import os import cv2 import numpy as np import json import random from PIL import Image, ImageDraw, ImageFont import asyncio import requests import base64 import gradio as gr # Set the machine number and model path machine_number = 0 model = os.path.join(os.path.dirname(__file__), "models", "eva", "Eva_0.png") # Define a mapping of model names to file paths MODEL_MAP = { "AI Model Rouyan_0": os.path.join("models", "rouyan_new", "rouyan_new\\Rouyan_0.png"), "AI Model Rouyan_1": os.path.join("models", "rouyan_new", "rouyan_new\\Rouyan_1.png"), "AI Model Rouyan_2": os.path.join("models", "rouyan_new", "rouyan_new\\Rouyan_2.png"), "AI Model Eva_0": os.path.join("models", "eva", "Eva_0.png"), "AI Model Eva_1": os.path.join("models", "eva", "Eva_1.png"), "AI Model Simon_0": os.path.join("models", "simon_online", "Simon_0.png"), "AI Model Simon_1": os.path.join("models", "simon_online", "Simon_1.png"), "AI Model Xuanxuan_0": os.path.join("models", "xiaoxuan_online", "Xuanxuan_0.png"), "AI Model Xuanxuan_1": os.path.join("models", "xiaoxuan_online", "Xuanxuan_1.png"), "AI Model Xuanxuan_2": os.path.join("models", "xiaoxuan_online", "Xuanxuan_2.png"), "AI Model Yaqi_0": os.path.join("models", "yaqi", "Yaqi_0.png"), "AI Model Yaqi_1": os.path.join("models", "yaqi", "Yaqi_1.png"), "AI Model Yaqi_2": os.path.join("models", "yaqi", "Yaqi_2.png"), "AI Model Yaqi_3": os.path.join("models", "yaqi", "Yaqi_3.png"), "AI Model Yifeng_0": os.path.join("models", "yifeng_online", "Yifeng_0.png"), "AI Model Yifeng_1": os.path.join("models", "yifeng_online", "Yifeng_1.png"), "AI Model Yifeng_2": os.path.join("models", "yifeng_online", "Yifeng_2.png"), "AI Model Yifeng_3": os.path.join("models", "yifeng_online", "Yifeng_3.png"), } # Function to add watermark text to image def add_waterprint(img): h, w, _ = img.shape img = cv2.putText(img, 'Powered by OutfitAnyone', (int(0.3*w), h-20), cv2.FONT_HERSHEY_PLAIN, 2, (128, 128, 128), 2, cv2.LINE_AA) return img # Function to process try-on results def get_tryon_result(model_name, garment1, garment2, seed=1234): if isinstance(model_name, np.ndarray): model_name = model_name[0] model_name = "AI Model " + model_name.split("\\")[-1].split(".")[0] # Handle Windows path print(type(model_name)) # Directly load the model image from the disk, no need for Gradio file upload model_image = cv2.imread(MODEL_MAP.get(model_name)) # Load model image from disk if model_image is None: raise ValueError(f"Model image {model_name} could not be loaded.") # Encode garments as base64 encoded_garment1 = cv2.imencode('.jpg', garment1)[1].tobytes() encoded_garment1 = base64.b64encode(encoded_garment1).decode('utf-8') if garment2 is not None: encoded_garment2 = cv2.imencode('.jpg', garment2)[1].tobytes() encoded_garment2 = base64.b64encode(encoded_garment2).decode('utf-8') else: encoded_garment2 = '' # Get the IP address from environment variable or default to localhost url = os.environ.get('OA_IP_ADDRESS', 'http://localhost:5000') headers = {'Content-Type': 'application/json'} seed = random.randint(0, 1222222222) # Prepare data for POST request data = { "garment1": encoded_garment1, "garment2": encoded_garment2, "model_name": model_name, "seed": seed } # Send POST request to server response = requests.post(url, headers=headers, data=json.dumps(data)) print("response code", response.status_code) if response.status_code == 200: result = response.json() result = base64.b64decode(result['images'][0]) result_np = np.frombuffer(result, np.uint8) result_img = cv2.imdecode(result_np, cv2.IMREAD_UNCHANGED) else: print('Server error!') final_img = add_waterprint(result_img) return final_img with gr.Blocks(css=".output-image, .input-image, .image-preview {height: 400px !important}") as demo: # Header Section gr.HTML( """

Outfit Anyone

Ultra-high quality virtual try-on for any clothing and any person

""" ) # UI Layout for Image Inputs and Text Description with gr.Row(): with gr.Column(): gr.Markdown("### Upload Your Model Image") init_image = gr.Image(sources='upload', type="numpy", label="Select a Model Image", value=None) example = gr.Examples(inputs=init_image, examples_per_page=4, examples=[os.path.join(os.path.dirname(__file__), MODEL_MAP.get('AI Model Rouyan_0'))]) with gr.Column(): gr.Markdown( """

Instructions

Please upload your model image and garment images (top and bottom). The models are pre-loaded and cannot be modified. For a dress or coat, you only need to upload the image for the 'Top Garment' section and leave the 'Bottom Garment' section empty.

""" ) with gr.Row(): garment_top = gr.Image(sources='upload', type="numpy", label="Top Garment") example_top = gr.Examples(inputs=garment_top, examples_per_page=5, examples=[os.path.join(os.path.dirname(__file__), "garments", "top222.JPG")]) garment_down = gr.Image(sources='upload', type="numpy", label="Bottom Garment") example_down = gr.Examples(inputs=garment_down, examples_per_page=5, examples=[os.path.join(os.path.dirname(__file__), "garments", "bottom1.png")]) run_button = gr.Button(value="Run Try-On") with gr.Column(): gallery = gr.Image(label="Try-On Result") run_button.click(fn=get_tryon_result, inputs=[init_image, garment_top, garment_down], outputs=[gallery], concurrency_limit=2) # Example Section gr.Markdown("## Example Try-On Results") with gr.Row(): reference_image1 = gr.Image(label="Model Example", scale=1, value="examples\\examples_basemodel.png") reference_image2 = gr.Image(label="Garment Example", scale=1, value="examples\\examples_garment1.jpg") reference_image3 = gr.Image(label="Result Example", scale=1, value="examples\\examples_result1.png") gr.Examples( examples=[["examples\\examples_basemodel.png", "examples\\examples_garment1.png", "examples\\examples_result1.png"]], inputs=[reference_image1, reference_image2, reference_image3], label="Check out our example outfits!", ) if __name__ == "__main__": ip = requests.get('http://ifconfig.me/ip', timeout=1).text.strip() print("IP address", ip) demo.queue(max_size=10) demo.launch()