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| from tkinter import W | |
| import gradio as gr | |
| from matplotlib import cm | |
| import torch | |
| import torchvision | |
| from dlclive import DLCLive, Processor | |
| import matplotlib | |
| from PIL import Image, ImageColor, ImageFont, ImageDraw | |
| import numpy as np | |
| import math | |
| import yaml | |
| import pdb | |
| ############################################ | |
| # Predict detections with MegaDetector v5a model | |
| def predict_md(im, | |
| megadetector_model, #Megadet_Models[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 | |
| # device | |
| if torch.cuda.is_available(): | |
| md_device = torch.device('cuda') | |
| else: | |
| md_device = torch.device('cpu') | |
| # megadetector | |
| MD_model = torch.hub.load('ultralytics/yolov5', # repo_or_dir | |
| 'custom', #model | |
| megadetector_model, # args for callable model | |
| force_reload=True, | |
| device=md_device) | |
| # send model to gpu if possible | |
| if (md_device == torch.device('cuda')): | |
| print('Sending model to GPU') | |
| MD_model.to(md_device) | |
| ## detect objects | |
| results = MD_model(im) # inference # vars(results).keys()= dict_keys(['imgs', 'pred', 'names', 'files', 'times', 'xyxy', 'xywh', 'xyxyn', 'xywhn', 'n', 't', 's']) | |
| return results | |
| ########################################## | |
| def crop_animal_detections(img_in, | |
| yolo_results, | |
| likelihood_th): | |
| ## Extract animal crops | |
| list_labels_as_str = [i for i in yolo_results.names.values()] # ['animal', 'person', 'vehicle'] | |
| list_np_animal_crops = [] | |
| # image to crop (scale as input for megadetector) | |
| img_in = img_in.resize((yolo_results.ims[0].shape[1], | |
| yolo_results.ims[0].shape[0])) | |
| # for every detection in the img | |
| for det_array in 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 | |
| if (pred_label == list_labels_as_str.index('animal')) and \ | |
| (pred_llk >= likelihood_th): | |
| area = (xmin_rd, ymin_rd, xmax_rd, ymax_rd) | |
| #pdb.set_trace() | |
| crop = img_in.crop(area) #Image.fromarray(img_in).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 = [] | |
| all_kypts = [] | |
| 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 | |
| #pdb.set_trace() | |
| 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) | |
| all_kypts.append(keypts_xyp) | |
| return list_kpts_per_crop |