app.py

from fastapi import FastAPI, File, UploadFile
import io
import numpy as np
from PIL import Image
import uvicorn
import cv2
from fastdepth import FastDepth
model = FastDepth(pretrained=True)
model.eval()
app = FastAPI()

import os
if not os.path.exists("fastdepth"):
    os.system("git clone https://github.com/dwofk/fast-depth.git fastdepth")

from fastdepth import FastDepth  # Import sau khi clone

app = FastAPI()

# 🟢 Load mô hình FastDepth
model = FastDepth(pretrained=True)
model.eval()

def analyzepath(image):
    depth_map = model(image).squeeze().cpu().numpy()
    return detect_path(depth_map)  # Xử lý đường đi nhanh hơn
@app.post("/analyze_path/")
async def analyze_path(file: UploadFile = File(...)):
    image_bytes = await file.read()
    image = Image.open(io.BytesIO(image_bytes)).convert("L")
    depth_map = np.array(image)

    # 🟢 Lật ảnh (nếu cần)
    flipped_depth_map = cv2.flip(depth_map, -1)
    transform = torchvision.transforms.Compose([
        torchvision.transforms.Resize((224, 224)),
        torchvision.transforms.ToTensor(),
    ])
    img_tensor = transform(image).unsqueeze(0).to(device)
    with torch.no_grad():
        depth_map = model(img_tensor).squeeze().cpu().numpy()
    # 🟢 Phân tích đường đi
    command = detect_path(flipped_depth_map)

    return {"command": command}

def detect_path(depth_map):
    _, thresh = cv2.threshold(depth_map, 200, 255, cv2.THRESH_BINARY)
    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    if len(contours) == 0:
        return "forward"
    
    left_region = np.mean(depth_map[:, :depth_map.shape[1]//3])
    right_region = np.mean(depth_map[:, 2*depth_map.shape[1]//3:])
    
    if left_region > right_region:
        return "left"
    else:
        return "right"