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UX Suggested minor changes: alphabetical order of models & font, default pose colour as red (black is hard to see)
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| # Built from https://huggingface.co/spaces/hlydecker/MegaDetector_v5 | |
| # Built from https://huggingface.co/spaces/sofmi/MegaDetector_DLClive/blob/main/app.py | |
| # Built from https://huggingface.co/spaces/Neslihan/megadetector_dlcmodels/blob/main/app.py | |
| import gradio as gr | |
| import torch | |
| import torchvision | |
| from dlclive import DLCLive, Processor | |
| from model.models import DownloadModel | |
| from PIL import Image, ImageColor, ImageFont, ImageDraw | |
| import numpy as np | |
| import math | |
| # import json | |
| import os | |
| import yaml | |
| import pdb | |
| ######################################### | |
| # Input params | |
| FONTS = {'amiko': "font/Amiko-Regular.ttf", | |
| 'nature': "font/LoveNature.otf", | |
| 'painter':"font/PainterDecorator.otf", | |
| 'animals': "font/UncialAnimals.ttf", | |
| 'zen': "font/ZEN.TTF"} | |
| Megadet_Models = {'md_v5a': "megadet_model/md_v5a.0.0.pt", | |
| 'md_v5b': "megadet_model/md_v5b.0.0.pt"} | |
| DLC_models = {'full_cat': "model/DLC_Cat_resnet_50_iteration-0_shuffle-0", | |
| 'full_dog': "model/DLC_Dog_resnet_50_iteration-0_shuffle-0", | |
| 'primate_face': "model/DLC_FacialLandmarks_resnet_50_iteration-1_shuffle-1", | |
| 'full_human': "model/DLC_human_dancing_resnet_101_iteration-0_shuffle-1", | |
| 'full_macaque': 'model/DLC_monkey_resnet_50_iteration-0_shuffle-1'} | |
| DLC_models_list = ['full_cat', 'full_dog','full_human', 'full_macaque', 'primate_face'] | |
| ######################################### | |
| # Draw keypoints on image | |
| def draw_keypoints_on_image(image, | |
| keypoints, | |
| map_label_id_to_str, | |
| flag_show_str_labels, | |
| use_normalized_coordinates=True, | |
| font_style='amiko', | |
| font_size=8, | |
| keypt_color="#ff0000", | |
| marker_size='2'): | |
| """Draws keypoints on an image. | |
| Modified from: | |
| https://www.programcreek.com/python/?code=fjchange%2Fobject_centric_VAD%2Fobject_centric_VAD-master%2Fobject_detection%2Futils%2Fvisualization_utils.py | |
| Args: | |
| image: a PIL.Image object. | |
| keypoints: a numpy array with shape [num_keypoints, 2]. | |
| map_label_id_to_str: dict with keys=label number and values= label string | |
| flag_show_str_labels: boolean to select whether or not to show string labels | |
| color: color to draw the keypoints with. Default is red. | |
| radius: keypoint radius. Default value is 2. | |
| use_normalized_coordinates: if True (default), treat keypoint values as | |
| relative to the image. Otherwise treat them as absolute. | |
| """ | |
| # get a drawing context | |
| draw = ImageDraw.Draw(image) | |
| im_width, im_height = image.size | |
| keypoints_x = [k[0] for k in keypoints] | |
| keypoints_y = [k[1] for k in keypoints] | |
| # adjust keypoints coords if required | |
| if use_normalized_coordinates: | |
| keypoints_x = tuple([im_width * x for x in keypoints_x]) | |
| keypoints_y = tuple([im_height * y for y in keypoints_y]) | |
| # draw ellipses around keypoints and add string labels | |
| for i, (keypoint_x, keypoint_y) in enumerate(zip(keypoints_x, keypoints_y)): | |
| draw.ellipse([(keypoint_x - marker_size, keypoint_y - marker_size), | |
| (keypoint_x + marker_size, keypoint_y + marker_size)], | |
| outline=keypt_color, | |
| fill=keypt_color) | |
| # add string labels around keypoints | |
| if flag_show_str_labels: | |
| font = ImageFont.truetype(FONTS[font_style], | |
| font_size) | |
| draw.text((keypoint_x + marker_size, keypoint_y + marker_size),#(0.5*im_width, 0.5*im_height), #------- | |
| map_label_id_to_str[i], | |
| ImageColor.getcolor(keypt_color, "RGB"), # rgb | |
| font=font) | |
| ############################################ | |
| # Predict detections with MegaDetector v5a model | |
| def predict_md(im, | |
| mega_model_input, | |
| size=640): | |
| # resize image | |
| g = (size / max(im.size)) # multipl factor to make max size of the image equal to input size | |
| im = im.resize((int(x * g) for x in im.size), | |
| Image.ANTIALIAS) # resize | |
| MD_model = torch.hub.load('ultralytics/yolov5', | |
| 'custom', | |
| path = Megadet_Models[mega_model_input], | |
| force_reload = True, | |
| source='github') | |
| ## detect objects | |
| results = MD_model(im) # inference # vars(results).keys()= dict_keys(['imgs', 'pred', 'names', 'files', 'times', 'xyxy', 'xywh', 'xyxyn', 'xywhn', 'n', 't', 's']) | |
| results.render() # updates results.imgs with boxes and labels | |
| return results | |
| ########################################## | |
| def crop_animal_detections(yolo_results, | |
| likelihood_th): | |
| ## Extract animal crops | |
| list_labels_as_str = [i for i in yolo_results.names.values()] # ['animal', 'person', 'vehicle'] # yolo_results.names | |
| list_np_animal_crops = [] | |
| # for every image | |
| for img, det_array in zip(yolo_results.ims, | |
| yolo_results.xyxy): | |
| # for every detection | |
| for j in range(det_array.shape[0]): | |
| # compute coords around bbox rounded to the nearest integer (for pasting later) | |
| xmin_rd = int(math.floor(det_array[j,0])) # int() should suffice? | |
| ymin_rd = int(math.floor(det_array[j,1])) | |
| xmax_rd = int(math.ceil(det_array[j,2])) | |
| ymax_rd = int(math.ceil(det_array[j,3])) | |
| pred_llk = det_array[j,4] | |
| pred_label = det_array[j,5] | |
| # keep animal crops above threshold | |
| # pdb.set_trace() | |
| if (pred_label == list_labels_as_str.index('animal')) and \ | |
| (pred_llk >= likelihood_th): | |
| area = (xmin_rd, ymin_rd, xmax_rd, ymax_rd) | |
| crop = Image.fromarray(img).crop(area) | |
| crop_np = np.asarray(crop) | |
| # add to list | |
| list_np_animal_crops.append(crop_np) | |
| return list_np_animal_crops | |
| ########################################## | |
| def predict_dlc(list_np_crops, | |
| kpts_likelihood_th, | |
| DLCmodel, | |
| dlc_proc): | |
| # run dlc thru list of crops | |
| dlc_live = DLCLive(DLCmodel, processor=dlc_proc) | |
| dlc_live.init_inference(list_np_crops[0]) | |
| list_kpts_per_crop = [] | |
| np_aux = np.empty((1,3)) # can I avoid hardcoding here? | |
| for crop in list_np_crops: | |
| # scale crop here? | |
| keypts_xyp = dlc_live.get_pose(crop) # third column is llk! | |
| # set kpts below threhsold to nan | |
| keypts_xyp[keypts_xyp[:,-1] < kpts_likelihood_th,:] = np_aux.fill(np.nan) | |
| # add kpts of this crop to list | |
| list_kpts_per_crop.append(keypts_xyp) | |
| return list_kpts_per_crop | |
| ##################################################### | |
| def predict_pipeline(img_input, | |
| mega_model_input, | |
| dlc_model_input_str, | |
| flag_dlc_only, | |
| flag_show_str_labels, | |
| bbox_likelihood_th, | |
| kpts_likelihood_th, | |
| font_style, | |
| font_size, | |
| keypt_color, | |
| marker_size, | |
| ): | |
| ############################################################ | |
| ## Get DLC model and labels as strings | |
| # TODO: make a dict as for megadetector | |
| # pdb.set_trace() | |
| path_to_DLCmodel = DLC_models[dlc_model_input_str] | |
| pose_cfg_path = os.path.join(path_to_DLCmodel,'pose_cfg.yaml') | |
| #path_to_DLCmodel = DownloadModel(dlc_model_input_str, './model/') | |
| #pose_cfg_path = './model/pose_cfg.yaml' | |
| # extract map label ids to strings | |
| # pose_cfg_dict['all_joints'] is a list of one-element lists, | |
| with open(pose_cfg_path, "r") as stream: | |
| pose_cfg_dict = yaml.safe_load(stream) | |
| map_label_id_to_str = dict([(k,v) for k,v in zip([el[0] for el in pose_cfg_dict['all_joints']], | |
| pose_cfg_dict['all_joints_names'])]) | |
| ############################################################ | |
| # ### Run Megadetector | |
| md_results = predict_md(img_input, | |
| mega_model_input, | |
| size=640) #Image.fromarray(results.imgs[0]) | |
| ################################################################ | |
| # Obtain animal crops for bboxes with confidence above th | |
| list_crops = crop_animal_detections(md_results, | |
| bbox_likelihood_th) | |
| ############################################################## | |
| # Run DLC | |
| dlc_proc = Processor() | |
| # if required: ignore MD crops and run DLC on full image [mostly for testing] | |
| if flag_dlc_only: | |
| # compute kpts on input img | |
| list_kpts_per_crop = predict_dlc([np.asarray(img_input)], | |
| kpts_likelihood_th, | |
| path_to_DLCmodel, | |
| dlc_proc) | |
| # draw kpts on input img | |
| draw_keypoints_on_image(img_input, | |
| list_kpts_per_crop[0], # a numpy array with shape [num_keypoints, 2]. | |
| map_label_id_to_str, | |
| flag_show_str_labels, | |
| use_normalized_coordinates=False, | |
| font_style=font_style, | |
| font_size=font_size, | |
| keypt_color=keypt_color, | |
| marker_size=marker_size) | |
| return img_input | |
| else: | |
| # Compute kpts for each crop | |
| list_kpts_per_crop = predict_dlc(list_crops, | |
| kpts_likelihood_th, | |
| path_to_DLCmodel, | |
| dlc_proc) | |
| # Produce final image | |
| img_background = Image.fromarray(md_results.ims[0]) # img_input or Image.fromarray(md_results.imgs[0])? | |
| # # Image.fromarray(md_results.imgs[0]) --> (640, 479) | |
| # # img_input.size ---> (259, 194) | |
| # resize image to match megadetector output | |
| # img_background = img_input | |
| # g = (640 / max(img_background.size)) # gain | |
| # img_background = img_background.resize((int(x * g) for x in img_background.size), | |
| # Image.ANTIALIAS) # resize | |
| for ic, (np_crop, kpts_crop) in enumerate(zip(list_crops, | |
| list_kpts_per_crop)): | |
| ## Draw keypts on crop | |
| img_crop = Image.fromarray(np_crop) | |
| draw_keypoints_on_image(img_crop, | |
| kpts_crop, # a numpy array with shape [num_keypoints, 2]. | |
| map_label_id_to_str, | |
| flag_show_str_labels, | |
| use_normalized_coordinates=False, # if True, then I should use md_results.xyxyn for list_kpts_crop | |
| font_style=font_style, | |
| font_size=font_size, | |
| keypt_color=keypt_color, | |
| marker_size=marker_size) | |
| ## Paste crop in original image | |
| # https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.Image.paste | |
| img_background.paste(img_crop, | |
| box = tuple([int(t) for t in md_results.xyxy[0][ic,:2]])) | |
| return img_background | |
| ############################################# | |
| # %% | |
| # User interface: inputs | |
| # Input image | |
| gr_image_input = gr.inputs.Image(type="pil", label="Input Image") | |
| # Models | |
| gr_dlc_model_input = gr.inputs.Dropdown(choices=list(DLC_models_list), # choices | |
| default='full_cat', # default option | |
| type='value', # Type of value to be returned by component. "value" returns the string of the choice selected, "index" returns the index of the choice selected. | |
| label='Select DeepLabCut model') | |
| gr_mega_model_input = gr.inputs.Dropdown(choices=list(Megadet_Models.keys()), | |
| default='md_v5a', # default option | |
| type='value', # Type of value to be returned by component. "value" returns the string of the choice selected, "index" returns the index of the choice selected. | |
| label='Select MegaDetector model') | |
| # Other inputs | |
| gr_dlc_only_checkbox = gr.inputs.Checkbox(False, | |
| label='Run DLClive only, directly on input image?') | |
| gr_str_labels_checkbox = gr.inputs.Checkbox(True, | |
| label='Show bodypart labels?') | |
| gr_slider_conf_bboxes = gr.inputs.Slider(0,1,.05,0.8, | |
| label='Set confidence threshold for animal detections') | |
| gr_slider_conf_keypoints = gr.inputs.Slider(0,1,.05,0.25, | |
| label='Set confidence threshold for keypoints') | |
| # Data viz | |
| gr_keypt_color = gr.ColorPicker(value ="#ff0000", label="choose color for keypoint label") | |
| gr_labels_font_style = gr.inputs.Dropdown(choices=['amiko', 'nature', 'painter', 'animals', 'zen'], | |
| default='amiko', | |
| type='value', | |
| label='Select keypoint label font') | |
| gr_slider_font_size = gr.inputs.Slider(5,30,1,8, | |
| label='Set font size') | |
| gr_slider_marker_size = gr.inputs.Slider(0.5,5,0.2,2, | |
| label='Set marker size') | |
| # list of inputs | |
| inputs = [gr_image_input, | |
| gr_mega_model_input, | |
| gr_dlc_model_input, | |
| gr_dlc_only_checkbox, | |
| gr_str_labels_checkbox, | |
| gr_slider_conf_bboxes, | |
| gr_slider_conf_keypoints, | |
| gr_labels_font_style, | |
| gr_slider_font_size, | |
| gr_keypt_color, | |
| gr_slider_marker_size, | |
| ] | |
| #################################################### | |
| # %% | |
| # User interface: outputs | |
| gr_image_output = gr.outputs.Image(type="pil", label="Output Image") | |
| outputs = [gr_image_output] | |
| ############################################## | |
| # User interace: description | |
| gr_title = "MegaDetector v5 + DeepLabCut-Live!" | |
| gr_description = "Contributed by Sofia Minano, Neslihan Wittek, Nirel Kadzo, VicShaoChih Chiang -- DLC AI Residents 2022..\ | |
| This App detects and estimate the pose of animals in camera trap images using <a href='https://github.com/microsoft/CameraTraps'>MegaDetector v5a</a> + <a href='https://github.com/DeepLabCut/DeepLabCut-live'>DeepLabCut-live</a>. \ | |
| We host models from the <a href='http://www.mackenziemathislab.org/dlc-modelzoo'>DeepLabCut ModelZoo Project</a>\, and two <a href='https://github.com/microsoft/CameraTraps/blob/main/megadetector.md'>MegaDetector Models</a>. Please carefully check their licensing information if you use this project. The App additionally builds upon on work from <a href='https://huggingface.co/spaces/hlydecker/MegaDetector_v5'>hlydecker/MegaDetector_v5</a> \ | |
| <a href='https://huggingface.co/spaces/sofmi/MegaDetector_DLClive'>sofmi/MegaDetector_DLClive</a> \ | |
| <a href='https://huggingface.co/spaces/Neslihan/megadetector_dlcmodels'>Neslihan/megadetector_dlcmodels</a>\." | |
| # article = "<p style='text-align: center'>This app makes predictions using a YOLOv5x6 model that was trained to detect animals, humans, and vehicles in camera trap images; find out more about the project on <a href='https://github.com/microsoft/CameraTraps'>GitHub</a>. This app was built by Henry Lydecker but really depends on code and models developed by <a href='http://ecologize.org/'>Ecologize</a> and <a href='http://aka.ms/aiforearth'>Microsoft AI for Earth</a>. Find out more about the YOLO model from the original creator, <a href='https://pjreddie.com/darknet/yolo/'>Joseph Redmon</a>. YOLOv5 is a family of compound-scaled object detection models trained on the COCO dataset and developed by Ultralytics, and includes simple functionality for Test Time Augmentation (TTA), model ensembling, hyperparameter evolution, and export to ONNX, CoreML and TFLite. <a href='https://github.com/ultralytics/yolov5'>Source code</a> | <a href='https://pytorch.org/hub/ultralytics_yolov5'>PyTorch Hub</a></p>" | |
| examples = [['example/monkey_full.jpg', 'md_v5a','full_macaque', False, True, 0.5, 0.3, 'amiko', 5, 'blue', 3], | |
| ['example/dog.jpeg', 'md_v5a', 'full_dog', False, True, 0.5, 0.05, 'amiko', 5, 'yellow', 3], | |
| ['example/cat.jpg', 'md_v5a', 'full_cat', False, True, 0.5, 0.05, 'amiko', 5, 'purple', 3], | |
| ['example/monkey_face.jpeg', 'md_v5a', 'primate_face', False, True, 0.5, 0.05, 'amiko', 5, 'orange', 3]] | |
| ################################################ | |
| # %% Define and launch gradio interface | |
| demo = gr.Interface(predict_pipeline, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title=gr_title, | |
| description=gr_description, | |
| examples=examples, | |
| theme="huggingface", | |
| #live=True | |
| ) | |
| demo.launch(enable_queue=True, share=True) | |