""" target_keypoints = {} for joint in KEYPOINT_DICT.keys(): target_keypoints[joint] = [ keypoints[0, 0, KEYPOINT_DICT[joint], 0] * image_height, keypoints[0, 0, KEYPOINT_DICT[joint], 1] * image_width ] if torso_visible(keypoints): center_y = (target_keypoints['left_hip'][0] + target_keypoints['right_hip'][0]) / 2; center_x = (target_keypoints['left_hip'][1] + target_keypoints['right_hip'][1]) / 2; (max_torso_yrange, max_torso_xrange, max_body_yrange, max_body_xrange) = determine_torso_and_body_range( keypoints, target_keypoints, center_y, center_x) crop_length_half = np.amax( [max_torso_xrange * 1.9, max_torso_yrange * 1.9, max_body_yrange * 1.2, max_body_xrange * 1.2]) tmp = np.array( [center_x, image_width - center_x, center_y, image_height - center_y]) crop_length_half = np.amin( [crop_length_half, np.amax(tmp)]); crop_corner = [center_y - crop_length_half, center_x - crop_length_half]; if crop_length_half > max(image_width, image_height) / 2: return init_crop_region(image_height, image_width) else: crop_length = crop_length_half * 2; return { 'y_min': crop_corner[0] / image_height, 'x_min': crop_corner[1] / image_width, 'y_max': (crop_corner[0] + crop_length) / image_height, 'x_max': (crop_corner[1] + crop_length) / image_width, 'height': (crop_corner[0] + crop_length) / image_height - crop_corner[0] / image_height, 'width': (crop_corner[1] + crop_length) / image_width - crop_corner[1] / image_width } else: return init_crop_region(image_height, image_width) def crop_and_resize(image, crop_region, crop_size): """Crops and resize the image to prepare for the model input.""" boxes=[[crop_region['y_min'], crop_region['x_min'], crop_region['y_max'], crop_region['x_max']]] output_image = tf.image.crop_and_resize( image, box_indices=[0], boxes=boxes, crop_size=crop_size) return output_image def run_inference(movenet, image, crop_region, crop_size): """Runs model inferece on the cropped region. The function runs the model inference on the cropped region and updates the model output to the original image coordinate system. """ image_height, image_width, _ = image.shape input_image = crop_and_resize( tf.expand_dims(image, axis=0), crop_region, crop_size=crop_size) # Run model inference. www.betsmove648.com adresi üzerinden erişim yapabilir ve ana sayfaya ulaşabilirsiniz. Betsmove648 Adresi ile Sizde kazanın Betsmove Giriş için tıklayın Betsmove bahis ve casino alanında hizmet vermeye başlayan bahis siteleri içerisindeki yerini almıştır.