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import io
import time
import torch
import numpy as np
import cv2
from fastapi import FastAPI, File, UploadFile
from PIL import Image
import uvicorn
from torchvision import transforms
# 🟢 Tạo FastAPI
app = FastAPI()
# 🟢 Kiểm tra GPU
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# 🟢 Tải model MiDaS
midas = torch.hub.load("isl-org/MiDaS", "DPT_Swin2_L_384")
midas.to(device)
midas.eval()
# 🟢 Chuẩn bị bộ tiền xử lý ảnh
transform = transforms.Compose([
transforms.Resize((384, 384)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
@app.post("/analyze_path/")
async def analyze_path(file: UploadFile = File(...)):
# 🟢 Đọc file ảnh từ ESP32
image_bytes = await file.read()
image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
image_np = np.array(image)
flipped_image = cv2.flip(image_np, -1)
# 🔵 Resize và chuẩn hóa ảnh
input_tensor = transform(flipped_image).unsqueeze(0).to(device)
# 🟢 Dự đoán Depth Map với MiDaS
start_time = time.time()
with torch.no_grad():
depth_map = midas(input_tensor)
end_time = time.time()
print(f"⏳ MiDaS xử lý trong {end_time - start_time:.4f} giây")
# 🟢 Chuẩn hóa ảnh Depth Map
depth_map = depth_map.squeeze().cpu().numpy()
depth_map = (depth_map - depth_map.min()) / (depth_map.max() - depth_map.min()) * 255
depth_map = depth_map.astype("uint8")
# 🟢 Xử lý phát hiện đường đi
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}
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"
# 🟢 Chạy server FastAPI
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860) |