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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)
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