Python库skimage绘制二值图像代码实例

 """ =========== Convex Hull =========== The convex hull of a binary image is the set of pixels included in the smallest convex polygon that surround all white pixels in the input. A good overview of the algorithm is given on `Steve Eddin's blog `__. """ import matplotlib.pyplot as plt from skimage.morphology import convex_hull_image from skimage import data, img_as_float from skimage.util import invert # The original image is inverted as the object must be white. image = invert(data.horse()) chull = convex_h<strong style="color:transparent">来源gaodai#ma#com搞@代~码网</strong>ull_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] = 2 fig, ax = plt.subplots() ax.imshow(chull_diff, cmap=plt.cm.gray) ax.set_title('Difference') plt.show()