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import io |
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import time |
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import numpy as np |
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import cv2 |
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import torch |
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from transformers import AutoImageProcessor, AutoModelForDepthEstimation |
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from fastapi import FastAPI, File, UploadFile, Response |
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from fastapi.responses import FileResponse, HTMLResponse |
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from PIL import Image |
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import uvicorn |
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app = FastAPI() |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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processor = AutoImageProcessor.from_pretrained("Intel/dpt-swinv2-tiny-256") |
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model = AutoModelForDepthEstimation.from_pretrained("Intel/dpt-swinv2-tiny-256").to(device) |
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model.eval() |
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@app.post("/analyze_path/") |
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async def analyze_path(file: UploadFile = File(...)): |
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"""Xử lý ảnh Depth Map và lưu ảnh để hiển thị""" |
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start_time = time.time() |
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image_bytes = await file.read() |
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image = Image.open(io.BytesIO(image_bytes)).convert("RGB") |
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image = image.resize((256, 256)) |
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image_np = np.array(image) |
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inputs = processor(images=image_np, return_tensors="pt").to(device) |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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predicted_depth = outputs.predicted_depth.squeeze().cpu().numpy() |
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depth_map = (predicted_depth * 255 / predicted_depth.max()).astype("uint8") |
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depth_colored = cv2.applyColorMap(depth_map, cv2.COLORMAP_INFERNO) |
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depth_pil = Image.fromarray(depth_colored) |
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depth_pil.save("depth_map.png") |
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end_time = time.time() |
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print(f"⏳ Model xử lý trong {end_time - start_time:.4f} giây") |
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return {"message": "Depth Map processed successfully. View at /view/"} |
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@app.get("/depth_map/") |
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async def get_depth_map(): |
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"""Trả về ảnh Depth Map để hiển thị trên trình duyệt""" |
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return FileResponse("depth_map.png", media_type="image/png") |
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@app.get("/view/", response_class=HTMLResponse) |
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async def view_depth_map(): |
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"""Trả về trang HTML để hiển thị ảnh Depth Map với auto-refresh""" |
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html_content = """ |
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<html> |
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<head> |
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<title>Depth Map Viewer</title> |
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<meta http-equiv="refresh" content="5"> |
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</head> |
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<body> |
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<h2>Depth Map (Tự động cập nhật mỗi 5 giây)</h2> |
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<img src="/depth_map/" width="500"> |
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</body> |
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</html> |
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""" |
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return HTMLResponse(content=html_content) |
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if __name__ == "__main__": |
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uvicorn.run(app, host="0.0.0.0", port=7860) |
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