def test_multiple_modes():
# Test that the filters with multiple mode cababilities for different
# dimensions give the same result as applying a single mode.
arr = np.array([[1., 0., 0.],
[1., 1., 0.],
[0., 0., 0.]])
mode1 = 'reflect'
mode2 = ['reflect', 'reflect']
assert_equal(sndi.gaussian_filter(arr, 1, mode=mode1),
sndi.gaussian_filter(arr, 1, mode=mode2))
assert_equal(sndi.prewitt(arr, mode=mode1),
sndi.prewitt(arr, mode=mode2))
assert_equal(sndi.sobel(arr, mode=mode1),
sndi.sobel(arr, mode=mode2))
assert_equal(sndi.laplace(arr, mode=mode1),
sndi.laplace(arr, mode=mode2))
assert_equal(sndi.gaussian_laplace(arr, 1, mode=mode1),
sndi.gaussian_laplace(arr, 1, mode=mode2))
assert_equal(sndi.maximum_filter(arr, size=5, mode=mode1),
sndi.maximum_filter(arr, size=5, mode=mode2))
assert_equal(sndi.minimum_filter(arr, size=5, mode=mode1),
sndi.minimum_filter(arr, size=5, mode=mode2))
assert_equal(sndi.gaussian_gradient_magnitude(arr, 1, mode=mode1),
sndi.gaussian_gradient_magnitude(arr, 1, mode=mode2))
assert_equal(sndi.uniform_filter(arr, 5, mode=mode1),
sndi.uniform_filter(arr, 5, mode=mode2))
def mexF2d(a, sigma2d):
"""
calculate -nd.gaussian_laplace sectionwise, using
output=None, mode="reflect", cval=0.0
forcing float32 output
"""
from scipy.ndimage import gaussian_laplace
out = zeroArrF(a.shape)
for tup in N.ndindex(a.shape[:-2]):
gaussian_laplace(a[tup], sigma2d, output=out[tup], mode="reflect", cval=0.0)
out[tup] *= -1
return out
def wall_points_phc_1(img, refsx, sigma, extra):
"""Get 4 reference points with coordinates on pipette walls
for Phase Contrast images
(walls are inner inflection points of inner minima on the profile)
"""
extra_walls = []
refs = np.array([])
refs_err = np.array([])
for refx in refsx:
if extra:
extra_wall = {}
jumps = jumps_pos_steep(ndimage.gaussian_laplace(img[:, refx], sigma).astype(float), 8)
y = img[:, refx]
fit = find_peak(y, jumps[1] + np.argmin(y[jumps[1] : jumps[2]]))
a, b, x0, s = fit[0]
refs = np.append(refs, np.asarray((x0 + s, refx)), axis=1)
# taking err_x0+err_s while ref = x0+s
refs_err = np.append(refs_err, sum(sqrt(np.diag(fit[1])[2:])))
if extra:
extra_wall["fit1"] = fit[0]
fit = find_peak(y, jumps[5] + np.argmin(y[jumps[5] : jumps[6]]))
a, b, x0, s = fit[0]
refs = np.append(refs, np.asarray((x0 - s, refx)), axis=1)
refs_err = np.append(refs_err, sum(sqrt(np.diag(fit[1])[2:])))
if extra:
extra_wall["fit2"] = fit[0]
extra_wall["profile"] = y
extra_walls.append(extra_wall)
return refs.reshape(4, 2), refs_err, extra_walls
def remove_dust(self):
self.seq = []
for i in xrange(0,self.n_frames):
# greyscale conversion
img = np.average(self.reader.get_data(i),axis=2)
self.seq.append(img)
self.seq = np.array(self.seq)
#var = np.var(self.seq, axis=0)
#min = np.min(self.seq, axis=0)
#max = np.max(self.seq, axis=0)
#delta = max - min
#var = stats.variation(self.seq, axis=0)
#gmean = stats.gmean(self.seq, axis=0)
a = np.average(self.seq, axis=0)
#grad = ndimage.gaussian_gradient_magnitude(a , 0.25)
#map = ndimage.prewitt(a)
map = ndimage.gaussian_laplace(a,2.5) * ndimage.gaussian_gradient_magnitude(a , 0.25)
cutoff = np.percentile(map,99.9)
map[map<cutoff]=0
map[map>0]=1
#map = grad
#map[map>300]=300
fig = plt.figure(figsize=(20,8), frameon=False)
fig.subplots_adjust(hspace=0)
fig.subplots_adjust(wspace=0)
ax1 = fig.add_subplot(1, 2, 1)
ax1.imshow(map,interpolation='nearest')
ax1.set_title('variance')
ax2 = fig.add_subplot(1, 2, 2)
ax2.imshow(self.seq[0], cmap='Greys_r',interpolation='nearest')
ax2.set_title('img')
fig.set_tight_layout(True)
plt.show()
def texton_visualization_filters(resolution, sigma, scales):
impulse = np.zeros((resolution,resolution))
impulse[resolution/2 + 1][resolution/2 + 1] = 1
filters = []
filters.append(gaussian_filter(impulse, sigma))
filters.append(gaussian_laplace(impulse, sigma))
for _ord in [1,2]:
for scale in scales:
filters.append(gaussian_filter(impulse, scale, order=[_ord,0]))
return filters
def sharpen_2pimage(image, laplace_sigma=0.7, low_percentile=3, high_percentile=99.9):
""" Apply a laplacian filter, clip pixel range and normalize.
:param np.array image: Array with raw two-photon images.
:param float laplace_sigma: Sigma of the gaussian used in the laplace filter.
:param float low_percentile, high_percentile: Percentiles at which to clip.
:returns: Array of same shape as input. Sharpened image.
"""
sharpened = image - ndimage.gaussian_laplace(image, laplace_sigma)
clipped = np.clip(sharpened, *np.percentile(sharpened, [low_percentile, high_percentile]))
norm = (clipped - clipped.mean()) / (clipped.max() - clipped.min() + 1e-7)
return norm
def test_multiple_modes_gaussian_laplace():
# Test gaussian_laplace filter for multiple extrapolation modes
arr = np.array([[1., 0., 0.],
[1., 1., 0.],
[0., 0., 0.]])
expected = np.array([[-0.28438687, 0.01559809, 0.19773499],
[-0.36630503, -0.20069774, 0.07483620],
[0.15849176, 0.18495566, 0.21934094]])
modes = ['reflect', 'wrap']
assert_almost_equal(expected,
sndi.gaussian_laplace(arr, 1, mode=modes))
def generate_filters(sigma, scales, angles):
"""
Parameters
----------
sigma: scalar
The scale of the Gaussian and the Laplacian of Gaussian filters.
scales: list of tuples
The scales of the anisotropic gaussian derivative filters.
angles: list of scalars
The angles of the anisotropic gaussian derivative filters.
Returns
----------
filters: list of functions
A list of functions which are the filters. These functions
take a single image as parameter and return a filter response.
"""
filters = []
# Gaussian filter
gauss_filter = lambda I: gaussian_filter(I, sigma)
filters.append(gauss_filter)
# Laplacian of Gaussian filter
laplacian_filter = lambda I: gaussian_laplace(I, sigma)
filters.append(laplacian_filter)
# Edge filters (first order Gaussian derivative)
for scale in scales:
filters.append(
lambda I, scale=scale: \
max_anisotropic_derivative_filter(I, scale, angles, 1))
# Bar filters (second order Gaussian derivative)
for scale in scales:
filters.append(
lambda I, scale=scale: \
max_anisotropic_derivative_filter(I, scale, angles, 2))
return filters
def update_features(self, img_array, index, use_memory):
if use_memory and (index in self.feats_dictionary):
self.integral_img, self.avg_rc, self.avg_gc, self.avg_bc, self.avg_rc_h, \
self.avg_gc_h, self.avg_bc_h, self.gauss1rc, self.gauss1gc, self.gauss1bc,\
self.gauss35rc,self.gauss35gc, self.gauss35bc, self.log2rc, self.log2gc, self.log2bc,\
self.log35rc, self.log35gc, self.log35bc = self.feats_dictionary[index]
return
img_array = mirror_borders(img_array, self.patch_size // 2)
# integral image
self.integral_img = cv2.integral(img_array[:,:,0])
# average image red and green channel patch size
self.avg_rc = cv2.blur(img_array[:,:,0], (self.patch_size, self.patch_size))
self.avg_gc = cv2.blur(img_array[:,:,1], (self.patch_size, self.patch_size))
self.avg_bc = cv2.blur(img_array[:,:,2], (self.patch_size, self.patch_size))
# average images all three channels
self.avg_rc_h = cv2.blur(img_array[:,:,0], (self.patch_size//2, self.patch_size//2))
self.avg_gc_h = cv2.blur(img_array[:,:,1], (self.patch_size//2, self.patch_size//2))
self.avg_bc_h = cv2.blur(img_array[:,:,2], (self.patch_size//2, self.patch_size//2))
# gassiuan smoothed sigma 1
self.gauss1rc = nd.gaussian_filter(img_array[:,:,0], 1)
self.gauss1gc = nd.gaussian_filter(img_array[:,:,1], 1)
self.gauss1bc = nd.gaussian_filter(img_array[:,:,2], 1)
# gaussian smoothed sigma 3.5
self.gauss35rc = nd.gaussian_filter(img_array[:, :, 0], 3.5)
self.gauss35gc = nd.gaussian_filter(img_array[:, :, 1], 3.5)
self.gauss35bc = nd.gaussian_filter(img_array[:, :, 2], 3.5)
# laplace of gaussian sigma 2 (all three chaannels)
self.log2rc = nd.gaussian_laplace(img_array[:,:,0], 2)
self.log2gc = nd.gaussian_laplace(img_array[:,:,1], 2)
self.log2bc = nd.gaussian_laplace(img_array[:,:,2], 2)
# laplace of gaussian sigma 3.5
self.log35rc = nd.gaussian_laplace(img_array[:,:,0], 3.5)
self.log35gc = nd.gaussian_laplace(img_array[:,:,1], 3.5)
self.log35bc = nd.gaussian_laplace(img_array[:,:,2], 3.5)
if use_memory:
# add the computed features to the dictionary
self.feats_dictionary[index] = self.integral_img, self.avg_rc, self.avg_gc, self.avg_bc, self.avg_rc_h,\
self.avg_gc_h, self.avg_bc_h, self.gauss1rc, self.gauss1gc, self.gauss1bc, self.gauss35rc, self.gauss35gc,\
self.gauss35bc, self.log2rc, self.log2gc, self.log2bc, self.log35rc, self.log35gc, self.log35bc
def test_gaussian_truncate():
# Test that Gaussian filters can be truncated at different widths.
# These tests only check that the result has the expected number
# of nonzero elements.
arr = np.zeros((100, 100), float)
arr[50, 50] = 1
num_nonzeros_2 = (sndi.gaussian_filter(arr, 5, truncate=2) > 0).sum()
assert_equal(num_nonzeros_2, 21**2)
num_nonzeros_5 = (sndi.gaussian_filter(arr, 5, truncate=5) > 0).sum()
assert_equal(num_nonzeros_5, 51**2)
# Test truncate when sigma is a sequence.
f = sndi.gaussian_filter(arr, [0.5, 2.5], truncate=3.5)
fpos = f > 0
n0 = fpos.any(axis=0).sum()
# n0 should be 2*int(2.5*3.5 + 0.5) + 1
assert_equal(n0, 19)
n1 = fpos.any(axis=1).sum()
# n1 should be 2*int(0.5*3.5 + 0.5) + 1
assert_equal(n1, 5)
# Test gaussian_filter1d.
x = np.zeros(51)
x[25] = 1
f = sndi.gaussian_filter1d(x, sigma=2, truncate=3.5)
n = (f > 0).sum()
assert_equal(n, 15)
# Test gaussian_laplace
y = sndi.gaussian_laplace(x, sigma=2, truncate=3.5)
nonzero_indices = np.nonzero(y != 0)[0]
n = nonzero_indices.ptp() + 1
assert_equal(n, 15)
# Test gaussian_gradient_magnitude
y = sndi.gaussian_gradient_magnitude(x, sigma=2, truncate=3.5)
nonzero_indices = np.nonzero(y != 0)[0]
n = nonzero_indices.ptp() + 1
assert_equal(n, 15)
def gaussian_Laplace1(left):
img = []
for i in range(left.shape[0]):
img.append(ndimage.gaussian_laplace(left[i, :, :], sigma=1))
return np.asarray(img)
def findLogZeros(image, logKernelSize, gradKernelSize, gradstrength,
gradmethod='any'):
''' improved implementation of find log zeros.
use np.diff of np.signbit to detect sign changes.
about 20 times faster than a loop like:
for y in range(ySize):
for x in range(xSize):
Also, keep track of positive gradient and negative gradient, and shift
appropriately so all edges are on the negative side of a zero contour,
rather than always appearing below/right of a zero, as is default behavior
for np.diff and the old for-loop logZeros method.
to find all zeros, set gradstrength to 0.
gradmethod:
'any' returns all edges where any pixel is greater than gradstrength
'mean' returns all edges where the average gradient is greater than
gradsthrength. 'mean' may be computationally quite taxing.
to find typical cell edges from a brightfield image,
logKernelSize = 4
gradKernelSize = 2
gradstrength = 0.05
gradmethod = 'any'
to find typical fluorescence features,
logKernelSize = 2.5
gradKernelSize = 2.5
gradstrength = 0.05
gradmethod = 'mean'
'''
# find raw zeros of the laplace of gaussian convolution of the input image
scaledImage = ((image.astype('float64') - image.min()) /
(image.max() - image.min()))
#laplace of gaussian
log = ndimage.gaussian_laplace(scaledImage, logKernelSize)
# initialize for zeros of laplace of gaussian
logZeros = np.zeros(log.shape, np.bool)
xZerosRaw = np.diff(np.signbit(log).astype(int),axis=1) # row zeros
yZerosRaw = np.diff(np.signbit(log).astype(int),axis=0) # column zeros
# find the indices for left, right, top and bottom edges
leftZerosIdx = np.where(xZerosRaw==1)
rightZerosIdx = np.where(xZerosRaw==-1)
topZerosIdx = np.where(yZerosRaw==1)
bottomZerosIdx = np.where(yZerosRaw==-1)
# left and top zeros must be shifted by one column/row respectively
logZeros[:,1:][leftZerosIdx] = True
logZeros[1:,:][topZerosIdx] = True
# right and bottom zeros can be added directly
logZeros[rightZerosIdx] = True
logZeros[bottomZerosIdx] = True
# filter by gradient, treating connected pixels as a single edge, and
# discarding any edge as specified by gradmethod
grad = ndimage.gaussian_gradient_magnitude(scaledImage, gradKernelSize)
lbl_logZeros, nEdges = ndimage.label(logZeros,
[[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
if gradmethod == 'mean':
for edge in range(nEdges):
if grad[lbl_logZeros==edge].mean() < gradstrength:
logZeros[lbl_logZeros==edge] = 0
elif gradmethod == 'any':
logZeros = np.zeros(log.shape, np.bool)
cutoffEdgeLabels = list(set(lbl_logZeros[grad > gradstrength]))[1:]
for edge in cutoffEdgeLabels:
logZeros[lbl_logZeros==edge] = 1
return logZeros
def road_transform(gray): dist = cv2.distanceTransform(gray, cv2.DIST_L2, 3) # get distanceTransform map dist_gauss = ndimage.gaussian_laplace(dist, sigma=3) # apply gaussian_laplace filter return dist, dist_gauss
def run(self, ips, snap, img, para = None):
nimg.gaussian_laplace(snap, para['sigma'], output=img)
def log_calc(image, sigma):
return ndimage.gaussian_laplace(image, sigma)
def blob_log(image,
min_sigma=1,
max_sigma=50,
num_sigma=10,
threshold=.2,
overlap=.5,
log_scale=False,
*,
exclude_border=False):
r"""Finds blobs in the given grayscale image.
Blobs are found using the Laplacian of Gaussian (LoG) method [1]_.
For each blob found, the method returns its coordinates and the standard
deviation of the Gaussian kernel that detected the blob.
Parameters
----------
image : 2D or 3D ndarray
Input grayscale image, blobs are assumed to be light on dark
background (white on black).
min_sigma : scalar or sequence of scalars, optional
the minimum standard deviation for Gaussian kernel. Keep this low to
detect smaller blobs. The standard deviations of the Gaussian filter
are given for each axis as a sequence, or as a single number, in
which case it is equal for all axes.
max_sigma : scalar or sequence of scalars, optional
The maximum standard deviation for Gaussian kernel. Keep this high to
detect larger blobs. The standard deviations of the Gaussian filter
are given for each axis as a sequence, or as a single number, in
which case it is equal for all axes.
num_sigma : int, optional
The number of intermediate values of standard deviations to consider
between `min_sigma` and `max_sigma`.
threshold : float, optional.
The absolute lower bound for scale space maxima. Local maxima smaller
than thresh are ignored. Reduce this to detect blobs with less
intensities.
overlap : float, optional
A value between 0 and 1. If the area of two blobs overlaps by a
fraction greater than `threshold`, the smaller blob is eliminated.
log_scale : bool, optional
If set intermediate values of standard deviations are interpolated
using a logarithmic scale to the base `10`. If not, linear
interpolation is used.
exclude_border : tuple of ints, int, or False, optional
If tuple of ints, the length of the tuple must match the input array's
dimensionality. Each element of the tuple will exclude peaks from
within `exclude_border`-pixels of the border of the image along that
dimension.
If nonzero int, `exclude_border` excludes peaks from within
`exclude_border`-pixels of the border of the image.
If zero or False, peaks are identified regardless of their
distance from the border.
Returns
-------
A : (n, image.ndim + sigma) ndarray
A 2d array with each row representing 2 coordinate values for a 2D
image, and 3 coordinate values for a 3D image, plus the sigma(s) used.
When a single sigma is passed, outputs are:
``(r, c, sigma)`` or ``(p, r, c, sigma)`` where ``(r, c)`` or
``(p, r, c)`` are coordinates of the blob and ``sigma`` is the standard
deviation of the Gaussian kernel which detected the blob. When an
anisotropic gaussian is used (sigmas per dimension), the detected sigma
is returned for each dimension.
References
----------
.. [1] https://en.wikipedia.org/wiki/Blob_detection#The_Laplacian_of_Gaussian
Examples
--------
>>> from skimage import data, feature, exposure
>>> img = data.coins()
>>> img = exposure.equalize_hist(img) # improves detection
>>> feature.blob_log(img, threshold = .3)
array([[124. , 336. , 11.88888889],
[198. , 155. , 11.88888889],
[194. , 213. , 17.33333333],
[121. , 272. , 17.33333333],
[263. , 244. , 17.33333333],
[194. , 276. , 17.33333333],
[266. , 115. , 11.88888889],
[128. , 154. , 11.88888889],
[260. , 174. , 17.33333333],
[198. , 103. , 11.88888889],
[126. , 208. , 11.88888889],
[127. , 102. , 11.88888889],
[263. , 302. , 17.33333333],
[197. , 44. , 11.88888889],
[185. , 344. , 17.33333333],
[126. , 46. , 11.88888889],
[113. , 323. , 1. ]])
Notes
-----
The radius of each blob is approximately :math:`\sqrt{2}\sigma` for
a 2-D image and :math:`\sqrt{3}\sigma` for a 3-D image.
"""
image = img_as_float(image)
float_dtype = _supported_float_type(image.dtype)
image = image.astype(float_dtype, copy=False)
# if both min and max sigma are scalar, function returns only one sigma
scalar_sigma = (True if np.isscalar(max_sigma) and np.isscalar(min_sigma)
else False)
# Gaussian filter requires that sequence-type sigmas have same
# dimensionality as image. This broadcasts scalar kernels
if np.isscalar(max_sigma):
max_sigma = np.full(image.ndim, max_sigma, dtype=float_dtype)
if np.isscalar(min_sigma):
min_sigma = np.full(image.ndim, min_sigma, dtype=float_dtype)
# Convert sequence types to array
min_sigma = np.asarray(min_sigma, dtype=float_dtype)
max_sigma = np.asarray(max_sigma, dtype=float_dtype)
if log_scale:
# for anisotropic data, we use the "highest resolution/variance" axis
standard_axis = np.argmax(min_sigma)
start = np.log10(min_sigma[standard_axis])
stop = np.log10(max_sigma[standard_axis])
scale = np.logspace(start, stop, num_sigma)[:, np.newaxis]
sigma_list = scale * min_sigma / np.max(min_sigma)
else:
scale = np.linspace(0, 1, num_sigma)[:, np.newaxis]
sigma_list = scale * (max_sigma - min_sigma) + min_sigma
# computing gaussian laplace
# average s**2 provides scale invariance
gl_images = [
-gaussian_laplace(image, s) * np.mean(s)**2 for s in sigma_list
]
image_cube = np.stack(gl_images, axis=-1)
exclude_border = _format_exclude_border(image.ndim, exclude_border)
local_maxima = peak_local_max(
image_cube,
threshold_abs=threshold,
footprint=np.ones((3, ) * (image.ndim + 1)),
threshold_rel=0.0,
exclude_border=exclude_border,
)
# Catch no peaks
if local_maxima.size == 0:
return np.empty((0, 3))
# Convert local_maxima to float64
lm = local_maxima.astype(float_dtype)
# translate final column of lm, which contains the index of the
# sigma that produced the maximum intensity value, into the sigma
sigmas_of_peaks = sigma_list[local_maxima[:, -1]]
if scalar_sigma:
# select one sigma column, keeping dimension
sigmas_of_peaks = sigmas_of_peaks[:, 0:1]
# Remove sigma index and replace with sigmas
lm = np.hstack([lm[:, :-1], sigmas_of_peaks])
sigma_dim = sigmas_of_peaks.shape[1]
return _prune_blobs(lm, overlap, sigma_dim=sigma_dim)
def blob_log(image, sigma_list=None, scale_choice='linear',
min_sigma=1, max_sigma=50, num_sigma=10,
threshold=.2, overlap=.5, sigma_ratio=2.,
weighting=None, merge_overlap_dist=1.0,
refine_shape=False, use_max_response=False):
"""Finds blobs in the given grayscale image.
Blobs are found using the Laplacian of Gaussian (LoG) method [1]_.
For each blob found, the method returns its coordinates and the standard
deviation of the Gaussian kernel that detected the blob.
Parameters
----------
image : ndarray
Input grayscale image, blobs are assumed to be light on dark
background (white on black).
sigma_list : np.ndarray, optional
Provide the list of sigmas to use.
scale_choice : str, optional
'log', 'linear' or 'ratio'. Determines how the scales are calculated
based on the given number, minimum, maximum, or ratio.
min_sigma : float, optional
The minimum standard deviation for Gaussian Kernel. Keep this low to
detect smaller blobs.
max_sigma : float, optional
The maximum standard deviation for Gaussian Kernel. Keep this high to
detect larger blobs.
num_sigma : int, optional
The number of intermediate values of standard deviations to consider
between `min_sigma` and `max_sigma`.
threshold : float, optional.
The absolute lower bound for scale space maxima. Local maxima smaller
than thresh are ignored. Reduce this to detect blobs with less
intensities.
overlap : float, optional
A value between 0 and 1. If the area of two blobs overlaps by a
fraction greater than `threshold`, the smaller blob is eliminated.
weighting : np.ndarray, optional
Used to weight certain scales differently when selecting local maxima
in the transform space. For example when searching for regions near
the beam size, the transform can be down-weighted to avoid spurious
detections. Must have the same number of elements as the scales.
merge_overlap_dist : float, optional
Controls the minimum overlap regions must have to be merged together.
Defaults to one sigma separation, where sigma is one of the scales
used in the transform.
Returns
-------
A : (n, 5) ndarray
A 2d array with each row representing 5 values,
``(y, x, semi-major sigma, semi-minor sigma, pa)``
where ``(y,x)`` are coordinates of the blob, ``semi-major sigma`` and
``semi-minor sigma`` are the standard deviations of the elliptical blob,
and ``pa`` is its position angle.
References
----------
.. [1] http://en.wikipedia.org/wiki/Blob_detection#The_Laplacian_of_Gaussian
Examples
--------
>>> from skimage import data, feature, exposure
>>> img = data.coins()
>>> img = exposure.equalize_hist(img) # improves detection
>>> feature.blob_log(img, threshold = .3)
array([[ 113. , 323. , 1. ],
[ 121. , 272. , 17.33333333],
[ 124. , 336. , 11.88888889],
[ 126. , 46. , 11.88888889],
[ 126. , 208. , 11.88888889],
[ 127. , 102. , 11.88888889],
[ 128. , 154. , 11.88888889],
[ 185. , 344. , 17.33333333],
[ 194. , 213. , 17.33333333],
[ 194. , 276. , 17.33333333],
[ 197. , 44. , 11.88888889],
[ 198. , 103. , 11.88888889],
[ 198. , 155. , 11.88888889],
[ 260. , 174. , 17.33333333],
[ 263. , 244. , 17.33333333],
[ 263. , 302. , 17.33333333],
[ 266. , 115. , 11.88888889]])
Notes
-----
The radius of each blob is approximately :math:`\sqrt{2}sigma`.
"""
# assert_nD(image, 2)
image = img_as_float(image)
if sigma_list is None:
if scale_choice is 'log':
start, stop = log(min_sigma, 10), log(max_sigma, 10)
sigma_list = np.logspace(start, stop, num_sigma)
elif scale_choice is 'linear':
sigma_list = np.linspace(min_sigma, max_sigma, num_sigma)
elif scale_choice is 'ratio':
# k such that min_sigma*(sigma_ratio**k) > max_sigma
k = int(log(float(max_sigma) / min_sigma, sigma_ratio)) + 1
sigma_list = np.array([min_sigma * (sigma_ratio ** i)
for i in range(k)])
else:
raise ValueError("scale_choice must be 'log', 'linear', or "
"'ratio'.")
if weighting is not None:
if len(weighting) != len(sigma_list):
raise IndexError("weighting must have the same number of elements"
" as scales (" + str(len(sigma_list)) + ").")
else:
weighting = np.ones_like(sigma_list)
# computing gaussian laplace
# s**2 provides scale invariance
# weighting by w changes the relative importance of each transform scale
gl_images = [gaussian_laplace(image, s) * s ** 2 * w for s, w in
zip(sigma_list, weighting)]
image_cube = np.dstack(gl_images)
if use_max_response:
scale_peaks = \
peak_local_max(image_cube.max(2),
threshold_abs=threshold,
threshold_rel=0.0,
min_distance=0.5 * np.sqrt(2) * sigma_list[0],
exclude_border=False)
argmaxes = image_cube.argmax(2)[scale_peaks[:, 0], scale_peaks[:, 1]]
radii = np.array([sigma_list[arg] for arg in argmaxes]) * np.sqrt(2)
radii = radii[:, np.newaxis]
responses = image_cube.max(2)[scale_peaks[:, 0], scale_peaks[:, 1]]
responses = responses[:, np.newaxis]
pas = np.zeros_like(radii)
local_maxima = np.hstack([scale_peaks, radii, radii, pas, responses])
else:
for i, scale in enumerate(sigma_list):
scale_peaks = peak_local_max(image_cube[:, :, i],
threshold_abs=threshold,
min_distance=np.sqrt(2) * scale,
threshold_rel=0.0,
exclude_border=False)
new_scale_peaks = np.empty((len(scale_peaks), 5))
if refine_shape:
for j, peak in enumerate(scale_peaks):
new_peak = np.array([peak[0], peak[1], scale, scale, 0.0])
new_scale_peaks[j] = \
shape_from_blob_moments(new_peak, image_cube[:, :, i])
else:
new_scale_peaks[:, :2] = scale_peaks
# sqrt(2) size correction
new_scale_peaks[:, 2:4] = np.sqrt(2) * scale
new_scale_peaks[:, 4] = 0.0
vals = \
np.array([image_cube[pos[0], pos[1], i]
for pos in scale_peaks]).reshape((len(scale_peaks),
1))
new_scale_peaks = np.hstack([new_scale_peaks, vals])
if i == 0:
local_maxima = new_scale_peaks
else:
local_maxima = np.vstack([local_maxima, new_scale_peaks])
if local_maxima.size == 0:
return local_maxima
# Merge regions into ellipses
local_maxima = _merge_blobs(local_maxima, merge_overlap_dist)
# Then prune and return them\
return local_maxima
if 'load' in sys.argv:
im = plt.imread('remote.jpeg')
target = {
'c1': [620, 1200], # [y1, x1]
'c2': [620, 1830], # [y1, x2]
'c3': [1096, 1830], # [y2, x2]
'c4': [1096, 1200]
} # [y2, x1]
c1 = target['c1']
c2 = target['c2']
c3 = target['c3']
c4 = target['c4']
sharpen = [[0, -1, 0], [-1, 5, -1], [0, -1, 0]]
# Create a Field of View
fov = im[c1[0]:c3[0], c1[1]:c3[1]]
fov = ndi.convolve(fov[:, :, 2], np.array(sharpen))
edges = 10 * fov / ndi.gaussian_laplace(im[c1[0]:c3[0], c1[1]:c3[1]][:, :, 2],
sigma=2)
f, ax = plt.subplots(1, 2, sharex=True, sharey=True)
#ax[0].imshow(im[c1[0]:c3[0],c1[1]:c3[1]], 'gray_r')
#ax[1].imshow(edges,'gray_r')
#plt.show()
plt.imsave('fov.jpeg', edges)
os.remove('remote.jpeg')
def log_filter(image, sigma, keep_dtype=False):
"""Apply a Laplacian of Gaussian filter to a 2-d or 3-d image.
The function returns the inverse of the filtered image such that the pixels
with the highest intensity from the original (smoothed) image have
positive values. Those with a low intensity returning a negative value are
clipped to zero.
Parameters
----------
image : np.ndarray
Image with shape (z, y, x) or (y, x).
sigma : float, int, Tuple(float, int) or List(float, int)
Sigma used for the gaussian filter (one for each dimension). If it's a
float, the same sigma is applied to every dimensions.
keep_dtype : bool
Cast output image as input image.
Returns
-------
image_filtered : np.ndarray
Filtered image.
"""
# check parameters
check_array(image,
ndim=[2, 3],
dtype=[np.uint8, np.uint16, np.float32, np.float64])
check_parameter(sigma=(float, int, tuple, list))
# we cast the data in np.float to allow negative values
if image.dtype == np.uint8:
image_float = cast_img_float32(image)
elif image.dtype == np.uint16:
image_float = cast_img_float64(image)
else:
image_float = image
# check sigma
if isinstance(sigma, (tuple, list)):
if len(sigma) != image.ndim:
raise ValueError("'sigma' must be a scalar or a sequence with the "
"same length as 'image.ndim'.")
# we apply LoG filter
image_filtered = gaussian_laplace(image_float, sigma=sigma)
# as the LoG filter makes the peaks in the original image appear as a
# reversed mexican hat, we inverse the result and clip negative values to 0
image_filtered = np.clip(-image_filtered, a_min=0, a_max=None)
# cast filtered image
if keep_dtype:
if image.dtype == np.uint8:
image_filtered = cast_img_uint8(image_filtered)
elif image.dtype == np.uint16:
image_filtered = cast_img_uint16(image_filtered)
else:
pass
return image_filtered
def main(vid_id):
print("process video: " + str(vid_id))
#if need to write video, you need to set the correct frame image path
IMAGE_PATH = '....../04_26_20/frames256_instances/{}/{}/image_{:05d}.jpg'
write_video = False
fps = 6
# deal with index/position
offset = 4 # this is after downsample, the end frame index of the current segment (4-th) of the first window
downsample = 3
offset *= downsample
# hyper-para for boundary detection
smooth_factor = 5
LoG_sigma = 15
# Update these
exp_path = '../../data/exp_TAPOS'
output_seg_dir = 'detect_seg'
SAVE_PATH = exp_path + output_seg_dir + '/{}/frame_{{:05d}}.jpg'
OUTPUT_PATH = exp_path + output_seg_dir + '/{}.mp4'
OUTPUT_BDY_PATH = exp_path + output_seg_dir + '/{}.pkl'
if not os.path.exists(exp_path + output_seg_dir):
os.makedirs(exp_path + output_seg_dir)
def detect_boundary(signal, offset, sigma):
bdy_idx = []
LoG_mean_errors = ndimage.gaussian_laplace(signal,
sigma=LoG_sigma,
mode='nearest')
delta_LoG = np.gradient(LoG_mean_errors)
for i in range(len(signal) - 1):
if delta_LoG[i] >= 0 and delta_LoG[i + 1] <= 0:
if delta_LoG[i] > -delta_LoG[i + 1]:
bdy_idx += [i + offset + 1]
else:
bdy_idx += [i + offset]
return bdy_idx
# load GT boundary timestamps and convert into index here
with open('../../data/export/tapos_annotation_timestamps_myfps.json',
'r') as f:
tapos_dict = json.load(f)
# read and pre-process prediction error for each video
windows = sorted(glob(exp_path + 'pred_err/err_vid_' + vid_id + '*.pkl'))
if windows == []:
return
pred_err = []
for window_path in windows:
with open(window_path, 'rb') as f:
data = pickle.load(f, encoding='latin1')
pred_err.append(data['pred_err'])
mean_errors = -np.nanmean(np.stack(pred_err, axis=0), axis=(1, 2, 3, 4))
save_path = SAVE_PATH.format(vid_id)
output_path = OUTPUT_PATH.format(vid_id)
output_bdy_path = OUTPUT_BDY_PATH.format(vid_id)
if osp.exists(osp.dirname(save_path)):
shutil.rmtree(osp.dirname(save_path))
os.mkdir(osp.dirname(save_path))
print(osp.dirname(save_path))
if len(mean_errors) < 2:
return
bdy_idx_list = detect_boundary(mean_errors, offset=offset, sigma=LoG_sigma)
bdy_idx_list_smt = detect_boundary(filters.gaussian_filter1d(
mean_errors, smooth_factor),
offset=offset,
sigma=LoG_sigma)
bdy_idx_save = {}
bdy_idx_save['bdy_idx_list'] = bdy_idx_list
bdy_idx_save['bdy_idx_list_smt'] = bdy_idx_list_smt
pickle.dump(bdy_idx_save, open(output_bdy_path, "wb"))
if write_video is False:
return
writer_og = VideoWriter('{img_dir}/{vid_id}_og.mp4'.format(
img_dir=osp.dirname(osp.dirname(SAVE_PATH)), vid_id=vid_id),
fps=fps)
# e.g. vid 2IO8DO_QbRE_s00018_5_324_13_160
v = vid_id[:11]
s = vid_id[12:]
myfps = tapos_dict[v][s]['myfps']
instance_start_idx = tapos_dict[v][s]['substages_myframeidx'][0]
bdy_idx_list_gt = []
for idx in tapos_dict[v][s]['substages_myframeidx'][1:-1]:
bdy_idx_list_gt.append(int((idx - instance_start_idx) / downsample))
for j in tqdm.tqdm(range(0, data['vlen'], downsample)):
x = np.arange(offset, offset + len(mean_errors))
fig = plt.figure(figsize=(10, 8))
gs = gridspec.GridSpec(nrows=5, ncols=2)
ax1 = fig.add_subplot(gs[0, 0])
# Signal is in mean_errors.
ax1.plot(x,
filters.gaussian_filter1d(mean_errors, smooth_factor),
lw=2)
ax1.axvline(j / downsample, color='red')
for bdy_idx in bdy_idx_list_gt: # plot GT boundaries
ax1.axvline(bdy_idx, color='cyan', ls='-')
for bdy_idx in bdy_idx_list_smt: # plot our detected boundaries
ax1.axvline(bdy_idx, color='green', ls='--')
ax1.set_xlim(0, len(mean_errors) + offset + offset_end)
ax1.set_title('Smooth Pred Acc')
ax2 = fig.add_subplot(gs[0, 1])
ax2.plot(x, mean_errors, lw=2)
ax2.axvline(j / downsample, color='red')
for bdy_idx in bdy_idx_list_gt: # plot GT boundaries
ax2.axvline(bdy_idx, color='cyan', ls='-')
for bdy_idx in bdy_idx_list:
ax2.axvline(bdy_idx, color='green', ls='--')
ax2.set_title('Pred Acc')
ax2.set_xlim(0, len(mean_errors) + offset + offset_end)
ax4 = fig.add_subplot(gs[1, 0])
LoG_smt_mean_errors = ndimage.gaussian_laplace(
filters.gaussian_filter1d(mean_errors, smooth_factor),
sigma=LoG_sigma,
mode='nearest')
ax4.plot(x, LoG_smt_mean_errors, lw=2)
ax4.axvline(j / downsample, color='red')
for bdy_idx in bdy_idx_list_gt: # plot GT boundaries
ax4.axvline(bdy_idx, color='cyan', ls='-')
for bdy_idx in bdy_idx_list_smt:
ax4.axvline(bdy_idx, color='green', ls='--')
ax4.set_title('LoG Smooth Pred Acc')
ax4.set_xlim(0, len(mean_errors) + offset + offset_end)
ax5 = fig.add_subplot(gs[1, 1])
LoG_mean_errors = ndimage.gaussian_laplace(mean_errors,
sigma=LoG_sigma,
mode='nearest')
ax5.plot(x, LoG_mean_errors, lw=2)
ax5.axvline(j / downsample, color='red')
for bdy_idx in bdy_idx_list_gt: # plot GT boundaries
ax5.axvline(bdy_idx, color='cyan', ls='-')
for bdy_idx in bdy_idx_list:
ax5.axvline(bdy_idx, color='green', ls='--')
ax5.set_title('LoG Pred Acc')
ax5.set_xlim(0, len(mean_errors) + offset + offset_end)
ax6 = fig.add_subplot(gs[2, 0])
ax6.plot(x, np.gradient(LoG_smt_mean_errors), lw=2)
ax6.axvline(j / downsample, color='red')
for bdy_idx in bdy_idx_list_gt: # plot GT boundaries
ax6.axvline(bdy_idx, color='cyan', ls='-')
for bdy_idx in bdy_idx_list_smt:
ax6.axvline(bdy_idx, color='green', ls='--')
ax6.axhline(0, color='green', ls='--')
ax6.set_title('$\Delta$ LoG Smooth Pred Acc')
ax6.set_xlim(0, len(mean_errors) + offset + offset_end)
ax7 = fig.add_subplot(gs[2, 1])
ax7.plot(x, np.gradient(LoG_mean_errors), lw=2)
ax7.axvline(j / downsample, color='red')
for bdy_idx in bdy_idx_list_gt: # plot GT boundaries
ax7.axvline(bdy_idx, color='cyan', ls='-')
for bdy_idx in bdy_idx_list:
ax7.axvline(bdy_idx, color='green', ls='--')
ax7.axhline(0, color='green', ls='--')
ax7.set_title('$\Delta$ LoG Pred Acc')
ax7.set_xlim(0, len(mean_errors) + offset + offset_end)
ax3 = fig.add_subplot(gs[-2:, :])
im = plt.imread(IMAGE_PATH.format(v, s, (j + 1)))
cv2.putText(im, str(j), (20, 20), 0, 1, (57, 255, 20), thickness=2)
writer_og.add_image(im)
ax3.imshow(im)
ax3.axis('off')
plt.tight_layout()
plt.savefig(fname=save_path.format(int(j / downsample)), dpi=150)
plt.close()
cmd = [
'ffmpeg',
'-y',
'-threads',
'16',
'-framerate',
str(fps),
'-i',
'{img_dir}/frame_%05d.jpg'.format(img_dir=osp.dirname(save_path)),
'-profile:v',
'baseline',
'-level',
'3.0',
'-c:v',
'libx264',
'-pix_fmt',
'yuv420p',
'-an',
# Note that if called as a string, ffmpeg needs quotes around the
# scale invocation.
'-vf',
'scale=trunc(iw/2)*2:trunc(ih/2)*2',
output_path,
]
print(' '.join(cmd))
try:
err = subprocess.call(cmd)
if err:
print('Subprocess failed')
except OSError:
print('Failed to run ffmpeg')
writer_og.make_video()
writer_og.close()
plt.plot(y, x, 'or', ms=4) from skimage import morphology markers = np.zeros(im_denoised.shape, dtype=np.int) markers[x.astype(np.int), y.astype(np.int)] = np.arange(len(x)) + 1 markers = morphology.dilation(markers, morphology.disk(7)) from scipy import ndimage from skimage import morphology # Black tophat transformation (see https://en.wikipedia.org/wiki/Top-hat_transform) hat = ndimage.black_tophat(im_denoised, 7) # Combine with denoised image hat -= 0.3 * im_denoised # Morphological dilation to try to remove some holes in hat image hat = morphology.dilation(hat) plt.imshow(hat, cmap='spectral') labels_hat = morphology.watershed(hat, markers) from skimage import color color_labels = color.label2rgb(labels_hat, im_denoised) plt.imshow(color_labels[:300, :300]) # A different markers image: laplace filter lap = ndimage.gaussian_laplace(im_denoised, sigma=0.7) lap = restoration.nl_means_denoising(lap, h=0.002) plt.imshow(lap, cmap='spectral') plt.colorbar() labels_lap = morphology.watershed(lap, markers) color_labels = color.label2rgb(labels_lap, im_denoised) plt.imshow(color_labels)
"""
for i in [1, 2, 3]:
if i == 1:
k = np.ones([5, 3])
k = k / k.sum()
title = "ndimage.convolve 5x3"
print(title)
smoothed = ndimage.convolve(data_2D, k, mode='nearest')
if i == 2:
if False:
title = "gaussian_laplace"
print("title")
#smoothed = data_2D + ndimage.gaussian_laplace(data_2D, 3, mode='nearest') * 10.
smoothed = ndimage.gaussian_laplace(data_2D, 3,
mode='nearest') * 10.
if True:
#sigma=1/2.*np.array([4.5,3.0]) # no artefacts more for shifted fields
sigma = 1 / 3. * np.array([4.5, 3.0
]) # conserves a bit better the maxima
title = "gaussian_filter " + str(sigma)
print("... image 2: ", title)
smoothed = ndimage.filters.gaussian_filter(data_2D,
sigma,
mode='nearest')
if i == 3:
if True:
sigma = 1 / 2. * np.array(
a = Image.open('resize.jpg').convert('L')
a = mi.fromimage(a)
thresh = filters.threshold_otsu(a)
im_otsu = a > thresh
im_otsu = mi.toimage(im_otsu)
im_otsu.save('otsu_semoutput.png')
im_canny = feature.canny(a, sigma=3)
fill_holes = nd.binary_fill_holes(im_canny)
fill_holes = mi.toimage(fill_holes)
im_canny = mi.toimage(im_canny)
im_canny.save('test_canny.jpg')
#to remove bushes try to adopt canny edges for leaves, fill holes and then negate obtained mask
fill_holes.save('fill_holes.jpg')
im_laplace = nd.gaussian_laplace(a, 3)
im_laplace = mi.toimage(im_laplace)
im_laplace.save('test_laplace.jpg')
im_elevation_map = filters.sobel(a)
markers = np.zeros_like(a)
markers[a < 30] = 1
markers[a > 150] = 2
segmentation = watershed(im_elevation_map, markers)
im_sobel = mi.toimage(segmentation)
im_sobel.save('im_sobel.jpg')
def addCell(self, eventTuple):
if self.maskOn:
if self.data.ndim == 2:
self.aveData = self.data.copy()
else:
self.aveData = self.data.mean(axis=2)
x, y = eventTuple
localValue = self.currentMask[x, y]
print str(self.mode) + " " + "x: " + str(x) + ", y: " + str(y) + ", mask val: " + str(localValue)
# ensure mask is uint16
self.currentMask = self.currentMask.astype("uint16")
sys.stdout.flush()
########## NORMAL MODE
if self.mode is None:
if localValue > 0 and localValue != self.currentMaskNumber:
print "we are altering mask at at %d, %d" % (x, y)
# copy the old mask
newMask = self.currentMask.copy()
# make a labeled image of the current mask
labeledCurrentMask = mahotas.label(newMask)[0]
roiNumber = labeledCurrentMask[x, y]
# set that ROI to zero
newMask[labeledCurrentMask == roiNumber] = self.currentMaskNumber
newMask = newMask.astype("uint16")
self.listOfMasks.append(newMask)
self.currentMask = self.listOfMasks[-1]
elif localValue > 0 and self.data.ndim == 3:
# update info panel
labeledCurrentMask = mahotas.label(self.currentMask.copy())[0]
roiNumber = labeledCurrentMask[x, y]
self.updateInfoPanel(ROI_number=roiNumber)
elif localValue == 0:
xmin = int(x - self.diskSize)
xmax = int(x + self.diskSize)
ymin = int(y - self.diskSize)
ymax = int(y + self.diskSize)
sub_region_image = self.aveData[xmin:xmax, ymin:ymax].copy()
# threshold = mahotas.otsu(self.data[xmin:xmax, ymin:ymax].astype('uint16'))
# do a gaussian_laplacian filter to find the edges and the center
g_l = nd.gaussian_laplace(
sub_region_image, 1
) # second argument is a free parameter, std of gaussian
g_l = mahotas.dilate(mahotas.erode(g_l >= 0))
g_l = mahotas.label(g_l)[0]
center = g_l == g_l[g_l.shape[0] / 2, g_l.shape[0] / 2]
# edges = mahotas.dilate(mahotas.dilate(mahotas.dilate(center))) - center
newCell = np.zeros_like(self.currentMask)
newCell[xmin:xmax, ymin:ymax] = center
newCell = mahotas.dilate(newCell)
if self.useNMF:
modes, thresh_modes, fit_data, this_cell, is_cell, nmf_limits = self.doLocalNMF(x, y, newCell)
for mode, mode_thresh, t, i in zip(modes, thresh_modes, this_cell, is_cell):
# need to place it in the right place
# have x and y
mode_width, mode_height = mode_thresh.shape
mode_thresh_fullsize = np.zeros_like(newCell)
mode_thresh_fullsize[
nmf_limits[0] : nmf_limits[1], nmf_limits[2] : nmf_limits[3]
] = mode_thresh
# need to add all modes belonging to this cell first,
# then remove the ones nearby.
if i:
if t:
valid_area = np.logical_and(
mahotas.dilate(
mahotas.dilate(mahotas.dilate(mahotas.dilate(newCell.astype(bool))))
),
mode_thresh_fullsize,
)
newCell = np.logical_or(newCell.astype(bool), valid_area)
else:
newCell = np.logical_and(
newCell.astype(bool), np.logical_not(mahotas.dilate(mode_thresh_fullsize))
)
newCell = mahotas.close_holes(newCell.astype(bool))
self.excludePixels(newCell, 2)
newCell = newCell.astype(self.currentMask.dtype)
# remove all pixels in and near current mask and filter for ROI size
newCell[mahotas.dilate(self.currentMask > 0)] = 0
newCell = self.excludePixels(newCell, 10)
newMask = (newCell * self.currentMaskNumber) + self.currentMask
newMask = newMask.astype("uint16")
self.listOfMasks.append(newMask.copy())
self.currentMask = newMask.copy()
elif self.mode is "OGB":
# build structuring elements
se = pymorph.sebox()
se2 = pymorph.sedisk(self.cellRadius, metric="city-block")
seJunk = pymorph.sedisk(max(np.floor(self.cellRadius / 4.0), 1), metric="city-block")
seExpand = pymorph.sedisk(self.diskSize, metric="city-block")
# add a disk around selected point, non-overlapping with adjacent cells
dilatedOrignal = mahotas.dilate(self.currentMask.astype(bool), Bc=se)
safeUnselected = np.logical_not(dilatedOrignal)
# tempMask is
tempMask = np.zeros_like(self.currentMask, dtype=bool)
tempMask[x, y] = True
tempMask = mahotas.dilate(tempMask, Bc=se2)
tempMask = np.logical_and(tempMask, safeUnselected)
# calculate the area we should add to this disk based on % of a threshold
cellMean = self.aveData[tempMask == 1.0].mean()
allMeanBw = self.aveData >= (cellMean * float(self.contrastThreshold))
tempLabel = mahotas.label(np.logical_and(allMeanBw, safeUnselected).astype(np.uint16))[0]
connMeanBw = tempLabel == tempLabel[x, y]
connMeanBw = np.logical_and(np.logical_or(connMeanBw, tempMask), safeUnselected).astype(np.bool)
# erode and then dilate to remove sharp bits and edges
erodedMean = mahotas.erode(connMeanBw, Bc=seJunk)
dilateMean = mahotas.dilate(erodedMean, Bc=seJunk)
dilateMean = mahotas.dilate(dilateMean, Bc=seExpand)
modes, thresh_modes, fit_data, this_cell, is_cell, limits = self.doLocaNMF(x, y)
newCell = np.logical_and(dilateMean, safeUnselected)
newMask = (newCell * self.currentMaskNumber) + self.currentMask
newMask = newMask.astype("uint16")
self.listOfMasks.append(newMask.copy())
self.currentMask = newMask.copy()
########## SQUARE MODE
elif self.mode is "square":
self.modeData.append((x, y))
if len(self.modeData) == 2:
square_mask = np.zeros_like(self.currentMask)
xstart = self.modeData[0][0]
ystart = self.modeData[0][1]
xend = self.modeData[1][0]
yend = self.modeData[1][1]
square_mask[xstart:xend, ystart:yend] = 1
# check if square_mask interfers with current mask, if so, abort
if np.any(np.logical_and(square_mask, self.currentMask)):
return None
# add square_mask to mask
newMask = (square_mask * self.currentMaskNumber) + self.currentMask
newMask = newMask.astype("uint16")
self.listOfMasks.append(newMask)
self.currentMask = self.listOfMasks[-1]
# clear current mode data
self.clearModeData()
########## CIRCLE MODE
elif self.mode is "circle":
# make a strel and move it in place to make circle_mask
if self.diskSize < 1:
return None
if self.diskSize is 1:
se = np.ones((1, 1))
elif self.diskSize is 2:
se = pymorph.secross(r=1)
else:
se = pymorph.sedisk(r=(self.diskSize - 1))
se_extent = int(se.shape[0] / 2)
circle_mask = np.zeros_like(self.currentMask)
circle_mask[x - se_extent : x + se_extent + 1, y - se_extent : y + se_extent + 1] = se * 1.0
circle_mask = circle_mask.astype(bool)
# check if circle_mask interfers with current mask, if so, abort
if np.any(np.logical_and(circle_mask, mahotas.dilate(self.currentMask.astype(bool)))):
return None
# add circle_mask to mask
newMask = (circle_mask * self.currentMaskNumber) + self.currentMask
newMask = newMask.astype("uint16")
self.listOfMasks.append(newMask)
self.currentMask = self.listOfMasks[-1]
########## POLY MODE
elif self.mode is "poly":
self.modeData.append((x, y))
sys.stdout.flush()
self.makeNewMaskAndBackgroundImage()
def __init__(self, image=[[0.0]], sigma=1, **options):
from scipy import ndimage
self.ax = ndimage.gaussian_laplace(image, sigma=sigma, **options)
# axes[2].imshow(img2, cmap='gray')
# axes[2].set_title('blur via Gaussian filter')
# plt.show()
'''
实验任务2- 仿照上面高斯滤波的例子，尝试自己编写 ndimage.gaussian_laplace 滤波器与卷积的对比。
'''
from scipy import fftpack
file_name = 'D:\Desktop/canoe.tif'
img = cv2.imread(file_name, 0)
img = (img - np.min(img)) / (np.max(img) - np.min(img))
fig, axes = plt.subplots(1, 3)
axes[0].imshow(img, cmap='gray')
axes[0].set_title('source')
img_cv = ndimage.gaussian_laplace(img, sigma=2)
img_cv = (img_cv - np.min(img_cv)) / (np.max(img_cv) - np.min(img_cv))
axes[1].imshow(img_cv, cmap='gray')
axes[1].set_title('convolve gaussian kernel')
#get the gaussian kernel
w = 100
h = 100
in_mask = np.zeros((h, w), dtype=np.float32)
in_mask[int(h / 2), int(w / 2)] = 1
img_kernel = ndimage.gaussian_laplace(in_mask, sigma=2)
#
kernel = img_kernel[50 - 10:50 + 10, 50 - 10:50 + 10]
kernel_ft = fftpack.fft2(kernel, shape=img.shape[:2], axes=(0, 1))
from scipy import ndimage, misc import matplotlib.pyplot as plt ascent = misc.ascent() fig = plt.figure() plt.gray() # show the filtered result in grayscale ax1 = fig.add_subplot(121) # left side ax2 = fig.add_subplot(122) # right side result = ndimage.gaussian_laplace(ascent, sigma=1) ax1.imshow(result) result = ndimage.gaussian_laplace(ascent, sigma=3) ax2.imshow(result) plt.show()
def blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, thresholds=[.2],
overlap=.5, log_scale=False, *, exclude_border=False):
r"""Finds blobs in the given grayscale image.
Blobs are found using the Laplacian of Gaussian (LoG) method [1]_.
For each blob found, the method returns its coordinates and the standard
deviation of the Gaussian kernel that detected the blob.
Parameters
----------
image : 2D or 3D ndarray
Input grayscale image, blobs are assumed to be light on dark
background (white on black).
min_sigma : scalar or sequence of scalars, optional
the minimum standard deviation for Gaussian kernel. Keep this low to
detect smaller blobs. The standard deviations of the Gaussian filter
are given for each axis as a sequence, or as a single number, in
which case it is equal for all axes.
max_sigma : scalar or sequence of scalars, optional
The maximum standard deviation for Gaussian kernel. Keep this high to
detect larger blobs. The standard deviations of the Gaussian filter
are given for each axis as a sequence, or as a single number, in
which case it is equal for all axes.
num_sigma : int, optional
The number of intermediate values of standard deviations to consider
between `min_sigma` and `max_sigma`.
threshold : float, optional.
The absolute lower bound for scale space maxima. Local maxima smaller
than thresh are ignored. Reduce this to detect blobs with less
intensities.
overlap : float, optional
A value between 0 and 1. If the area of two blobs overlaps by a
fraction greater than `threshold`, the smaller blob is eliminated.
log_scale : bool, optional
If set intermediate values of standard deviations are interpolated
using a logarithmic scale to the base `10`. If not, linear
interpolation is used.
exclude_border : int or bool, optional
If nonzero int, `exclude_border` excludes blobs from
within `exclude_border`-pixels of the border of the image.
Returns
-------
A : (n, image.ndim + sigma) ndarray
A 2d array with each row representing 2 coordinate values for a 2D
image, and 3 coordinate values for a 3D image, plus the sigma(s) used.
When a single sigma is passed, outputs are:
``(r, c, sigma)`` or ``(p, r, c, sigma)`` where ``(r, c)`` or
``(p, r, c)`` are coordinates of the blob and ``sigma`` is the standard
deviation of the Gaussian kernel which detected the blob. When an
anisotropic gaussian is used (sigmas per dimension), the detected sigma
is returned for each dimension.
References
----------
.. [1] https://en.wikipedia.org/wiki/Blob_detection#The_Laplacian_of_Gaussian
Examples
--------
>>> from skimage import data, feature, exposure
>>> img = data.coins()
>>> img = exposure.equalize_hist(img) # improves detection
>>> feature.blob_log(img, threshold = .3)
array([[ 266. , 115. , 11.88888889],
[ 263. , 302. , 17.33333333],
[ 263. , 244. , 17.33333333],
[ 260. , 174. , 17.33333333],
[ 198. , 155. , 11.88888889],
[ 198. , 103. , 11.88888889],
[ 197. , 44. , 11.88888889],
[ 194. , 276. , 17.33333333],
[ 194. , 213. , 17.33333333],
[ 185. , 344. , 17.33333333],
[ 128. , 154. , 11.88888889],
[ 127. , 102. , 11.88888889],
[ 126. , 208. , 11.88888889],
[ 126. , 46. , 11.88888889],
[ 124. , 336. , 11.88888889],
[ 121. , 272. , 17.33333333],
[ 113. , 323. , 1. ]])
Notes
-----
The radius of each blob is approximately :math:`\sqrt{2}\sigma` for
a 2-D image and :math:`\sqrt{3}\sigma` for a 3-D image.
"""
image = img_as_float(image)
# if both min and max sigma are scalar, function returns only one sigma
scalar_sigma = (
True if np.isscalar(max_sigma) and np.isscalar(min_sigma) else False
)
# Gaussian filter requires that sequence-type sigmas have same
# dimensionality as image. This broadcasts scalar kernels
if np.isscalar(max_sigma):
max_sigma = np.full(image.ndim, max_sigma, dtype=float)
if np.isscalar(min_sigma):
min_sigma = np.full(image.ndim, min_sigma, dtype=float)
# Convert sequence types to array
min_sigma = np.asarray(min_sigma, dtype=float)
max_sigma = np.asarray(max_sigma, dtype=float)
if log_scale:
start, stop = np.log10(min_sigma)[:, None], np.log10(max_sigma)[:, None]
space = np.concatenate(
[start, stop, np.full_like(start, num_sigma)], axis=1)
sigma_list = np.stack([np.logspace(*s) for s in space], axis=1)
else:
scale = np.linspace(0, 1, num_sigma)[:, None]
sigma_list = scale * (max_sigma - min_sigma) + min_sigma
import time
start = time.time()
# computing gaussian laplace
# average s**2 provides scale invariance
gl_images = [-gaussian_laplace(image, s) * s ** 2
for s in np.mean(sigma_list, axis=1)]
image_cube = np.stack(gl_images, axis=-1)
# print("LoG filter took {}s".format(round(time.time() - start, 2)))
ret = list()
times = list()
for threshold in thresholds:
start = time.time()
local_maxima = peak_local_max(image_cube, threshold_abs=threshold,
footprint=np.ones((3,) * (image.ndim + 1)),
threshold_rel=0.0,
exclude_border=exclude_border)
# Catch no peaks
if local_maxima.size == 0:
ret.append(np.empty((0, 4)))
continue
# Convert local_maxima to float64
lm = local_maxima.astype(np.float64)
# translate final column of lm, which contains the index of the
# sigma that produced the maximum intensity value, into the sigma
sigmas_of_peaks = sigma_list[local_maxima[:, -1]]
if scalar_sigma:
# select one sigma column, keeping dimension
sigmas_of_peaks = sigmas_of_peaks[:, 0:1]
# Remove sigma index and replace with sigmas
lm = np.hstack([lm[:, :-1], sigmas_of_peaks])
ret.append(_prune_blobs(lm, overlap))
times.append(time.time() - start)
# print("Thresholds took on avg: {}s".format(round(np.average(np.array(times)),2)))
return np.asarray(ret)
# def blob_doh(image, min_sigma=1, max_sigma=30, num_sigma=10, threshold=0.01,
# overlap=.5, log_scale=False):
# """Finds blobs in the given grayscale image.
# Blobs are found using the Determinant of Hessian method [1]_. For each blob
# found, the method returns its coordinates and the standard deviation
# of the Gaussian Kernel used for the Hessian matrix whose determinant
# detected the blob. Determinant of Hessians is approximated using [2]_.
# Parameters
# ----------
# image : 2D ndarray
# Input grayscale image.Blobs can either be light on dark or vice versa.
# min_sigma : float, optional
# The minimum standard deviation for Gaussian Kernel used to compute
# Hessian matrix. Keep this low to detect smaller blobs.
# max_sigma : float, optional
# The maximum standard deviation for Gaussian Kernel used to compute
# Hessian matrix. Keep this high to detect larger blobs.
# num_sigma : int, optional
# The number of intermediate values of standard deviations to consider
# between `min_sigma` and `max_sigma`.
# threshold : float, optional.
# The absolute lower bound for scale space maxima. Local maxima smaller
# than thresh are ignored. Reduce this to detect less prominent blobs.
# overlap : float, optional
# A value between 0 and 1. If the area of two blobs overlaps by a
# fraction greater than `threshold`, the smaller blob is eliminated.
# log_scale : bool, optional
# If set intermediate values of standard deviations are interpolated
# using a logarithmic scale to the base `10`. If not, linear
# interpolation is used.
# Returns
# -------
# A : (n, 3) ndarray
# A 2d array with each row representing 3 values, ``(y,x,sigma)``
# where ``(y,x)`` are coordinates of the blob and ``sigma`` is the
# standard deviation of the Gaussian kernel of the Hessian Matrix whose
# determinant detected the blob.
# References
# ----------
# .. [1] https://en.wikipedia.org/wiki/Blob_detection#The_determinant_of_the_Hessian
# .. [2] Herbert Bay, Andreas Ess, Tinne Tuytelaars, Luc Van Gool,
# "SURF: Speeded Up Robust Features"
# ftp://ftp.vision.ee.ethz.ch/publications/articles/eth_biwi_00517.pdf
# Examples
# --------
# >>> from skimage import data, feature
# >>> img = data.coins()
# >>> feature.blob_doh(img)
# array([[ 270. , 363. , 30. ],
# [ 265. , 113. , 23.55555556],
# [ 262. , 243. , 23.55555556],
# [ 260. , 173. , 30. ],
# [ 197. , 153. , 20.33333333],
# [ 197. , 44. , 20.33333333],
# [ 195. , 100. , 23.55555556],
# [ 193. , 275. , 23.55555556],
# [ 192. , 212. , 23.55555556],
# [ 185. , 348. , 30. ],
# [ 156. , 302. , 30. ],
# [ 126. , 153. , 20.33333333],
# [ 126. , 101. , 20.33333333],
# [ 124. , 336. , 20.33333333],
# [ 123. , 205. , 20.33333333],
# [ 123. , 44. , 23.55555556],
# [ 121. , 271. , 30. ]])
# Notes
# -----
# The radius of each blob is approximately `sigma`.
# Computation of Determinant of Hessians is independent of the standard
# deviation. Therefore detecting larger blobs won't take more time. In
# methods line :py:meth:`blob_dog` and :py:meth:`blob_log` the computation
# of Gaussians for larger `sigma` takes more time. The downside is that
# this method can't be used for detecting blobs of radius less than `3px`
# due to the box filters used in the approximation of Hessian Determinant.
# """
# assert_nD(image, 2)
# image = img_as_float(image)
# image = integral_image(image)
# if log_scale:
# start, stop = log(min_sigma, 10), log(max_sigma, 10)
# sigma_list = np.logspace(start, stop, num_sigma)
# else:
# sigma_list = np.linspace(min_sigma, max_sigma, num_sigma)
# hessian_images = [_hessian_matrix_det(image, s) for s in sigma_list]
# image_cube = np.dstack(hessian_images)
# local_maxima = peak_local_max(image_cube, threshold_abs=threshold,
# footprint=np.ones((3,) * image_cube.ndim),
# threshold_rel=0.0,
# exclude_border=False)
# # Catch no peaks
# if local_maxima.size == 0:
# return np.empty((0, 3))
# # Convert local_maxima to float64
# lm = local_maxima.astype(np.float64)
# # Convert the last index to its corresponding scale value
# lm[:, -1] = sigma_list[local_maxima[:, -1]]
# return _prune_blobs(lm, overlap)
def _update_spline_plot(self):
"""Update the spline plot."""
knots = np.mgrid[0:1:((self.num_internal_knots + 2) * 1j)][1:-1]
medial_repr = self.current_object.aligned_version.medial_repr
dependent_variable = np.mgrid[0:1:(medial_repr.length * 1j)]
laplacian = ndimage.gaussian_laplace(medial_repr.width_curve,
self.smoothing,
mode='constant',
cval=np.nan)
m_spline = LSQUnivariateSpline(dependent_variable,
medial_repr.medial_axis, knots)
w_spline = LSQUnivariateSpline(dependent_variable,
medial_repr.width_curve, knots)
# sample at double the frequency
spl_dep_var = np.mgrid[0:1:(medial_repr.length * 2j)]
plots = self.plots
if plots is None:
# Render the plot for the first time.
plotdata = ArrayPlotData(
medial_x=dependent_variable,
medial_y=medial_repr.medial_axis,
width_x=dependent_variable,
width_y=medial_repr.width_curve,
medial_spline_x=spl_dep_var,
medial_spline_y=m_spline(spl_dep_var),
width_spline_x=spl_dep_var,
width_spline_y=w_spline(spl_dep_var),
laplacian_y=laplacian,
)
plot = Plot(plotdata)
# Width data
self._width_data_renderer, = plot.plot(
("width_x", "width_y"),
type="line",
color="blue",
name="Original width curve data")
filterdata = ArrayPlotData(x=dependent_variable,
laplacian=laplacian)
filterplot = Plot(filterdata)
self._laplacian_renderer, = filterplot.plot(
("x", "laplacian"),
type="line",
color="black",
name="Laplacian-of-Gaussian")
# Titles for plot & axes
plot.title = "Width curves"
plot.x_axis.title = "Normalized position on medial axis"
plot.y_axis.title = "Fraction of medial axis width"
# Legend mangling stuff
legend = plot.legend
plot.legend = None
legend.set(component=None,
visible=True,
resizable="",
auto_size=True,
bounds=[250, 70],
padding_top=plot.padding_top)
filterlegend = filterplot.legend
filterplot.legend = None
filterlegend.set(component=None,
visible=True,
resizable="",
auto_size=True,
bounds=[250, 50],
padding_top=filterplot.padding_top)
self.plots = GridPlotContainer(plot,
legend,
filterplot,
filterlegend,
shape=(2, 2),
valign="top",
bgcolor="transparent")
else:
# Update the real width curve
self._width_data_renderer.index.set_data(dependent_variable)
self._width_data_renderer.value.set_data(medial_repr.width_curve)
# Render the Laplacian
self._laplacian_renderer.index.set_data(dependent_variable)
self._laplacian_renderer.value.set_data(laplacian)
def extract_data(img_rgb,laplacians,df,dir_input):
image_gray = rgb2gray(img_rgb)
img_red = img_rgb[:,:,0]
img_red = gaussian(img_red, 3)
s = 1.5/sqrt(2)
img_log = ndimage.gaussian_laplace(img_red, s) * s ** 2
img_log = np.float32(img_log)
t_scales,row,col = laplacians.shape
training_set = np.array([])
#fig, ax = plt.subplots(nrows=1, ncols=1)
#ax.set_title("RGB Image")
#ax.imshow(image_gray,interpolation='nearest')
for index,rows in df.iterrows():
x = rows['x']#col
y = rows['y']#fila
w = rows['w']
h = rows['h']
label = rows['label']
mask = np.zeros(image_gray.shape)
if label == 'juvenil':
r = w/2 if w < h else h/2
s = np.linspace(0, 2*np.pi, 400)
px = x + r + r*np.cos(s)
py = y + r + r*np.sin(s)
init = np.array([px, py]).T
snake = segmentation.active_contour(img_log,init,alpha=0.5, beta=0.5,w_line=-0.4)
px,py = snake[:,0].astype(int),snake[:,1].astype(int)
#ax.plot(snake[:, 0], snake[:, 1], '-r', lw=3)
#plt.show()
else:
px = [ i for i in range(x,x+w)]
py = [ i for i in range(y,y+h)]
"""
py = [ i for i in range(x,x+w)]
px = [ i for i in range(y,y+h)]
"""
positions = []
for x,y in itertools.product(px,py):
positions.append([x,y])
positions = np.array(positions)
px,py = positions[:,0],positions[:,1]
features = np.array([])
region = laplacians[:,py,px]
for scale in range(t_scales):
mean = np.mean(region[scale,:])
std = np.std(region[scale,:])
data = np.array([mean,std])
features = np.vstack([features, data ]) if features.size else data
r = img_rgb[py,px,0]
g = img_rgb[py,px,1]
b = img_rgb[py,px,2]
color = np.mean(image_gray[py,px])
r_color = np.mean(r)
g_color = np.mean(g)
b_color = np.mean(b)
features = np.insert(features,0,color)
features = np.insert(features,1,r_color)
features = np.insert(features,2,g_color)
features = np.insert(features,3,b_color)
training_set = np.vstack([training_set, features ]) if training_set.size else features
attr = ["color","r","g","b"]
for i in range(t_scales):
attr.append("mean_" + str(i))
attr.append("std_" + str(i))
df_training = pd.DataFrame(data = training_set)
df_training['label'] = df['label'].values
filename = dir_input.split('.tif')[0]
df_training.to_csv(filename + "_training_set.csv",index=False)
def extract_features(image_number, slice_number):
# extracts features for [image_number]_[slice_number]_t1.tif and [image_number]_[slice_number]_t2.tif
# Input:
# image_number - Which subject (scalar)
# slice_number - Which slice (scalar)
# Output:
# X - N x k dataset, where N is the number of pixels and k is the total number of features
# features - k x 1 cell array describing each of the k features
base_dir = '../data/dataset_brains/'
t1 = plt.imread(base_dir + str(image_number) + '_' + str(slice_number) +
'_t1.tif')
t2 = plt.imread(base_dir + str(image_number) + '_' + str(slice_number) +
'_t2.tif')
fig = plt.figure(figsize=(10, 10))
ax1 = fig.add_subplot(181)
ax1.imshow(t1)
ax2 = fig.add_subplot(182)
ax2.imshow(t2)
n = t1.shape[0]
features = ()
#display image
t1f = t1.flatten().T.astype(float)
t1f = t1f.reshape(-1, 1)
t2f = t2.flatten().T.astype(float)
t2f = t2f.reshape(-1, 1)
X = np.concatenate((t1f, t2f), axis=1)
features += ('T1 intensity', )
features += ('T2 intensity', )
#------------------------------------------------------------------#
# TODO: Extract more features and add them to X.
# Don't forget to provide (short) descriptions for the features
#add blurred images to features
t1b = ndimage.gaussian_filter(t1, sigma=2) #blurred t1
t2b = ndimage.gaussian_filter(t2, sigma=2) #blurred t2
#display altered images
ax3 = fig.add_subplot(183)
ax3.imshow(t1b)
ax4 = fig.add_subplot(184)
ax4.imshow(t2b)
t1b = t1b.flatten().T.astype(float)
t1b = t1b.reshape(-1, 1)
t2b = t2b.flatten().T.astype(float)
t2b = t2b.reshape(-1, 1)
X = np.concatenate((X, t1b), axis=1)
X = np.concatenate((X, t2b), axis=1)
features += ('T1 blurred intensity', )
features += ('T2 blurred intensity', )
#minimum filter
t1min = ndimage.minimum_filter(t1, size=5)
t2min = ndimage.minimum_filter(t2, size=5)
ax5 = fig.add_subplot(185)
ax5.imshow(t1min)
ax6 = fig.add_subplot(186)
ax6.imshow(t2min)
t1min = t1min.flatten().T.astype(float)
t1min = t1min.reshape(-1, 1)
t2min = t2min.flatten().T.astype(float)
t2min = t2min.reshape(-1, 1)
X = np.concatenate((X, t1min), axis=1)
X = np.concatenate((X, t2min), axis=1)
features += ('T1 minimum intensity', )
features += ('T2 minimum intensity', )
#maximum filter
t1max = ndimage.maximum_filter(t1, size=5)
t2max = ndimage.maximum_filter(t2, size=5)
ax7 = fig.add_subplot(187)
ax7.imshow(t1max)
ax8 = fig.add_subplot(188)
ax8.imshow(t2max)
t1max = t1max.flatten().T.astype(float)
t1max = t1max.reshape(-1, 1)
t2max = t2max.flatten().T.astype(float)
t2max = t2max.reshape(-1, 1)
X = np.concatenate((X, t1max), axis=1)
X = np.concatenate((X, t2max), axis=1)
features += ('T1 maximum intensity', )
features += ('T2 maximum intensity', )
#gaussian gradient magnitude
t1_ggm = ndimage.gaussian_gradient_magnitude(t1, sigma=2)
t2_ggm = ndimage.gaussian_gradient_magnitude(t2, sigma=2)
fig1 = plt.figure(figsize=(10, 10))
ax9 = fig1.add_subplot(181)
ax9.imshow(t1_ggm)
ax10 = fig1.add_subplot(182)
ax10.imshow(t2_ggm)
t1_ggm = t1_ggm.flatten().T.astype(float)
t1_ggm = t1_ggm.reshape(-1, 1)
t2_ggm = t2_ggm.flatten().T.astype(float)
t2_ggm = t2_ggm.reshape(-1, 1)
X = np.concatenate((X, t1_ggm), axis=1)
X = np.concatenate((X, t2_ggm), axis=1)
features += ('T1 gaussian gradient magnitude intensity', )
features += ('T2 gaussian gradient magnitude intensity', )
#gaussian la place (second derivative)
t1_glp = ndimage.gaussian_laplace(t1, sigma=1)
t2_glp = ndimage.gaussian_laplace(t2, sigma=1)
ax11 = fig1.add_subplot(183)
ax11.imshow(t1_glp)
ax12 = fig1.add_subplot(184)
ax12.imshow(t2_glp)
t1_glp = t1_glp.flatten().T.astype(float)
t1_glp = t1_glp.reshape(-1, 1)
t2_glp = t2_glp.flatten().T.astype(float)
t2_glp = t2_glp.reshape(-1, 1)
X = np.concatenate((X, t1_glp), axis=1)
X = np.concatenate((X, t2_glp), axis=1)
features += ('T1 gaussian la place intensity', )
features += ('T2 gaussian la place intensity', )
#median filter (smoothning filter)
t1_median = ndimage.median_filter(t1, size=5)
t2_median = ndimage.median_filter(t2, size=5)
ax13 = fig1.add_subplot(185)
ax13.imshow(t1_median)
ax14 = fig1.add_subplot(186)
ax14.imshow(t2_median)
t1_median = t1_median.flatten().T.astype(float)
t1_median = t1_median.reshape(-1, 1)
t2_median = t2_median.flatten().T.astype(float)
t2_median = t2_median.reshape(-1, 1)
X = np.concatenate((X, t1_median), axis=1)
X = np.concatenate((X, t2_median), axis=1)
features += ('T1 median intensity', )
features += ('T2 median intensity', )
#sobel filter (edge detection, derivative filter, horizontal/vertical lines, some smoothning)
t1_sobel = ndimage.sobel(t1)
t2_sobel = ndimage.sobel(t2)
ax15 = fig1.add_subplot(187)
ax15.imshow(t1_sobel)
ax16 = fig1.add_subplot(188)
ax16.imshow(t2_sobel)
t1_sobel = t1_sobel.flatten().T.astype(float)
t1_sobel = t1_sobel.reshape(-1, 1)
t2_sobel = t2_sobel.flatten().T.astype(float)
t2_sobel = t2_sobel.reshape(-1, 1)
X = np.concatenate((X, t1_sobel), axis=1)
X = np.concatenate((X, t2_sobel), axis=1)
features += ('T1 sobel intensity', )
features += ('T2 sobel intensity', )
# rank filter (smoothning filter)
t1_rank = ndimage.rank_filter(t1, rank=30, size=10)
t2_rank = ndimage.rank_filter(t2, rank=30, size=10)
fig2 = plt.figure(figsize=(10, 10))
ax17 = fig2.add_subplot(181)
ax17.imshow(t1_rank)
ax18 = fig2.add_subplot(182)
ax18.imshow(t2_rank)
t1_rank = t1_rank.flatten().T.astype(float)
t1_rank = t1_rank.reshape(-1, 1)
t2_rank = t2_rank.flatten().T.astype(float)
t2_rank = t2_rank.reshape(-1, 1)
X = np.concatenate((X, t1_sobel), axis=1)
X = np.concatenate((X, t2_sobel), axis=1)
features += ('T1 rank intensity', )
features += ('T2 rank intensity', )
# prewitt filter (edge detection, derivative filter, horizontal/vertical lines, some smoothning)
# looks like sobel filter but has different kernel, only horizonal or vertical lines
t1_prewitt = ndimage.prewitt(t1)
t2_prewitt = ndimage.prewitt(t2)
ax19 = fig2.add_subplot(183)
ax19.imshow(t1_prewitt)
ax20 = fig2.add_subplot(184)
ax20.imshow(t2_prewitt)
t1_prewitt = t1_prewitt.flatten().T.astype(float)
t1_prewitt = t1_prewitt.reshape(-1, 1)
t2_prewitt = t2_prewitt.flatten().T.astype(float)
t2_prewitt = t2_prewitt.reshape(-1, 1)
X = np.concatenate((X, t1_prewitt), axis=1)
X = np.concatenate((X, t2_prewitt), axis=1)
features += ('T1 prewitt intensity', )
features += ('T2 prewitt intensity', )
#------------------------------------------------------------------#
return X, features
def enhance_edges(img, HPASS, NUCRAD):
img = simple_highpassfilter(img.astype(np.float64), HPASS)
lapgauss_img = -gaussian_laplace(img.astype(np.float32), NUCRAD / 3) * (
NUCRAD / 3)**2
edge = -lapgauss_img
return edge
def blob_log(image,
sigma_list=None,
scale_choice='linear',
min_sigma=1,
max_sigma=50,
num_sigma=10,
threshold=.2,
overlap=.5,
sigma_ratio=2.,
weighting=None,
merge_overlap_dist=1.0,
refine_shape=False,
use_max_response=False):
"""Finds blobs in the given grayscale image.
Blobs are found using the Laplacian of Gaussian (LoG) method [1]_.
For each blob found, the method returns its coordinates and the standard
deviation of the Gaussian kernel that detected the blob.
Parameters
----------
image : ndarray
Input grayscale image, blobs are assumed to be light on dark
background (white on black).
sigma_list : np.ndarray, optional
Provide the list of sigmas to use.
scale_choice : str, optional
'log', 'linear' or 'ratio'. Determines how the scales are calculated
based on the given number, minimum, maximum, or ratio.
min_sigma : float, optional
The minimum standard deviation for Gaussian Kernel. Keep this low to
detect smaller blobs.
max_sigma : float, optional
The maximum standard deviation for Gaussian Kernel. Keep this high to
detect larger blobs.
num_sigma : int, optional
The number of intermediate values of standard deviations to consider
between `min_sigma` and `max_sigma`.
threshold : float, optional.
The absolute lower bound for scale space maxima. Local maxima smaller
than thresh are ignored. Reduce this to detect blobs with less
intensities.
overlap : float, optional
A value between 0 and 1. If the area of two blobs overlaps by a
fraction greater than `threshold`, the smaller blob is eliminated.
weighting : np.ndarray, optional
Used to weight certain scales differently when selecting local maxima
in the transform space. For example when searching for regions near
the beam size, the transform can be down-weighted to avoid spurious
detections. Must have the same number of elements as the scales.
merge_overlap_dist : float, optional
Controls the minimum overlap regions must have to be merged together.
Defaults to one sigma separation, where sigma is one of the scales
used in the transform.
Returns
-------
A : (n, 5) ndarray
A 2d array with each row representing 5 values,
``(y, x, semi-major sigma, semi-minor sigma, pa)``
where ``(y,x)`` are coordinates of the blob, ``semi-major sigma`` and
``semi-minor sigma`` are the standard deviations of the elliptical blob,
and ``pa`` is its position angle.
References
----------
.. [1] http://en.wikipedia.org/wiki/Blob_detection#The_Laplacian_of_Gaussian
Examples
--------
>>> from skimage import data, feature, exposure
>>> img = data.coins()
>>> img = exposure.equalize_hist(img) # improves detection
>>> feature.blob_log(img, threshold = .3)
array([[ 113. , 323. , 1. ],
[ 121. , 272. , 17.33333333],
[ 124. , 336. , 11.88888889],
[ 126. , 46. , 11.88888889],
[ 126. , 208. , 11.88888889],
[ 127. , 102. , 11.88888889],
[ 128. , 154. , 11.88888889],
[ 185. , 344. , 17.33333333],
[ 194. , 213. , 17.33333333],
[ 194. , 276. , 17.33333333],
[ 197. , 44. , 11.88888889],
[ 198. , 103. , 11.88888889],
[ 198. , 155. , 11.88888889],
[ 260. , 174. , 17.33333333],
[ 263. , 244. , 17.33333333],
[ 263. , 302. , 17.33333333],
[ 266. , 115. , 11.88888889]])
Notes
-----
The radius of each blob is approximately :math:`\sqrt{2}sigma`.
"""
# assert_nD(image, 2)
image = img_as_float(image)
if sigma_list is None:
if scale_choice is 'log':
start, stop = log(min_sigma, 10), log(max_sigma, 10)
sigma_list = np.logspace(start, stop, num_sigma)
elif scale_choice is 'linear':
sigma_list = np.linspace(min_sigma, max_sigma, num_sigma)
elif scale_choice is 'ratio':
# k such that min_sigma*(sigma_ratio**k) > max_sigma
k = int(log(float(max_sigma) / min_sigma, sigma_ratio)) + 1
sigma_list = np.array(
[min_sigma * (sigma_ratio**i) for i in range(k)])
else:
raise ValueError("scale_choice must be 'log', 'linear', or "
"'ratio'.")
if weighting is not None:
if len(weighting) != len(sigma_list):
raise IndexError("weighting must have the same number of elements"
" as scales (" + str(len(sigma_list)) + ").")
else:
weighting = np.ones_like(sigma_list)
# computing gaussian laplace
# s**2 provides scale invariance
# weighting by w changes the relative importance of each transform scale
gl_images = [
gaussian_laplace(image, s) * s**2 * w
for s, w in zip(sigma_list, weighting)
]
image_cube = np.dstack(gl_images)
if use_max_response:
scale_peaks = \
peak_local_max(image_cube.max(2),
threshold_abs=threshold,
threshold_rel=0.0,
min_distance=0.5 * np.sqrt(2) * sigma_list[0],
exclude_border=False)
argmaxes = image_cube.argmax(2)[scale_peaks[:, 0], scale_peaks[:, 1]]
radii = np.array([sigma_list[arg] for arg in argmaxes]) * np.sqrt(2)
radii = radii[:, np.newaxis]
responses = image_cube.max(2)[scale_peaks[:, 0], scale_peaks[:, 1]]
responses = responses[:, np.newaxis]
pas = np.zeros_like(radii)
local_maxima = np.hstack([scale_peaks, radii, radii, pas, responses])
else:
for i, scale in enumerate(sigma_list):
scale_peaks = peak_local_max(image_cube[:, :, i],
threshold_abs=threshold,
min_distance=np.sqrt(2) * scale,
threshold_rel=0.0,
exclude_border=False)
new_scale_peaks = np.empty((len(scale_peaks), 5))
if refine_shape:
for j, peak in enumerate(scale_peaks):
new_peak = np.array([peak[0], peak[1], scale, scale, 0.0])
new_scale_peaks[j] = \
shape_from_blob_moments(new_peak, image_cube[:, :, i])
else:
new_scale_peaks[:, :2] = scale_peaks
# sqrt(2) size correction
new_scale_peaks[:, 2:4] = np.sqrt(2) * scale
new_scale_peaks[:, 4] = 0.0
vals = \
np.array([image_cube[pos[0], pos[1], i]
for pos in scale_peaks]).reshape((len(scale_peaks),
1))
new_scale_peaks = np.hstack([new_scale_peaks, vals])
if i == 0:
local_maxima = new_scale_peaks
else:
local_maxima = np.vstack([local_maxima, new_scale_peaks])
if local_maxima.size == 0:
return local_maxima
# Merge regions into ellipses
local_maxima = _merge_blobs(local_maxima, merge_overlap_dist)
# Then prune and return them\
return local_maxima
def laplacian_of_gaussian(img, sig):
return nd.gaussian_laplace(img, sigma=sig)
def run(self, ips, snap, img, para=None):
nimg.gaussian_laplace(snap, para['sigma'], output=img)
img *= -1
if para['uniform']:
np.add(img, np.mean(ips.range), out=img, casting='unsafe')
def blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=.2,
overlap=.5, log_scale=False):
"""Finds blobs in the given grayscale image.
Blobs are found using the Laplacian of Gaussian (LoG) method [1]_.
For each blob found, the method returns its coordinates and the standard
deviation of the Gaussian kernel that detected the blob.
Parameters
----------
image : 2D or 3D ndarray
Input grayscale image, blobs are assumed to be light on dark
background (white on black).
min_sigma : float, optional
The minimum standard deviation for Gaussian Kernel. Keep this low to
detect smaller blobs.
max_sigma : float, optional
The maximum standard deviation for Gaussian Kernel. Keep this high to
detect larger blobs.
num_sigma : int, optional
The number of intermediate values of standard deviations to consider
between `min_sigma` and `max_sigma`.
threshold : float, optional.
The absolute lower bound for scale space maxima. Local maxima smaller
than thresh are ignored. Reduce this to detect blobs with less
intensities.
overlap : float, optional
A value between 0 and 1. If the area of two blobs overlaps by a
fraction greater than `threshold`, the smaller blob is eliminated.
log_scale : bool, optional
If set intermediate values of standard deviations are interpolated
using a logarithmic scale to the base `10`. If not, linear
interpolation is used.
Returns
-------
A : (n, image.ndim + 1) ndarray
A 2d array with each row representing 3 values for a 2D image,
and 4 values for a 3D image: ``(r, c, sigma)`` or ``(p, r, c, sigma)``
where ``(r, c)`` or ``(p, r, c)`` are coordinates of the blob and
``sigma`` is the standard deviation of the Gaussian kernel which
detected the blob.
References
----------
.. [1] http://en.wikipedia.org/wiki/Blob_detection#The_Laplacian_of_Gaussian
Examples
--------
>>> from skimage import data, feature, exposure
>>> img = data.coins()
>>> img = exposure.equalize_hist(img) # improves detection
>>> feature.blob_log(img, threshold = .3)
array([[ 266. , 115. , 11.88888889],
[ 263. , 302. , 17.33333333],
[ 263. , 244. , 17.33333333],
[ 260. , 174. , 17.33333333],
[ 198. , 155. , 11.88888889],
[ 198. , 103. , 11.88888889],
[ 197. , 44. , 11.88888889],
[ 194. , 276. , 17.33333333],
[ 194. , 213. , 17.33333333],
[ 185. , 344. , 17.33333333],
[ 128. , 154. , 11.88888889],
[ 127. , 102. , 11.88888889],
[ 126. , 208. , 11.88888889],
[ 126. , 46. , 11.88888889],
[ 124. , 336. , 11.88888889],
[ 121. , 272. , 17.33333333],
[ 113. , 323. , 1. ]])
Notes
-----
The radius of each blob is approximately :math:`\sqrt{2}\sigma` for
a 2-D image and :math:`\sqrt{3}\sigma` for a 3-D image.
"""
image = img_as_float(image)
if log_scale:
start, stop = log(min_sigma, 10), log(max_sigma, 10)
sigma_list = np.logspace(start, stop, num_sigma)
else:
sigma_list = np.linspace(min_sigma, max_sigma, num_sigma)
# computing gaussian laplace
# s**2 provides scale invariance
gl_images = [-gaussian_laplace(image, s) * s ** 2 for s in sigma_list]
image_cube = np.stack(gl_images, axis=-1)
local_maxima = peak_local_max(image_cube, threshold_abs=threshold,
footprint=np.ones((3,) * (image.ndim + 1)),
threshold_rel=0.0,
exclude_border=False)
# Catch no peaks
if local_maxima.size == 0:
return np.empty((0, 3))
# Convert local_maxima to float64
lm = local_maxima.astype(np.float64)
# Convert the last index to its corresponding scale value
lm[:, -1] = sigma_list[local_maxima[:, -1]]
return _prune_blobs(lm, overlap)
# from skimage.external import tifffile
# tif_path = r"C:\Users\Administrator\Desktop\realData\ins_gt(1).tif"
# a = tifffile.imread(tif_path)
# a = np.zeros((10, 10, 10))
# a[3] = 1
# a[2:9, 3:5, 4:6] = 1
# a = np.zeros((100, 100))
# a[23:, 78:81] = 1
# a[13:, 23:28] = 1
distance, ind = ndimage.distance_transform_edt(a, return_indices=True)
df = direct_field3D(a)
gl0 = ndimage.gaussian_laplace(df[0, ...], sigma=1)
gl1 = ndimage.gaussian_laplace(df[1, ...], sigma=1)
sq = np.abs(gl0) + np.abs(gl1)
import matplotlib.pyplot as plt
gl0_ = (gl0 < -0.1).astype(int)
gl1_ = (gl1 < -0.1).astype(int)
gl_sq = (sq > 0.4).astype(int)
# plt.imshow(np.stack([gl0_, gl1_]), cmap=plt.cm.gray)
_, axs = plt.subplots(1, 3)
axs[0].imshow(gl0_)
axs[1].imshow(gl1_)
axs[2].imshow(gl_sq)
plt.show()
def blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=.2,
overlap=.5, log_scale=False, *, exclude_border=False):
r"""Finds blobs in the given grayscale image.
Blobs are found using the Laplacian of Gaussian (LoG) method [1]_.
For each blob found, the method returns its coordinates and the standard
deviation of the Gaussian kernel that detected the blob.
Parameters
----------
image : 2D or 3D ndarray
Input grayscale image, blobs are assumed to be light on dark
background (white on black).
min_sigma : scalar or sequence of scalars, optional
the minimum standard deviation for Gaussian kernel. Keep this low to
detect smaller blobs. The standard deviations of the Gaussian filter
are given for each axis as a sequence, or as a single number, in
which case it is equal for all axes.
max_sigma : scalar or sequence of scalars, optional
The maximum standard deviation for Gaussian kernel. Keep this high to
detect larger blobs. The standard deviations of the Gaussian filter
are given for each axis as a sequence, or as a single number, in
which case it is equal for all axes.
num_sigma : int, optional
The number of intermediate values of standard deviations to consider
between `min_sigma` and `max_sigma`.
threshold : float, optional.
The absolute lower bound for scale space maxima. Local maxima smaller
than thresh are ignored. Reduce this to detect blobs with less
intensities.
overlap : float, optional
A value between 0 and 1. If the area of two blobs overlaps by a
fraction greater than `threshold`, the smaller blob is eliminated.
log_scale : bool, optional
If set intermediate values of standard deviations are interpolated
using a logarithmic scale to the base `10`. If not, linear
interpolation is used.
exclude_border : int or bool, optional
If nonzero int, `exclude_border` excludes blobs from
within `exclude_border`-pixels of the border of the image.
Returns
-------
A : (n, image.ndim + sigma) ndarray
A 2d array with each row representing 2 coordinate values for a 2D
image, and 3 coordinate values for a 3D image, plus the sigma(s) used.
When a single sigma is passed, outputs are:
``(r, c, sigma)`` or ``(p, r, c, sigma)`` where ``(r, c)`` or
``(p, r, c)`` are coordinates of the blob and ``sigma`` is the standard
deviation of the Gaussian kernel which detected the blob. When an
anisotropic gaussian is used (sigmas per dimension), the detected sigma
is returned for each dimension.
References
----------
.. [1] https://en.wikipedia.org/wiki/Blob_detection#The_Laplacian_of_Gaussian
Examples
--------
>>> from skimage import data, feature, exposure
>>> img = data.coins()
>>> img = exposure.equalize_hist(img) # improves detection
>>> feature.blob_log(img, threshold = .3)
array([[ 266. , 115. , 11.88888889],
[ 263. , 302. , 17.33333333],
[ 263. , 244. , 17.33333333],
[ 260. , 174. , 17.33333333],
[ 198. , 155. , 11.88888889],
[ 198. , 103. , 11.88888889],
[ 197. , 44. , 11.88888889],
[ 194. , 276. , 17.33333333],
[ 194. , 213. , 17.33333333],
[ 185. , 344. , 17.33333333],
[ 128. , 154. , 11.88888889],
[ 127. , 102. , 11.88888889],
[ 126. , 208. , 11.88888889],
[ 126. , 46. , 11.88888889],
[ 124. , 336. , 11.88888889],
[ 121. , 272. , 17.33333333],
[ 113. , 323. , 1. ]])
Notes
-----
The radius of each blob is approximately :math:`\sqrt{2}\sigma` for
a 2-D image and :math:`\sqrt{3}\sigma` for a 3-D image.
"""
image = img_as_float(image)
# if both min and max sigma are scalar, function returns only one sigma
scalar_sigma = (
True if np.isscalar(max_sigma) and np.isscalar(min_sigma) else False
)
# Gaussian filter requires that sequence-type sigmas have same
# dimensionality as image. This broadcasts scalar kernels
if np.isscalar(max_sigma):
max_sigma = np.full(image.ndim, max_sigma, dtype=float)
if np.isscalar(min_sigma):
min_sigma = np.full(image.ndim, min_sigma, dtype=float)
# Convert sequence types to array
min_sigma = np.asarray(min_sigma, dtype=float)
max_sigma = np.asarray(max_sigma, dtype=float)
if log_scale:
start, stop = np.log10(min_sigma)[:, None], np.log10(max_sigma)[:, None]
space = np.concatenate(
[start, stop, np.full_like(start, num_sigma)], axis=1)
sigma_list = np.stack([np.logspace(*s) for s in space], axis=1)
else:
scale = np.linspace(0, 1, num_sigma)[:, None]
sigma_list = scale * (max_sigma - min_sigma) + min_sigma
# computing gaussian laplace
# average s**2 provides scale invariance
gl_images = [-gaussian_laplace(image, s) * s ** 2
for s in np.mean(sigma_list, axis=1)]
image_cube = np.stack(gl_images, axis=-1)
local_maxima = peak_local_max(image_cube, threshold_abs=threshold,
footprint=np.ones((3,) * (image.ndim + 1)),
threshold_rel=0.0,
exclude_border=exclude_border)
# Catch no peaks
if local_maxima.size == 0:
return np.empty((0, 3))
# Convert local_maxima to float64
lm = local_maxima.astype(np.float64)
# translate final column of lm, which contains the index of the
# sigma that produced the maximum intensity value, into the sigma
sigmas_of_peaks = sigma_list[local_maxima[:, -1]]
if scalar_sigma:
# select one sigma column, keeping dimension
sigmas_of_peaks = sigmas_of_peaks[:, 0:1]
# Remove sigma index and replace with sigmas
lm = np.hstack([lm[:, :-1], sigmas_of_peaks])
return _prune_blobs(lm, overlap)
def gaussianLaplaceBlobDetection(originalImage): originalImageArray = numpy.asarray(originalImage) blobDetectedImageArray = ndimage.gaussian_laplace(originalImageArray,sigma=2) blobDetectedImage = Image.fromarray(blobDetectedImageArray) return blobDetectedImage
def _update_spline_plot(self):
"""Update the spline plot."""
knots = np.mgrid[0:1:((self.num_internal_knots + 2)*1j)][1:-1]
medial_repr = self.current_object.aligned_version.medial_repr
dependent_variable = np.mgrid[0:1:(medial_repr.length * 1j)]
laplacian = ndimage.gaussian_laplace(medial_repr.width_curve,
self.smoothing, mode='constant', cval=np.nan)
m_spline = LSQUnivariateSpline(dependent_variable,
medial_repr.medial_axis, knots)
w_spline = LSQUnivariateSpline(dependent_variable,
medial_repr.width_curve, knots)
# sample at double the frequency
spl_dep_var = np.mgrid[0:1:(medial_repr.length * 2j)]
plots = self.plots
if plots is None:
# Render the plot for the first time.
plotdata = ArrayPlotData(medial_x=dependent_variable,
medial_y=medial_repr.medial_axis,
width_x=dependent_variable,
width_y=medial_repr.width_curve,
medial_spline_x=spl_dep_var,
medial_spline_y=m_spline(spl_dep_var),
width_spline_x=spl_dep_var,
width_spline_y=w_spline(spl_dep_var),
laplacian_y=laplacian,
)
plot = Plot(plotdata)
# Width data
self._width_data_renderer, = plot.plot(("width_x", "width_y"),
type="line", color="blue", name="Original width curve data")
filterdata = ArrayPlotData(
x=dependent_variable,
laplacian=laplacian
)
filterplot = Plot(filterdata)
self._laplacian_renderer, = filterplot.plot(("x",
"laplacian"), type="line", color="black",
name="Laplacian-of-Gaussian")
# Titles for plot & axes
plot.title = "Width curves"
plot.x_axis.title = "Normalized position on medial axis"
plot.y_axis.title = "Fraction of medial axis width"
# Legend mangling stuff
legend = plot.legend
plot.legend = None
legend.set(
component = None,
visible = True,
resizable = "",
auto_size=True,
bounds = [250, 70],
padding_top = plot.padding_top)
filterlegend = filterplot.legend
filterplot.legend = None
filterlegend.set(
component = None,
visible = True,
resizable = "",
auto_size=True,
bounds = [250, 50],
padding_top = filterplot.padding_top)
self.plots = GridPlotContainer(plot, legend, filterplot,
filterlegend, shape=(2,2),
valign="top", bgcolor="transparent")
else:
# Update the real width curve
self._width_data_renderer.index.set_data(dependent_variable)
self._width_data_renderer.value.set_data(medial_repr.width_curve)
# Render the Laplacian
self._laplacian_renderer.index.set_data(dependent_variable)
self._laplacian_renderer.value.set_data(laplacian)
def gaussian_Laplace1(left):
return ndimage.gaussian_laplace(left,sigma=1)
def find_peaks(img, band_shape=(3, 25), show_images=False, save_images=False):
"""
:param img:
:param band_shape: (height, width) akak (y, x)
:param show_images:
:param save_images:
:return:
"""
# Fixed: peaks are shifted, probably because opening() makes a shift from the structuring element.
# Edit, no it is the convolution that does it...
#
img_org = img
img = img.astype('f') # cast to float, otherwise all calculations become inaccurate
images = []
band_selem = np.ones((3, 29))
ploti = 0 # start at zero, and show_image will deal with it.
if show_images:
ploti = show_image(img, title="original", plotidx=ploti)
title = "original"
descr = "img" # aka "history"
images.append((img, title, descr))
# #
# # opening - small:
# title, descr = "opening-3x21", "opening(%s)" % descr
# print(title)
# opened = opening(img, selem=np.ones((3, 21)))
# # images.append((img, title, descr))
# if show_images:
# ploti = show_image(opened, title=title, plotidx=ploti)
#
# #
# # opening - medium:
# title, descr = "opening-3x25", "opening(%s)" % descr
# print(title)
# opened = opening(img, selem=np.ones((3, 25)))
# # images.append((img, title, descr))
# if show_images:
# ploti = show_image(opened, title=title, plotidx=ploti)
#
#
# opening - larger:
title, descr = "opening-3x23", "opening(%s)" % descr
print(title)
img = opened1 = opening(img, selem=np.ones((3, 23)))
images.append((img, title, descr))
if show_images:
ploti = show_image(img, title=title, plotidx=ploti)
#
# subtract global percentile:
title = "subtract_global_percentile"
descr = "%s(%s)" % (title, descr)
print(title)
img = minus_global_pct_bg = subtract_global_percentile(img, percentile=30)
images.append((img, title, descr))
if show_images:
ploti = show_image(img, title=title, plotidx=ploti)
#
# rolling-minimum background subtraction with large ellipse:
title = "rolling_5percentile_bg_el"
descr = "%s(%s)" % (title, descr)
print(title)
rol_min_el = rolling_minimum_background(gaussian_filter(img, sigma=2), percentile=5,
size=(71, 71), # height, width (should be odd integers)
geometry='ellipse')
if show_images:
ploti = show_image(rol_min_el, title=title, plotidx=ploti, clim_percentile=99.9)
# subtract the background:
title = "minus-rol_min_el"
descr = "%s(%s)" % (title, descr)
print(title)
img = np.clip(img - rol_min_el, 0, None) # remember to clip at zero
images.append((img, title, descr))
if show_images:
ploti = show_image(img, title=title, plotidx=ploti)
#
# rolling-minimum background subtraction
title = "rolling_5percentile_bg"
descr = "%s(%s)" % (title, descr)
print(title)
rol_min_bg = rolling_minimum_background(gaussian_filter(img, sigma=2), percentile=5)
if show_images:
ploti = show_image(rol_min_bg, title=title, plotidx=ploti, clim_percentile=99.9)
# subtract the background:
title = "minus-rol_min_bg"
descr = "%s(%s)" % (title, descr)
print(title)
img = np.clip(img - rol_min_bg, 0, None) # remember to clip at zero
images.append((img, title, descr))
if show_images:
ploti = show_image(img, title=title, plotidx=ploti)
print("np.all(rol_min_bg == rol_min_el):", np.all(rol_min_bg == rol_min_el))
# #
# # subtract_row_col_percentile background subtraction:
# title = "subtract_row_col_percentile"
# descr = "%s(%s)" % (title, descr)
# print(title)
# img = background_subtracted_rcp = subtract_row_col_percentile(
# img, percentile=30, filters=savgol_filter, window_length=11, polyorder=1
# )
# images.append((img, title, descr))
# if show_images:
# ploti = show_image(img, title=title, plotidx=ploti)
#
# opening, again:
title, descr = "opening-%sx%s" % band_shape, "opening(%s)" % descr
print(title)
img = opened2 = opening(img, selem=np.ones(band_shape))
images.append((img, title, descr))
if show_images:
ploti = show_image(img, title=title, plotidx=ploti)
#
# convolve:
# default mode='full' will shift output, use mode='same' to prevent shifting
title, descr = "convolved", "convolved(%s)" % descr
print(title)
img = convolved = convolve2d(img, band_selem/band_selem.sum(), mode='same')
images.append((img, title, descr))
if show_images:
ploti = show_image(img, title=title, plotidx=ploti)
#
# low-percentile filter to narrow the bands:
# (don't apply further openings or band-shape specific convolutions after narrowing the bands!)
size = (3, 21)
title = "pct_filtered-%sx%s" % size
descr = "%s(%s)" % (title, descr)
print(title)
img = pct_filtered = percentile_filter(img, percentile=10, size=size)
images.append((img, title, descr))
if show_images:
ploti = show_image(img, title=title, plotidx=ploti)
#
# gaussian:
title, descr = "gaussian_filter", "gaussian_filter(%s)" % descr
print(title)
img = gaussianed = gaussian_filter(img, sigma=1)
images.append((img, title, descr))
# if show_images:
# ploti = show_image(img, title="gaussianed", plotidx=ploti)
# Peaks!
print("Finding peaks...")
peak_pos = peak_local_max(img,
min_distance=10,
# threshold_abs=3,
# threshold_rel=0.01 # values must be 0.01 * maximum_value
)
print("peak_pos.shape", peak_pos.shape)
#
# Draw peaks on a copy of the image:
img = img.copy() # otherwise we will write on convolved
for pos in peak_pos:
draw_rectangle(img, pos, width=2, val=255, border=0, center_val=None)
ploti = show_image(img, title="peaks", plotidx=ploti)
#
# Other visualizations:
ggm_filtered = gaussian_gradient_magnitude(convolved, sigma=1.0)
ploti = show_image(ggm_filtered, title="gaussian_gradient_magnitude",
plotidx=ploti, clim=(0, np.percentile(ggm_filtered, 99.9)))
title, descr = "glaplace of convolved", "laplace of convolved"
# laplaced = laplace(convolved)
laplaced = gaussian_laplace(convolved, sigma=2)
ploti = show_image(laplaced, title=title, plotidx=ploti,
cmap="gray_r",
clim_percentile=(1, 99))
lggm = laplaced*(ggm_filtered-3)
ploti = show_image(lggm, title="laplaced*(ggm_filtered-3)", plotidx=ploti,
cmap="gray_r",
clim_percentile=(1, 99))
title, descr = "glaplace of opened1", "laplace of opened1"
# laplaced = laplace(convolved)
laplaced = gaussian_laplace(opened1, sigma=2)
ploti = show_image(laplaced, title=title, plotidx=ploti,
cmap="gray_r",
clim_percentile=(10, 90))
if save_images:
return peak_pos, images
else:
return peak_pos
def gaussian_second_deri(image):
"""
TODO:need to change the input of sigma here. Using gaussian second derivatives.
"""
return ndimage.gaussian_laplace(image, sigma=2)
def LoG(image, sigma):
result = ndimage.gaussian_laplace(image, sigma)
return result
def SpotDetector(self, input_image, AnalysisGui, nuclei_image,
spot_channel):
self.UPDATE_SPOT_ANALYSIS_PARAMS()
if AnalysisGui.spotchannelselect.currentText() == 'All':
params_to_pass = self.spot_params_dict['Ch1']
else:
params_to_pass = self.spot_params_dict[spot_channel]
uint8_max_val = 255
## First blurring round
median_img = cv2.medianBlur(nuclei_image, 11)
## Threhsolding and Binarizing
ret, thresh = cv2.threshold(median_img, 0, uint8_max_val,
cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
bin_img = (1 - thresh / uint8_max_val).astype('bool')
## Binary image filling
filled = ndimage.binary_fill_holes(bin_img)
struct = ndimage.generate_binary_structure(2, 2)
filled = ndimage.binary_dilation(filled,
structure=struct).astype(filled.dtype)
filled = ndimage.binary_dilation(filled,
structure=struct).astype(filled.dtype)
#### this part is for removing bright junk in the image################
labeled_nuc, num_features_nuc = label(filled)
props = regionprops_table(labeled_nuc,
input_image,
properties=('label', 'area', 'max_intensity',
'mean_intensity'))
props_df = pd.DataFrame(props)
mean_intensity_ave = props_df['mean_intensity'].mean()
max_intensity_max = props_df['max_intensity'].max()
for ind, row in props_df.iterrows():
if row['mean_intensity'] > 2 * mean_intensity_ave:
input_image[labeled_nuc == row['label']] = 0
input_image[input_image > max_intensity_max] = 0
input_image = cv2.normalize(input_image,
None,
0,
255,
cv2.NORM_MINMAX,
dtype=cv2.CV_8U)
########################################
sig = params_to_pass[3]
if str(params_to_pass[0]) == '0':
log_result = ndimage.gaussian_laplace(input_image, sigma=sig)
if str(params_to_pass[1]) == '0':
ret_log, thresh_log = cv2.threshold(
log_result, 0, 255,
cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
bin_img_log = (1 - thresh_log / 255).astype('bool')
if str(params_to_pass[1]) == '1':
manual_threshold = np.ceil(params_to_pass[2] *
2.55).astype(int)
thresh_log = log_result > manual_threshold
bin_img_log = thresh_log
# bin_img_log = (1-thresh_log/255).astype('bool')
spots_img_log = (bin_img_log * 255).astype('uint8')
kernel = np.ones((3, 3), np.uint8)
spot_openned_log = cv2.morphologyEx(spots_img_log, cv2.MORPH_OPEN,
kernel)
final_spots = np.multiply(spot_openned_log, filled)
if str(params_to_pass[0]) == '1':
result_gaussian = ndimage.gaussian_filter(input_image, sigma=sig)
if str(params_to_pass[1]) == '0':
ret_log, thresh_log = cv2.threshold(
result_gaussian, 0, 255,
cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
bin_img_g = (1 - thresh_log / 255).astype('bool')
if str(params_to_pass[1]) == '1':
manual_threshold = np.ceil(params_to_pass[2] *
2.55).astype(int)
thresh_log = result_gaussian > manual_threshold
bin_img_g = thresh_log
spots_img_g = (bin_img_g * 255).astype('uint8')
kernel = np.ones((3, 3), np.uint8)
spot_openned_g = cv2.morphologyEx(spots_img_g, cv2.MORPH_OPEN,
kernel)
final_spots = np.multiply(spot_openned_g, filled)
### center of mass calculation
if str(AnalysisGui.SpotLocationCbox.currentIndex()) == '0':
labeled_spots, num_features = label(final_spots)
spot_labels = np.unique(labeled_spots)
bin_img = (final_spots / uint8_max_val).astype('bool')
## Binary image filling
masked_spots = np.multiply(input_image, bin_img)
spot_locations = ndimage.measurements.center_of_mass(
masked_spots, labeled_spots, spot_labels[spot_labels > 0])
###### Brightest spot calculation
if str(AnalysisGui.SpotLocationCbox.currentIndex()) == '1':
labeled_spots, num_features = label(final_spots)
spot_labels = np.unique(labeled_spots)
bin_img = (final_spots / uint8_max_val).astype('bool')
masked_spots = np.multiply(input_image, bin_img)
spot_locations = peak_local_max(masked_spots,
labels=labeled_spots,
num_peaks_per_label=1)
##### Centroid calculation
if str(AnalysisGui.SpotLocationCbox.currentIndex()) == '2':
labeled_spots, num_features = label(final_spots)
spot_labels = np.unique(labeled_spots)
spot_locations = ndimage.measurements.center_of_mass(
final_spots, labeled_spots, spot_labels[spot_labels > 0])
return spot_locations, final_spots
def blob_log(image, min_sigma=1, max_sigma=50, num_sigma=10, threshold=.2,
overlap=.5, log_scale=False):
"""Finds blobs in the given grayscale image.
Blobs are found using the Laplacian of Gaussian (LoG) method [1]_.
For each blob found, the method returns its coordinates and the standard
deviation of the Gaussian kernel that detected the blob.
Parameters
----------
image : 2D or 3D ndarray
Input grayscale image, blobs are assumed to be light on dark
background (white on black).
min_sigma : float, optional
The minimum standard deviation for Gaussian Kernel. Keep this low to
detect smaller blobs.
max_sigma : float, optional
The maximum standard deviation for Gaussian Kernel. Keep this high to
detect larger blobs.
num_sigma : int, optional
The number of intermediate values of standard deviations to consider
between `min_sigma` and `max_sigma`.
threshold : float, optional.
The absolute lower bound for scale space maxima. Local maxima smaller
than thresh are ignored. Reduce this to detect blobs with less
intensities.
overlap : float, optional
A value between 0 and 1. If the area of two blobs overlaps by a
fraction greater than `threshold`, the smaller blob is eliminated.
log_scale : bool, optional
If set intermediate values of standard deviations are interpolated
using a logarithmic scale to the base `10`. If not, linear
interpolation is used.
Returns
-------
A : (n, image.ndim + 1) ndarray
A 2d array with each row representing 3 values for a 2D image,
and 4 values for a 3D image: ``(r, c, sigma)`` or ``(f, r, c, sigma)``
where ``(r, c)`` or ``(f, r, c)`` are coordinates of the blob and
``sigma`` is the standard deviation of the Gaussian kernel which
detected the blob.
References
----------
.. [1] http://en.wikipedia.org/wiki/Blob_detection#The_Laplacian_of_Gaussian
Examples
--------
>>> from skimage import data, feature, exposure
>>> img = data.coins()
>>> img = exposure.equalize_hist(img) # improves detection
>>> feature.blob_log(img, threshold = .3)
array([[ 113. , 323. , 1. ],
[ 121. , 272. , 17.33333333],
[ 124. , 336. , 11.88888889],
[ 126. , 46. , 11.88888889],
[ 126. , 208. , 11.88888889],
[ 127. , 102. , 11.88888889],
[ 128. , 154. , 11.88888889],
[ 185. , 344. , 17.33333333],
[ 194. , 213. , 17.33333333],
[ 194. , 276. , 17.33333333],
[ 197. , 44. , 11.88888889],
[ 198. , 103. , 11.88888889],
[ 198. , 155. , 11.88888889],
[ 260. , 174. , 17.33333333],
[ 263. , 244. , 17.33333333],
[ 263. , 302. , 17.33333333],
[ 266. , 115. , 11.88888889]])
Notes
-----
The radius of each blob is approximately :math:`\sqrt{2}sigma` for
a 2-D image and :math:`\sqrt{3}sigma` for a 3-D image.
"""
image = img_as_float(image)
if log_scale:
start, stop = log(min_sigma, 10), log(max_sigma, 10)
sigma_list = np.logspace(start, stop, num_sigma)
else:
sigma_list = np.linspace(min_sigma, max_sigma, num_sigma)
# computing gaussian laplace
# s**2 provides scale invariance
gl_images = [-gaussian_laplace(image, s) * s ** 2 for s in sigma_list]
# Replace by image_cube = np.stack(hessian_images, axis=-1)
# When we upgrade minimal requirements to NumPy 1.10
sl = (slice(None),) * image.ndim + (np.newaxis,)
arrays = [np.asanyarray(arr) for arr in gl_images]
extended_arrays = [arr[sl] for arr in arrays]
image_cube = np.concatenate(extended_arrays, axis=-1)
local_maxima = peak_local_max(image_cube, threshold_abs=threshold,
footprint=np.ones((3,) * (image.ndim + 1)),
threshold_rel=0.0,
exclude_border=False)
# Convert local_maxima to float64
lm = local_maxima.astype(np.float64)
# Convert the last index to its corresponding scale value
lm[:, -1] = sigma_list[local_maxima[:, -1]]
return _prune_blobs(lm, overlap)
def train(self):
"""Train encoder, generator and discriminator."""
#====================================== Training ===========================================#
#===========================================================================================#
unet_path = os.path.join(
self.current_model_saving_path, '%s-%s-%.4f-%d-%d-%d-best.pkl' %
(self.model_type, self.optimizer_choice, self.initial_lr,
self.num_epochs, self.batch_size, self.down_factor))
last_unet_path = os.path.join(
self.current_model_saving_path, '%s-%s-%.4f-%d-%d-%d-last.pkl' %
(self.model_type, self.optimizer_choice, self.initial_lr,
self.num_epochs, self.batch_size, self.down_factor))
print('The U-Net path is {}'.format(unet_path))
# U-Net Train
# Train loss history (R&R)
train_loss_history = []
# Validation loss history (R&R)
validation_loss_history = []
if os.path.isfile(unet_path):
# Load the pretrained Encoder
self.unet.load_state_dict(torch.load(unet_path))
print('%s is Successfully Loaded from %s' %
(self.model_type, unet_path))
else:
# Train for Encoder
best_unet_score = 0.
print('Start training. The initial learning rate is: {}'.format(
self.initial_lr))
# Write the first line of the train and validation loss history csv file.
with open(os.path.join(self.current_loss_history_path, 'train_and_validation_history.csv'), 'a', \
encoding = 'utf-8', newline= '') as f:
wr = csv.writer(f)
wr.writerow([
'Mode', 'Current Epoch', 'Total Epoch', 'Batch Size',
'Metric', 'Loss'
])
f.close()
for epoch in range(self.num_epochs):
self.unet.train(True)
train_epoch_loss = 0
validation_epoch_loss = 0
length = 0
start_time = timeit.default_timer()
for batch, (img, GT) in enumerate(self.train_loader):
img = img.to(self.device)
GT = GT.to(self.device)
# Reshape the images and GTs to 4-dimensional so that they can get fed to the conv2d layer. (R&R)
# The new shape has to be (batch_size, num_channels, img_dim1, img_dim2).
if self.img_ch == 1:
img = img[:, np.newaxis, :, :]
else:
img = img.transpose(1, 3)
img = img.transpose(2, 3)
if self.GT_ch == 1:
GT = GT[:, np.newaxis, :, :]
else:
GT = GT.transpose(1, 3)
GT = GT.transpose(2, 3)
# SR : Segmentation Result
SR = torch.sigmoid(self.unet(img))
# Flatten the prediction, target, and training field.
SR_flat = SR.view(SR.size(0), -1)
GT_flat = GT.view(GT.size(0), -1)
# Compute the loss for this batch.
if self.loss_function_name == 'Dice':
train_loss = self.dice_coeff_loss(SR_flat, GT_flat)
else:
train_loss = self.loss_function(SR_flat, GT_flat)
# Add the loss of this batch to the loss of this epoch.
train_epoch_loss += train_loss.item()
if self.edge_enhance == 'True':
GT_edge_enhanced = ndimage.gaussian_laplace(np.squeeze(
GT.cpu().detach().numpy()),
sigma=5)
GT_edge1 = torch.tensor(
np.int64(GT_edge_enhanced < -0.001))
GT_edge2 = torch.tensor(
np.int64(GT_edge_enhanced > 0.001))
y_hat = torch.cat((torch.squeeze(SR_flat),
torch.squeeze(SR)[GT_edge1 == 1],
torch.squeeze(SR)[GT_edge2 == 1]),
0)
y = torch.cat((torch.squeeze(GT_flat),
torch.squeeze(GT)[GT_edge1 == 1],
torch.squeeze(GT)[GT_edge2 == 1]), 0)
elif self.edge_enhance == 'Double':
GT_edge_enhanced = ndimage.gaussian_laplace(np.squeeze(
GT.cpu().detach().numpy()),
sigma=5)
GT_edge1 = torch.tensor(
np.int64(GT_edge_enhanced < -0.001))
GT_edge2 = torch.tensor(
np.int64(GT_edge_enhanced > 0.001))
y_hat = torch.cat((torch.squeeze(SR_flat),
torch.squeeze(SR)[GT_edge1 == 1],
torch.squeeze(SR)[GT_edge1 == 1],
torch.squeeze(SR)[GT_edge2 == 1]),
0)
y = torch.cat((torch.squeeze(GT_flat),
torch.squeeze(GT)[GT_edge1 == 1],
torch.squeeze(GT)[GT_edge1 == 1],
torch.squeeze(GT)[GT_edge2 == 1]), 0)
train_loss = self.loss_function(y_hat, y)
# Backprop + optimize
self.reset_grad()
train_loss.backward()
self.optimizer.step()
### if batch size = 1 ###
length += 1
if batch % 200 == 0:
print(
'[Training] Epoch [{}/{}], Batch: {}, Batch size: {}, Average {} Error: {}'
.format(epoch + 1, self.num_epochs, batch,
self.batch_size, self.loss_function_name,
train_epoch_loss / length))
# Empty cache to free up memory at the end of each batch.
del batch, img, GT, SR, GT_flat, SR_flat, train_loss
torch.cuda.empty_cache()
end_time = timeit.default_timer()
# Normalize the train loss over the length of the epoch (number of images in this epoch).
train_epoch_loss = train_epoch_loss / length
# Print the log info
print(
'[Training] Epoch [%d/%d], Train Loss: %.6f, Run Time: %.4f [h]'
% (epoch + 1, self.num_epochs, train_epoch_loss,
(end_time - start_time) / 60 / 60))
# Append train loss to train loss history (R&R)
train_loss_history.append(train_epoch_loss)
with open(os.path.join(self.current_loss_history_path, 'train_and_validation_history.csv'), 'a', \
encoding = 'utf-8', newline= '') as f:
wr = csv.writer(f)
wr.writerow(['Training', '%d' % (epoch + 1), '%d' % (self.num_epochs), '%d' % (self.batch_size), \
'%s' % self.loss_function_name, '%.6f' % train_epoch_loss])
f.close()
#===================================== Validation ====================================#
self.unet.train(False)
self.unet.eval()
length = 0
start_time = timeit.default_timer()
for batch, (img, GT) in enumerate(self.validation_loader):
# Read, reshape the GTs and images, and compute the target images.
img = img.to(self.device)
GT = GT.to(self.device)
# Reshape the images and GTs to 4-dimensional so that they can get fed to the conv2d layer. (R&R)
# The new shape has to be (batch_size, num_channels, img_dim1, img_dim2).
if self.img_ch == 1:
img = img[:, np.newaxis, :, :]
else:
img = img.transpose(1, 3)
img = img.transpose(2, 3)
if self.GT_ch == 1:
GT = GT[:, np.newaxis, :, :]
else:
GT = GT.transpose(1, 3)
GT = GT.transpose(2, 3)
# SR : Segmentation Result
SR = torch.sigmoid(self.unet(img))
# Flatten the prediction and target.
SR_flat = SR.view(SR.size(0), -1)
GT_flat = GT.view(GT.size(0), -1)
# Compute the loss for this batch.
if self.loss_function_name == 'Dice':
validation_loss = self.dice_coeff_loss(
SR_flat, GT_flat)
else:
validation_loss = self.loss_function(SR_flat, GT_flat)
length += 1
validation_epoch_loss += validation_loss.item()
# Empty cache to free up memory at the end of each batch.
del img, GT, SR, GT_flat, SR_flat, validation_loss
torch.cuda.empty_cache()
# Normalize the validation loss.
validation_epoch_loss = validation_epoch_loss / length
end_time = timeit.default_timer()
# Define the decisive score of the network as 1 - validation_epoch_loss.
unet_score = 1. - validation_epoch_loss
print('Current learning rate: {}'.format(self.current_lr))
print(
'[Validation] Epoch [%d/%d] Validation Loss: %.6f, Run Time: %.4f [h]'
% (epoch + 1, self.num_epochs, validation_epoch_loss,
(end_time - start_time) / 60 / 60))
# Append validation loss to train loss history (R&R)
validation_loss_history.append(validation_epoch_loss)
with open(os.path.join(self.current_loss_history_path, 'train_and_validation_history.csv'), 'a', \
encoding = 'utf-8', newline= '') as f:
wr = csv.writer(f)
wr.writerow(['Validation', '%d' % (epoch + 1), '%d' % (self.num_epochs), '%d' % (self.batch_size), \
'%s' % self.loss_function_name, '%.6f' % validation_epoch_loss])
f.close()
# Make sure we save the best and last unets.
if unet_score > best_unet_score:
best_unet_score = unet_score
best_epoch = epoch
best_unet = self.unet.state_dict()
print('Best %s model score : %.6f' %
(self.model_type, best_unet_score))
torch.save(best_unet, unet_path)
if (epoch == self.num_epochs - 1):
last_unet = self.unet.state_dict()
torch.save(last_unet, last_unet_path)
if epoch % 10 == 0 and epoch != 0:
epoch_unet_path_component = unet_path.split('/')
file_name = epoch_unet_path_component[-1].replace(
'best', 'epoch%d' % epoch)
epoch_unet_path_component[-1] = file_name
epoch_unet_path = '/'.join(epoch_unet_path_component)
epoch_unet = self.unet.state_dict()
torch.save(epoch_unet, epoch_unet_path)
# Adaptive Learning Rate (R&R)
try:
previous_epoch = self.adaptive_lr_handler(
3, 0.01 * self.initial_lr, epoch, previous_epoch, 0.98,
0.5, validation_loss_history)
except:
previous_epoch = self.adaptive_lr_handler(
3, 0.01 * self.initial_lr, epoch, 0, 0.98, 0.5,
validation_loss_history)
# Early stop (R&R)
if (self.early_stop == True):
if (len(validation_loss_history) > 9):
if (np.mean(validation_loss_history[-10:-5]) <=
np.mean(validation_loss_history[-5:])):
print(
'Validation loss stop decreasing. Stop training.'
)
last_unet = self.unet.state_dict()
torch.save(last_unet, last_unet_path)
break
del self.unet
try:
del best_unet
torch.cuda.empty_cache()
except:
print(
'Cannot delete the variable "best_unet": variable does not exist.'
)
return train_loss_history, validation_loss_history
