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| import os | |
| os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' | |
| os.environ['CUDA_VISIBLE_DEVICES'] = '0' | |
| import tensorflow as tf | |
| import tf_bodypix | |
| from tf_bodypix.api import download_model, load_model, BodyPixModelPaths | |
| from tf_bodypix.draw import draw_poses | |
| from tensorflow.keras import preprocessing | |
| import cv2 | |
| import json | |
| from matplotlib import pyplot as plt | |
| import numpy as np | |
| from calculations import measure_body_sizes | |
| import gradio as gr | |
| import pandas as pd | |
| # Load BodyPix model | |
| bodypix_model = load_model(download_model(BodyPixModelPaths.MOBILENET_FLOAT_50_STRIDE_16)) | |
| rainbow = [ | |
| [110, 64, 170], [143, 61, 178], [178, 60, 178], [210, 62, 167], | |
| [238, 67, 149], [255, 78, 125], [255, 94, 99], [255, 115, 75], | |
| [255, 140, 56], [239, 167, 47], [217, 194, 49], [194, 219, 64], | |
| [175, 240, 91], [135, 245, 87], [96, 247, 96], [64, 243, 115], | |
| [40, 234, 141], [28, 219, 169], [26, 199, 194], [33, 176, 213], | |
| [47, 150, 224], [65, 125, 224], [84, 101, 214], [99, 81, 195] | |
| ] | |
| def process_images(front_img, side_img, real_height_cm): | |
| fimage_array = preprocessing.image.img_to_array(front_img) | |
| simage_array = preprocessing.image.img_to_array(side_img) | |
| # bodypix prediction | |
| frontresult = bodypix_model.predict_single(fimage_array) | |
| sideresult = bodypix_model.predict_single(simage_array) | |
| front_mask = frontresult.get_mask(threshold=0.75) | |
| side_mask = sideresult.get_mask(threshold=0.75) | |
| # preprocessing.image.save_img(f'{output_path}/frontbodypix-mask.jpg',front_mask) | |
| # preprocessing.image.save_img(f'{output_path}/sidebodypix-mask.jpg',side_mask) | |
| front_colored_mask = frontresult.get_colored_part_mask(front_mask, rainbow) | |
| side_colored_mask = sideresult.get_colored_part_mask(side_mask, rainbow) | |
| # preprocessing.image.save_img(f'{output_path}/frontbodypix-colored-mask.jpg',front_colored_mask) | |
| # preprocessing.image.save_img(f'{output_path}/sidebodypix-colored-mask.jpg',side_colored_mask) | |
| frontposes = frontresult.get_poses() | |
| front_image_with_poses = draw_poses( | |
| fimage_array.copy(), # create a copy to ensure we are not modifing the source image | |
| frontposes, | |
| keypoints_color=(255, 100, 100), | |
| skeleton_color=(100, 100, 255) | |
| ) | |
| sideposes = sideresult.get_poses() | |
| side_image_with_poses = draw_poses( | |
| simage_array.copy(), # create a copy to ensure we are not modifing the source image | |
| sideposes, | |
| keypoints_color=(255, 100, 100), | |
| skeleton_color=(100, 100, 255) | |
| ) | |
| # print(np.array(simage).shape) | |
| # print(np.array(side_colored_mask).shape) | |
| # preprocessing.image.save_img(f'{output_path}/frontbodypix-poses.jpg', front_image_with_poses) | |
| # preprocessing.image.save_img(f'{output_path}/sidebodypix-poses.jpg', side_image_with_poses) | |
| body_sizes = measure_body_sizes(side_colored_mask, front_colored_mask, sideposes, frontposes, real_height_cm, rainbow) | |
| measurements_df = pd.DataFrame([body_sizes[0]]) | |
| return measurements_df | |
| # Create the Gradio interface | |
| interface = gr.Interface( | |
| fn=process_images, | |
| inputs=[ | |
| gr.Image(label="Upload Front Pose"), | |
| gr.Image(label="Upload Side Pose"), | |
| gr.Number(label="Enter Height (cm)") | |
| ], | |
| outputs=[ | |
| gr.DataFrame(label="Body Measurements") | |
| ], | |
| title="Body Sizing System Demo", | |
| description="Upload two images: Front View and Side View, and input the height in cm." | |
| ) | |
| # Launch the app | |
| interface.launch(share=False) |