cat in categorization: + assert len(categorization[cat]) == len(categorization_unordered[cat]) + assert set(categorization[cat]) == set(categorization_unordered[cat]) + +# Cutoff objects and categorization +selected_objects = [o for o, v in objects.items() if v["count"] > CUTOFF] +selected_categorization = {} +for k, v in categorization.items(): + selected_categorization[k] = [o for o in v if o in selected_objects] + +# Add a color for each object +# All objects in one category use the same category color +for cat, objs in selected_categorization.items(): + colors = [ + cat_colors[cat](x) + for x in np.linspace(COLOR_RANGE[0], COLOR_RANGE[1], len(selected_categorization[cat]))] + color_index = 0 + for obj in objs: + objects[obj]['color'] = colors[color_index] + color_index += 1 + +# Plot: histogramm +fig = plt.figure(figsize=(25, 22)) +gs = gridspec.GridSpec(1, 1) +gs.update(wspace=0.15, hspace=0.15) +axes = [] +for elem in gs: + axes.append(fig.add_subplot(elem)) +axes[0].grid(axis='x', zorder=0) +axes[0].set_xticks(range(0, 500, 50)) +sns.barplot( + x=list(v["count"] for o, v in objects.items() if o in selected_objects), + y=list(selected_objects), + ax=axes[0], + zorder=3, + palette=list(v["color"] for o, v in objects.items() if o in selected_objects)) +axes[0].set_xticklabels(axes[0].get_xticklabels(), rotation=90) +plt.xticks(rotation=0, fontsize=FONTSIZE) +plt.yticks(fontsize=FONTSIZE) +plt.xlabel('# images', labelpad=30, fontsize=FONTSIZE) + +# Plot: pie chart +right_inset_ax = fig.add_axes([.3, .1, .5, .5], facecolor='k') +outer_colors = [c(0.5) for c in cat_colors.values()] +labels = [word.replace(' ', '\n') for word in list(selected_categorization)] +# Need to be orderd by category +vals = [] +groups = {cat: 0 for cat in selected_categorization.keys()} +inner_colors = [] +for cat, members in selected_categorization.items(): + for _ in members: + vals.append(objects[_]["count"]) + inner_colors.append(objects[_]["color"]) + groups[cat] += objects[_]["count"] +right_inset_ax.pie( + list(groups.values()), + radius=1, + colors=outer_colors, + # labels=labels, + autopct='%.1f%%', + pctdistance=0.82, + # labeldistance=1.5, + textprops={'color': 'white', 'fontsize': FONTSIZE}, + wedgeprops={'width': PIE_INNER_RADIUS, 'edgecolor': 'w'}) +right_inset_ax.pie( + vals, + radius=1-PIE_INNER_RADIUS, + colors=inner_colors, + wedgeprops={'width': PIE_INNER_RADIUS, 'edgecolor': 'w'}) + +# Plot: add legend and save +plt.legend( + labels, bbox_to_anchor=(1.2, 1.45), + loc='upper right', prop={'size': 1.5*FONTSIZE}) +plt.savefig(OUTPUT) +# plt.show() diff --git a/hot_analysis/jc_graphs/plot_partwise_contacts.py b/hot_analysis/jc_graphs/plot_partwise_contacts.py new file mode 100644 index 0000000000000000000000000000000000000000..87eacb8a53169b66bc4a001b797c81efbb42d50f --- /dev/null +++ b/hot_analysis/jc_graphs/plot_partwise_contacts.py @@ -0,0 +1,42 @@ +import csv +from pathlib import Path + +from matplotlib import gridspec +from matplotlib import pyplot as plt +import seaborn as sns + +# Input/output files +CSV_FOLDER = Path(__file__).parent / "documents-export-2023-03-07" +CONTACT_FILE = CSV_FOLDER / "partwise_contact_graph.csv" +OUTPUT = 'output.pdf' + +# Some configuration +FONTSIZE = 30 +plt.rcParams['grid.color'] = (0.5, 0.5, 0.5, 0.1) # low grid opacity + +# Read CSV file +body_parts = [] +body_part_counts = [] +with open(CONTACT_FILE, newline='', encoding="utf-8") as csvfile: + reader = csv.DictReader(csvfile) + for row in reader: + body_parts.append(row['']) + body_part_counts.append(int(row['count'])) + +# Plot +fig = plt.figure(figsize=(25, 22)) +gs = gridspec.GridSpec(1, 1) +gs.update(wspace=0.15, hspace=0.15) +axes = [] +for elem in gs: + axes.append(fig.add_subplot(elem)) +axes[0].grid(axis='y', zorder=0) +sns.barplot( + x=body_parts, y=body_part_counts, ax=axes[0], zorder=3, + facecolor=(0.2, 0.4, 0.6, 0.6)) +axes[0].set_xticklabels(axes[0].get_xticklabels(), rotation=90) +plt.xticks(rotation=45, ha='right', fontsize=FONTSIZE) +plt.yticks(fontsize=FONTSIZE) +plt.ylabel('# images', labelpad=30, fontsize=FONTSIZE) +plt.savefig(OUTPUT) +# plt.show() diff --git a/hot_analysis/object_nest_piechart.py b/hot_analysis/object_nest_piechart.py new file mode 100644 index 0000000000000000000000000000000000000000..07fed60d35dbebe2bdd50a1bb63ae46e8adcf9c6 --- /dev/null +++ b/hot_analysis/object_nest_piechart.py @@ -0,0 +1,136 @@ +# make a nestest plotly pie chart given a list of labels and a list of values +import os.path as osp +import json +import plotly.express as px +import plotly.io as pio + +version = '1' +dir = '/is/cluster/work/stripathi/pycharm_remote/dca_contact/hot_analysis/' +out_dir_hico = osp.join(dir, f'filtered_data/v_{version}/hico') +out_dir_vcoco = osp.join(dir, f'filtered_data/v_{version}/vcoco') + +objectwise_img_dict_hico = osp.join(out_dir_hico, 'imgnames_per_object_dict.json') +objectwise_img_dict_vcoco = osp.join(out_dir_vcoco, 'imgnames_per_object_dict.json') + +with open(objectwise_img_dict_hico, 'r') as fp: + objectwise_img_dict_hico = json.load(fp) + # replace underscore with space + objectwise_img_dict_hico = {k.replace('_', ' '): v for k, v in objectwise_img_dict_hico.items()} + # replace values with lenght of values + objectwise_img_dict_hico = {k: len(v) for k, v in objectwise_img_dict_hico.items()} +with open(objectwise_img_dict_vcoco, 'r') as fp: + objectwise_img_dict_vcoco = json.load(fp) + # replace underscore with space + objectwise_img_dict_vcoco = {k.replace('_', ' '): v for k, v in objectwise_img_dict_vcoco.items()} + # replace values with lenght of values + objectwise_img_dict_vcoco = {k: len(v) for k, v in objectwise_img_dict_vcoco.items()} + +# combine the dicts +objectwise_img_dict = objectwise_img_dict_hico.copy() +objectwise_img_dict.update(objectwise_img_dict_vcoco) + + +categorization = {"transportation": ['motorcycle','bicycle','boat','car','truck','bus','train','airplane',], + "accessories": ['backpack', 'tie', 'handbag', 'baseball glove'], + "furniture": ['bench','chair','couch','bed','toilet','dining table'], + 'everyday objects': ['book','umbrella','cell phone','laptop','kite','suitcase','bottle','remote', + 'toothbrush','teddy bear','scissors','keyboard','hair drier','traffic light', + 'fire hydrant','stop sign','tv','vase','parking meter','clock','potted plant','mouse',], + 'sports equipment': ['frisbee','sports ball','tennis racket','baseball bat','skateboard','snowboard','skis','surfboard',], + 'food items': ['banana','cake','apple','carrot','pizza','donut','hot dog','sandwich','broccoli','orange'], + 'kitchen appliances': ['knife', 'spoon', 'cup', 'wine glass', 'oven', 'fork', 'bowl', 'refrigerator', 'toaster', 'sink', 'microwave',]} + +# get total lengths of each category +objectwise_img_dict_categorized = {} +for k, v in categorization.items(): + objectwise_img_dict_categorized[k] = sum([objectwise_img_dict[obj] for obj in v]) + +# reverse categorization +categorization_rev = {} +for k, v in categorization.items(): + for obj in v: + categorization_rev[obj] = k + + + +data = dict( + character=list(categorization.keys()) + list(categorization_rev.keys()), + parent= ["objects"] * len(categorization.keys()) + list(categorization_rev.values()), + value =[0] * len(categorization.keys()) + list(objectwise_img_dict.values()), +) + +# save data as pandas +import pandas as pd +df = pd.DataFrame(data) +df.to_csv(osp.join(dir, 'object_nest_piechart_data.csv')) + + +fig = px.sunburst( + data, + names='character', + parents='parent', + values='value', +) + +# chage font size of the innermost level +fig.update_traces(textfont_size=30) + +# save the figure +out_path = osp.join(dir, "object_nest_piechart.html") +fig.write_html(out_path) +# Save plot as PNG wihtout transparent background +out_path = osp.join(dir, "object_nest_piechart.png") +fig.write_image(out_path, + format='png', + width=2000, height=1000, scale=1, engine='kaleido') + + +# Set layout +fig.update_layout( + margin=dict(t=0, l=0, r=0, b=0), + plot_bgcolor='rgba(0, 0, 0, 0)', + paper_bgcolor='rgba(0, 0, 0, 0)' +) + +# Save plot as HTML with transparent background +out_path = osp.join(dir, "object_nest_piechart_transparent.html") +pio.write_html(fig, file=out_path, auto_open=False, include_plotlyjs='cdn', config=dict(displayModeBar=False)) + +# Save plot as PNG with transparent background +out_path = osp.join(dir, "object_nest_piechart_transparent.png") +pio.write_image(fig, file=out_path, + format='png', + width=2000, height=1000, scale=1, engine='kaleido') + +import matplotlib.pyplot as plt +import seaborn as sns + +# make a bar plot of objectwise_img_dict + +# sort the dict +objectwise_img_dict = {k: v for k, v in sorted(objectwise_img_dict.items(), key=lambda item: item[1], reverse=True)} + +# save as pandas +import pandas as pd +df = pd.DataFrame.from_dict(objectwise_img_dict, orient='index', columns=['count']) +df.to_csv(osp.join(dir, 'image_per_object_category.csv')) + + +plt.figure(figsize=(20, 10)) +sns.barplot(x=list(objectwise_img_dict.keys()), y=list(objectwise_img_dict.values())) +# make horizontal grid lines +plt.grid(axis='y', alpha=0.5) + +# Add x-axis and y-axis labels +plt.xticks(rotation=45, ha='right', fontsize=15) +plt.yticks(fontsize=15) +plt.show() +# avoid clipping of x-axis labels +plt.tight_layout() + +# save the figure +out_path = osp.join(dir, "image_per_object_category.png") +plt.savefig(out_path, transparent=False) +out_path = osp.join(dir, "image_per_object_category_transparent.png") +plt.savefig(out_path, transparent=True) + diff --git a/hot_analysis/partwise_contact_graph.py b/hot_analysis/partwise_contact_graph.py new file mode 100644 index 0000000000000000000000000000000000000000..95b60e02d9a52e9378924fce8e44d63c9f72f80d --- /dev/null +++ b/hot_analysis/partwise_contact_graph.py @@ -0,0 +1,98 @@ +import numpy as np +import os +import json +import os.path as osp + + +# Load the combined dca train, val and test npzs +dir = '/is/cluster/work/stripathi/pycharm_remote/dca_contact/data/dataset_extras' +trainval_npz = np.load(os.path.join(dir, 'hot_dca_trainval.npz'), allow_pickle=True) +test_npz = np.load(os.path.join(dir, 'hot_dca_test.npz'), allow_pickle=True) + +# combine the two npz +combined_npz = {} +for key in trainval_npz.keys(): + combined_npz[key] = np.concatenate([trainval_npz[key], test_npz[key]], axis=0) + +segmentation_path = 'data/smpl_vert_segmentation.json' +with open(segmentation_path, 'rb') as f: + part_segmentation = json.load(f) + +# reverse the part segmentation +part_segmentation_rev = {} +for part in part_segmentation: + for vert in part_segmentation[part]: + part_segmentation_rev[vert] = part + +part_names_combined = [] +for cls in combined_npz['contact_label']: + cls_idx = np.where(cls == 1)[0] + part_names = [part_segmentation_rev[vert] for vert in cls_idx] + # find count for every part in part_names + part_count = {} + for part in part_names: + if part not in part_count: + part_count[part] = 0 + part_count[part] += 1 + # find the parts with count > 10 + part_names = [part for part in part_count if part_count[part] > 10] + part_names_combined.append(part_names) + +# make a histogram of the part names +part_names_combined = [item for sublist in part_names_combined for item in sublist] +part_names_combined = np.array(part_names_combined) +unique, counts = np.unique(part_names_combined, return_counts=True) +part_names_combined = dict(zip(unique, counts)) + +print('Total number of parts: ', len(part_names_combined)) +print(part_names_combined.keys()) +# set keys to combine; +combine_keys = {'leftFoot': ['leftToeBase'], + 'rightFoot': ['rightToeBase'], + 'leftHand': ['leftHandIndex1'], + 'rightHand': ['rightHandIndex1'], + 'spine': ['spine1', 'spine2'], + 'head': ['neck'],} + +for key in combine_keys: + for subkey in combine_keys[key]: + part_names_combined[key] += part_names_combined[subkey] + del part_names_combined[subkey] + +print('Total number of parts: ', len(part_names_combined)) + +# sort the dictionary +part_names_combined = {k: v for k, v in sorted(part_names_combined.items(), key=lambda item: item[1], reverse=True)} + +# convert to pandas +import pandas as pd +df = pd.DataFrame.from_dict(part_names_combined, orient='index', columns=['count']) +df.to_csv(osp.join(dir, 'partwise_contact_graph.csv')) + + + +# make a beautiful bar plot using seabord and change the default color palette +import seaborn as sns +import matplotlib.pyplot as plt + +# set the color palette + +# make the plot +fig, ax = plt.subplots(figsize=(20, 10)) +sns.barplot(x=list(part_names_combined.keys()), y=list(part_names_combined.values()), ax=ax, + palette=sns.color_palette("rocket", len(part_names_combined))) +ax.set_xticklabels(ax.get_xticklabels(), rotation=90) +plt.xticks(rotation=45, ha='right', fontsize=30) +plt.yticks(fontsize=30) +# avoid clipping of xtick labels +plt.tight_layout() + + + +# save the plot +outdir = "/is/cluster/work/stripathi/pycharm_remote/dca_contact/hot_analysis" +out_path = os.path.join(outdir, "partwise_contact_graph.png") +plt.savefig(out_path, transparent=True, dpi=300) + + + diff --git a/hot_analysis/vcoco_analysis.ipynb b/hot_analysis/vcoco_analysis.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..062ae397e03d68459a841527c122e3af0a510c2d --- /dev/null +++ b/hot_analysis/vcoco_analysis.ipynb @@ -0,0 +1,276 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import pandas as pd \n", + "import ipdb\n", + "import os\n", + "import pickle as pkl\n", + "import os.path as osp\n", + "import numpy as np\n", + "from PIL import Image" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total images found: 5459\n", + "Images after KP filtering: 2386\n", + "['vcoco_000000051899.jpg', 'vcoco_000000093127.jpg', 'vcoco_000000455665.jpg', 'vcoco_000000248389.jpg', 'vcoco_000000403947.jpg']\n" + ] + } + ], + "source": [ + "# Load Agniv VITpose-base hot dict\n", + "filter_path = \"./agniv_pose_filter/hot_dict.pkl\"\n", + "with open(filter_path, 'rb') as f:\n", + " pose_md_dict = pkl.load(f)\n", + " \n", + "vcoco_dict = {}\n", + "\n", + "for k, v in pose_md_dict.items():\n", + " if 'vcoco' in k:\n", + " vcoco_dict[k] = v\n", + " \n", + "print(f'Total images found: {len(vcoco_dict)}')\n", + "\n", + "# Filter out images with < 10 visible keypoints \n", + "kp_thresh = 10\n", + "\n", + "filter_img_names = {}\n", + "\n", + "for imgname, kp_md in vcoco_dict.items():\n", + " if kp_md == 0:\n", + " continue\n", + " if kp_md[\"num_persons\"] == 1 and kp_md[\"num_kpt\"][0.5][0] > kp_thresh:\n", + " filter_img_names[imgname] = kp_md[\"num_kpt\"][0.5][0]\n", + " \n", + "print(f'Images after KP filtering: {len(filter_img_names)}')\n", + "\n", + "print(list(filter_img_names.keys())[:5])" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loading annotations into memory...\n", + "Done (t=1.30s)\n", + "creating index...\n", + "index created!\n", + "Final number of images 2368\n" + ] + } + ], + "source": [ + "from pycocotools.coco import COCO\n", + "version = '1'\n", + "out_dir = f'./filtered_data/v_{version}/vcoco'\n", + "os.makedirs(out_dir, exist_ok=True)\n", + "\n", + "coco_dir = '/is/cluster/work/stripathi/pycharm_remote/v-coco/data'\n", + "coco = COCO(os.path.join(coco_dir, 'instances_vcoco_all_2014.json'))\n", + "\n", + "imgwise_object_names = {}\n", + "img_dir = '/ps/project/datasets/HOT/Contact_Data/images/training/'\n", + "\n", + "bad_object_names = ['bird', 'cat', 'dog', 'horse', 'sheep', 'cow', \n", + " 'elephant', 'bear', 'zebra', 'giraffe']\n", + "\n", + "# # Get all coco objects\n", + "# count = 0\n", + "# for id in range(91):\n", + "# try: \n", + "# print(coco.loadCats(id))\n", + "# count += 1\n", + "# except:\n", + "# print(f'ID:{id} does not exist')\n", + "# continue\n", + "# print(count)\n", + "\n", + "objectwise_img_names = {}\n", + "for img_name in filter_img_names.keys():\n", + " img_id = int(os.path.splitext(img_name.split('_')[-1])[0])\n", + " ann_ids = coco.getAnnIds([img_id])\n", + " anns = coco.loadAnns(ann_ids)\n", + " object_list = []\n", + " for ann in anns:\n", + " object_cat = coco.loadCats(ann[\"category_id\"])\n", + " if len(object_cat) > 1:\n", + " ipdb.set_trace()\n", + " object_name = object_cat[0]['name']\n", + " if object_name in bad_object_names:\n", + " continue\n", + " if object_name != 'person':\n", + " object_list.append(object_name)\n", + " if len(object_list) != 0:\n", + " imgwise_object_names[img_name] = list(set(object_list))\n", + " \n", + " for obj_name in set(object_list):\n", + " objectwise_img_names.setdefault(obj_name,[]).append(img_name)\n", + " \n", + "# # Visualize images \n", + "# print(img_name)\n", + "# print(list(set(object_list)))\n", + "# img = Image.open(osp.join(img_dir,img_name))\n", + "# display(img)\n", + " \n", + " \n", + "print(f'Final number of images {len(imgwise_object_names)}')\n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "saved at ./filtered_data/v_1/vcoco/object_per_image_dict.json\n", + "saved at ./filtered_data/v_1/vcoco/vcoco_imglist_all_170223.txt\n", + "saved at ./filtered_data/v_1/vcoco/imgnames_per_object_dict.json\n" + ] + } + ], + "source": [ + "# Save the imagewise_object dict\n", + "out_path = osp.join(out_dir, 'object_per_image_dict.json')\n", + "with open(out_path, 'w') as fp:\n", + " json.dump(imgwise_object_names, fp)\n", + " print(f'saved at {out_path}')\n", + " \n", + "# save image_list \n", + "out_path = osp.join(out_dir, 'vcoco_imglist_all_170223.txt')\n", + "with open(out_path, 'w') as f:\n", + " f.write('\\n'.join(imgwise_object_names.keys()))\n", + " print(f'saved at {out_path}')\n", + "\n", + "\n", + "# Save the object_wise dict\n", + "out_path = osp.join(out_dir, 'imgnames_per_object_dict.json')\n", + "with open(out_path, 'w') as fp:\n", + " json.dump(objectwise_img_names, fp)\n", + " print(f'saved at {out_path}')\n", + "\n", + " \n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5072\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "# sort the dictionary \n", + "objectwise_img_names = dict(sorted(objectwise_img_names.items(), key=lambda x: len(x[1]), reverse=True))\n", + "\n", + "# Extract object names and image counts\n", + "obj_names = list(objectwise_img_names.keys())\n", + "img_counts = [len(objectwise_img_names[obj]) for obj in objectwise_img_names]\n", + "print(sum(img_counts))\n", + "\n", + "# Create bar plot\n", + "sns.barplot(x=obj_names, y=img_counts)\n", + "\n", + "# Add x-axis and y-axis labels\n", + "plt.xlabel('Object')\n", + "plt.ylabel('Number of Images')\n", + "\n", + "plt.xticks(rotation=45, ha='right', fontsize=3)\n", + "\n", + "# Save the plot as a high-resolution image file\n", + "out_path = osp.join(out_dir, 'image_per_object_category.png')\n", + "plt.savefig(out_path, dpi=300)\n", + "\n", + "# Show plot\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/inference.py b/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..b1d04b7c37b4358dd8f71b0418314f32ed616a4d --- /dev/null +++ b/inference.py @@ -0,0 +1,192 @@ +import torch +import os +import glob +import argparse +import numpy as np +import cv2 +import PIL.Image as pil_img +from loguru import logger +import shutil + +import trimesh +import pyrender + +from models.deco import DECO +from common import constants + +os.environ['PYOPENGL_PLATFORM'] = 'egl' + +if torch.cuda.is_available(): + device = torch.device('cuda') +else: + device = torch.device('cpu') + +def initiate_model(args): + deco_model = DECO('hrnet', True, device) + + logger.info(f'Loading weights from {args.model_path}') + checkpoint = torch.load(args.model_path) + deco_model.load_state_dict(checkpoint['deco'], strict=True) + + deco_model.eval() + + return deco_model + +def render_image(scene, img_res, img=None, viewer=False): + ''' + Render the given pyrender scene and return the image. Can also overlay the mesh on an image. + ''' + if viewer: + pyrender.Viewer(scene, use_raymond_lighting=True) + return 0 + else: + r = pyrender.OffscreenRenderer(viewport_width=img_res, + viewport_height=img_res, + point_size=1.0) + color, _ = r.render(scene, flags=pyrender.RenderFlags.RGBA) + color = color.astype(np.float32) / 255.0 + + if img is not None: + valid_mask = (color[:, :, -1] > 0)[:, :, np.newaxis] + input_img = img.detach().cpu().numpy() + output_img = (color[:, :, :-1] * valid_mask + + (1 - valid_mask) * input_img) + else: + output_img = color + return output_img + +def create_scene(mesh, img, focal_length=500, camera_center=250, img_res=500): + # Setup the scene + scene = pyrender.Scene(bg_color=[1.0, 1.0, 1.0, 1.0], + ambient_light=(0.3, 0.3, 0.3)) + # add mesh for camera + camera_pose = np.eye(4) + camera_rotation = np.eye(3, 3) + camera_translation = np.array([0., 0, 2.5]) + camera_pose[:3, :3] = camera_rotation + camera_pose[:3, 3] = camera_rotation @ camera_translation + pyrencamera = pyrender.camera.IntrinsicsCamera( + fx=focal_length, fy=focal_length, + cx=camera_center, cy=camera_center) + scene.add(pyrencamera, pose=camera_pose) + # create and add light + light = pyrender.PointLight(color=[1.0, 1.0, 1.0], intensity=1) + light_pose = np.eye(4) + for lp in [[1, 1, 1], [-1, 1, 1], [1, -1, 1], [-1, -1, 1]]: + light_pose[:3, 3] = mesh.vertices.mean(0) + np.array(lp) + # out_mesh.vertices.mean(0) + np.array(lp) + scene.add(light, pose=light_pose) + # add body mesh + material = pyrender.MetallicRoughnessMaterial( + metallicFactor=0.0, + alphaMode='OPAQUE', + baseColorFactor=(1.0, 1.0, 0.9, 1.0)) + mesh_images = [] + + # resize input image to fit the mesh image height + img_height = img_res + img_width = int(img_height * img.shape[1] / img.shape[0]) + img = cv2.resize(img, (img_width, img_height)) + mesh_images.append(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) + + for sideview_angle in [0, 90, 180, 270]: + out_mesh = mesh.copy() + rot = trimesh.transformations.rotation_matrix( + np.radians(sideview_angle), [0, 1, 0]) + out_mesh.apply_transform(rot) + out_mesh = pyrender.Mesh.from_trimesh( + out_mesh, + material=material) + mesh_pose = np.eye(4) + scene.add(out_mesh, pose=mesh_pose, name='mesh') + output_img = render_image(scene, img_res) + output_img = pil_img.fromarray((output_img * 255).astype(np.uint8)) + output_img = np.asarray(output_img)[:, :, :3] + mesh_images.append(output_img) + # delete the previous mesh + prev_mesh = scene.get_nodes(name='mesh').pop() + scene.remove_node(prev_mesh) + + # show upside down view + for topview_angle in [90, 270]: + out_mesh = mesh.copy() + rot = trimesh.transformations.rotation_matrix( + np.radians(topview_angle), [1, 0, 0]) + out_mesh.apply_transform(rot) + out_mesh = pyrender.Mesh.from_trimesh( + out_mesh, + material=material) + mesh_pose = np.eye(4) + scene.add(out_mesh, pose=mesh_pose, name='mesh') + output_img = render_image(scene, img_res) + output_img = pil_img.fromarray((output_img * 255).astype(np.uint8)) + output_img = np.asarray(output_img)[:, :, :3] + mesh_images.append(output_img) + # delete the previous mesh + prev_mesh = scene.get_nodes(name='mesh').pop() + scene.remove_node(prev_mesh) + + # stack images + IMG = np.hstack(mesh_images) + IMG = pil_img.fromarray(IMG) + IMG.thumbnail((3000, 3000)) + return IMG + +def main(args): + if os.path.isdir(args.img_src): + images = glob.iglob(args.img_src + '/*', recursive=True) + else: + images = [args.img_src] + + deco_model = initiate_model(args) + + smpl_path = os.path.join(constants.SMPL_MODEL_DIR, 'smpl_neutral_tpose.ply') + + for img_name in images: + img = cv2.imread(img_name) + img = cv2.resize(img, (256, 256), cv2.INTER_CUBIC) + img = img.transpose(2,0,1)/255.0 + img = img[np.newaxis,:,:,:] + img = torch.tensor(img, dtype = torch.float32).to(device) + + cont, _, _ = deco_model(img) + cont = cont.detach().cpu().numpy().squeeze() + cont_smpl = [] + for indx, i in enumerate(cont): + if i >= 0.5: + cont_smpl.append(indx) + + img = img.detach().cpu().numpy() + img = np.transpose(img[0], (1, 2, 0)) + img = img * 255 + img = img.astype(np.uint8) + + contact_smpl = np.zeros((1, 1, 6890)) + contact_smpl[0][0][cont_smpl] = 1 + + body_model_smpl = trimesh.load(smpl_path, process=False) + for vert in range(body_model_smpl.visual.vertex_colors.shape[0]): + body_model_smpl.visual.vertex_colors[vert] = args.mesh_colour + body_model_smpl.visual.vertex_colors[cont_smpl] = args.annot_colour + + rend = create_scene(body_model_smpl, img) + os.makedirs(os.path.join(args.out_dir, 'Renders'), exist_ok=True) + rend.save(os.path.join(args.out_dir, 'Renders', os.path.basename(img_name).split('.')[0] + '.png')) + + out_dir = os.path.join(args.out_dir, 'Preds', os.path.basename(img_name).split('.')[0]) + os.makedirs(out_dir, exist_ok=True) + + logger.info(f'Saving mesh to {out_dir}') + shutil.copyfile(img_name, os.path.join(out_dir, os.path.basename(img_name))) + body_model_smpl.export(os.path.join(out_dir, 'pred.obj')) + +if __name__=='__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--img_src', help='Source of image(s). Can be file or directory', default='./demo_out', type=str) + parser.add_argument('--out_dir', help='Where to store images', default='./demo_out', type=str) + parser.add_argument('--model_path', help='Path to best model weights', default='./checkpoints/Release_Checkpoint/deco_best.pth', type=str) + parser.add_argument('--mesh_colour', help='Colour of the mesh', nargs='+', type=int, default=[130, 130, 130, 255]) + parser.add_argument('--annot_colour', help='Colour of the mesh', nargs='+', type=int, default=[0, 255, 0, 255]) + args = parser.parse_args() + + main(args) diff --git a/models/__init__.py b/models/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/models/backbones/hrnet/cls_hrnet.py b/models/backbones/hrnet/cls_hrnet.py new file mode 100644 index 0000000000000000000000000000000000000000..6957d356fb605d9e5628addcb47772e60e7d40b3 --- /dev/null +++ b/models/backbones/hrnet/cls_hrnet.py @@ -0,0 +1,518 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# Modified by Ke Sun (sunk@mail.ustc.edu.cn) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import logging +import torch +import torch.nn as nn +import torch._utils +import torch.nn.functional as F + +BN_MOMENTUM = 0.1 +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) + +ch = logging.StreamHandler() +ch.setLevel(logging.INFO) +logger.addHandler(ch) + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, + bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion, + momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class HighResolutionModule(nn.Module): + def __init__(self, num_branches, blocks, num_blocks, num_inchannels, + num_channels, fuse_method, multi_scale_output=True): + super(HighResolutionModule, self).__init__() + self._check_branches( + num_branches, blocks, num_blocks, num_inchannels, num_channels) + + self.num_inchannels = num_inchannels + self.fuse_method = fuse_method + self.num_branches = num_branches + + self.multi_scale_output = multi_scale_output + + self.branches = self._make_branches( + num_branches, blocks, num_blocks, num_channels) + self.fuse_layers = self._make_fuse_layers() + self.relu = nn.ReLU(False) + + def _check_branches(self, num_branches, blocks, num_blocks, + num_inchannels, num_channels): + if num_branches != len(num_blocks): + error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format( + num_branches, len(num_blocks)) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_channels): + error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format( + num_branches, len(num_channels)) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_inchannels): + error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format( + num_branches, len(num_inchannels)) + logger.error(error_msg) + raise ValueError(error_msg) + + def _make_one_branch(self, branch_index, block, num_blocks, num_channels, + stride=1): + downsample = None + if stride != 1 or \ + self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.num_inchannels[branch_index], + num_channels[branch_index] * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(num_channels[branch_index] * block.expansion, + momentum=BN_MOMENTUM), + ) + + layers = [] + layers.append(block(self.num_inchannels[branch_index], + num_channels[branch_index], stride, downsample)) + self.num_inchannels[branch_index] = \ + num_channels[branch_index] * block.expansion + for i in range(1, num_blocks[branch_index]): + layers.append(block(self.num_inchannels[branch_index], + num_channels[branch_index])) + + return nn.Sequential(*layers) + + def _make_branches(self, num_branches, block, num_blocks, num_channels): + branches = [] + + for i in range(num_branches): + branches.append( + self._make_one_branch(i, block, num_blocks, num_channels)) + + return nn.ModuleList(branches) + + def _make_fuse_layers(self): + if self.num_branches == 1: + return None + + num_branches = self.num_branches + num_inchannels = self.num_inchannels + fuse_layers = [] + for i in range(num_branches if self.multi_scale_output else 1): + fuse_layer = [] + for j in range(num_branches): + if j > i: + fuse_layer.append(nn.Sequential( + nn.Conv2d(num_inchannels[j], + num_inchannels[i], + 1, + 1, + 0, + bias=False), + nn.BatchNorm2d(num_inchannels[i], + momentum=BN_MOMENTUM), + nn.Upsample(scale_factor=2 ** (j - i), mode='nearest'))) + elif j == i: + fuse_layer.append(None) + else: + conv3x3s = [] + for k in range(i - j): + if k == i - j - 1: + num_outchannels_conv3x3 = num_inchannels[i] + conv3x3s.append(nn.Sequential( + nn.Conv2d(num_inchannels[j], + num_outchannels_conv3x3, + 3, 2, 1, bias=False), + nn.BatchNorm2d(num_outchannels_conv3x3, + momentum=BN_MOMENTUM))) + else: + num_outchannels_conv3x3 = num_inchannels[j] + conv3x3s.append(nn.Sequential( + nn.Conv2d(num_inchannels[j], + num_outchannels_conv3x3, + 3, 2, 1, bias=False), + nn.BatchNorm2d(num_outchannels_conv3x3, + momentum=BN_MOMENTUM), + nn.ReLU(False))) + fuse_layer.append(nn.Sequential(*conv3x3s)) + fuse_layers.append(nn.ModuleList(fuse_layer)) + + return nn.ModuleList(fuse_layers) + + def get_num_inchannels(self): + return self.num_inchannels + + def forward(self, x): + if self.num_branches == 1: + return [self.branches[0](x[0])] + + for i in range(self.num_branches): + x[i] = self.branches[i](x[i]) + + x_fuse = [] + for i in range(len(self.fuse_layers)): + y = x[0] if i == 0 else self.fuse_layers[i][0](x[0]) + for j in range(1, self.num_branches): + if i == j: + y = y + x[j] + else: + y = y + self.fuse_layers[i][j](x[j]) + x_fuse.append(self.relu(y)) + + return x_fuse + + +blocks_dict = { + 'BASIC': BasicBlock, + 'BOTTLENECK': Bottleneck +} + + +class HighResolutionNet(nn.Module): + + def __init__(self, cfg, **kwargs): + super(HighResolutionNet, self).__init__() + + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, + bias=False) + self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, + bias=False) + self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + + self.stage1_cfg = cfg['MODEL']['EXTRA']['STAGE1'] + num_channels = self.stage1_cfg['NUM_CHANNELS'][0] + block = blocks_dict[self.stage1_cfg['BLOCK']] + num_blocks = self.stage1_cfg['NUM_BLOCKS'][0] + self.layer1 = self._make_layer(block, 64, num_channels, num_blocks) + stage1_out_channel = block.expansion * num_channels + + self.stage2_cfg = cfg['MODEL']['EXTRA']['STAGE2'] + num_channels = self.stage2_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage2_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels))] + self.transition1 = self._make_transition_layer( + [stage1_out_channel], num_channels) + self.stage2, pre_stage_channels = self._make_stage( + self.stage2_cfg, num_channels) + + self.stage3_cfg = cfg['MODEL']['EXTRA']['STAGE3'] + num_channels = self.stage3_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage3_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels))] + self.transition2 = self._make_transition_layer( + pre_stage_channels, num_channels) + self.stage3, pre_stage_channels = self._make_stage( + self.stage3_cfg, num_channels) + + self.stage4_cfg = cfg['MODEL']['EXTRA']['STAGE4'] + num_channels = self.stage4_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage4_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels))] + self.transition3 = self._make_transition_layer( + pre_stage_channels, num_channels) + self.stage4, pre_stage_channels = self._make_stage( + self.stage4_cfg, num_channels, multi_scale_output=True) + + # Classification Head + self.incre_modules, self.downsamp_modules, \ + self.final_layer = self._make_head(pre_stage_channels) + + # self.classifier = nn.Linear(2048, 1000) + + def _make_head(self, pre_stage_channels): + head_block = Bottleneck + head_channels = [32, 64, 128, 256] + + # Increasing the #channels on each resolution + # from C, 2C, 4C, 8C to 128, 256, 512, 1024 + incre_modules = [] + for i, channels in enumerate(pre_stage_channels): + incre_module = self._make_layer(head_block, + channels, + head_channels[i], + 1, + stride=1) + incre_modules.append(incre_module) + incre_modules = nn.ModuleList(incre_modules) + + # downsampling modules + downsamp_modules = [] + for i in range(len(pre_stage_channels) - 1): + in_channels = head_channels[i] * head_block.expansion + out_channels = head_channels[i + 1] * head_block.expansion + + downsamp_module = nn.Sequential( + nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=3, + stride=2, + padding=1), + nn.BatchNorm2d(out_channels, momentum=BN_MOMENTUM), + nn.ReLU(inplace=True) + ) + + downsamp_modules.append(downsamp_module) + downsamp_modules = nn.ModuleList(downsamp_modules) + + final_layer = nn.Sequential( + nn.Conv2d( + in_channels=head_channels[3] * head_block.expansion, + out_channels=2048, + kernel_size=1, + stride=1, + padding=0 + ), + nn.BatchNorm2d(2048, momentum=BN_MOMENTUM), + nn.ReLU(inplace=True) + ) + + return incre_modules, downsamp_modules, final_layer + + def _make_transition_layer( + self, num_channels_pre_layer, num_channels_cur_layer): + num_branches_cur = len(num_channels_cur_layer) + num_branches_pre = len(num_channels_pre_layer) + + transition_layers = [] + for i in range(num_branches_cur): + if i < num_branches_pre: + if num_channels_cur_layer[i] != num_channels_pre_layer[i]: + transition_layers.append(nn.Sequential( + nn.Conv2d(num_channels_pre_layer[i], + num_channels_cur_layer[i], + 3, + 1, + 1, + bias=False), + nn.BatchNorm2d( + num_channels_cur_layer[i], momentum=BN_MOMENTUM), + nn.ReLU(inplace=True))) + else: + transition_layers.append(None) + else: + conv3x3s = [] + for j in range(i + 1 - num_branches_pre): + inchannels = num_channels_pre_layer[-1] + outchannels = num_channels_cur_layer[i] \ + if j == i - num_branches_pre else inchannels + conv3x3s.append(nn.Sequential( + nn.Conv2d( + inchannels, outchannels, 3, 2, 1, bias=False), + nn.BatchNorm2d(outchannels, momentum=BN_MOMENTUM), + nn.ReLU(inplace=True))) + transition_layers.append(nn.Sequential(*conv3x3s)) + + return nn.ModuleList(transition_layers) + + def _make_layer(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM), + ) + + layers = [] + layers.append(block(inplanes, planes, stride, downsample)) + inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(inplanes, planes)) + + return nn.Sequential(*layers) + + def _make_stage(self, layer_config, num_inchannels, + multi_scale_output=True): + num_modules = layer_config['NUM_MODULES'] + num_branches = layer_config['NUM_BRANCHES'] + num_blocks = layer_config['NUM_BLOCKS'] + num_channels = layer_config['NUM_CHANNELS'] + block = blocks_dict[layer_config['BLOCK']] + fuse_method = layer_config['FUSE_METHOD'] + + modules = [] + for i in range(num_modules): + # multi_scale_output is only used last module + if not multi_scale_output and i == num_modules - 1: + reset_multi_scale_output = False + else: + reset_multi_scale_output = True + + modules.append( + HighResolutionModule(num_branches, + block, + num_blocks, + num_inchannels, + num_channels, + fuse_method, + reset_multi_scale_output) + ) + num_inchannels = modules[-1].get_num_inchannels() + + return nn.Sequential(*modules), num_inchannels + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.conv2(x) + x = self.bn2(x) + x = self.relu(x) + x = self.layer1(x) + + x_list = [] + for i in range(self.stage2_cfg['NUM_BRANCHES']): + if self.transition1[i] is not None: + x_list.append(self.transition1[i](x)) + else: + x_list.append(x) + y_list = self.stage2(x_list) + + x_list = [] + for i in range(self.stage3_cfg['NUM_BRANCHES']): + if self.transition2[i] is not None: + x_list.append(self.transition2[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage3(x_list) + + x_list = [] + for i in range(self.stage4_cfg['NUM_BRANCHES']): + if self.transition3[i] is not None: + x_list.append(self.transition3[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage4(x_list) + + # Classification Head + y = self.incre_modules[0](y_list[0]) + for i in range(len(self.downsamp_modules)): + y = self.incre_modules[i + 1](y_list[i + 1]) + \ + self.downsamp_modules[i](y) + + y = self.final_layer(y) + + if torch._C._get_tracing_state(): + y = y.flatten(start_dim=2).mean(dim=2) + else: + y = F.avg_pool2d(y, kernel_size=y.size()[2:]).view(y.size(0), -1) + + # y = self.classifier(y) + + return y + + def init_weights(self, pretrained='', ): + logger.info('=> pretrained: ' + pretrained) + if os.path.isfile(pretrained): + pretrained_dict = torch.load(pretrained) + # logger.info('=> loading pretrained model {}'.format(pretrained)) + # model_dict = self.state_dict() + # pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict.keys()} + # for k, _ in pretrained_dict.items(): + # logger.info('=> loading {} pretrained model {}'.format(k, pretrained)) + # model_dict.update(pretrained_dict) + # self.load_state_dict(model_dict) + self.load_state_dict(pretrained_dict, strict=True) + else: + logger.info('=> init weights from normal distribution') + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + +def get_cls_net(config, **kwargs): + model = HighResolutionNet(config, **kwargs) + model.init_weights(**kwargs) + return model diff --git a/models/backbones/hrnet/hrnet_config/__init__.py b/models/backbones/hrnet/hrnet_config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..a44e926b9b71389cb32a727d33d904bfdbcaaffb --- /dev/null +++ b/models/backbones/hrnet/hrnet_config/__init__.py @@ -0,0 +1,9 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from .default import _C as cfg +from .default import update_config +from .models import MODEL_EXTRAS diff --git a/models/backbones/hrnet/hrnet_config/default.py b/models/backbones/hrnet/hrnet_config/default.py new file mode 100644 index 0000000000000000000000000000000000000000..e22a15477edd33004650215e7a3d33b4eed793ae --- /dev/null +++ b/models/backbones/hrnet/hrnet_config/default.py @@ -0,0 +1,136 @@ + +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# Modified by Hanbin Dai (daihanbin.ac@gmail.com) and Feng Zhang (zhangfengwcy@gmail.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from yacs.config import CfgNode as CN + + +_C = CN() + +_C.OUTPUT_DIR = '' +_C.LOG_DIR = '' +_C.DATA_DIR = '' +_C.GPUS = (0,) +_C.WORKERS = 4 +_C.PRINT_FREQ = 20 +_C.AUTO_RESUME = False +_C.PIN_MEMORY = True +_C.RANK = 0 + +# Cudnn related params +_C.CUDNN = CN() +_C.CUDNN.BENCHMARK = True +_C.CUDNN.DETERMINISTIC = False +_C.CUDNN.ENABLED = True + +# common params for NETWORK +_C.MODEL = CN() +_C.MODEL.NAME = 'pose_hrnet' +_C.MODEL.INIT_WEIGHTS = True +_C.MODEL.PRETRAINED = '' +_C.MODEL.NUM_JOINTS = 17 +_C.MODEL.TAG_PER_JOINT = True +_C.MODEL.TARGET_TYPE = 'gaussian' +_C.MODEL.IMAGE_SIZE = [256, 256] # width * height, ex: 192 * 256 +_C.MODEL.HEATMAP_SIZE = [64, 64] # width * height, ex: 24 * 32 +_C.MODEL.SIGMA = 2 +_C.MODEL.EXTRA = CN(new_allowed=True) + +_C.LOSS = CN() +_C.LOSS.USE_OHKM = False +_C.LOSS.TOPK = 8 +_C.LOSS.USE_TARGET_WEIGHT = True +_C.LOSS.USE_DIFFERENT_JOINTS_WEIGHT = False + +# DATASET related params +_C.DATASET = CN() +_C.DATASET.ROOT = '' +_C.DATASET.DATASET = 'mpii' +_C.DATASET.TRAIN_SET = 'train' +_C.DATASET.TEST_SET = 'valid' +_C.DATASET.DATA_FORMAT = 'jpg' +_C.DATASET.HYBRID_JOINTS_TYPE = '' +_C.DATASET.SELECT_DATA = False + +# training data augmentation +_C.DATASET.FLIP = True +_C.DATASET.SCALE_FACTOR = 0.25 +_C.DATASET.ROT_FACTOR = 30 +_C.DATASET.PROB_HALF_BODY = 0.0 +_C.DATASET.NUM_JOINTS_HALF_BODY = 8 +_C.DATASET.COLOR_RGB = False + +# train +_C.TRAIN = CN() + +_C.TRAIN.LR_FACTOR = 0.1 +_C.TRAIN.LR_STEP = [90, 110] +_C.TRAIN.LR = 0.001 + +_C.TRAIN.OPTIMIZER = 'adam' +_C.TRAIN.MOMENTUM = 0.9 +_C.TRAIN.WD = 0.0001 +_C.TRAIN.NESTEROV = False +_C.TRAIN.GAMMA1 = 0.99 +_C.TRAIN.GAMMA2 = 0.0 + +_C.TRAIN.BEGIN_EPOCH = 0 +_C.TRAIN.END_EPOCH = 140 + +_C.TRAIN.RESUME = False +_C.TRAIN.CHECKPOINT = '' + +_C.TRAIN.BATCH_SIZE_PER_GPU = 32 +_C.TRAIN.SHUFFLE = True + +# testing +_C.TEST = CN() + +# size of images for each device +_C.TEST.BATCH_SIZE_PER_GPU = 32 +# Test Model Epoch +_C.TEST.FLIP_TEST = False +_C.TEST.POST_PROCESS = False +_C.TEST.BLUR_KERNEL = 11 + +_C.TEST.USE_GT_BBOX = False + +# nms +_C.TEST.IMAGE_THRE = 0.1 +_C.TEST.NMS_THRE = 0.6 +_C.TEST.SOFT_NMS = False +_C.TEST.OKS_THRE = 0.5 +_C.TEST.IN_VIS_THRE = 0.0 +_C.TEST.COCO_BBOX_FILE = '' +_C.TEST.BBOX_THRE = 1.0 +_C.TEST.MODEL_FILE = '' + +# debug +_C.DEBUG = CN() +_C.DEBUG.DEBUG = False +_C.DEBUG.SAVE_BATCH_IMAGES_GT = False +_C.DEBUG.SAVE_BATCH_IMAGES_PRED = False +_C.DEBUG.SAVE_HEATMAPS_GT = False +_C.DEBUG.SAVE_HEATMAPS_PRED = False + + +def update_config(cfg, config_file): + cfg.defrost() + cfg.merge_from_file(config_file) + cfg.freeze() + + +if __name__ == '__main__': + import sys + with open(sys.argv[1], 'w') as f: + print(_C, file=f) diff --git a/models/backbones/hrnet/hrnet_config/models.py b/models/backbones/hrnet/hrnet_config/models.py new file mode 100644 index 0000000000000000000000000000000000000000..8e04c4f75a42429142131e1fe4cbbd67fbf4acb8 --- /dev/null +++ b/models/backbones/hrnet/hrnet_config/models.py @@ -0,0 +1,58 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from yacs.config import CfgNode as CN + + +# pose_resnet related params +POSE_RESNET = CN() +POSE_RESNET.NUM_LAYERS = 50 +POSE_RESNET.DECONV_WITH_BIAS = False +POSE_RESNET.NUM_DECONV_LAYERS = 3 +POSE_RESNET.NUM_DECONV_FILTERS = [256, 256, 256] +POSE_RESNET.NUM_DECONV_KERNELS = [4, 4, 4] +POSE_RESNET.FINAL_CONV_KERNEL = 1 +POSE_RESNET.PRETRAINED_LAYERS = ['*'] + +# pose_multi_resoluton_net related params +POSE_HIGH_RESOLUTION_NET = CN() +POSE_HIGH_RESOLUTION_NET.PRETRAINED_LAYERS = ['*'] +POSE_HIGH_RESOLUTION_NET.STEM_INPLANES = 64 +POSE_HIGH_RESOLUTION_NET.FINAL_CONV_KERNEL = 1 + +POSE_HIGH_RESOLUTION_NET.STAGE2 = CN() +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_MODULES = 1 +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_BRANCHES = 2 +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_BLOCKS = [4, 4] +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_CHANNELS = [32, 64] +POSE_HIGH_RESOLUTION_NET.STAGE2.BLOCK = 'BASIC' +POSE_HIGH_RESOLUTION_NET.STAGE2.FUSE_METHOD = 'SUM' + +POSE_HIGH_RESOLUTION_NET.STAGE3 = CN() +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_MODULES = 1 +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_BRANCHES = 3 +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_BLOCKS = [4, 4, 4] +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_CHANNELS = [32, 64, 128] +POSE_HIGH_RESOLUTION_NET.STAGE3.BLOCK = 'BASIC' +POSE_HIGH_RESOLUTION_NET.STAGE3.FUSE_METHOD = 'SUM' + +POSE_HIGH_RESOLUTION_NET.STAGE4 = CN() +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_MODULES = 1 +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_BRANCHES = 4 +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_BLOCKS = [4, 4, 4, 4] +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_CHANNELS = [32, 64, 128, 256] +POSE_HIGH_RESOLUTION_NET.STAGE4.BLOCK = 'BASIC' +POSE_HIGH_RESOLUTION_NET.STAGE4.FUSE_METHOD = 'SUM' + + +MODEL_EXTRAS = { + 'pose_resnet': POSE_RESNET, + 'pose_high_resolution_net': POSE_HIGH_RESOLUTION_NET, +} diff --git a/models/backbones/hrnet/models/cls_hrnet_w48_sgd_lr5e-2_wd1e-4_bs32_x100.yaml b/models/backbones/hrnet/models/cls_hrnet_w48_sgd_lr5e-2_wd1e-4_bs32_x100.yaml new file mode 100644 index 0000000000000000000000000000000000000000..b28cae60056eb54d8e86df50bb5276789b8d1aed --- /dev/null +++ b/models/backbones/hrnet/models/cls_hrnet_w48_sgd_lr5e-2_wd1e-4_bs32_x100.yaml @@ -0,0 +1,92 @@ +GPUS: (0,1,2,3) +LOG_DIR: 'log/' +DATA_DIR: '' +OUTPUT_DIR: 'output/' +WORKERS: 4 +PRINT_FREQ: 1000 + +MODEL: + NAME: cls_hrnet + IMAGE_SIZE: + - 224 + - 224 + EXTRA: + STAGE1: + NUM_MODULES: 1 + NUM_RANCHES: 1 + BLOCK: BOTTLENECK + NUM_BLOCKS: + - 4 + NUM_CHANNELS: + - 64 + FUSE_METHOD: SUM + STAGE2: + NUM_MODULES: 1 + NUM_BRANCHES: 2 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + NUM_CHANNELS: + - 48 + - 96 + FUSE_METHOD: SUM + STAGE3: + NUM_MODULES: 4 + NUM_BRANCHES: 3 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + - 4 + NUM_CHANNELS: + - 48 + - 96 + - 192 + FUSE_METHOD: SUM + STAGE4: + NUM_MODULES: 3 + NUM_BRANCHES: 4 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + - 4 + - 4 + NUM_CHANNELS: + - 48 + - 96 + - 192 + - 384 + FUSE_METHOD: SUM +CUDNN: + BENCHMARK: true + DETERMINISTIC: false + ENABLED: true +DATASET: + DATASET: 'imagenet' + DATA_FORMAT: 'jpg' + ROOT: 'data/imagenet/' + TEST_SET: 'val' + TRAIN_SET: 'train' +TEST: + BATCH_SIZE_PER_GPU: 32 + MODEL_FILE: '' +TRAIN: + BATCH_SIZE_PER_GPU: 32 + BEGIN_EPOCH: 0 + END_EPOCH: 100 + RESUME: true + LR_FACTOR: 0.1 + LR_STEP: + - 30 + - 60 + - 90 + OPTIMIZER: sgd + LR: 0.05 + WD: 0.0001 + MOMENTUM: 0.9 + NESTEROV: true + SHUFFLE: true +DEBUG: + DEBUG: false diff --git a/models/backbones/hrnet/models/cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml b/models/backbones/hrnet/models/cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml new file mode 100644 index 0000000000000000000000000000000000000000..65d710339c15ac3fc94b91bc54b51196feb433b1 --- /dev/null +++ b/models/backbones/hrnet/models/cls_hrnet_w64_sgd_lr5e-2_wd1e-4_bs32_x100.yaml @@ -0,0 +1,92 @@ +GPUS: (0,1,2,3) +LOG_DIR: 'log/' +DATA_DIR: '' +OUTPUT_DIR: 'output/' +WORKERS: 4 +PRINT_FREQ: 1000 + +MODEL: + NAME: cls_hrnet + IMAGE_SIZE: + - 224 + - 224 + EXTRA: + STAGE1: + NUM_MODULES: 1 + NUM_RANCHES: 1 + BLOCK: BOTTLENECK + NUM_BLOCKS: + - 4 + NUM_CHANNELS: + - 64 + FUSE_METHOD: SUM + STAGE2: + NUM_MODULES: 1 + NUM_BRANCHES: 2 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + NUM_CHANNELS: + - 64 + - 128 + FUSE_METHOD: SUM + STAGE3: + NUM_MODULES: 4 + NUM_BRANCHES: 3 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + - 4 + NUM_CHANNELS: + - 64 + - 128 + - 256 + FUSE_METHOD: SUM + STAGE4: + NUM_MODULES: 3 + NUM_BRANCHES: 4 + BLOCK: BASIC + NUM_BLOCKS: + - 4 + - 4 + - 4 + - 4 + NUM_CHANNELS: + - 64 + - 128 + - 256 + - 512 + FUSE_METHOD: SUM +CUDNN: + BENCHMARK: true + DETERMINISTIC: false + ENABLED: true +DATASET: + DATASET: 'imagenet' + DATA_FORMAT: 'jpg' + ROOT: 'data/imagenet/' + TEST_SET: 'val' + TRAIN_SET: 'train' +TEST: + BATCH_SIZE_PER_GPU: 32 + MODEL_FILE: '' +TRAIN: + BATCH_SIZE_PER_GPU: 32 + BEGIN_EPOCH: 0 + END_EPOCH: 100 + RESUME: true + LR_FACTOR: 0.1 + LR_STEP: + - 30 + - 60 + - 90 + OPTIMIZER: sgd + LR: 0.05 + WD: 0.0001 + MOMENTUM: 0.9 + NESTEROV: true + SHUFFLE: true +DEBUG: + DEBUG: false diff --git a/models/backbones/resnet.py b/models/backbones/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..1c72616c6cdacd5fdf436c8d24a3ce6b986454b5 --- /dev/null +++ b/models/backbones/resnet.py @@ -0,0 +1,94 @@ +# Copyright (c) 2019, University of Pennsylvania, Max Planck Institute for Intelligent Systems +# This script is borrowed and extended from SPIN + +import torch.nn as nn + + +class Bottleneck(nn.Module): + """ Redefinition of Bottleneck residual block + Adapted from the official PyTorch implementation + """ + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class ResNet(nn.Module): + def __init__(self, layers, block=Bottleneck): + super().__init__() + self.inplanes = 64 + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d((8, 6), stride=1) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.view(x.size(0), -1) + return x + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) diff --git a/models/cliff_hr48/cliff.py b/models/cliff_hr48/cliff.py new file mode 100644 index 0000000000000000000000000000000000000000..e431a6ef06d86cd302450e39fd6fea3b72cab2ae --- /dev/null +++ b/models/cliff_hr48/cliff.py @@ -0,0 +1,105 @@ +# 2022.07.19 - Changed for CLIFF +# Huawei Technologies Co., Ltd. + +# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. +# Copyright (c) 2019, University of Pennsylvania, Max Planck Institute for Intelligent Systems + +# This program is free software; you can redistribute it and/or modify it +# under the terms of the MIT license. + +# This program is distributed in the hope that it will be useful, but WITHOUT ANY +# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +# PARTICULAR PURPOSE. See the MIT License for more details. + +# This script is borrowed and extended from SPIN + +import torch +import torch.nn as nn +import numpy as np +import math +import os.path as osp + +from common.imutils import rot6d_to_rotmat +from models.backbones.hrnet.cls_hrnet import HighResolutionNet +from models.backbones.hrnet.hrnet_config import cfg +from models.backbones.hrnet.hrnet_config import update_config + + +class CLIFF(nn.Module): + """ SMPL Iterative Regressor with ResNet50 backbone""" + + def __init__(self, smpl_mean_params, img_feat_num=2048): + super(CLIFF, self).__init__() + curr_dir = osp.dirname(osp.abspath(__file__)) + config_file = osp.join(curr_dir, "../backbones/hrnet/models/cls_hrnet_w48_sgd_lr5e-2_wd1e-4_bs32_x100.yaml") + update_config(cfg, config_file) + self.encoder = HighResolutionNet(cfg) + + npose = 24 * 6 + nshape = 10 + ncam = 3 + nbbox = 3 + + fc1_feat_num = 1024 + fc2_feat_num = 1024 + final_feat_num = fc2_feat_num + reg_in_feat_num = img_feat_num + nbbox + npose + nshape + ncam + # CUDA Error: an illegal memory access was encountered + # the above error will occur, if use mobilenet v3 with BN, so don't use BN + self.fc1 = nn.Linear(reg_in_feat_num, fc1_feat_num) + self.drop1 = nn.Dropout() + self.fc2 = nn.Linear(fc1_feat_num, fc2_feat_num) + self.drop2 = nn.Dropout() + + self.decpose = nn.Linear(final_feat_num, npose) + self.decshape = nn.Linear(final_feat_num, nshape) + self.deccam = nn.Linear(final_feat_num, ncam) + + nn.init.xavier_uniform_(self.decpose.weight, gain=0.01) + nn.init.xavier_uniform_(self.decshape.weight, gain=0.01) + nn.init.xavier_uniform_(self.deccam.weight, gain=0.01) + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + mean_params = np.load(smpl_mean_params) + init_pose = torch.from_numpy(mean_params['pose'][:]).unsqueeze(0) + init_shape = torch.from_numpy(mean_params['shape'][:].astype('float32')).unsqueeze(0) + init_cam = torch.from_numpy(mean_params['cam']).unsqueeze(0) + self.register_buffer('init_pose', init_pose) + self.register_buffer('init_shape', init_shape) + self.register_buffer('init_cam', init_cam) + + def forward(self, x, bbox, init_pose=None, init_shape=None, init_cam=None, n_iter=3): + batch_size = x.shape[0] + if init_pose is None: + init_pose = self.init_pose.expand(batch_size, -1) + if init_shape is None: + init_shape = self.init_shape.expand(batch_size, -1) + if init_cam is None: + init_cam = self.init_cam.expand(batch_size, -1) + + xf = self.encoder(x) + + pred_pose = init_pose + pred_shape = init_shape + pred_cam = init_cam + for i in range(n_iter): + xc = torch.cat([xf, bbox, pred_pose, pred_shape, pred_cam], 1) + + xc = self.fc1(xc) + xc = self.drop1(xc) + xc = self.fc2(xc) + xc = self.drop2(xc) + + pred_pose = self.decpose(xc) + pred_pose + pred_shape = self.decshape(xc) + pred_shape + pred_cam = self.deccam(xc) + pred_cam + + pred_rotmat = rot6d_to_rotmat(pred_pose).view(batch_size, 24, 3, 3) + + return pred_rotmat, pred_shape, pred_cam diff --git a/models/cliff_res50/cliff.py b/models/cliff_res50/cliff.py new file mode 100644 index 0000000000000000000000000000000000000000..ef30e0f5b893f8c207353d4fd3bb2bf3d7a54137 --- /dev/null +++ b/models/cliff_res50/cliff.py @@ -0,0 +1,97 @@ +# 2022.07.19 - Changed for CLIFF +# Huawei Technologies Co., Ltd. + +# Copyright (C) 2022. Huawei Technologies Co., Ltd. All rights reserved. +# Copyright (c) 2019, University of Pennsylvania, Max Planck Institute for Intelligent Systems + +# This program is free software; you can redistribute it and/or modify it +# under the terms of the MIT license. + +# This program is distributed in the hope that it will be useful, but WITHOUT ANY +# WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A +# PARTICULAR PURPOSE. See the MIT License for more details. + +# This script is borrowed and extended from SPIN + +import torch +import torch.nn as nn +import numpy as np +import math + +from common.imutils import rot6d_to_rotmat +from models.backbones.resnet import ResNet + + +class CLIFF(nn.Module): + """ SMPL Iterative Regressor with ResNet50 backbone""" + + def __init__(self, smpl_mean_params, img_feat_num=2048): + super(CLIFF, self).__init__() + self.encoder = ResNet(layers=[3, 4, 6, 3]) + + npose = 24 * 6 + nshape = 10 + ncam = 3 + nbbox = 3 + + fc1_feat_num = 1024 + fc2_feat_num = 1024 + final_feat_num = fc2_feat_num + reg_in_feat_num = img_feat_num + nbbox + npose + nshape + ncam + self.fc1 = nn.Linear(reg_in_feat_num, fc1_feat_num) + self.drop1 = nn.Dropout() + self.fc2 = nn.Linear(fc1_feat_num, fc2_feat_num) + self.drop2 = nn.Dropout() + + self.decpose = nn.Linear(final_feat_num, npose) + self.decshape = nn.Linear(final_feat_num, nshape) + self.deccam = nn.Linear(final_feat_num, ncam) + + nn.init.xavier_uniform_(self.decpose.weight, gain=0.01) + nn.init.xavier_uniform_(self.decshape.weight, gain=0.01) + nn.init.xavier_uniform_(self.deccam.weight, gain=0.01) + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + mean_params = np.load(smpl_mean_params) + init_pose = torch.from_numpy(mean_params['pose'][:]).unsqueeze(0) + init_shape = torch.from_numpy(mean_params['shape'][:].astype('float32')).unsqueeze(0) + init_cam = torch.from_numpy(mean_params['cam']).unsqueeze(0) + self.register_buffer('init_pose', init_pose) + self.register_buffer('init_shape', init_shape) + self.register_buffer('init_cam', init_cam) + + def forward(self, x, bbox, init_pose=None, init_shape=None, init_cam=None, n_iter=3): + batch_size = x.shape[0] + if init_pose is None: + init_pose = self.init_pose.expand(batch_size, -1) + if init_shape is None: + init_shape = self.init_shape.expand(batch_size, -1) + if init_cam is None: + init_cam = self.init_cam.expand(batch_size, -1) + + xf = self.encoder(x) + + pred_pose = init_pose + pred_shape = init_shape + pred_cam = init_cam + for i in range(n_iter): + xc = torch.cat([xf, bbox, pred_pose, pred_shape, pred_cam], 1) + + xc = self.fc1(xc) + xc = self.drop1(xc) + xc = self.fc2(xc) + xc = self.drop2(xc) + + pred_pose = self.decpose(xc) + pred_pose + pred_shape = self.decshape(xc) + pred_shape + pred_cam = self.deccam(xc) + pred_cam + + pred_rotmat = rot6d_to_rotmat(pred_pose).view(batch_size, 24, 3, 3) + + return pred_rotmat, pred_shape, pred_cam diff --git a/models/components.py b/models/components.py new file mode 100644 index 0000000000000000000000000000000000000000..93c0c90d02db50bf61ef48df3a338af6c94477df --- /dev/null +++ b/models/components.py @@ -0,0 +1,130 @@ +import torch +import torchvision +import torch.nn as nn +import numpy as np + +from utils.hrnet import hrnet_w32 + +class Encoder(nn.Module): + def __init__(self, encoder='hrnet', pretrained=True): + super(Encoder, self).__init__() + + if encoder == 'swin': + '''Swin Transformer encoder''' + self.encoder = torchvision.models.swin_b(weights='DEFAULT') + self.encoder.head = nn.GELU() + elif encoder == 'hrnet': + '''HRNet encoder''' + self.encoder = hrnet_w32(pretrained=pretrained) + else: + raise NotImplementedError('Encoder not implemented') + + def forward(self, x): + out = self.encoder(x) + return out + +class Self_Attn(nn.Module): + """ Self attention Layer for Feature Map dimension""" + def __init__(self, in_dim, out_dim): + super(Self_Attn, self).__init__() + self.channel_in = in_dim + self.query_conv = nn.Conv1d(in_channels = in_dim, out_channels = out_dim, kernel_size = 1) + self.key_conv = nn.Conv1d(in_channels = in_dim, out_channels = out_dim, kernel_size = 1) + self.value_conv = nn.Conv1d(in_channels = in_dim, out_channels = out_dim, kernel_size = 1) + self.softmax = nn.Softmax(dim = -1) + + def forward(self, q, k, v): + """ + inputs : + x : input feature maps(B X C X H X W) + returns : + out : self attention value + input feature + attention: B X N X N (N is Height * Width) + """ + batchsize, C, height = q.size() + # proj_query: reshape to B x N x c, N = H x W + proj_query = self.query_conv(q.permute(0, 2, 1)) + # proj_query: reshape to B x c x N, N = H x W + proj_key = self.key_conv(k.permute(0, 2, 1)) + # transpose check, energy: B x N x N, N = H x W + energy = torch.bmm(proj_query, proj_key.permute(0, 2, 1)) + # attention: B x N x N, N = H x W + attention = self.softmax(energy) + # proj_value is normal convolution, B x C x N + proj_value = self.value_conv(v.permute(0, 2, 1)) + # out: B x C x N + out = torch.bmm(attention, proj_value) + out = out.view(batchsize, C, height) + out = out/np.sqrt(self.channel_in) + + return out + +class Cross_Att(nn.Module): + def __init__(self, in_dim, out_dim): + super(Cross_Att, self).__init__() + + self.cross_attn_1 = Self_Attn(in_dim, out_dim) + self.cross_attn_2 = Self_Attn(in_dim, out_dim) + self.layer_norm = nn.LayerNorm([1, in_dim]) + + def forward(self, sem_seg, part_seg): + cross1 = self.cross_attn_1(sem_seg, part_seg, part_seg) + cross2 = self.cross_attn_1(part_seg, sem_seg, sem_seg) + + out = cross1 * cross2 + out = self.layer_norm(out) + + return out + +class Decoder(nn.Module): + def __init__(self, in_dim, out_dim, encoder='hrnet'): + super(Decoder, self).__init__() + self.out_dim = out_dim + if encoder == 'swin': + self.upsample = nn.Sequential( + nn.ConvTranspose2d(in_dim, out_dim, kernel_size=3, stride=2, padding=1, output_padding=1), + nn.BatchNorm2d(out_dim), + nn.ReLU(), + nn.ConvTranspose2d(out_dim, out_dim, kernel_size=3, stride=2, padding=1, output_padding=1), + nn.BatchNorm2d(out_dim), + nn.ReLU(), + nn.ConvTranspose2d(out_dim, out_dim, kernel_size=3, stride=2, padding=1, output_padding=1), + nn.BatchNorm2d(out_dim), + nn.Softmax(1) + ) + elif encoder == 'hrnet': + self.upsample = nn.Sequential( + nn.ConvTranspose2d(in_dim, out_dim, kernel_size=3, stride=2, padding=1, output_padding=1), + nn.BatchNorm2d(out_dim), + nn.ReLU(), + nn.ConvTranspose2d(out_dim, out_dim, kernel_size=3, stride=2, padding=1, output_padding=1), + nn.BatchNorm2d(out_dim), + # nn.ReLU(), + # nn.ConvTranspose2d(out_dim, out_dim, kernel_size=3, stride=2, padding=1, output_padding=1), + # nn.BatchNorm2d(out_dim), + nn.Softmax(1) + ) + else: + raise NotImplementedError('Decoder not implemented') + + def forward(self, x): + out = self.upsample(x) + return out + + +class Classifier(nn.Module): + def __init__(self, in_dim, out_dim=6890): + super(Classifier, self).__init__() + + self.out_dim = out_dim + + self.classifier = nn.Sequential( + nn.Linear(in_dim, 4096, True), + nn.ReLU(), + nn.Linear(4096, out_dim, True), + nn.Sigmoid() + ) + + def forward(self, x): + out = self.classifier(x) + return out.reshape(-1, self.out_dim) \ No newline at end of file diff --git a/models/deco.py b/models/deco.py new file mode 100644 index 0000000000000000000000000000000000000000..c15b0000ac29364d1b83e037d4f3dbbfa4fc94d5 --- /dev/null +++ b/models/deco.py @@ -0,0 +1,78 @@ +from models.components import Encoder, Cross_Att, Decoder, Classifier +import torch.nn as nn +import torch + +class DECO(nn.Module): + def __init__(self, encoder, context, device): + super(DECO, self).__init__() + self.encoder_type = encoder + self.context = context + + self.encoder_sem = Encoder(encoder=encoder).to(device) + self.encoder_part = Encoder(encoder=encoder).to(device) + if self.encoder_type == 'hrnet': + if self.context: + self.decoder_sem = Decoder(480, 133, encoder=encoder).to(device) + self.decoder_part = Decoder(480, 26, encoder=encoder).to(device) + self.sem_pool = nn.AdaptiveAvgPool2d((1)) + self.part_pool = nn.AdaptiveAvgPool2d((1)) + self.cross_att = Cross_Att(480, 480).to(device) + self.classif = Classifier(480).to(device) + elif self.encoder_type == 'swin': + self.correction_conv = nn.Conv1d(768, 1024, 1).to(device) + if self.context: + self.decoder_sem = Decoder(1, 133, encoder=encoder).to(device) + self.decoder_part = Decoder(1, 26, encoder=encoder).to(device) + self.cross_att = Cross_Att(1024, 1024).to(device) + self.classif = Classifier(1024).to(device) + else: + NotImplementedError('Encoder type not implemented') + + self.device = device + + def forward(self, img): + if self.encoder_type == 'hrnet': + sem_enc_out = self.encoder_sem(img) + part_enc_out = self.encoder_part(img) + + if self.context: + sem_mask_pred = self.decoder_sem(sem_enc_out) + part_mask_pred = self.decoder_part(part_enc_out) + + sem_enc_out = self.sem_pool(sem_enc_out) + sem_enc_out = sem_enc_out.squeeze(2) + sem_enc_out = sem_enc_out.squeeze(2) + sem_enc_out = sem_enc_out.unsqueeze(1) + + part_enc_out = self.part_pool(part_enc_out) + part_enc_out = part_enc_out.squeeze(2) + part_enc_out = part_enc_out.squeeze(2) + part_enc_out = part_enc_out.unsqueeze(1) + + att = self.cross_att(sem_enc_out, part_enc_out) + cont = self.classif(att) + else: + sem_enc_out = self.encoder_sem(img) + part_enc_out = self.encoder_part(img) + + sem_seg = torch.reshape(sem_enc_out, (-1, 768, 1)) + part_seg = torch.reshape(part_enc_out, (-1, 768, 1)) + + sem_seg = self.correction_conv(sem_seg) + part_seg = self.correction_conv(part_seg) + + sem_seg = torch.reshape(sem_seg, (-1, 1, 32, 32)) + part_seg = torch.reshape(part_seg, (-1, 1, 32, 32)) + + if self.context: + sem_mask_pred = self.decoder_sem(sem_seg) + part_mask_pred = self.decoder_part(part_seg) + + sem_enc_out = torch.reshape(sem_seg, (-1, 1, 1024)) + part_enc_out = torch.reshape(part_seg, (-1, 1, 1024)) + + att = self.cross_att(sem_enc_out, part_enc_out) + cont = self.classif(att) + + if self.context: return cont, sem_mask_pred, part_mask_pred + return cont \ No newline at end of file diff --git a/models/hmr.py b/models/hmr.py new file mode 100644 index 0000000000000000000000000000000000000000..5a4a862d7f0627051118b1515c0b6f7e45bf5213 --- /dev/null +++ b/models/hmr.py @@ -0,0 +1,182 @@ +import math +import numpy as np + +import torch +import torch.nn as nn +from torch.nn import functional as F +import torchvision.models.resnet as resnet + + +def rot6d_to_rotmat(x): + """Convert 6D rotation representation to 3x3 rotation matrix. + Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019 + Input: + (B,6) Batch of 6-D rotation representations + Output: + (B,3,3) Batch of corresponding rotation matrices + """ + x = x.view(-1,3,2) + a1 = x[:, :, 0] + a2 = x[:, :, 1] + b1 = F.normalize(a1) + b2 = F.normalize(a2 - torch.einsum('bi,bi->b', b1, a2).unsqueeze(-1) * b1) + b3 = torch.cross(b1, b2) + return torch.stack((b1, b2, b3), dim=-1) + +class Bottleneck(nn.Module): + """ Redefinition of Bottleneck residual block + Adapted from the official PyTorch implementation + """ + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + +class HMR(nn.Module): + """ SMPL Iterative Regressor with ResNet50 backbone + """ + + def __init__(self, block, layers, smpl_mean_params): + self.inplanes = 64 + super(HMR, self).__init__() + npose = 24 * 6 + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d(7, stride=1) + self.fc1 = nn.Linear(512 * block.expansion + npose + 13, 1024) + self.drop1 = nn.Dropout() + self.fc2 = nn.Linear(1024, 1024) + self.drop2 = nn.Dropout() + self.decpose = nn.Linear(1024, npose) + self.decshape = nn.Linear(1024, 10) + self.deccam = nn.Linear(1024, 3) + nn.init.xavier_uniform_(self.decpose.weight, gain=0.01) + nn.init.xavier_uniform_(self.decshape.weight, gain=0.01) + nn.init.xavier_uniform_(self.deccam.weight, gain=0.01) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + mean_params = np.load(smpl_mean_params) + init_pose = torch.from_numpy(mean_params['pose'][:]).unsqueeze(0) + init_shape = torch.from_numpy(mean_params['shape'][:].astype('float32')).unsqueeze(0) + init_cam = torch.from_numpy(mean_params['cam']).unsqueeze(0) + self.register_buffer('init_pose', init_pose) + self.register_buffer('init_shape', init_shape) + self.register_buffer('init_cam', init_cam) + + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + + def forward(self, x, init_pose=None, init_shape=None, init_cam=None, n_iter=3): + + batch_size = x.shape[0] + + if init_pose is None: + init_pose = self.init_pose.expand(batch_size, -1) + if init_shape is None: + init_shape = self.init_shape.expand(batch_size, -1) + if init_cam is None: + init_cam = self.init_cam.expand(batch_size, -1) + + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x1 = self.layer1(x) + x2 = self.layer2(x1) + x3 = self.layer3(x2) + x4 = self.layer4(x3) + + xf = self.avgpool(x4) + xf = xf.view(xf.size(0), -1) + + pred_pose = init_pose + pred_shape = init_shape + pred_cam = init_cam + for i in range(n_iter): + xc = torch.cat([xf, pred_pose, pred_shape, pred_cam],1) + xc = self.fc1(xc) + xc = self.drop1(xc) + xc = self.fc2(xc) + xc = self.drop2(xc) + pred_pose = self.decpose(xc) + pred_pose + pred_shape = self.decshape(xc) + pred_shape + pred_cam = self.deccam(xc) + pred_cam + + pred_rotmat = rot6d_to_rotmat(pred_pose).view(batch_size, 24, 3, 3) + + return pred_rotmat, pred_shape, pred_cam + +def hmr(smpl_mean_params, pretrained=True, **kwargs): + """ Constructs an HMR model with ResNet50 backbone. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = HMR(Bottleneck, [3, 4, 6, 3], smpl_mean_params, **kwargs) + if pretrained: + resnet_imagenet = resnet.resnet50(pretrained=True) + model.load_state_dict(resnet_imagenet.state_dict(),strict=False) + return model + diff --git a/models/smpl.py b/models/smpl.py new file mode 100644 index 0000000000000000000000000000000000000000..3cf6dfcd12bc43e5883408de8709c8e7b5ce7ad8 --- /dev/null +++ b/models/smpl.py @@ -0,0 +1,110 @@ +import torch +import numpy as np +from smplx import SMPL as _SMPL +try: + from smplx.body_models import ModelOutput as SMPLOutput +except Exception as e: + from smplx.body_models import SMPLOutput +from smplx.lbs import vertices2joints + +JOINT_NAMES = [ +# 25 OpenPose joints (in the order provided by OpenPose) +'OP Nose', +'OP Neck', +'OP RShoulder', +'OP RElbow', +'OP RWrist', +'OP LShoulder', +'OP LElbow', +'OP LWrist', +'OP MidHip', +'OP RHip', +'OP RKnee', +'OP RAnkle', +'OP LHip', +'OP LKnee', +'OP LAnkle', +'OP REye', +'OP LEye', +'OP REar', +'OP LEar', +'OP LBigToe', +'OP LSmallToe', +'OP LHeel', +'OP RBigToe', +'OP RSmallToe', +'OP RHeel', +# 24 Ground Truth joints (superset of joints from different datasets) +'Right Ankle', +'Right Knee', +'Right Hip', +'Left Hip', +'Left Knee', +'Left Ankle', +'Right Wrist', +'Right Elbow', +'Right Shoulder', +'Left Shoulder', +'Left Elbow', +'Left Wrist', +'Neck (LSP)', +'Top of Head (LSP)', +'Pelvis (MPII)', +'Thorax (MPII)', +'Spine (H36M)', +'Jaw (H36M)', +'Head (H36M)', +'Nose', +'Left Eye', +'Right Eye', +'Left Ear', +'Right Ear' +] + +# Dict containing the joints in numerical order +JOINT_IDS = {JOINT_NAMES[i]: i for i in range(len(JOINT_NAMES))} + +# Map joints to SMPL joints +JOINT_MAP = { +'OP Nose': 24, 'OP Neck': 12, 'OP RShoulder': 17, +'OP RElbow': 19, 'OP RWrist': 21, 'OP LShoulder': 16, +'OP LElbow': 18, 'OP LWrist': 20, 'OP MidHip': 0, +'OP RHip': 2, 'OP RKnee': 5, 'OP RAnkle': 8, +'OP LHip': 1, 'OP LKnee': 4, 'OP LAnkle': 7, +'OP REye': 25, 'OP LEye': 26, 'OP REar': 27, +'OP LEar': 28, 'OP LBigToe': 29, 'OP LSmallToe': 30, +'OP LHeel': 31, 'OP RBigToe': 32, 'OP RSmallToe': 33, 'OP RHeel': 34, +'Right Ankle': 8, 'Right Knee': 5, 'Right Hip': 45, +'Left Hip': 46, 'Left Knee': 4, 'Left Ankle': 7, +'Right Wrist': 21, 'Right Elbow': 19, 'Right Shoulder': 17, +'Left Shoulder': 16, 'Left Elbow': 18, 'Left Wrist': 20, +'Neck (LSP)': 47, 'Top of Head (LSP)': 48, +'Pelvis (MPII)': 49, 'Thorax (MPII)': 50, +'Spine (H36M)': 51, 'Jaw (H36M)': 52, +'Head (H36M)': 53, 'Nose': 24, 'Left Eye': 26, +'Right Eye': 25, 'Left Ear': 28, 'Right Ear': 27 +} + +class SMPL(_SMPL): + """ Extension of the official SMPL implementation to support more joints """ + + def __init__(self, *args, **kwargs): + super(SMPL, self).__init__(*args, **kwargs) + joints = [JOINT_MAP[i] for i in JOINT_NAMES] + J_regressor_extra = np.load('data/J_regressor_extra.npy') + self.register_buffer('J_regressor_extra', torch.tensor(J_regressor_extra, dtype=torch.float32)) + self.joint_map = torch.tensor(joints, dtype=torch.long) + + def forward(self, *args, **kwargs): + kwargs['get_skin'] = True + smpl_output = super(SMPL, self).forward(*args, **kwargs) + extra_joints = vertices2joints(self.J_regressor_extra, smpl_output.vertices) + joints = torch.cat([smpl_output.joints, extra_joints], dim=1) + joints = joints[:, self.joint_map, :] + output = SMPLOutput(vertices=smpl_output.vertices, + global_orient=smpl_output.global_orient, + body_pose=smpl_output.body_pose, + joints=joints, + betas=smpl_output.betas, + full_pose=smpl_output.full_pose) + return output diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..8a1246308adb2a500a712beae441ce343b09a066 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,14 @@ +opencv-python +loguru +monai +pyrender==0.1.33 +smplx==0.1.28 +scikit-learn +scikit-image +jpeg4py +matplotlib +pandas +configargparse +flatten_dict +chumpy +numpy==1.23.1 \ No newline at end of file diff --git a/scripts/datascripts/add_imgname_column_to_deco_csv.py b/scripts/datascripts/add_imgname_column_to_deco_csv.py new file mode 100644 index 0000000000000000000000000000000000000000..94a966c91e382e79f9618ca60020b5f7733fd39c --- /dev/null +++ b/scripts/datascripts/add_imgname_column_to_deco_csv.py @@ -0,0 +1,23 @@ +#python scripts/datascripts/add_imgname_column_to_deco_csv.py --csv_path /ps/scratch/ps_shared/stripathi/deco/4agniv/DCA/mturk_csvs_combined_temp.csv --out_path /ps/scratch/ps_shared/stripathi/deco/4agniv/DCA/mturk_csvs_combined_temp_with_imgnames.csv + +import argparse +import pandas as pd +import os + +def add_imagename_column(csv_path, out_path): + # Load csv + csv_data = pd.read_csv(csv_path) + + # Add column with image name from vertices column, where every element is a dictionary with key the image names as keys and values as the vertices + csv_data['imgnames'] = csv_data['vertices'].apply(lambda x: os.path.basename(list(eval(x).keys())[0])) + + # Write combined data to new CSV file + csv_data.to_csv(out_path, index=False) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--csv_path', type=str, default='/ps/scratch/ps_shared/stripathi/deco/4agniv/hot/dca.csv') + parser.add_argument('--out_path', type=str, default='/ps/scratch/ps_shared/stripathi/deco/4agniv/hot/dca_with_imgname.csv') + args = parser.parse_args() + add_imagename_column(args.csv_path, args.out_path) + diff --git a/scripts/datascripts/combine_cropped_npz.py b/scripts/datascripts/combine_cropped_npz.py new file mode 100644 index 0000000000000000000000000000000000000000..a0d1c32c88a1b77160438d87611c9d69f6a4be84 --- /dev/null +++ b/scripts/datascripts/combine_cropped_npz.py @@ -0,0 +1,63 @@ +''' +Combine cropped npz given a folder of npz +''' + +import argparse +import os +import numpy as np + +def combine_npz(npz_dir, out_npz): + # get all npz files in the folder + + npz_files = [os.path.join(npz_dir, out_dir, f) for f in os.listdir(npz_dir, out_dir) if f.endswith('.npz')] + print('Found {} npz files'.format(len(npz_files))) + + # combine all the values in all keys in all npz files + c_imgname = [] + c_pose = [] + c_transl = [] + c_shape = [] + c_cam_k = [] + c_contact_label = [] + c_scene_seg = [] + c_part_seg = [] + + for npz_file in npz_files: + npz= np.load(npz_file) + c_imgname.extend(npz['imgname']) + c_pose.extend(npz['pose']) + c_transl.extend(npz['transl']) + c_shape.extend(npz['shape']) + c_cam_k.extend(npz['cam_k']) + c_contact_label.extend(npz['contact_label']) + c_scene_seg.extend(npz['scene_seg']) + c_part_seg.extend(npz['part_seg']) + + # convert to numpy arrays + c_imgname = np.concatenate(c_imgname, axis=0) + c_pose = np.concatenate(c_pose, axis=0) + c_transl = np.concatenate(c_transl, axis=0) + c_shape = np.concatenate(c_shape, axis=0) + c_cam_k = np.concatenate(c_cam_k, axis=0) + c_contact_label = np.concatenate(c_contact_label, axis=0) + c_scene_seg = np.concatenate(c_scene_seg, axis=0) + c_part_seg = np.concatenate(c_part_seg, axis=0) + + # save the new npz + np.savez(out_npz, + imgname=c_imgname, + pose=c_pose, + transl=c_transl, + shape=c_shape, + cam_k=c_cam_k, + contact_label=c_contact_label, + scene_seg=c_scene_seg, + part_seg=c_part_seg) + print('Saved combined npz to {}'.format(out_npz)) + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--orig_npz_dir', type=str, default='/is/cluster/fast/achatterjee/rich/scene_npzs/train') + parser.add_argument('--out_npz', type=str, required=True,default='/is/cluster/fast/achatterjee/rich/scene_npzs/train_combined.npz') + args = parser.parse_args() + combine_npz(npz_dir = args.orig_npz_dir, out_npz = args.out_npz) \ No newline at end of file diff --git a/scripts/datascripts/combine_mturk_csvs.py b/scripts/datascripts/combine_mturk_csvs.py new file mode 100644 index 0000000000000000000000000000000000000000..c7a3c5d29ef43be83ab47a1be0fdfebc9435b18e --- /dev/null +++ b/scripts/datascripts/combine_mturk_csvs.py @@ -0,0 +1,33 @@ +# write a script to find all csvs in a folder and merge them + +import os +import pandas as pd +import argparse + +def combine_csvs(folder_path, out_path): + # Create empty DataFrame to store combined data + combined_data = pd.DataFrame() + + # Loop through all files in folder with .csv extension + for file in os.listdir(folder_path): + if file.endswith(".csv"): + file_path = os.path.join(folder_path, file) + # Read CSV file and append to combined_data + csv_data = pd.read_csv(file_path) + combined_data = combined_data.append(csv_data, ignore_index=True) + + # Write combined data to new CSV file + combined_data.to_csv(out_path, index=False) + print(f"Combined CSV file saved to {out_path}") + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--folder_path', type=str, default='/ps/scratch/ps_shared/stripathi/deco/4agniv/hot/') + parser.add_argument('--out_path', type=str, default='/ps/scratch/ps_shared/stripathi/deco/4agniv/hot/combined.csv') + args = parser.parse_args() + folder_path = args.folder_path + out_path = args.out_path + combine_csvs(folder_path, out_path) + + + diff --git a/scripts/datascripts/combine_npzs.py b/scripts/datascripts/combine_npzs.py new file mode 100644 index 0000000000000000000000000000000000000000..e84ca28982f312118511ed508f19cddba08922a2 --- /dev/null +++ b/scripts/datascripts/combine_npzs.py @@ -0,0 +1,41 @@ +''' +Combine the npzs +''' + +import numpy as np + +d1 = np.load('/is/cluster/work/stripathi/pycharm_remote/dca_contact/data/dataset_extras/hot_noprox_supporting_False_trainval_0.npz',allow_pickle=True) +d2 = np.load('/is/cluster/work/stripathi/pycharm_remote/dca_contact/data/dataset_extras/hot_noprox_supporting_False_trainval_1.npz',allow_pickle=True) +d3 = np.load('/is/cluster/work/stripathi/pycharm_remote/dca_contact/data/dataset_extras/hot_noprox_supporting_False_trainval_2.npz',allow_pickle=True) +d4 = np.load('/is/cluster/work/stripathi/pycharm_remote/dca_contact/data/dataset_extras/hot_noprox_supporting_False_trainval_3.npz',allow_pickle=True) + + +print(list(d1.keys())) +print(type(d1['imgname'])) +print(d1['imgname'].shape) + +c_imgname = np.concatenate((d1['imgname'], d2['imgname'], d3['imgname'], d4['imgname']), axis=0) +c_pose = np.concatenate((d1['pose'], d2['pose'], d3['pose'], d4['pose']), axis=0) +c_transl = np.concatenate((d1['transl'], d2['transl'], d3['transl'], d4['transl']), axis=0) +c_shape = np.concatenate((d1['shape'], d2['shape'], d3['shape'], d4['shape']), axis=0) +c_cam_k = np.concatenate((d1['cam_k'], d2['cam_k'], d3['cam_k'], d4['cam_k']), axis=0) +c_polygon_2d_contact = np.concatenate((d1['polygon_2d_contact'], d2['polygon_2d_contact'], + d3['polygon_2d_contact'], d4['polygon_2d_contact']), axis=0) +c_contact_label = np.concatenate((d1['contact_label'], d2['contact_label'], d3['contact_label'], + d4['contact_label']), axis=0) +c_scene_seg = np.concatenate((d1['scene_seg'], d2['scene_seg'], d3['scene_seg'], d4['scene_seg']), axis=0) +c_part_seg = np.concatenate((d1['part_seg'], d2['part_seg'], d3['part_seg'], d4['part_seg']), axis=0) + +outfile = '/is/cluster/work/stripathi/pycharm_remote/dca_contact/data/dataset_extras/hot_noprox_supporting_False_trainval_combined.npz' +np.savez(outfile, + imgname=c_imgname, + pose=c_pose, + transl=c_transl, + shape=c_shape, + cam_k=c_cam_k, + polygon_2d_contact=c_polygon_2d_contact, + contact_label=c_contact_label, + scene_seg=c_scene_seg, + part_seg=c_part_seg + ) +print(f'Saved to {outfile}') \ No newline at end of file diff --git a/scripts/datascripts/convert_rich_npz_to_cropped.py b/scripts/datascripts/convert_rich_npz_to_cropped.py new file mode 100644 index 0000000000000000000000000000000000000000..e0b9864ea03a793b6a158f3f50e69a210a0821eb --- /dev/null +++ b/scripts/datascripts/convert_rich_npz_to_cropped.py @@ -0,0 +1,75 @@ +''' +Fix paths to cropped images, partmasks and segmasks +''' + +import argparse +import os +import numpy as np +from tqdm import tqdm + +def convert_rich_npz(orig_npz, out_dir): + # go through all keys in the npz + # if the key is imgname, partmask or segmask, replace the path with the new path + # save the new npz + + # structs we use + imgnames_ = [] + poses_, shapes_, transls_ = [], [], [] + cams_k_ = [] + contact_label_ = [] + scene_seg_, part_seg_ = [], [] + + # load the npz + npz = np.load(orig_npz) + for i in tqdm(range(len(npz['imgname']))): + + if not os.path.exists(npz['imgname'][i]): + print(npz['imgname'][i]) + continue + + new_scene_seg = os.path.exists(npz['scene_seg'][i].replace('seg_masks_new', 'segmentation_masks')) + + if not new_scene_seg: + print(new_scene_seg) + continue + + if not os.path.exists(npz['part_seg'][i]): + print(npz['part_seg'][i]) + continue + + imgnames_.append(npz['imgname'][i]) + poses_.append(npz['pose'][i]) + transls_.append(npz['transl'][i]) + shapes_.append(npz['shape'][i]) + cams_k_.append(npz['cam_k'][i]) + contact_label_.append(npz['contact_label'][i]) + scene_seg_.append(npz['scene_seg'][i].replace('seg_masks_new', 'segmentation_masks')) + part_seg_.append(npz['part_seg'][i]) + + # save the new npz + out_dir = out_dir+'_cropped' + os.makedirs(out_dir, exist_ok=True) + out_file = os.path.join(out_dir, os.path.basename(args.orig_npz)) + np.savez(out_file, + imgname=imgnames_, + pose=poses_, + transl=transls_, + shape=shapes_, + cam_k=cams_k_, + contact_label=contact_label_, + scene_seg=scene_seg_, + part_seg=part_seg_) + + print('Saved to: ', out_file) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument('--orig_npz_dir', type=str, default='/is/cluster/fast/achatterjee/rich/scene_npzs/train') + parser.add_argument('--cluster_idx', type=int) + args = parser.parse_args() + # get all npz files in the directory + npz_files = [os.path.join(args.orig_npz_dir, f) for f in os.listdir(args.orig_npz_dir) if f.endswith('.npz')] + # get the npz file for this cluster + orig_npz = npz_files[args.cluster_idx] + convert_rich_npz(orig_npz, out_dir=args.orig_npz_dir) \ No newline at end of file diff --git a/scripts/datascripts/get_part_seg_mask.py b/scripts/datascripts/get_part_seg_mask.py new file mode 100644 index 0000000000000000000000000000000000000000..2083e31e2eb4e17fb815cafdff7f293b1db9a6b8 --- /dev/null +++ b/scripts/datascripts/get_part_seg_mask.py @@ -0,0 +1,199 @@ +# python scripts/datascripts/get_part_seg_mask.py --data_npz data/rich_val_smplx_small.npz --model_type 'smplx' + + +import os +import sys +sys.path.append('/is/cluster/work/achatterjee/dca_contact') +import cv2 +import argparse +import numpy as np +import torch +from common import constants +from models.smpl import SMPL +from smplx import SMPLX +from utils.mesh_utils import save_results_mesh +import trimesh +from tqdm import tqdm +from utils.image_utils import get_body_part_texture, generate_part_labels +from utils.diff_renderer import Pytorch3D + +class PART_LABELER: + def __init__(self, body_params, img_w, img_h, model_type, debug=False): + """ + Get part segmentation masks for images + + Args: + body_params: SMPL parameters + img_w: image width + img_h: image height + model_type: 'smpl' or 'smplx' + """ + self.device = torch.device('cuda:{}'.format(args.gpu)) if torch.cuda.is_available() else torch.device('cpu') + + self.model_type = model_type + + # Setup the SMPL model + if self.model_type == 'smpl': + self.body_model = SMPL(constants.SMPL_MODEL_DIR).to(self.device) + if self.model_type == 'smplx': + self.body_model = SMPLX(constants.SMPL_MODEL_DIR, + num_betas=10, + use_pca=False).to(self.device) + + self.body_part_vertex_colors, self.body_part_texture = get_body_part_texture(self.body_model.faces, + model_type=self.model_type, + non_parametric=False) + # bins are discrete part labels, add eps to avoid quantization error + eps = 1e-2 + # self.part_label_bins = (torch.arange(int(constants.N_PARTS)) / float(constants.N_PARTS)) + eps + self.part_label_bins = torch.linspace(0, constants.N_PARTS-1, constants.N_PARTS) + eps + + ## Run SMPL forward + self.body_params = body_params + + self.smpl_verts, self.smpl_joints = self.get_posed_mesh(debug) + + # Assumbe same focal lenght for all frames in a seq + focal_length = self.body_params['cam_k'][0, 0, 0] + # focal_length = focal_length[0] + # Setup Pyrender renderer + # self.renderer = Renderer(focal_length=focal_length, img_w=img_w, img_h=img_h, + # faces=self.smpl_model.faces, + # same_mesh_color=False) + + # Setup Pytorch3D Renderer + focal_length = torch.FloatTensor([focal_length]) + smpl_faces = torch.from_numpy(self.body_model.faces.astype(np.int32)).to(self.device) + self.renderer = Pytorch3D(img_h=img_h, + img_w=img_w, + focal_length=focal_length, + smpl_faces=smpl_faces, + texture_mode='partseg', + vertex_colors=self.body_part_vertex_colors, + face_textures=self.body_part_texture, + model_type=self.model_type) + + def get_posed_mesh(self, debug=False): + betas = torch.from_numpy(self.body_params['shape']).float().to(self.device) + pose = torch.from_numpy(self.body_params['pose']).float().to(self.device) + transl = torch.from_numpy(self.body_params['transl']).float().to(self.device) + + # extra smplx params + extra_args = {'jaw_pose': torch.zeros((betas.shape[0], 3)).float().to(self.device), + 'leye_pose': torch.zeros((betas.shape[0], 3)).float().to(self.device), + 'reye_pose': torch.zeros((betas.shape[0], 3)).float().to(self.device), + 'expression': torch.zeros((betas.shape[0], 10)).float().to(self.device), + 'left_hand_pose': torch.zeros((betas.shape[0], 45)).float().to(self.device), + 'right_hand_pose': torch.zeros((betas.shape[0], 45)).float().to(self.device)} + + smpl_output = self.body_model(betas=betas, + body_pose=pose[:, 3:], + global_orient=pose[:, :3], + pose2rot=True, + transl=transl, + **extra_args) + smpl_verts = smpl_output.vertices.detach().cpu().numpy() + smpl_joints = smpl_output.joints.detach().cpu().numpy() + + if debug: + for mesh_i in range(smpl_verts.shape[0]): + out_dir = 'temp_meshes' + os.makedirs(out_dir, exist_ok=True) + out_file = os.path.join(out_dir, f'temp_mesh_{mesh_i:04d}.obj') + save_results_mesh(smpl_verts[mesh_i], self.body_model.faces, out_file) + return smpl_verts, smpl_joints + + def bucketize_part_image(self, color_rgb, mask): + # make single channel + body_parts = color_rgb.clone() + body_parts *= 255. # multiply it with 255 to make labels distant + body_parts = body_parts.max(-1)[0] # reduce to single channel + body_parts = torch.bucketize(body_parts, self.part_label_bins, right=True) # np.digitize(body_parts, bins, right=True) + # add 1 to make background label 0 + body_parts = body_parts.long() + 1 + body_parts = body_parts * mask.detach() + return body_parts.long() + + def create_part_masks(self, body_parts): + # extract every pixel as a separate mask + part_masks = [] + for part_id in range(1, constants.N_PARTS+1): # first one is for background + part_mask = (body_parts == part_id) + part_masks.append(part_mask) + return part_masks + + def render_part_mask_p3d(self, img_paths, out_dir): + with torch.no_grad(): + # os.makedirs(out_dir, exist_ok=True) + for index, img_path in tqdm(enumerate(img_paths), dynamic_ncols=True): + # Load the image + if not os.path.exists(img_path): + if 'train' in img_path: + split = 'train' + elif 'val' in img_path: + split = 'val' + else: + split = 'test' + new_img_name = img_path[img_path.index(split)+4:].replace('/', '_') + new_path = os.path.join('/is/cluster/work/achatterjee/rich/images', split, new_img_name.replace('jpeg', 'bmp')) + if not os.path.exists(new_path): + new_path = new_path.replace('bmp', 'png') + img_path = new_path + if os.path.exists(out_dir[index]): + continue + # img_bgr = cv2.imread(img_path) + chosen_vert_arr = torch.FloatTensor(self.smpl_verts[[index]]).to(self.device) + front_view = self.renderer(chosen_vert_arr) + front_view_rgb = front_view[0, :3, :, :].permute(1,2,0).detach().cpu() + front_view_mask = front_view[0, 3, :, :].detach().cpu() + # front_view_depth = front_view[0, 4, :, :].detach().cpu() + + body_parts = self.bucketize_part_image(front_view_rgb, front_view_mask) + body_parts = body_parts.numpy() + front_view_rgb = front_view_rgb.numpy() + + # body_part_masks = self.create_part_masks(body_parts) + # display part masks + # for part_id, part_mask in enumerate(body_part_masks): + # part_mask = part_mask * 255 + # part_dir = os.path.join(out_dir, f'frame_{index:04d}_parts') + # os.makedirs(part_dir, exist_ok=True) + # out_file = os.path.join(part_dir, f'part_{part_id:02d}_{index:04d}.png') + # cv2.imwrite(out_file, part_mask) + + # out_file = os.path.join(out_dir, f'front_view_{index:04d}.png') + # cv2.imwrite(out_file, front_view_rgb[: ,:, [2, 1, 0]]*255) + # print(f'wrote front view to {out_file}') + body_parts = cv2.merge((body_parts, body_parts, body_parts)) + # out_file = os.path.join(out_dir, f'body_parts_{index:04d}.png') + out_file = out_dir[index] + cv2.imwrite(out_file, body_parts) + # print(f'wrote body part masks to {out_file}') + + +def main(args): + out_dir = args.out_dir + data_md = np.load(args.data_npz) + + # get all the jpg files in the folder + img_paths = data_md['imgname'] + seg_paths = data_md['part_seg'] + print(f'found {len(img_paths)} images') + # load first image + img = cv2.imread(img_paths[0]) + img_h, img_w, _ = img.shape + + labeler = PART_LABELER(body_params=data_md, img_w=img_w, img_h=img_h, + model_type=args.model_type, debug=args.debug) + labeler.render_part_mask_p3d(img_paths=img_paths, out_dir=seg_paths) + +if __name__=='__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--out_dir', type=str, default='./temp_part_masks/', help='image folder') + parser.add_argument('--data_npz', type=str, default='.', help='folder with smpl/smpl-x npz') + parser.add_argument('--model_type', type=str, default='smplx', choices=['smpl', 'smplx'], help='model type') + parser.add_argument('--gpu', type=int, default=0, help='gpu id') + parser.add_argument('--debug', action='store_true', help='debug mode', default=False) + args = parser.parse_args() + + main(args) diff --git a/scripts/datascripts/test_chore_phosa.py b/scripts/datascripts/test_chore_phosa.py new file mode 100644 index 0000000000000000000000000000000000000000..cde856e72c2e1ae638dc893bf58f56612b2f16ee --- /dev/null +++ b/scripts/datascripts/test_chore_phosa.py @@ -0,0 +1,234 @@ +# python test_chore_phosa.py --dataset_root /is/cluster/fast/achatterjee/CHORE_DECO/phosa_test --npz_file /is/cluster/fast/achatterjee/Datasets/hot_phosa/hot_phosa_test.npz --json_file /ps/scratch/ps_shared/stripathi/deco/4agniv/hot_phosa_split/imgnames_per_object_dict.json --th 0.05 + +import numpy as np +import pandas as pd +import torch +import os +import os.path as osp +import json +import trimesh +import argparse +import collections +from tqdm import tqdm + +DIST_MATRIX = np.load('/is/cluster/fast/achatterjee/dca_contact/data/smpl/smpl_neutral_geodesic_dist.npy') + +def precision_recall_f1score(gt, pred): + """ + Compute precision, recall, and f1 + """ + precision = np.zeros(gt.shape[0]) + recall = np.zeros(gt.shape[0]) + f1 = np.zeros(gt.shape[0]) + + for b in range(gt.shape[0]): + tp_num = gt[b, pred[b, :] >= 0.5].sum() + precision_denominator = (pred[b, :] >= 0.5).sum() + recall_denominator = (gt[b, :]).sum() + + precision_ = tp_num / precision_denominator + recall_ = tp_num / recall_denominator + if precision_denominator == 0: # if no pred + precision_ = 1. + recall_ = 0. + f1_ = 0. + elif recall_denominator == 0: # if no GT + precision_ = 0. + recall_ = 1. + f1_ = 0. + elif (precision_ + recall_) <= 1e-10: # to avoid precision issues + precision_= 0. + recall_= 0. + f1_ = 0. + else: + f1_ = 2 * precision_ * recall_ / (precision_ + recall_) + + precision[b] = precision_ + recall[b] = recall_ + f1[b] = f1_ + + return precision, recall, f1 + +def det_error_metric(gt, pred): + dist_matrix = torch.tensor(DIST_MATRIX) + + false_positive_dist = torch.zeros(gt.shape[0]) + false_negative_dist = torch.zeros(gt.shape[0]) + + for b in range(gt.shape[0]): + gt_columns = dist_matrix[:, gt[b, :]==1] if any(gt[b, :]==1) else dist_matrix + error_matrix = gt_columns[pred[b, :] >= 0.5, :] if any(pred[b, :] >= 0.5) else gt_columns + + false_positive_dist_ = error_matrix.min(dim=1)[0].mean() + false_negative_dist_ = error_matrix.min(dim=0)[0].mean() + + false_positive_dist[b] = false_positive_dist_ + false_negative_dist[b] = false_negative_dist_ + + return false_positive_dist, false_negative_dist + +def main(args): + with open(args.json_file, 'r') as fp: + img_list_dict = json.load(fp) + + d = np.load(args.npz_file) + img_count = 0 + + tot_pre = 0 + tot_rec = 0 + tot_f1 = 0 + tot_fp_err = 0 + + # for i, img in tqdm(enumerate(d['imgname']), dynamic_ncols=True): + # gt = d['contact_label'][i] + # gt = np.reshape(gt, (1, -1)) + + # imgname = osp.basename(img) + # for k, v in img_list_dict.items(): + # if imgname in v: + # object_class = k + # break + # # pred_path = osp.join(args.dataset_root, object_class, imgname, 'chore-test', f'k1.smpl_colored_{args.th}.ply') + # pred_path = osp.join(args.dataset_root, object_class, imgname, f'k1.smpl_colored_{args.th}.obj') + # if not osp.exists(pred_path): + # # print(pred_path) + # continue + # pred_mesh = trimesh.load(pred_path, process=False) + + # pred = np.zeros((1, 6890)) + # for i in range(pred_mesh.visual.vertex_colors.shape[0]): + # c = pred_mesh.visual.vertex_colors[i] + # if collections.Counter(c) == collections.Counter([255, 0, 0, 255]): + # pred[0, i] = 1 + + # pre, rec, f1 = precision_recall_f1score(gt, pred) + # fp_err, _ = det_error_metric(pred, gt) + + # tot_pre += pre.sum() + # tot_rec += rec.sum() + # tot_f1 += f1.sum() + # tot_fp_err += fp_err.numpy().sum() + + # img_count += 1 + + # print(f'Dataset size: {img_count}') + # print(f'Threshold: {args.th}\n') + + # print(f'Test Precision: {tot_pre/img_count}') + # print(f'Test Recall: {tot_rec/img_count}') + # print(f'Test F1 Score: {tot_f1/img_count}') + # print(f'Test FP Error: {tot_fp_err/img_count}') + + # Part of code for checking a random image + img_search = osp.join('/ps/project/datasets/HOT/Contact_Data/images/training', 'vcoco_000000542163.jpg') + i = np.where(d['imgname'] == img_search)[0][0] + print(i) + # i = 50 + img = d['imgname'][i] + gt = d['contact_label'][i] + gt = np.reshape(gt, (1, -1)) + + imgname = osp.basename(img) + print(f'Image: {imgname}') + for k, v in img_list_dict.items(): + if imgname in v: + object_class = k + break + print(f'Object: {object_class}') + # pred_path = osp.join(args.dataset_root, object_class, imgname, 'chore-test', f'k1.smpl_colored_{args.th}.ply') + pred_path = osp.join(args.dataset_root, object_class, imgname, f'k1.smpl_colored_{args.th}.obj') + if not osp.exists(pred_path): + print(f'Missing file: {pred_path}') + pred_mesh = trimesh.load(pred_path, process=False) + + pred = np.zeros((1, 6890)) + for i in range(pred_mesh.visual.vertex_colors.shape[0]): + c = pred_mesh.visual.vertex_colors[i] + if collections.Counter(c) == collections.Counter([255, 0, 0, 255]): + pred[0, i] = 1 + + pre, rec, f1 = precision_recall_f1score(gt, pred) + fp_err, _ = det_error_metric(pred, gt) + + tot_pre += pre.sum() + tot_rec += rec.sum() + tot_f1 += f1.sum() + tot_fp_err += fp_err.numpy().sum() + + print(f'Test Precision: {tot_pre}') + print(f'Test Recall: {tot_rec}') + print(f'Test F1 Score: {tot_f1}') + print(f'Test FP Error: {tot_fp_err}') + + # best_pre = 0 + # best_rec = 0 + # best_f1 = 0 + # best_fp_err = 0 + # best_imgname = '' + # best_obj = '' + + # for i, img in tqdm(enumerate(d['imgname']), dynamic_ncols=True): + # gt = d['contact_label'][i] + # gt = np.reshape(gt, (1, -1)) + + # imgname = osp.basename(img) + # for k, v in img_list_dict.items(): + # if imgname in v: + # object_class = k + # break + # # pred_path = osp.join(args.dataset_root, object_class, imgname, 'chore-test', f'k1.smpl_colored_{args.th}.ply') + # pred_path = osp.join(args.dataset_root, object_class, imgname, f'k1.smpl_colored_{args.th}.obj') + # if not osp.exists(pred_path): + # # print(pred_path) + # continue + # pred_mesh = trimesh.load(pred_path, process=False) + + # pred = np.zeros((1, 6890)) + # for i in range(pred_mesh.visual.vertex_colors.shape[0]): + # c = pred_mesh.visual.vertex_colors[i] + # if collections.Counter(c) == collections.Counter([255, 0, 0, 255]): + # pred[0, i] = 1 + + # pre, rec, f1 = precision_recall_f1score(gt, pred) + # fp_err, _ = det_error_metric(pred, gt) + + # tot_pre += pre.sum() + # tot_rec += rec.sum() + # tot_f1 += f1.sum() + # tot_fp_err += fp_err.numpy().sum() + + # if f1.sum() > best_f1: + # best_pre = pre.sum() + # best_rec = rec.sum() + # best_f1 = f1.sum() + # best_fp_err = fp_err.numpy().sum() + # best_imgname = imgname + # best_obj = object_class + + # img_count += 1 + + # print(f'Dataset size: {img_count}') + # print(f'Threshold: {args.th}\n') + + # print(f'Test Precision: {tot_pre/img_count}') + # print(f'Test Recall: {tot_rec/img_count}') + # print(f'Test F1 Score: {tot_f1/img_count}') + # print(f'Test FP Error: {tot_fp_err/img_count}\n') + + # print(f'Best Precision: {best_pre}') + # print(f'Best Recall: {best_rec}') + # print(f'Best F1 Score: {best_f1}') + # print(f'Best FP Error: {best_fp_err}') + # print(f'Best Image: {best_imgname} and Object: {best_obj}') + + +if __name__=='__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--dataset_root', type=str, default='/is/cluster/fast/achatterjee/CHORE_DECO/hot_test') + parser.add_argument('--npz_file', type=str, default='/is/cluster/fast/achatterjee/Datasets/hot_behave/hot_behave_test_reduced.npz') + parser.add_argument('--json_file', type=str, default='/ps/scratch/ps_shared/stripathi/deco/4agniv/hot_behave_split/imgnames_per_object_dict_reduced.json') + parser.add_argument('--th', type=float) + args = parser.parse_args() + + main(args) + \ No newline at end of file diff --git a/tester.py b/tester.py new file mode 100644 index 0000000000000000000000000000000000000000..9f536b618c1d63d4ac75d2ff5be274168818efbc --- /dev/null +++ b/tester.py @@ -0,0 +1,61 @@ +import torch +from torch.utils.data import DataLoader +from loguru import logger + +from train.trainer_step import TrainStepper +from train.base_trainer import evaluator +from data.base_dataset import BaseDataset +from models.deco import DECO +from utils.config import parse_args, run_grid_search_experiments + +def test(hparams): + deco_model = DECO(hparams.TRAINING.ENCODER, hparams.TRAINING.CONTEXT, device) + pytorch_total_params = sum(p.numel() for p in deco_model.parameters() if p.requires_grad) + print('Total number of trainable parameters: ', pytorch_total_params) + + solver = TrainStepper(deco_model, hparams.TRAINING.CONTEXT, hparams.OPTIMIZER.LR, hparams.TRAINING.LOSS_WEIGHTS, hparams.TRAINING.PAL_LOSS_WEIGHTS, device) + + logger.info(f'Loading weights from {hparams.TRAINING.BEST_MODEL_PATH}') + _, _ = solver.load(hparams.TRAINING.BEST_MODEL_PATH) + + # Run testing + for test_loader in val_loaders: + dataset_name = test_loader.dataset.dataset + test_dict, total_time = evaluator(test_loader, solver, hparams, 0, dataset_name, return_dict=True) + + print('Test Contact Precision: ', test_dict['cont_precision']) + print('Test Contact Recall: ', test_dict['cont_recall']) + print('Test Contact F1 Score: ', test_dict['cont_f1']) + print('Test Contact FP Geo. Error: ', test_dict['fp_geo_err']) + print('Test Contact FN Geo. Error: ', test_dict['fn_geo_err']) + if hparams.TRAINING.CONTEXT: + print('Test Contact Semantic Segmentation IoU: ', test_dict['sem_iou']) + print('Test Contact Part Segmentation IoU: ', test_dict['part_iou']) + print('\nTime taken per image for evaluation: ', total_time) + print('-'*50) + +if __name__ == '__main__': + args = parse_args() + hparams = run_grid_search_experiments( + args, + script='tester.py', + change_wt_name=False + ) + + if torch.cuda.is_available(): + device = torch.device('cuda') + else: + device = torch.device('cpu') + + val_datasets = [] + for ds in hparams.VALIDATION.DATASETS: + if ds in ['rich', 'prox']: + val_datasets.append(BaseDataset(ds, 'val', model_type='smplx', normalize=hparams.DATASET.NORMALIZE_IMAGES)) + elif ds in ['damon']: + val_datasets.append(BaseDataset(ds, 'val', model_type='smpl', normalize=hparams.DATASET.NORMALIZE_IMAGES)) + else: + raise ValueError('Dataset not supported') + + val_loaders = [DataLoader(val_dataset, batch_size=hparams.DATASET.BATCH_SIZE, shuffle=False, num_workers=hparams.DATASET.NUM_WORKERS) for val_dataset in val_datasets] + + test(hparams) \ No newline at end of file diff --git a/train.py b/train.py new file mode 100644 index 0000000000000000000000000000000000000000..971df7467d2d7b4711ac36c25f02e202c8ab4e56 --- /dev/null +++ b/train.py @@ -0,0 +1,94 @@ +import torch +from torch.utils.data import DataLoader +import os + +from train.trainer_step import TrainStepper +from train.base_trainer import trainer, evaluator +from data.base_dataset import BaseDataset +from data.mixed_dataset import MixedDataset +from models.deco import DECO +from utils.config import parse_args, run_grid_search_experiments + +def train(hparams): + deco_model = DECO(hparams.TRAINING.ENCODER, hparams.TRAINING.CONTEXT, device) + + solver = TrainStepper(deco_model, hparams.TRAINING.CONTEXT, hparams.OPTIMIZER.LR, hparams.TRAINING.LOSS_WEIGHTS, hparams.TRAINING.PAL_LOSS_WEIGHTS, device) + + vb_f1 = 0 + start_ep = 0 + num = 0 + k = True + latest_model_path = hparams.TRAINING.BEST_MODEL_PATH.replace('best', 'latest') + if os.path.exists(latest_model_path): + _, vb_f1 = solver.load(hparams.TRAINING.BEST_MODEL_PATH) + start_ep, _ = solver.load(latest_model_path) + + for epoch in range(start_ep+1, hparams.TRAINING.NUM_EPOCHS + 1): + # Train one epoch + trainer(epoch, train_loader, solver, hparams) + # Run evaluation + vc_f1 = None + for val_loader in val_loaders: + dataset_name = val_loader.dataset.dataset + vc_f1_ds = evaluator(val_loader, solver, hparams, epoch, dataset_name, normalize=hparams.DATASET.NORMALIZE_IMAGES) + if dataset_name == hparams.VALIDATION.MAIN_DATASET: + vc_f1 = vc_f1_ds + if vc_f1 is None: + raise ValueError('Main dataset not found in validation datasets') + + print('Learning rate: ', solver.lr) + + print('---------------------------------------------') + print('---------------------------------------------') + + solver.save(epoch, vc_f1, latest_model_path) + + if epoch % hparams.TRAINING.CHECKPOINT_EPOCHS == 0: + inter_model_path = latest_model_path.replace('latest', 'epoch_'+str(epoch).zfill(3)) + solver.save(epoch, vc_f1, inter_model_path) + + if vc_f1 < vb_f1: + num += 1 + print('Not Saving model: Best Val F1 = ', vb_f1, ' Current Val F1 = ', vc_f1) + else: + num = 0 + vb_f1 = vc_f1 + print('Saving model...') + solver.save(epoch, vb_f1, hparams.TRAINING.BEST_MODEL_PATH) + + if num >= hparams.OPTIMIZER.NUM_UPDATE_LR: solver.update_lr() + if num >= hparams.TRAINING.NUM_EARLY_STOP: + print('Early Stop') + k = False + + if k: continue + else: break + + +if __name__ == '__main__': + args = parse_args() + hparams = run_grid_search_experiments( + args, + script='train.py', + ) + + if torch.cuda.is_available(): + device = torch.device('cuda') + else: + device = torch.device('cpu') + + train_dataset = MixedDataset(hparams.TRAINING.DATASETS, 'train', dataset_mix_pdf=hparams.TRAINING.DATASET_MIX_PDF, normalize=hparams.DATASET.NORMALIZE_IMAGES) + + val_datasets = [] + for ds in hparams.VALIDATION.DATASETS: + if ds in ['rich', 'prox']: + val_datasets.append(BaseDataset(ds, 'val', model_type='smplx', normalize=hparams.DATASET.NORMALIZE_IMAGES)) + elif ds in ['damon']: + val_datasets.append(BaseDataset(ds, 'val', model_type='smpl', normalize=hparams.DATASET.NORMALIZE_IMAGES)) + else: + raise ValueError('Dataset not supported') + + train_loader = DataLoader(train_dataset, hparams.DATASET.BATCH_SIZE, shuffle=True, num_workers=hparams.DATASET.NUM_WORKERS) + val_loaders = [DataLoader(val_dataset, batch_size=hparams.DATASET.BATCH_SIZE, shuffle=False, num_workers=hparams.DATASET.NUM_WORKERS) for val_dataset in val_datasets] + + train(hparams) diff --git a/train/__init__.py b/train/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/train/base_trainer.py b/train/base_trainer.py new file mode 100644 index 0000000000000000000000000000000000000000..f230310549e7f6fe2ec5831f02ec5bc4592e1b4e --- /dev/null +++ b/train/base_trainer.py @@ -0,0 +1,103 @@ +from tqdm import tqdm +from utils.metrics import metric, precision_recall_f1score, det_error_metric +import torch +import numpy as np +from vis.visualize import gen_render + + +def trainer(epoch, train_loader, solver, hparams, compute_metrics=False): + + total_epochs = hparams.TRAINING.NUM_EPOCHS + print('Training Epoch {}/{}'.format(epoch, total_epochs)) + + length = len(train_loader) + iterator = tqdm(enumerate(train_loader), total=length, leave=False, desc=f'Training Epoch: {epoch}/{total_epochs}') + for step, batch in iterator: + losses, output = solver.optimize(batch) + return losses, output + +@torch.no_grad() +def evaluator(val_loader, solver, hparams, epoch=0, dataset_name='Unknown', normalize=True, return_dict=False): + total_epochs = hparams.TRAINING.NUM_EPOCHS + + batch_size = val_loader.batch_size + dataset_size = len(val_loader.dataset) + print(f'Dataset size: {dataset_size}') + + val_epoch_cont_pre = np.zeros(dataset_size) + val_epoch_cont_rec = np.zeros(dataset_size) + val_epoch_cont_f1 = np.zeros(dataset_size) + val_epoch_fp_geo_err = np.zeros(dataset_size) + val_epoch_fn_geo_err = np.zeros(dataset_size) + if hparams.TRAINING.CONTEXT: + val_epoch_sem_iou = np.zeros(dataset_size) + val_epoch_part_iou = np.zeros(dataset_size) + + val_epoch_cont_loss = np.zeros(dataset_size) + + total_time = 0 + + rend_images = [] + + eval_dict = {} + + length = len(val_loader) + iterator = tqdm(enumerate(val_loader), total=length, leave=False, desc=f'Evaluating {dataset_name.capitalize()} Epoch: {epoch}/{total_epochs}') + for step, batch in iterator: + curr_batch_size = batch['img'].shape[0] + losses, output, time_taken = solver.evaluate(batch) + + val_epoch_cont_loss[step * batch_size:step * batch_size + curr_batch_size] = losses['cont_loss'].cpu().numpy() + + # compute metrics + contact_labels_3d = output['contact_labels_3d_gt'] + has_contact_3d = output['has_contact_3d'] + # check if any value in has_contact_3d tensor is 0 + assert torch.any(has_contact_3d == 0) == False, 'has_contact_3d tensor has 0 values' + + contact_labels_3d_pred = output['contact_labels_3d_pred'] + if hparams.TRAINING.CONTEXT: + sem_mask_gt = output['sem_mask_gt'] + sem_seg_pred = output['sem_mask_pred'] + part_mask_gt = output['part_mask_gt'] + part_seg_pred = output['part_mask_pred'] + + cont_pre, cont_rec, cont_f1 = precision_recall_f1score(contact_labels_3d, contact_labels_3d_pred) + fp_geo_err, fn_geo_err = det_error_metric(contact_labels_3d_pred, contact_labels_3d) + if hparams.TRAINING.CONTEXT: + sem_iou = metric(sem_mask_gt, sem_seg_pred) + part_iou = metric(part_mask_gt, part_seg_pred) + + val_epoch_cont_pre[step * batch_size:step * batch_size + curr_batch_size] = cont_pre.cpu().numpy() + val_epoch_cont_rec[step * batch_size:step * batch_size + curr_batch_size] = cont_rec.cpu().numpy() + val_epoch_cont_f1[step * batch_size:step * batch_size + curr_batch_size] = cont_f1.cpu().numpy() + val_epoch_fp_geo_err[step * batch_size:step * batch_size + curr_batch_size] = fp_geo_err.cpu().numpy() + val_epoch_fn_geo_err[step * batch_size:step * batch_size + curr_batch_size] = fn_geo_err.cpu().numpy() + if hparams.TRAINING.CONTEXT: + val_epoch_sem_iou[step * batch_size:step * batch_size + curr_batch_size] = sem_iou.cpu().numpy() + val_epoch_part_iou[step * batch_size:step * batch_size + curr_batch_size] = part_iou.cpu().numpy() + + total_time += time_taken + + # logging every summary_steps steps + if step % hparams.VALIDATION.SUMMARY_STEPS == 0: + if hparams.TRAINING.CONTEXT: + rend = gen_render(output, normalize) + rend_images.append(rend) + + eval_dict['cont_precision'] = np.sum(val_epoch_cont_pre) / dataset_size + eval_dict['cont_recall'] = np.sum(val_epoch_cont_rec) / dataset_size + eval_dict['cont_f1'] = np.sum(val_epoch_cont_f1) / dataset_size + eval_dict['fp_geo_err'] = np.sum(val_epoch_fp_geo_err) / dataset_size + eval_dict['fn_geo_err'] = np.sum(val_epoch_fn_geo_err) / dataset_size + if hparams.TRAINING.CONTEXT: + eval_dict['sem_iou'] = np.sum(val_epoch_sem_iou) / dataset_size + eval_dict['part_iou'] = np.sum(val_epoch_part_iou) / dataset_size + eval_dict['images'] = rend_images + + total_time /= dataset_size + + val_epoch_cont_loss = np.sum(val_epoch_cont_loss) / dataset_size + if return_dict: + return eval_dict, total_time + return eval_dict['cont_f1'] \ No newline at end of file diff --git a/train/trainer_step.py b/train/trainer_step.py new file mode 100644 index 0000000000000000000000000000000000000000..e17953ba0f639da2896a4ee70db120da9e87a750 --- /dev/null +++ b/train/trainer_step.py @@ -0,0 +1,291 @@ +from utils.loss import sem_loss_function, class_loss_function, pixel_anchoring_function +import torch +import os +import time + + +class TrainStepper(): + def __init__(self, deco_model, context, learning_rate, loss_weight, pal_loss_weight, device): + self.device = device + + self.model = deco_model + self.context = context + + if self.context: + self.optimizer_sem = torch.optim.Adam(params=list(self.model.encoder_sem.parameters()) + list(self.model.decoder_sem.parameters()), + lr=learning_rate, weight_decay=0.0001) + self.optimizer_part = torch.optim.Adam( + params=list(self.model.encoder_part.parameters()) + list(self.model.decoder_part.parameters()), lr=learning_rate, + weight_decay=0.0001) + self.optimizer_contact = torch.optim.Adam( + params=list(self.model.encoder_sem.parameters()) + list(self.model.encoder_part.parameters()) + list( + self.model.cross_att.parameters()) + list(self.model.classif.parameters()), lr=learning_rate, weight_decay=0.0001) + + if self.context: self.sem_loss = sem_loss_function().to(device) + self.class_loss = class_loss_function().to(device) + self.pixel_anchoring_loss_smplx = pixel_anchoring_function(model_type='smplx').to(device) + self.pixel_anchoring_loss_smpl = pixel_anchoring_function(model_type='smpl').to(device) + self.lr = learning_rate + self.loss_weight = loss_weight + self.pal_loss_weight = pal_loss_weight + + def optimize(self, batch): + self.model.train() + + img_paths = batch['img_path'] + img = batch['img'].to(self.device) + + img_scale_factor = batch['img_scale_factor'].to(self.device) + + pose = batch['pose'].to(self.device) + betas = batch['betas'].to(self.device) + transl = batch['transl'].to(self.device) + has_smpl = batch['has_smpl'].to(self.device) + is_smplx = batch['is_smplx'].to(self.device) + + cam_k = batch['cam_k'].to(self.device) + + gt_contact_labels_3d = batch['contact_label_3d'].to(self.device) + has_contact_3d = batch['has_contact_3d'].to(self.device) + + if self.context: + sem_mask_gt = batch['sem_mask'].to(self.device) + part_mask_gt = batch['part_mask'].to(self.device) + + polygon_contact_2d = batch['polygon_contact_2d'].to(self.device) + has_polygon_contact_2d = batch['has_polygon_contact_2d'].to(self.device) + + # Forward pass + if self.context: + cont, sem_mask_pred, part_mask_pred = self.model(img) + else: + cont = self.model(img) + + if self.context: + loss_sem = self.sem_loss(sem_mask_gt, sem_mask_pred) + loss_part = self.sem_loss(part_mask_gt, part_mask_pred) + valid_contact_3d = has_contact_3d + loss_cont = self.class_loss(gt_contact_labels_3d, cont, valid_contact_3d) + valid_polygon_contact_2d = has_polygon_contact_2d + + if self.pal_loss_weight > 0 and (is_smplx == 0).sum() > 0: + smpl_body_params = {'pose': pose[is_smplx == 0], 'betas': betas[is_smplx == 0], + 'transl': transl[is_smplx == 0], + 'has_smpl': has_smpl[is_smplx == 0]} + loss_pix_anchoring_smpl, contact_2d_pred_rgb_smpl, _ = self.pixel_anchoring_loss_smpl(cont[is_smplx == 0], + smpl_body_params, + cam_k[is_smplx == 0], + img_scale_factor[ + is_smplx == 0], + polygon_contact_2d[ + is_smplx == 0], + valid_polygon_contact_2d[ + is_smplx == 0]) + # weigh the smpl loss based on the number of smpl sample + loss_pix_anchoring = loss_pix_anchoring_smpl * (is_smplx == 0).sum() / len(is_smplx) + contact_2d_pred_rgb = contact_2d_pred_rgb_smpl + else: + loss_pix_anchoring = 0 + contact_2d_pred_rgb = torch.zeros_like(polygon_contact_2d) + + if self.context: loss = loss_sem + loss_part + self.loss_weight * loss_cont + self.pal_loss_weight * loss_pix_anchoring + else: loss = self.loss_weight * loss_cont + self.pal_loss_weight * loss_pix_anchoring + + if self.context: + self.optimizer_sem.zero_grad() + self.optimizer_part.zero_grad() + self.optimizer_contact.zero_grad() + + loss.backward() + + if self.context: + self.optimizer_sem.step() + self.optimizer_part.step() + self.optimizer_contact.step() + + if self.context: + losses = {'sem_loss': loss_sem, + 'part_loss': loss_part, + 'cont_loss': loss_cont, + 'pal_loss': loss_pix_anchoring, + 'total_loss': loss} + else: + losses = {'cont_loss': loss_cont, + 'pal_loss': loss_pix_anchoring, + 'total_loss': loss} + + if self.context: + output = { + 'img': img, + 'sem_mask_gt': sem_mask_gt, + 'sem_mask_pred': sem_mask_pred, + 'part_mask_gt': part_mask_gt, + 'part_mask_pred': part_mask_pred, + 'has_contact_2d': has_polygon_contact_2d, + 'contact_2d_gt': polygon_contact_2d, + 'contact_2d_pred_rgb': contact_2d_pred_rgb, + 'has_contact_3d': has_contact_3d, + 'contact_labels_3d_gt': gt_contact_labels_3d, + 'contact_labels_3d_pred': cont} + else: + output = { + 'img': img, + 'has_contact_2d': has_polygon_contact_2d, + 'contact_2d_gt': polygon_contact_2d, + 'contact_2d_pred_rgb': contact_2d_pred_rgb, + 'has_contact_3d': has_contact_3d, + 'contact_labels_3d_gt': gt_contact_labels_3d, + 'contact_labels_3d_pred': cont} + + return losses, output + + @torch.no_grad() + def evaluate(self, batch): + self.model.eval() + + img_paths = batch['img_path'] + img = batch['img'].to(self.device) + + img_scale_factor = batch['img_scale_factor'].to(self.device) + + pose = batch['pose'].to(self.device) + betas = batch['betas'].to(self.device) + transl = batch['transl'].to(self.device) + has_smpl = batch['has_smpl'].to(self.device) + is_smplx = batch['is_smplx'].to(self.device) + + cam_k = batch['cam_k'].to(self.device) + + gt_contact_labels_3d = batch['contact_label_3d'].to(self.device) + has_contact_3d = batch['has_contact_3d'].to(self.device) + + if self.context: + sem_mask_gt = batch['sem_mask'].to(self.device) + part_mask_gt = batch['part_mask'].to(self.device) + + polygon_contact_2d = batch['polygon_contact_2d'].to(self.device) + has_polygon_contact_2d = batch['has_polygon_contact_2d'].to(self.device) + + # Forward pass + initial_time = time.time() + if self.context: cont, sem_mask_pred, part_mask_pred = self.model(img) + else: cont = self.model(img) + time_taken = time.time() - initial_time + + if self.context: + loss_sem = self.sem_loss(sem_mask_gt, sem_mask_pred) + loss_part = self.sem_loss(part_mask_gt, part_mask_pred) + valid_contact_3d = has_contact_3d + loss_cont = self.class_loss(gt_contact_labels_3d, cont, valid_contact_3d) + valid_polygon_contact_2d = has_polygon_contact_2d + + if self.pal_loss_weight > 0 and (is_smplx == 0).sum() > 0: # PAL loss only on 2D contacts in HOT which only has SMPL + smpl_body_params = {'pose': pose[is_smplx == 0], 'betas': betas[is_smplx == 0], 'transl': transl[is_smplx == 0], + 'has_smpl': has_smpl[is_smplx == 0]} + loss_pix_anchoring_smpl, contact_2d_pred_rgb_smpl, _ = self.pixel_anchoring_loss_smpl(cont[is_smplx == 0], + smpl_body_params, + cam_k[is_smplx == 0], + img_scale_factor[ + is_smplx == 0], + polygon_contact_2d[ + is_smplx == 0], + valid_polygon_contact_2d[ + is_smplx == 0]) + # weight the smpl loss based on the number of smpl samples + contact_2d_pred_rgb = contact_2d_pred_rgb_smpl + loss_pix_anchoring = loss_pix_anchoring_smpl * (is_smplx == 0).sum() / len(is_smplx) + else: + loss_pix_anchoring = 0 + contact_2d_pred_rgb = torch.zeros_like(polygon_contact_2d) + + if self.context: loss = loss_sem + loss_part + self.loss_weight * loss_cont + self.pal_loss_weight * loss_pix_anchoring + else: loss = self.loss_weight * loss_cont + self.pal_loss_weight * loss_pix_anchoring + + if self.context: + losses = {'sem_loss': loss_sem, + 'part_loss': loss_part, + 'cont_loss': loss_cont, + 'pal_loss': loss_pix_anchoring, + 'total_loss': loss} + else: + losses = {'cont_loss': loss_cont, + 'pal_loss': loss_pix_anchoring, + 'total_loss': loss} + + if self.context: + output = { + 'img': img, + 'sem_mask_gt': sem_mask_gt, + 'sem_mask_pred': sem_mask_pred, + 'part_mask_gt': part_mask_gt, + 'part_mask_pred': part_mask_pred, + 'has_contact_2d': has_polygon_contact_2d, + 'contact_2d_gt': polygon_contact_2d, + 'contact_2d_pred_rgb': contact_2d_pred_rgb, + 'has_contact_3d': has_contact_3d, + 'contact_labels_3d_gt': gt_contact_labels_3d, + 'contact_labels_3d_pred': cont} + else: + output = { + 'img': img, + 'has_contact_2d': has_polygon_contact_2d, + 'contact_2d_gt': polygon_contact_2d, + 'contact_2d_pred_rgb': contact_2d_pred_rgb, + 'has_contact_3d': has_contact_3d, + 'contact_labels_3d_gt': gt_contact_labels_3d, + 'contact_labels_3d_pred': cont} + + return losses, output, time_taken + + def save(self, ep, f1, model_path): + # create model directory if it does not exist + os.makedirs(os.path.dirname(model_path), exist_ok=True) + if self.context: + torch.save({ + 'epoch': ep, + 'deco': self.model.state_dict(), + 'f1': f1, + 'sem_optim': self.optimizer_sem.state_dict(), + 'part_optim': self.optimizer_part.state_dict(), + 'contact_optim': self.optimizer_contact.state_dict() + }, + model_path) + else: + torch.save({ + 'epoch': ep, + 'deco': self.model.state_dict(), + 'f1': f1, + 'sem_optim': self.optimizer_sem.state_dict(), + 'part_optim': self.optimizer_part.state_dict(), + 'contact_optim': self.optimizer_contact.state_dict() + }, + model_path) + + def load(self, model_path): + print(f'~~~ Loading existing checkpoint from {model_path} ~~~') + checkpoint = torch.load(model_path) + self.model.load_state_dict(checkpoint['deco'], strict=True) + + if self.context: + self.optimizer_sem.load_state_dict(checkpoint['sem_optim']) + self.optimizer_part.load_state_dict(checkpoint['part_optim']) + self.optimizer_contact.load_state_dict(checkpoint['contact_optim']) + epoch = checkpoint['epoch'] + f1 = checkpoint['f1'] + return epoch, f1 + + def update_lr(self, factor=2): + if factor: + new_lr = self.lr / factor + + if self.context: + self.optimizer_sem = torch.optim.Adam(params=list(self.model.encoder_sem.parameters()) + list(self.model.decoder_sem.parameters()), + lr=new_lr, weight_decay=0.0001) + self.optimizer_part = torch.optim.Adam( + params=list(self.model.encoder_part.parameters()) + list(self.model.decoder_part.parameters()), lr=new_lr, weight_decay=0.0001) + self.optimizer_contact = torch.optim.Adam( + params=list(self.model.encoder_sem.parameters()) + list(self.model.encoder_part.parameters()) + list( + self.model.cross_att.parameters()) + list(self.model.classif.parameters()), lr=new_lr, weight_decay=0.0001) + + print('update learning rate: %f -> %f' % (self.lr, new_lr)) + self.lr = new_lr \ No newline at end of file diff --git a/utils/__init__.py b/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/utils/cluster.py b/utils/cluster.py new file mode 100644 index 0000000000000000000000000000000000000000..c052ef292ebbf04e5cb768b88fdb623540435e7b --- /dev/null +++ b/utils/cluster.py @@ -0,0 +1,99 @@ +import os +import sys +import stat +import shutil +import subprocess + +from loguru import logger + +GPUS = { + 'v100-v16': ('\"Tesla V100-PCIE-16GB\"', 'tesla', 16000), + 'v100-p32': ('\"Tesla V100-PCIE-32GB\"', 'tesla', 32000), + 'v100-s32': ('\"Tesla V100-SXM2-32GB\"', 'tesla', 32000), + 'v100-p16': ('\"Tesla P100-PCIE-16GB\"', 'tesla', 16000), +} + +def get_gpus(min_mem=10000, arch=('tesla', 'quadro', 'rtx')): + gpu_names = [] + for k, (gpu_name, gpu_arch, gpu_mem) in GPUS.items(): + if gpu_mem >= min_mem and gpu_arch in arch: + gpu_names.append(gpu_name) + + assert len(gpu_names) > 0, 'Suitable GPU model could not be found' + + return gpu_names + + +def execute_task_on_cluster( + script, + exp_name, + output_dir, + condor_dir, + cfg_file, + num_exp=1, + exp_opts=None, + bid_amount=10, + num_workers=2, + memory=64000, + gpu_min_mem=10000, + gpu_arch=('tesla', 'quadro', 'rtx'), + num_gpus=1 +): + # copy config to a new experiment directory and source from there. + # this makes sure the correct config is copied even if you change the config file + # after starting the experiment and before the first job is submitted + temp_config_dir = os.path.join(os.path.dirname(condor_dir), 'temp_configs', exp_name) + os.makedirs(temp_config_dir, exist_ok=True) + new_cfg_file = os.path.join(temp_config_dir, 'config.yaml') + shutil.copy(src=cfg_file, dst=new_cfg_file) + + gpus = get_gpus(min_mem=gpu_min_mem, arch=gpu_arch) + + gpus = ' || '.join([f'CUDADeviceName=={x}' for x in gpus]) + + condor_log_dir = os.path.join(condor_dir, 'condorlog', exp_name) + os.makedirs(condor_log_dir, exist_ok=True) + submission = f'executable = {condor_log_dir}/{exp_name}_run.sh\n' \ + 'arguments = $(Process) $(Cluster)\n' \ + f'error = {condor_log_dir}/{exp_name}_$(Cluster).$(Process).err\n' \ + f'output = {condor_log_dir}/{exp_name}_$(Cluster).$(Process).out\n' \ + f'log = {condor_log_dir}/{exp_name}_$(Cluster).$(Process).log\n' \ + f'request_memory = {memory}\n' \ + f'request_cpus={int(num_workers)}\n' \ + f'request_gpus={num_gpus}\n' \ + f'requirements={gpus}\n' \ + f'+MaxRunningPrice = 500\n' \ + f'queue {num_exp}' + # f'request_cpus={int(num_workers/2)}\n' \ + # f'+RunningPriceExceededAction = \"kill\"\n' \ + print('<<< Condor Submission >>> ') + print(submission) + + with open(f'{condor_log_dir}/{exp_name}_submit.sub', 'w') as f: + f.write(submission) + + # output_dir = os.path.join(output_dir, exp_name) + logger.info(f'The logs for this experiments can be found under: {condor_log_dir}') + logger.info(f'The outputs for this experiments can be found under: {output_dir}') + ## This is the trick. Notice there is no --cluster here + bash = 'export PYTHONBUFFERED=1\n export PATH=$PATH\n ' \ + f'{sys.executable} {script} --cfg {new_cfg_file} --cfg_id $1' + + if exp_opts is not None: + bash += ' --opts ' + for opt in exp_opts: + bash += f'{opt} ' + bash += 'SYSTEM.CLUSTER_NODE $2.$1' + else: + bash += ' --opts SYSTEM.CLUSTER_NODE $2.$1' + + executable_path = f'{condor_log_dir}/{exp_name}_run.sh' + + with open(executable_path, 'w') as f: + f.write(bash) + + os.chmod(executable_path, stat.S_IRWXU) + + cmd = ['condor_submit_bid', f'{bid_amount}', f'{condor_log_dir}/{exp_name}_submit.sub'] + logger.info('Executing ' + ' '.join(cmd)) + subprocess.call(cmd) diff --git a/utils/colorwheel.py b/utils/colorwheel.py new file mode 100644 index 0000000000000000000000000000000000000000..354ee157b64e098bf1f1e06ac961a1b97d827cd6 --- /dev/null +++ b/utils/colorwheel.py @@ -0,0 +1,22 @@ +import cv2 +import numpy as np + + +def make_color_wheel_image(img_width, img_height): + """ + Creates a color wheel based image of given width and height + Args: + img_width (int): + img_height (int): + + Returns: + opencv image (numpy array): color wheel based image + """ + hue = np.fromfunction(lambda i, j: (np.arctan2(i-img_height/2, img_width/2-j) + np.pi)*(180/np.pi)/2, + (img_height, img_width), dtype=np.float) + saturation = np.ones((img_height, img_width)) * 255 + value = np.ones((img_height, img_width)) * 255 + hsl = np.dstack((hue, saturation, value)) + color_map = cv2.cvtColor(np.array(hsl, dtype=np.uint8), cv2.COLOR_HSV2BGR) + return color_map + diff --git a/utils/config.py b/utils/config.py new file mode 100644 index 0000000000000000000000000000000000000000..61757f71b77d787c5e1a2422f8c38af7db04ca6d --- /dev/null +++ b/utils/config.py @@ -0,0 +1,196 @@ +import itertools +import operator +import os +import shutil +import time +from functools import reduce +from typing import List, Union + +import configargparse +import yaml +from flatten_dict import flatten, unflatten +from loguru import logger +from yacs.config import CfgNode as CN + +from utils.cluster import execute_task_on_cluster +from utils.default_hparams import hparams + + +def parse_args(): + def add_common_cmdline_args(parser): + # for cluster runs + parser.add_argument('--cfg', required=True, type=str, help='cfg file path') + parser.add_argument('--opts', default=[], nargs='*', help='additional options to update config') + parser.add_argument('--cfg_id', type=int, default=0, help='cfg id to run when multiple experiments are spawned') + parser.add_argument('--cluster', default=False, action='store_true', help='creates submission files for cluster') + parser.add_argument('--bid', type=int, default=10, help='amount of bid for cluster') + parser.add_argument('--memory', type=int, default=64000, help='memory amount for cluster') + parser.add_argument('--gpu_min_mem', type=int, default=12000, help='minimum amount of GPU memory') + parser.add_argument('--gpu_arch', default=['tesla', 'quadro', 'rtx'], + nargs='*', help='additional options to update config') + parser.add_argument('--num_cpus', type=int, default=8, help='num cpus for cluster') + return parser + + # For Blender main parser + arg_formatter = configargparse.ArgumentDefaultsHelpFormatter + cfg_parser = configargparse.YAMLConfigFileParser + description = 'PyTorch implementation of DECO' + + parser = configargparse.ArgumentParser(formatter_class=arg_formatter, + config_file_parser_class=cfg_parser, + description=description, + prog='deco') + + parser = add_common_cmdline_args(parser) + + args = parser.parse_args() + print(args, end='\n\n') + + return args + +def get_hparams_defaults(): + """Get a yacs hparamsNode object with default values for my_project.""" + # Return a clone so that the defaults will not be altered + # This is for the "local variable" use pattern + return hparams.clone() + +def update_hparams(hparams_file): + hparams = get_hparams_defaults() + hparams.merge_from_file(hparams_file) + return hparams.clone() + +def update_hparams_from_dict(cfg_dict): + hparams = get_hparams_defaults() + cfg = hparams.load_cfg(str(cfg_dict)) + hparams.merge_from_other_cfg(cfg) + return hparams.clone() + +def get_grid_search_configs(config, excluded_keys=[]): + """ + :param config: dictionary with the configurations + :return: The different configurations + """ + + def bool_to_string(x: Union[List[bool], bool]) -> Union[List[str], str]: + """ + boolean to string conversion + :param x: list or bool to be converted + :return: string converted thinghat + """ + if isinstance(x, bool): + return [str(x)] + for i, j in enumerate(x): + x[i] = str(j) + return x + + # exclude from grid search + + flattened_config_dict = flatten(config, reducer='path') + hyper_params = [] + + for k,v in flattened_config_dict.items(): + if isinstance(v,list): + if k in excluded_keys: + flattened_config_dict[k] = ['+'.join(v)] + elif len(v) > 1: + hyper_params += [k] + + if isinstance(v, list) and isinstance(v[0], bool) : + flattened_config_dict[k] = bool_to_string(v) + + if not isinstance(v,list): + if isinstance(v, bool): + flattened_config_dict[k] = bool_to_string(v) + else: + flattened_config_dict[k] = [v] + + keys, values = zip(*flattened_config_dict.items()) + experiments = [dict(zip(keys, v)) for v in itertools.product(*values)] + + for exp_id, exp in enumerate(experiments): + for param in excluded_keys: + exp[param] = exp[param].strip().split('+') + for param_name, param_value in exp.items(): + # print(param_name,type(param_value)) + if isinstance(param_value, list) and (param_value[0] in ['True', 'False']): + exp[param_name] = [True if x == 'True' else False for x in param_value] + if param_value in ['True', 'False']: + if param_value == 'True': + exp[param_name] = True + else: + exp[param_name] = False + + + experiments[exp_id] = unflatten(exp, splitter='path') + + return experiments, hyper_params + +def get_from_dict(dict, keys): + return reduce(operator.getitem, keys, dict) + +def save_dict_to_yaml(obj, filename, mode='w'): + with open(filename, mode) as f: + yaml.dump(obj, f, default_flow_style=False) + +def run_grid_search_experiments( + args, + script='train.py', + change_wt_name=True +): + cfg = yaml.safe_load(open(args.cfg)) + # parse config file to split into a list of configs with tuning hyperparameters separated + # Also return the names of tuned hyperparameters hyperparameters + different_configs, hyperparams = get_grid_search_configs( + cfg, + excluded_keys=['TRAINING/DATASETS', 'TRAINING/DATASET_MIX_PDF', 'VALIDATION/DATASETS'], + ) + logger.info(f'Grid search hparams: \n {hyperparams}') + + # The config file may be missing some default values, so we need to add them + different_configs = [update_hparams_from_dict(c) for c in different_configs] + logger.info(f'======> Number of experiment configurations is {len(different_configs)}') + + config_to_run = CN(different_configs[args.cfg_id]) + + if args.cluster: + execute_task_on_cluster( + script=script, + exp_name=config_to_run.EXP_NAME, + output_dir=config_to_run.OUTPUT_DIR, + condor_dir=config_to_run.CONDOR_DIR, + cfg_file=args.cfg, + num_exp=len(different_configs), + bid_amount=args.bid, + num_workers=config_to_run.DATASET.NUM_WORKERS, + memory=args.memory, + exp_opts=args.opts, + gpu_min_mem=args.gpu_min_mem, + gpu_arch=args.gpu_arch, + ) + exit() + + # ==== create logdir using hyperparam settings + logtime = time.strftime('%d-%m-%Y_%H-%M-%S') + logdir = f'{logtime}_{config_to_run.EXP_NAME}' + wt_file = config_to_run.EXP_NAME + '_' + for hp in hyperparams: + v = get_from_dict(different_configs[args.cfg_id], hp.split('/')) + logdir += f'_{hp.replace("/", ".").replace("_", "").lower()}-{v}' + wt_file += f'{hp.replace("/", ".").replace("_", "").lower()}-{v}_' + logdir = os.path.join(config_to_run.OUTPUT_DIR, logdir) + os.makedirs(logdir, exist_ok=True) + config_to_run.LOGDIR = logdir + + wt_file += 'best.pth' + wt_path = os.path.join(os.path.dirname(config_to_run.TRAINING.BEST_MODEL_PATH), wt_file) + if change_wt_name: config_to_run.TRAINING.BEST_MODEL_PATH = wt_path + + shutil.copy(src=args.cfg, dst=os.path.join(logdir, 'config.yaml')) + + # save config + save_dict_to_yaml( + unflatten(flatten(config_to_run)), + os.path.join(config_to_run.LOGDIR, 'config_to_run.yaml') + ) + + return config_to_run \ No newline at end of file diff --git a/utils/default_hparams.py b/utils/default_hparams.py new file mode 100644 index 0000000000000000000000000000000000000000..84fe09d89d7e98c2724f39a6264ef459451abaf3 --- /dev/null +++ b/utils/default_hparams.py @@ -0,0 +1,45 @@ +from yacs.config import CfgNode as CN + +# Set default hparams to construct new default config +# Make sure the defaults are same as in parser +hparams = CN() + +# General settings +hparams.EXP_NAME = 'default' +hparams.PROJECT_NAME = 'default' +hparams.OUTPUT_DIR = 'deco_results/' +hparams.CONDOR_DIR = '/is/cluster/work/achatterjee/condor/rich/' +hparams.LOGDIR = '' + +# Dataset hparams +hparams.DATASET = CN() +hparams.DATASET.BATCH_SIZE = 64 +hparams.DATASET.NUM_WORKERS = 4 +hparams.DATASET.NORMALIZE_IMAGES = True + +# Optimizer hparams +hparams.OPTIMIZER = CN() +hparams.OPTIMIZER.TYPE = 'adam' +hparams.OPTIMIZER.LR = 5e-5 +hparams.OPTIMIZER.NUM_UPDATE_LR = 10 + +# Training hparams +hparams.TRAINING = CN() +hparams.TRAINING.ENCODER = 'hrnet' +hparams.TRAINING.CONTEXT = True +hparams.TRAINING.NUM_EPOCHS = 50 +hparams.TRAINING.SUMMARY_STEPS = 100 +hparams.TRAINING.CHECKPOINT_EPOCHS = 5 +hparams.TRAINING.NUM_EARLY_STOP = 10 +hparams.TRAINING.DATASETS = ['rich'] +hparams.TRAINING.DATASET_MIX_PDF = ['1.'] +hparams.TRAINING.DATASET_ROOT_PATH = '/is/cluster/work/achatterjee/rich/npzs' +hparams.TRAINING.BEST_MODEL_PATH = '/is/cluster/work/achatterjee/weights/rich/exp/rich_exp.pth' +hparams.TRAINING.LOSS_WEIGHTS = 1. +hparams.TRAINING.PAL_LOSS_WEIGHTS = 1. + +# Training hparams +hparams.VALIDATION = CN() +hparams.VALIDATION.SUMMARY_STEPS = 100 +hparams.VALIDATION.DATASETS = ['rich'] +hparams.VALIDATION.MAIN_DATASET = 'rich' diff --git a/utils/diff_renderer.py b/utils/diff_renderer.py new file mode 100644 index 0000000000000000000000000000000000000000..62358a361cd6c5f5d79dc863ddef07946df19bb0 --- /dev/null +++ b/utils/diff_renderer.py @@ -0,0 +1,287 @@ +# from https://gitlab.tuebingen.mpg.de/mkocabas/projects/-/blob/master/pare/pare/utils/diff_renderer.py + +import torch +import numpy as np +import torch.nn as nn + +from pytorch3d.renderer import ( + PerspectiveCameras, + RasterizationSettings, + DirectionalLights, + BlendParams, + HardFlatShader, + MeshRasterizer, + TexturesVertex, + TexturesAtlas + ) +from pytorch3d.structures import Meshes + +from .image_utils import get_default_camera +from .smpl_uv import get_tenet_texture + + +class MeshRendererWithDepth(nn.Module): + """ + A class for rendering a batch of heterogeneous meshes. The class should + be initialized with a rasterizer and shader class which each have a forward + function. + """ + + def __init__(self, rasterizer, shader): + super().__init__() + self.rasterizer = rasterizer + self.shader = shader + + def forward(self, meshes_world, **kwargs) -> torch.Tensor: + """ + Render a batch of images from a batch of meshes by rasterizing and then + shading. + + NOTE: If the blur radius for rasterization is > 0.0, some pixels can + have one or more barycentric coordinates lying outside the range [0, 1]. + For a pixel with out of bounds barycentric coordinates with respect to a + face f, clipping is required before interpolating the texture uv + coordinates and z buffer so that the colors and depths are limited to + the range for the corresponding face. + """ + fragments = self.rasterizer(meshes_world, **kwargs) + images = self.shader(fragments, meshes_world, **kwargs) + + mask = (fragments.zbuf > -1).float() + + zbuf = fragments.zbuf.view(images.shape[0], -1) + # print(images.shape, zbuf.shape) + depth = (zbuf - zbuf.min(-1, keepdims=True).values) / \ + (zbuf.max(-1, keepdims=True).values - zbuf.min(-1, keepdims=True).values) + depth = depth.reshape(*images.shape[:3] + (1,)) + + images = torch.cat([images[:, :, :, :3], mask, depth], dim=-1) + return images + + +class DifferentiableRenderer(nn.Module): + def __init__( + self, + img_h, + img_w, + focal_length, + device='cuda', + background_color=(0.0, 0.0, 0.0), + texture_mode='smplpix', + vertex_colors=None, + face_textures=None, + smpl_faces=None, + is_train=False, + is_cam_batch=False, + ): + super(DifferentiableRenderer, self).__init__() + self.x = 'a' + self.img_h = img_h + self.img_w = img_w + self.device = device + self.focal_length = focal_length + K, R = get_default_camera(focal_length, img_h, img_w, is_cam_batch=is_cam_batch) + K, R = K.to(device), R.to(device) + + # T = torch.tensor([[0, 0, 2.5 * self.focal_length / max(self.img_h, self.img_w)]]).to(device) + if is_cam_batch: + T = torch.zeros((K.shape[0], 3)).to(device) + else: + T = torch.tensor([[0.0, 0.0, 0.0]]).to(device) + self.background_color = background_color + self.renderer = None + smpl_faces = smpl_faces + + if texture_mode == 'smplpix': + face_colors = get_tenet_texture(mode=texture_mode).to(device).float() + vertex_colors = torch.from_numpy( + np.load(f'data/smpl/{texture_mode}_vertex_colors.npy')[:,:3] + ).unsqueeze(0).to(device).float() + if texture_mode == 'partseg': + vertex_colors = vertex_colors[..., :3].unsqueeze(0).to(device) + face_colors = face_textures.to(device) + if texture_mode == 'deco': + vertex_colors = vertex_colors[..., :3].to(device) + face_colors = face_textures.to(device) + + self.register_buffer('K', K) + self.register_buffer('R', R) + self.register_buffer('T', T) + self.register_buffer('face_colors', face_colors) + self.register_buffer('vertex_colors', vertex_colors) + self.register_buffer('smpl_faces', smpl_faces) + + self.set_requires_grad(is_train) + + def set_requires_grad(self, val=False): + self.K.requires_grad_(val) + self.R.requires_grad_(val) + self.T.requires_grad_(val) + self.face_colors.requires_grad_(val) + self.vertex_colors.requires_grad_(val) + # check if smpl_faces is a FloatTensor as requires_grad_ is not defined for LongTensor + if isinstance(self.smpl_faces, torch.FloatTensor): + self.smpl_faces.requires_grad_(val) + + def forward(self, vertices, faces=None, R=None, T=None): + raise NotImplementedError + + +class Pytorch3D(DifferentiableRenderer): + def __init__( + self, + img_h, + img_w, + focal_length, + device='cuda', + background_color=(0.0, 0.0, 0.0), + texture_mode='smplpix', + vertex_colors=None, + face_textures=None, + smpl_faces=None, + model_type='smpl', + is_train=False, + is_cam_batch=False, + ): + super(Pytorch3D, self).__init__( + img_h, + img_w, + focal_length, + device=device, + background_color=background_color, + texture_mode=texture_mode, + vertex_colors=vertex_colors, + face_textures=face_textures, + smpl_faces=smpl_faces, + is_train=is_train, + is_cam_batch=is_cam_batch, + ) + + # this R converts the camera from pyrender NDC to + # OpenGL coordinate frame. It is basicall R(180, X) x R(180, Y) + # I manually defined it here for convenience + self.R = self.R @ torch.tensor( + [[[ -1.0, 0.0, 0.0], + [ 0.0, -1.0, 0.0], + [ 0.0, 0.0, 1.0]]], + dtype=self.R.dtype, device=self.R.device, + ) + + if is_cam_batch: + focal_length = self.focal_length + else: + focal_length = self.focal_length[None, :] + + principal_point = ((self.img_w // 2, self.img_h // 2),) + image_size = ((self.img_h, self.img_w),) + + cameras = PerspectiveCameras( + device=self.device, + focal_length=focal_length, + principal_point=principal_point, + R=self.R, + T=self.T, + in_ndc=False, + image_size=image_size, + ) + + for param in cameras.parameters(): + param.requires_grad_(False) + + raster_settings = RasterizationSettings( + image_size=(self.img_h, self.img_w), + blur_radius=0.0, + max_faces_per_bin=20000, + faces_per_pixel=1, + ) + + lights = DirectionalLights( + device=self.device, + ambient_color=((1.0, 1.0, 1.0),), + diffuse_color=((0.0, 0.0, 0.0),), + specular_color=((0.0, 0.0, 0.0),), + direction=((0, 1, 0),), + ) + + blend_params = BlendParams(background_color=self.background_color) + + shader = HardFlatShader(device=self.device, + cameras=cameras, + blend_params=blend_params, + lights=lights) + + self.textures = TexturesVertex(verts_features=self.vertex_colors) + + self.renderer = MeshRendererWithDepth( + rasterizer=MeshRasterizer( + cameras=cameras, + raster_settings=raster_settings + ), + shader=shader, + ) + + def forward(self, vertices, faces=None, R=None, T=None, face_atlas=None): + batch_size = vertices.shape[0] + if faces is None: + faces = self.smpl_faces.expand(batch_size, -1, -1) + + if R is None: + R = self.R.expand(batch_size, -1, -1) + + if T is None: + T = self.T.expand(batch_size, -1) + + # convert camera translation to pytorch3d coordinate frame + T = torch.bmm(R, T.unsqueeze(-1)).squeeze(-1) + + vertex_textures = TexturesVertex( + verts_features=self.vertex_colors.expand(batch_size, -1, -1) + ) + + # face_textures needed because vertex_texture cause interpolation at boundaries + if face_atlas: + face_textures = TexturesAtlas(atlas=face_atlas) + else: + face_textures = TexturesAtlas(atlas=self.face_colors) + + # we may need to rotate the mesh + meshes = Meshes(verts=vertices, faces=faces, textures=face_textures) + images = self.renderer(meshes, R=R, T=T) + images = images.permute(0, 3, 1, 2) + return images + + +class NeuralMeshRenderer(DifferentiableRenderer): + def __init__(self, *args, **kwargs): + import neural_renderer as nr + + super(NeuralMeshRenderer, self).__init__(*args, **kwargs) + + self.neural_renderer = nr.Renderer( + dist_coeffs=None, + orig_size=self.img_size, + image_size=self.img_size, + light_intensity_ambient=1, + light_intensity_directional=0, + anti_aliasing=False, + ) + + def forward(self, vertices, faces=None, R=None, T=None): + batch_size = vertices.shape[0] + if faces is None: + faces = self.smpl_faces.expand(batch_size, -1, -1) + + if R is None: + R = self.R.expand(batch_size, -1, -1) + + if T is None: + T = self.T.expand(batch_size, -1) + rgb, depth, mask = self.neural_renderer( + vertices, + faces, + textures=self.face_colors.expand(batch_size, -1, -1, -1, -1, -1), + K=self.K.expand(batch_size, -1, -1), + R=R, + t=T.unsqueeze(1), + ) + return torch.cat([rgb, depth.unsqueeze(1), mask.unsqueeze(1)], dim=1) \ No newline at end of file diff --git a/utils/get_cfg.py b/utils/get_cfg.py new file mode 100644 index 0000000000000000000000000000000000000000..f11a5cc1da59fb593590cc34ad5907b0700fbdce --- /dev/null +++ b/utils/get_cfg.py @@ -0,0 +1,17 @@ +from yacs.config import CfgNode + +_VALID_TYPES = {tuple, list, str, int, float, bool} + + +def convert_to_dict(cfg_node, key_list=[]): + """ Convert a config node to dictionary """ + if not isinstance(cfg_node, CfgNode): + if type(cfg_node) not in _VALID_TYPES: + print("Key {} with value {} is not a valid type; valid types: {}".format( + ".".join(key_list), type(cfg_node), _VALID_TYPES), ) + return cfg_node + else: + cfg_dict = dict(cfg_node) + for k, v in cfg_dict.items(): + cfg_dict[k] = convert_to_dict(v, key_list + [k]) + return cfg_dict \ No newline at end of file diff --git a/utils/hrnet.py b/utils/hrnet.py new file mode 100644 index 0000000000000000000000000000000000000000..c07e03bef991829d4ce04355d7bfd7d6f9c895f7 --- /dev/null +++ b/utils/hrnet.py @@ -0,0 +1,625 @@ +import os + +import torch +import torch.nn as nn +from loguru import logger +import torch.nn.functional as F +from yacs.config import CfgNode as CN + +models = [ + 'hrnet_w32', + 'hrnet_w48', +] + +BN_MOMENTUM = 0.1 + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, + bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion, + momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class HighResolutionModule(nn.Module): + def __init__(self, num_branches, blocks, num_blocks, num_inchannels, + num_channels, fuse_method, multi_scale_output=True): + super(HighResolutionModule, self).__init__() + self._check_branches( + num_branches, blocks, num_blocks, num_inchannels, num_channels) + + self.num_inchannels = num_inchannels + self.fuse_method = fuse_method + self.num_branches = num_branches + + self.multi_scale_output = multi_scale_output + + self.branches = self._make_branches( + num_branches, blocks, num_blocks, num_channels) + self.fuse_layers = self._make_fuse_layers() + self.relu = nn.ReLU(True) + + def _check_branches(self, num_branches, blocks, num_blocks, + num_inchannels, num_channels): + if num_branches != len(num_blocks): + error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format( + num_branches, len(num_blocks)) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_channels): + error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format( + num_branches, len(num_channels)) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_inchannels): + error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format( + num_branches, len(num_inchannels)) + logger.error(error_msg) + raise ValueError(error_msg) + + def _make_one_branch(self, branch_index, block, num_blocks, num_channels, + stride=1): + downsample = None + if stride != 1 or \ + self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion: + downsample = nn.Sequential( + nn.Conv2d( + self.num_inchannels[branch_index], + num_channels[branch_index] * block.expansion, + kernel_size=1, stride=stride, bias=False + ), + nn.BatchNorm2d( + num_channels[branch_index] * block.expansion, + momentum=BN_MOMENTUM + ), + ) + + layers = [] + layers.append( + block( + self.num_inchannels[branch_index], + num_channels[branch_index], + stride, + downsample + ) + ) + self.num_inchannels[branch_index] = \ + num_channels[branch_index] * block.expansion + for i in range(1, num_blocks[branch_index]): + layers.append( + block( + self.num_inchannels[branch_index], + num_channels[branch_index] + ) + ) + + return nn.Sequential(*layers) + + def _make_branches(self, num_branches, block, num_blocks, num_channels): + branches = [] + + for i in range(num_branches): + branches.append( + self._make_one_branch(i, block, num_blocks, num_channels) + ) + + return nn.ModuleList(branches) + + def _make_fuse_layers(self): + if self.num_branches == 1: + return None + + num_branches = self.num_branches + num_inchannels = self.num_inchannels + fuse_layers = [] + for i in range(num_branches if self.multi_scale_output else 1): + fuse_layer = [] + for j in range(num_branches): + if j > i: + fuse_layer.append( + nn.Sequential( + nn.Conv2d( + num_inchannels[j], + num_inchannels[i], + 1, 1, 0, bias=False + ), + nn.BatchNorm2d(num_inchannels[i]), + nn.Upsample(scale_factor=2**(j-i), mode='nearest') + ) + ) + elif j == i: + fuse_layer.append(None) + else: + conv3x3s = [] + for k in range(i-j): + if k == i - j - 1: + num_outchannels_conv3x3 = num_inchannels[i] + conv3x3s.append( + nn.Sequential( + nn.Conv2d( + num_inchannels[j], + num_outchannels_conv3x3, + 3, 2, 1, bias=False + ), + nn.BatchNorm2d(num_outchannels_conv3x3) + ) + ) + else: + num_outchannels_conv3x3 = num_inchannels[j] + conv3x3s.append( + nn.Sequential( + nn.Conv2d( + num_inchannels[j], + num_outchannels_conv3x3, + 3, 2, 1, bias=False + ), + nn.BatchNorm2d(num_outchannels_conv3x3), + nn.ReLU(True) + ) + ) + fuse_layer.append(nn.Sequential(*conv3x3s)) + fuse_layers.append(nn.ModuleList(fuse_layer)) + + return nn.ModuleList(fuse_layers) + + def get_num_inchannels(self): + return self.num_inchannels + + def forward(self, x): + if self.num_branches == 1: + return [self.branches[0](x[0])] + + for i in range(self.num_branches): + x[i] = self.branches[i](x[i]) + + x_fuse = [] + + for i in range(len(self.fuse_layers)): + y = x[0] if i == 0 else self.fuse_layers[i][0](x[0]) + for j in range(1, self.num_branches): + if i == j: + y = y + x[j] + else: + y = y + self.fuse_layers[i][j](x[j]) + x_fuse.append(self.relu(y)) + + return x_fuse + + +blocks_dict = { + 'BASIC': BasicBlock, + 'BOTTLENECK': Bottleneck +} + + +class PoseHighResolutionNet(nn.Module): + + def __init__(self, cfg): + self.inplanes = 64 + extra = cfg['MODEL']['EXTRA'] + super(PoseHighResolutionNet, self).__init__() + + self.cfg = extra + + # stem net + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, + bias=False) + self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, + bias=False) + self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.layer1 = self._make_layer(Bottleneck, 64, 4) + + self.stage2_cfg = extra['STAGE2'] + num_channels = self.stage2_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage2_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels)) + ] + self.transition1 = self._make_transition_layer([256], num_channels) + self.stage2, pre_stage_channels = self._make_stage( + self.stage2_cfg, num_channels) + + self.stage3_cfg = extra['STAGE3'] + num_channels = self.stage3_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage3_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels)) + ] + self.transition2 = self._make_transition_layer( + pre_stage_channels, num_channels) + self.stage3, pre_stage_channels = self._make_stage( + self.stage3_cfg, num_channels) + + self.stage4_cfg = extra['STAGE4'] + num_channels = self.stage4_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage4_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels)) + ] + self.transition3 = self._make_transition_layer( + pre_stage_channels, num_channels) + self.stage4, pre_stage_channels = self._make_stage( + self.stage4_cfg, num_channels, multi_scale_output=True) + + self.final_layer = nn.Conv2d( + in_channels=pre_stage_channels[0], + out_channels=cfg['MODEL']['NUM_JOINTS'], + kernel_size=extra['FINAL_CONV_KERNEL'], + stride=1, + padding=1 if extra['FINAL_CONV_KERNEL'] == 3 else 0 + ) + + self.pretrained_layers = extra['PRETRAINED_LAYERS'] + + if extra.DOWNSAMPLE and extra.USE_CONV: + self.downsample_stage_1 = self._make_downsample_layer(3, num_channel=self.stage2_cfg['NUM_CHANNELS'][0]) + self.downsample_stage_2 = self._make_downsample_layer(2, num_channel=self.stage2_cfg['NUM_CHANNELS'][-1]) + self.downsample_stage_3 = self._make_downsample_layer(1, num_channel=self.stage3_cfg['NUM_CHANNELS'][-1]) + elif not extra.DOWNSAMPLE and extra.USE_CONV: + self.upsample_stage_2 = self._make_upsample_layer(1, num_channel=self.stage2_cfg['NUM_CHANNELS'][-1]) + self.upsample_stage_3 = self._make_upsample_layer(2, num_channel=self.stage3_cfg['NUM_CHANNELS'][-1]) + self.upsample_stage_4 = self._make_upsample_layer(3, num_channel=self.stage4_cfg['NUM_CHANNELS'][-1]) + + def _make_transition_layer( + self, num_channels_pre_layer, num_channels_cur_layer): + num_branches_cur = len(num_channels_cur_layer) + num_branches_pre = len(num_channels_pre_layer) + + transition_layers = [] + for i in range(num_branches_cur): + if i < num_branches_pre: + if num_channels_cur_layer[i] != num_channels_pre_layer[i]: + transition_layers.append( + nn.Sequential( + nn.Conv2d( + num_channels_pre_layer[i], + num_channels_cur_layer[i], + 3, 1, 1, bias=False + ), + nn.BatchNorm2d(num_channels_cur_layer[i]), + nn.ReLU(inplace=True) + ) + ) + else: + transition_layers.append(None) + else: + conv3x3s = [] + for j in range(i+1-num_branches_pre): + inchannels = num_channels_pre_layer[-1] + outchannels = num_channels_cur_layer[i] \ + if j == i-num_branches_pre else inchannels + conv3x3s.append( + nn.Sequential( + nn.Conv2d( + inchannels, outchannels, 3, 2, 1, bias=False + ), + nn.BatchNorm2d(outchannels), + nn.ReLU(inplace=True) + ) + ) + transition_layers.append(nn.Sequential(*conv3x3s)) + + return nn.ModuleList(transition_layers) + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d( + self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False + ), + nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def _make_stage(self, layer_config, num_inchannels, + multi_scale_output=True): + num_modules = layer_config['NUM_MODULES'] + num_branches = layer_config['NUM_BRANCHES'] + num_blocks = layer_config['NUM_BLOCKS'] + num_channels = layer_config['NUM_CHANNELS'] + block = blocks_dict[layer_config['BLOCK']] + fuse_method = layer_config['FUSE_METHOD'] + + modules = [] + for i in range(num_modules): + # multi_scale_output is only used last module + if not multi_scale_output and i == num_modules - 1: + reset_multi_scale_output = False + else: + reset_multi_scale_output = True + + modules.append( + HighResolutionModule( + num_branches, + block, + num_blocks, + num_inchannels, + num_channels, + fuse_method, + reset_multi_scale_output + ) + ) + num_inchannels = modules[-1].get_num_inchannels() + + return nn.Sequential(*modules), num_inchannels + + def _make_upsample_layer(self, num_layers, num_channel, kernel_size=3): + layers = [] + for i in range(num_layers): + layers.append(nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)) + layers.append( + nn.Conv2d( + in_channels=num_channel, out_channels=num_channel, + kernel_size=kernel_size, stride=1, padding=1, bias=False, + ) + ) + layers.append(nn.BatchNorm2d(num_channel, momentum=BN_MOMENTUM)) + layers.append(nn.ReLU(inplace=True)) + + return nn.Sequential(*layers) + + def _make_downsample_layer(self, num_layers, num_channel, kernel_size=3): + layers = [] + for i in range(num_layers): + layers.append( + nn.Conv2d( + in_channels=num_channel, out_channels=num_channel, + kernel_size=kernel_size, stride=2, padding=1, bias=False, + ) + ) + layers.append(nn.BatchNorm2d(num_channel, momentum=BN_MOMENTUM)) + layers.append(nn.ReLU(inplace=True)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.conv2(x) + x = self.bn2(x) + x = self.relu(x) + x = self.layer1(x) + + x_list = [] + for i in range(self.stage2_cfg['NUM_BRANCHES']): + if self.transition1[i] is not None: + x_list.append(self.transition1[i](x)) + else: + x_list.append(x) + y_list = self.stage2(x_list) + + x_list = [] + for i in range(self.stage3_cfg['NUM_BRANCHES']): + if self.transition2[i] is not None: + x_list.append(self.transition2[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage3(x_list) + + x_list = [] + for i in range(self.stage4_cfg['NUM_BRANCHES']): + if self.transition3[i] is not None: + x_list.append(self.transition3[i](y_list[-1])) + else: + x_list.append(y_list[i]) + x = self.stage4(x_list) + + if self.cfg.DOWNSAMPLE: + if self.cfg.USE_CONV: + # Downsampling with strided convolutions + x1 = self.downsample_stage_1(x[0]) + x2 = self.downsample_stage_2(x[1]) + x3 = self.downsample_stage_3(x[2]) + x = torch.cat([x1, x2, x3, x[3]], 1) + else: + # Downsampling with interpolation + x0_h, x0_w = x[3].size(2), x[3].size(3) + x1 = F.interpolate(x[0], size=(x0_h, x0_w), mode='bilinear', align_corners=True) + x2 = F.interpolate(x[1], size=(x0_h, x0_w), mode='bilinear', align_corners=True) + x3 = F.interpolate(x[2], size=(x0_h, x0_w), mode='bilinear', align_corners=True) + x = torch.cat([x1, x2, x3, x[3]], 1) + else: + if self.cfg.USE_CONV: + # Upsampling with interpolations + convolutions + x1 = self.upsample_stage_2(x[1]) + x2 = self.upsample_stage_3(x[2]) + x3 = self.upsample_stage_4(x[3]) + x = torch.cat([x[0], x1, x2, x3], 1) + else: + # Upsampling with interpolation + x0_h, x0_w = x[0].size(2), x[0].size(3) + x1 = F.interpolate(x[1], size=(x0_h, x0_w), mode='bilinear', align_corners=True) + x2 = F.interpolate(x[2], size=(x0_h, x0_w), mode='bilinear', align_corners=True) + x3 = F.interpolate(x[3], size=(x0_h, x0_w), mode='bilinear', align_corners=True) + x = torch.cat([x[0], x1, x2, x3], 1) + + return x + + def init_weights(self, pretrained=''): + logger.info('=> init weights from normal distribution') + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + nn.init.normal_(m.weight, std=0.001) + for name, _ in m.named_parameters(): + if name in ['bias']: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.ConvTranspose2d): + nn.init.normal_(m.weight, std=0.001) + for name, _ in m.named_parameters(): + if name in ['bias']: + nn.init.constant_(m.bias, 0) + + if os.path.isfile(pretrained): + pretrained_state_dict = torch.load(pretrained) + logger.info('=> loading pretrained model {}'.format(pretrained)) + + need_init_state_dict = {} + for name, m in pretrained_state_dict.items(): + if name.split('.')[0] in self.pretrained_layers \ + or self.pretrained_layers[0] is '*': + need_init_state_dict[name] = m + self.load_state_dict(need_init_state_dict, strict=False) + elif pretrained: + logger.warning('IMPORTANT WARNING!! Please download pre-trained models if you are in TRAINING mode!') + # raise ValueError('{} is not exist!'.format(pretrained)) + + +def get_pose_net(cfg, is_train): + model = PoseHighResolutionNet(cfg) + + if is_train and cfg['MODEL']['INIT_WEIGHTS']: + model.init_weights(cfg['MODEL']['PRETRAINED']) + + return model + + +def get_cfg_defaults(pretrained, width=32, downsample=False, use_conv=False): + # pose_multi_resoluton_net related params + HRNET = CN() + HRNET.PRETRAINED_LAYERS = [ + 'conv1', 'bn1', 'conv2', 'bn2', 'layer1', 'transition1', + 'stage2', 'transition2', 'stage3', 'transition3', 'stage4', + ] + HRNET.STEM_INPLANES = 64 + HRNET.FINAL_CONV_KERNEL = 1 + HRNET.STAGE2 = CN() + HRNET.STAGE2.NUM_MODULES = 1 + HRNET.STAGE2.NUM_BRANCHES = 2 + HRNET.STAGE2.NUM_BLOCKS = [4, 4] + HRNET.STAGE2.NUM_CHANNELS = [width, width*2] + HRNET.STAGE2.BLOCK = 'BASIC' + HRNET.STAGE2.FUSE_METHOD = 'SUM' + HRNET.STAGE3 = CN() + HRNET.STAGE3.NUM_MODULES = 4 + HRNET.STAGE3.NUM_BRANCHES = 3 + HRNET.STAGE3.NUM_BLOCKS = [4, 4, 4] + HRNET.STAGE3.NUM_CHANNELS = [width, width*2, width*4] + HRNET.STAGE3.BLOCK = 'BASIC' + HRNET.STAGE3.FUSE_METHOD = 'SUM' + HRNET.STAGE4 = CN() + HRNET.STAGE4.NUM_MODULES = 3 + HRNET.STAGE4.NUM_BRANCHES = 4 + HRNET.STAGE4.NUM_BLOCKS = [4, 4, 4, 4] + HRNET.STAGE4.NUM_CHANNELS = [width, width*2, width*4, width*8] + HRNET.STAGE4.BLOCK = 'BASIC' + HRNET.STAGE4.FUSE_METHOD = 'SUM' + HRNET.DOWNSAMPLE = downsample + HRNET.USE_CONV = use_conv + + cfg = CN() + cfg.MODEL = CN() + cfg.MODEL.INIT_WEIGHTS = True + cfg.MODEL.PRETRAINED = pretrained # 'data/pretrained_models/hrnet_w32-36af842e.pth' + cfg.MODEL.EXTRA = HRNET + cfg.MODEL.NUM_JOINTS = 24 + return cfg + + +def hrnet_w32( + pretrained=True, + pretrained_ckpt='data/weights/pose_hrnet_w32_256x192.pth', + downsample=False, + use_conv=False, +): + cfg = get_cfg_defaults(pretrained_ckpt, width=32, downsample=downsample, use_conv=use_conv) + return get_pose_net(cfg, is_train=True) + + +def hrnet_w48( + pretrained=True, + pretrained_ckpt='data/weights/pose_hrnet_w48_256x192.pth', + downsample=False, + use_conv=False, +): + cfg = get_cfg_defaults(pretrained_ckpt, width=48, downsample=downsample, use_conv=use_conv) + return get_pose_net(cfg, is_train=True) \ No newline at end of file diff --git a/utils/image_utils.py b/utils/image_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..fb2410c4e5bb6413bb6118a88743dd1aefda3693 --- /dev/null +++ b/utils/image_utils.py @@ -0,0 +1,444 @@ +""" +This file contains functions that are used to perform data augmentation. +""" +import cv2 +import torch +import json +from skimage.transform import rotate, resize +import numpy as np +import jpeg4py as jpeg +from trimesh.visual import color + +# from ..core import constants +# from .vibe_image_utils import gen_trans_from_patch_cv +from .kp_utils import map_smpl_to_common, get_smpl_joint_names + +def get_transform(center, scale, res, rot=0): + """Generate transformation matrix.""" + h = 200 * scale + t = np.zeros((3, 3)) + t[0, 0] = float(res[1]) / h + t[1, 1] = float(res[0]) / h + t[0, 2] = res[1] * (-float(center[0]) / h + .5) + t[1, 2] = res[0] * (-float(center[1]) / h + .5) + t[2, 2] = 1 + if not rot == 0: + rot = -rot # To match direction of rotation from cropping + rot_mat = np.zeros((3, 3)) + rot_rad = rot * np.pi / 180 + sn, cs = np.sin(rot_rad), np.cos(rot_rad) + rot_mat[0, :2] = [cs, -sn] + rot_mat[1, :2] = [sn, cs] + rot_mat[2, 2] = 1 + # Need to rotate around center + t_mat = np.eye(3) + t_mat[0, 2] = -res[1] / 2 + t_mat[1, 2] = -res[0] / 2 + t_inv = t_mat.copy() + t_inv[:2, 2] *= -1 + t = np.dot(t_inv, np.dot(rot_mat, np.dot(t_mat, t))) + return t + + +def transform(pt, center, scale, res, invert=0, rot=0): + """Transform pixel location to different reference.""" + t = get_transform(center, scale, res, rot=rot) + if invert: + t = np.linalg.inv(t) + new_pt = np.array([pt[0] - 1, pt[1] - 1, 1.]).T + new_pt = np.dot(t, new_pt) + return new_pt[:2].astype(int) + 1 + + +def crop(img, center, scale, res, rot=0): + """Crop image according to the supplied bounding box.""" + # Upper left point + ul = np.array(transform([1, 1], center, scale, res, invert=1)) - 1 + # Bottom right point + br = np.array(transform([res[0] + 1, + res[1] + 1], center, scale, res, invert=1)) - 1 + + # Padding so that when rotated proper amount of context is included + pad = int(np.linalg.norm(br - ul) / 2 - float(br[1] - ul[1]) / 2) + if not rot == 0: + ul -= pad + br += pad + + new_shape = [br[1] - ul[1], br[0] - ul[0]] + if len(img.shape) > 2: + new_shape += [img.shape[2]] + new_img = np.zeros(new_shape) + + # Range to fill new array + new_x = max(0, -ul[0]), min(br[0], len(img[0])) - ul[0] + new_y = max(0, -ul[1]), min(br[1], len(img)) - ul[1] + # Range to sample from original image + old_x = max(0, ul[0]), min(len(img[0]), br[0]) + old_y = max(0, ul[1]), min(len(img), br[1]) + new_img[new_y[0]:new_y[1], new_x[0]:new_x[1]] = img[old_y[0]:old_y[1], + old_x[0]:old_x[1]] + + if not rot == 0: + # Remove padding + + new_img = rotate(new_img, rot) # scipy.misc.imrotate(new_img, rot) + new_img = new_img[pad:-pad, pad:-pad] + + # resize image + new_img = resize(new_img, res) # scipy.misc.imresize(new_img, res) + return new_img + + +def crop_cv2(img, center, scale, res, rot=0): + c_x, c_y = center + c_x, c_y = int(round(c_x)), int(round(c_y)) + patch_width, patch_height = int(round(res[0])), int(round(res[1])) + bb_width = bb_height = int(round(scale * 200.)) + + trans = gen_trans_from_patch_cv( + c_x, c_y, bb_width, bb_height, + patch_width, patch_height, + scale=1.0, rot=rot, inv=False, + ) + + crop_img = cv2.warpAffine( + img, trans, (int(patch_width), int(patch_height)), + flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT + ) + + return crop_img + + +def get_random_crop_coords(height, width, crop_height, crop_width, h_start, w_start): + y1 = int((height - crop_height) * h_start) + y2 = y1 + crop_height + x1 = int((width - crop_width) * w_start) + x2 = x1 + crop_width + return x1, y1, x2, y2 + + +def random_crop(center, scale, crop_scale_factor, axis='all'): + ''' + center: bbox center [x,y] + scale: bbox height / 200 + crop_scale_factor: amount of cropping to be applied + axis: axis which cropping will be applied + "x": center the y axis and get random crops in x + "y": center the x axis and get random crops in y + "all": randomly crop from all locations + ''' + orig_size = int(scale * 200.) + ul = (center - (orig_size / 2.)).astype(int) + + crop_size = int(orig_size * crop_scale_factor) + + if axis == 'all': + h_start = np.random.rand() + w_start = np.random.rand() + elif axis == 'x': + h_start = np.random.rand() + w_start = 0.5 + elif axis == 'y': + h_start = 0.5 + w_start = np.random.rand() + else: + raise ValueError(f'axis {axis} is undefined!') + + x1, y1, x2, y2 = get_random_crop_coords( + height=orig_size, + width=orig_size, + crop_height=crop_size, + crop_width=crop_size, + h_start=h_start, + w_start=w_start, + ) + scale = (y2 - y1) / 200. + center = ul + np.array([(y1 + y2) / 2, (x1 + x2) / 2]) + return center, scale + + +def uncrop(img, center, scale, orig_shape, rot=0, is_rgb=True): + """'Undo' the image cropping/resizing. + This function is used when evaluating mask/part segmentation. + """ + res = img.shape[:2] + # Upper left point + ul = np.array(transform([1, 1], center, scale, res, invert=1)) - 1 + # Bottom right point + br = np.array(transform([res[0] + 1, res[1] + 1], center, scale, res, invert=1)) - 1 + # size of cropped image + crop_shape = [br[1] - ul[1], br[0] - ul[0]] + + new_shape = [br[1] - ul[1], br[0] - ul[0]] + if len(img.shape) > 2: + new_shape += [img.shape[2]] + new_img = np.zeros(orig_shape, dtype=np.uint8) + # Range to fill new array + new_x = max(0, -ul[0]), min(br[0], orig_shape[1]) - ul[0] + new_y = max(0, -ul[1]), min(br[1], orig_shape[0]) - ul[1] + # Range to sample from original image + old_x = max(0, ul[0]), min(orig_shape[1], br[0]) + old_y = max(0, ul[1]), min(orig_shape[0], br[1]) + img = resize(img, crop_shape) #, interp='nearest') # scipy.misc.imresize(img, crop_shape, interp='nearest') + new_img[old_y[0]:old_y[1], old_x[0]:old_x[1]] = img[new_y[0]:new_y[1], new_x[0]:new_x[1]] + return new_img + + +def rot_aa(aa, rot): + """Rotate axis angle parameters.""" + # pose parameters + R = np.array([[np.cos(np.deg2rad(-rot)), -np.sin(np.deg2rad(-rot)), 0], + [np.sin(np.deg2rad(-rot)), np.cos(np.deg2rad(-rot)), 0], + [0, 0, 1]]) + # find the rotation of the body in camera frame + per_rdg, _ = cv2.Rodrigues(aa) + # apply the global rotation to the global orientation + resrot, _ = cv2.Rodrigues(np.dot(R, per_rdg)) + aa = (resrot.T)[0] + return aa + + +def flip_img(img): + """Flip rgb images or masks. + channels come last, e.g. (256,256,3). + """ + img = np.fliplr(img) + return img + + +def flip_kp(kp): + """Flip keypoints.""" + if len(kp) == 24: + flipped_parts = constants.J24_FLIP_PERM + elif len(kp) == 49: + flipped_parts = constants.J49_FLIP_PERM + kp = kp[flipped_parts] + kp[:, 0] = - kp[:, 0] + return kp + + +def flip_pose(pose): + """Flip pose. + The flipping is based on SMPL parameters. + """ + flipped_parts = constants.SMPL_POSE_FLIP_PERM + pose = pose[flipped_parts] + # we also negate the second and the third dimension of the axis-angle + pose[1::3] = -pose[1::3] + pose[2::3] = -pose[2::3] + return pose + + +def denormalize_images(images): + images = images * torch.tensor([0.229, 0.224, 0.225], device=images.device).reshape(1, 3, 1, 1) + images = images + torch.tensor([0.485, 0.456, 0.406], device=images.device).reshape(1, 3, 1, 1) + return images + + +def read_img(img_fn): + # return pil_img.fromarray( + # cv2.cvtColor(cv2.imread(img_fn), cv2.COLOR_BGR2RGB)) + # with open(img_fn, 'rb') as f: + # img = pil_img.open(f).convert('RGB') + # return img + if img_fn.endswith('jpeg') or img_fn.endswith('jpg'): + try: + with open(img_fn, 'rb') as f: + img = np.array(jpeg.JPEG(f).decode()) + except jpeg.JPEGRuntimeError: + # logger.warning('{} produced a JPEGRuntimeError', img_fn) + img = cv2.cvtColor(cv2.imread(img_fn), cv2.COLOR_BGR2RGB) + else: + # elif img_fn.endswith('png') or img_fn.endswith('JPG') or img_fn.endswith(''): + img = cv2.cvtColor(cv2.imread(img_fn), cv2.COLOR_BGR2RGB) + return img.astype(np.float32) + + +def generate_heatmaps_2d(joints, joints_vis, num_joints=24, heatmap_size=56, image_size=224, sigma=1.75): + ''' + :param joints: [num_joints, 3] + :param joints_vis: [num_joints, 3] + :return: target, target_weight(1: visible, 0: invisible) + ''' + target_weight = np.ones((num_joints, 1), dtype=np.float32) + target_weight[:, 0] = joints_vis[:, 0] + + target = np.zeros((num_joints, heatmap_size, heatmap_size), dtype=np.float32) + + tmp_size = sigma * 3 + + # denormalize joint into heatmap coordinates + joints = (joints + 1.) * (image_size / 2.) + + for joint_id in range(num_joints): + feat_stride = image_size / heatmap_size + mu_x = int(joints[joint_id][0] / feat_stride + 0.5) + mu_y = int(joints[joint_id][1] / feat_stride + 0.5) + # Check that any part of the gaussian is in-bounds + ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)] + br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)] + if ul[0] >= heatmap_size or ul[1] >= heatmap_size \ + or br[0] < 0 or br[1] < 0: + # If not, just return the image as is + target_weight[joint_id] = 0 + continue + + # # Generate gaussian + size = 2 * tmp_size + 1 + x = np.arange(0, size, 1, np.float32) + y = x[:, np.newaxis] + x0 = y0 = size // 2 + # The gaussian is not normalized, we want the center value to equal 1 + g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2)) + + # Usable gaussian range + g_x = max(0, -ul[0]), min(br[0], heatmap_size) - ul[0] + g_y = max(0, -ul[1]), min(br[1], heatmap_size) - ul[1] + # Image range + img_x = max(0, ul[0]), min(br[0], heatmap_size) + img_y = max(0, ul[1]), min(br[1], heatmap_size) + + v = target_weight[joint_id] + if v > 0.5: + target[joint_id][img_y[0]:img_y[1], img_x[0]:img_x[1]] = \ + g[g_y[0]:g_y[1], g_x[0]:g_x[1]] + + return target, target_weight + + +def generate_part_labels(vertices, faces, cam_t, neural_renderer, body_part_texture, K, R, part_bins): + batch_size = vertices.shape[0] + + body_parts, depth, mask = neural_renderer( + vertices, + faces.expand(batch_size, -1, -1), + textures=body_part_texture.expand(batch_size, -1, -1, -1, -1, -1), + K=K.expand(batch_size, -1, -1), + R=R.expand(batch_size, -1, -1), + t=cam_t.unsqueeze(1), + ) + + render_rgb = body_parts.clone() + + body_parts = body_parts.permute(0, 2, 3, 1) + body_parts *= 255. # multiply it with 255 to make labels distant + body_parts, _ = body_parts.max(-1) # reduce to single channel + + body_parts = torch.bucketize(body_parts.detach(), part_bins, right=True) # np.digitize(body_parts, bins, right=True) + + # add 1 to make background label 0 + body_parts = body_parts.long() + 1 + body_parts = body_parts * mask.detach() + + return body_parts.long(), render_rgb + + +def generate_heatmaps_2d_batch(joints, num_joints=24, heatmap_size=56, image_size=224, sigma=1.75): + batch_size = joints.shape[0] + + joints = joints.detach().cpu().numpy() + joints_vis = np.ones_like(joints) + + heatmaps = [] + heatmaps_vis = [] + for i in range(batch_size): + hm, hm_vis = generate_heatmaps_2d(joints[i], joints_vis[i], num_joints, heatmap_size, image_size, sigma) + heatmaps.append(hm) + heatmaps_vis.append(hm_vis) + + return torch.from_numpy(np.stack(heatmaps)).float().to('cuda'), \ + torch.from_numpy(np.stack(heatmaps_vis)).float().to('cuda') + + +def get_body_part_texture(faces, model_type='smpl', non_parametric=False): + if model_type == 'smpl': + n_vertices = 6890 + segmentation_path = 'data/smpl_vert_segmentation.json' + if model_type == 'smplx': + n_vertices = 10475 + segmentation_path = 'data/smplx_vert_segmentation.json' + + with open(segmentation_path, 'rb') as f: + part_segmentation = json.load(f) + + # map all vertex ids to the joint ids + joint_names = get_smpl_joint_names() + smplx_extra_joint_names = ['leftEye', 'eyeballs', 'rightEye'] + body_vert_idx = np.zeros((n_vertices), dtype=np.int32) - 1 # -1 for missing label + for i, (k, v) in enumerate(part_segmentation.items()): + if k in smplx_extra_joint_names and model_type == 'smplx': + k = 'head' # map all extra smplx face joints to head + body_joint_idx = joint_names.index(k) + body_vert_idx[v] = body_joint_idx + + # pare implementation + # import joblib + # part_segmentation = joblib.load('data/smpl_partSegmentation_mapping.pkl') + # body_vert_idx = part_segmentation['smpl_index'] + + n_parts = 24. + + if non_parametric: + # reduce the number of body_parts to 14 + # by mapping some joints to others + n_parts = 14. + joint_mapping = map_smpl_to_common() + + for jm in joint_mapping: + for j in jm[0]: + body_vert_idx[body_vert_idx==j] = jm[1] + + vertex_colors = np.ones((n_vertices, 4)) + vertex_colors[:, :3] = body_vert_idx[..., None] + + vertex_colors = color.to_rgba(vertex_colors) + vertex_colors = vertex_colors[:, :3]/255. + + face_colors = vertex_colors[faces].min(axis=1) + texture = np.zeros((1, faces.shape[0], 1, 1, 3), dtype=np.float32) + # texture[0, :, 0, 0, :] = face_colors[:, :3] / n_parts + texture[0, :, 0, 0, :] = face_colors[:, :3] + + vertex_colors = torch.from_numpy(vertex_colors).float() + texture = torch.from_numpy(texture).float() + return vertex_colors, texture + + +def get_default_camera(focal_length, img_h, img_w, is_cam_batch=False): + if not is_cam_batch: + K = torch.eye(3) + K[0, 0] = focal_length + K[1, 1] = focal_length + K[2, 2] = 1 + K[0, 2] = img_w / 2. + K[1, 2] = img_h / 2. + K = K[None, :, :] + R = torch.eye(3)[None, :, :] + else: + bs = focal_length.shape[0] + K = torch.eye(3)[None, :, :].repeat(bs, 1, 1) + K[:, 0, 0] = focal_length[:, 0] + K[:, 1, 1] = focal_length[:, 1] + K[:, 2, 2] = 1 + K[:, 0, 2] = img_w / 2. + K[:, 1, 2] = img_h / 2. + R = torch.eye(3)[None, :, :].repeat(bs, 1, 1) + return K, R + + +def read_exif_data(img_fname): + import PIL.Image + import PIL.ExifTags + + img = PIL.Image.open(img_fname) + exif_data = img._getexif() + + if exif_data == None: + return None + + exif = { + PIL.ExifTags.TAGS[k]: v + for k, v in exif_data.items() + if k in PIL.ExifTags.TAGS + } + return exif \ No newline at end of file diff --git a/utils/kp_utils.py b/utils/kp_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..066ff243b3bb245cd93a1deef11c33d8480a01fd --- /dev/null +++ b/utils/kp_utils.py @@ -0,0 +1,1114 @@ +import numpy as np + + +def keypoint_hflip(kp, img_width): + # Flip a keypoint horizontally around the y-axis + # kp N,2 + if len(kp.shape) == 2: + kp[:,0] = (img_width - 1.) - kp[:,0] + elif len(kp.shape) == 3: + kp[:, :, 0] = (img_width - 1.) - kp[:, :, 0] + return kp + + +def convert_kps(joints2d, src, dst): + src_names = eval(f'get_{src}_joint_names')() + dst_names = eval(f'get_{dst}_joint_names')() + + out_joints2d = np.zeros((joints2d.shape[0], len(dst_names), joints2d.shape[-1])) + + for idx, jn in enumerate(dst_names): + if jn in src_names: + out_joints2d[:, idx] = joints2d[:, src_names.index(jn)] + + return out_joints2d + + +def get_perm_idxs(src, dst): + src_names = eval(f'get_{src}_joint_names')() + dst_names = eval(f'get_{dst}_joint_names')() + idxs = [src_names.index(h) for h in dst_names if h in src_names] + return idxs + + +def get_mpii3d_test_joint_names(): + return [ + 'headtop', # 'head_top', + 'neck', + 'rshoulder',# 'right_shoulder', + 'relbow',# 'right_elbow', + 'rwrist',# 'right_wrist', + 'lshoulder',# 'left_shoulder', + 'lelbow', # 'left_elbow', + 'lwrist', # 'left_wrist', + 'rhip', # 'right_hip', + 'rknee', # 'right_knee', + 'rankle',# 'right_ankle', + 'lhip',# 'left_hip', + 'lknee',# 'left_knee', + 'lankle',# 'left_ankle' + 'hip',# 'pelvis', + 'Spine (H36M)',# 'spine', + 'Head (H36M)',# 'head' + ] + + +def get_mpii3d_joint_names(): + return [ + 'spine3', # 0, + 'spine4', # 1, + 'spine2', # 2, + 'Spine (H36M)', #'spine', # 3, + 'hip', # 'pelvis', # 4, + 'neck', # 5, + 'Head (H36M)', # 'head', # 6, + "headtop", # 'head_top', # 7, + 'left_clavicle', # 8, + "lshoulder", # 'left_shoulder', # 9, + "lelbow", # 'left_elbow',# 10, + "lwrist", # 'left_wrist',# 11, + 'left_hand',# 12, + 'right_clavicle',# 13, + 'rshoulder',# 'right_shoulder',# 14, + 'relbow',# 'right_elbow',# 15, + 'rwrist',# 'right_wrist',# 16, + 'right_hand',# 17, + 'lhip', # left_hip',# 18, + 'lknee', # 'left_knee',# 19, + 'lankle', #left ankle # 20 + 'left_foot', # 21 + 'left_toe', # 22 + "rhip", # 'right_hip',# 23 + "rknee", # 'right_knee',# 24 + "rankle", #'right_ankle', # 25 + 'right_foot',# 26 + 'right_toe' # 27 + ] + + +# def get_insta_joint_names(): +# return [ +# 'rheel' , # 0 +# 'rknee' , # 1 +# 'rhip' , # 2 +# 'lhip' , # 3 +# 'lknee' , # 4 +# 'lheel' , # 5 +# 'rwrist' , # 6 +# 'relbow' , # 7 +# 'rshoulder' , # 8 +# 'lshoulder' , # 9 +# 'lelbow' , # 10 +# 'lwrist' , # 11 +# 'neck' , # 12 +# 'headtop' , # 13 +# 'nose' , # 14 +# 'leye' , # 15 +# 'reye' , # 16 +# 'lear' , # 17 +# 'rear' , # 18 +# 'lbigtoe' , # 19 +# 'rbigtoe' , # 20 +# 'lsmalltoe' , # 21 +# 'rsmalltoe' , # 22 +# 'lankle' , # 23 +# 'rankle' , # 24 +# ] + + +def get_insta_joint_names(): + return [ + 'OP RHeel', + 'OP RKnee', + 'OP RHip', + 'OP LHip', + 'OP LKnee', + 'OP LHeel', + 'OP RWrist', + 'OP RElbow', + 'OP RShoulder', + 'OP LShoulder', + 'OP LElbow', + 'OP LWrist', + 'OP Neck', + 'headtop', + 'OP Nose', + 'OP LEye', + 'OP REye', + 'OP LEar', + 'OP REar', + 'OP LBigToe', + 'OP RBigToe', + 'OP LSmallToe', + 'OP RSmallToe', + 'OP LAnkle', + 'OP RAnkle', + ] + + +def get_mmpose_joint_names(): + # this naming is for the first 23 joints of MMPose + # does not include hands and face + return [ + 'OP Nose', # 1 + 'OP LEye', # 2 + 'OP REye', # 3 + 'OP LEar', # 4 + 'OP REar', # 5 + 'OP LShoulder', # 6 + 'OP RShoulder', # 7 + 'OP LElbow', # 8 + 'OP RElbow', # 9 + 'OP LWrist', # 10 + 'OP RWrist', # 11 + 'OP LHip', # 12 + 'OP RHip', # 13 + 'OP LKnee', # 14 + 'OP RKnee', # 15 + 'OP LAnkle', # 16 + 'OP RAnkle', # 17 + 'OP LBigToe', # 18 + 'OP LSmallToe', # 19 + 'OP LHeel', # 20 + 'OP RBigToe', # 21 + 'OP RSmallToe', # 22 + 'OP RHeel', # 23 + ] + + +def get_insta_skeleton(): + return np.array( + [ + [0 , 1], + [1 , 2], + [2 , 3], + [3 , 4], + [4 , 5], + [6 , 7], + [7 , 8], + [8 , 9], + [9 ,10], + [2 , 8], + [3 , 9], + [10,11], + [8 ,12], + [9 ,12], + [12,13], + [12,14], + [14,15], + [14,16], + [15,17], + [16,18], + [0 ,20], + [20,22], + [5 ,19], + [19,21], + [5 ,23], + [0 ,24], + ]) + + +def get_staf_skeleton(): + return np.array( + [ + [0, 1], + [1, 2], + [2, 3], + [3, 4], + [1, 5], + [5, 6], + [6, 7], + [1, 8], + [8, 9], + [9, 10], + [10, 11], + [8, 12], + [12, 13], + [13, 14], + [0, 15], + [0, 16], + [15, 17], + [16, 18], + [2, 9], + [5, 12], + [1, 19], + [20, 19], + ] + ) + + +def get_staf_joint_names(): + return [ + 'OP Nose', # 0, + 'OP Neck', # 1, + 'OP RShoulder', # 2, + 'OP RElbow', # 3, + 'OP RWrist', # 4, + 'OP LShoulder', # 5, + 'OP LElbow', # 6, + 'OP LWrist', # 7, + 'OP MidHip', # 8, + 'OP RHip', # 9, + 'OP RKnee', # 10, + 'OP RAnkle', # 11, + 'OP LHip', # 12, + 'OP LKnee', # 13, + 'OP LAnkle', # 14, + 'OP REye', # 15, + 'OP LEye', # 16, + 'OP REar', # 17, + 'OP LEar', # 18, + 'Neck (LSP)', # 19, + 'Top of Head (LSP)', # 20, + ] + + +def get_spin_op_joint_names(): + return [ + 'OP Nose', # 0 + 'OP Neck', # 1 + 'OP RShoulder', # 2 + 'OP RElbow', # 3 + 'OP RWrist', # 4 + 'OP LShoulder', # 5 + 'OP LElbow', # 6 + 'OP LWrist', # 7 + 'OP MidHip', # 8 + 'OP RHip', # 9 + 'OP RKnee', # 10 + 'OP RAnkle', # 11 + 'OP LHip', # 12 + 'OP LKnee', # 13 + 'OP LAnkle', # 14 + 'OP REye', # 15 + 'OP LEye', # 16 + 'OP REar', # 17 + 'OP LEar', # 18 + 'OP LBigToe', # 19 + 'OP LSmallToe', # 20 + 'OP LHeel', # 21 + 'OP RBigToe', # 22 + 'OP RSmallToe', # 23 + 'OP RHeel', # 24 + ] + + +def get_openpose_joint_names(): + return [ + 'OP Nose', # 0 + 'OP Neck', # 1 + 'OP RShoulder', # 2 + 'OP RElbow', # 3 + 'OP RWrist', # 4 + 'OP LShoulder', # 5 + 'OP LElbow', # 6 + 'OP LWrist', # 7 + 'OP MidHip', # 8 + 'OP RHip', # 9 + 'OP RKnee', # 10 + 'OP RAnkle', # 11 + 'OP LHip', # 12 + 'OP LKnee', # 13 + 'OP LAnkle', # 14 + 'OP REye', # 15 + 'OP LEye', # 16 + 'OP REar', # 17 + 'OP LEar', # 18 + 'OP LBigToe', # 19 + 'OP LSmallToe', # 20 + 'OP LHeel', # 21 + 'OP RBigToe', # 22 + 'OP RSmallToe', # 23 + 'OP RHeel', # 24 + ] + + +def get_spin_joint_names(): + return [ + 'OP Nose', # 0 + 'OP Neck', # 1 + 'OP RShoulder', # 2 + 'OP RElbow', # 3 + 'OP RWrist', # 4 + 'OP LShoulder', # 5 + 'OP LElbow', # 6 + 'OP LWrist', # 7 + 'OP MidHip', # 8 + 'OP RHip', # 9 + 'OP RKnee', # 10 + 'OP RAnkle', # 11 + 'OP LHip', # 12 + 'OP LKnee', # 13 + 'OP LAnkle', # 14 + 'OP REye', # 15 + 'OP LEye', # 16 + 'OP REar', # 17 + 'OP LEar', # 18 + 'OP LBigToe', # 19 + 'OP LSmallToe', # 20 + 'OP LHeel', # 21 + 'OP RBigToe', # 22 + 'OP RSmallToe', # 23 + 'OP RHeel', # 24 + 'rankle', # 25 + 'rknee', # 26 + 'rhip', # 27 + 'lhip', # 28 + 'lknee', # 29 + 'lankle', # 30 + 'rwrist', # 31 + 'relbow', # 32 + 'rshoulder', # 33 + 'lshoulder', # 34 + 'lelbow', # 35 + 'lwrist', # 36 + 'neck', # 37 + 'headtop', # 38 + 'hip', # 39 'Pelvis (MPII)', # 39 + 'thorax', # 40 'Thorax (MPII)', # 40 + 'Spine (H36M)', # 41 + 'Jaw (H36M)', # 42 + 'Head (H36M)', # 43 + 'nose', # 44 + 'leye', # 45 'Left Eye', # 45 + 'reye', # 46 'Right Eye', # 46 + 'lear', # 47 'Left Ear', # 47 + 'rear', # 48 'Right Ear', # 48 + ] + +def get_muco3dhp_joint_names(): + return [ + 'headtop', + 'thorax', + 'rshoulder', + 'relbow', + 'rwrist', + 'lshoulder', + 'lelbow', + 'lwrist', + 'rhip', + 'rknee', + 'rankle', + 'lhip', + 'lknee', + 'lankle', + 'hip', + 'Spine (H36M)', + 'Head (H36M)', + 'R_Hand', + 'L_Hand', + 'R_Toe', + 'L_Toe' + ] + +def get_h36m_joint_names(): + return [ + 'hip', # 0 + 'lhip', # 1 + 'lknee', # 2 + 'lankle', # 3 + 'rhip', # 4 + 'rknee', # 5 + 'rankle', # 6 + 'Spine (H36M)', # 7 + 'neck', # 8 + 'Head (H36M)', # 9 + 'headtop', # 10 + 'lshoulder', # 11 + 'lelbow', # 12 + 'lwrist', # 13 + 'rshoulder', # 14 + 'relbow', # 15 + 'rwrist', # 16 + ] + + +def get_spin_skeleton(): + return np.array( + [ + [0 , 1], + [1 , 2], + [2 , 3], + [3 , 4], + [1 , 5], + [5 , 6], + [6 , 7], + [1 , 8], + [8 , 9], + [9 ,10], + [10,11], + [8 ,12], + [12,13], + [13,14], + [0 ,15], + [0 ,16], + [15,17], + [16,18], + [21,19], + [19,20], + [14,21], + [11,24], + [24,22], + [22,23], + [0 ,38], + ] + ) + + +def get_openpose_skeleton(): + return np.array( + [ + [0 , 1], + [1 , 2], + [2 , 3], + [3 , 4], + [1 , 5], + [5 , 6], + [6 , 7], + [1 , 8], + [8 , 9], + [9 ,10], + [10,11], + [8 ,12], + [12,13], + [13,14], + [0 ,15], + [0 ,16], + [15,17], + [16,18], + [21,19], + [19,20], + [14,21], + [11,24], + [24,22], + [22,23], + ] + ) + + +def get_posetrack_joint_names(): + return [ + "nose", + "neck", + "headtop", + "lear", + "rear", + "lshoulder", + "rshoulder", + "lelbow", + "relbow", + "lwrist", + "rwrist", + "lhip", + "rhip", + "lknee", + "rknee", + "lankle", + "rankle" + ] + + +def get_posetrack_original_kp_names(): + return [ + 'nose', + 'head_bottom', + 'head_top', + 'left_ear', + 'right_ear', + 'left_shoulder', + 'right_shoulder', + 'left_elbow', + 'right_elbow', + 'left_wrist', + 'right_wrist', + 'left_hip', + 'right_hip', + 'left_knee', + 'right_knee', + 'left_ankle', + 'right_ankle' + ] + + +def get_pennaction_joint_names(): + return [ + "headtop", # 0 + "lshoulder", # 1 + "rshoulder", # 2 + "lelbow", # 3 + "relbow", # 4 + "lwrist", # 5 + "rwrist", # 6 + "lhip" , # 7 + "rhip" , # 8 + "lknee", # 9 + "rknee" , # 10 + "lankle", # 11 + "rankle" # 12 + ] + + +def get_common_joint_names(): + return [ + "rankle", # 0 "lankle", # 0 + "rknee", # 1 "lknee", # 1 + "rhip", # 2 "lhip", # 2 + "lhip", # 3 "rhip", # 3 + "lknee", # 4 "rknee", # 4 + "lankle", # 5 "rankle", # 5 + "rwrist", # 6 "lwrist", # 6 + "relbow", # 7 "lelbow", # 7 + "rshoulder", # 8 "lshoulder", # 8 + "lshoulder", # 9 "rshoulder", # 9 + "lelbow", # 10 "relbow", # 10 + "lwrist", # 11 "rwrist", # 11 + "neck", # 12 "neck", # 12 + "headtop", # 13 "headtop", # 13 + ] + + +def get_common_paper_joint_names(): + return [ + "Right Ankle", # 0 "lankle", # 0 + "Right Knee", # 1 "lknee", # 1 + "Right Hip", # 2 "lhip", # 2 + "Left Hip", # 3 "rhip", # 3 + "Left Knee", # 4 "rknee", # 4 + "Left Ankle", # 5 "rankle", # 5 + "Right Wrist", # 6 "lwrist", # 6 + "Right Elbow", # 7 "lelbow", # 7 + "Right Shoulder", # 8 "lshoulder", # 8 + "Left Shoulder", # 9 "rshoulder", # 9 + "Left Elbow", # 10 "relbow", # 10 + "Left Wrist", # 11 "rwrist", # 11 + "Neck", # 12 "neck", # 12 + "Head", # 13 "headtop", # 13 + ] + + +def get_common_skeleton(): + return np.array( + [ + [ 0, 1 ], + [ 1, 2 ], + [ 3, 4 ], + [ 4, 5 ], + [ 6, 7 ], + [ 7, 8 ], + [ 8, 2 ], + [ 8, 9 ], + [ 9, 3 ], + [ 2, 3 ], + [ 8, 12], + [ 9, 10], + [12, 9 ], + [10, 11], + [12, 13], + ] + ) + + +def get_coco_joint_names(): + return [ + "nose", # 0 + "leye", # 1 + "reye", # 2 + "lear", # 3 + "rear", # 4 + "lshoulder", # 5 + "rshoulder", # 6 + "lelbow", # 7 + "relbow", # 8 + "lwrist", # 9 + "rwrist", # 10 + "lhip", # 11 + "rhip", # 12 + "lknee", # 13 + "rknee", # 14 + "lankle", # 15 + "rankle", # 16 + ] + + +def get_ochuman_joint_names(): + return [ + 'rshoulder', + 'relbow', + 'rwrist', + 'lshoulder', + 'lelbow', + 'lwrist', + 'rhip', + 'rknee', + 'rankle', + 'lhip', + 'lknee', + 'lankle', + 'headtop', + 'neck', + 'rear', + 'lear', + 'nose', + 'reye', + 'leye' + ] + + +def get_crowdpose_joint_names(): + return [ + 'lshoulder', + 'rshoulder', + 'lelbow', + 'relbow', + 'lwrist', + 'rwrist', + 'lhip', + 'rhip', + 'lknee', + 'rknee', + 'lankle', + 'rankle', + 'headtop', + 'neck' + ] + +def get_coco_skeleton(): + # 0 - nose, + # 1 - leye, + # 2 - reye, + # 3 - lear, + # 4 - rear, + # 5 - lshoulder, + # 6 - rshoulder, + # 7 - lelbow, + # 8 - relbow, + # 9 - lwrist, + # 10 - rwrist, + # 11 - lhip, + # 12 - rhip, + # 13 - lknee, + # 14 - rknee, + # 15 - lankle, + # 16 - rankle, + return np.array( + [ + [15, 13], + [13, 11], + [16, 14], + [14, 12], + [11, 12], + [ 5, 11], + [ 6, 12], + [ 5, 6 ], + [ 5, 7 ], + [ 6, 8 ], + [ 7, 9 ], + [ 8, 10], + [ 1, 2 ], + [ 0, 1 ], + [ 0, 2 ], + [ 1, 3 ], + [ 2, 4 ], + [ 3, 5 ], + [ 4, 6 ] + ] + ) + + +def get_mpii_joint_names(): + return [ + "rankle", # 0 + "rknee", # 1 + "rhip", # 2 + "lhip", # 3 + "lknee", # 4 + "lankle", # 5 + "hip", # 6 + "thorax", # 7 + "neck", # 8 + "headtop", # 9 + "rwrist", # 10 + "relbow", # 11 + "rshoulder", # 12 + "lshoulder", # 13 + "lelbow", # 14 + "lwrist", # 15 + ] + + +def get_mpii_skeleton(): + # 0 - rankle, + # 1 - rknee, + # 2 - rhip, + # 3 - lhip, + # 4 - lknee, + # 5 - lankle, + # 6 - hip, + # 7 - thorax, + # 8 - neck, + # 9 - headtop, + # 10 - rwrist, + # 11 - relbow, + # 12 - rshoulder, + # 13 - lshoulder, + # 14 - lelbow, + # 15 - lwrist, + return np.array( + [ + [ 0, 1 ], + [ 1, 2 ], + [ 2, 6 ], + [ 6, 3 ], + [ 3, 4 ], + [ 4, 5 ], + [ 6, 7 ], + [ 7, 8 ], + [ 8, 9 ], + [ 7, 12], + [12, 11], + [11, 10], + [ 7, 13], + [13, 14], + [14, 15] + ] + ) + + +def get_aich_joint_names(): + return [ + "rshoulder", # 0 + "relbow", # 1 + "rwrist", # 2 + "lshoulder", # 3 + "lelbow", # 4 + "lwrist", # 5 + "rhip", # 6 + "rknee", # 7 + "rankle", # 8 + "lhip", # 9 + "lknee", # 10 + "lankle", # 11 + "headtop", # 12 + "neck", # 13 + ] + + +def get_aich_skeleton(): + # 0 - rshoulder, + # 1 - relbow, + # 2 - rwrist, + # 3 - lshoulder, + # 4 - lelbow, + # 5 - lwrist, + # 6 - rhip, + # 7 - rknee, + # 8 - rankle, + # 9 - lhip, + # 10 - lknee, + # 11 - lankle, + # 12 - headtop, + # 13 - neck, + return np.array( + [ + [ 0, 1 ], + [ 1, 2 ], + [ 3, 4 ], + [ 4, 5 ], + [ 6, 7 ], + [ 7, 8 ], + [ 9, 10], + [10, 11], + [12, 13], + [13, 0 ], + [13, 3 ], + [ 0, 6 ], + [ 3, 9 ] + ] + ) + + +def get_3dpw_joint_names(): + return [ + "nose", # 0 + "thorax", # 1 + "rshoulder", # 2 + "relbow", # 3 + "rwrist", # 4 + "lshoulder", # 5 + "lelbow", # 6 + "lwrist", # 7 + "rhip", # 8 + "rknee", # 9 + "rankle", # 10 + "lhip", # 11 + "lknee", # 12 + "lankle", # 13 + ] + + +def get_3dpw_skeleton(): + return np.array( + [ + [ 0, 1 ], + [ 1, 2 ], + [ 2, 3 ], + [ 3, 4 ], + [ 1, 5 ], + [ 5, 6 ], + [ 6, 7 ], + [ 2, 8 ], + [ 5, 11], + [ 8, 11], + [ 8, 9 ], + [ 9, 10], + [11, 12], + [12, 13] + ] + ) + + +def get_smplcoco_joint_names(): + return [ + "rankle", # 0 + "rknee", # 1 + "rhip", # 2 + "lhip", # 3 + "lknee", # 4 + "lankle", # 5 + "rwrist", # 6 + "relbow", # 7 + "rshoulder", # 8 + "lshoulder", # 9 + "lelbow", # 10 + "lwrist", # 11 + "neck", # 12 + "headtop", # 13 + "nose", # 14 + "leye", # 15 + "reye", # 16 + "lear", # 17 + "rear", # 18 + ] + + +def get_smplcoco_skeleton(): + return np.array( + [ + [ 0, 1 ], + [ 1, 2 ], + [ 3, 4 ], + [ 4, 5 ], + [ 6, 7 ], + [ 7, 8 ], + [ 8, 12], + [12, 9 ], + [ 9, 10], + [10, 11], + [12, 13], + [14, 15], + [15, 17], + [16, 18], + [14, 16], + [ 8, 2 ], + [ 9, 3 ], + [ 2, 3 ], + ] + ) + + +def get_smpl_joint_names(): + return [ + 'hips', # 0 + 'leftUpLeg', # 1 + 'rightUpLeg', # 2 + 'spine', # 3 + 'leftLeg', # 4 + 'rightLeg', # 5 + 'spine1', # 6 + 'leftFoot', # 7 + 'rightFoot', # 8 + 'spine2', # 9 + 'leftToeBase', # 10 + 'rightToeBase', # 11 + 'neck', # 12 + 'leftShoulder', # 13 + 'rightShoulder', # 14 + 'head', # 15 + 'leftArm', # 16 + 'rightArm', # 17 + 'leftForeArm', # 18 + 'rightForeArm', # 19 + 'leftHand', # 20 + 'rightHand', # 21 + 'leftHandIndex1', # 22 + 'rightHandIndex1', # 23 + ] + + +def get_smpl_paper_joint_names(): + return [ + 'Hips', # 0 + 'Left Hip', # 1 + 'Right Hip', # 2 + 'Spine', # 3 + 'Left Knee', # 4 + 'Right Knee', # 5 + 'Spine_1', # 6 + 'Left Ankle', # 7 + 'Right Ankle', # 8 + 'Spine_2', # 9 + 'Left Toe', # 10 + 'Right Toe', # 11 + 'Neck', # 12 + 'Left Shoulder', # 13 + 'Right Shoulder', # 14 + 'Head', # 15 + 'Left Arm', # 16 + 'Right Arm', # 17 + 'Left Elbow', # 18 + 'Right Elbow', # 19 + 'Left Hand', # 20 + 'Right Hand', # 21 + 'Left Thumb', # 22 + 'Right Thumb', # 23 + ] + + +def get_smpl_neighbor_triplets(): + return [ + [ 0, 1, 2 ], # 0 + [ 1, 4, 0 ], # 1 + [ 2, 0, 5 ], # 2 + [ 3, 0, 6 ], # 3 + [ 4, 7, 1 ], # 4 + [ 5, 2, 8 ], # 5 + [ 6, 3, 9 ], # 6 + [ 7, 10, 4 ], # 7 + [ 8, 5, 11], # 8 + [ 9, 13, 14], # 9 + [10, 7, 4 ], # 10 + [11, 8, 5 ], # 11 + [12, 9, 15], # 12 + [13, 16, 9 ], # 13 + [14, 9, 17], # 14 + [15, 9, 12], # 15 + [16, 18, 13], # 16 + [17, 14, 19], # 17 + [18, 20, 16], # 18 + [19, 17, 21], # 19 + [20, 22, 18], # 20 + [21, 19, 23], # 21 + [22, 20, 18], # 22 + [23, 19, 21], # 23 + ] + + +def get_smpl_skeleton(): + return np.array( + [ + [ 0, 1 ], + [ 0, 2 ], + [ 0, 3 ], + [ 1, 4 ], + [ 2, 5 ], + [ 3, 6 ], + [ 4, 7 ], + [ 5, 8 ], + [ 6, 9 ], + [ 7, 10], + [ 8, 11], + [ 9, 12], + [ 9, 13], + [ 9, 14], + [12, 15], + [13, 16], + [14, 17], + [16, 18], + [17, 19], + [18, 20], + [19, 21], + [20, 22], + [21, 23], + ] + ) + + +def map_spin_joints_to_smpl(): + # this function primarily will be used to copy 2D keypoint + # confidences to pose parameters + return [ + [(39, 27, 28), 0], # hip,lhip,rhip->hips + [(28,), 1], # lhip->leftUpLeg + [(27,), 2], # rhip->rightUpLeg + [(41, 27, 28, 39), 3], # Spine->spine + [(29,), 4], # lknee->leftLeg + [(26,), 5], # rknee->rightLeg + [(41, 40, 33, 34,), 6], # spine, thorax ->spine1 + [(30,), 7], # lankle->leftFoot + [(25,), 8], # rankle->rightFoot + [(40, 33, 34), 9], # thorax,shoulders->spine2 + [(30,), 10], # lankle -> leftToe + [(25,), 11], # rankle -> rightToe + [(37, 42, 33, 34), 12], # neck, shoulders -> neck + [(34,), 13], # lshoulder->leftShoulder + [(33,), 14], # rshoulder->rightShoulder + [(33, 34, 38, 43, 44, 45, 46, 47, 48,), 15], # nose, eyes, ears, headtop, shoulders->head + [(34,), 16], # lshoulder->leftArm + [(33,), 17], # rshoulder->rightArm + [(35,), 18], # lelbow->leftForeArm + [(32,), 19], # relbow->rightForeArm + [(36,), 20], # lwrist->leftHand + [(31,), 21], # rwrist->rightHand + [(36,), 22], # lhand -> leftHandIndex + [(31,), 23], # rhand -> rightHandIndex + ] + + +def map_smpl_to_common(): + return [ + [(11, 8), 0], # rightToe, rightFoot -> rankle + [(5,), 1], # rightleg -> rknee, + [(2,), 2], # rhip + [(1,), 3], # lhip + [(4,), 4], # leftLeg -> lknee + [(10, 7), 5], # lefttoe, leftfoot -> lankle + [(21, 23), 6], # rwrist + [(18,), 7], # relbow + [(17, 14), 8], # rshoulder + [(16, 13), 9], # lshoulder + [(19,), 10], # lelbow + [(20, 22), 11], # lwrist + [(0, 3, 6, 9, 12), 12], # neck + [(15,), 13], # headtop + ] + + +def relation_among_spin_joints(): + # this function primarily will be used to copy 2D keypoint + # confidences to 3D joints + return [ + [(), 25], + [(), 26], + [(39,), 27], + [(39,), 28], + [(), 29], + [(), 30], + [(), 31], + [(), 32], + [(), 33], + [(), 34], + [(), 35], + [(), 36], + [(40,42,44,43,38,33,34,), 37], + [(43,44,45,46,47,48,33,34,), 38], + [(27,28,), 39], + [(27,28,37,41,42,), 40], + [(27,28,39,40,), 41], + [(37,38,44,45,46,47,48,), 42], + [(44,45,46,47,48,38,42,37,33,34,), 43], + [(44,45,46,47,48,38,42,37,33,34), 44], + [(44,45,46,47,48,38,42,37,33,34), 45], + [(44,45,46,47,48,38,42,37,33,34), 46], + [(44,45,46,47,48,38,42,37,33,34), 47], + [(44,45,46,47,48,38,42,37,33,34), 48], + ] \ No newline at end of file diff --git a/utils/loss.py b/utils/loss.py new file mode 100644 index 0000000000000000000000000000000000000000..81188fa8c8488688f42f33b2a4d6252cd887aeab --- /dev/null +++ b/utils/loss.py @@ -0,0 +1,207 @@ +import torch +import torch.nn as nn +from common import constants +from models.smpl import SMPL +from smplx import SMPLX +import pickle as pkl +import numpy as np +from utils.mesh_utils import save_results_mesh +from utils.diff_renderer import Pytorch3D +import os +import cv2 + + +class sem_loss_function(nn.Module): + def __init__(self): + super(sem_loss_function, self).__init__() + self.ce = nn.BCELoss() + + def forward(self, y_true, y_pred): + loss = self.ce(y_pred, y_true) + return loss + + +class class_loss_function(nn.Module): + def __init__(self): + super(class_loss_function, self).__init__() + self.ce_loss = nn.BCELoss() + # self.ce_loss = nn.MultiLabelSoftMarginLoss() + # self.ce_loss = nn.MultiLabelMarginLoss() + + def forward(self, y_true, y_pred, valid_mask): + # y_true = torch.squeeze(y_true, 1).long() + # y_true = torch.squeeze(y_true, 1) + # y_pred = torch.squeeze(y_pred, 1) + bs = y_true.shape[0] + if bs != 1: + y_pred = y_pred[valid_mask == 1] + y_true = y_true[valid_mask == 1] + if len(y_pred) > 0: + return self.ce_loss(y_pred, y_true) + else: + return torch.tensor(0.0).to(y_pred.device) + + +class pixel_anchoring_function(nn.Module): + def __init__(self, model_type, device='cuda'): + super(pixel_anchoring_function, self).__init__() + + self.device = device + + self.model_type = model_type + + if self.model_type == 'smplx': + # load mapping from smpl vertices to smplx vertices + mapping_pkl = os.path.join(constants.CONTACT_MAPPING_PATH, "smpl_to_smplx.pkl") + with open(mapping_pkl, 'rb') as f: + smpl_to_smplx_mapping = pkl.load(f) + smpl_to_smplx_mapping = smpl_to_smplx_mapping["matrix"] + self.smpl_to_smplx_mapping = torch.from_numpy(smpl_to_smplx_mapping).float().to(self.device) + + + # Setup the SMPL model + if self.model_type == 'smpl': + self.n_vertices = 6890 + self.body_model = SMPL(constants.SMPL_MODEL_DIR).to(self.device) + if self.model_type == 'smplx': + self.n_vertices = 10475 + self.body_model = SMPLX(constants.SMPLX_MODEL_DIR, + num_betas=10, + use_pca=False).to(self.device) + self.body_faces = torch.LongTensor(self.body_model.faces.astype(np.int32)).to(self.device) + + self.ce_loss = nn.BCELoss() + + def get_posed_mesh(self, body_params, debug=False): + betas = body_params['betas'] + pose = body_params['pose'] + transl = body_params['transl'] + + # extra smplx params + extra_args = {'jaw_pose': torch.zeros((betas.shape[0], 3)).float().to(self.device), + 'leye_pose': torch.zeros((betas.shape[0], 3)).float().to(self.device), + 'reye_pose': torch.zeros((betas.shape[0], 3)).float().to(self.device), + 'expression': torch.zeros((betas.shape[0], 10)).float().to(self.device), + 'left_hand_pose': torch.zeros((betas.shape[0], 45)).float().to(self.device), + 'right_hand_pose': torch.zeros((betas.shape[0], 45)).float().to(self.device)} + + smpl_output = self.body_model(betas=betas, + body_pose=pose[:, 3:], + global_orient=pose[:, :3], + pose2rot=True, + transl=transl, + **extra_args) + smpl_verts = smpl_output.vertices + smpl_joints = smpl_output.joints + + if debug: + for mesh_i in range(smpl_verts.shape[0]): + out_dir = 'temp_meshes' + os.makedirs(out_dir, exist_ok=True) + out_file = os.path.join(out_dir, f'temp_mesh_{mesh_i:04d}.obj') + save_results_mesh(smpl_verts[mesh_i], self.body_model.faces, out_file) + return smpl_verts, smpl_joints + + + def render_batch(self, smpl_verts, cam_k, img_scale_factor, vertex_colors=None, face_textures=None, debug=False): + + bs = smpl_verts.shape[0] + + # Incorporate resizing factor into the camera + img_w = 256 # TODO: Remove hardcoding + img_h = 256 # TODO: Remove hardcoding + focal_length_x = cam_k[:, 0, 0] * img_scale_factor[:, 0] + focal_length_y = cam_k[:, 1, 1] * img_scale_factor[:, 1] + # convert to float for pytorch3d + focal_length_x, focal_length_y = focal_length_x.float(), focal_length_y.float() + + # concatenate focal length + focal_length = torch.stack([focal_length_x, focal_length_y], dim=1) + + # Setup renderer + renderer = Pytorch3D(img_h=img_h, + img_w=img_w, + focal_length=focal_length, + smpl_faces=self.body_faces, + texture_mode='deco', + vertex_colors=vertex_colors, + face_textures=face_textures, + is_train=True, + is_cam_batch=True) + front_view = renderer(smpl_verts) + if debug: + # visualize the front view as images in a temp_image folder + for i in range(bs): + front_view_rgb = front_view[i, :3, :, :].permute(1, 2, 0).detach().cpu() + front_view_mask = front_view[i, 3, :, :].detach().cpu() + out_dir = 'temp_images' + os.makedirs(out_dir, exist_ok=True) + out_file_rgb = os.path.join(out_dir, f'{i:04d}_rgb.png') + out_file_mask = os.path.join(out_dir, f'{i:04d}_mask.png') + cv2.imwrite(out_file_rgb, front_view_rgb.numpy()*255) + cv2.imwrite(out_file_mask, front_view_mask.numpy()*255) + + return front_view + + def paint_contact(self, pred_contact): + """ + Paints the contact vertices on the SMPL mesh + + Args: + pred_contact: prbabilities of contact vertices + + Returns: + pred_rgb: RGB colors for the contact vertices + """ + bs = pred_contact.shape[0] + + # initialize black and while colors + colors = torch.tensor([[0, 0, 0], [1, 1, 1]]).float().to(self.device) + colors = torch.unsqueeze(colors, 0).expand(bs, -1, -1) + + # add another dimension to the contact probabilities for inverse probabilities + pred_contact = torch.unsqueeze(pred_contact, 2) + pred_contact = torch.cat((1 - pred_contact, pred_contact), 2) + + # get pred_rgb colors + pred_vert_rgb = torch.bmm(pred_contact, colors) + pred_face_rgb = pred_vert_rgb[:, self.body_faces, :][:, :, 0, :] # take the first vertex color + pred_face_texture = torch.zeros((bs, self.body_faces.shape[0], 1, 1, 3), dtype=torch.float32).to(self.device) + pred_face_texture[:, :, 0, 0, :] = pred_face_rgb + return pred_vert_rgb, pred_face_texture + + def forward(self, pred_contact, body_params, cam_k, img_scale_factor, gt_contact_polygon, valid_mask): + """ + Takes predicted contact labels (probabilities), transfers them to the posed mesh and + renders to the image. Loss is computed between the rendered contact and the ground truth + polygons from HOT. + + Args: + pred_contact: predicted contact labels (probabilities) + body_params: SMPL parameters in camera coords + cam_k: camera intrinsics + gt_contact_polygon: ground truth polygons from HOT + """ + # convert pred_contact to smplx + bs = pred_contact.shape[0] + if self.model_type == 'smplx': + smpl_to_smplx_mapping = self.smpl_to_smplx_mapping[None].expand(bs, -1, -1) + pred_contact = torch.bmm(smpl_to_smplx_mapping, pred_contact[..., None]) + pred_contact = pred_contact.squeeze() + + # get the posed mesh + smpl_verts, smpl_joints = self.get_posed_mesh(body_params) + + # paint the contact vertices on the mesh + vertex_colors, face_textures = self.paint_contact(pred_contact) + + # render the mesh + front_view = self.render_batch(smpl_verts, cam_k, img_scale_factor, vertex_colors, face_textures) + front_view_rgb = front_view[:, :3, :, :].permute(0, 2, 3, 1) + front_view_mask = front_view[:, 3, :, :] + + # compute segmentation loss between rendered contact mask and ground truth contact mask + front_view_rgb = front_view_rgb[valid_mask == 1] + gt_contact_polygon = gt_contact_polygon[valid_mask == 1] + loss = self.ce_loss(front_view_rgb, gt_contact_polygon) + return loss, front_view_rgb, front_view_mask diff --git a/utils/mesh_utils.py b/utils/mesh_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..18a6380cf536320f7dc56fbb0e0724b9395f05d8 --- /dev/null +++ b/utils/mesh_utils.py @@ -0,0 +1,6 @@ +import trimesh + +def save_results_mesh(vertices, faces, filename): + mesh = trimesh.Trimesh(vertices, faces, process=False) + mesh.export(filename) + print(f'save results to {filename}') \ No newline at end of file diff --git a/utils/metrics.py b/utils/metrics.py new file mode 100644 index 0000000000000000000000000000000000000000..2df5395d8acb89f2cf23e9dfa77764c98e76b57e --- /dev/null +++ b/utils/metrics.py @@ -0,0 +1,106 @@ +import numpy as np +import torch +import monai.metrics as metrics +from common.constants import DIST_MATRIX_PATH + +DIST_MATRIX = np.load(DIST_MATRIX_PATH) + +def metric(mask, pred, back=True): + iou = metrics.compute_meaniou(pred, mask, back, False) + iou = iou.mean() + + return iou + +def precision_recall_f1score(gt, pred): + """ + Compute precision, recall, and f1 + """ + + # gt = gt.numpy() + # pred = pred.numpy() + + precision = torch.zeros(gt.shape[0]) + recall = torch.zeros(gt.shape[0]) + f1 = torch.zeros(gt.shape[0]) + + for b in range(gt.shape[0]): + tp_num = gt[b, pred[b, :] >= 0.5].sum() + precision_denominator = (pred[b, :] >= 0.5).sum() + recall_denominator = (gt[b, :]).sum() + + precision_ = tp_num / precision_denominator + recall_ = tp_num / recall_denominator + if precision_denominator == 0: # if no pred + precision_ = 1. + recall_ = 0. + f1_ = 0. + elif recall_denominator == 0: # if no GT + precision_ = 0. + recall_ = 1. + f1_ = 0. + elif (precision_ + recall_) <= 1e-10: # to avoid precision issues + precision_= 0. + recall_= 0. + f1_ = 0. + else: + f1_ = 2 * precision_ * recall_ / (precision_ + recall_) + + precision[b] = precision_ + recall[b] = recall_ + f1[b] = f1_ + + # return precision, recall, f1 + return precision, recall, f1 + +def acc_precision_recall_f1score(gt, pred): + """ + Compute acc, precision, recall, and f1 + """ + + # gt = gt.numpy() + # pred = pred.numpy() + + acc = torch.zeros(gt.shape[0]) + precision = torch.zeros(gt.shape[0]) + recall = torch.zeros(gt.shape[0]) + f1 = torch.zeros(gt.shape[0]) + + for b in range(gt.shape[0]): + tp_num = gt[b, pred[b, :] >= 0.5].sum() + precision_denominator = (pred[b, :] >= 0.5).sum() + recall_denominator = (gt[b, :]).sum() + tn_num = gt.shape[-1] - precision_denominator - recall_denominator + tp_num + + acc_ = (tp_num + tn_num) / gt.shape[-1] + precision_ = tp_num / (precision_denominator + 1e-10) + recall_ = tp_num / (recall_denominator + 1e-10) + f1_ = 2 * precision_ * recall_ / (precision_ + recall_ + 1e-10) + + acc[b] = acc_ + precision[b] = precision_ + recall[b] = recall_ + + # return precision, recall, f1 + return acc, precision, recall, f1 + +def det_error_metric(pred, gt): + + gt = gt.detach().cpu() + pred = pred.detach().cpu() + + dist_matrix = torch.tensor(DIST_MATRIX) + + false_positive_dist = torch.zeros(gt.shape[0]) + false_negative_dist = torch.zeros(gt.shape[0]) + + for b in range(gt.shape[0]): + gt_columns = dist_matrix[:, gt[b, :]==1] if any(gt[b, :]==1) else dist_matrix + error_matrix = gt_columns[pred[b, :] >= 0.5, :] if any(pred[b, :] >= 0.5) else gt_columns + + false_positive_dist_ = error_matrix.min(dim=1)[0].mean() + false_negative_dist_ = error_matrix.min(dim=0)[0].mean() + + false_positive_dist[b] = false_positive_dist_ + false_negative_dist[b] = false_negative_dist_ + + return false_positive_dist, false_negative_dist \ No newline at end of file diff --git a/utils/smpl_uv.py b/utils/smpl_uv.py new file mode 100644 index 0000000000000000000000000000000000000000..c540578f2cf3f78ed4ad4d652c07dee361ad0476 --- /dev/null +++ b/utils/smpl_uv.py @@ -0,0 +1,167 @@ +import torch +import trimesh +import numpy as np +import skimage.io as io +from PIL import Image +from smplx import SMPL +from matplotlib import cm as mpl_cm, colors as mpl_colors +from trimesh.visual.color import face_to_vertex_color, vertex_to_face_color, to_rgba + +from common import constants +from .colorwheel import make_color_wheel_image + + +def get_smpl_uv(): + uv_obj = 'data/body_models/smpl_uv_20200910/smpl_uv.obj' + + uv_map = [] + with open(uv_obj) as f: + for line in f.readlines(): + if line.startswith('vt'): + coords = [float(x) for x in line.split(' ')[1:]] + uv_map.append(coords) + + uv_map = np.array(uv_map) + + return uv_map + + +def show_uv_texture(): + # image = io.imread('data/body_models/smpl_uv_20200910/smpl_uv_20200910.png') + image = make_color_wheel_image(1024, 1024) + image = Image.fromarray(image) + + uv = np.load('data/body_models/smpl_uv_20200910/uv_table.npy') # get_smpl_uv() + material = trimesh.visual.texture.SimpleMaterial(image=image) + tex_visuals = trimesh.visual.TextureVisuals(uv=uv, image=image, material=material) + + smpl = SMPL(constants.SMPL_MODEL_DIR) + + faces = smpl.faces + verts = smpl().vertices[0].detach().numpy() + + # assert(len(uv) == len(verts)) + print(uv.shape) + vc = tex_visuals.to_color().vertex_colors + fc = trimesh.visual.color.vertex_to_face_color(vc, faces) + face_colors = fc.copy() + fc = fc.astype(float) + vc = vc.astype(float) + fc[:,:3] = fc[:,:3] / 255. + vc[:,:3] = vc[:,:3] / 255. + print(fc[:,:3].max(), fc[:,:3].min(), fc[:,:3].mean()) + print(vc[:, :3].max(), vc[:, :3].min(), vc[:, :3].mean()) + np.save('data/body_models/smpl/color_wheel_face_colors.npy', fc) + np.save('data/body_models/smpl/color_wheel_vertex_colors.npy', vc) + print(fc.shape) + mesh = trimesh.Trimesh(verts, faces, validate=True, process=False, face_colors=face_colors) + # mesh = trimesh.load('data/body_models/smpl_uv_20200910/smpl_uv.obj', process=False) + # mesh.visual = tex_visuals + + # import ipdb; ipdb.set_trace() + # print(vc.shape) + mesh.show() + + +def show_colored_mesh(): + cm = mpl_cm.get_cmap('jet') + norm_gt = mpl_colors.Normalize() + + smpl = SMPL(constants.SMPL_MODEL_DIR) + + faces = smpl.faces + verts = smpl().vertices[0].detach().numpy() + + m = trimesh.Trimesh(verts, faces, process=False) + + mode = 1 + if mode == 0: + # mano_segm_labels = m.triangles_center + face_labels = m.triangles_center + face_colors = (face_labels - face_labels.min()) / np.ptp(face_labels) + + elif mode == 1: + # print(face_labels.shape) + face_labels = m.triangles_center + face_labels = np.argsort(np.linalg.norm(face_labels, axis=-1)) + face_colors = np.ones((13776, 4)) + face_colors[:, 3] = 1.0 + face_colors[:, :3] = cm(norm_gt(face_labels))[:, :3] + elif mode == 2: + # breakpoint() + fc = np.load('data/body_models/smpl_uv_20200910/data/vertex_texture.npy')[0, :, 0, 0, 0, :] + face_colors = np.ones((13776, 4)) + face_colors[:, :3] = fc + mesh = trimesh.Trimesh(verts, faces, process=False, face_colors=face_colors) + mesh.show() + + +def get_tenet_texture(mode='smplpix'): + # mode = 'smplpix', 'decomr' + + smpl = SMPL(constants.SMPL_MODEL_DIR) + + faces = smpl.faces + verts = smpl().vertices[0].detach().numpy() + + m = trimesh.Trimesh(verts, faces, process=False) + if mode == 'smplpix': + # mano_segm_labels = m.triangles_center + face_labels = m.triangles_center + face_colors = (face_labels - face_labels.min()) / np.ptp(face_labels) + texture = np.zeros((1, faces.shape[0], 1, 1, 1, 3), dtype=np.float32) + texture[0, :, 0, 0, 0, :] = face_colors[:, :3] + texture = torch.from_numpy(texture).float() + elif mode == 'decomr': + texture = np.load('data/body_models/smpl_uv_20200910/data/vertex_texture.npy') + texture = torch.from_numpy(texture).float() + elif mode == 'colorwheel': + face_colors = np.load('data/body_models/smpl/color_wheel_face_colors.npy') + texture = np.zeros((1, faces.shape[0], 1, 1, 1, 3), dtype=np.float32) + texture[0, :, 0, 0, 0, :] = face_colors[:, :3] + texture = torch.from_numpy(texture).float() + else: + raise ValueError(f'{mode} is not defined!') + + return texture + + +def save_tenet_textures(mode='smplpix'): + # mode = 'smplpix', 'decomr' + + smpl = SMPL(constants.SMPL_MODEL_DIR) + + faces = smpl.faces + verts = smpl().vertices[0].detach().numpy() + + m = trimesh.Trimesh(verts, faces, process=False) + + if mode == 'smplpix': + # mano_segm_labels = m.triangles_center + face_labels = m.triangles_center + face_colors = (face_labels - face_labels.min()) / np.ptp(face_labels) + texture = np.zeros((1, faces.shape[0], 1, 1, 1, 3), dtype=np.float32) + texture[0, :, 0, 0, 0, :] = face_colors[:, :3] + texture = torch.from_numpy(texture).float() + + vert_colors = face_to_vertex_color(m, face_colors).astype(float) / 255.0 + + elif mode == 'decomr': + texture = np.load('data/body_models/smpl_uv_20200910/data/vertex_texture.npy') + texture = torch.from_numpy(texture).float() + face_colors = texture[0, :, 0, 0, 0, :] + vert_colors = face_to_vertex_color(m, face_colors).astype(float) / 255.0 + + elif mode == 'colorwheel': + face_colors = np.load('data/body_models/smpl/color_wheel_face_colors.npy') + texture = np.zeros((1, faces.shape[0], 1, 1, 1, 3), dtype=np.float32) + texture[0, :, 0, 0, 0, :] = face_colors[:, :3] + texture = torch.from_numpy(texture).float() + face_colors[:, :3] *= 255 + vert_colors = face_to_vertex_color(m, face_colors).astype(float) / 255.0 + else: + raise ValueError(f'{mode} is not defined!') + + print(vert_colors.shape, vert_colors.max()) + np.save(f'data/body_models/smpl/{mode}_vertex_colors.npy', vert_colors) + return texture \ No newline at end of file diff --git a/vis/__pycache__/visualize.cpython-37.pyc b/vis/__pycache__/visualize.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..88fd4b392b3c5310adc684445ece6934c93c8f91 Binary files /dev/null and b/vis/__pycache__/visualize.cpython-37.pyc differ diff --git a/vis/visualize.py b/vis/visualize.py new file mode 100644 index 0000000000000000000000000000000000000000..a9b386e6956c1170e79b1b56baf9de0a618fb000 --- /dev/null +++ b/vis/visualize.py @@ -0,0 +1,209 @@ +import cv2 +import os +import trimesh +import PIL.Image as pil_img +import numpy as np +import pyrender +from common import constants + +os.environ['PYOPENGL_PLATFORM'] = 'egl' + +def render_image(scene, img_res, img=None, viewer=False): + ''' + Render the given pyrender scene and return the image. Can also overlay the mesh on an image. + ''' + if viewer: + pyrender.Viewer(scene, use_raymond_lighting=True) + return 0 + else: + r = pyrender.OffscreenRenderer(viewport_width=img_res, + viewport_height=img_res, + point_size=1.0) + color, _ = r.render(scene, flags=pyrender.RenderFlags.RGBA) + color = color.astype(np.float32) / 255.0 + + if img is not None: + valid_mask = (color[:, :, -1] > 0)[:, :, np.newaxis] + input_img = img.detach().cpu().numpy() + output_img = (color[:, :, :-1] * valid_mask + + (1 - valid_mask) * input_img) + else: + output_img = color + return output_img + +def create_scene(mesh, img, focal_length=500, camera_center=250, img_res=500): + # Setup the scene + scene = pyrender.Scene(bg_color=[1.0, 1.0, 1.0, 1.0], + ambient_light=(0.3, 0.3, 0.3)) + # add mesh for camera + camera_pose = np.eye(4) + camera_rotation = np.eye(3, 3) + camera_translation = np.array([0., 0, 2.5]) + camera_pose[:3, :3] = camera_rotation + camera_pose[:3, 3] = camera_rotation @ camera_translation + pyrencamera = pyrender.camera.IntrinsicsCamera( + fx=focal_length, fy=focal_length, + cx=camera_center, cy=camera_center) + scene.add(pyrencamera, pose=camera_pose) + # create and add light + light = pyrender.PointLight(color=[1.0, 1.0, 1.0], intensity=1) + light_pose = np.eye(4) + for lp in [[1, 1, 1], [-1, 1, 1], [1, -1, 1], [-1, -1, 1]]: + light_pose[:3, 3] = mesh.vertices.mean(0) + np.array(lp) + # out_mesh.vertices.mean(0) + np.array(lp) + scene.add(light, pose=light_pose) + # add body mesh + material = pyrender.MetallicRoughnessMaterial( + metallicFactor=0.0, + alphaMode='OPAQUE', + baseColorFactor=(1.0, 1.0, 0.9, 1.0)) + mesh_images = [] + + # resize input image to fit the mesh image height + # print(img.shape) + img_height = img_res + img_width = int(img_height * img.shape[1] / img.shape[0]) + img = cv2.resize(img, (img_width, img_height)) + mesh_images.append(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) + + for sideview_angle in [0, 90, 180, 270]: + out_mesh = mesh.copy() + rot = trimesh.transformations.rotation_matrix( + np.radians(sideview_angle), [0, 1, 0]) + out_mesh.apply_transform(rot) + out_mesh = pyrender.Mesh.from_trimesh( + out_mesh, + material=material) + mesh_pose = np.eye(4) + scene.add(out_mesh, pose=mesh_pose, name='mesh') + output_img = render_image(scene, img_res) + output_img = pil_img.fromarray((output_img * 255).astype(np.uint8)) + output_img = np.asarray(output_img)[:, :, :3] + mesh_images.append(output_img) + # delete the previous mesh + prev_mesh = scene.get_nodes(name='mesh').pop() + scene.remove_node(prev_mesh) + + # show upside down view + for topview_angle in [90, 270]: + out_mesh = mesh.copy() + rot = trimesh.transformations.rotation_matrix( + np.radians(topview_angle), [1, 0, 0]) + out_mesh.apply_transform(rot) + out_mesh = pyrender.Mesh.from_trimesh( + out_mesh, + material=material) + mesh_pose = np.eye(4) + scene.add(out_mesh, pose=mesh_pose, name='mesh') + output_img = render_image(scene, img_res) + output_img = pil_img.fromarray((output_img * 255).astype(np.uint8)) + output_img = np.asarray(output_img)[:, :, :3] + mesh_images.append(output_img) + # delete the previous mesh + prev_mesh = scene.get_nodes(name='mesh').pop() + scene.remove_node(prev_mesh) + + # stack images + IMG = np.hstack(mesh_images) + IMG = pil_img.fromarray(IMG) + IMG.thumbnail((3000, 3000)) + return IMG + +# img = cv2.imread('../samples/prox_N3OpenArea_03301_01_s001_frame_00694.jpg') +# mesh = trimesh.load('../samples/mesh.ply', process=False) +# comb_img = create_scene(mesh, img) +# comb_img.save('../samples/combined_image.png') + +def unsplit(img, palette): + rgb_img = np.zeros((img.shape[0], img.shape[1], 3)) + for i in range(img.shape[0]): + for j in range(img.shape[1]): + id = np.argmax(img[i, j, :]) + rgb_img[i, j, :] = palette[id] + + return rgb_img + +def gen_render(output, normalize=True): + img = output['img'].cpu().numpy() + contact_labels_3d = output['contact_labels_3d_gt'].cpu().numpy() + contact_labels_3d_pred = output['contact_labels_3d_pred'].cpu().numpy() + sem_mask_gt = output['sem_mask_gt'].cpu().numpy() + sem_mask_pred = output['sem_mask_pred'].cpu().numpy() + part_mask_gt = output['part_mask_gt'].cpu().numpy() + part_mask_pred = output['part_mask_pred'].cpu().numpy() + contact_2d_gt_rgb = output['contact_2d_gt'].cpu().numpy() + contact_2d_pred_rgb = output['contact_2d_pred_rgb'].cpu().numpy() + + mesh_path = './data/smpl/smpl_neutral_tpose.ply' + gt_mesh = trimesh.load(mesh_path, process=False) + pred_mesh = trimesh.load(mesh_path, process=False) + + img = np.transpose(img[0], (1, 2, 0)) + if normalize: + # unnormalize the image before displaying + mean = np.array(constants.IMG_NORM_MEAN, dtype=np.float32) + std = np.array(constants.IMG_NORM_STD, dtype=np.float32) + img = img * std + mean + img = img * 255 + img = img.astype(np.uint8) + color = np.array([0, 0, 0, 255]) + th = 0.5 + + contact_labels_3d = contact_labels_3d[0, :] + for vid, val in enumerate(contact_labels_3d): + if val >= th: + gt_mesh.visual.vertex_colors[vid] = color + + contact_labels_3d_pred = contact_labels_3d_pred[0, :] + for vid, val in enumerate(contact_labels_3d_pred): + if val >= th: + pred_mesh.visual.vertex_colors[vid] = color + + gt_rend = create_scene(gt_mesh, img) + pred_rend = create_scene(pred_mesh, img) + + sem_palette = [[220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100], [0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30], [165, 42, 42], [255, 77, 255], [0, 226, 252], [182, 182, 255], [0, 82, 0], [120, 166, 157], [110, 76, 0], [174, 57, 255], [199, 100, 0], [72, 0, 118], [255, 179, 240], [0, 125, 92], [209, 0, 151], [188, 208, 182], [0, 220, 176], [255, 99, 164], [92, 0, 73], [133, 129, 255], [78, 180, 255], [0, 228, 0], [174, 255, 243], [45, 89, 255], [134, 134, 103], [145, 148, 174], [255, 208, 186], [197, 226, 255], [171, 134, 1], [109, 63, 54], [207, 138, 255], [151, 0, 95], [9, 80, 61], [84, 105, 51], [74, 65, 105], [166, 196, 102], [208, 195, 210], [255, 109, 65], [0, 143, 149], [179, 0, 194], [209, 99, 106], [5, 121, 0], [227, 255, 205], [147, 186, 208], [153, 69, 1], [3, 95, 161], [163, 255, 0], [119, 0, 170], [0, 182, 199], [0, 165, 120], [183, 130, 88], [95, 32, 0], [130, 114, 135], [110, 129, 133], [166, 74, 118], [219, 142, 185], [79, 210, 114], [178, 90, 62], [65, 70, 15], [127, 167, 115], [59, 105, 106], [142, 108, 45], [196, 172, 0], [95, 54, 80], [128, 76, 255], [201, 57, 1], [246, 0, 122], [191, 162, 208], [255, 255, 128], [147, 211, 203], [150, 100, 100], [168, 171, 172], [146, 112, 198], [210, 170, 100], [92, 136, 89], [218, 88, 184], [241, 129, 0], [217, 17, 255], [124, 74, 181], [70, 70, 70], [255, 228, 255], [154, 208, 0], [193, 0, 92], [76, 91, 113], [255, 180, 195], [106, 154, 176], [230, 150, 140], [60, 143, 255], [128, 64, 128], [92, 82, 55], [254, 212, 124], [73, 77, 174], [255, 160, 98], [255, 255, 255], [104, 84, 109], [169, 164, 131], [225, 199, 255], [137, 54, 74], [135, 158, 223], [7, 246, 231], [107, 255, 200], [58, 41, 149], [183, 121, 142], [255, 73, 97], [107, 142, 35], [190, 153, 153], [146, 139, 141], [70, 130, 180], [134, 199, 156], [209, 226, 140], [96, 36, 108], [96, 96, 96], [64, 170, 64], [152, 251, 152], [208, 229, 228], [206, 186, 171], [152, 161, 64], [116, 112, 0], [0, 114, 143], [102, 102, 156], [250, 141, 255]] + # part_palette = [(0,0,0), (128,0,0), (255,0,0), (0,85,0), (170,0,51), (255,85,0), (0,0,85), (0,119,221), (85,85,0), (0,85,85), (85,51,0), (52,86,128), (0,128,0), (0,0,255), (51,170,221), (0,255,255), (85,255,170), (170,255,85), (255,255,0), (255,170,0)] + part_palette = [[0, 0, 0], [220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100], [0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30], [165, 42, 42], [255, 77, 255], [0, 226, 252], [182, 182, 255], [0, 82, 0], [120, 166, 157], [110, 76, 0], [174, 57, 255], [199, 100, 0], [72, 0, 118], [255, 179, 240]] + hot_palette = [[0, 0, 0], [220, 20, 60], [119, 11, 32], [0, 0, 142], [0, 0, 230], [106, 0, 228], [0, 60, 100], [0, 80, 100], [0, 0, 70], [0, 0, 192], [250, 170, 30], [100, 170, 30], [220, 220, 0], [175, 116, 175], [250, 0, 30], [165, 42, 42], [255, 77, 255], [0, 226, 252]] + + sem_mask_gt = np.transpose(sem_mask_gt[0], (1, 2, 0))*255 + sem_mask_gt = sem_mask_gt.astype(np.uint8) + sem_mask_pred = np.transpose(sem_mask_pred[0], (1, 2, 0))*255 + sem_mask_pred = sem_mask_pred.astype(np.uint8) + part_mask_gt = np.transpose(part_mask_gt[0], (1, 2, 0))*255 + part_mask_gt = part_mask_gt.astype(np.uint8) + part_mask_pred = np.transpose(part_mask_pred[0], (1, 2, 0))*255 + part_mask_pred = part_mask_pred.astype(np.uint8) + contact_2d_gt_rgb = contact_2d_gt_rgb[0]*255 + contact_2d_gt_rgb = contact_2d_gt_rgb.astype(np.uint8) + contact_2d_pred_rgb = contact_2d_pred_rgb[0]*255 + contact_2d_pred_rgb = contact_2d_pred_rgb.astype(np.uint8) + + sem_mask_rgb = unsplit(sem_mask_gt, sem_palette) + sem_pred_rgb = unsplit(sem_mask_pred, sem_palette) + part_mask_rgb = unsplit(part_mask_gt, part_palette) + part_pred_rgb = unsplit(part_mask_pred, part_palette) + + sem_mask_rgb = sem_mask_rgb.astype(np.uint8) + sem_pred_rgb = sem_pred_rgb.astype(np.uint8) + part_mask_rgb = part_mask_rgb.astype(np.uint8) + part_pred_rgb = part_pred_rgb.astype(np.uint8) + + sem_mask_rgb = pil_img.fromarray(sem_mask_rgb) + sem_pred_rgb = pil_img.fromarray(sem_pred_rgb) + part_mask_rgb = pil_img.fromarray(part_mask_rgb) + part_pred_rgb = pil_img.fromarray(part_pred_rgb) + contact_2d_gt_rgb = pil_img.fromarray(contact_2d_gt_rgb) + contact_2d_pred_rgb = pil_img.fromarray(contact_2d_pred_rgb) + + tot_rend = pil_img.new('RGB', (3000, 2000)) + tot_rend.paste(gt_rend, (0, 0)) + tot_rend.paste(pred_rend, (0, 450)) + tot_rend.paste(sem_mask_rgb, (0, 900)) + tot_rend.paste(sem_pred_rgb, (400, 900)) + tot_rend.paste(part_mask_rgb, (0, 1300)) + tot_rend.paste(part_pred_rgb, (400, 1300)) + tot_rend.paste(contact_2d_gt_rgb, (0, 1700)) + tot_rend.paste(contact_2d_pred_rgb, (400, 1700)) + return tot_rend \ No newline at end of file