.

app.py

@@ -1,9 +1,11 @@
 import io
 import cv2
 import numpy as np
+import base64
 from PIL import Image
-from fastapi import FastAPI, File, UploadFile
+from fastapi import FastAPI, HTTPException
 from fastapi.responses import StreamingResponse, JSONResponse
+from pydantic import BaseModel
 
 app = FastAPI(
     version="0.0.1",
@@ -15,59 +17,65 @@ app = FastAPI(
     ],
 )
 
+# Pydantic 모델 정의
+class ImageData(BaseModel):
+    image_base64: str
+
 @app.post("/process-image/")
-def process_image(file: UploadFile = File(...)):
+async def process_image(data: ImageData):
     try:
-        # Load the image using OpenCV
-        image = cv2.imdecode(np.frombuffer(file.file.read(), np.uint8), cv2.IMREAD_COLOR)
+        # Base64 문자열을 디코딩하여 이미지 데이터를 생성
+        image_data = base64.b64decode(data.image_base64)
+        # NumPy 배열로 변환
+        nparr = np.frombuffer(image_data, np.uint8)
+        # OpenCV를 사용하여 이미지 디코딩
+        image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
 
         if image is None:
-            return JSONResponse(status_code=400, content={"message": "Invalid image file."})
+            raise HTTPException(status_code=400, detail="Invalid image data")
 
-        # Convert the image to grayscale
+        # 그레이스케일로 변환
         gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
 
-        # Apply Canny edge detection
+        # Canny 에지 검출 적용
         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
+        # 흰색 배경 생성
         white_background = Image.new("RGB", edges_image.size, (255, 255, 255))
 
-        # Convert edges to an image with black edges on white background
+        # 검은색 에지를 흰색 배경 위에 합성
         edges_on_white = Image.composite(Image.new("RGB", edges_image.size, (0, 0, 0)), white_background, edges_image)
 
-        # Save the final Canny image to a BytesIO object
+        # 최종 Canny 이미지를 BytesIO 객체에 저장
         canny_output_io = io.BytesIO()
         edges_on_white.save(canny_output_io, format='JPEG')
         canny_output_io.seek(0)
 
-        # 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
+        # Sobel 연산자 적용
+        sobelx = cv2.Sobel(gray_image, cv2.CV_64F, 1, 0, ksize=5)  # X 축에 대한 Sobel 연산자
+        sobely = cv2.Sobel(gray_image, cv2.CV_64F, 0, 1, ksize=5)  # Y 축에 대한 Sobel 연산자
 
-        # Combine the two gradients
+        # 두 기울기 결합
         sobel_combined = cv2.magnitude(sobelx, sobely)
 
-        # Normalize the result to the range [0, 255]
+        # 결과를 [0, 255] 범위로 정규화
         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
+        # 결과를 PIL 이미지로 변환
         sobel_image = Image.fromarray(sobel_combined)
 
-        # Composite the Sobel edges on a white background
+        # Sobel 에지를 흰색 배경 위에 합성
         edges_on_white_sobel = Image.composite(Image.new("RGB", sobel_image.size, (0, 0, 0)), white_background, sobel_image)
 
-        # Save the final Sobel image to a BytesIO object
+        # 최종 Sobel 이미지를 BytesIO 객체에 저장
         sobel_output_io = io.BytesIO()
         edges_on_white_sobel.save(sobel_output_io, format='JPEG')
         sobel_output_io.seek(0)
 
         # StreamingResponse를 사용하여 파일 반환
         return StreamingResponse(sobel_output_io, media_type='image/jpeg')
-    
+
     except Exception as e:
         return JSONResponse(status_code=500, content={"message": str(e)})

app.py.backup

@@ -0,0 +1,73 @@
+import io
+import cv2
+import numpy as np
+from PIL import Image
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import StreamingResponse, JSONResponse
+
+app = FastAPI(
+    version="0.0.1",
+    servers=[
+        {
+            "url": "https://leekwoon-edge-api.hf.space",
+            "description": "image edge detection API",
+        }
+    ],
+)
+
+@app.post("/process-image/")
+def process_image(file: UploadFile = File(...)):
+    try:
+        # Load the image using OpenCV
+        image = cv2.imdecode(np.frombuffer(file.file.read(), np.uint8), cv2.IMREAD_COLOR)
+
+        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 to a BytesIO object
+        canny_output_io = io.BytesIO()
+        edges_on_white.save(canny_output_io, format='JPEG')
+        canny_output_io.seek(0)
+
+        # 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 to a BytesIO object
+        sobel_output_io = io.BytesIO()
+        edges_on_white_sobel.save(sobel_output_io, format='JPEG')
+        sobel_output_io.seek(0)
+
+        # StreamingResponse를 사용하여 파일 반환
+        return StreamingResponse(sobel_output_io, media_type='image/jpeg')
+    
+    except Exception as e:
+        return JSONResponse(status_code=500, content={"message": str(e)})