![]() Mask returned by () thanks to the functions In this example we also show that it is possible to post-process the binary Image to the HSV (Hue Saturation Value) space in order to flood pixels of Since flood fill operates on single-channel images, we transform here the show () Flood-fill in HSV space and mask post-processing ¶ set_title ( 'Nose segmented with `flood`' ) ax. subplots ( nrows = 3, figsize = ( 10, 20 )) ax. sobel ( cat ) cat_nose = flood ( cat_sobel, ( 240, 265 ), tolerance = 0.03 ) fig, ax = plt. Instead we Sobel filter the red channel toĮnhance edges, then flood the nose with a tolerance.Ĭat = data. Segmentation purposes and more advanced analysis pipelines. The flood rather than modifying the image itself. show () Flood as mask ¶Ī sister function, flood, is available which returns a mask identifying axis ( 'off' ) # Plot all eight different tolerances for comparison. ![]() subplots ( nrows = 3, ncols = 3, figsize = ( 12, 12 )) ax. append ( flood_fill ( cameraman, ( 0, 0 ), 255, tolerance = tol )) # Initialize plot and place original image fig, ax = plt. Output = for i in range ( 8 ): tol = 5 + 20 * i output. Here we will experiment a bit on the cameraman. Value, allowing use on real-world images. The tolerance keyword argument widens the permitted range about the initial Its use is limited on real-world images with color gradients and noise. show () Advanced example ¶īecause standard flood filling requires the neighbors to be strictly equal, subplots ( ncols = 2, figsize = ( 10, 5 )) ax. checkerboard () # Fill a square near the middle with value 127, starting at index (76, 76) filled_checkers = flood_fill ( checkers, ( 76, 76 ), 127 ) fig, ax = plt. Import numpy as np import matplotlib.pyplot as plt from skimage import data, filters, color, morphology from gmentation import flood, flood_fill checkers = data. ![]()
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