import gradio as gr import torch import torchvision import numpy as np from PIL import Image import PIL.ImageDraw as ImageDraw import math import pdb from dlclive import DLCLive, Processor import matplotlib.pyplot as plt ######################################### # https://www.programcreek.com/python/?code=fjchange%2Fobject_centric_VAD%2Fobject_centric_VAD-master%2Fobject_detection%2Futils%2Fvisualization_utils.py def draw_keypoints_on_image(image, keypoints, color='red', radius=2, use_normalized_coordinates=True): """Draws keypoints on an image. Args: image: a PIL.Image object. keypoints: a numpy array with shape [num_keypoints, 2]. 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[1] for k in keypoints] keypoints_y = [k[0] 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 for keypoint_x, keypoint_y in zip(keypoints_x, keypoints_y): draw.ellipse([(keypoint_x - radius, keypoint_y - radius), (keypoint_x + radius, keypoint_y + radius)], outline=color, fill=color) ############################################ # Predict detections with MegaDetector v5a model def predict_md(im, size=640): # resize image g = (size / max(im.size)) # gain im = im.resize((int(x * g) for x in im.size), Image.ANTIALIAS) # resize ## 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 #Image.fromarray(results.imgs[0]) ---return animals only? def crop_animal_detections(yolo_results, likelihood_th): ## crop if animal and return list of crops list_labels_as_str = yolo_results.names #['animal', 'person', 'vehicle'] list_np_animal_crops = [] # for every image for img, det_array in zip(yolo_results.imgs, 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] #-----TODO: filter based on likelihood? pred_label = det_array[j,5] 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) # for detections_dict in img_data["detections"]: # index = img_data["detections"].index(detections_dict) # if detections_dict["conf"] > 0.8: # x1, y1,w_box, h_box = detections_dict["bbox"] # ymin,xmin,ymax, xmax = y1, x1, y1 + h_box, x1 + w_box # imageWidth=img.size[0] # imageHeight= img.size[1] # area = (xmin * imageWidth, ymin * imageHeight, xmax * imageWidth, # ymax * imageHeight) # crop = img.crop(area) # crop_np = np.asarray(crop) # # if detections_dict["category"] == "1": return list_np_animal_crops def predict_dlc(list_np_crops,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 = [] for crop in list_np_crops: # scale crop? keypts = dlc_live.get_pose(crop) # third column is llk! list_kpts_per_crop.append(keypts) return list_kpts_per_crop def predict_pipeline(img_input): # these eventually user inputs.... path_to_DLCmodel = "DLC_models/DLC_Cat_resnet_50_iteration-0_shuffle-0" likelihood_th = 0.8 # Run Megadetector md_results = predict_md(img_input) #Image.fromarray(results.imgs[0]) # Obtain animal crops with confidence above th list_crops = crop_animal_detections(md_results, likelihood_th) # Run DLC # TODO: add llk threshold for kpts too? dlc_proc = Processor() list_kpts_per_crop = predict_dlc(list_crops, path_to_DLCmodel, dlc_proc) # # Produce final image # fig = plt.Figure(md_results.imgs[0].shape[:2]) #figsize=(10,10)) #md_results.imgs[0].shape) 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]. color='red', radius=2, use_normalized_coordinates=False) # if True, then I should use md_results.xyxyn ## Paste crop in original image # https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.Image.paste img_input.paste(img_crop, box = tuple([int(math.floor(t)) for t in md_results.xyxy[0][ic,:2]])) # plt.imshow(np_crop) # plt.scatter(kpts_crop[:,0], kpts_crop[:,1], 40, # color='r') # img_overlay = Image.frombytes('RGB', # fig.canvas.get_width_height(), # fig.canvas.tostring_rgb()) return img_input #Image.fromarray(list_crops[0]) #Image.fromarray(md_results.imgs[0]) # ########################################################## # Get MegaDetector model # TODO: Allow user selectable model? # models = ["model_weights/md_v5a.0.0.pt","model_weights/md_v5b.0.0.pt"] MD_model = torch.hub.load('ultralytics/yolov5', 'custom', "model_weights/md_v5a.0.0.pt") #################################################### # Create user interface and launch #inputs = [image, chosen_model, size] inputs = gr.inputs.Image(type="pil", label="Input Image") outputs = gr.outputs.Image(type="pil", label="Output Image") #image = gr.inputs.Image(type="pil", label="Input Image") #chosen_model = gr.inputs.Dropdown(choices = models, value = "model_weights/md_v5a.0.0.pt",type = "value", label="Model Weight") #size = 640 title = "MegaDetector v5 + DLC live" description = "Detect and estimate pose of animals camera trap images using MegaDetector v5a + DLClive" # article = "

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 GitHub. This app was built by Henry Lydecker but really depends on code and models developed by Ecologize and Microsoft AI for Earth. Find out more about the YOLO model from the original creator, Joseph Redmon. 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. Source code | PyTorch Hub

" # examples = [['data/Macropod.jpg'], ['data/koala2.jpg'],['data/cat.jpg'],['data/BrushtailPossum.jpg']] gr.Interface(predict_pipeline, inputs, outputs, title=title, description=description, theme="huggingface").launch(enable_queue=True) # def dlclive_pose(model, crop_np, crop, fname, index,dlc_proc): # dlc_live = DLCLive(model, processor=dlc_proc) # dlc_live.init_inference(crop_np) # keypts = dlc_live.get_pose(crop_np) # savetxt(str(fname)+ '_' + str(index) + '.csv' , keypts, delimiter=',') # xpose = [] # ypose = [] # for key in keypts[:,2]: # # if key > 0.05: # which value do we need here? # i = np.where(keypts[:,2]==key) # xpose.append(keypts[i,0]) # ypose.append(keypts[i,1]) # plt.imshow(crop) # plt.scatter(xpose[:], ypose[:], 40, color='cyan') # plt.savefig(str(fname)+ '_' + str(index) + '.png') # plt.show() # plt.clf()