app.py

import os
import asyncio
import requests
from flask import Flask, request, jsonify,send_file
from PIL import Image
from io import BytesIO
import torch
import base64
import io 
import logging
import gradio as gr
import numpy as np
import spaces
import uuid
import random
from src.tryon_pipeline import StableDiffusionXLInpaintPipeline as TryonPipeline
from src.unet_hacked_garmnet import UNet2DConditionModel as UNet2DConditionModel_ref
from src.unet_hacked_tryon import UNet2DConditionModel
from transformers import (
    CLIPImageProcessor,
    CLIPVisionModelWithProjection,
    CLIPTextModel,
    CLIPTextModelWithProjection,
    AutoTokenizer,
)
from diffusers import DDPMScheduler, AutoencoderKL
from utils_mask import get_mask_location
from torchvision import transforms
import apply_net
from preprocess.humanparsing.run_parsing import Parsing
from preprocess.openpose.run_openpose import OpenPose
from detectron2.data.detection_utils import convert_PIL_to_numpy, _apply_exif_orientation
from torchvision.transforms.functional import to_pil_image

app = Flask(__name__)

# Chemins de base pour les modèles
base_path = 'yisol/IDM-VTON'

# Chargement des modèles
unet = UNet2DConditionModel.from_pretrained(
    base_path,
    subfolder="unet",
    torch_dtype=torch.float16,
    force_download=False
)
tokenizer_one = AutoTokenizer.from_pretrained(
    base_path,
    subfolder="tokenizer",
    use_fast=False,
    force_download=False
)
tokenizer_two = AutoTokenizer.from_pretrained(
    base_path,
    subfolder="tokenizer_2",
    use_fast=False,
    force_download=False
)
noise_scheduler = DDPMScheduler.from_pretrained(base_path, subfolder="scheduler")
text_encoder_one = CLIPTextModel.from_pretrained(base_path, subfolder="text_encoder", torch_dtype=torch.float16)
text_encoder_two = CLIPTextModelWithProjection.from_pretrained(base_path, subfolder="text_encoder_2", torch_dtype=torch.float16)
image_encoder = CLIPVisionModelWithProjection.from_pretrained(base_path, subfolder="image_encoder", torch_dtype=torch.float16)
vae = AutoencoderKL.from_pretrained(base_path, subfolder="vae", torch_dtype=torch.float16)
UNet_Encoder = UNet2DConditionModel_ref.from_pretrained(base_path, subfolder="unet_encoder", torch_dtype=torch.float16)

parsing_model = Parsing(0)
openpose_model = OpenPose(0)

# Préparation du pipeline Tryon
pipe = TryonPipeline.from_pretrained(
    base_path,
    unet=unet,
    vae=vae,
    feature_extractor=CLIPImageProcessor(),
    text_encoder=text_encoder_one,
    text_encoder_2=text_encoder_two,
    tokenizer=tokenizer_one,
    tokenizer_2=tokenizer_two,
    scheduler=noise_scheduler,
    image_encoder=image_encoder,
    torch_dtype=torch.float16,
    force_download=False
)
pipe.unet_encoder = UNet_Encoder

# Utilisation des transformations d'images
tensor_transfrom = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5]),
])

def pil_to_binary_mask(pil_image, threshold=0):
    np_image = np.array(pil_image)
    grayscale_image = Image.fromarray(np_image).convert("L")
    binary_mask = np.array(grayscale_image) > threshold
    mask = np.zeros(binary_mask.shape, dtype=np.uint8)
    mask[binary_mask] = 1
    return Image.fromarray((mask * 255).astype(np.uint8))
    


def get_image_from_url(url):
    try:
        response = requests.get(url)
        response.raise_for_status()  # Vérifie les erreurs HTTP
        img = Image.open(BytesIO(response.content))
        return img
    except Exception as e:
        logging.error(f"Error fetching image from URL: {e}")
        raise

def decode_image_from_base64(base64_str):
    try:
        img_data = base64.b64decode(base64_str)
        img = Image.open(BytesIO(img_data))
        return img
    except Exception as e:
        logging.error(f"Error decoding image: {e}")
        raise

def encode_image_to_base64(img):
    try:
        buffered = BytesIO()
        img.save(buffered, format="PNG")
        return base64.b64encode(buffered.getvalue()).decode("utf-8")
    except Exception as e:
        logging.error(f"Error encoding image: {e}")
        raise

def save_image(img):
    unique_name = str(uuid.uuid4()) + ".webp"  
    img.save(unique_name, format="WEBP", lossless=True) 
    return unique_name

@spaces.GPU
def start_tryon(dict, garm_img, garment_des, is_checked, is_checked_crop, denoise_steps, seed, categorie = 'upper_body'):
    device = "cuda"
    openpose_model.preprocessor.body_estimation.model.to(device)
    pipe.to(device)
    pipe.unet_encoder.to(device)

    garm_img = garm_img.convert("RGB").resize((768, 1024))
    human_img_orig = dict["background"].convert("RGB")

    if is_checked_crop:
        width, height = human_img_orig.size
        target_width = int(min(width, height * (3 / 4)))
        target_height = int(min(height, width * (4 / 3)))
        left = (width - target_width) / 2
        top = (height - target_height) / 2
        right = (width + target_width) / 2
        bottom = (height + target_height) / 2
        cropped_img = human_img_orig.crop((left, top, right, bottom))
        crop_size = cropped_img.size
        human_img = cropped_img.resize((768, 1024))
    else:
        human_img = human_img_orig.resize((768, 1024))

    if is_checked:
        keypoints = openpose_model(human_img.resize((384, 512)))
        model_parse, _ = parsing_model(human_img.resize((384, 512)))
        mask, mask_gray = get_mask_location('hd', categorie , model_parse, keypoints)
        mask = mask.resize((768, 1024))
    else:
        mask = dict['layers'][0].convert("RGB").resize((768, 1024))#pil_to_binary_mask(dict['layers'][0].convert("RGB").resize((768, 1024)))
    mask_gray = (1 - transforms.ToTensor()(mask)) * tensor_transfrom(human_img)
    mask_gray = to_pil_image((mask_gray + 1.0) / 2.0)

    human_img_arg = _apply_exif_orientation(human_img.resize((384, 512)))
    human_img_arg = convert_PIL_to_numpy(human_img_arg, format="BGR")

    args = apply_net.create_argument_parser().parse_args(('show', './configs/densepose_rcnn_R_50_FPN_s1x.yaml', './ckpt/densepose/model_final_162be9.pkl', 'dp_segm', '-v', '--opts', 'MODEL.DEVICE', 'cuda'))
    pose_img = args.func(args, human_img_arg)
    pose_img = pose_img[:, :, ::-1]
    pose_img = Image.fromarray(pose_img).resize((768, 1024))

    with torch.no_grad():
        with torch.cuda.amp.autocast():
            prompt = "model is wearing " + garment_des
            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
            with torch.inference_mode():
                (
                    prompt_embeds,
                    negative_prompt_embeds,
                    pooled_prompt_embeds,
                    negative_pooled_prompt_embeds,
                ) = pipe.encode_prompt(
                    prompt,
                    num_images_per_prompt=1,
                    do_classifier_free_guidance=True,
                    negative_prompt=negative_prompt,
                )

                prompt = "a photo of " + garment_des
                negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality , change color"
                if not isinstance(prompt, list):
                    prompt = [prompt] * 1
                if not isinstance(negative_prompt, list):
                    negative_prompt = [negative_prompt] * 1
                with torch.inference_mode():
                    (
                        prompt_embeds_c,
                        _,
                        _,
                        _,
                    ) = pipe.encode_prompt(
                        prompt,
                        num_images_per_prompt=1,
                        do_classifier_free_guidance=False,
                        negative_prompt=negative_prompt,
                    )

                pose_img = tensor_transfrom(pose_img).unsqueeze(0).to(device, torch.float16)
                garm_tensor = tensor_transfrom(garm_img).unsqueeze(0).to(device, torch.float16)
                generator = torch.Generator(device).manual_seed(seed) if seed is not None else None
                images = pipe(
                    prompt_embeds=prompt_embeds.to(device, torch.float16),
                    negative_prompt_embeds=negative_prompt_embeds.to(device, torch.float16),
                    pooled_prompt_embeds=pooled_prompt_embeds.to(device, torch.float16),
                    negative_pooled_prompt_embeds=negative_pooled_prompt_embeds.to(device, torch.float16),
                    num_inference_steps=denoise_steps,
                    generator=generator,
                    strength=1.5,
                    pose_img=pose_img.to(device, torch.float16),
                    text_embeds_cloth=prompt_embeds_c.to(device, torch.float16),
                    cloth=garm_tensor.to(device, torch.float16),
                    mask_image=mask,
                    image=human_img,
                    height=1024,
                    width=768,
                    ip_adapter_image=garm_img.resize((768, 1024)),
                    guidance_scale=1.5,
                )[0]

    if is_checked_crop:
        out_img = images[0].resize(crop_size)
        human_img_orig.paste(out_img, (int(left), int(top)))
        return human_img_orig, mask_gray
    else:
        return images[0], mask_gray , mask

        
@app.route('/tryon-v2', methods=['POST'])
def tryon_v2():

    data = request.json
    human_image_data = data['human_image']
    garment_image_data = data['garment_image']

    human_image = process_image(human_image_data)
    garment_image = process_image(garment_image_data)

    description = data.get('description')
    use_auto_mask = data.get('use_auto_mask', True)
    use_auto_crop = data.get('use_auto_crop', False)
    denoise_steps = int(data.get('denoise_steps', 30))
    seed = int(data.get('seed', random.randint(0, 9999999)))
    categorie = data.get('categorie', 'upper_body')

    mask_image = None
    if 'mask_image' in data:
        mask_image_data = data['mask_image']
        mask_image = process_image(mask_image_data)

    human_dict = {
        'background': human_image,
        'layers': [mask_image] if not use_auto_mask else None,
        'composite': None
    }

    output_image, mask_image , mask = start_tryon(human_dict, garment_image, description, use_auto_mask, use_auto_crop, denoise_steps, seed, categorie)
    return jsonify({
        'image_id': save_image(output_image),
        'mask_gray_id' : save_image(mask_image),
        'mask_id' : save_image(mask)
    })

def clear_gpu_memory():
    torch.cuda.empty_cache()
    torch.cuda.synchronize()

def process_image(image_data):
    # Vérifie si l'image est en base64 ou URL
    if image_data.startswith('http://') or image_data.startswith('https://'):
        return get_image_from_url(image_data)  # Télécharge l'image depuis l'URL
    else:
        return decode_image_from_base64(image_data)  # Décode l'image base64

@app.route('/tryon', methods=['POST'])
def tryon():
    data = request.json
    human_image = process_image(data['human_image'])
    garment_image = process_image(data['garment_image'])
    description = data.get('description')
    use_auto_mask = data.get('use_auto_mask', True)
    use_auto_crop = data.get('use_auto_crop', False)
    denoise_steps = int(data.get('denoise_steps', 30))
    seed = int(data.get('seed', 42))
    categorie = data.get('categorie' , 'upper_body')
    human_dict = {
        'background': human_image,
        'layers': [human_image] if not use_auto_mask else None,
        'composite': None
    }
    clear_gpu_memory()

    output_image, mask_image = start_tryon(human_dict, garment_image, description, use_auto_mask, use_auto_crop, denoise_steps, seed , categorie)

    output_base64 = encode_image_to_base64(output_image)
    mask_base64 = encode_image_to_base64(mask_image)

    return jsonify({
        'output_image': output_base64,
        'mask_image': mask_base64
    })

@app.route('/get_mask', methods=['POST'])
def get_mask():
    try:
        # Récupérer l'image du corps à partir de la requête
        data = request.json
        img_file = process_image(data['image'])
        img = img_file.convert("RGB").resize((384, 512))
        categorie = request.form.get('categorie', 'upper_body')  # Paramètre avec valeur par défaut

        # Appliquer la détection des points clés
        keypoints = openpose_model(img)  # Utilise votre modèle
        model_parse, _ = parsing_model(img)  # Utilise votre modèle

        # Obtenir le masque
        mask, mask_gray = get_mask_location('hd', categorie , model_parse, keypoints)
        
        # Convertir le masque en image (si nécessaire)
        mask_gray = (1 - transforms.ToTensor()(mask_gray)) * tensor_transfrom(img)
        mask_gray = to_pil_image((mask_gray + 1.0) / 2.0)

        # Convertir l'image en base64 si besoin pour le retour
        img_byte_arr = io.BytesIO()
        mask_gray.save(img_byte_arr, format='PNG')
        img_byte_arr.seek(0)
        return jsonify({'mask': img_byte_arr.getvalue().decode('latin1')})  # Utiliser une méthode appropriée pour l'encodage

    except Exception as e:
        return jsonify({'error': str(e)}), 500

# Route index
@app.route('/', methods=['GET'])
def index():

    # Renvoyer l'image
    try:
        return 'Welcome to IDM VTON API'
    except FileNotFoundError:
        return jsonify({'error': 'Image not found'}), 404

# Route pour récupérer l'image générée
@app.route('/api/get_image/<image_id>', methods=['GET'])
def get_image(image_id):
    # Construire le chemin complet de l'image
    image_path = image_id  # Assurez-vous que le nom de fichier correspond à celui que vous avez utilisé lors de la sauvegarde

    # Renvoyer l'image
    try:
        return send_file(image_path, mimetype='image/webp')
    except FileNotFoundError:
        return jsonify({'error': 'Image not found'}), 404

if __name__ == "__main__":
    app.run(debug=False, host="0.0.0.0", port=7860)