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PEP8 compliance and doc formatting fixes.

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This commit is contained in:
Geoffrey French
2014-09-02 19:16:41 +01:00
parent e6bda5accd
commit 5342299572
7 changed files with 75 additions and 48 deletions
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doc/examples/plot_windowed_histogram.py
+7 -7
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@@ -57,7 +57,7 @@ def windowed_histogram_similarity(image, selem, reference_hist, n_bins):
# Reshape coin histogram to (1,1,N) for broadcast when we want to use it in
# arithmetic operations with the windowed histograms from the image
reference_hist = reference_hist.reshape((1,1) + reference_hist.shape)
reference_hist = reference_hist.reshape((1, 1) + reference_hist.shape)
# Compute Chi squared distance metric: sum((X-Y)^2 / (X+Y));
# a measure of distance between histograms
@@ -66,7 +66,7 @@ def windowed_histogram_similarity(image, selem, reference_hist, n_bins):
num = (X-Y)*(X-Y)
denom = X+Y
frac = num / denom
frac[denom==0] = 0
frac[denom == 0] = 0
chi_sqr = np.sum(frac, axis=2) * 0.5
# Generate a similarity measure. It needs to be low when distance is high
@@ -80,17 +80,18 @@ def windowed_histogram_similarity(image, selem, reference_hist, n_bins):
# Load the `skimage.data.coins` image
img = img_as_ubyte(data.coins())
# Quantize to 16 levels of grayscale; this way the output image will have a
# Quantize to 16 levels of greyscale; this way the output image will have a
# 16-dimensional feature vector per pixel
quantized_img = img//16
# Select the coin from the 4th column, second row.
# Co-ordinate ordering: [x1,y1,x2,y2]
coin_coords = [184,100,228,148] # 44 x 44 region
coin = quantized_img[coin_coords[1]:coin_coords[3], coin_coords[0]:coin_coords[2]]
coin_coords = [184, 100, 228, 148] # 44 x 44 region
coin = quantized_img[coin_coords[1]:coin_coords[3],
coin_coords[0]:coin_coords[2]]
# Compute coin histogram and normalize
coin_hist, _ = np.histogram(coin.flatten(), bins=16, range=(0,16))
coin_hist, _ = np.histogram(coin.flatten(), bins=16, range=(0, 16))
coin_hist = coin_hist.astype(float) / np.sum(coin_hist)
@@ -114,7 +115,6 @@ rotated_similarity = windowed_histogram_similarity(quantized_rotated_image,
coin_hist.shape[0])
# Plot it all
fig, axes = plt.subplots(nrows=5, figsize=(6, 18))
ax0, ax1, ax2, ax3, ax4 = axes
skimage/filter/rank/bilateral.py
+2 -1
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@@ -158,8 +158,9 @@ def pop_bilateral(image, selem, out=None, mask=None, shift_x=False,
return _apply(bilateral_cy._pop, image, selem, out=out,
mask=mask, shift_x=shift_x, shift_y=shift_y, s0=s0, s1=s1)
def sum_bilateral(image, selem, out=None, mask=None, shift_x=False,
shift_y=False, s0=10, s1=10):
shift_y=False, s0=10, s1=10):
"""Apply a flat kernel bilateral filter.
This is an edge-preserving and noise reducing denoising filter. It averages
skimage/filter/rank/bilateral_cy.pyx
+2
View File
@@ -51,6 +51,7 @@ cdef inline void _kernel_pop(dtype_t_out* out, Py_ssize_t odepth,
else:
out[0] = <dtype_t_out>0
cdef inline void _kernel_sum(dtype_t_out* out, Py_ssize_t odepth,
Py_ssize_t* histo,
double pop, dtype_t g,
@@ -96,6 +97,7 @@ def _pop(dtype_t[:, ::1] image,
_core(_kernel_pop[dtype_t_out, dtype_t], image, selem, mask, out,
shift_x, shift_y, 0, 0, s0, s1, max_bin)
def _sum(dtype_t[:, ::1] image,
char[:, ::1] selem,
char[:, ::1] mask,
skimage/filter/rank/core_cy.pyx
+8 -8
View File
@@ -42,8 +42,8 @@ cdef inline char is_in_mask(Py_ssize_t rows, Py_ssize_t cols,
return 0
cdef void _core(void kernel(dtype_t_out*, Py_ssize_t, Py_ssize_t*, double, dtype_t,
Py_ssize_t, Py_ssize_t, double,
cdef void _core(void kernel(dtype_t_out*, Py_ssize_t, Py_ssize_t*, double,
dtype_t, Py_ssize_t, Py_ssize_t, double,
double, Py_ssize_t, Py_ssize_t),
dtype_t[:, ::1] image,
char[:, ::1] selem,
@@ -173,8 +173,8 @@ cdef void _core(void kernel(dtype_t_out*, Py_ssize_t, Py_ssize_t*, double, dtype
if is_in_mask(rows, cols, rr, cc, mask_data):
histogram_decrement(histo, &pop, image[rr, cc])
kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin, mid_bin,
p0, p1, s0, s1)
kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin,
mid_bin, p0, p1, s0, s1)
r += 1 # pass to the next row
if r >= rows:
@@ -193,8 +193,8 @@ cdef void _core(void kernel(dtype_t_out*, Py_ssize_t, Py_ssize_t*, double, dtype
if is_in_mask(rows, cols, rr, cc, mask_data):
histogram_decrement(histo, &pop, image[rr, cc])
kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin, mid_bin,
p0, p1, s0, s1)
kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin,
mid_bin, p0, p1, s0, s1)
# ---> east to west
for c in range(cols - 2, -1, -1):
@@ -210,8 +210,8 @@ cdef void _core(void kernel(dtype_t_out*, Py_ssize_t, Py_ssize_t*, double, dtype
if is_in_mask(rows, cols, rr, cc, mask_data):
histogram_decrement(histo, &pop, image[rr, cc])
kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin, mid_bin,
p0, p1, s0, s1)
kernel(&out[r, c, 0], odepth, histo, pop, image[r, c], max_bin,
mid_bin, p0, p1, s0, s1)
r += 1 # pass to the next row
if r >= rows:
skimage/filter/rank/generic.py
+53 -31
View File
@@ -68,7 +68,8 @@ def _handle_input(image, selem, out, mask, out_dtype=None, pixel_size=1):
return image, selem, out, mask, max_bin
def _apply_scalar_per_pixel(func, image, selem, out, mask, shift_x, shift_y, out_dtype=None):
def _apply_scalar_per_pixel(func, image, selem, out, mask, shift_x, shift_y,
out_dtype=None):
image, selem, out, mask, max_bin = _handle_input(image, selem, out, mask,
out_dtype)
@@ -79,10 +80,12 @@ def _apply_scalar_per_pixel(func, image, selem, out, mask, shift_x, shift_y, out
return out.reshape(out.shape[:2])
def _apply_vector_per_pixel(func, image, selem, out, mask, shift_x, shift_y, out_dtype=None, pixel_size=1):
def _apply_vector_per_pixel(func, image, selem, out, mask, shift_x, shift_y,
out_dtype=None, pixel_size=1):
image, selem, out, mask, max_bin = _handle_input(image, selem, out, mask,
out_dtype, pixel_size=pixel_size)
out_dtype,
pixel_size=pixel_size)
func(image, selem, shift_x=shift_x, shift_y=shift_y, mask=mask,
out=out, max_bin=max_bin)
@@ -128,7 +131,8 @@ def autolevel(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""
return _apply_scalar_per_pixel(generic_cy._autolevel, image, selem,
out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -169,7 +173,8 @@ def bottomhat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""
return _apply_scalar_per_pixel(generic_cy._bottomhat, image, selem,
out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -207,7 +212,8 @@ def equalize(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""
return _apply_scalar_per_pixel(generic_cy._equalize, image, selem,
out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -245,7 +251,8 @@ def gradient(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""
return _apply_scalar_per_pixel(generic_cy._gradient, image, selem,
out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -292,7 +299,8 @@ def maximum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""
return _apply_scalar_per_pixel(generic_cy._maximum, image, selem,
out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -330,7 +338,8 @@ def mean(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""
return _apply_scalar_per_pixel(generic_cy._mean, image, selem, out=out,
mask=mask, shift_x=shift_x, shift_y=shift_y)
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def subtract_mean(image, selem, out=None, mask=None, shift_x=False,
@@ -369,7 +378,8 @@ def subtract_mean(image, selem, out=None, mask=None, shift_x=False,
"""
return _apply_scalar_per_pixel(generic_cy._subtract_mean, image, selem,
out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -407,7 +417,8 @@ def median(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""
return _apply_scalar_per_pixel(generic_cy._median, image, selem,
out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -454,7 +465,8 @@ def minimum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""
return _apply_scalar_per_pixel(generic_cy._minimum, image, selem,
out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -494,7 +506,8 @@ def modal(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""
return _apply_scalar_per_pixel(generic_cy._modal, image, selem,
out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def enhance_contrast(image, selem, out=None, mask=None, shift_x=False,
@@ -537,7 +550,8 @@ def enhance_contrast(image, selem, out=None, mask=None, shift_x=False,
"""
return _apply_scalar_per_pixel(generic_cy._enhance_contrast, image, selem,
out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -586,7 +600,8 @@ def pop(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""
return _apply_scalar_per_pixel(generic_cy._pop, image, selem, out=out,
mask=mask, shift_x=shift_x, shift_y=shift_y)
mask=mask, shift_x=shift_x,
shift_y=shift_y)
def sum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -635,7 +650,8 @@ def sum(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""
return _apply_scalar_per_pixel(generic_cy._sum, image, selem, out=out,
mask=mask, shift_x=shift_x, shift_y=shift_y)
mask=mask, shift_x=shift_x,
shift_y=shift_y)
def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -684,7 +700,8 @@ def threshold(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""
return _apply_scalar_per_pixel(generic_cy._threshold, image, selem,
out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -725,7 +742,8 @@ def tophat(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""
return _apply_scalar_per_pixel(generic_cy._tophat, image, selem,
out=out, mask=mask, shift_x=shift_x, shift_y=shift_y)
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def noise_filter(image, selem, out=None, mask=None, shift_x=False,
@@ -775,8 +793,9 @@ def noise_filter(image, selem, out=None, mask=None, shift_x=False,
selem_cpy = selem.copy()
selem_cpy[centre_r, centre_c] = 0
return _apply_scalar_per_pixel(generic_cy._noise_filter, image, selem_cpy, out=out,
mask=mask, shift_x=shift_x, shift_y=shift_y)
return _apply_scalar_per_pixel(generic_cy._noise_filter, image, selem_cpy,
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y)
def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -821,8 +840,9 @@ def entropy(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""
return _apply_scalar_per_pixel(generic_cy._entropy, image, selem,
out=out, mask=mask, shift_x=shift_x, shift_y=shift_y,
out_dtype=np.double)
out=out, mask=mask,
shift_x=shift_x, shift_y=shift_y,
out_dtype=np.double)
def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
@@ -865,10 +885,12 @@ def otsu(image, selem, out=None, mask=None, shift_x=False, shift_y=False):
"""
return _apply_scalar_per_pixel(generic_cy._otsu, image, selem, out=out,
mask=mask, shift_x=shift_x, shift_y=shift_y)
mask=mask, shift_x=shift_x,
shift_y=shift_y)
def windowed_histogram(image, selem, out=None, mask=None, shift_x=False, shift_y=False, n_bins=None):
def windowed_histogram(image, selem, out=None, mask=None,
shift_x=False, shift_y=False, n_bins=None):
"""Normalized sliding window histogram
Parameters
@@ -887,18 +909,18 @@ def windowed_histogram(image, selem, out=None, mask=None, shift_x=False, shift_y
to the structuring element sizes (center must be inside the given
structuring element).
n_bins : int or None
The number of histogram bins. Will default to `image.max() + 1`
The number of histogram bins. Will default to ``image.max() + 1``
if None is passed.
Returns
-------
out : 3-D array with float dtype of dimensions (H,W,N), where (H,W) are
the dimensions of the input image and N is n_bins or image.max()+1
if no value is provided as a parameter. Effectively, each pixel
is a N-D feature vector that is the histogram. The sum of the
elements in the feature vector will be 1, unless no pixels in the
window were covered by both selem and mask, in which case all
elements will be 0.
the dimensions of the input image and N is n_bins or
``image.max() + 1`` if no value is provided as a parameter.
Effectively, each pixel is a N-D feature vector that is the histogram.
The sum of the elements in the feature vector will be 1, unless no
pixels in the window were covered by both selem and mask, in which
case all elements will be 0.
Examples
--------
skimage/filter/rank/generic_cy.pyx
+1 -1
View File
@@ -407,7 +407,6 @@ cdef inline void _kernel_win_hist(dtype_t_out* out, Py_ssize_t odepth,
out[i] = <dtype_t_out>0
def _autolevel(dtype_t[:, ::1] image,
char[:, ::1] selem,
char[:, ::1] mask,
@@ -527,6 +526,7 @@ def _pop(dtype_t[:, ::1] image,
_core(_kernel_pop[dtype_t_out, dtype_t], image, selem, mask, out,
shift_x, shift_y, 0, 0, 0, 0, max_bin)
def _sum(dtype_t[:, ::1] image,
char[:, ::1] selem,
char[:, ::1] mask,
skimage/filter/rank/percentile_cy.pyx
+2
View File
@@ -285,6 +285,7 @@ def _mean(dtype_t[:, ::1] image,
_core(_kernel_mean[dtype_t_out, dtype_t], image, selem, mask, out,
shift_x, shift_y, p0, p1, 0, 0, max_bin)
def _sum(dtype_t[:, ::1] image,
char[:, ::1] selem,
char[:, ::1] mask,
@@ -295,6 +296,7 @@ def _sum(dtype_t[:, ::1] image,
_core(_kernel_sum[dtype_t_out, dtype_t], image, selem, mask, out,
shift_x, shift_y, p0, p1, 0, 0, max_bin)
def _subtract_mean(dtype_t[:, ::1] image,
char[:, ::1] selem,
char[:, ::1] mask,