import numpy as np import os import time import sys import torch import zipfile import gradio as gr import u2net_load import u2net_run from rembg import remove from PIL import Image, ImageOps from predict_pose import generate_pose_keypoints # Use GPU if available if torch.cuda.is_available(): device = torch.device("cuda") else: device = torch.device("cpu") print(f"####Using {device}.#####") # Make directories os.system("mkdir ./Data_preprocessing") os.system("mkdir ./Data_preprocessing/test_color") os.system("mkdir ./Data_preprocessing/test_colormask") os.system("mkdir ./Data_preprocessing/test_edge") os.system("mkdir ./Data_preprocessing/test_img") os.system("mkdir ./Data_preprocessing/test_label") os.system("mkdir ./Data_preprocessing/test_mask") os.system("mkdir ./Data_preprocessing/test_pose") os.system("mkdir ./inputs") os.system("mkdir ./inputs/img") os.system("mkdir ./inputs/cloth") os.system("mkdir ./saved_models/") os.system("mkdir ./saved_models/u2net") os.system("mkdir ./saved_models/u2netp") os.system("mkdir ./pose") os.system("mkdir ./checkpoints") # Get pose model if not os.path.exists("./pose/pose_deploy_linevec.prototxt"): os.system("wget -O ./pose/pose_deploy_linevec.prototxt https://github.com/hasibzunair/fifa-demo/releases/download/v1.0/pose_deploy_linevec.prototxt") if not os.path.exists("./pose/pose_iter_440000.caffemodel"): os.system("wget -O ./pose/pose_iter_440000.caffemodel https://github.com/hasibzunair/fifa-demo/releases/download/v1.0/pose_iter_440000.caffemodel") # For segmentation mask generation if not os.path.exists("lip_final.pth"): os.system("wget https://github.com/hasibzunair/fifa-demo/releases/download/v1.0/lip_final.pth") # Get U-2-Net weights if not os.path.exists("saved_models/u2netp/u2netp.pth"): os.system("wget -P saved_models/u2netp/ https://github.com/hasibzunair/fifa-demo/releases/download/v1.0/u2netp.pth") if not os.path.exists("saved_models/u2net/u2net.pth"): os.system("wget -P saved_models/u2net/ https://github.com/hasibzunair/fifa-demo/releases/download/v1.0/u2net.pth") # Get model checkpoints if not os.path.exists("./checkpoints/decavtonfifapretrain/"): os.system("wget -O ./checkpoints/decavtonfifapretrain.zip https://github.com/hasibzunair/vton-demo/releases/download/v1.0/decavtonfifapretrain.zip") with zipfile.ZipFile('./checkpoints/decavtonfifapretrain.zip', 'r') as zip_ref: zip_ref.extractall('./checkpoints/') print("########################Setup done!########################") # Load U-2-Net model print(f"####Using {device}.#####") u2net = u2net_load.model(model_name = 'u2netp') def composite_background(img_mask, person_image_path, tryon_image_path): """Put background back on the person image after tryon.""" person = np.array(Image.open(person_image_path)) # tryon image tryon = np.array(Image.open(tryon_image_path)) # persom image mask from rembg p_mask = np.array(img_mask) # make binary mask p_mask = np.where(p_mask>0, 1, 0) # invert mask p_mask_inv = np.logical_not(p_mask) # make bg without person background = person * np.stack((p_mask_inv, p_mask_inv, p_mask_inv), axis=2) # make tryon image without background tryon_nobg = tryon * np.stack((p_mask, p_mask, p_mask), axis=2) tryon_nobg = tryon_nobg.astype("uint8") # composite tryon_with_bg = np.add(tryon_nobg, background) tryon_with_bg_pil = Image.fromarray(np.uint8(tryon_with_bg)).convert('RGB') tryon_with_bg_pil.save("results/test/try-on/tryon_with_bg.png") # Main inference function def inference(clothing_image, person_image, retrieve_bg): """ Do try-on! """ remove_bg = "no" # Read cloth and person images cloth = Image.open(clothing_image) # cloth person = Image.open(person_image) # person # Save cloth and person images in "input" folder cloth.save(os.path.join("inputs/cloth/cloth.png")) person.save(os.path.join("inputs/img/person.png")) ############## Clothing image pre-processing cloth_name = 'cloth.png' cloth_path = os.path.join('inputs/cloth', sorted(os.listdir('inputs/cloth'))[0]) cloth = Image.open(cloth_path) # Resize cloth image cloth = ImageOps.fit(cloth, (192, 256), Image.BICUBIC).convert("RGB") # Save resized cloth image cloth.save(os.path.join('Data_preprocessing/test_color', cloth_name)) # 1. Get binary mask for clothing image u2net_run.infer(u2net, 'Data_preprocessing/test_color', 'Data_preprocessing/test_edge') ############## Person image pre-processing start_time = time.time() # Person image img_name = 'person.png' img_path = os.path.join('inputs/img', sorted(os.listdir('inputs/img'))[0]) img = Image.open(img_path) if remove_bg == "yes": # Remove background img = remove(img, alpha_matting=True, alpha_matting_erode_size=15) print("Removing background from person image..") img = ImageOps.fit(img, (192, 256), Image.BICUBIC).convert("RGB") # Get binary from person image, used in def_composite_background img_mask = remove(img, alpha_matting=True, alpha_matting_erode_size=15, only_mask=True) img_path = os.path.join('Data_preprocessing/test_img', img_name) img.save(img_path) resize_time = time.time() print('Resized image in {}s'.format(resize_time-start_time)) # 2. Get parsed person image (test_label), uses person image os.system("python Self-Correction-Human-Parsing-for-ACGPN/simple_extractor.py --dataset 'lip' --model-restore 'lip_final.pth' --input-dir 'Data_preprocessing/test_img' --output-dir 'Data_preprocessing/test_label'") parse_time = time.time() print('Parsing generated in {}s'.format(parse_time-resize_time)) # 3. Get pose map from person image pose_path = os.path.join('Data_preprocessing/test_pose', img_name.replace('.png', '_keypoints.json')) generate_pose_keypoints(img_path, pose_path) pose_time = time.time() print('Pose map generated in {}s'.format(pose_time-parse_time)) os.system("rm -rf Data_preprocessing/test_pairs.txt") # Format: person, cloth image with open('Data_preprocessing/test_pairs.txt','w') as f: f.write('person.png cloth.png') # Do try-on os.system("python test.py --name decavtonfifapretrain") tryon_image = Image.open("results/test/try-on/person.png") print("Size of image is: ", tryon_image.size) # Return try-on with background added back on the person image if retrieve_bg == "yes": composite_background(img_mask, 'Data_preprocessing/test_img/person.png', 'results/test/try-on/person.png') return os.path.join("results/test/try-on/tryon_with_bg.png") # Return only try-on result else: return os.path.join("results/test/try-on/person.png") title = "Image based Virtual Try-On" description = "This is a demo for an image based virtual try-on system. It generates a synthetic image of a person wearing a target clothing item. To use it, simply upload your clothing item and person images, or click one of the examples to load them. This demo currently uses a temporary GPU. You can always run the demo locally, of course on a machine with a GPU!" article = "

Fill in Fabrics: Body-Aware Self-Supervised Inpainting for Image-Based Virtual Try-On (Under Review!) | Github

" thumbnail = None # "./pathtothumbnail.png" # todos # train model with background removed then add feature, also add remove_bg in inferene() # add gr.inputs.Radio(choices=["yes","no"], default="no", label="Remove background from the person image?") in inputs gr.Interface( inference, [gr.inputs.Image(type='filepath', label="Clothing Image"), gr.inputs.Image(type='filepath', label="Person Image"), gr.inputs.Radio(choices=["yes","no"], default="no", label="Retrieve original background from the person image?")], gr.outputs.Image(type="filepath", label="Predicted Output"), examples=[["./sample_images/1/cloth.jpg", "./sample_images/1/person.jpg"], ["./sample_images/2/cloth.jpg", "./sample_images/2/person.jpg"]], cache_examples=False, title=title, description=description, article=article, allow_flagging=False, analytics_enabled=False, thumbnail=thumbnail, ).launch(debug=True, enable_queue=True)