.

Dockerfile

@@ -0,0 +1,18 @@
+# Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+
+

app.py

@@ -0,0 +1,88 @@
+import os
+import cv2
+import shutil
+from PIL import Image
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import FileResponse, JSONResponse
+import numpy as np
+
+app = FastAPI()
+
+@app.post("/process-image/")
+async def process_image(file: UploadFile = File(...)):
+    """
+    Upload an image file and get the processed image with Canny and Sobel edge detection applied.
+    """
+    try:
+        # Create a temporary directory
+        temp_dir = "temp"
+        os.makedirs(temp_dir, exist_ok=True)
+
+        # Save the uploaded file temporarily
+        input_file_path = os.path.join(temp_dir, file.filename)
+        with open(input_file_path, "wb") as buffer:
+            shutil.copyfileobj(file.file, buffer)
+
+        # Load the image using OpenCV
+        image = cv2.imread(input_file_path)
+
+        if image is None:
+            return JSONResponse(status_code=400, content={"message": "Invalid image file."})
+
+        # Convert the image to grayscale
+        gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+        # Apply Canny edge detection
+        edges = cv2.Canny(gray_image, threshold1=100, threshold2=200)
+
+        # Convert the edges to a PIL Image for display
+        edges_image = Image.fromarray(edges)
+
+        # Create a white background
+        white_background = Image.new("RGB", edges_image.size, (255, 255, 255))
+
+        # Convert edges to an image with black edges on white background
+        edges_on_white = Image.composite(Image.new("RGB", edges_image.size, (0, 0, 0)), white_background, edges_image)
+
+        # Save the final Canny image
+        output_canny_path = os.path.join(temp_dir, "canny_edges_white_bg_image.jpg")
+        edges_on_white.save(output_canny_path)
+
+        # Apply the Sobel operator for edge detection
+        sobelx = cv2.Sobel(gray_image, cv2.CV_64F, 1, 0, ksize=5)  # Sobel operator on the X axis
+        sobely = cv2.Sobel(gray_image, cv2.CV_64F, 0, 1, ksize=5)  # Sobel operator on the Y axis
+
+        # Combine the two gradients
+        sobel_combined = cv2.magnitude(sobelx, sobely)
+
+        # Normalize the result to the range [0, 255]
+        sobel_combined = cv2.normalize(sobel_combined, None, 0, 255, cv2.NORM_MINMAX)
+        sobel_combined = sobel_combined.astype('uint8')
+
+        # Convert the result to a PIL Image
+        sobel_image = Image.fromarray(sobel_combined)
+
+        # Composite the Sobel edges on a white background
+        edges_on_white_sobel = Image.composite(Image.new("RGB", sobel_image.size, (0, 0, 0)), white_background, sobel_image)
+
+        # Save the final Sobel image
+        output_sobel_path = os.path.join(temp_dir, "sobel_edges_white_bg_image.jpg")
+        edges_on_white_sobel.save(output_sobel_path)
+
+        # Choose which image to return, for example, return the Canny edge detection image
+        return FileResponse(path=output_canny_path, filename="canny_edges_white_bg_image.jpg", media_type='image/jpeg')
+
+        # If you want to return Sobel edge detection image, uncomment the next line and comment the previous return line
+        # return FileResponse(path=output_sobel_path, filename="sobel_edges_white_bg_image.jpg", media_type='image/jpeg')
+
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"message": str(e)})
+
+    finally:
+        # Clean up temporary files
+        if os.path.exists(input_file_path):
+            os.remove(input_file_path)
+        if os.path.exists(output_canny_path):
+            os.remove(output_canny_path)
+        if os.path.exists(output_sobel_path):
+            os.remove(output_sobel_path)

requirements.txt

@@ -0,0 +1,7 @@
+fastapi  # FastAPI 웹 프레임워크
+uvicorn  # FastAPI 서버 실행을 위한 Uvicorn ASGI 서버
+
+opencv-python-headless  # OpenCV, 이미지 처리 라이브러리, GUI 기능이 필요 없는 환경에서는 headless 버전을 사용
+Pillow  # Python Imaging Library (PIL), 이미지 작업을 위한 라이브러리
+
+numpy  # NumPy, OpenCV 및 일반 수치 계산에 필요