mirror of
https://github.com/wassname/scikit-image.git
synced 2026-08-09 12:30:07 +08:00
COSMIT some manual pep8, removed unused imports, removed unused variables and fixed a bug in a ValueError statement.
This commit is contained in:
Andreas Mueller
@@ -63,7 +63,6 @@ except ImportError:
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def _setup_test(verbose=False):
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import gzip
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import functools
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args = ['', '--exe', '-w', pkg_dir]
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@@ -49,7 +49,7 @@ def lena():
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def text():
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""" Gray-level "text" image used for corner detection.
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""" Gray-level "text" image used for corner detection.
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Notes
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-----
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@@ -60,7 +60,8 @@ def text():
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"""
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return load("text.png")
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return load("text.png")
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def checkerboard():
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"""Checkerboard image.
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@@ -1,6 +1,5 @@
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import skimage.data as data
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from numpy.testing import assert_equal, assert_array_equal
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import numpy as np
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from numpy.testing import assert_equal
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def test_lena():
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@@ -17,7 +16,7 @@ def test_camera():
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def test_checkerboard():
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""" Test that checkerboard image can be loaded. """
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checkerboard = data.checkerboard()
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data.checkerboard()
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if __name__ == "__main__":
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from numpy.testing import run_module_suite
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@@ -70,7 +70,7 @@ def hsobel(image, mask=None):
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mask = np.ones(image.shape, bool)
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big_mask = binary_erosion(mask,
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generate_binary_structure(2, 2),
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border_value = 0)
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border_value=0)
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result = np.abs(convolve(image,
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np.array([[ 1, 2, 1],
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[ 0, 0, 0],
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@@ -78,6 +78,7 @@ def hsobel(image, mask=None):
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result[big_mask == False] = 0
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return result
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def vsobel(image, mask=None):
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"""Find the vertical edges of an image using the Sobel transform.
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@@ -5,6 +5,7 @@ from collections import deque
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_param_options = ('high', 'low')
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def find_contours(array, level,
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fully_connected='low', positive_orientation='low'):
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"""Find iso-valued contours in a 2D array for a given level value.
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@@ -83,10 +84,10 @@ def find_contours(array, level,
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This means that to find reasonable contours, it is best to find contours
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midway between the expected "light" and "dark" values. In particular,
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given a binarized array, *do not* choose to find contours at the low or high
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value of the array. This will often yield degenerate contours, especially
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around structures that are a single array element wide. Instead choose
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a middle value, as above.
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given a binarized array, *do not* choose to find contours at the low or
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high value of the array. This will often yield degenerate contours,
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especially around structures that are a single array element wide. Instead
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choose a middle value, as above.
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References
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----------
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@@ -129,7 +130,8 @@ def _assemble_contours(points_iterator):
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# This happens when (and only when) one vertex of the square is
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# exactly the contour level, and the rest are above or below.
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# This degnerate vertex will be picked up later by neighboring squares.
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if from_point == to_point: continue
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if from_point == to_point:
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continue
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tail_data = starts.get(to_point)
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head_data = ends.get(from_point)
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@@ -21,39 +21,40 @@ r = np.sqrt(x**2 + y**2)
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def test_binary():
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contours = find_contours(a, 0.5)
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assert len(contours) == 1
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assert_array_equal(contours[0],
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[[ 6. , 1.5],
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[ 5. , 1.5],
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[ 4. , 1.5],
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[ 3. , 1.5],
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[ 2. , 1.5],
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[ 1.5, 2. ],
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[ 1.5, 3. ],
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[ 1.5, 4. ],
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[ 1.5, 5. ],
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[ 1.5, 6. ],
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[ 1. , 6.5],
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[ 0.5, 6. ],
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[ 0.5, 5. ],
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[ 0.5, 4. ],
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[ 0.5, 3. ],
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[ 0.5, 2. ],
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[ 0.5, 1. ],
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[ 1. , 0.5],
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[ 2. , 0.5],
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[ 3. , 0.5],
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[ 4. , 0.5],
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[ 5. , 0.5],
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[ 6. , 0.5],
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[ 6.5, 1. ],
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[ 6. , 1.5]])
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contours = find_contours(a, 0.5)
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assert len(contours) == 1
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assert_array_equal(contours[0],
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[[6. , 1.5],
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[5. , 1.5],
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[4. , 1.5],
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[3. , 1.5],
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[2. , 1.5],
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[1.5, 2. ],
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[1.5, 3. ],
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[1.5, 4. ],
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[1.5, 5. ],
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[1.5, 6. ],
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[1. , 6.5],
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[0.5, 6. ],
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[0.5, 5. ],
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[0.5, 4. ],
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[0.5, 3. ],
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[0.5, 2. ],
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[0.5, 1. ],
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[1. , 0.5],
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[2. , 0.5],
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[3. , 0.5],
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[4. , 0.5],
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[5. , 0.5],
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[6. , 0.5],
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[6.5, 1. ],
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[6. , 1.5]])
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def test_float():
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contours = find_contours(r, 0.5)
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assert len(contours) == 1
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assert_array_equal(contours[0],
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contours = find_contours(r, 0.5)
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assert len(contours) == 1
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assert_array_equal(contours[0],
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[[ 2., 3.],
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[ 1., 2.],
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[ 2., 1.],
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@@ -61,7 +62,6 @@ def test_float():
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[ 2., 3.]])
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if __name__ == '__main__':
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from numpy.testing import run_module_suite
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run_module_suite()
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@@ -79,7 +79,7 @@ def skeletonize(image):
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[0, 0, 0, 0, 1, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=uint8)
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"""
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# look up table - there is one entry for each of the 2^8=256 possible
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# combinations of 8 binary neighbours. 1's, 2's and 3's are candidates
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@@ -318,9 +318,9 @@ def _pattern_of(index):
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def _table_lookup(image, table):
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"""
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Perform a morphological transform on an image, directed by its
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Perform a morphological transform on an image, directed by its
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neighbors
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Parameters
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----------
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image : ndarray
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@@ -330,7 +330,7 @@ def _table_lookup(image, table):
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the values of that pixel and its 8-connected neighbors.
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border_value : bool
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The value of pixels beyond the border of the image.
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Returns
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-------
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result : ndarray of same shape as `image`
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@@ -340,7 +340,7 @@ def _table_lookup(image, table):
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-----
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The pixels are numbered like this::
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0 1 2
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3 4 5
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6 7 8
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@@ -358,11 +358,11 @@ def _table_lookup(image, table):
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indexer[1:, 1:] += image[:-1, :-1] * 2**0
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indexer[1:, :] += image[:-1, :] * 2**1
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indexer[1:, :-1] += image[:-1, 1:] * 2**2
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indexer[:, 1:] += image[:, :-1] * 2**3
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indexer[:, :] += image[:, :] * 2**4
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indexer[:, :-1] += image[:, 1:] * 2**5
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indexer[:-1, 1:] += image[1:, :-1] * 2**6
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indexer[:-1, :] += image[1:, :] * 2**7
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indexer[:-1, :-1] += image[1:, 1:] * 2**8
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@@ -370,4 +370,3 @@ def _table_lookup(image, table):
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indexer = _table_lookup_index(np.ascontiguousarray(image, np.uint8))
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image = table[indexer]
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return image
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@@ -3,6 +3,7 @@ from numpy.testing import assert_array_equal, run_module_suite
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from skimage.morphology import label
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class TestConnectedComponents:
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def setup(self):
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self.x = np.array([[0, 0, 3, 2, 1, 9],
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@@ -43,7 +43,6 @@ Original author: Lee Kamentsky
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import math
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import time
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import unittest
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import numpy as np
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@@ -54,6 +53,7 @@ from skimage.morphology.watershed import watershed, \
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eps = 1e-12
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def diff(a, b):
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if not isinstance(a, np.ndarray):
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a = np.asarray(a)
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@@ -122,28 +122,27 @@ class TestWatershed(unittest.TestCase):
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def test_watershed02(self):
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"watershed 2"
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data = np.array([[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 1, 0, 0, 0, 1, 0],
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[0, 1, 0, 0, 0, 1, 0],
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[0, 1, 0, 0, 0, 1, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0]], np.uint8)
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markers = np.array([[-1, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 1, 0, 0, 0, 1, 0],
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[0, 1, 0, 0, 0, 1, 0],
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[0, 1, 0, 0, 0, 1, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0]], np.uint8)
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markers = np.array([[ -1, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 1, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0]],
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np.int8)
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 1, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0]], np.int8)
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out = watershed(data, markers)
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error = diff([[-1, -1, -1, -1, -1, -1, -1],
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[-1, -1, -1, -1, -1, -1, -1],
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@@ -161,26 +160,25 @@ class TestWatershed(unittest.TestCase):
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def test_watershed03(self):
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"watershed 3"
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data = np.array([[0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 1, 1, 1, 1, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0]], np.uint8)
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markers = np.array([[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 2, 0, 3, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, 0],
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[ 0, 0, 0, 0, 0, 0, -1]],
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np.int8)
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[0, 1, 1, 1, 1, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 1, 1, 1, 1, 0],
|
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0]], np.uint8)
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markers = np.array([[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
|
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[0, 0, 0, 0, 0, 0, 0],
|
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[0, 0, 2, 0, 3, 0, 0],
|
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[0, 0, 0, 0, 0, 0, 0],
|
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[0, 0, 0, 0, 0, 0, 0],
|
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[0, 0, 0, 0, 0, 0, 0],
|
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, -1]], np.int8)
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out = watershed(data, markers)
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error = diff([[-1, -1, -1, -1, -1, -1, -1],
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[-1, 0, 2, 0, 3, 0, -1],
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@@ -202,21 +200,20 @@ class TestWatershed(unittest.TestCase):
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[0, 1, 0, 1, 0, 1, 0],
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[0, 1, 0, 1, 0, 1, 0],
|
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[0, 1, 1, 1, 1, 1, 0],
|
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[ 0, 0, 0, 0, 0, 0, 0],
|
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[ 0, 0, 0, 0, 0, 0, 0],
|
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[ 0, 0, 0, 0, 0, 0, 0],
|
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[0, 0, 0, 0, 0, 0, 0],
|
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[0, 0, 0, 0, 0, 0, 0],
|
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[0, 0, 0, 0, 0, 0, 0],
|
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[0, 0, 0, 0, 0, 0, 0]], np.uint8)
|
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markers = np.array([[ 0, 0, 0, 0, 0, 0, 0],
|
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[ 0, 0, 0, 0, 0, 0, 0],
|
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[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 2, 0, 3, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, -1]],
|
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np.int8)
|
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markers = np.array([[0, 0, 0, 0, 0, 0, 0],
|
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[0, 0, 0, 0, 0, 0, 0],
|
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[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 2, 0, 3, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, -1]], np.int8)
|
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out = watershed(data, markers, self.eight)
|
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error = diff([[-1, -1, -1, -1, -1, -1, -1],
|
||||
[-1, 2, 2, 0, 3, 3, -1],
|
||||
@@ -224,35 +221,34 @@ class TestWatershed(unittest.TestCase):
|
||||
[-1, 2, 2, 0, 3, 3, -1],
|
||||
[-1, 2, 2, 0, 3, 3, -1],
|
||||
[-1, 2, 2, 0, 3, 3, -1],
|
||||
[-1, -1, -1, -1, -1, -1, -1],
|
||||
[-1, -1, -1, -1, -1, -1, -1],
|
||||
[-1, -1, -1, -1, -1, -1, -1],
|
||||
[-1, -1, -1, -1, -1, -1, -1],
|
||||
[-1, -1, -1, -1, -1, -1, -1],
|
||||
[-1, -1, -1, -1, -1, -1, -1],
|
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[-1, -1, -1, -1, -1, -1, -1]], out)
|
||||
self.failUnless(error < eps)
|
||||
|
||||
def test_watershed05(self):
|
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"watershed 5"
|
||||
data = np.array([[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 1, 1, 1, 1, 1, 0],
|
||||
[0, 1, 0, 1, 0, 1, 0],
|
||||
[0, 1, 0, 1, 0, 1, 0],
|
||||
[0, 1, 0, 1, 0, 1, 0],
|
||||
[0, 1, 1, 1, 1, 1, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0]], np.uint8)
|
||||
markers = np.array([[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 3, 0, 2, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, -1]],
|
||||
np.int8)
|
||||
[0, 1, 1, 1, 1, 1, 0],
|
||||
[0, 1, 0, 1, 0, 1, 0],
|
||||
[0, 1, 0, 1, 0, 1, 0],
|
||||
[0, 1, 0, 1, 0, 1, 0],
|
||||
[0, 1, 1, 1, 1, 1, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0]], np.uint8)
|
||||
markers = np.array([[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 3, 0, 2, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, -1]], np.int8)
|
||||
out = watershed(data, markers, self.eight)
|
||||
error = diff([[-1, -1, -1, -1, -1, -1, -1],
|
||||
[-1, 3, 3, 0, 2, 2, -1],
|
||||
@@ -269,24 +265,23 @@ class TestWatershed(unittest.TestCase):
|
||||
def test_watershed06(self):
|
||||
"watershed 6"
|
||||
data = np.array([[0, 1, 0, 0, 0, 1, 0],
|
||||
[0, 1, 0, 0, 0, 1, 0],
|
||||
[0, 1, 0, 0, 0, 1, 0],
|
||||
[0, 1, 1, 1, 1, 1, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0]], np.uint8)
|
||||
markers = np.array([[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 1, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ 0, 0, 0, 0, 0, 0, 0],
|
||||
[ -1, 0, 0, 0, 0, 0, 0]],
|
||||
np.int8)
|
||||
[0, 1, 0, 0, 0, 1, 0],
|
||||
[0, 1, 0, 0, 0, 1, 0],
|
||||
[0, 1, 1, 1, 1, 1, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0]], np.uint8)
|
||||
markers = np.array([[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 1, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[0, 0, 0, 0, 0, 0, 0],
|
||||
[-1, 0, 0, 0, 0, 0, 0]], np.int8)
|
||||
out = watershed(data, markers, self.eight)
|
||||
error = diff([[-1, 1, 1, 1, 1, 1, -1],
|
||||
[-1, 1, 1, 1, 1, 1, -1],
|
||||
@@ -302,28 +297,28 @@ class TestWatershed(unittest.TestCase):
|
||||
def test_watershed07(self):
|
||||
"A regression test of a competitive case that failed"
|
||||
data = np.array([[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
|
||||
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
|
||||
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
|
||||
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
|
||||
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
|
||||
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
|
||||
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
|
||||
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
|
||||
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
|
||||
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
|
||||
[255,255,255,255,255,204,153,103,103,153,204,255,255,255,255,255],
|
||||
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
|
||||
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
|
||||
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
|
||||
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
|
||||
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
|
||||
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
|
||||
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
|
||||
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
|
||||
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
|
||||
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255]])
|
||||
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
|
||||
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
|
||||
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
|
||||
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
|
||||
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
|
||||
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
|
||||
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
|
||||
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
|
||||
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
|
||||
[255,255,255,255,255,204,153,103,103,153,204,255,255,255,255,255],
|
||||
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
|
||||
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
|
||||
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
|
||||
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
|
||||
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
|
||||
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
|
||||
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
|
||||
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
|
||||
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
|
||||
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255]])
|
||||
mask = (data != 255)
|
||||
markers = np.zeros(data.shape,int)
|
||||
markers = np.zeros(data.shape, int)
|
||||
markers[6, 7] = 1
|
||||
markers[14, 7] = 2
|
||||
out = watershed(data, markers, self.eight, mask=mask)
|
||||
@@ -338,30 +333,30 @@ class TestWatershed(unittest.TestCase):
|
||||
def test_watershed08(self):
|
||||
"The border pixels + an edge are all the same value"
|
||||
data = np.array([[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
|
||||
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
|
||||
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
|
||||
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
|
||||
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
|
||||
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
|
||||
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
|
||||
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
|
||||
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
|
||||
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
|
||||
[255,255,255,255,255,204,153,141,141,153,204,255,255,255,255,255],
|
||||
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
|
||||
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
|
||||
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
|
||||
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
|
||||
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
|
||||
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
|
||||
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
|
||||
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
|
||||
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
|
||||
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255]])
|
||||
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
|
||||
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
|
||||
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
|
||||
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
|
||||
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
|
||||
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
|
||||
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
|
||||
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
|
||||
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
|
||||
[255,255,255,255,255,204,153,141,141,153,204,255,255,255,255,255],
|
||||
[255,255,255,255,204,183,141, 94, 94,141,183,204,255,255,255,255],
|
||||
[255,255,255,204,183,141,111, 72, 72,111,141,183,204,255,255,255],
|
||||
[255,255,204,183,141,111, 72, 39, 39, 72,111,141,183,204,255,255],
|
||||
[255,255,204,153,111, 72, 39, 1, 1, 39, 72,111,153,204,255,255],
|
||||
[255,255,204,153,111, 94, 72, 52, 52, 72, 94,111,153,204,255,255],
|
||||
[255,255,204,183,153,141,111,103,103,111,141,153,183,204,255,255],
|
||||
[255,255,255,204,204,183,153,153,153,153,183,204,204,255,255,255],
|
||||
[255,255,255,255,255,204,204,204,204,204,204,255,255,255,255,255],
|
||||
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255],
|
||||
[255,255,255,255,255,255,255,255,255,255,255,255,255,255,255,255]])
|
||||
mask = (data != 255)
|
||||
markers = np.zeros(data.shape,int)
|
||||
markers[6,7] = 1
|
||||
markers[14,7] = 2
|
||||
markers = np.zeros(data.shape, int)
|
||||
markers[6, 7] = 1
|
||||
markers[14, 7] = 2
|
||||
out = watershed(data, markers, self.eight, mask=mask)
|
||||
#
|
||||
# The two objects should be the same size, except possibly for the
|
||||
@@ -379,20 +374,17 @@ class TestWatershed(unittest.TestCase):
|
||||
"""
|
||||
image = np.zeros((1000, 1000))
|
||||
coords = np.random.uniform(0, 1000, (100, 2)).astype(int)
|
||||
markers = np.zeros((1000, 1000),int)
|
||||
markers = np.zeros((1000, 1000), int)
|
||||
idx = 1
|
||||
for x,y in coords:
|
||||
image[x,y] = 1
|
||||
for x, y in coords:
|
||||
image[x, y] = 1
|
||||
markers[x, y] = idx
|
||||
idx += 1
|
||||
|
||||
image = scipy.ndimage.gaussian_filter(image, 4)
|
||||
before = time.clock()
|
||||
out = watershed(image, markers, self.eight)
|
||||
elapsed = time.clock() - before
|
||||
before = time.clock()
|
||||
out = scipy.ndimage.watershed_ift(image.astype(np.uint16), markers, self.eight)
|
||||
elapsed = time.clock() - before
|
||||
watershed(image, markers, self.eight)
|
||||
scipy.ndimage.watershed_ift(image.astype(np.uint16), markers,
|
||||
self.eight)
|
||||
|
||||
|
||||
class TestIsLocalMaximum(unittest.TestCase):
|
||||
@@ -431,48 +423,48 @@ class TestIsLocalMaximum(unittest.TestCase):
|
||||
self.assertTrue(np.all(result == expected))
|
||||
|
||||
def test_01_04_not_adjacent_and_different(self):
|
||||
image = np.zeros((10,20))
|
||||
labels = np.zeros((10,20), int)
|
||||
image[5,5] = 1
|
||||
image[5,8] = .5
|
||||
image = np.zeros((10, 20))
|
||||
labels = np.zeros((10, 20), int)
|
||||
image[5, 5] = 1
|
||||
image[5, 8] = .5
|
||||
labels[image > 0] = 1
|
||||
expected = (labels == 1)
|
||||
result = is_local_maximum(image, labels, np.ones((3,3), bool))
|
||||
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
|
||||
self.assertTrue(np.all(result == expected))
|
||||
|
||||
def test_01_05_two_objects(self):
|
||||
image = np.zeros((10,20))
|
||||
labels = np.zeros((10,20), int)
|
||||
image[5,5] = 1
|
||||
image[5,15] = .5
|
||||
labels[5,5] = 1
|
||||
labels[5,15] = 2
|
||||
image = np.zeros((10, 20))
|
||||
labels = np.zeros((10, 20), int)
|
||||
image[5, 5] = 1
|
||||
image[5, 15] = .5
|
||||
labels[5, 5] = 1
|
||||
labels[5, 15] = 2
|
||||
expected = (labels > 0)
|
||||
result = is_local_maximum(image, labels, np.ones((3,3), bool))
|
||||
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
|
||||
self.assertTrue(np.all(result == expected))
|
||||
|
||||
def test_01_06_adjacent_different_objects(self):
|
||||
image = np.zeros((10,20))
|
||||
labels = np.zeros((10,20), int)
|
||||
image[5,5] = 1
|
||||
image[5,6] = .5
|
||||
labels[5,5] = 1
|
||||
labels[5,6] = 2
|
||||
image = np.zeros((10, 20))
|
||||
labels = np.zeros((10, 20), int)
|
||||
image[5, 5] = 1
|
||||
image[5, 6] = .5
|
||||
labels[5, 5] = 1
|
||||
labels[5, 6] = 2
|
||||
expected = (labels > 0)
|
||||
result = is_local_maximum(image, labels, np.ones((3,3), bool))
|
||||
result = is_local_maximum(image, labels, np.ones((3, 3), bool))
|
||||
self.assertTrue(np.all(result == expected))
|
||||
|
||||
def test_02_01_four_quadrants(self):
|
||||
np.random.seed(21)
|
||||
image = np.random.uniform(size=(40,60))
|
||||
i,j = np.mgrid[0:40,0:60]
|
||||
image = np.random.uniform(size=(40, 60))
|
||||
i, j = np.mgrid[0:40, 0:60]
|
||||
labels = 1 + (i >= 20) + (j >= 30) * 2
|
||||
i,j = np.mgrid[-3:4,-3:4]
|
||||
i, j = np.mgrid[-3:4, -3:4]
|
||||
footprint = (i * i + j * j <= 9)
|
||||
expected = np.zeros(image.shape, float)
|
||||
for imin, imax in ((0, 20), (20, 40)):
|
||||
for jmin, jmax in ((0, 30), (30, 60)):
|
||||
expected[imin:imax,jmin:jmax] = scipy.ndimage.maximum_filter(
|
||||
expected[imin:imax, jmin:jmax] = scipy.ndimage.maximum_filter(
|
||||
image[imin:imax, jmin:jmax], footprint=footprint)
|
||||
expected = (expected == image)
|
||||
result = is_local_maximum(image, labels, footprint)
|
||||
@@ -484,9 +476,9 @@ class TestIsLocalMaximum(unittest.TestCase):
|
||||
Test is_local_maximum when every point is a local maximum
|
||||
'''
|
||||
np.random.seed(31)
|
||||
image = np.random.uniform(size=(10,20))
|
||||
image = np.random.uniform(size=(10, 20))
|
||||
footprint = np.array([[1]])
|
||||
result = is_local_maximum(image, np.ones((10,20)), footprint)
|
||||
result = is_local_maximum(image, np.ones((10, 20)), footprint)
|
||||
self.assertTrue(np.all(result))
|
||||
result = is_local_maximum(image, footprint=footprint)
|
||||
self.assertTrue(np.all(result))
|
||||
|
||||
@@ -24,13 +24,12 @@ All rights reserved.
|
||||
Original author: Lee Kamentsky
|
||||
"""
|
||||
|
||||
from _heapq import heapify, heappush, heappop
|
||||
from _heapq import heappush, heappop
|
||||
import numpy as np
|
||||
import scipy.ndimage
|
||||
from ..filter import rank_order
|
||||
|
||||
from . import _watershed
|
||||
import warnings
|
||||
|
||||
|
||||
def watershed(image, markers, connectivity=None, offset=None, mask=None):
|
||||
@@ -123,17 +122,17 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
|
||||
The algorithm works also for 3-D images, and can be used for example to
|
||||
separate overlapping spheres.
|
||||
"""
|
||||
|
||||
|
||||
if connectivity == None:
|
||||
c_connectivity = scipy.ndimage.generate_binary_structure(image.ndim, 1)
|
||||
else:
|
||||
c_connectivity = np.array(connectivity, bool)
|
||||
if c_connectivity.ndim != image.ndim:
|
||||
raise ValueError,"Connectivity dimension must be same as image"
|
||||
raise ValueError("Connectivity dimension must be same as image")
|
||||
if offset == None:
|
||||
if any([x%2==0 for x in c_connectivity.shape]):
|
||||
raise ValueError,"Connectivity array must have an unambiguous \
|
||||
center"
|
||||
if any([x % 2 == 0 for x in c_connectivity.shape]):
|
||||
raise ValueError("Connectivity array must have an unambiguous "
|
||||
"center")
|
||||
#
|
||||
# offset to center of connectivity array
|
||||
#
|
||||
@@ -144,7 +143,7 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
|
||||
pads = offset
|
||||
|
||||
def pad(im):
|
||||
new_im = np.zeros([i + 2*p for i, p in zip(im.shape, pads)], im.dtype)
|
||||
new_im = np.zeros([i + 2 * p for i, p in zip(im.shape, pads)], im.dtype)
|
||||
new_im[[slice(p, -p, None) for p in pads]] = im
|
||||
return new_im
|
||||
|
||||
@@ -158,9 +157,8 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
|
||||
c_image = rank_order(image)[0].astype(np.int32)
|
||||
c_markers = np.ascontiguousarray(markers, dtype=np.int32)
|
||||
if c_markers.ndim != c_image.ndim:
|
||||
raise ValueError,\
|
||||
"markers (ndim=%d) must have same # of dimensions "\
|
||||
"as image (ndim=%d)"%(c_markers.ndim, cimage.ndim)
|
||||
raise ValueError("markers (ndim=%d) must have same # of dimensions "
|
||||
"as image (ndim=%d)" % (c_markers.ndim, c_image.ndim))
|
||||
if c_markers.shape != c_image.shape:
|
||||
raise ValueError("image and markers must have the same shape")
|
||||
if mask != None:
|
||||
@@ -190,7 +188,6 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
|
||||
indexes = []
|
||||
ignore = True
|
||||
for j in range(len(c_connectivity.shape)):
|
||||
elems = c_image.shape[j]
|
||||
idx = (i // multiplier) % c_connectivity.shape[j]
|
||||
off = idx - offset[j]
|
||||
if off:
|
||||
@@ -231,7 +228,7 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
|
||||
def is_local_maximum(image, labels=None, footprint=None):
|
||||
"""
|
||||
Return a boolean array of points that are local maxima
|
||||
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image: ndarray (2-D, 3-D, ...)
|
||||
|
||||
@@ -140,7 +140,6 @@ def iradon(radon_image, theta=None, output_size=None,
|
||||
# zero pad input image
|
||||
img.resize((order, img.shape[1]))
|
||||
# construct the fourier filter
|
||||
freqs = np.zeros((order, 1))
|
||||
|
||||
f = fftshift(abs(np.mgrid[-1:1:2 / order])).reshape(-1, 1)
|
||||
w = 2 * np.pi * f
|
||||
|
||||
@@ -59,8 +59,9 @@ def test_probabilistic_hough():
|
||||
img[i, i] = 100
|
||||
# decrease default theta sampling because similar orientations may confuse
|
||||
# as mentioned in article of Galambos et al
|
||||
theta=np.linspace(0, np.pi, 45)
|
||||
lines = probabilistic_hough(img, theta=theta, threshold=10, line_length=10, line_gap=1)
|
||||
theta = np.linspace(0, np.pi, 45)
|
||||
lines = probabilistic_hough(img, theta=theta, threshold=10, line_length=10,
|
||||
line_gap=1)
|
||||
# sort the lines according to the x-axis
|
||||
sorted_lines = []
|
||||
for line in lines:
|
||||
@@ -73,4 +74,3 @@ def test_probabilistic_hough():
|
||||
|
||||
if __name__ == "__main__":
|
||||
run_module_suite()
|
||||
|
||||
|
||||
@@ -6,6 +6,7 @@ from skimage.transform import homography, fast_homography
|
||||
from skimage import data
|
||||
from skimage.color import rgb2gray
|
||||
|
||||
|
||||
def test_stackcopy():
|
||||
layers = 4
|
||||
x = np.empty((3, 3, layers))
|
||||
@@ -17,10 +18,10 @@ def test_stackcopy():
|
||||
|
||||
def test_homography():
|
||||
x = np.arange(9, dtype=np.uint8).reshape((3, 3)) + 1
|
||||
theta = -np.pi/2
|
||||
M = np.array([[np.cos(theta),-np.sin(theta),0],
|
||||
[np.sin(theta), np.cos(theta),2],
|
||||
[0, 0, 1]])
|
||||
theta = -np.pi / 2
|
||||
M = np.array([[np.cos(theta), -np.sin(theta), 0],
|
||||
[np.sin(theta), np.cos(theta), 2],
|
||||
[0, 0, 1]])
|
||||
x90 = homography(x, M, order=1)
|
||||
assert_array_almost_equal(x90, np.rot90(x))
|
||||
|
||||
|
||||
@@ -40,6 +40,7 @@ def test_radon_iradon():
|
||||
image = np.tri(size) + np.tri(size)[::-1]
|
||||
reconstructed = iradon(radon(image), filter="ramp", interpolation="nearest")
|
||||
|
||||
|
||||
def test_iradon_angles():
|
||||
"""
|
||||
Test with different number of projections
|
||||
@@ -62,10 +63,12 @@ def test_iradon_angles():
|
||||
s = radon_image_80.sum(axis=0)
|
||||
assert np.allclose(s, s[0], rtol=0.01)
|
||||
reconstructed = iradon(radon_image_80)
|
||||
delta_80 = np.mean(abs(image/np.max(image) - reconstructed/np.max(reconstructed)))
|
||||
delta_80 = np.mean(abs(image / np.max(image) -
|
||||
reconstructed / np.max(reconstructed)))
|
||||
# Loss of quality when the number of projections is reduced
|
||||
assert delta_80 > delta_200
|
||||
|
||||
|
||||
def test_radon_minimal():
|
||||
"""
|
||||
Test for small images for various angles
|
||||
|
||||
Reference in New Issue
Block a user