canny

canny

skimage.feature.canny(image, sigma=1.0, low_threshold=None, high_threshold=None, mask=None, use_quantiles=False) [source]

Edge filter an image using the Canny algorithm.

Parameters:

image : 2D array Greyscale input image to detect edges on; can be of any dtype. sigma : float Standard deviation of the Gaussian filter. low_threshold : float Lower bound for hysteresis thresholding (linking edges). If None, low_threshold is set to 10% of dtype’s max. high_threshold : float Upper bound for hysteresis thresholding (linking edges). If None, high_threshold is set to 20% of dtype’s max. mask : array, dtype=bool, optional Mask to limit the application of Canny to a certain area. use_quantiles : bool, optional If True then treat low_threshold and high_threshold as quantiles of the edge magnitude image, rather than absolute edge magnitude values. If True then the thresholds must be in the range [0, 1].

Returns:

output : 2D array (image) The binary edge map.

See also

skimage.sobel

Notes

The steps of the algorithm are as follows:

• Smooth the image using a Gaussian with sigma width.
• Apply the horizontal and vertical Sobel operators to get the gradients within the image. The edge strength is the norm of the gradient.
• Thin potential edges to 1-pixel wide curves. First, find the normal to the edge at each point. This is done by looking at the signs and the relative magnitude of the X-Sobel and Y-Sobel to sort the points into 4 categories: horizontal, vertical, diagonal and antidiagonal. Then look in the normal and reverse directions to see if the values in either of those directions are greater than the point in question. Use interpolation to get a mix of points instead of picking the one that’s the closest to the normal.
• Perform a hysteresis thresholding: first label all points above the high threshold as edges. Then recursively label any point above the low threshold that is 8-connected to a labeled point as an edge.

References

Canny, J., A Computational Approach To Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 8:679-714, 1986

William Green’s Canny tutorial http://dasl.mem.drexel.edu/alumni/bGreen/www.pages.drexel.edu/_weg22/can_tut.html

Examples

>>> from skimage import feature
>>> # Generate noisy image of a square
>>> im = np.zeros((256, 256))
>>> im[64:-64, 64:-64] = 1
>>> im += 0.2 * np.random.rand(*im.shape)
>>> # First trial with the Canny filter, with the default smoothing
>>> edges1 = feature.canny(im)
>>> # Increase the smoothing for better results
>>> edges2 = feature.canny(im, sigma=3)