import numpy as np import pandas as pd def get_width(mask, keypoint_y): pred_np = mask.numpy() # Find the index of the first occurrence of the mask label first_index = np.argmax(pred_np[keypoint_y,:] > 0) # Find the index of the last occurrence of the mask label last_index = len(pred_np[keypoint_y,:]) - np.argmax(np.flip(pred_np[keypoint_y,:]) > 0) - 1 return last_index-first_index def calculate_bbox(original_image, keypoint_1, keypoint_2): # Determine bounding box coordinates min_x = 0 min_y = int(min(keypoint_1[1], keypoint_2[1])) max_y = int(max(keypoint_1[1], keypoint_2[1])) # Set max_x to be the width of the image max_x = original_image.size[0] return [min_x, max_x, min_y, max_y] def get_volume_result(mask_of_interest, original_image, max_x, min_x, max_y, min_y): # thr = mask_of_interest[mask_of_interest > 0].mean() thr = 0 if min_x < 0 or max_x > mask_of_interest.shape[1] or min_y < 0 or max_y > mask_of_interest.shape[0]: print("Warning: Invalid indices for slicing.") if min_x < max_x and min_y < max_y: region_inside_bounding_box = mask_of_interest[min_y:max_y, min_x:max_x] > thr return region_inside_bounding_box.sum() else: print("Invalid bounding box indices.") return None def calculate_all_measures(front_image, side_image, front_keypoint_data, side_keypoint_data, front_seg_mask, side_rcnn_mask): # Initialize an empty DataFrame results = [] results_dict = {} front_keypoints = front_keypoint_data['keypoints'] front_keypoint_names = front_keypoint_data['keypoint_names'] side_keypoints = side_keypoint_data['keypoints'] side_keypoint_names = side_keypoint_data['keypoint_names'] # calculate the body length # pick the longer from the two ankle keypoints on y coordinate side_body_length = side_keypoints[15][1] if side_keypoints[15][1] > side_keypoints[16][1] else side_keypoints[16][1] # 5: 'left_shoulder' left_shoulder_y = round(front_keypoints[5][1]) # print("shoulder width", get_width(front_seg_mask, left_shoulder_y)) results_dict['shoulder_width'] = get_width(front_seg_mask, left_shoulder_y) # remove left-arm mask front_seg_mask[front_seg_mask == 14] = 0 # remove right-arm mask front_seg_mask[front_seg_mask == 15] = 0 # 11: 'left_hip' left_hip_y = round(front_keypoints[11][1]) # print("hip width", get_width(front_seg_mask, left_hip_y)) results_dict['hip_width'] = get_width(front_seg_mask, left_hip_y) # calculate shoulder_to_hip distance shoulder_to_hip_distance = front_keypoints[11][1] - front_keypoints[5][1] # print("Shoulder to hip distance:", shoulder_to_hip_distance) results_dict['shoulder_to_hip_distance'] = shoulder_to_hip_distance # calculate hip_to_ankle distance hip_to_ankle_distance = front_keypoints[16][1] - front_keypoints[12][1] # print("Hip to ankle distance:", hip_to_ankle_distance) results_dict['hip_to_ankle_distance'] = hip_to_ankle_distance # calculate torso_to_leg proportions torso_to_leg_proportions = shoulder_to_hip_distance / hip_to_ankle_distance # print("Torso to leg proportions:", torso_to_leg_proportions) results_dict['torso_to_leg_ratio'] = torso_to_leg_proportions # waist # assuming waistline is x % higher from hips # hip_y axis - (40 % of shoulder to hip distance) waist_y = round(front_keypoints[11][1] - (shoulder_to_hip_distance * 0.40)) # print("waist width", get_width(front_seg_mask, waist_y)) results_dict['waist_width'] = get_width(front_seg_mask, waist_y) # Calculate bounding box for thigh right_knee = side_keypoints[side_keypoint_names.index("right_knee")] right_hip = side_keypoints[side_keypoint_names.index("right_hip")] # Calculate bounding box for torso right_shoulder = side_keypoints[side_keypoint_names.index("right_shoulder")] # Replace keypoints, keypoint_names, combined_mask, and original_image with your actual data thigh_bbox = calculate_bbox(side_image, right_knee, right_hip) torso_bbox = calculate_bbox(side_image, right_hip, right_shoulder) # Calculate midpoint coordinates torso_midpoint = [0, (right_hip[1] + right_shoulder[1]) / 2] lower_torso_bbox = calculate_bbox(side_image, right_hip, torso_midpoint) upper_torso_bbox = calculate_bbox(side_image, torso_midpoint, right_shoulder) # Replace keypoints, keypoint_names, combined_mask, and original_image with your actual data thigh_area = get_volume_result(side_rcnn_mask, side_image, thigh_bbox[1], thigh_bbox[0], thigh_bbox[3], thigh_bbox[2]) # Thigh volume torso_area = get_volume_result(side_rcnn_mask, side_image, torso_bbox[1], torso_bbox[0], torso_bbox[3], torso_bbox[2]) # Torso volume lower_torso_area = get_volume_result(side_rcnn_mask, side_image, lower_torso_bbox[1], lower_torso_bbox[0], lower_torso_bbox[3], lower_torso_bbox[2]) # Lower torso volume upper_torso_area = get_volume_result(side_rcnn_mask, side_image, upper_torso_bbox[1], upper_torso_bbox[0], upper_torso_bbox[3], upper_torso_bbox[2]) # Upper torso volume full_side_body_area = (side_rcnn_mask > 0).sum() # print(f"Thigh area: {thigh_area}") # print(f"Torso area: {torso_area}") # print(f"Lower torso area: {lower_torso_area}") # print(f"Upper torso area: {upper_torso_area}") results_dict['thigh_area'] = thigh_area results_dict['torso_area'] = torso_area results_dict['lower_torso_area'] = lower_torso_area results_dict['upper_torso_area'] = upper_torso_area results_dict['full_side_body_area'] = full_side_body_area # # calculate ratios results_dict['thigh_normalised'] = thigh_area / side_body_length results_dict['torso_normalised'] = torso_area / side_body_length results_dict['thigh_to_torso_ratio_normalised'] = results_dict['thigh_normalised'] / results_dict['torso_normalised'] results_dict['thigh_to_torso_ratio'] = thigh_area / torso_area results_dict['upper_torso_normalised'] = upper_torso_area / side_body_length results_dict['lower_torso_normalised'] = lower_torso_area / side_body_length results_dict['upper_to_lower_torso_normalised_ratio'] = results_dict['upper_torso_normalised'] / results_dict['lower_torso_normalised'] results_dict['upper_to_lower_torso_ratio'] = upper_torso_area / lower_torso_area results_dict['shoulder_to_hip_ratio'] = results_dict['shoulder_width'] / results_dict['hip_width'] results_dict['shoulder_to_waist_ratio'] = results_dict['shoulder_width'] / results_dict['waist_width'] results_dict['waist_to_hip_ratio'] = results_dict['waist_width'] / results_dict['hip_width'] results_dict['thigh_to_body_ratio'] = thigh_area / full_side_body_area results_dict['upper_torso_to_body_ratio'] = upper_torso_area / full_side_body_area results_dict['upper_torso_to_body_ratio'] = upper_torso_area / full_side_body_area # TODO: Temporary to force thigns to work. results_dict['id'] = "1" results.append(results_dict) results_df = pd.DataFrame(results) return results_df