app.py

import gradio as gr
from PIL import Image,ImageFilter
import cv2
import numpy as np
import base64
import spaces
from loadimg import load_img
from io import BytesIO
import numpy as np
import insightface
import onnxruntime as ort
import huggingface_hub
from SegCloth import segment_clothing
from transparent_background import Remover
import uuid
from transformers import AutoModelForImageSegmentation
import torch
from torchvision import transforms

# Load the model lazily
model = None
detector = None
def load_model():
    global model, detector
    path = huggingface_hub.hf_hub_download("public-data/insightface", "models/scrfd_person_2.5g.onnx")
    options = ort.SessionOptions()
    options.intra_op_num_threads = 8
    options.inter_op_num_threads = 8
    session = ort.InferenceSession(
        path, sess_options=options, providers=["CPUExecutionProvider", "CUDAExecutionProvider"]
    )
    model = insightface.model_zoo.retinaface.RetinaFace(model_file=path, session=session)
    model.prepare(-1, nms_thresh=0.5, input_size=(640, 640))
    detector = model

# Load the segmentation model
torch.set_float32_matmul_precision(["high", "highest"][0])
birefnet = AutoModelForImageSegmentation.from_pretrained("ZhengPeng7/BiRefNet", trust_remote_code=True)
birefnet.to("cuda")
transform_image = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])

def refine_edges(image):
    """
    Affine les contours de l'image en sortie en utilisant un filtre de détection de contours et du lissage.
    """
    # Convertir l'image PIL en format numpy pour OpenCV
    img_np = np.array(image)

    # Convertir en niveaux de gris pour traiter les contours
    gray = cv2.cvtColor(img_np, cv2.COLOR_RGBA2GRAY)

    # Détection des bords avec Canny
    edges = cv2.Canny(gray, threshold1=50, threshold2=150)

    # Dilater les bords pour renforcer les contours
    kernel = np.ones((3, 3), np.uint8)
    edges_dilated = cv2.dilate(edges, kernel, iterations=1)

    # Lisser les bords (anti-aliasing)
    blurred = cv2.GaussianBlur(edges_dilated, (5, 5), 0)

    # Ajouter les bords comme masque alpha
    alpha = Image.fromarray(blurred).convert("L")
    image.putalpha(alpha)

    # Filtrage supplémentaire pour améliorer l'esthétique
    refined_image = image.filter(ImageFilter.SMOOTH_MORE)

    return refined_image

def save_image(img):
    unique_name = str(uuid.uuid4()) + ".png"
    img.save(unique_name)
    return unique_name

@spaces.GPU
def rm_background(image):
    im = load_img(image, output_type="pil")
    im = im.convert("RGB")
    image_size = im.size
    origin = im.copy()
    image = load_img(im)
    input_images = transform_image(image).unsqueeze(0).to("cuda")
    # Prediction
    with torch.no_grad():
        preds = birefnet(input_images)[-1].sigmoid().cpu()
    pred = preds[0].squeeze()
    pred_pil = transforms.ToPILImage()(pred)
    mask = pred_pil.resize(image_size)
    image.putalpha(mask)
    return image

@spaces.GPU
def detect_and_segment_persons(image, clothes):
    img = np.array(image)
    img = img[:, :, ::-1]  # RGB -> BGR

    if detector is None:
        load_model()  # Ensure the model is loaded

    bboxes, kpss = detector.detect(img)
    if bboxes.shape[0] == 0:
        return [rm_background(image)]

    height, width, _ = img.shape
    bboxes = np.round(bboxes[:, :4]).astype(int)
    bboxes[:, 0] = np.clip(bboxes[:, 0], 0, width)
    bboxes[:, 1] = np.clip(bboxes[:, 1], 0, height)
    bboxes[:, 2] = np.clip(bboxes[:, 2], 0, width)
    bboxes[:, 3] = np.clip(bboxes[:, 3], 0, height)

    all_segmented_images = []
    for i in range(bboxes.shape[0]):
        bbox = bboxes[i]
        x1, y1, x2, y2 = bbox
        person_img = img[y1:y2, x1:x2]
        pil_img = Image.fromarray(person_img[:, :, ::-1])

        img_rm_background = rm_background(pil_img)
        segmented_result = segment_clothing(img_rm_background, clothes)
        all_segmented_images.extend(segmented_result)

    return all_segmented_images

@spaces.GPU
def process_image(input_image):
    try:
        clothes = ["Upper-clothes", "Skirt", "Pants", "Dress"]
        results = detect_and_segment_persons(input_image, clothes)
        # results = [refine_edges(image) for image in results]

        return results
    except Exception as e:
        return f"Error occurred: {e}"

# Gradio Interface
def gradio_interface(image):
    results = process_image(image)
    if isinstance(results, list):
        return results
    else:
        return "Error: " + results

# Create Gradio app
interface = gr.Interface(
    fn=gradio_interface,
    inputs=gr.Image(type="pil"),
    outputs=gr.Gallery(label="Segmented Results"),
    title="Clothing Segmentation API"
)

interface.queue(max_size=20).launch(server_name="0.0.0.0", server_port=7860)