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| 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 | |