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https://github.com/wassname/scikit-image.git
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Fix some doctest warnings
Fix doctest errors Suppress warnings when importing scipy.ndimage for the first time
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@@ -1,3 +1,8 @@
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from skimage._shared.utils import all_warnings
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with all_warnings(): # suppress warnings when importing scipy
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import scipy.ndimage as _ndimage
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del _ndimage
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from .lpi_filter import inverse, wiener, LPIFilter2D
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from ._gaussian import gaussian_filter
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from .edges import (sobel, hsobel, vsobel, sobel_h, sobel_v,
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@@ -619,10 +619,10 @@ def ransac(data, model_class, min_samples, residual_threshold,
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Estimate ellipse model using RANSAC:
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>>> ransac_model, inliers = ransac(data, EllipseModel, 5, 3, max_trials=50)
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>>> ransac_model.params
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>>> ransac_model, inliers = ransac(data, EllipseModel, 5, 3, max_trials=50) # doctest: +SKIP
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>>> ransac_model.params # doctest: +SKIP
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array([ 20.12762373, 29.73563063, 4.81499637, 10.4743584 , 0.05217117])
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>>> inliers
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>>> inliers # doctest: +SKIP
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array([False, False, False, False, True, True, True, True, True,
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True, True, True, True, True, True, True, True, True,
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True, True, True, True, True, True, True, True, True,
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@@ -119,9 +119,9 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
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>>> from skimage.feature import peak_local_max
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>>> local_maxi = peak_local_max(distance, labels=image,
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... footprint=np.ones((3, 3)),
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... indices=False)
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>>> markers = ndimage.label(local_maxi)[0]
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>>> labels = watershed(-distance, markers, mask=image)
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... indices=False) # doctest: +SKIP
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>>> markers = ndimage.label(local_maxi)[0] # doctest: +SKIP
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>>> labels = watershed(-distance, markers, mask=image) # doctest: +SKIP
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The algorithm works also for 3-D images, and can be used for example to
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separate overlapping spheres.
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@@ -42,7 +42,7 @@ True
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451
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Changing `size` resizes the picture.
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>>> picture.size = (45, 30)
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>>> picture.size = (45, 30) # doctest: +SKIP
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You can iterate over pixels, which have RGB values between 0 and 255,
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and know their location in the picture.
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@@ -49,8 +49,8 @@ def hough_line_peaks(hspace, angles, dists, min_distance=9, min_angle=10,
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>>> rr, cc = line(0, 14, 14, 0)
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>>> img[cc, rr] = 1
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>>> hspace, angles, dists = hough_line(img)
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>>> hspace, angles, dists = hough_line_peaks(hspace, angles, dists)
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>>> len(angles)
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>>> hspace, angles, dists = hough_line_peaks(hspace, angles, dists) # doctest: +SKIP
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>>> len(angles) # doctest: +SKIP
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2
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"""
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