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| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # Copyright (c) Meta Platforms, Inc. All Rights Reserved | |
| import os | |
| os.system('pip install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio==0.10.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html') | |
| try: | |
| import detectron2 | |
| except: | |
| import os | |
| # os.system('cd /home/user/app/third_party/CLIP && pip install -Ue .') | |
| os.system('pip install git+https://github.com/Jun-CEN/CLIP.git') | |
| os.system('pip install git+https://github.com/facebookresearch/detectron2.git') | |
| os.system('pip install git+https://github.com/facebookresearch/pytorch3d.git') | |
| os.system('pip install git+https://github.com/facebookresearch/segment-anything.git') | |
| import argparse | |
| import glob | |
| import multiprocessing as mp | |
| import os | |
| import time | |
| import cv2 | |
| import tqdm | |
| import numpy as np | |
| import gradio as gr | |
| from tools.util import * | |
| from detectron2.config import get_cfg | |
| from detectron2.projects.deeplab import add_deeplab_config | |
| from detectron2.data.detection_utils import read_image | |
| from detectron2.utils.logger import setup_logger | |
| from open_vocab_seg import add_ovseg_config | |
| from open_vocab_seg.utils import VisualizationDemo, VisualizationDemoIndoor | |
| # constants | |
| WINDOW_NAME = "Open vocabulary segmentation" | |
| def setup_cfg(args): | |
| # load config from file and command-line arguments | |
| cfg = get_cfg() | |
| # for poly lr schedule | |
| add_deeplab_config(cfg) | |
| add_ovseg_config(cfg) | |
| cfg.merge_from_file(args.config_file) | |
| cfg.merge_from_list(args.opts) | |
| cfg.freeze() | |
| return cfg | |
| def get_parser(): | |
| parser = argparse.ArgumentParser(description="Detectron2 demo for open vocabulary segmentation") | |
| parser.add_argument( | |
| "--config-file", | |
| default="configs/ovseg_swinB_vitL_demo.yaml", | |
| metavar="FILE", | |
| help="path to config file", | |
| ) | |
| parser.add_argument( | |
| "--input", | |
| default=["/mnt/lustre/jkyang/PSG4D/sailvos3d/downloads/sailvos3d/trevor_1_int/images/000160.bmp"], | |
| nargs="+", | |
| help="A list of space separated input images; " | |
| "or a single glob pattern such as 'directory/*.jpg'", | |
| ) | |
| parser.add_argument( | |
| "--class-names", | |
| default=["person", "car", "motorcycle", "truck", "bird", "dog", "handbag", "suitcase", "bottle", "cup", "bowl", "chair", "potted plant", "bed", "dining table", "tv", "laptop", "cell phone", "bag", "bin", "box", "door", "road barrier", "stick", "lamp", "floor", "wall"], | |
| nargs="+", | |
| help="A list of user-defined class_names" | |
| ) | |
| parser.add_argument( | |
| "--output", | |
| default = "./pred", | |
| help="A file or directory to save output visualizations. " | |
| "If not given, will show output in an OpenCV window.", | |
| ) | |
| parser.add_argument( | |
| "--opts", | |
| help="Modify config options using the command-line 'KEY VALUE' pairs", | |
| default=["MODEL.WEIGHTS", "ovseg_swinbase_vitL14_ft_mpt.pth"], | |
| nargs=argparse.REMAINDER, | |
| ) | |
| return parser | |
| args = get_parser().parse_args() | |
| def greet_sailvos3d(rgb_input, depth_map_input, rage_matrices_input, class_candidates): | |
| print(args.class_names) | |
| print(class_candidates[0], class_candidates[1], class_candidates[2], class_candidates[3],) | |
| print(class_candidates.split(', ')) | |
| args.input = [rgb_input] | |
| args.class_names = class_candidates.split(', ') | |
| depth_map_path = depth_map_input.name | |
| rage_matrices_path = rage_matrices_input.name | |
| print(args.input, args.class_names, depth_map_path, rage_matrices_path) | |
| mp.set_start_method("spawn", force=True) | |
| setup_logger(name="fvcore") | |
| logger = setup_logger() | |
| logger.info("Arguments: " + str(args)) | |
| cfg = setup_cfg(args) | |
| demo = VisualizationDemo(cfg) | |
| class_names = args.class_names | |
| print(args.input) | |
| if args.input: | |
| if len(args.input) == 1: | |
| args.input = glob.glob(os.path.expanduser(args.input[0])) | |
| assert args.input, "The input path(s) was not found" | |
| for path in tqdm.tqdm(args.input, disable=not args.output): | |
| # use PIL, to be consistent with evaluation | |
| start_time = time.time() | |
| predictions, visualized_output_rgb, visualized_output_depth, visualized_output_rgb_sam, visualized_output_depth_sam = demo.run_on_image_sam(path, class_names, depth_map_path, rage_matrices_path) | |
| logger.info( | |
| "{}: {} in {:.2f}s".format( | |
| path, | |
| "detected {} instances".format(len(predictions["instances"])) | |
| if "instances" in predictions | |
| else "finished", | |
| time.time() - start_time, | |
| ) | |
| ) | |
| if args.output: | |
| if os.path.isdir(args.output): | |
| assert os.path.isdir(args.output), args.output | |
| out_filename = os.path.join(args.output, os.path.basename(path)) | |
| else: | |
| assert len(args.input) == 1, "Please specify a directory with args.output" | |
| out_filename = args.output | |
| visualized_output_rgb.save('outputs/RGB_Semantic_SAM.png') | |
| visualized_output_depth.save('outputs/Depth_Semantic_SAM.png') | |
| visualized_output_rgb_sam.save('outputs/RGB_Semantic_SAM_Mask.png') | |
| visualized_output_depth_sam.save('outputs/Depth_Semantic_SAM_Mask.png') | |
| rgb_3d_sam = demo.get_xyzrgb('outputs/RGB_Semantic_SAM.png', depth_map_path, rage_matrices_path) | |
| depth_3d_sam = demo.get_xyzrgb('outputs/Depth_Semantic_SAM.png', depth_map_path, rage_matrices_path) | |
| rgb_3d_sam_mask = demo.get_xyzrgb('outputs/RGB_Semantic_SAM_Mask.png', depth_map_path, rage_matrices_path) | |
| depth_3d_sam_mask = demo.get_xyzrgb('outputs/Depth_Semantic_SAM_Mask.png', depth_map_path, rage_matrices_path) | |
| np.savez('outputs/xyzrgb.npz', rgb_3d_sam = rgb_3d_sam, depth_3d_sam = depth_3d_sam, rgb_3d_sam_mask = rgb_3d_sam_mask, depth_3d_sam_mask = depth_3d_sam_mask) | |
| demo.render_3d_video('outputs/xyzrgb.npz', depth_map_path) | |
| else: | |
| cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) | |
| cv2.imshow(WINDOW_NAME, visualized_output_rgb.get_image()[:, :, ::-1]) | |
| if cv2.waitKey(0) == 27: | |
| break # esc to quit | |
| else: | |
| raise NotImplementedError | |
| Depth_Semantic_SAM_Mask = read_image('outputs/Depth_Semantic_SAM_Mask.png') | |
| RGB_Semantic_SAM_Mask = read_image('outputs/RGB_Semantic_SAM_Mask.png') | |
| Depth_Semantic_SAM = read_image('outputs/Depth_Semantic_SAM.png') | |
| RGB_Semantic_SAM = read_image('outputs/RGB_Semantic_SAM.png') | |
| two_image_to_gif(Depth_Semantic_SAM_Mask, Depth_Semantic_SAM, 'Depth_Semantic_SAM_2D') | |
| two_image_to_gif(RGB_Semantic_SAM_Mask, RGB_Semantic_SAM, 'RGB_Semantic_SAM_2D') | |
| Depth_Semantic_SAM_2D = 'outputs/Depth_Semantic_SAM_2D.mp4' | |
| RGB_Semantic_SAM_2D = 'outputs/RGB_Semantic_SAM_2D.mp4' | |
| Depth_map = read_image('outputs/Depth_rendered.png') | |
| Depth_Semantic_SAM_Mask_gif = 'outputs/Depth_3D_All.mp4' | |
| RGB_Semantic_SAM_Mask_gif = 'outputs/RGB_3D_All.mp4' | |
| return RGB_Semantic_SAM_2D, RGB_Semantic_SAM_Mask_gif, Depth_map, Depth_Semantic_SAM_2D, Depth_Semantic_SAM_Mask_gif | |
| def greet_scannet(rgb_input, depth_map_input, class_candidates): | |
| rgb_input = rgb_input | |
| depth_map_input = depth_map_input.name | |
| class_candidates = class_candidates.split(', ') | |
| print(rgb_input, depth_map_input, class_candidates) | |
| mp.set_start_method("spawn", force=True) | |
| args = get_parser().parse_args() | |
| setup_logger(name="fvcore") | |
| logger = setup_logger() | |
| logger.info("Arguments: " + str(args)) | |
| cfg = setup_cfg(args) | |
| demo = VisualizationDemoIndoor(cfg) | |
| """ args.input = glob.glob(os.path.expanduser(args.input[0])) | |
| assert args.input, "The input path(s) was not found" """ | |
| start_time = time.time() | |
| predictions, output2D, output3D = demo.run_on_pcd_ui(rgb_input, depth_map_input, class_candidates) | |
| output2D['sem_seg_on_rgb'].save('outputs/RGB_Semantic_SAM.png') | |
| output2D['sem_seg_on_depth'].save('outputs/Depth_Semantic_SAM.png') | |
| output2D['sam_seg_on_rgb'].save('outputs/RGB_Semantic_SAM_Mask.png') | |
| output2D['sam_seg_on_depth'].save('outputs/Depth_Semantic_SAM_Mask.png') | |
| """ rgb_3d_sam = demo.get_xyzrgb('outputs/RGB_Semantic_SAM.png', path) | |
| depth_3d_sam = demo.get_xyzrgb('outputs/Depth_Semantic_SAM.png', path) | |
| rgb_3d_sam_mask = demo.get_xyzrgb('outputs/RGB_Semantic_SAM_Mask.png', path) | |
| depth_3d_sam_mask = demo.get_xyzrgb(outputs/'Depth_Semantic_SAM_Mask.png', path) """ | |
| rgb_3d_sem = output3D['rgb_3d_sem'] | |
| depth_3d_sem = output3D['depth_3d_sem'] | |
| rgb_3d_sam = output3D['rgb_3d_sam'] | |
| depth_3d_sam = output3D['depth_3d_sam'] | |
| np.savez('outputs/xyzrgb.npz', rgb_3d_sam = rgb_3d_sem, depth_3d_sam = depth_3d_sem, rgb_3d_sam_mask = rgb_3d_sam, depth_3d_sam_mask = depth_3d_sam) | |
| demo.render_3d_video('outputs/xyzrgb.npz') | |
| Depth_Semantic_SAM_Mask = read_image('outputs/Depth_Semantic_SAM_Mask.png') | |
| RGB_Semantic_SAM_Mask = read_image('outputs/RGB_Semantic_SAM_Mask.png') | |
| Depth_Semantic_SAM = read_image('outputs/Depth_Semantic_SAM.png') | |
| RGB_Semantic_SAM = read_image('outputs/RGB_Semantic_SAM.png') | |
| two_image_to_gif(Depth_Semantic_SAM_Mask, Depth_Semantic_SAM, 'Depth_Semantic_SAM_2D') | |
| two_image_to_gif(RGB_Semantic_SAM_Mask, RGB_Semantic_SAM, 'RGB_Semantic_SAM_2D') | |
| Depth_Semantic_SAM_2D = 'outputs/Depth_Semantic_SAM_2D.mp4' | |
| RGB_Semantic_SAM_2D = 'outputs/RGB_Semantic_SAM_2D.mp4' | |
| Depth_map = read_image('outputs/Depth_rendered.png') | |
| Depth_Semantic_SAM_Mask_gif = 'outputs/Depth_3D_All.mp4' | |
| RGB_Semantic_SAM_Mask_gif = 'outputs/RGB_3D_All.mp4' | |
| return RGB_Semantic_SAM_2D, RGB_Semantic_SAM_Mask_gif, Depth_map, Depth_Semantic_SAM_2D, Depth_Semantic_SAM_Mask_gif | |
| SHARED_UI_WARNING = f'''### [NOTE] It may be very slow in this shared UI. | |
| You can duplicate and use it with a paid private GPU. | |
| <a class="duplicate-button" style="display:inline-block" target="_blank" href="https://huggingface.co/spaces/mmlab-ntu/Segment-Any-RGBD?duplicate=true"><img style="margin-top:0;margin-bottom:0" src="https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-xl-dark.svg" alt="Duplicate Space"></a> | |
| Alternatively, you can also use the demo on your own computer. | |
| <a style="display:inline-block" href="https://github.com/Jun-CEN/SegmentAnyRGBD/"><img style="margin-top:0;margin-bottom:0" src="https://img.shields.io/badge/Project%20Page-online-brightgreen"></a> | |
| ''' | |
| with gr.Blocks(analytics_enabled=False) as segrgbd_iface: | |
| with gr.Box(): | |
| gr.Markdown(SHARED_UI_WARNING) | |
| #######t2v####### | |
| with gr.Tab(label="Dataset: Sailvos3D"): | |
| with gr.Column(): | |
| with gr.Row(): | |
| # with gr.Tab(label='input'): | |
| with gr.Column(): | |
| with gr.Row(): | |
| Input_RGB_Component = gr.Image(label = 'RGB_Input', type = 'filepath').style(width=320, height=200) | |
| Depth_Map_Output_Component = gr.Image(label = "Vis_Depth_Map").style(width=320, height=200) | |
| with gr.Row(): | |
| Depth_Map_Input_Component = gr.File(label = 'input_Depth_map') | |
| Component_2D_to_3D_Projection_Parameters = gr.File(label = '2D_to_3D_Projection_Parameters') | |
| with gr.Row(): | |
| Class_Candidates_Component = gr.Text(label = 'Class_Candidates') | |
| vc_end_btn = gr.Button("Send") | |
| with gr.Tab(label='Result'): | |
| with gr.Row(): | |
| RGB_Semantic_SAM_Mask_Component = gr.Video(label = "RGB_Semantic_SAM_Mask").style(width=320, height=200) | |
| RGB_Semantic_SAM_Mask_3D_Component = gr.Video(label = "Video_3D_RGB_Semantic_SAM_Mask").style(width=320, height=200) | |
| with gr.Row(): | |
| Depth_Semantic_SAM_Mask_Component = gr.Video(label = "Depth_Semantic_SAM_Mask").style(width=320, height=200) | |
| Depth_Semantic_SAM_Mask_3D_Component = gr.Video(label = "Video_3D_Depth_Semantic_SAM_Mask").style(width=320, height=200) | |
| with gr.Row(): | |
| gr.Markdown("<b> It takes around 2 to 5 minutes to get the final results. The framework initialization, SAM segmentation, zero-shot semantic segmentation and 3D results rendering take long time.</b>") | |
| gr.Examples(examples=[ | |
| [ | |
| 'UI/sailvos3d/ex1/inputs/rgb_000160.bmp', | |
| 'UI/sailvos3d/ex1/inputs/depth_000160.npy', | |
| 'UI/sailvos3d/ex1/inputs/rage_matrices_000160.npz', | |
| 'person, car, motorcycle, truck, bird, dog, handbag, suitcase, bottle, cup, bowl, chair, potted plant, bed, dining table, tv, laptop, cell phone, bag, bin, box, door, road barrier, stick, lamp, floor, wall', | |
| ], | |
| [ | |
| 'UI/sailvos3d/ex2/inputs/rgb_000540.bmp', | |
| 'UI/sailvos3d/ex2/inputs/depth_000540.npy', | |
| 'UI/sailvos3d/ex2/inputs/rage_matrices_000540.npz', | |
| 'person, car, motorcycle, truck, bird, dog, handbag, suitcase, bottle, cup, bowl, chair, potted plant, bed, dining table, tv, laptop, cell phone, bag, bin, box, door, road barrier, stick, lamp, floor, wall', | |
| ]], | |
| inputs=[Input_RGB_Component, Depth_Map_Input_Component, Component_2D_to_3D_Projection_Parameters, Class_Candidates_Component], | |
| outputs=[RGB_Semantic_SAM_Mask_Component, RGB_Semantic_SAM_Mask_3D_Component, Depth_Map_Output_Component, Depth_Semantic_SAM_Mask_Component, Depth_Semantic_SAM_Mask_3D_Component], | |
| fn=greet_sailvos3d) | |
| vc_end_btn.click(inputs=[Input_RGB_Component, Depth_Map_Input_Component, Component_2D_to_3D_Projection_Parameters, Class_Candidates_Component], | |
| outputs=[RGB_Semantic_SAM_Mask_Component, RGB_Semantic_SAM_Mask_3D_Component, Depth_Map_Output_Component, Depth_Semantic_SAM_Mask_Component, Depth_Semantic_SAM_Mask_3D_Component], | |
| fn=greet_sailvos3d) | |
| with gr.Tab(label="Dataset: Scannet"): | |
| with gr.Column(): | |
| with gr.Row(): | |
| # with gr.Tab(label='input'): | |
| with gr.Column(): | |
| with gr.Row(): | |
| Input_RGB_Component = gr.Image(label = 'RGB_Input', type = 'filepath').style(width=320, height=200) | |
| Depth_Map_Output_Component = gr.Image(label = "Vis_Depth_Map").style(width=320, height=200) | |
| with gr.Row(): | |
| Depth_Map_Input_Component = gr.File(label = "Input_Depth_Map") | |
| Class_Candidates_Component = gr.Text(label = 'Class_Candidates') | |
| vc_end_btn = gr.Button("Send") | |
| with gr.Tab(label='Result'): | |
| with gr.Row(): | |
| RGB_Semantic_SAM_Mask_Component = gr.Video(label = "RGB_Semantic_SAM_Mask").style(width=320, height=200) | |
| RGB_Semantic_SAM_Mask_3D_Component = gr.Video(label = "Video_3D_RGB_Semantic_SAM_Mask").style(width=320, height=200) | |
| with gr.Row(): | |
| Depth_Semantic_SAM_Mask_Component = gr.Video(label = "Depth_Semantic_SAM_Mask").style(width=320, height=200) | |
| Depth_Semantic_SAM_Mask_3D_Component = gr.Video(label = "Video_3D_Depth_Semantic_SAM_Mask").style(width=320, height=200) | |
| with gr.Row(): | |
| gr.Markdown("<b> It takes around 2 to 5 minutes to get the final results. The framework initialization, SAM segmentation, zero-shot semantic segmentation and 3D results rendering take long time.</b>") | |
| gr.Examples(examples=[ | |
| [ | |
| 'UI/scannetv2/examples/scene0000_00/color/1660.jpg', | |
| 'UI/scannetv2/examples/scene0000_00/depth/1660.png', | |
| 'wall, floor, cabinet, bed, chair, sofa, table, door, window, bookshelf, picture, counter, desk, curtain, refrigerator, shower curtain, toilet, sink, bathtub, other furniture', | |
| ], | |
| [ | |
| 'UI/scannetv2/examples/scene0000_00/color/5560.jpg', | |
| 'UI/scannetv2/examples/scene0000_00/depth/5560.png', | |
| 'wall, floor, cabinet, bed, chair, sofa, table, door, window, bookshelf, picture, counter, desk, curtain, refrigerator, shower curtain, toilet, sink, bathtub, other furniture', | |
| ]], | |
| inputs=[Input_RGB_Component, Depth_Map_Input_Component, Class_Candidates_Component], | |
| outputs=[RGB_Semantic_SAM_Mask_Component, RGB_Semantic_SAM_Mask_3D_Component, Depth_Map_Output_Component, Depth_Semantic_SAM_Mask_Component, Depth_Semantic_SAM_Mask_3D_Component], | |
| fn=greet_scannet) | |
| vc_end_btn.click(inputs=[Input_RGB_Component, Depth_Map_Input_Component, Class_Candidates_Component], | |
| outputs=[RGB_Semantic_SAM_Mask_Component, RGB_Semantic_SAM_Mask_3D_Component, Depth_Map_Output_Component, Depth_Semantic_SAM_Mask_Component, Depth_Semantic_SAM_Mask_3D_Component], | |
| fn=greet_scannet) | |
| demo = segrgbd_iface | |
| demo.launch() | |