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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) | |
| front_colored_mask = frontresult.get_colored_part_mask(front_mask, rainbow) | |
| side_colored_mask = sideresult.get_colored_part_mask(side_mask, rainbow) | |
| frontposes = frontresult.get_poses() | |
| sideposes = sideresult.get_poses() | |
| # Calculate body sizes | |
| body_sizes = measure_body_sizes(side_colored_mask, front_colored_mask, sideposes, frontposes, real_height_cm, rainbow) | |
| print(body_sizes) | |
| measurements_df = pd.DataFrame([body_sizes]) if isinstance(body_sizes, dict) else pd.DataFrame(body_sizes) | |
| # Save measurements to CSV | |
| csv_file = "Body-measurement.csv" | |
| if not os.path.exists(csv_file): | |
| measurements_df.to_csv(csv_file, index=False) | |
| else: | |
| measurements_df.to_csv(csv_file, mode='a', header=False, index=False) | |
| # Prepare measurements for display | |
| measurement_display = measurements_df.to_html(index=False, justify="center", border=1) | |
| print(measurement_display) | |
| return f""" | |
| <h3 style="text-align: center;">Body Measurements</h3> | |
| <div style="text-align: center;"> | |
| </div> | |
| {measurement_display} | |
| """ | |
| # Create the Gradio interface | |
| interface = gr.Interface( | |
| fn=process_images, | |
| inputs=[ | |
| gr.Image(sources=["webcam", "upload"], type="numpy", label="Front Pose"), | |
| gr.Image(sources=["webcam", "upload"], type="numpy", label="Side Pose"), | |
| gr.Number(label="Enter Your Height (cm)") | |
| ], | |
| outputs=gr.HTML(label="Measurement Results"), # Use HTML output to display the measurements | |
| title="Body Sizing System Demo", | |
| description="Capture two webcam images: Front View and Side View, and input your height in cm." | |
| ) | |
| # Launch the app | |
| interface.launch(share=True) |