bounding_box = { 'cam_00_10_30': bbox1, 'cam_01_10_30': bbox2, 'cam_02_10_30': bbox3, 'cam_03_10_30': bbox4 } img_paths = os.listdir(params['test_img_directory']) for img_path in img_paths: img = cv2.imread(os.path.join(params['test_img_directory'], img_path)) bbox = bounding_box[img_path[0:12]] size = max((bbox[2] - bbox[0]), (bbox[3] - bbox[1])) img_crop = np.copy(img)[bbox[1]:bbox[1] + size, bbox[0]:bbox[0] + size] img_resize = cv2.resize(img_crop, (256, 256)) #test_img = cv2.cvtColor(test_img, cv2.COLOR_BGR2RGB) if predictJoints: predictions = model.predictJoints(img_resize, mode='gpu') print(' Predict on {}'.format(img_path)) print( np.add( np.asarray(predictions) * (bbox[1] - bbox[0]) / 256, np.array([bbox[0], bbox[2]]))) show_prections(img_resize, predictions, img_path[0:-4] + '_pred' + img_path[-4:]) else: # output heatmap = 1*64 x 64 x outputDim out_heatmap = np.squeeze(model.predictHM(img_resize)) for i in range(out_heatmap.shape[2]): joint_hm = np.asarray(cv2.resize(255 * out_heatmap[:, :, i], (256, 256)), dtype=np.uint8) joint_hm = cv2.applyColorMap(joint_hm, cv2.COLORMAP_JET)
keypt = (int(coord[0]), int(coord[1])) text_loc = (keypt[0]+5, keypt[1]+7) cv2.circle(img, keypt, 3, RED, -1) cv2.putText(img, str(i), text_loc, cv2.FONT_HERSHEY_DUPLEX, 0.5, RED, 1, cv2.LINE_AA) np.savetxt(a.output_dir+filename[:-4]+'_pred.csv', predictions , delimiter=",", fmt='%i') # print(a.output_dir+filename+'_pred.csv') cv2.imwrite(a.output_dir+filename[:-4]+'_pred.png',img) # cv2.imshow('img', img) # cv2.waitKey(0) if __name__ == '__main__': print('--Parsing Config File') params = process_config('config.cfg') from os import listdir ImageFileNames=listdir(a.input_dir) model = Inference(model=a.checkpoint) for i in range(len(ImageFileNames)): #ImageName = a.input_dir+'/'+ImageFileNames[i] img = cv2.imread(os.path.join( a.input_dir, ImageFileNames[i])) test_img=img predictions = model.predictJoints(test_img, mode='gpu') show_prections(test_img, predictions,ImageFileNames[i])