blocks|key|4564662|text|我们在积极开发的scikit-image中有分割和边缘检测算法，您可能会发现这些算法很有用：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4564663|Scikit+Images+Examples|4564664|entityMap|0|LINK|mutability|MUTABLE|url|http://scikit-image.org/docs/dev/auto_examples/^0|8|C|0|0|M|0|0^^$0|@$1|2|3|4|5|6|7|P|8|@$9|Q|A|R|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|S|8|@]|D|@$9|T|A|U|1|V]]|E|$]]|$1|H|3|-4|5|6|7|W|8|@]|D|@]|E|$]]]|I|$J|$5|K|L|M|E|$N|O]]]]

We have segmentation and edge detection algorithms in the actively developed <code>scikit-image</code> that you may find useful:

<a href="http://scikit-image.org/docs/dev/auto_examples/" rel="noreferrer">Scikit Images Examples</a>

blocks|key|1511716|text|您可以在python中使用scipy轻松实现边缘检测。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1511717|from+scipy+import+ndimage
edge_horizont+=+ndimage.sobel(greyscale,+0)
edge_vertical+=+ndimage.sobel(greyscale,+1)
magnitude+=+np.hypot(edge_horizont,+edge_vertical)|code-block|syntax|javascript|1511718|这里是原始图像和边缘检测后的图像的示例。|1511719|📷|atomic|offset|length|1511720|​|1511721|在scikit+image中，有一个关于如何进行边缘检测的special+page+with+explanations。|1511722|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/2414b4b760e33c02a88049fb1de16192.png|imageAlt|1|LINK|MUTABLE|url|http://scikit-image.org/docs/dev/auto_examples/plot_canny.html^0|0|0|0|0|1|0|0|0|T|U|1|0^^$0|@$1|2|3|4|5|6|7|15|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|16|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|17|8|@]|9|@]|A|$]]|$1|I|3|J|5|K|7|18|8|@]|9|@$L|19|M|1A|1|1B]]|A|$]]|$1|N|3|O|5|6|7|1C|8|@]|9|@]|A|$]]|$1|P|3|Q|5|6|7|1D|8|@]|9|@$L|1E|M|1F|1|1G]]|A|$]]|$1|R|3|-4|5|6|7|1H|8|@]|9|@]|A|$]]]|S|$T|$5|U|V|W|A|$X|Y|Z|-4]]|10|$5|11|V|12|A|$13|14]]]]

You can easily achieve edge detection with scipy in python.

<pre><code>from scipy import ndimage
edge_horizont = ndimage.sobel(greyscale, 0)
edge_vertical = ndimage.sobel(greyscale, 1)
magnitude = np.hypot(edge_horizont, edge_vertical)
</code></pre>

And here is an example of original image and the image after edge detection. <a href="https://i.stack.imgur.com/RQaBC.png" rel="noreferrer"><img src="https://i.stack.imgur.com/RQaBC.png" alt="enter image description here"></a>

In scikit-image, there is a <a href="http://scikit-image.org/docs/dev/auto_examples/plot_canny.html" rel="noreferrer">special page with explanations</a> of how to do edge detection.

blocks|key|526668|text|有一种非常简单的方法可以在python中使用scikit图像来查找轮廓。它实际上只有几行代码，如下所示：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|526669|++++from+skimage+import+measure
++++contours+=+measure.find_contours(gimg,+0.8)|code-block|syntax|javascript|526670|这将返回轮廓线的矢量表示。在每一行的单独数组中。也很容易通过计算近似值来减少直线上的点数。这里有一个更长的源代码描述：image+vectorization+with+python|offset|length|526671|entityMap|0|LINK|mutability|MUTABLE|url|http://goo.gl/pc5h7^0|0|0|1N|V|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@]|9|@$I|V|J|W|1|X]]|A|$]]|$1|K|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|L|$M|$5|N|O|P|A|$Q|R]]]]

There is a very simple way to find contours in python with scikit image. It's really just a couple line of code, like this:

<pre><code> from skimage import measure
 contours = measure.find_contours(gimg, 0.8)
</code></pre>

This returns the vector representation of the contour lines. In a separate array for each line. And it's also easy to decrease the number of points in a line by calculating an approximation. Here is a bit longer description with source code: <a href="http://goo.gl/pc5h7" rel="nofollow">image vectorization with python</a>

blocks|key|4564803|text|你可以使用不同的边缘检测器:+Canny，Sobel，Laplacian，Scharr，Prewitt，Roberts。您可以使用OpenCV来完成此操作|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4564804|import+cv2
import+numpy+as+np

img+=+cv2.imread('your_image.jpg',+0)

#+Canny
edges_canny+=+cv2.Canny(img,+100,+100)

#+Sobel
sobel_x+=+cv2.Sobel(img,+cv2.CV_64F,+1,+0,+ksize=5)
sobel_y+=+cv2.Sobel(img,+cv2.CV_64F,+0,+1,+ksize=5)
edges_sobel+=+np.hypot(sobel_x,+sobel_y)
edges_sobel+*=+255.0+/+np.max(edges_sobel)

#+Laplacian
edges_laplacian+=+cv2.Laplacian(img,+cv2.CV_64F)

#+Scharr
schar_x+=+cv2.Scharr(img,+cv2.CV_64F,+1,+0)
schar_y+=+cv2.Scharr(img,+cv2.CV_64F,+0,+1)
edges_scharr+=+np.hypot(schar_x,+schar_y)
edges_scharr+*=+255.0+/+np.max(edges_scharr)|code-block|syntax|javascript|4564805|或使用scikit-image|4564806|import+cv2
from+skimage+import+feature,+filters

img+=+cv2.imread('your_image.jpg',+0)

edges_canny+=+feature.canny(img)+#+Canny
edges_sobel+=+filters.sobel(img)+#+Sobel
edges_laplace+=+filters.laplace(img)+#+Laplacian
edges_scharr+=+filters.scharr(img)+#+Scharr
edges_prewitt+=+filters.prewitt(img)+#+Prewitt
edges_roberts+=+filters.roberts(img)+#+Roberts|4564807|坎尼边缘检测器可能是最常用和最有效的方法，但也是最复杂的。有关上述方法之间的区别的更多详细信息，请查看this+blog+post。|style|BOLD|4564808|entityMap|0|LINK|mutability|MUTABLE|url|https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_canny/py_canny.html#canny-edge-detection-in-opencv|1|https://scikit-image.org/docs/dev/api/skimage.feature.html?highlight=canny#skimage.feature.canny|2|https://medium.com/@nikatsanka/comparing-edge-detection-methods-638a2919476e^0|1T|6|0|0|0|3|C|1|0|0|0|7|1F|E|2|0^^$0|@$1|2|3|4|5|6|7|12|8|@]|9|@$A|13|B|14|1|15]]|C|$]]|$1|D|3|E|5|F|7|16|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|17|8|@]|9|@$A|18|B|19|1|1A]]|C|$]]|$1|K|3|L|5|F|7|1B|8|@]|9|@]|C|$G|H]]|$1|M|3|N|5|6|7|1C|8|@$A|1D|B|1E|O|P]]|9|@$A|1F|B|1G|1|1H]]|C|$]]|$1|Q|3|-4|5|6|7|1I|8|@]|9|@]|C|$]]]|R|$S|$5|T|U|V|C|$W|X]]|Y|$5|T|U|V|C|$W|Z]]|10|$5|T|U|V|C|$W|11]]]]

There are different edge detectors you can use: Canny, Sobel, Laplacian, Scharr, Prewitt, Roberts. You can do it with <a href="https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_canny/py_canny.html#canny-edge-detection-in-opencv" rel="nofollow noreferrer">OpenCV</a>:

<pre><code>import cv2
import numpy as np

img = cv2.imread('your_image.jpg', 0)

# Canny
edges_canny = cv2.Canny(img, 100, 100)

# Sobel
sobel_x = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=5)
sobel_y = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5)
edges_sobel = np.hypot(sobel_x, sobel_y)
edges_sobel *= 255.0 / np.max(edges_sobel)

# Laplacian
edges_laplacian = cv2.Laplacian(img, cv2.CV_64F)

# Scharr
schar_x = cv2.Scharr(img, cv2.CV_64F, 1, 0)
schar_y = cv2.Scharr(img, cv2.CV_64F, 0, 1)
edges_scharr = np.hypot(schar_x, schar_y)
edges_scharr *= 255.0 / np.max(edges_scharr)
</code></pre>

or with <a href="https://scikit-image.org/docs/dev/api/skimage.feature.html?highlight=canny#skimage.feature.canny" rel="nofollow noreferrer">scikit-image</a>:

<pre><code>import cv2
from skimage import feature, filters

img = cv2.imread('your_image.jpg', 0)

edges_canny = feature.canny(img) # Canny
edges_sobel = filters.sobel(img) # Sobel
edges_laplace = filters.laplace(img) # Laplacian
edges_scharr = filters.scharr(img) # Scharr
edges_prewitt = filters.prewitt(img) # Prewitt
edges_roberts = filters.roberts(img) # Roberts
</code></pre>

Canny edge detector is probably the most commonly used and most effective method, but also the most complex. For more details on what is the difference between the mentioned methods check <a href="https://medium.com/@nikatsanka/comparing-edge-detection-methods-638a2919476e" rel="nofollow noreferrer">this blog post</a>.

I know the edge detection problem has been posted before (in Java: <a href="https://stackoverflow.com/questions/2952165/count-the-number-of-objects-in-an-image">Count the number of objects in an Image</a>, language independent: <a href="https://stackoverflow.com/questions/3540910/image-edge-detection">Image edge detection</a>), but I want to know how to implement it in python.

I'm doing edge detection and curvature calculation around the edge on some simple shapes (binary shape with some noise). I know there are some wrapper for OpenCV, but not sure which one is better: pyopencv, pycv, pycvf?

Since I'm basically only doing this two tasks, I'm also not sure whether it would be faster to implement it by myself rather than using the library.

Python edge detection and curvature calculation

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我知道边缘检测问题以前已经发布过(在Java：，语言无关：中)，但我想知道如何在Python语言中实现它。我在一些简单的形状(带有一些噪声的二进制形状)的边缘周围做边缘检测和曲率计算。我知道有一些OpenCV的包装器，但不确定哪一个更好: pyopencv，pycv，pycvf？由于我基本上只做这两个任务，我也不确定自己实现它是否比使用库更快。

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