from flask import Flask, request, jsonify
from PIL import Image
import base64
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 threading
import logging

app = Flask(__name__)

# Configure logging
logging.basicConfig(level=logging.INFO)

# 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
    logging.info("Model loaded successfully.")

# Function to decode a base64 image to PIL.Image.Image
def decode_image_from_base64(image_data):
    image_data = base64.b64decode(image_data)
    image = Image.open(BytesIO(image_data)).convert("RGB")
    return image

# Function to encode a PIL image to base64
def encode_image_to_base64(image):
    buffered = BytesIO()
    image.save(buffered, format="JPEG")  # Use JPEG for potentially better performance
    return base64.b64encode(buffered.getvalue()).decode('utf-8')

def remove_background(image):
    remover = Remover()
    if isinstance(image, Image.Image):
        output = remover.process(image)
    elif isinstance(image, np.ndarray):
        image_pil = Image.fromarray(image)
        output = remover.process(image_pil)
    else:
        raise TypeError("Unsupported image type")
    return output

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 [encode_image_to_base64(remove_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 = remove_background(pil_img)
        segmented_result = segment_clothing(img_rm_background, clothes)

        all_segmented_images.extend(segmented_result)

    return all_segmented_images

@app.route('/', methods=['GET'])
def welcome():
    return "Welcome to Clothing Segmentation API"

@app.route('/api/detect', methods=['POST'])
def detect():
    try:
        data = request.json
        image_base64 = data['image']
        image = decode_image_from_base64(image_base64)

        clothes = ["Upper-clothes", "Skirt", "Pants", "Dress"]
        
        # Run the detection and segmentation in a separate thread
        result = []

        def process_image():
            nonlocal result
            result = detect_and_segment_persons(image, clothes)

        thread = threading.Thread(target=process_image)
        thread.start()
        thread.join()  # Wait for the thread to finish

        return jsonify({'images': result})
    except Exception as e:
        logging.error(f"Error occurred: {e}")
        return jsonify({'error': str(e)}), 500

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