二值圖像的凸殼指的是包圍輸入二值圖像白色區域的最小的凸多邊形的像素集合。

skimage中的函數

from skimage.morphology import convex_hull_imagechull = convex_hull_image(image)

完整代碼：

'''===========Convex Hull===========The convex hull of a binary image is the set of pixels included in thesmallest convex polygon that surround all white pixels in the input.A good overview of the algorithm is given on `Steve Eddin’s blog<http://blogs.mathworks.com/steve/2011/10/04/binary-image-convex-hull-algorithm-notes/>`__.'''import matplotlib.pyplot as pltfrom skimage.morphology import convex_hull_imagefrom skimage import data, img_as_floatfrom skimage.util import invert# The original image is inverted as the object must be white.image = invert(data.horse())chull = convex_hull_image(image)fig, axes = plt.subplots(1, 2, figsize=(8, 4))ax = axes.ravel()ax[0].set_title(’Original picture’)ax[0].imshow(image, cmap=plt.cm.gray)ax[0].set_axis_off()ax[1].set_title(’Transformed picture’)ax[1].imshow(chull, cmap=plt.cm.gray)ax[1].set_axis_off()plt.tight_layout()plt.show()####################################################################### We prepare a second plot to show the difference.#chull_diff = img_as_float(chull.copy())chull_diff[image] = 2fig, ax = plt.subplots()ax.imshow(chull_diff, cmap=plt.cm.gray)ax.set_title(’Difference’)plt.show()

實驗輸出

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