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:
        start_time = time.time()
        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!")
        end_time = time.time()

        start_detect_time = time.time()
        command = detect_path(depth_map)
        end_detect_time = time.time()
        print(f"⏳ detect_path() xử lý trong {end_detect_time - start_detect_time:.4f} giây")

        return {"command": command}
    except Exception as e:
        print("❌ Lỗi xử lý ảnh:", str(e))
        return {"error": str(e)}
def detect_path(depth_map):
    """Phân tích đường đi từ ảnh Depth Map"""
    h, w = depth_map.shape
    center_x = w // 2
    scan_y = int(h * 0.8)  # Quét dòng 80% từ trên xuống

    left_region = np.mean(depth_map[scan_y, :center_x])
    right_region = np.mean(depth_map[scan_y, center_x:])
    center_region = np.mean(depth_map[scan_y, center_x - 40:center_x + 40])

    # 🟢 Cải thiện logic xử lý
    threshold = 100  # Ngưỡng phân biệt vật cản
    if center_region > threshold:
        return "forward"
    elif left_region > right_region:
        return "left"
    elif right_region > left_region:
        return "right"
    else:
        return "backward"

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