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Merge pull request #1289 from ahojnnes/random

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STY: Misc PEP8 fixes
This commit is contained in:
Josh Warner
2014-12-13 23:25:37 -06:00
parent dad2b18546 3a1079a180
commit 1213a3ea1a
44 changed files with 160 additions and 147 deletions
Download Patch File Download Diff File Expand all files Collapse all files
skimage/_shared/tests/test_safe_as_int.py
+4 -4
View File
@@ -8,14 +8,14 @@ def test_int_cast_not_possible():
np.testing.assert_raises(ValueError, safe_as_int, np.r_[7.1, 0.9])
np.testing.assert_raises(ValueError, safe_as_int, (7.1, 0.9))
np.testing.assert_raises(ValueError, safe_as_int, ((3, 4, 1),
(2, 7.6, 289)))
(2, 7.6, 289)))
np.testing.assert_raises(ValueError, safe_as_int, 7.1, 0.09)
np.testing.assert_raises(ValueError, safe_as_int, [7.1, 0.9], 0.09)
np.testing.assert_raises(ValueError, safe_as_int, np.r_[7.1, 0.9], 0.09)
np.testing.assert_raises(ValueError, safe_as_int, (7.1, 0.9), 0.09)
np.testing.assert_raises(ValueError, safe_as_int, ((3, 4, 1),
(2, 7.6, 289)), 0.25)
(2, 7.6, 289)), 0.25)
def test_int_cast_possible():
@@ -25,8 +25,8 @@ def test_int_cast_possible():
np.testing.assert_array_equal(safe_as_int([2, 42, 5789234.0, 87, 4]),
np.r_[2, 42, 5789234, 87, 4])
np.testing.assert_array_equal(safe_as_int(np.r_[[[3, 4, 1.000000001],
[7, 2, -8.999999999],
[6, 9, -4234918347.]]]),
[7, 2, -8.999999999],
[6, 9, -4234918347.]]]),
np.r_[[[3, 4, 1],
[7, 2, -9],
[6, 9, -4234918347]]])
skimage/_shared/tests/test_testing.py
+1
View File
@@ -11,6 +11,7 @@ def test_skipper():
pass
class c():
def __init__(self):
self.me = "I think, therefore..."
skimage/_shared/utils.py
+3 -3
View File
@@ -52,9 +52,9 @@ class deprecated(object):
func_code = six.get_function_code(func)
warnings.simplefilter('always', skimage_deprecation)
warnings.warn_explicit(msg,
category=skimage_deprecation,
filename=func_code.co_filename,
lineno=func_code.co_firstlineno + 1)
category=skimage_deprecation,
filename=func_code.co_filename,
lineno=func_code.co_firstlineno + 1)
elif self.behavior == 'raise':
raise skimage_deprecation(msg)
return func(*args, **kwargs)
skimage/color/colorconv.py
+16 -16
View File
@@ -299,8 +299,8 @@ sb_primaries = np.array([1. / 155, 1. / 190, 1. / 225]) * 1e5
# From sRGB specification
xyz_from_rgb = np.array([[0.412453, 0.357580, 0.180423],
[0.212671, 0.715160, 0.072169],
[0.019334, 0.119193, 0.950227]])
[0.212671, 0.715160, 0.072169],
[0.019334, 0.119193, 0.950227]])
rgb_from_xyz = linalg.inv(xyz_from_rgb)
@@ -978,19 +978,19 @@ def xyz2luv(xyz, illuminant="D65", observer="2"):
L[mask] = 116. * np.power(L[mask], 1. / 3.) - 16.
L[~mask] = 903.3 * L[~mask]
u0 = 4*xyz_ref_white[0] / np.dot([1, 15, 3], xyz_ref_white)
v0 = 9*xyz_ref_white[1] / np.dot([1, 15, 3], xyz_ref_white)
u0 = 4 * xyz_ref_white[0] / np.dot([1, 15, 3], xyz_ref_white)
v0 = 9 * xyz_ref_white[1] / np.dot([1, 15, 3], xyz_ref_white)
# u' and v' helper functions
def fu(X, Y, Z):
return (4.*X) / (X + 15.*Y + 3.*Z + eps)
return (4. * X) / (X + 15. * Y + 3. * Z + eps)
def fv(X, Y, Z):
return (9.*Y) / (X + 15.*Y + 3.*Z + eps)
return (9. * Y) / (X + 15. * Y + 3. * Z + eps)
# compute u and v using helper functions
u = 13.*L * (fu(x, y, z) - u0)
v = 13.*L * (fv(x, y, z) - v0)
u = 13. * L * (fu(x, y, z) - u0)
v = 13. * L * (fv(x, y, z) - v0)
return np.concatenate([q[..., np.newaxis] for q in [L, u, v]], axis=-1)
@@ -1043,24 +1043,24 @@ def luv2xyz(luv, illuminant="D65", observer="2"):
# compute y
y = L.copy()
mask = y > 7.999625
y[mask] = np.power((y[mask]+16.) / 116., 3.)
y[mask] = np.power((y[mask] + 16.) / 116., 3.)
y[~mask] = y[~mask] / 903.3
xyz_ref_white = get_xyz_coords(illuminant, observer)
y *= xyz_ref_white[1]
# reference white x,z
uv_weights = [1, 15, 3]
u0 = 4*xyz_ref_white[0] / np.dot(uv_weights, xyz_ref_white)
v0 = 9*xyz_ref_white[1] / np.dot(uv_weights, xyz_ref_white)
u0 = 4 * xyz_ref_white[0] / np.dot(uv_weights, xyz_ref_white)
v0 = 9 * xyz_ref_white[1] / np.dot(uv_weights, xyz_ref_white)
# compute intermediate values
a = u0 + u / (13.*L + eps)
b = v0 + v / (13.*L + eps)
c = 3*y * (5*b-3)
a = u0 + u / (13. * L + eps)
b = v0 + v / (13. * L + eps)
c = 3 * y * (5 * b - 3)
# compute x and z
z = ((a-4)*c - 15*a*b*y) / (12*b)
x = -(c/b + 3.*z)
z = ((a - 4) * c - 15 * a * b * y) / (12 * b)
x = -(c / b + 3. * z)
return np.concatenate([q[..., np.newaxis] for q in [x, y, z]], axis=-1)
skimage/color/delta_e.py
+1 -1
View File
@@ -336,4 +336,4 @@ def get_dH2(lab1, lab2):
C2 = np.hypot(a2, b2)
term = (C1 * C2) - (a1 * a2 + b1 * b2)
return 2*term
return 2 * term
skimage/data/__init__.py
+4 -3
View File
@@ -65,18 +65,19 @@ def lena():
"""
return load("lena.png")
def astronaut():
"""Colour image of the astronaut Eileen Collins.
Photograph of Eileen Collins, an American astronaut. She was selected
Photograph of Eileen Collins, an American astronaut. She was selected
as an astronaut in 1992 and first piloted the space shuttle STS-63 in
1995. She retired in 2006 after spending a total of 38 days, 8 hours
1995. She retired in 2006 after spending a total of 38 days, 8 hours
and 10 minutes in outer space.
This image was downloaded from the NASA Great Images database
<http://grin.hq.nasa.gov/ABSTRACTS/GPN-2000-001177.html>`__.
No known copyright restrictions, released into the public domain.
No known copyright restrictions, released into the public domain.
"""
skimage/exposure/_adapthist.py
+1 -1
View File
@@ -22,7 +22,7 @@ from skimage.util import view_as_blocks, pad
MAX_REG_X = 16 # max. # contextual regions in x-direction */
MAX_REG_Y = 16 # max. # contextual regions in y-direction */
NR_OF_GREY = 2**14 # number of grayscale levels to use in CLAHE algorithm
NR_OF_GREY = 2 ** 14 # number of grayscale levels to use in CLAHE algorithm
@adapt_rgb(hsv_value)
skimage/exposure/exposure.py
+5 -5
View File
@@ -11,9 +11,9 @@ __all__ = ['histogram', 'cumulative_distribution', 'equalize_hist',
DTYPE_RANGE = dtype_range.copy()
DTYPE_RANGE.update((d.__name__, limits) for d, limits in dtype_range.items())
DTYPE_RANGE.update({'uint10': (0, 2**10 - 1),
'uint12': (0, 2**12 - 1),
'uint14': (0, 2**14 - 1),
DTYPE_RANGE.update({'uint10': (0, 2 ** 10 - 1),
'uint12': (0, 2 ** 12 - 1),
'uint14': (0, 2 ** 14 - 1),
'bool': dtype_range[np.bool_],
'float': dtype_range[np.float64]})
@@ -457,8 +457,8 @@ def adjust_sigmoid(image, cutoff=0.5, gain=10, inv=False):
scale = float(dtype_limits(image, True)[1] - dtype_limits(image, True)[0])
if inv:
out = (1 - 1 / (1 + np.exp(gain * (cutoff - image/scale)))) * scale
out = (1 - 1 / (1 + np.exp(gain * (cutoff - image / scale)))) * scale
return dtype(out)
out = (1 / (1 + np.exp(gain * (cutoff - image/scale)))) * scale
out = (1 / (1 + np.exp(gain * (cutoff - image / scale)))) * scale
return dtype(out)
skimage/feature/_hog.py
+3 -3
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@@ -104,7 +104,7 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
cell are used to vote into the orientation histogram.
"""
magnitude = sqrt(gx**2 + gy**2)
magnitude = sqrt(gx ** 2 + gy ** 2)
orientation = arctan2(gy, gx) * (180 / pi) % 180
sy, sx = image.shape
@@ -119,7 +119,7 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
subsample = np.index_exp[cy // 2:cy * n_cellsy:cy,
cx // 2:cx * n_cellsx:cx]
for i in range(orientations):
#create new integral image for this orientation
# create new integral image for this orientation
# isolate orientations in this range
temp_ori = np.where(orientation < 180.0 / orientations * (i + 1),
@@ -177,7 +177,7 @@ def hog(image, orientations=9, pixels_per_cell=(8, 8),
for y in range(n_blocksy):
block = orientation_histogram[y:y + by, x:x + bx, :]
eps = 1e-5
normalised_blocks[y, x, :] = block / sqrt(block.sum()**2 + eps)
normalised_blocks[y, x, :] = block / sqrt(block.sum() ** 2 + eps)
"""
The final step collects the HOG descriptors from all blocks of a dense
skimage/feature/blob.py
+1 -1
View File
@@ -185,7 +185,7 @@ def blob_dog(image, min_sigma=1, max_sigma=50, sigma_ratio=1.6, threshold=2.0,
# a geometric progression of standard deviations for gaussian kernels
sigma_list = np.array([min_sigma * (sigma_ratio ** i)
for i in range(k + 1)])
for i in range(k + 1)])
gaussian_images = [gaussian_filter(image, s) for s in sigma_list]
skimage/feature/brief.py
+2 -2
View File
@@ -171,13 +171,13 @@ class BRIEF(DescriptorExtractor):
# Removing keypoints that are within (patch_size / 2) distance from the
# image border
self.mask = _mask_border_keypoints(image.shape, keypoints,
patch_size // 2)
patch_size // 2)
keypoints = np.array(keypoints[self.mask, :], dtype=np.intp,
order='C', copy=False)
self.descriptors = np.zeros((keypoints.shape[0], desc_size),
dtype=bool, order='C')
dtype=bool, order='C')
_brief_loop(image, self.descriptors.view(np.uint8), keypoints,
pos1, pos2)
skimage/feature/censure.py
+5 -6
View File
@@ -31,7 +31,7 @@ STAR_FILTER_SHAPE = [(1, 0), (3, 1), (4, 2), (5, 3), (7, 4), (8, 5),
def _filter_image(image, min_scale, max_scale, mode):
response = np.zeros((image.shape[0], image.shape[1],
max_scale - min_scale + 1), dtype=np.double)
max_scale - min_scale + 1), dtype=np.double)
if mode == 'dob':
@@ -48,8 +48,8 @@ def _filter_image(image, min_scale, max_scale, mode):
# Constant multipliers for the outer region and the inner region
# of the bi-level filters with the constraint of keeping the
# DC bias 0.
inner_weight = (1.0 / (2 * n + 1)**2)
outer_weight = (1.0 / (12 * n**2 + 4 * n))
inner_weight = (1.0 / (2 * n + 1) ** 2)
outer_weight = (1.0 / (12 * n ** 2 + 4 * n))
_censure_dob_loop(n, integral_img, response[:, :, i],
inner_weight, outer_weight)
@@ -79,8 +79,8 @@ def _filter_image(image, min_scale, max_scale, mode):
def _octagon_kernel(mo, no, mi, ni):
outer = (mo + 2 * no)**2 - 2 * no * (no + 1)
inner = (mi + 2 * ni)**2 - 2 * ni * (ni + 1)
outer = (mo + 2 * no) ** 2 - 2 * no * (no + 1)
inner = (mi + 2 * ni) ** 2 - 2 * ni * (ni + 1)
outer_weight = 1.0 / (outer - inner)
inner_weight = 1.0 / inner
c = ((mo + 2 * no) - (mi + 2 * ni)) // 2
@@ -110,7 +110,6 @@ def _suppress_lines(feature_mask, image, sigma, line_threshold):
> line_threshold * (Axx * Ayy - Axy ** 2)] = False
class CENSURE(FeatureDetector):
"""CENSURE keypoint detector.
skimage/feature/corner.py
+8 -8
View File
@@ -323,8 +323,8 @@ def corner_kitchen_rosenfeld(image, mode='constant', cval=0):
imxx, imxy = _compute_derivatives(imx, mode=mode, cval=cval)
imyx, imyy = _compute_derivatives(imy, mode=mode, cval=cval)
numerator = (imxx * imy**2 + imyy * imx**2 - 2 * imxy * imx * imy)
denominator = (imx**2 + imy**2)
numerator = (imxx * imy ** 2 + imyy * imx ** 2 - 2 * imxy * imx * imy)
denominator = (imx ** 2 + imy ** 2)
response = np.zeros_like(image, dtype=np.double)
@@ -403,12 +403,12 @@ def corner_harris(image, method='k', k=0.05, eps=1e-6, sigma=1):
Axx, Axy, Ayy = structure_tensor(image, sigma)
# determinant
detA = Axx * Ayy - Axy**2
detA = Axx * Ayy - Axy ** 2
# trace
traceA = Axx + Ayy
if method == 'k':
response = detA - k * traceA**2
response = detA - k * traceA ** 2
else:
response = 2 * detA / (traceA + eps)
@@ -473,7 +473,7 @@ def corner_shi_tomasi(image, sigma=1):
Axx, Axy, Ayy = structure_tensor(image, sigma)
# minimum eigenvalue of A
response = ((Axx + Ayy) - np.sqrt((Axx - Ayy)**2 + 4 * Axy**2)) / 2
response = ((Axx + Ayy) - np.sqrt((Axx - Ayy) ** 2 + 4 * Axy ** 2)) / 2
return response
@@ -543,7 +543,7 @@ def corner_foerstner(image, sigma=1):
Axx, Axy, Ayy = structure_tensor(image, sigma)
# determinant
detA = Axx * Ayy - Axy**2
detA = Axx * Ayy - Axy ** 2
# trace
traceA = Axx + Ayy
@@ -553,7 +553,7 @@ def corner_foerstner(image, sigma=1):
mask = traceA != 0
w[mask] = detA[mask] / traceA[mask]
q[mask] = 4 * detA[mask] / traceA[mask]**2
q[mask] = 4 * detA[mask] / traceA[mask] ** 2
return w, q
@@ -692,7 +692,7 @@ def corner_subpix(image, corners, window_size=11, alpha=0.99):
b_edge = np.zeros((2, ), dtype=np.double)
# critical statistical test values
redundancy = window_size**2 - 2
redundancy = window_size ** 2 - 2
t_crit_dot = stats.f.isf(1 - alpha, redundancy, redundancy)
t_crit_edge = stats.f.isf(alpha, redundancy, redundancy)
skimage/feature/orb.py
+3 -3
View File
@@ -16,7 +16,7 @@ OFAST_MASK = np.zeros((31, 31))
OFAST_UMAX = [15, 15, 15, 15, 14, 14, 14, 13, 13, 12, 11, 10, 9, 8, 6, 3]
for i in range(-15, 16):
for j in range(-OFAST_UMAX[abs(i)], OFAST_UMAX[abs(i)] + 1):
OFAST_MASK[15 + j, 15 + i] = 1
OFAST_MASK[15 + j, 15 + i] = 1
class ORB(FeatureDetector, DescriptorExtractor):
@@ -185,7 +185,7 @@ class ORB(FeatureDetector, DescriptorExtractor):
keypoints_list.append(keypoints * self.downscale ** octave)
orientations_list.append(orientations)
scales_list.append(self.downscale ** octave
scales_list.append(self.downscale ** octave
* np.ones(keypoints.shape[0], dtype=np.intp))
responses_list.append(responses)
@@ -314,7 +314,7 @@ class ORB(FeatureDetector, DescriptorExtractor):
keypoints_list.append(keypoints[mask] * self.downscale ** octave)
responses_list.append(responses[mask])
orientations_list.append(orientations[mask])
scales_list.append(self.downscale ** octave
scales_list.append(self.downscale ** octave
* np.ones(keypoints.shape[0], dtype=np.intp))
descriptors_list.append(descriptors)
skimage/feature/template.py
+6 -6
View File
@@ -9,11 +9,11 @@ def _window_sum_2d(image, window_shape):
window_sum = np.cumsum(image, axis=0)
window_sum = (window_sum[window_shape[0]:-1]
- window_sum[:-window_shape[0]-1])
- window_sum[:-window_shape[0] - 1])
window_sum = np.cumsum(window_sum, axis=1)
window_sum = (window_sum[:, window_shape[1]:-1]
- window_sum[:, :-window_shape[1]-1])
- window_sum[:, :-window_shape[1] - 1])
return window_sum
@@ -24,7 +24,7 @@ def _window_sum_3d(image, window_shape):
window_sum = np.cumsum(window_sum, axis=2)
window_sum = (window_sum[:, :, window_shape[2]:-1]
- window_sum[:, :, :-window_shape[2]-1])
- window_sum[:, :, :-window_shape[2] - 1])
return window_sum
@@ -126,13 +126,13 @@ def match_template(image, template, pad_input=False, mode='constant',
# computation of integral images
if image.ndim == 2:
image_window_sum = _window_sum_2d(image, template.shape)
image_window_sum2 = _window_sum_2d(image**2, template.shape)
image_window_sum2 = _window_sum_2d(image ** 2, template.shape)
elif image.ndim == 3:
image_window_sum = _window_sum_3d(image, template.shape)
image_window_sum2 = _window_sum_3d(image**2, template.shape)
image_window_sum2 = _window_sum_3d(image ** 2, template.shape)
template_volume = np.prod(template.shape)
template_ssd = np.sum((template - template.mean())**2)
template_ssd = np.sum((template - template.mean()) ** 2)
if image.ndim == 2:
xcorr = fftconvolve(image, template[::-1, ::-1],
skimage/filters/_gabor.py
+4 -3
View File
@@ -9,7 +9,8 @@ __all__ = ['gabor_kernel', 'gabor_filter']
def _sigma_prefactor(bandwidth):
b = bandwidth
# See http://www.cs.rug.nl/~imaging/simplecell.html
return 1.0 / np.pi * np.sqrt(np.log(2)/2.0) * (2.0**b + 1) / (2.0**b - 1)
return 1.0 / np.pi * np.sqrt(np.log(2) / 2.0) * \
(2.0 ** b + 1) / (2.0 ** b - 1)
def gabor_kernel(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None,
@@ -80,13 +81,13 @@ def gabor_kernel(frequency, theta=0, bandwidth=1, sigma_x=None, sigma_y=None,
np.abs(n_stds * sigma_y * np.sin(theta)), 1))
y0 = np.ceil(max(np.abs(n_stds * sigma_y * np.cos(theta)),
np.abs(n_stds * sigma_x * np.sin(theta)), 1))
y, x = np.mgrid[-y0:y0+1, -x0:x0+1]
y, x = np.mgrid[-y0:y0 + 1, -x0:x0 + 1]
rotx = x * np.cos(theta) + y * np.sin(theta)
roty = -x * np.sin(theta) + y * np.cos(theta)
g = np.zeros(y.shape, dtype=np.complex)
g[:] = np.exp(-0.5 * (rotx**2 / sigma_x**2 + roty**2 / sigma_y**2))
g[:] = np.exp(-0.5 * (rotx ** 2 / sigma_x ** 2 + roty ** 2 / sigma_y ** 2))
g /= 2 * np.pi * sigma_x * sigma_y
g *= np.exp(1j * (2 * np.pi * frequency * rotx + offset))
skimage/filters/_gaussian.py
+6 -8
View File
@@ -11,12 +11,10 @@ __all__ = ['gaussian_filter']
def gaussian_filter(image, sigma, output=None, mode='nearest', cval=0,
multichannel=None):
"""
Multi-dimensional Gaussian filter
"""Multi-dimensional Gaussian filter
Parameters
----------
image : array-like
input image (grayscale or color) to filter.
sigma : scalar or sequence of scalars
@@ -43,13 +41,11 @@ def gaussian_filter(image, sigma, output=None, mode='nearest', cval=0,
Returns
-------
filtered_image : ndarray
the filtered array
Notes
-----
This function is a wrapper around :func:`scipy.ndimage.gaussian_filter`.
Integer arrays are converted to float.
@@ -87,12 +83,14 @@ def gaussian_filter(image, sigma, output=None, mode='nearest', cval=0,
>>> from skimage.data import astronaut
>>> image = astronaut()
>>> filtered_img = gaussian_filter(image, sigma=1, multichannel=True)
"""
spatial_dims = guess_spatial_dimensions(image)
if spatial_dims is None and multichannel is None:
msg = ("Images with dimensions (M, N, 3) are interpreted as 2D+RGB" +
" by default. Use `multichannel=False` to interpret as " +
" 3D image with last dimension of length 3.")
msg = ("Images with dimensions (M, N, 3) are interpreted as 2D+RGB "
"by default. Use `multichannel=False` to interpret as "
"3D image with last dimension of length 3.")
warnings.warn(RuntimeWarning(msg))
multichannel = True
if multichannel:
skimage/filters/_rank_order.py
+1 -1
View File
@@ -28,7 +28,7 @@ def rank_order(image):
n - 1, where n is the number of distinct unique values in
`image`.
original_values: 1-d ndarray
original_values: 1-D ndarray
Unique original values of `image`
Examples
skimage/filters/lpi_filter.py
+2 -1
View File
@@ -43,6 +43,7 @@ class LPIFilter2D(object):
"""Linear Position-Invariant Filter (2-dimensional)
"""
def __init__(self, impulse_response, **filter_params):
"""
Parameters
@@ -239,7 +240,7 @@ def wiener(data, impulse_response=None, filter_params={}, K=0.25,
F, G = filt._prepare(data)
_min_limit(F)
H_mag_sqr = np.abs(F)**2
H_mag_sqr = np.abs(F) ** 2
F = 1 / F * H_mag_sqr / (H_mag_sqr + K)
return _centre(np.abs(ifftshift(np.dual.ifftn(G * F))), data.shape)
skimage/graph/spath.py
+2 -2
View File
@@ -73,8 +73,8 @@ def shortest_path(arr, reach=1, axis=-1, output_indexlist=False):
if not output_indexlist:
traceback = np.array(traceback)
traceback = np.concatenate([traceback[:, :axis], traceback[:, axis + 1:]],
axis=1)
traceback = np.concatenate([traceback[:, :axis],
traceback[:, axis + 1:]], axis=1)
traceback = np.squeeze(traceback)
return traceback, cost
skimage/io/__init__.py
-1
View File
@@ -36,7 +36,6 @@ def _update_doc(doc):
"""
from textwrap import wrap
info_table = [(p, plugin_info(p).get('description', 'no description'))
for p in available_plugins if not p == 'test']
skimage/io/collection.py
+2 -1
View File
@@ -206,7 +206,8 @@ class ImageCollection(object):
im.getdata()[0]
except IOError:
site = "http://pillow.readthedocs.org/en/latest/installation.html#external-libraries"
raise ValueError('Could not load "%s"\nPlease see documentation at: %s' % (fname, site))
raise ValueError(
'Could not load "%s"\nPlease see documentation at: %s' % (fname, site))
else:
i = 0
while True:
skimage/io/manage_plugins.py
+4 -4
View File
@@ -119,7 +119,7 @@ def _scan_plugins():
for p in provides:
if not p in plugin_store:
print("Plugin `%s` wants to provide non-existent `%s`." \
print("Plugin `%s` wants to provide non-existent `%s`."
" Ignoring." % (name, p))
# Add plugins that provide 'imread' as provider of 'imread_collection'.
@@ -201,7 +201,7 @@ def call_plugin(kind, *args, **kwargs):
try:
func = [f for (p, f) in plugin_funcs if p == plugin][0]
except IndexError:
raise RuntimeError('Could not find the plugin "%s" for %s.' % \
raise RuntimeError('Could not find the plugin "%s" for %s.' %
(plugin, kind))
return func(*args, **kwargs)
@@ -240,7 +240,7 @@ def use_plugin(name, kind=None):
kind = plugin_store.keys()
else:
if not kind in plugin_provides[name]:
raise RuntimeError("Plugin %s does not support `%s`." % \
raise RuntimeError("Plugin %s does not support `%s`." %
(name, kind))
if kind == 'imshow':
@@ -299,7 +299,7 @@ def _load(plugin):
if p == 'imread_collection':
_inject_imread_collection_if_needed(plugin_module)
elif not hasattr(plugin_module, p):
print("Plugin %s does not provide %s as advertised. Ignoring." % \
print("Plugin %s does not provide %s as advertised. Ignoring." %
(plugin, p))
continue
skimage/io/sift.py
+4 -4
View File
@@ -42,15 +42,15 @@ def _sift_read(f, mode='SIFT'):
if mode == 'SIFT':
nr_features, feature_len = map(int, f.readline().split())
datatype = np.dtype([('row', float), ('column', float),
('scale', float), ('orientation', float),
('data', (float, feature_len))])
('scale', float), ('orientation', float),
('data', (float, feature_len))])
else:
mode = 'SURF'
feature_len = int(f.readline()) - 1
nr_features = int(f.readline())
datatype = np.dtype([('column', float), ('row', float),
('second_moment', (float, 3)),
('sign', float), ('data', (float, feature_len))])
('second_moment', (float, 3)),
('sign', float), ('data', (float, feature_len))])
data = np.fromfile(f, sep=' ')
if data.size != nr_features * datatype.itemsize / np.dtype(float).itemsize:
raise IOError("Invalid %s feature file." % mode)
skimage/measure/_structural_similarity.py
+11 -12
View File
@@ -7,6 +7,7 @@ import numpy as np
from ..util.dtype import dtype_range
from ..util.shape import view_as_windows
def structural_similarity(X, Y, win_size=7,
gradient=False, dynamic_range=None):
"""Compute the mean structural similarity index between two images.
@@ -64,34 +65,32 @@ def structural_similarity(X, Y, win_size=7,
uy = np.mean(np.mean(YW, axis=2), axis=2)
# Compute variances var(X), var(Y) and var(X, Y)
cov_norm = 1 / (win_size**2 - 1)
cov_norm = 1 / (win_size ** 2 - 1)
XWM = XW - ux[..., None, None]
YWM = YW - uy[..., None, None]
vx = cov_norm * np.sum(np.sum(XWM**2, axis=2), axis=2)
vy = cov_norm * np.sum(np.sum(YWM**2, axis=2), axis=2)
vx = cov_norm * np.sum(np.sum(XWM ** 2, axis=2), axis=2)
vy = cov_norm * np.sum(np.sum(YWM ** 2, axis=2), axis=2)
vxy = cov_norm * np.sum(np.sum(XWM * YWM, axis=2), axis=2)
R = dynamic_range
K1 = 0.01
K2 = 0.03
C1 = (K1 * R)**2
C2 = (K2 * R)**2
C1 = (K1 * R) ** 2
C2 = (K2 * R) ** 2
A1, A2, B1, B2 = (v[..., None, None] for v in
(2 * ux * uy + C1,
2 * vxy + C2,
ux**2 + uy**2 + C1,
ux ** 2 + uy ** 2 + C1,
vx + vy + C2))
S = np.mean((A1 * A2) / (B1 * B2))
if gradient:
local_grad = 2 / (NP * B1**2 * B2**2) * \
(
A1 * B1 * (B2 * XW - A2 * YW) - \
B1 * B2 * (A2 - A1) * ux[..., None, None] + \
A1 * A2 * (B1 - B2) * uy[..., None, None]
)
local_grad = 2 / (NP * B1 ** 2 * B2 ** 2) * \
(A1 * B1 * (B2 * XW - A2 * YW) -
B1 * B2 * (A2 - A1) * ux[..., None, None] +
A1 * A2 * (B1 - B2) * uy[..., None, None])
grad = np.zeros_like(X, dtype=float)
OW = view_as_windows(grad, (win_size, win_size))
skimage/measure/profile.py
+1 -2
View File
@@ -9,7 +9,7 @@ def profile_line(img, src, dst, linewidth=1,
Parameters
----------
img : numeric array, shape (M, N[, C])
The image, either grayscale (2D array) or multichannel
The image, either grayscale (2D array) or multichannel
(3D array, where the final axis contains the channel
information).
src : 2-tuple of numeric scalar (float or int)
@@ -109,4 +109,3 @@ def _line_profile_coordinates(src, dst, linewidth=1):
perp_cols = np.array([np.linspace(col_i - col_width, col_i + col_width,
linewidth) for col_i in line_col])
return np.array([perp_rows, perp_cols])
skimage/morphology/misc.py
+1 -1
View File
@@ -30,7 +30,7 @@ def default_fallback(func):
Returns
-------
func_out : function
If the image dimentionality is greater than 2D, the ndimage
If the image dimensionality is greater than 2D, the ndimage
function is returned, otherwise skimage function is used.
"""
@functools.wraps(func)
skimage/morphology/selem.py
+15 -9
View File
@@ -171,9 +171,9 @@ def octahedron(radius, dtype=np.uint8):
"""
# note that in contrast to diamond(), this method allows non-integer radii
n = 2 * radius + 1
Z, Y, X = np.mgrid[-radius:radius:n*1j,
-radius:radius:n*1j,
-radius:radius:n*1j]
Z, Y, X = np.mgrid[-radius:radius:n * 1j,
-radius:radius:n * 1j,
-radius:radius:n * 1j]
s = np.abs(X) + np.abs(Y) + np.abs(Z)
return np.array(s <= radius, dtype=dtype)
@@ -202,10 +202,10 @@ def ball(radius, dtype=np.uint8):
are 1 and 0 otherwise.
"""
n = 2 * radius + 1
Z, Y, X = np.mgrid[-radius:radius:n*1j,
-radius:radius:n*1j,
-radius:radius:n*1j]
s = X**2 + Y**2 + Z**2
Z, Y, X = np.mgrid[-radius:radius:n * 1j,
-radius:radius:n * 1j,
-radius:radius:n * 1j]
s = X ** 2 + Y ** 2 + Z ** 2
return np.array(s <= radius * radius, dtype=dtype)
@@ -236,7 +236,7 @@ def octagon(m, n, dtype=np.uint8):
"""
from . import convex_hull_image
selem = np.zeros((m + 2*n, m + 2*n))
selem = np.zeros((m + 2 * n, m + 2 * n))
selem[0, n] = 1
selem[n, 0] = 1
selem[0, m + n - 1] = 1
@@ -274,20 +274,26 @@ def star(a, dtype=np.uint8):
"""
from . import convex_hull_image
if a == 1:
bfilter = np.zeros((3, 3), dtype)
bfilter[:] = 1
return bfilter
m = 2 * a + 1
n = a // 2
selem_square = np.zeros((m + 2 * n, m + 2 * n))
selem_square[n: m + n, n: m + n] = 1
c = (m + 2 * n - 1) // 2
selem_rotated = np.zeros((m + 2 * n, m + 2 * n))
selem_rotated[0, c] = selem_rotated[-1, c] = selem_rotated[c, 0] = selem_rotated[c, -1] = 1
selem_rotated[0, c] = selem_rotated[-1, c] = 1
selem_rotated[c, 0] = selem_rotated[c, -1] = 1
selem_rotated = convex_hull_image(selem_rotated).astype(int)
selem = selem_square + selem_rotated
selem[selem > 0] = 1
return selem.astype(dtype)
skimage/morphology/watershed.py
+4 -3
View File
@@ -136,7 +136,7 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
if offset is None:
if any([x % 2 == 0 for x in c_connectivity.shape]):
raise ValueError("Connectivity array must have an unambiguous "
"center")
"center")
#
# offset to center of connectivity array
#
@@ -147,7 +147,8 @@ 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
@@ -220,7 +221,7 @@ def watershed(image, markers, connectivity=None, offset=None, mask=None):
c_mask,
c_output)
c_output = c_output.reshape(c_image.shape)[[slice(1, -1, None)] *
image.ndim]
image.ndim]
try:
return c_output.astype(markers.dtype)
except:
skimage/novice/_novice.py
+2
View File
@@ -80,6 +80,7 @@ class Pixel(object):
Transparency component (0-255), 255 (opaque) by default
"""
def __init__(self, pic, array, x, y, rgb, alpha=255):
self._picture = pic
self._x = x
@@ -239,6 +240,7 @@ class Picture(object):
>>> pic[:, pic.height-1] = (255, 0, 0)
"""
def __init__(self, path=None, array=None, xy_array=None):
self._modified = False
self.scale = 1
skimage/restoration/_denoise.py
+6 -6
View File
@@ -1,8 +1,8 @@
# coding: utf-8
import numpy as np
from skimage import img_as_float
from skimage.restoration._denoise_cy import _denoise_bilateral, \
_denoise_tv_bregman
from skimage.restoration._denoise_cy import (_denoise_bilateral,
_denoise_tv_bregman)
def denoise_bilateral(image, win_size=5, sigma_range=None, sigma_spatial=1,
@@ -151,12 +151,12 @@ def _denoise_tv_chambolle_3d(im, weight=100, eps=2.e-4, n_iter_max=200):
d[:, :, 1:] += pz[:, :, :-1]
out = im + d
E = (d**2).sum()
E = (d ** 2).sum()
gx[:-1] = np.diff(out, axis=0)
gy[:, :-1] = np.diff(out, axis=1)
gz[:, :, :-1] = np.diff(out, axis=2)
norm = np.sqrt(gx**2 + gy**2 + gz**2)
norm = np.sqrt(gx ** 2 + gy ** 2 + gz ** 2)
E += weight * norm.sum()
norm *= 0.5 / weight
norm += 1.
@@ -231,10 +231,10 @@ def _denoise_tv_chambolle_2d(im, weight=50, eps=2.e-4, n_iter_max=200):
d[:, 1:] += py[:, :-1]
out = im + d
E = (d**2).sum()
E = (d ** 2).sum()
gx[:-1] = np.diff(out, axis=0)
gy[:, :-1] = np.diff(out, axis=1)
norm = np.sqrt(gx**2 + gy**2)
norm = np.sqrt(gx ** 2 + gy ** 2)
E += weight * norm.sum()
norm *= 0.5 / weight
norm += 1
skimage/restoration/deconvolution.py
+4 -4
View File
@@ -126,8 +126,8 @@ def wiener(image, psf, balance, reg=None, is_real=True, clip=True):
else:
trans_func = psf
wiener_filter = np.conj(trans_func) / (np.abs(trans_func)**2 +
balance * np.abs(reg)**2)
wiener_filter = np.conj(trans_func) / (np.abs(trans_func) ** 2 +
balance * np.abs(reg) ** 2)
if is_real:
deconv = uft.uirfft2(wiener_filter * uft.urfft2(image),
shape=image.shape)
@@ -261,8 +261,8 @@ def unsupervised_wiener(image, psf, reg=None, user_params=None, is_real=True,
# The correlation of the object in Fourier space (if size is big,
# this can reduce computation time in the loop)
areg2 = np.abs(reg)**2
atf2 = np.abs(trans_fct)**2
areg2 = np.abs(reg) ** 2
atf2 = np.abs(trans_fct) ** 2
# The Fourier transfrom may change the image.size attribut, so we
# store it.
skimage/restoration/uft.py
+3 -3
View File
@@ -333,10 +333,10 @@ def image_quad_norm(inarray):
"""
# If there is a Hermitian symmetry
if inarray.shape[-1] != inarray.shape[-2]:
return (2 * np.sum(np.sum(np.abs(inarray)**2, axis=-1), axis=-1) -
np.sum(np.abs(inarray[..., 0])**2, axis=-1))
return (2 * np.sum(np.sum(np.abs(inarray) ** 2, axis=-1), axis=-1) -
np.sum(np.abs(inarray[..., 0]) ** 2, axis=-1))
else:
return np.sum(np.sum(np.abs(inarray)**2, axis=-1), axis=-1)
return np.sum(np.sum(np.abs(inarray) ** 2, axis=-1), axis=-1)
def ir2tf(imp_resp, shape, dim=None, is_real=True):
skimage/segmentation/_felzenszwalb.py
+2 -1
View File
@@ -45,7 +45,8 @@ def felzenszwalb(image, scale=1, sigma=0.8, min_size=20):
if image.ndim == 2:
# assume single channel image
return _felzenszwalb_grey(image, scale=scale, sigma=sigma, min_size=min_size)
return _felzenszwalb_grey(image, scale=scale, sigma=sigma,
min_size=min_size)
elif image.ndim != 3:
raise ValueError("Felzenswalb segmentation can only operate on RGB and"
skimage/segmentation/boundaries.py
+2 -1
View File
@@ -13,7 +13,8 @@ def find_boundaries(label_img):
return boundaries
def mark_boundaries(image, label_img, color=(1, 1, 0), outline_color=(0, 0, 0)):
def mark_boundaries(image, label_img, color=(1, 1, 0),
outline_color=(0, 0, 0)):
"""Return image with boundaries between labeled regions highlighted.
Parameters
skimage/segmentation/random_walker_segmentation.py
+2 -2
View File
@@ -454,8 +454,8 @@ def random_walker(data, labels, beta=130, mode='bf', tol=1.e-3, copy=True,
# Clean up results
if return_full_prob:
labels = labels.astype(np.float)
X = np.array([_clean_labels_ar(Xline, labels,
copy=True).reshape(dims) for Xline in X])
X = np.array([_clean_labels_ar(Xline, labels, copy=True).reshape(dims)
for Xline in X])
for i in range(1, int(labels.max()) + 1):
mask_i = np.squeeze(labels == i)
X[:, mask_i] = 0
skimage/segmentation/slic_superpixels.py
+2 -1
View File
@@ -6,7 +6,8 @@ from scipy import ndimage
import warnings
from skimage.util import img_as_float, regular_grid
from skimage.segmentation._slic import _slic_cython, _enforce_label_connectivity_cython
from skimage.segmentation._slic import (_slic_cython,
_enforce_label_connectivity_cython)
from skimage.color import rgb2lab
skimage/transform/_warps.py
+2 -2
View File
@@ -284,8 +284,8 @@ def _swirl_mapping(xy, center, rotation, strength, radius):
radius = radius / 5 * np.log(2)
theta = rotation + strength * \
np.exp(-rho / radius) + \
np.arctan2(y - y0, x - x0)
np.exp(-rho / radius) + \
np.arctan2(y - y0, x - x0)
xy[..., 0] = x0 + rho * np.cos(theta)
xy[..., 1] = y0 + rho * np.sin(theta)
skimage/transform/pyramids.py
+2 -1
View File
@@ -10,7 +10,8 @@ def _smooth(image, sigma, mode, cval):
smoothed = np.empty(image.shape, dtype=np.double)
if image.ndim == 3: # apply Gaussian filter to all dimensions independently
# apply Gaussian filter to all dimensions independently
if image.ndim == 3:
for dim in range(image.shape[2]):
ndimage.gaussian_filter(image[..., dim], sigma,
output=smoothed[..., dim],
skimage/transform/radon_transform.py
+6 -5
View File
@@ -63,8 +63,9 @@ def radon(image, theta=None, circle=False):
if circle:
radius = min(image.shape) // 2
c0, c1 = np.ogrid[0:image.shape[0], 0:image.shape[1]]
reconstruction_circle = ((c0 - image.shape[0] // 2)**2
+ (c1 - image.shape[1] // 2)**2) <= radius**2
reconstruction_circle = ((c0 - image.shape[0] // 2) ** 2
+ (c1 - image.shape[1] // 2) ** 2)
reconstruction_circle = reconstruction_circle <= radius ** 2
if not np.all(reconstruction_circle | (image == 0)):
raise ValueError('Image must be zero outside the reconstruction'
' circle')
@@ -189,7 +190,7 @@ def iradon(radon_image, theta=None, output_size=None,
if circle:
output_size = radon_image.shape[0]
else:
output_size = int(np.floor(np.sqrt((radon_image.shape[0])**2
output_size = int(np.floor(np.sqrt((radon_image.shape[0]) ** 2
/ 2.0)))
if circle:
radon_image = _sinogram_circle_to_square(radon_image)
@@ -198,7 +199,7 @@ def iradon(radon_image, theta=None, output_size=None,
# resize image to next power of two (but no less than 64) for
# Fourier analysis; speeds up Fourier and lessens artifacts
projection_size_padded = \
max(64, int(2**np.ceil(np.log2(2 * radon_image.shape[0]))))
max(64, int(2 ** np.ceil(np.log2(2 * radon_image.shape[0]))))
pad_width = ((0, projection_size_padded - radon_image.shape[0]), (0, 0))
img = util.pad(radon_image, pad_width, mode='constant', constant_values=0)
@@ -249,7 +250,7 @@ def iradon(radon_image, theta=None, output_size=None,
reconstructed += backprojected
if circle:
radius = output_size // 2
reconstruction_circle = (xpr**2 + ypr**2) <= radius**2
reconstruction_circle = (xpr ** 2 + ypr ** 2) <= radius ** 2
reconstructed[~reconstruction_circle] = 0.
return reconstructed * np.pi / (2 * len(th))
skimage/util/_regular_grid.py
+3 -3
View File
@@ -59,9 +59,9 @@ def regular_grid(ar_shape, n_points):
if (sorted_dims < stepsizes).any():
for dim in range(ndim):
stepsizes[dim] = sorted_dims[dim]
space_size = float(np.prod(sorted_dims[dim+1:]))
stepsizes[dim+1:] = ((space_size / n_points) **
(1.0 / (ndim - dim - 1)))
space_size = float(np.prod(sorted_dims[dim + 1:]))
stepsizes[dim + 1:] = ((space_size / n_points) **
(1.0 / (ndim - dim - 1)))
if (sorted_dims >= stepsizes).all():
break
starts = (stepsizes // 2).astype(int)
skimage/util/dtype.py
+2 -2
View File
@@ -304,7 +304,7 @@ def img_as_uint(image, force_copy=False):
Notes
-----
Negative input values will be clipped.
Negative input values will be clipped.
Positive values are scaled between 0 and 65535.
"""
@@ -353,7 +353,7 @@ def img_as_ubyte(image, force_copy=False):
Notes
-----
Negative input values will be clipped.
Negative input values will be clipped.
Positive values are scaled between 0 and 255.
"""
skimage/util/montage.py
+1 -1
View File
@@ -98,7 +98,7 @@ def montage2d(arr_in, fill='mean', rescale_intensity=False, grid_shape=None):
if fill == 'mean':
fill = arr_in.mean()
n_missing = int((alpha_y * alpha_x) - n_images)
n_missing = int((alpha_y * alpha_x) - n_images)
missing = np.ones((n_missing, height, width), dtype=arr_in.dtype) * fill
arr_out = np.vstack((arr_in, missing))
skimage/util/shape.py
+1 -1
View File
@@ -240,7 +240,7 @@ def view_as_windows(arr_in, window_shape, step=1):
arr_in = np.ascontiguousarray(arr_in)
new_shape = tuple((arr_shape - window_shape) // step + 1) + \
tuple(window_shape)
tuple(window_shape)
arr_strides = np.array(arr_in.strides)
new_strides = np.concatenate((arr_strides * step, arr_strides))