.

app.py.backup

@@ -1,73 +0,0 @@
-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)})

app.py.bacup2

@@ -1,81 +0,0 @@
-import io
-import cv2
-import numpy as np
-import base64
-from PIL import Image
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import StreamingResponse, JSONResponse
-from pydantic import BaseModel
-
-app = FastAPI(
-    version="0.0.1",
-    servers=[
-        {
-            "url": "https://leekwoon-edge-api.hf.space",
-            "description": "image edge detection API",
-        }
-    ],
-)
-
-# Pydantic 모델 정의
-class ImageData(BaseModel):
-    image_base64: str
-
-@app.post("/process-image/")
-def process_image(data: ImageData):
-    try:
-        # 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:
-            raise HTTPException(status_code=400, detail="Invalid image data")
-
-        # 그레이스케일로 변환
-        gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-
-        # Canny 에지 검출 적용
-        edges = cv2.Canny(gray_image, threshold1=100, threshold2=200)
-        edges_image = Image.fromarray(edges)
-
-        # 흰색 배경 생성
-        white_background = Image.new("RGB", edges_image.size, (255, 255, 255))
-
-        # 검은색 에지를 흰색 배경 위에 합성
-        edges_on_white = Image.composite(Image.new("RGB", edges_image.size, (0, 0, 0)), white_background, edges_image)
-
-        # 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)
-
-        # 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
-
-        # 두 기울기 결합
-        sobel_combined = cv2.magnitude(sobelx, sobely)
-
-        # 결과를 [0, 255] 범위로 정규화
-        sobel_combined = cv2.normalize(sobel_combined, None, 0, 255, cv2.NORM_MINMAX)
-        sobel_combined = sobel_combined.astype('uint8')
-
-        # 결과를 PIL 이미지로 변환
-        sobel_image = Image.fromarray(sobel_combined)
-
-        # 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)
-
-        # 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)})

requirements.txt

@@ -5,5 +5,4 @@ opencv-python-headless  # OpenCV, 이미지 처리 라이브러리, GUI 기능
 Pillow  # Python Imaging Library (PIL), 이미지 작업을 위한 라이브러리
 
 numpy  # NumPy, OpenCV 및 일반 수치 계산에 필요
-python-multipart
 gdown