使用OPENCV改善边缘检测
因此，我正在研究一个项目来扫描模拟照片。该程序应自动检测边缘和裁剪。为此，我正在使用Python和OpenCV。我的问题是，有时我的程序由于间隙而无法将照片视为整个矩形。这是一个示例：
这是我使用Canny的代码台：
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # convert the image to gray scale
blur = cv2.GaussianBlur(gray, (1, 1), 0) # Add Gaussian blur to remove noise
edged = cv2.Canny(blur, 75, 200) # Apply the Canny algorithm to find the edges
我还注意到调整模糊参数仅使其与查找边缘更加不准确。
当照片边界的颜色与表面不同时，该程序似乎起作用。
有什么方法可以改进代码，因此检测照片边缘的更准确性？
so I am working on a project to scan analog photos. The program should detect the edges and crop it automatically. For that I am using Python and OpenCV. My problem is that sometimes my program can not detect the photo as a whole rectangle due to gaps. Here is an example:
This is the codepart where I am using canny:
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # convert the image to gray scale
blur = cv2.GaussianBlur(gray, (1, 1), 0) # Add Gaussian blur to remove noise
edged = cv2.Canny(blur, 75, 200) # Apply the Canny algorithm to find the edges
I also noticed that tweaking up the blur parameters only makes it more inaccurate with finding the edges.
The program seems to work when the colour at the border of the photo is different compared to the surface.
Is there any way to improve the code so it is way more accurate in detecting the edges of a photo?
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