app.py

from fastapi import FastAPI, UploadFile, File, Response
import cv2
import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
import io

app = FastAPI()

# Load AI Model MiDaS
midas = torch.hub.load("intel-isl/MiDaS", "MiDaS_small")
midas.eval()
transform = T.Compose([
    T.Resize((256, 256)), 
    T.ToTensor(), 
    T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

@app.post("/upload/")
async def upload_image(file: UploadFile = File(...)):
    try:
        image_bytes = await file.read()
        print(f"📷 Ảnh nhận được ({len(image_bytes)} bytes)")

        image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
        print("✅ Ảnh mở thành công!")
        image = image.transpose(Image.FLIP_TOP_BOTTOM)
        image = image.transpose(Image.FLIP_LEFT_RIGHT)
        # Chuyển đổi ảnh sang tensor
        img_tensor = transform(image).unsqueeze(0)
        with torch.no_grad():
            depth_map = midas(img_tensor).squeeze().cpu().numpy()

        # Chuẩn hóa depth map
        depth_map = cv2.normalize(depth_map, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
        depth_resized = cv2.resize(depth_map, (160, 120))

        # Mã hóa ảnh thành JPEG
        _, buffer = cv2.imencode(".jpg", depth_resized)
        print("✅ Depth Map đã được tạo!")

        return Response(content=buffer.tobytes(), media_type="image/jpeg")  # 🟢 Trả ảnh trực tiếp
    
    except Exception as e:
        print("❌ Lỗi xử lý ảnh:", str(e))
        return {"error": str(e)}

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