def test_zeros():
zeros = np.zeros((64,64), np.uint8)
feats = texture.haralick(zeros)
assert not np.any(np.isnan(feats))
feats = texture.haralick(zeros, ignore_zeros=True)
assert not np.any(np.isnan(feats))
def test_zeros():
zeros = np.zeros((64,64), np.uint8)
feats = texture.haralick(zeros)
assert not np.any(np.isnan(feats))
feats = texture.haralick(zeros, ignore_zeros=True)
assert not np.any(np.isnan(feats))
def test_haralick3d():
np.random.seed(22)
img = mahotas.stretch(255 * np.random.rand(20, 20, 4))
features = texture.haralick(img)
assert features.shape == (13, 13)
features = texture.haralick(img[:, :, 0])
assert features.shape == (4, 13)
def test_haralick3d():
np.random.seed(22)
img = mahotas.stretch(255*np.random.rand(20,20,4))
features = texture.haralick(img)
assert features.shape == (13,13)
features = texture.haralick(img[:,:,0])
assert features.shape == (4,13)
def test_haralick3d():
np.random.seed(22)
img = mahotas.stretch(255*np.random.rand(20,20,4))
features = texture.haralick(img)
assert features.shape == (13,13)
features = texture.haralick(img[:,:,0])
assert features.shape == (4,13)
features = texture.haralick(img.max(0), ignore_zeros=True, preserve_haralick_bug=True, compute_14th_feature=True)
assert features.shape == (4,14)
def test_haralick3d():
np.random.seed(22)
img = mahotas.stretch(255*np.random.rand(20,20,4))
features = texture.haralick(img)
assert features.shape == (13,13)
features = texture.haralick(img[:,:,0])
assert features.shape == (4,13)
features = texture.haralick(img.max(0), ignore_zeros=True, preserve_haralick_bug=True, compute_14th_feature=True)
assert features.shape == (4,14)
def compute_fluor_info(seg, fluor_img):
rps = regionprops(seg)
mean_intensity = np.zeros((len(rps)))
std_intensity = np.zeros((len(rps)))
intensity_range = np.zeros((len(rps)))
fluor_haralick = []
for i in range(len(rps)):
cell_num = int(i + 1)
r = rps[i]
cell_mask = (seg == cell_num)
region_cell_mask = cell_mask[r.bbox[0]:r.bbox[2], r.bbox[1]:r.bbox[3]]
crop_img = fluor_img[r.bbox[0]:r.bbox[2], r.bbox[1]:r.bbox[3]]
cell_img = (fluor_img * cell_mask)[r.bbox[0]:r.bbox[2],
r.bbox[1]:r.bbox[3]]
mean_intensity[i] = np.sum(cell_img) * 1.0 / r.area
std_intensity[i] = np.std(cell_img[region_cell_mask])
min_value, max_value = np.amin(cell_img[region_cell_mask]), np.amax(
cell_img[region_cell_mask])
min_value = min_value - 1
intensity_range[i] = max_value - min_value
#the haralick features have four directions, to meet rotation invariance,use average for each feature
fl_hara = np.mean(mht.haralick(cell_img, ignore_zeros=True), axis=0)
fluor_haralick.append(fl_hara)
fluor_haralick = np.array(fluor_haralick)
return mean_intensity, std_intensity, intensity_range, fluor_haralick
def _calculateStatistics(self, img, haralick=False, zernike=False):
result = []
# 3-bin histogram
result.extend(mquantiles(img))
# First four moments
result.extend([img.mean(), img.var(), skew(img, axis=None), kurtosis(img, axis=None)])
# Haralick features
if haralick:
integerImage = dtype.img_as_ubyte(img)
result.extend(texture.haralick(integerImage).flatten())
# Zernike moments
if zernike:
result.extend(zernike_moments(img, int(self.rows) / 2 + 1))
return result
def _calculateStatistics(self, img, haralick=False, zernike=False):
result = []
#3-bin histogram
result.extend(mquantiles(img))
#First four moments
result.extend([
img.mean(),
img.var(),
skew(img, axis=None),
kurtosis(img, axis=None)
])
#Haralick features
if haralick:
integerImage = dtype.img_as_ubyte(img)
result.extend(texture.haralick(integerImage).flatten())
#Zernike moments
if zernike:
result.extend(zernike_moments(img, int(self.rows) / 2 + 1))
return result
def _compute_haralick_features(patch, distance):
"""Compute the haralick feature.
This function is used for parallel processing.
Parameters
----------
patch: ndarray, (patch_size)
The patch to consider to compute the cooccurence matrix.
distance: int,
The distance to use to compute the cooccurence matrix.
Returns
-------
haralick_features: ndarray, shape (4, 13)
The haralick features.
"""
return haralick(patch, distance=distance)
def img_feature_extraction(self, img):
# Change CHW to HWC
HWC_img = img_as_float(img.permute(1, 2, 0))
segments_slic = slic(HWC_img, n_segments=20, compactness=10, sigma=1, start_label=1)
unique_lables = np.unique(segments_slic)
if 0 in unique_lables:
unique_lables = np.delete(unique_lables, 0)
N = len(unique_lables)
image = np.zeros((N, 19))
# image = np.zeros((N, 6))
regions = measure.regionprops(segments_slic, intensity_image=HWC_img)
for (i, segVal) in enumerate(unique_lables):
# convert image to grayscale
ubyte_img = img_as_ubyte(HWC_img)
ubyte_img[ubyte_img == 0] = 1
gray = cv2.cvtColor(ubyte_img, cv2.COLOR_BGR2GRAY)
# construct a mask for the segment
mask = np.zeros(gray.shape, dtype=np.bool)
mask[segments_slic != segVal] = True
gray[mask] = 0
# extract texture feature from mahotas package
texture_features = texture.haralick(gray, ignore_zeros=True)
ht_mean = texture_features.mean(axis=0)
# extract mean_intensity feature
mean_intensity = regions[i].mean_intensity
# extract centroid feature
centroid = regions[i].centroid
#extract area feature
area = regions[i].area
image[i] = np.hstack((mean_intensity,centroid, area, ht_mean))
# image[i] = np.hstack((mean_intensity, centroid, area))
return image
def test_4d_image():
texture.haralick(np.arange(4**5).reshape((4,4,4,4,4)))
def test_ignore_zeros_raise():
zeros = np.zeros((64,64), np.uint8)
texture.haralick(zeros, ignore_zeros=True)
regions = measure.regionprops(segments_slic, intensity_image=img)
print(len(regions))
if 0 in c:
c = np.delete(c, 0)
for (i, segVal) in enumerate(c):
# construct a mask for the segment
print(i)
new = img_as_ubyte(img)
new[new == 0] = 1
gray = cv2.cvtColor(new, cv2.COLOR_BGR2GRAY)
# print(i)
# print(new)
mask = np.zeros(gray.shape, dtype=np.bool)
mask[segments_slic != segVal] = True
gray[mask] = 0
features = texture.haralick(gray, ignore_zeros=True)
ht_mean = features.mean(axis=0)
a = np.hstack((regions[i].mean_intensity, regions[i].centroid, ht_mean))
print(regions[i].mean_intensity)
# print(regions[i].centroid)
# print(ht_mean)
# print(a.shape)
fig, ax = plt.subplots(2, 2, figsize=(10, 10), sharex=True, sharey=True)
ax[0, 1].imshow(mark_boundaries(img, segments_slic))
ax[0, 1].set_title('SLIC')
for a in ax.ravel():
a.set_axis_off()
def test_float_haralick():
A = np.zeros((5,5), np.float32)
A[2,2]=12
texture.haralick(A)
def test_haralick3():
np.random.seed(123)
f = np.random.rand(34, 12, 8)
f = (f * 255).astype(np.int32)
feats = texture.haralick(f)
assert not np.any(np.isnan(feats))
def test_ignore_zeros_raise():
zeros = np.zeros((64, 64), np.uint8)
texture.haralick(zeros, ignore_zeros=True)
def test_4d_image():
with pytest.raises(ValueError):
texture.haralick(np.arange(4**5).reshape((4, 4, 4, 4, 4)))
def test_ignore_zeros_raise():
zeros = np.zeros((64, 64), np.uint8)
with pytest.raises(ValueError):
texture.haralick(zeros, ignore_zeros=True)
def test_float_haralick():
A = np.zeros((5, 5), np.float32)
A[2, 2] = 12
with pytest.raises(TypeError):
texture.haralick(A)
def test_haralick3():
np.random.seed(123)
f = np.random.rand(34, 12, 8)
f = (f * 255).astype(np.int32)
feats = texture.haralick(f)
assert not np.any(np.isnan(feats))
def test_4d_image():
texture.haralick(np.arange(4**5).reshape((4,4,4,4,4)))
def test_float_haralick():
A = np.zeros((5,5), np.float32)
A[2,2]=12
texture.haralick(A)
