二值圖像的凸殼指的是包圍輸入二值圖像白色區(qū)域的最小的凸多邊形的像素集合。

skimage中的函數(shù)

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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