mirror of
https://github.com/wassname/scikit-image.git
synced 2026-07-13 17:45:20 +08:00
Refactor to use np.pad, naming changes, update test.
This commit is contained in:
blink1073
@@ -26,7 +26,7 @@ NR_OF_GREY = 16384 # number of grayscale levels to use in CLAHE algorithm
|
||||
|
||||
|
||||
def equalize_adapthist(image, ntiles_x=8, ntiles_y=8, clip_limit=0.01,
|
||||
nbins=256, mode='ignore'):
|
||||
nbins=256):
|
||||
"""Contrast Limited Adaptive Histogram Equalization.
|
||||
|
||||
Parameters
|
||||
@@ -121,40 +121,39 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128):
|
||||
if clip_limit == 1.0:
|
||||
return image # is OK, immediately returns original image.
|
||||
|
||||
y_res = image.shape[0] - image.shape[0] % ntiles_y
|
||||
x_res = image.shape[1] - image.shape[1] % ntiles_x
|
||||
h_inner = image.shape[0] - image.shape[0] % ntiles_y
|
||||
w_inner = image.shape[1] - image.shape[1] % ntiles_x
|
||||
|
||||
# make the tile size divisible by 2
|
||||
while y_res % (2 * ntiles_y):
|
||||
y_res -= 1
|
||||
while x_res % (2 * ntiles_x):
|
||||
x_res -= 1
|
||||
while h_inner % (2 * ntiles_y):
|
||||
h_inner -= 1
|
||||
while w_inner % (2 * ntiles_x):
|
||||
w_inner -= 1
|
||||
|
||||
orig_shape = image.shape
|
||||
x_size = x_res // ntiles_x # Actual size of contextual regions
|
||||
y_size = y_res // ntiles_y
|
||||
width = w_inner // ntiles_x # Actual size of contextual regions
|
||||
height = h_inner // ntiles_y
|
||||
|
||||
if y_res != image.shape[0]:
|
||||
if h_inner != image.shape[0]:
|
||||
ntiles_y += 1
|
||||
if x_res != image.shape[1]:
|
||||
if w_inner != image.shape[1]:
|
||||
ntiles_x += 1
|
||||
if y_res != image.shape[1] or x_res != image.shape[0]:
|
||||
hgt = y_size * ntiles_y - image.shape[0]
|
||||
wid = x_size * ntiles_x - image.shape[1]
|
||||
image = np.vstack((image, image[-hgt:, :]))
|
||||
image = np.hstack((image, image[:, -wid:]))
|
||||
y_res, x_res = image.shape
|
||||
if h_inner != image.shape[1] or w_inner != image.shape[0]:
|
||||
h_pad = height * ntiles_y - image.shape[0]
|
||||
w_pad = width * ntiles_x - image.shape[1]
|
||||
image = np.pad(image, ((0, h_pad), (0, w_pad)), 'reflect')
|
||||
h_inner, w_inner = image.shape
|
||||
|
||||
bin_size = 1 + NR_OF_GREY / nbins
|
||||
aLUT = np.arange(NR_OF_GREY)
|
||||
aLUT //= bin_size
|
||||
img_blocks = view_as_blocks(image, (y_size, x_size))
|
||||
lut = np.arange(NR_OF_GREY)
|
||||
lut //= bin_size
|
||||
img_blocks = view_as_blocks(image, (height, width))
|
||||
|
||||
map_array = np.zeros((ntiles_y, ntiles_x, nbins), dtype=int)
|
||||
n_pixels = x_size * y_size
|
||||
n_pixels = width * height
|
||||
|
||||
if clip_limit > 0.0: # Calculate actual cliplimit
|
||||
clip_limit = int(clip_limit * (x_size * y_size) / nbins)
|
||||
clip_limit = int(clip_limit * (width * height) / nbins)
|
||||
if clip_limit < 1:
|
||||
clip_limit = 1
|
||||
else:
|
||||
@@ -164,7 +163,7 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128):
|
||||
for y in range(ntiles_y):
|
||||
for x in range(ntiles_x):
|
||||
sub_img = img_blocks[y, x]
|
||||
hist = aLUT[sub_img.ravel()]
|
||||
hist = lut[sub_img.ravel()]
|
||||
hist = np.bincount(hist)
|
||||
hist = np.append(hist, np.zeros(nbins - hist.size, dtype=int))
|
||||
hist = clip_histogram(hist, clip_limit)
|
||||
@@ -176,29 +175,29 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128):
|
||||
for y in range(ntiles_y + 1):
|
||||
xstart = 0
|
||||
if y == 0: # special case: top row
|
||||
ystep = y_size / 2.0
|
||||
ystep = height / 2.0
|
||||
yU = 0
|
||||
yB = 0
|
||||
elif y == ntiles_y: # special case: bottom row
|
||||
ystep = y_size / 2.0
|
||||
ystep = height / 2.0
|
||||
yU = ntiles_y - 1
|
||||
yB = yU
|
||||
else: # default values
|
||||
ystep = y_size
|
||||
ystep = height
|
||||
yU = y - 1
|
||||
yB = yB + 1
|
||||
|
||||
for x in range(ntiles_x + 1):
|
||||
if x == 0: # special case: left column
|
||||
xstep = x_size / 2.0
|
||||
xstep = width / 2.0
|
||||
xL = 0
|
||||
xR = 0
|
||||
elif x == ntiles_x: # special case: right column
|
||||
xstep = x_size / 2.0
|
||||
xstep = width / 2.0
|
||||
xL = ntiles_x - 1
|
||||
xR = xL
|
||||
else: # default values
|
||||
xstep = x_size
|
||||
xstep = width
|
||||
xL = x - 1
|
||||
xR = xL + 1
|
||||
|
||||
@@ -210,7 +209,7 @@ def _clahe(image, ntiles_x, ntiles_y, clip_limit, nbins=128):
|
||||
xslice = np.arange(xstart, xstart + xstep)
|
||||
yslice = np.arange(ystart, ystart + ystep)
|
||||
interpolate(image, xslice, yslice,
|
||||
mapLU, mapRU, mapLB, mapRB, aLUT)
|
||||
mapLU, mapRU, mapLB, mapRB, lut)
|
||||
|
||||
xstart += xstep # set pointer on next matrix */
|
||||
|
||||
@@ -305,7 +304,7 @@ def map_histogram(hist, min_val, max_val, n_pixels):
|
||||
|
||||
|
||||
def interpolate(image, xslice, yslice,
|
||||
mapLU, mapRU, mapLB, mapRB, aLUT):
|
||||
mapLU, mapRU, mapLB, mapRB, lut):
|
||||
"""Find the new grayscale level for a region using bilinear interpolation.
|
||||
|
||||
Parameters
|
||||
@@ -316,7 +315,7 @@ def interpolate(image, xslice, yslice,
|
||||
Indices of the region.
|
||||
map* : ndarray
|
||||
Mappings of greylevels from histograms.
|
||||
aLUT : ndarray
|
||||
lut : ndarray
|
||||
Maps grayscale levels in image to histogram levels.
|
||||
|
||||
Returns
|
||||
@@ -338,7 +337,7 @@ def interpolate(image, xslice, yslice,
|
||||
|
||||
view = image[int(yslice[0]):int(yslice[-1] + 1),
|
||||
int(xslice[0]):int(xslice[-1] + 1)]
|
||||
im_slice = aLUT[view]
|
||||
im_slice = lut[view]
|
||||
new = ((y_inv_coef * (x_inv_coef * mapLU[im_slice]
|
||||
+ x_coef * mapRU[im_slice])
|
||||
+ y_coef * (x_inv_coef * mapLB[im_slice]
|
||||
|
||||
@@ -172,7 +172,7 @@ def test_adapthist_grayscale():
|
||||
nbins=128)
|
||||
assert_almost_equal = np.testing.assert_almost_equal
|
||||
assert img.shape == adapted.shape
|
||||
assert_almost_equal(peak_snr(img, adapted), 104.3168, 3)
|
||||
assert_almost_equal(peak_snr(img, adapted), 104.307, 3)
|
||||
assert_almost_equal(norm_brightness_err(img, adapted), 0.0265, 3)
|
||||
return data, adapted
|
||||
|
||||
|
||||
Reference in New Issue
Block a user