File size: 1,496 Bytes
02cc722 49ff6ee 02cc722 49ff6ee 02cc722 49ff6ee b59c16a 49ff6ee b59c16a 38420c4 49ff6ee 02cc722 49ff6ee 02cc722 b59c16a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
from fastapi import FastAPI, UploadFile, File
import cv2
import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
import io
app = FastAPI()
# Load MiDaS model
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"📷 Nhận ảnh ({len(image_bytes)} bytes)")
image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
print("✅ Ảnh mở thành công!")
# 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, (128, 64))
_, buffer = cv2.imencode(".jpg", depth_resized)
print("✅ Depth Map đã được tạo!")
return {"depth_map": buffer.tobytes()}
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)
|