def segment3DLungs(image_path):
# Lungs Parameters
params = {}
# Parameters for intensity (fixed)
params['lungMinValue'] = 0
params['lungMaxValue'] = 800
params['lungThreshold'] = -900
# Parameters for lung segmentation (fixed)
params['xRangeRatio1'] = 0.4
params['xRangeRatio2'] = 0.75
params['zRangeRatio1'] = 0.5
params['zRangeRatio2'] = 0.75
# Load image
Img = nib.load(image_path)
I = Img.get_data()
# Intensity thresholding & Morphological operations
M = np.zeros(I.shape)
M[I > params['lungMinValue']] = 1
M[I > params['lungMaxValue']] = 0
struct_s = ndimage.generate_binary_structure(3, 1)
struct_m = ndimage.iterate_structure(struct_s, 2)
struct_l = ndimage.iterate_structure(struct_s, 3)
M = ndimage.binary_closing(M, structure=struct_s, iterations=1)
M = ndimage.binary_opening(M, structure=struct_m, iterations=1)
# Estimate lung filed of view
[m, n, p] = I.shape
medx = int(m / 2)
medy = int(n / 2)
xrange1 = int(m / 2 * params['xRangeRatio1'])
xrange2 = int(m / 2 * params['xRangeRatio2'])
zrange1 = int(p * params['zRangeRatio1'])
zrange2 = int(p * params['zRangeRatio2'])
# Select largest connected components
M = measure.label(M)
label1 = M[medx - xrange2:medx - xrange1, medy, zrange1:zrange2]
label2 = M[medx + xrange1:medx + xrange2, medy, zrange1:zrange2]
label1 = stats.mode(label1[label1 > 0])[0][0]
label2 = stats.mode(label2[label2 > 0])[0][0]
M[M == label1] = -1
M[M == label2] = -1
M[M > 0] = 0
M = M * -1
SegImage = nib.Nifti1Image(M, Img.affine, Img.header)
return SegImage
def compute_mask(aparc, labels=[0, 5000]):
import nibabel as nb
import numpy as np
import os.path as op
import scipy.ndimage as nd
segnii = nb.load(aparc)
seg = segnii.get_data()
mask = np.ones_like(seg, dtype=np.uint8)
for l in labels:
mask[seg == l] = 0
struct = nd.iterate_structure(
nd.generate_binary_structure(3, 1), 4)
mask = nd.binary_dilation(mask, structure=struct).astype(np.uint8)
mask = nd.binary_closing(mask, structure=struct)
mask = nd.binary_fill_holes(mask, structure=struct).astype(np.uint8)
mask[mask > 0] = 1
mask[mask <= 0] = 0
hdr = segnii.get_header().copy()
hdr.set_data_dtype(np.uint8)
hdr.set_xyzt_units('mm', 'sec')
out_file = op.abspath('nobstem_mask.nii.gz')
nii = nb.Nifti1Image(mask, segnii.get_affine(), hdr).to_filename(
out_file)
return out_file
def watershed(self, mask, sigma=0.5, watershed_line=True):
"""
Run watershed segmentation to generate segment label mask.
Args:
mask (np.ndarray[bool]) - binary foreground mask
sigma (float) - parameter for smoothing distance mask
watershed_line (bool) - if True, include 1px line between contours
"""
# define distances
distances = distance_transform_edt(mask)
distances = gaussian_filter(distances, sigma=sigma)
# run segmentation
connectivity = iterate_structure(generate_binary_structure(2, 1), 1)
markers = self.get_segment_mask(distances, self.seeds)
self.labels = watershed(-distances,
markers=markers,
mask=mask,
connectivity=connectivity,
watershed_line=watershed_line)
def compute_mask(aparc, labels=[0, 5000]):
import nibabel as nb
import numpy as np
import os.path as op
import scipy.ndimage as nd
segnii = nb.load(aparc)
seg = segnii.get_data()
mask = np.ones_like(seg, dtype=np.uint8)
for l in labels:
mask[seg == l] = 0
struct = nd.iterate_structure(nd.generate_binary_structure(3, 1), 4)
mask = nd.binary_dilation(mask, structure=struct).astype(np.uint8)
mask = nd.binary_closing(mask, structure=struct)
mask = nd.binary_fill_holes(mask, structure=struct).astype(np.uint8)
mask[mask > 0] = 1
mask[mask <= 0] = 0
hdr = segnii.get_header().copy()
hdr.set_data_dtype(np.uint8)
hdr.set_xyzt_units("mm", "sec")
out_file = op.abspath("nobstem_mask.nii.gz")
nii = nb.Nifti1Image(mask, segnii.get_affine(), hdr).to_filename(out_file)
return out_file
def get_peaks(img):
"""
create a binary structure to specify shape. use this shape to filter peaks
from the spectrogram image passed as param. erode the background and apply
xor operation to get final image(a 2d array) containing only peaks.
find the amplitude at the found peaks.
extract time and frequency location from the original spectrogram using these found peak.
filter through the peaks. i used only the peaks whose amps are more than the avg amp.
zip all these into a list and return for hashing
:param img:
:return: list of tuples containing time and frequency location in the structure [(time,freq)...]
"""
struct = generate_binary_structure(2, 2)
neighbor = iterate_structure(structure=struct, iterations=20)
avg_peak = np.mean(img)
maximas = maximum_filter(input=img, footprint=neighbor) == img
background = img == 0
eroded_back = binary_erosion(input=background,
structure=neighbor,
border_value=1)
peaks = maximas ^ eroded_back # this will return a numpy array containing boolean values that are true at the peaks
amps = img[peaks]
t, f = np.where(peaks)
zipped_peaks = list(zip(t, f, amps))
# filtering through our peaks
time_loc = [zips[0] for zips in zipped_peaks if zips[2] >= avg_peak]
freq_loc = [zips[1] for zips in zipped_peaks if zips[2] >= avg_peak]
return list(zip(time_loc, freq_loc))
def seperate_skull(image):
marker_internal, marker_external, marker_watershed = generate_markers(
image)
sobel_filtered_dx = ndimage.sobel(image, 1)
sobel_filtered_dy = ndimage.sobel(image, 0)
sobel_gradient = np.hypot(sobel_filtered_dx, sobel_filtered_dy)
sobel_gradient *= 255.0 / np.max(sobel_gradient)
watershed = morphology.watershed(sobel_gradient, marker_watershed)
outline = ndimage.morphological_gradient(watershed, size=(3, 3))
outline = outline.astype(bool)
blackhat_struct = [[0, 0, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 0, 0]]
blackhat_struct = ndimage.iterate_structure(blackhat_struct, 8)
outline += ndimage.black_tophat(outline, structure=blackhat_struct)
lungfilter = np.bitwise_or(marker_internal, outline)
lungfilter = ndimage.morphology.binary_closing(
lungfilter, structure=np.ones((5, 5)), iterations=3)
segmented = np.where(lungfilter == 1, image, -2000 * np.ones((512, 512)))
return segmented
def granulometry(BW, T=35, filename="simu"):
# total original area
A = ndimage.measurements.sum(BW)
# number of objects
label, N = ndimage.measurements.label(BW)
area = np.zeros((T, ), dtype=np.float)
number = np.zeros((T, ), dtype=np.float)
"""
Warning: the structuring elements must verify B(n) = B(n-1) o B(1).
"""
se = ndimage.generate_binary_structure(2, 1)
for i in np.arange(T):
SE = ndimage.iterate_structure(se, i - 1)
m = ndimage.morphology.binary_erosion(BW, structure=SE)
G = ndimage.morphology.binary_propagation(m, mask=BW)
area[i] = 100 * ndimage.measurements.sum(G) / A
label, n = ndimage.measurements.label(G)
number[i] = 100 * n / N # beware of integer division
plt.figure()
plt.plot(area, label='Area')
plt.plot(number, label='Number')
plt.legend()
plt.savefig("granulo_" + filename + "1.pdf", bbox_inches='tight')
plt.show()
plt.figure()
plt.plot(-np.diff(area), label='Area derivative')
plt.plot(-np.diff(number), label='Number derivative')
plt.legend()
plt.savefig("granulo_" + filename + "2.pdf", bbox_inches='tight')
plt.show()
def build_mask_sphere(self, sphere_radius):
"""build_mask_sphere
"""
sphere_vertex_number_1d = np.ceil(2.0 * sphere_radius / self.mesh_voxel_size).astype('int')
sphere_element = ndimage.generate_binary_structure(3,1)
sphere = ndimage.iterate_structure(sphere_element, np.ceil(sphere_vertex_number_1d / 3).astype('int'))
return sphere
def find_local_max(img, d_rad, threshold=1e-15):
"""
This is effectively a replacement for pkfnd in the matlab/IDL code.
The output of this function is meant to be feed into :py:func:`~subpixel_centroid`
The magic of numpy means this should work for any dimension data.
:param img: an ndarray representing the data to find the local maxes
:param d_rad: the radius of the dilation, the smallest possible spacing between local maximum
:param threshold: optional, voxels < threshold are ignored.
:rtype: (d,N) array of the local maximums.
"""
d_rad = int(d_rad)
img = np.array(np.squeeze(img)) # knock out singleton dimensions
img[img < threshold] = -np.inf # mask out pixels below threshold
dim = img.ndim # get the dimension of data
# make structuring element
s = ndimage.generate_binary_structure(dim, 1)
# scale it up to the desired size
d_struct = ndimage.iterate_structure(s, int(d_rad))
dilated_img = ndimage.grey_dilation(img,
footprint=d_struct,
cval=0,
mode='constant') # do the dilation
# find the locations that are the local maximum
# TODO clean this up
local_max = np.where(np.exp(img - dilated_img) > (1 - 1e-15))
# the extra [::-1] is because matplotlib and ndimage disagree an xy vs yx
return np.vstack(local_max[::-1])
def subpixel_centroid(img, local_maxes, mask_rad, struct_shape='circle'):
'''
This is effectively a replacement for cntrd in the matlab/IDL code.
Works for 2D data only. Accelerated by numba.
:param img: the data
:param local_maxes: a (d,N) array with the location of the local maximums (as generated by :py:func:`~find_local_max`)
:param mask_rad: the radius of the mask used for the averaging.
:param struct_shape: ['circle' | 'diamond'] Shape of mask over each particle.
:rtype: (d,N) array of positions, (d,) array of masses, (d,) array of r2,
'''
# First, check that all local maxes are within 'mask_rad' of the image
# edges. Otherwise we will be going outside the bounds of the array in
# _refine_centroids_loop()
if not all(_local_max_within_bounds(img.shape, local_maxes, mask_rad)):
raise IndexError(
'One or more local maxes are too close to the image edge. Use local_max_crop().'
)
# Make coordinate order compatible with upcoming code
local_maxes = local_maxes[::-1]
# do some data checking/munging
img = np.squeeze(img) # knock out singleton dimensions
dim = img.ndim
if dim > 2:
raise ValueError('Use subpixel_centroid_nd() for dimension > 2')
so = [slice(-mask_rad, mask_rad + 1)] * dim
# Make circular structuring element
if struct_shape == 'circle':
d_struct = (np.sum(np.mgrid[so]**2, 0) <= mask_rad**2).astype(np.int8)
elif struct_shape == 'diamond':
s = ndimage.generate_binary_structure(dim, 1)
# scale it up to the desired size
d_struct = ndimage.iterate_structure(s, int(mask_rad))
else:
raise ValueError('Shape must be diamond or circle')
offset_masks = np.array([d_struct * os
for os in np.mgrid[so]]).astype(np.int8)
r2_mask = np.zeros(d_struct.shape)
for o in offset_masks:
r2_mask += o**2
r2_mask = np.sqrt(r2_mask).astype(float)
results = _refine_centroids_loop(img, local_maxes, mask_rad, offset_masks,
d_struct, r2_mask)
pos = (results[0:2, :] + local_maxes)[::-1, :]
#m = results[2,:]
#r2 = results[3,:]
#return pos, m, r2
peaks = np.array(3, pos.shape[1])
peaks[0:2, :] = pos
peaks[2, :] = img[peaks[0, :], peaks[1, :]]
#for i in range(0,pos.shape[1]):
# x = pos[0][i]
#y = pos[1][i]
# try: peaks.append([x, y, img[y,x]])
#except: pass
return peaks
def draw_spheres(self,mid_mask,thres):
all_mask = mid_mask * (self.img > thres)
struct = ndimage.generate_binary_structure(3, 2)
struct_iter = ndimage.iterate_structure(struct,self.s_iter).astype(int)
spheres = ndimage.binary_dilation(all_mask, structure = struct_iter, iterations=self.d_iter).astype(all_mask[:,:,:].dtype)
struct_size = np.sum(struct_iter)
return spheres, all_mask, struct_size
def seperate_lungs(self, image):
segemented_array = np.zeros(image.shape)
marker_internal, marker_external, marker_watershed = self.generate_marker(
image)
# value of gradient (slope) in X and Y-direction
sobel_filtered_dx = ndimage.sobel(
image, 1) # vertical derivate ( detects horizontal edges)
sobel_filtered_dy = ndimage.sobel(
image, 0) # horizontal derivate (detects vertical edges)
sobel_gradient = np.hypot(
sobel_filtered_dx, sobel_filtered_dy
) # magnitude of gradient, gets rids of a (-)ve sign
sobel_gradient *= 255.0 / np.max(
sobel_gradient
) # normalize (This is our landscape image and we will fit it with water)
watershed = morphology.watershed(sobel_gradient, marker_watershed)
outline = ndimage.morphological_gradient(watershed, size=(3, 3))
outline = outline.astype(bool)
# Creation of the disk-kernel
blackhat_struct = [[0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 0, 0]]
blackhat_struct = ndimage.iterate_structure(blackhat_struct, 7)
outline += ndimage.black_tophat(outline, structure=blackhat_struct)
lungfilter = np.bitwise_or(marker_internal, outline)
lungfilter = ndimage.morphology.binary_closing(lungfilter,
structure=np.ones(
(5, 5)),
iterations=3)
segmented = np.where(lungfilter == 1, image, -2000 * np.ones(
(512, 512)))
segemented_array = segmented
return segmented
def _findPeaks(spectrogram, min_amplitude=DEFAULT_MIN_AMPLITUDE):
# Находим локальные максимумы, используя binary_erosion
neighbourhood_structure = generate_binary_structure(2, 1)
neighborhood = iterate_structure(neighbourhood_structure,
Extractor.PEAK_NEIGHBORHOOD_SIZE)
local_max = maximum_filter(spectrogram,
footprint=neighborhood) == spectrogram
background = (spectrogram == 0)
eroded_background = binary_erosion(background,
structure=neighborhood,
border_value=1)
detected_peaks = local_max - eroded_background
# Формируем массив пик
peak_amplitudes = spectrogram[detected_peaks].flatten()
peak_frequencies, peak_times = np.where(detected_peaks)
raw_peaks = []
for i in range(
0,
min(peak_amplitudes.size, peak_frequencies.size,
peak_times.size)):
raw_peaks.append(
SpectogramPeak(peak_frequencies[i], peak_times[i],
peak_amplitudes[i]))
# Выбираем только пики с нормальной для нас амплитудой
peaks = [peak for peak in raw_peaks if peak.amplitude > min_amplitude]
return peaks
def detect_peaks(self, spectrogram):
"""
Takes an image of the spectrogram and detect the peaks using the local
maximum filter.
Input:
spectrogram: a matrix of time-frequency strengths from matplotlibs specgram
method.
peak_sensitivity: How large of a neighborhood structure to consider when
looking for peaks
Returns a boolean mask of the peaks (i.e. 1 when
the pixel's value is the neighborhood maximum, 0 otherwise)
"""
# define a connected neighborhood and find max values in neighborhood
neighborhood_structure = generate_binary_structure(2, 1)
neighborhood = ndi.iterate_structure(neighborhood_structure,
self.peak_sensitivity)
local_max = ndi.filters.maximum_filter(
spectrogram, footprint=neighborhood) == spectrogram
background = (spectrogram == 0)
eroded_background = binary_erosion(background,
structure=neighborhood,
border_value=1)
detected_peaks = local_max != eroded_background
if self.min_peak_amplitude:
filtered_peak_locations = self.filter_peaks_by_size(
detected_peaks, spectrogram)
else:
filtered_peak_locations = detected_peaks
return filtered_peak_locations
def _process_image(filename,
out_format,
resize=None,
dilate=None,
require_binary_output=False):
"""Process a single image file.
Args:
filename: string, path to an image file e.g., '/path/to/example.JPG'.
out_format: string, output format type e.g., 'PNG', 'JPEG'
Returns:
image_buffer: string, encoding of image in out_format
height: integer, image height in pixels.
width: integer, image width in pixels.
"""
# Read the image file.
with tf.gfile.FastGFile(filename, 'rb') as f:
raw_image_data = f.read()
# Convert any format to PNG for consistency.
#pil_img = Image.open(StringIO(raw_image_data))
pil_img = Image.open(BytesIO(raw_image_data))
# dilate image if requested so - create structering element of appropriate size
if dilate is not None:
dilation_se = iterate_structure(generate_binary_structure(2, 1),
(int)((dilate - 1) / 2))
im = binary_dilation(np.array(pil_img), structure=dilation_se)
pil_img = Image.fromarray(np.uint8(im) * 255)
if resize is not None:
pil_img = pil_img.resize(
resize[::-1]
) # NOTE: use reversed order of resize to make input consistent with tensorflow
# if output should be in binary then we must do binarization to remove interpolation values from resize
if require_binary_output:
im = (np.array(pil_img) > 0)
pil_img = Image.fromarray(np.uint8(im) * 255)
try:
#image_data = StringIO()
image_data = BytesIO()
pil_img.save(image_data, out_format)
except Exception as e:
print("exception inside _process_image:", e)
height = pil_img.size[1]
width = pil_img.size[0]
if pil_img.mode in ['RGBA', 'CMYK']:
num_chanels = 4
elif pil_img.mode in ['RGB', 'LAB', 'HSV', 'YCbCr']:
num_chanels = 3
else:
num_chanels = 1
return image_data.getvalue(), height, width, num_chanels
def find_local_max(img, d_rad, threshold=1e-15, inplace=False):
"""
This is effectively a replacement for pkfnd in the matlab/IDL code.
The output of this function is meant to be feed into :py:func:`~subpixel_centroid`
The magic of numpy means this should work for any dimension data.
:param img: an ndarray representing the data to find the local maxes
:param d_rad: the radius of the dilation, the smallest possible spacing between local maximum
:param threshold: optional, voxels < threshold are ignored.
:param inplace: If True, `img` is modified.
:rtype: (d,N) array of the local maximums.
"""
d_rad = int(d_rad)
# knock out singleton dimensions,
# and prepare to change values in thresholding step.
img = np.array(np.squeeze(img))
if not inplace:
img = img.copy() # Otherwise we could mess up use of 'img' by subsequent code.
img[img < threshold] = -np.inf # mask out pixels below threshold
dim = img.ndim # get the dimension of data
# make structuring element
s = ndimage.generate_binary_structure(dim, 1)
# scale it up to the desired size
d_struct = ndimage.iterate_structure(s, int(d_rad))
dilated_img = ndimage.grey_dilation(img,
footprint=d_struct,
cval=0,
mode='constant') # do the dilation
# find the locations that are the local maximum
# TODO clean this up
maxima = np.vstack(np.where(np.exp(img - dilated_img) > (1 - 1e-15))).T
count=0
while True:
duplicates = KDTree(maxima, 30).query_pairs(d_rad)
if len(duplicates) == 0:
break
count += len(duplicates)
to_drop = []
for pair in duplicates:
# Take the average position.
# This is just a starting point, so we won't go into subpx precision here.
merged = maxima[pair[0]]
merged = maxima[[pair[0], pair[1]]].mean(0).astype(int)
maxima[pair[0]] = merged # overwrite one
to_drop.append(pair[1]) # queue other to be dropped
maxima = np.delete(maxima, to_drop, 0)
# the extra [::-1] is because matplotlib and ndimage disagree an xy vs yx.
# Finally, there should be nothing within 'd_rad' of the edges of the image
print '%i peaks were removed' %count
return np.vstack(maxima).T[::-1]
def get_filtered_lung(image):
# Creation of the internal Marker
marker_internal = image < -500
marker_internal = segmentation.clear_border(marker_internal)
marker_internal_labels = measure.label(marker_internal)
areas = [r.area for r in measure.regionprops(marker_internal_labels)]
areas.sort()
if len(areas) > 2:
for region in measure.regionprops(marker_internal_labels):
if region.area < areas[-2]:
for coordinates in region.coords:
marker_internal_labels[coordinates[0], coordinates[1]] = 0
marker_internal = marker_internal_labels > 0
# Creation of the external Marker
external_a = ndimage.binary_dilation(marker_internal, iterations=10)
external_b = ndimage.binary_dilation(marker_internal, iterations=55)
marker_external = external_b ^ external_a
# Creation of the Watershed Marker matrix
marker_watershed = np.zeros((512, 512), dtype=np.int)
marker_watershed += marker_internal * 255
marker_watershed += marker_external * 128
# Creation of the Sobel-Gradient
sobel_filtered_dx = ndimage.sobel(image, 1)
sobel_filtered_dy = ndimage.sobel(image, 0)
sobel_gradient = np.hypot(sobel_filtered_dx, sobel_filtered_dy)
sobel_gradient *= 255.0 / np.max(sobel_gradient)
# Watershed algorithm
watershed = morphology.watershed(sobel_gradient, marker_watershed)
# Reducing the image created by the Watershed algorithm to its outline
outline = ndimage.morphological_gradient(watershed, size=(3, 3))
outline = outline.astype(bool)
# Performing Black-Tophat Morphology for reinclusion
# Creation of the disk-kernel and increasing its size a bit
blackhat_struct = [[0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 0, 0]]
blackhat_struct = ndimage.iterate_structure(blackhat_struct, 8)
# Perform the Black-Hat
outline += ndimage.black_tophat(outline, structure=blackhat_struct)
# Use the internal marker and the Outline that was just created to generate the lungfilter
lungfilter = np.bitwise_or(marker_internal, outline)
# Close holes in the lungfilter
# fill_holes is not used here, since in some slices the heart would be reincluded by accident
lungfilter = ndimage.morphology.binary_closing(lungfilter,
structure=np.ones((5, 5)),
iterations=3)
return lungfilter
def local_maxima(image, radius, separation, threshold):
ndim = image.ndim
threshold -= 1
# The intersection of the image with its dilation gives local maxima.
if not np.issubdtype(image.dtype, np.integer):
raise TypeError("Perform dilation on exact (i.e., integer) data.")
#footprint = self.binary_mask(radius, ndim)
s = ndimage.generate_binary_structure(ndim, 2)
# scale it up to the desired size
footprint = ndimage.iterate_structure(s, int(radius))
dilation = ndimage.grey_dilation(image,
footprint=footprint,
mode='constant')
maxima = np.vstack(np.where((image == dilation)
& (image > threshold))).T[:, ::-1]
if not np.size(maxima) > 0:
#warnings.warn("Image contains no local maxima.", UserWarning)
return np.empty((0, ndim))
# Flat peaks return multiple nearby maxima. Eliminate duplicates.
if len(maxima) > 0:
while True:
duplicates = cKDTree(maxima, 30).query_pairs(separation)
if len(duplicates) == 0:
break
to_drop = []
for pair in duplicates:
# Take the average position.
# This is just a starting point, so we won't go into subpx precision here.
merged = maxima[pair[0]]
merged = maxima[[pair[0], pair[1]]].mean(0).astype(int)
maxima[pair[0]] = merged # overwrite one
to_drop.append(pair[1]) # queue other to be dropped
maxima = np.delete(maxima, to_drop, 0)
# Do not accept peaks near the edges.
shape = np.array(image.shape)
margin = int(separation) // 2
near_edge = np.any((maxima < margin) | (maxima > (shape - margin)), 1)
maxima = maxima[~near_edge]
#if not np.size(maxima) > 0:
#warnings.warn("All local maxima were in the margins.", UserWarning)
#x, y = maxima[:,0], maxima[:,1]
#max_val = image[x,y].reshape(1,len(maxima))
#peaks = np.concatenate((maxima,max_val), axis = 1)
return maxima[:, 0], maxima[:,
1], image[maxima[:, 0],
maxima[:,
1]].reshape(1, len(maxima))
def wiggle_room_precision_recall(pred, boundary, margin=2, connectivity=1):
struct = nd.generate_binary_structure(boundary.ndim, connectivity)
gtd = nd.binary_dilation(boundary, struct, margin)
struct_m = nd.iterate_structure(struct, margin)
pred_dil = nd.grey_dilation(pred, footprint=struct_m)
missing = np.setdiff1d(np.unique(pred), np.unique(pred_dil))
for m in missing:
pred_dil.ravel()[np.flatnonzero(pred == m)[0]] = m
prec, _, ts = precision_recall_curve(gtd.ravel(), pred.ravel())
_, rec, _ = precision_recall_curve(boundary.ravel(), pred_dil.ravel())
return zip(ts, prec, rec)
def wiggle_room_precision_recall(pred, boundary, margin=2, connectivity=1):
struct = nd.generate_binary_structure(boundary.ndim, connectivity)
gtd = nd.binary_dilation(boundary, struct, margin)
struct_m = nd.iterate_structure(struct, margin)
pred_dil = nd.grey_dilation(pred, footprint=struct_m)
missing = np.setdiff1d(np.unique(pred), np.unique(pred_dil))
for m in missing:
pred_dil.ravel()[np.flatnonzero(pred==m)[0]] = m
prec, _, ts = precision_recall_curve(gtd.ravel(), pred.ravel())
_, rec, _ = precision_recall_curve(boundary.ravel(), pred_dil.ravel())
return zip(ts, prec, rec)
def local_maxima(image, radius, separation, percentile=64):
"""Find local maxima whose brightness is above a given percentile."""
ndim = image.ndim
# Compute a threshold based on percentile.
not_black = image[np.nonzero(image)]
if len(not_black) == 0:
warnings.warn("Image is completely black.", UserWarning)
return np.empty((0, ndim))
threshold = stats.scoreatpercentile(not_black, percentile)
# The intersection of the image with its dilation gives local maxima.
if not np.issubdtype(image.dtype, np.integer):
raise TypeError("Perform dilation on exact (i.e., integer) data.")
#footprint = binary_mask(radius, ndim, separation)
s = ndimage.generate_binary_structure(ndim, 2)
# scale it up to the desired size
footprint = ndimage.iterate_structure(s, int(d_rad))
dilation = ndimage.grey_dilation(image, footprint=footprint,
mode='constant')
maxima = np.vstack(np.where((image == dilation) & (image > threshold))).T
if not np.size(maxima) > 0:
warnings.warn("Image contains no local maxima.", UserWarning)
return np.empty((0, ndim))
# Flat peaks return multiple nearby maxima. Eliminate duplicates.
while True:
duplicates = cKDTree(maxima, 30).query_pairs(separation)
if len(duplicates) == 0:
break
to_drop = []
for pair in duplicates:
# Take the average position.
# This is just a starting point, so we won't go into subpx precision here.
merged = maxima[pair[0]]
merged = maxima[[pair[0], pair[1]]].mean(0).astype(int)
maxima[pair[0]] = merged # overwrite one
to_drop.append(pair[1]) # queue other to be dropped
maxima = np.delete(maxima, to_drop, 0)
# Do not accept peaks near the edges.
shape = np.array(image.shape)
margin = int(separation) // 2
near_edge = np.any((maxima < margin) | (maxima > (shape - margin)), 1)
maxima = maxima[~near_edge]
if not np.size(maxima) > 0:
warnings.warn("All local maxima were in the margins.", UserWarning)
# Return coords in as a numpy array shaped so it can be passed directly
# to the DataFrame constructor.
return maxima
def separate_lungs(image, return_list=None, iteration=-1):
"""
This only takes in a 2D slice to make he lung segmentation and takes really long to run.
But supposedly will get all corner cases. Not sure if mask from this is very good.
Looks like the mask might be too dilated.
:param image:
:param return_list:
:param iteration:
:return:
"""
#Creation of the markers as shown above:
marker_internal, marker_external, marker_watershed = generate_markers(
image)
#Creation of the Sobel-Gradient
sobel_filtered_dx = ndimage.sobel(image, 1)
sobel_filtered_dy = ndimage.sobel(image, 0)
sobel_gradient = np.hypot(sobel_filtered_dx, sobel_filtered_dy)
sobel_gradient *= 255.0 / np.max(sobel_gradient)
#Watershed algorithm
watershed = morphology.watershed(sobel_gradient, marker_watershed)
#Reducing the image created by the Watershed algorithm to its outline
outline = ndimage.morphological_gradient(watershed, size=(3, 3))
outline = outline.astype(bool)
#Performing Black-Tophat Morphology for reinclusion
#Creation of the disk-kernel and increasing its size a bit
blackhat_struct = [[0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 0, 0]]
blackhat_struct = ndimage.iterate_structure(blackhat_struct, 8)
#Perform the Black-Hat
outline += ndimage.black_tophat(outline, structure=blackhat_struct)
#Use the internal marker and the Outline that was just created to generate the lungfilter
lungfilter = np.bitwise_or(marker_internal, outline)
#Close holes in the lungfilter
#fill_holes is not used here, since in some slices the heart would be reincluded by accident
lungfilter = ndimage.morphology.binary_closing(lungfilter,
structure=np.ones((5, 5)),
iterations=3)
# #Apply the lungfilter (note the filtered areas being assigned -2000 HU)
# segmented = np.where(lungfilter == 1, image, -2000*np.ones((512, 512)))
if iteration >= 0 and return_list:
return_list[iteration] = lungfilter
else:
return lungfilter
def subpixel_centroid(img, local_maxes, mask_rad, struct_shape='circle'):
'''
This is effectively a replacement for cntrd in the matlab/IDL code.
Works for 2D data only. Accelerated by numba.
:param img: the data
:param local_maxes: a (d,N) array with the location of the local maximums (as generated by :py:func:`~find_local_max`)
:param mask_rad: the radius of the mask used for the averaging.
:param struct_shape: ['circle' | 'diamond'] Shape of mask over each particle.
:rtype: (d,N) array of positions, (d,) array of masses, (d,) array of r2,
'''
# First, check that all local maxes are within 'mask_rad' of the image
# edges. Otherwise we will be going outside the bounds of the array in
# _refine_centroids_loop()
if not all(_local_max_within_bounds(img.shape, local_maxes, mask_rad)):
raise IndexError('One or more local maxes are too close to the image edge. Use local_max_crop().')
# Make coordinate order compatible with upcoming code
local_maxes = local_maxes[::-1]
# do some data checking/munging
img = np.squeeze(img) # knock out singleton dimensions
dim = img.ndim
if dim > 2: raise ValueError('Use subpixel_centroid_nd() for dimension > 2')
so = [slice(-mask_rad, mask_rad + 1)] * dim
# Make circular structuring element
if struct_shape == 'circle':
d_struct = (np.sum(np.mgrid[so]**2, 0) <= mask_rad**2).astype(np.int8)
elif struct_shape == 'diamond':
s = ndimage.generate_binary_structure(dim, 1)
# scale it up to the desired size
d_struct = ndimage.iterate_structure(s, int(mask_rad))
else: raise ValueError('Shape must be diamond or circle')
offset_masks = np.array([d_struct * os for os in np.mgrid[so]]).astype(np.int8)
r2_mask = np.zeros(d_struct.shape)
for o in offset_masks:
r2_mask += o ** 2
r2_mask = np.sqrt(r2_mask).astype(float)
results = _refine_centroids_loop(img, local_maxes, mask_rad, offset_masks, d_struct, r2_mask)
pos = (results[0:2,:] + local_maxes)[::-1,:]
#m = results[2,:]
#r2 = results[3,:]
#return pos, m, r2
peaks=[]
for i in range(0,pos.shape[1]):
x = pos[0][i]
y = pos[1][i]
peaks.append([x, y, img[y,x]])
return peaks
def make_dilation_kernel(dil_param):
kernel = ndimage.generate_binary_structure(2, 1)
kernel = ndimage.iterate_structure(kernel, dil_param)
z_component = np.zeros(kernel.shape, dtype=kernel.dtype)
width = kernel.shape[-1]
mid = width // 2
z_component[mid, mid] = 1
# kernel = np.stack((z_component,kernel,z_component),axis=0)
kernel = np.stack((kernel, kernel, kernel), axis=0)
return kernel.reshape((1, 1, 3, width, width))
def get_upd_flag(self, i_t, distance, entropy):
"""
:param i_t:
:param distance: sum of M nearest neighbours' distances, D
:param entropy: entropy of physics guided state evolution estimate distribution, E
:return: pf_upd_flag: dimension (n_lat, n_lon)
pf_upd_flag[i,j] = 1 denotes skip estimate correction for the subarea(i,j), i,e the
equation: E - alpha * D > 0 sustain.
"""
pf_upd_flag = np.zeros((self.n_lat, self.n_lon))
if self.flag_empty(i_t):
return pf_upd_flag
# need to be modified
# all areas update
if self.alg_upd == 1:
pf_upd_flag = np.ones((self.n_lat, self.n_lon))
# update the collected data areas
elif self.alg_upd == 2:
idx = self.data.smp_cnt_upd[i_t] > 0
pf_upd_flag[idx] = 1
# update collected data areas + good compensated data areas + dilation --- need to modify
elif self.alg_upd == 3:
# didn't make sense
# idx = self.data.ver_re_err_adp[i_t-1] >= self.ver_re_err_th
# pf_upd_flag[idx] = 1
idx = np.nonzero(self.data.smp_cnt_upd[i_t] > 0)
pf_upd_flag[idx] = 1
# didn't make sense
# idx = np.nonzero(self.data.ver_var_adp[i_t-1] >= self.ver_var_th)
# pf_upd_flag[idx] = 1
# idx = np.nonzero(pf_upd_flag > 0)
struct = generate_binary_structure(2, 1) # se = strel('square',3);
se = iterate_structure(struct, 3).astype(int)
pf_upd_flag = binary_dilation(pf_upd_flag, structure=struct)
# use ver_re_err and ver_var at i_t - 1 to get the update flag matrix
# for adaptive scheme
elif self.alg_upd == 4:
# TODO distance - entropy????
feature_tmp = np.reshape(distance - entropy,
(self.n_lat, self.n_lon))
idx = feature_tmp <= 0
pf_upd_flag[idx] = 1
idx1 = self.data.smp_cnt_upd[i_t] > 0
pf_upd_flag[idx1] = 1
return pf_upd_flag
def seperate_lungs(image):
"""
Function conducts lung segmentation process using watershed algorithm
:param image: a pixel array
:return: segmented, lungfilter, outline, watershed, sobel_gradient, marker_internal, marker_external, marker_watershed:
np ndarrays of segmented lungs (segmented) and other markers used in the process
"""
# Creation of the markers as shown above:
marker_internal, marker_external, marker_watershed = SegmentationA.generate_markers(
image)
# Creation of the Sobel-Gradient
sobel_filtered_dx = ndimage.sobel(image, 1)
sobel_filtered_dy = ndimage.sobel(image, 0)
sobel_gradient = np.hypot(sobel_filtered_dx, sobel_filtered_dy)
sobel_gradient *= 255.0 / np.max(sobel_gradient)
# Watershed algorithm
watershed = morphology.watershed(sobel_gradient, marker_watershed)
# Reducing the image created by the Watershed algorithm to its outline
outline = ndimage.morphological_gradient(watershed, size=(3, 3))
outline = outline.astype(bool)
# Performing Black-Tophat Morphology for reinclusion
# Creation of the disk-kernel and increasing its size a bit
blackhat_struct = [[0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 0, 0]]
blackhat_struct = ndimage.iterate_structure(blackhat_struct, 8)
# Perform the Black-Hat
outline += ndimage.black_tophat(outline, structure=blackhat_struct)
# Use the internal marker and the Outline that was just created to generate the lungfilter
lungfilter = np.bitwise_or(marker_internal, outline)
# Close holes in the lungfilter
# fill_holes is not used here, since in some slices the heart would be reincluded by accident
lungfilter = ndimage.morphology.binary_closing(lungfilter,
structure=np.ones(
(5, 5)),
iterations=3)
# Apply the lungfilter (note the filtered areas being assigned -2000 HU)
segmented = np.where(lungfilter == 1, image, -2000 * np.ones(
(len(image), len(image[0]))))
return segmented, lungfilter
def fill_border_holes_with_black_hat(self, outline):
blackhat_struct = [[0, 0, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 0, 0]]
blackhat_struct = ndimage.iterate_structure(blackhat_struct, 2)
outline += ndimage.black_tophat(outline, structure=blackhat_struct)
return outline
def local_maxima(self, image, radius, separation, threshold):
ndim = image.ndim
threshold -= 1
# The intersection of the image with its dilation gives local maxima.
if not np.issubdtype(image.dtype, np.integer):
raise TypeError("Perform dilation on exact (i.e., integer) data.")
#footprint = self.binary_mask(radius, ndim)
s = ndimage.generate_binary_structure(ndim, 2)
# scale it up to the desired size
footprint = ndimage.iterate_structure(s, int(radius))
dilation = ndimage.grey_dilation(image, footprint=footprint, mode='constant')
maxima = np.vstack(np.where((image == dilation) & (image > threshold))).T[:,::-1]
if not np.size(maxima) > 0:
#warnings.warn("Image contains no local maxima.", UserWarning)
return np.empty((0, ndim))
# Flat peaks return multiple nearby maxima. Eliminate duplicates.
if len(maxima) > 0:
while True:
duplicates = cKDTree(maxima, 30).query_pairs(separation)
if len(duplicates) == 0:
break
to_drop = []
for pair in duplicates:
# Take the average position.
# This is just a starting point, so we won't go into subpx precision here.
merged = maxima[pair[0]]
merged = maxima[[pair[0], pair[1]]].mean(0).astype(int)
maxima[pair[0]] = merged # overwrite one
to_drop.append(pair[1]) # queue other to be dropped
maxima = np.delete(maxima, to_drop, 0)
# Do not accept peaks near the edges.
shape = np.array(image.shape)
margin = int(separation) // 2
near_edge = np.any((maxima < margin) | (maxima > (shape - margin)), 1)
maxima = maxima[~near_edge]
#if not np.size(maxima) > 0:
#warnings.warn("All local maxima were in the margins.", UserWarning)
x, y = maxima[:,0], maxima[:,1]
max_val = image[x,y].reshape(len(maxima),1)
peaks = np.concatenate((maxima,max_val), axis = 1)
return peaks
def gradient_threshold(in_file, in_segm, thresh=1.0, out_file=None):
""" Compute a threshold from the histogram of the magnitude gradient image """
import os.path as op
import numpy as np
import nibabel as nb
from scipy import ndimage as sim
struc = sim.iterate_structure(sim.generate_binary_structure(3, 2), 2)
if out_file is None:
fname, ext = op.splitext(op.basename(in_file))
if ext == '.gz':
fname, ext2 = op.splitext(fname)
ext = ext2 + ext
out_file = op.abspath('{}_gradmask{}'.format(fname, ext))
imnii = nb.load(in_file)
hdr = imnii.get_header().copy()
hdr.set_data_dtype(np.uint8) # pylint: disable=no-member
data = imnii.get_data().astype(np.float32)
mask = np.zeros_like(data, dtype=np.uint8) # pylint: disable=no-member
mask[data > 15.] = 1
segdata = nb.load(in_segm).get_data().astype(np.uint8)
segdata[segdata > 0] = 1
segdata = sim.binary_dilation(segdata, struc, iterations=2,
border_value=1).astype(np.uint8) # pylint: disable=no-member
mask[segdata > 0] = 1
mask = sim.binary_closing(mask, struc, iterations=2).astype(np.uint8) # pylint: disable=no-member
# Remove small objects
label_im, nb_labels = sim.label(mask)
artmsk = np.zeros_like(mask)
if nb_labels > 2:
sizes = sim.sum(mask, label_im, list(range(nb_labels + 1)))
ordered = list(reversed(sorted(zip(sizes,
list(range(nb_labels + 1))))))
for _, label in ordered[2:]:
mask[label_im == label] = 0
artmsk[label_im == label] = 1
mask = sim.binary_fill_holes(mask, struc).astype(np.uint8) # pylint: disable=no-member
nb.Nifti1Image(mask, imnii.get_affine(), hdr).to_filename(out_file)
return out_file
def segment_lung(params, I, I_affine):
#####################################################
# Intensity thresholding & Morphological operations
#####################################################
M = np.zeros(I.shape)
M[I > params['lungMinValue']] = 1
M[I > params['lungMaxValue']] = 0
struct_s = ndimage.generate_binary_structure(3, 1)
struct_m = ndimage.iterate_structure(struct_s, 2)
M = ndimage.binary_closing(M, structure=struct_s, iterations=1)
M = ndimage.binary_opening(M, structure=struct_m, iterations=1)
#####################################################
# Estimate lung filed of view
#####################################################
[m, n, p] = I.shape
medx = int(m / 2)
medy = int(n / 2)
xrange1 = int(m / 2 * params['xRangeRatio1'])
xrange2 = int(m / 2 * params['xRangeRatio2'])
zrange1 = int(p * params['zRangeRatio1'])
zrange2 = int(p * params['zRangeRatio2'])
#####################################################
# Select largest connected components & save nii
#####################################################
M = measure.label(M)
label1 = M[medx - xrange2:medx - xrange1, medy, zrange1:zrange2]
label2 = M[medx + xrange1:medx + xrange2, medy, zrange1:zrange2]
label1 = stats.mode(label1[label1 > 0])[0][0]
label2 = stats.mode(label2[label2 > 0])[0][0]
M[M == label1] = -1
M[M == label2] = -1
M[M > 0] = 0
M = M * -1
M = ndimage.binary_closing(M, structure=struct_m, iterations=1)
M = ndimage.binary_fill_holes(M)
Mlung = np.int8(M)
nib.Nifti1Image(Mlung,
I_affine).to_filename('./result/sample_lungaw.nii.gz')
return Mlung
def gradient_threshold(in_file, thresh=1.0, out_file=None):
""" Compute a threshold from the histogram of the magnitude gradient image """
import os.path as op
import numpy as np
import nibabel as nb
from scipy import ndimage as sim
thresh *= 1e-2
if out_file is None:
fname, ext = op.splitext(op.basename(in_file))
if ext == '.gz':
fname, ext2 = op.splitext(fname)
ext = ext2 + ext
out_file = op.abspath('%s_gradmask%s' % (fname, ext))
imnii = nb.load(in_file)
data = imnii.get_data()
hist, bin_edges = np.histogram(data[data > 0], bins=128, density=True) # pylint: disable=no-member
# Find threshold at 1% frequency
for i, freq in reversed(list(enumerate(hist))):
binw = bin_edges[i+1] - bin_edges[i]
if (freq * binw) >= thresh:
out_thresh = 0.5 * binw
break
mask = np.zeros_like(data, dtype=np.uint8) # pylint: disable=no-member
mask[data > out_thresh] = 1
struc = sim.iterate_structure(sim.generate_binary_structure(3, 2), 2)
mask = sim.binary_opening(mask, struc).astype(np.uint8) # pylint: disable=no-member
# Remove small objects
label_im, nb_labels = sim.label(mask)
if nb_labels > 2:
sizes = sim.sum(mask, label_im, range(nb_labels + 1))
ordered = list(reversed(sorted(zip(sizes, range(nb_labels + 1)))))
for _, label in ordered[2:]:
mask[label_im == label] = 0
mask = sim.binary_closing(mask, struc).astype(np.uint8) # pylint: disable=no-member
mask = sim.binary_fill_holes(mask, struc).astype(np.uint8) # pylint: disable=no-member
hdr = imnii.get_header().copy()
hdr.set_data_dtype(np.uint8) # pylint: disable=no-member
nb.Nifti1Image(mask, imnii.get_affine(), hdr).to_filename(out_file)
return out_file
def applyImageThresholds(self,
image,
highThreshold=None,
lowThreshold=None,
regularizationWidth=2):
"""Restrict image values to be between upper and lower limits.
This method flags all pixels in an image that are outside of the given
threshold values. The threshold values are taken from a reference
image, so noisy pixels are likely to get flagged. In order to exclude
those noisy pixels, the array of flags is eroded and dilated, which
removes isolated pixels outside of the thresholds from the list of
pixels to be modified. Pixels that remain flagged after this operation
have their values set to the appropriate upper or lower threshold
value.
Parameters
----------
image : `numpy.ndarray`
The image to apply the thresholds to.
The values will be modified in place.
highThreshold : `numpy.ndarray`, optional
Array of upper limit values for each pixel of ``image``.
lowThreshold : `numpy.ndarray`, optional
Array of lower limit values for each pixel of ``image``.
regularizationWidth : `int`, optional
Minimum radius of a region to include in regularization, in pixels.
"""
# Generate the structure for binary erosion and dilation, which is used
# to remove noise-like pixels. Groups of pixels with a radius smaller
# than ``regularizationWidth`` will be excluded from regularization.
filterStructure = ndimage.iterate_structure(
ndimage.generate_binary_structure(2, 1), regularizationWidth)
if highThreshold is not None:
highPixels = image > highThreshold
if regularizationWidth > 0:
# Erode and dilate ``highPixels`` to exclude noisy pixels.
highPixels = ndimage.morphology.binary_opening(
highPixels, structure=filterStructure)
image[highPixels] = highThreshold[highPixels]
if lowThreshold is not None:
lowPixels = image < lowThreshold
if regularizationWidth > 0:
# Erode and dilate ``lowPixels`` to exclude noisy pixels.
lowPixels = ndimage.morphology.binary_opening(
lowPixels, structure=filterStructure)
image[lowPixels] = lowThreshold[lowPixels]
def gradient_threshold(in_file, thresh=1.0, out_file=None):
""" Compute a threshold from the histogram of the magnitude gradient image """
import os.path as op
import numpy as np
import nibabel as nb
from scipy import ndimage as sim
thresh *= 1e-2
if out_file is None:
fname, ext = op.splitext(op.basename(in_file))
if ext == '.gz':
fname, ext2 = op.splitext(fname)
ext = ext2 + ext
out_file = op.abspath('%s_gradmask%s' % (fname, ext))
imnii = nb.load(in_file)
data = imnii.get_data()
hist, bin_edges = np.histogram(data[data > 0], bins=128, density=True) # pylint: disable=no-member
# Find threshold at 1% frequency
for i, freq in reversed(list(enumerate(hist))):
binw = bin_edges[i + 1] - bin_edges[i]
if (freq * binw) >= thresh:
out_thresh = 0.5 * binw
break
mask = np.zeros_like(data, dtype=np.uint8) # pylint: disable=no-member
mask[data > out_thresh] = 1
struc = sim.iterate_structure(sim.generate_binary_structure(3, 2), 2)
mask = sim.binary_opening(mask, struc).astype(np.uint8) # pylint: disable=no-member
# Remove small objects
label_im, nb_labels = sim.label(mask)
if nb_labels > 2:
sizes = sim.sum(mask, label_im, range(nb_labels + 1))
ordered = list(reversed(sorted(zip(sizes, range(nb_labels + 1)))))
for _, label in ordered[2:]:
mask[label_im == label] = 0
mask = sim.binary_closing(mask, struc).astype(np.uint8) # pylint: disable=no-member
mask = sim.binary_fill_holes(mask, struc).astype(np.uint8) # pylint: disable=no-member
hdr = imnii.get_header().copy()
hdr.set_data_dtype(np.uint8) # pylint: disable=no-member
nb.Nifti1Image(mask, imnii.get_affine(), hdr).to_filename(out_file)
return out_file
def gradient_threshold(in_file, in_segm, thresh=1.0, out_file=None):
""" Compute a threshold from the histogram of the magnitude gradient image """
import os.path as op
import numpy as np
import nibabel as nb
from scipy import ndimage as sim
struc = sim.iterate_structure(sim.generate_binary_structure(3, 2), 2)
if out_file is None:
fname, ext = op.splitext(op.basename(in_file))
if ext == '.gz':
fname, ext2 = op.splitext(fname)
ext = ext2 + ext
out_file = op.abspath('{}_gradmask{}'.format(fname, ext))
imnii = nb.load(in_file)
hdr = imnii.get_header().copy()
hdr.set_data_dtype(np.uint8) # pylint: disable=no-member
data = imnii.get_data().astype(np.float32)
mask = np.zeros_like(data, dtype=np.uint8) # pylint: disable=no-member
mask[data > 15.] = 1
segdata = nb.load(in_segm).get_data().astype(np.uint8)
segdata[segdata > 0] = 1
segdata = sim.binary_dilation(segdata, struc, iterations=2, border_value=1).astype(np.uint8) # pylint: disable=no-member
mask[segdata > 0] = 1
mask = sim.binary_closing(mask, struc, iterations=2).astype(np.uint8) # pylint: disable=no-member
# Remove small objects
label_im, nb_labels = sim.label(mask)
artmsk = np.zeros_like(mask)
if nb_labels > 2:
sizes = sim.sum(mask, label_im, list(range(nb_labels + 1)))
ordered = list(reversed(sorted(zip(sizes, list(range(nb_labels + 1))))))
for _, label in ordered[2:]:
mask[label_im == label] = 0
artmsk[label_im == label] = 1
mask = sim.binary_fill_holes(mask, struc).astype(np.uint8) # pylint: disable=no-member
nb.Nifti1Image(mask, imnii.get_affine(), hdr).to_filename(out_file)
return out_file
def testGetCenterAndRfromImgBinary(self):
structOri = generate_binary_structure(2, 1).astype(int)
iterations = 7
donut = iterate_structure(structOri, iterations)
dY, dX = donut.shape
cornerX = 10
cornerY = 20
imgBinary = np.zeros((120, 120), dtype=int)
imgBinary[cornerY:cornerY + dY, cornerX:cornerX + dX] = donut
x, y, r = self.centroid.getCenterAndRfromImgBinary(imgBinary)
self.assertEqual(x, cornerX + iterations)
self.assertEqual(y, cornerY + iterations)
self.assertAlmostEqual(r, 5.9974, places=3)
def get_segmented_lungs(image):
#Creation of the markers as shown above:
marker_internal, marker_external, marker_watershed = generate_markers(
image)
#Creation of the Sobel-Gradient
sobel_filtered_dx = ndimage.sobel(image, 1)
sobel_filtered_dy = ndimage.sobel(image, 0)
sobel_gradient = np.hypot(sobel_filtered_dx, sobel_filtered_dy)
sobel_gradient *= 255.0 / np.max(sobel_gradient)
#Watershed algorithm
watershed = morphology.watershed(sobel_gradient, marker_watershed)
#Reducing the image created by the Watershed algorithm to its outline
outline = ndimage.morphological_gradient(watershed, size=(3, 3))
outline = outline.astype(bool)
#Performing Black-Tophat Morphology for reinclusion
#Creation of the disk-kernel and increasing its size a bit
blackhat_struct = [[0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 0, 0]]
#blackhat_struct = ndimage.iterate_structure(blackhat_struct, 8)
blackhat_struct = ndimage.iterate_structure(
blackhat_struct, 14
) # <- retains more of the area, 12 works well. Changed to 14, 12 still excluded some parts.
#Perform the Black-Hat
outline += ndimage.black_tophat(outline, structure=blackhat_struct)
#Use the internal marker and the Outline that was just created to generate the lungfilter
lungfilter = np.bitwise_or(marker_internal, outline)
#Close holes in the lungfilter
#fill_holes is not used here, since in some slices the heart would be reincluded by accident
lungfilter = ndimage.morphology.binary_closing(lungfilter,
structure=np.ones((5, 5)),
iterations=3)
#Apply the lungfilter (note the filtered areas being assigned threshold_min HU)
segmented = np.where(lungfilter == 1, image,
threshold_min * np.ones(image.shape))
#return segmented, lungfilter, outline, watershed, sobel_gradient, marker_internal, marker_external, marker_watershed
return segmented
def subpixel_centroid(img, local_maxes, mask_rad):
'''
This is effectively a replacement for cntrd in the matlab/IDL code.
Should work for any dimension data
:param img: the data
:param local_maxes: a (d,N) array with the location of the local maximums (as generated by :py:func:`~find_local_max`)
:param mask_rad: the radius of the mask used for the averaging.
:rtype: (d,N) array of positions, (d,) array of masses, (d,) array of r2,
'''
local_maxes = local_maxes[::-1]
# do some data checking/munging
mask_rad = int(mask_rad)
img = np.squeeze(img) # knock out singleton dimensions
# make sure local_maxes.shape makes sense
dim = img.ndim
s = ndimage.generate_binary_structure(dim, 1)
# scale it up to the desired size
d_struct = ndimage.iterate_structure(s, int(mask_rad))
so = [slice(-mask_rad, mask_rad + 1)] * dim
offset_masks = [d_struct * os for os in np.mgrid[so]]
r2_mask = np.zeros(d_struct.shape)
for o in offset_masks:
r2_mask += o ** 2
r2_mask = np.sqrt(r2_mask)
shifts_lst = []
mass_lst = []
r2_lst = []
for loc in itertools.izip(*local_maxes):
window = [slice(p - mask_rad, p + mask_rad + 1) for p in loc]
img_win = img[window]
mass = np.sum(img_win * d_struct)
mass_lst.append(mass)
shifts_lst.append([np.sum(img_win * o) / mass for o in offset_masks])
r2_lst.append(np.sum(r2_mask * img_win))
sub_pixel = np.array(shifts_lst).T + local_maxes
return sub_pixel[::-1], mass_lst, r2_lst
def segment_lung(params, I, I_affine):
#####################################################
# Intensity thresholding & Morphological operations
#####################################################
M = np.zeros(I.shape)
M[I > params["lungMinValue"]] = 1
M[I > params["lungMaxValue"]] = 0
struct_s = ndimage.generate_binary_structure(3, 1)
struct_m = ndimage.iterate_structure(struct_s, 2)
M = ndimage.binary_closing(M, structure=struct_s, iterations=1)
M = ndimage.binary_opening(M, structure=struct_m, iterations=1)
#####################################################
# Estimate lung filed of view
#####################################################
[m, n, p] = I.shape
medx = int(m / 2)
medy = int(n / 2)
xrange1 = int(m / 2 * params["xRangeRatio1"])
xrange2 = int(m / 2 * params["xRangeRatio2"])
zrange1 = int(p * params["zRangeRatio1"])
zrange2 = int(p * params["zRangeRatio2"])
#####################################################
# Select largest connected components & save nii
#####################################################
M = measure.label(M)
label1 = M[medx - xrange2:medx - xrange1, medy, zrange1:zrange2]
label2 = M[medx + xrange1:medx + xrange2, medy, zrange1:zrange2]
label1 = stats.mode(label1[label1 > 0])[0][0]
label2 = stats.mode(label2[label2 > 0])[0][0]
M[M == label1] = -1
M[M == label2] = -1
M[M > 0] = 0
M = M * -1
M = ndimage.binary_closing(M, structure=struct_m, iterations=1)
M = ndimage.binary_fill_holes(M)
Mlung = np.int8(M)
# Note: Skip writing "lungaw.nii.gz" to disk, as we don't use it
return Mlung
def get_segmented_lungs(image):
#Creation of the markers as shown above:
marker_internal, marker_external, marker_watershed = generate_markers(image)
#Creation of the Sobel-Gradient
sobel_filtered_dx = ndimage.sobel(image, 1)
sobel_filtered_dy = ndimage.sobel(image, 0)
sobel_gradient = np.hypot(sobel_filtered_dx, sobel_filtered_dy)
sobel_gradient *= 255.0 / np.max(sobel_gradient)
#Watershed algorithm
watershed = morphology.watershed(sobel_gradient, marker_watershed)
#Reducing the image created by the Watershed algorithm to its outline
outline = ndimage.morphological_gradient(watershed, size=(3,3))
outline = outline.astype(bool)
#Performing Black-Tophat Morphology for reinclusion
#Creation of the disk-kernel and increasing its size a bit
blackhat_struct = [[0, 0, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 0, 0]]
#blackhat_struct = ndimage.iterate_structure(blackhat_struct, 8)
blackhat_struct = ndimage.iterate_structure(blackhat_struct, 14) # <- retains more of the area, 12 works well. Changed to 14, 12 still excluded some parts.
#Perform the Black-Hat
outline += ndimage.black_tophat(outline, structure=blackhat_struct)
#Use the internal marker and the Outline that was just created to generate the lungfilter
lungfilter = np.bitwise_or(marker_internal, outline)
#Close holes in the lungfilter
#fill_holes is not used here, since in some slices the heart would be reincluded by accident
lungfilter = ndimage.morphology.binary_closing(lungfilter, structure=np.ones((5,5)), iterations=3)
#Apply the lungfilter (note the filtered areas being assigned threshold_min HU)
segmented = np.where(lungfilter == 1, image, threshold_min*np.ones(image.shape))
#return segmented, lungfilter, outline, watershed, sobel_gradient, marker_internal, marker_external, marker_watershed
return segmented
def applyImageThresholds(self, image, highThreshold=None, lowThreshold=None, regularizationWidth=2):
"""Restrict image values to be between upper and lower limits.
This method flags all pixels in an image that are outside of the given
threshold values. The threshold values are taken from a reference image,
so noisy pixels are likely to get flagged. In order to exclude those
noisy pixels, the array of flags is eroded and dilated, which removes
isolated pixels outside of the thresholds from the list of pixels to be
modified. Pixels that remain flagged after this operation have their
values set to the appropriate upper or lower threshold value.
Parameters
----------
image : `numpy.ndarray`
The image to apply the thresholds to.
The values will be modified in place.
highThreshold : `numpy.ndarray`, optional
Array of upper limit values for each pixel of ``image``.
lowThreshold : `numpy.ndarray`, optional
Array of lower limit values for each pixel of ``image``.
regularizationWidth : `int`, optional
Minimum radius of a region to include in regularization, in pixels.
"""
# Generate the structure for binary erosion and dilation, which is used to remove noise-like pixels.
# Groups of pixels with a radius smaller than ``regularizationWidth``
# will be excluded from regularization.
filterStructure = ndimage.iterate_structure(ndimage.generate_binary_structure(2, 1),
regularizationWidth)
if highThreshold is not None:
highPixels = image > highThreshold
if regularizationWidth > 0:
# Erode and dilate ``highPixels`` to exclude noisy pixels.
highPixels = ndimage.morphology.binary_opening(highPixels, structure=filterStructure)
image[highPixels] = highThreshold[highPixels]
if lowThreshold is not None:
lowPixels = image < lowThreshold
if regularizationWidth > 0:
# Erode and dilate ``lowPixels`` to exclude noisy pixels.
lowPixels = ndimage.morphology.binary_opening(lowPixels, structure=filterStructure)
image[lowPixels] = lowThreshold[lowPixels]
def wiggle_room_precision_recall(pred, boundary, margin=2, connectivity=1):
"""Voxel-wise, continuous value precision recall curve allowing drift.
Voxel-wise precision recall evaluates predictions against a ground truth.
Wiggle-room precision recall (WRPR, "warper") allows calls from nearby
voxels to be counted as correct. Specifically, if a voxel is predicted to
be a boundary within a dilation distance of `margin` (distance defined
according to `connectivity`) of a true boundary voxel, it will be counted
as a True Positive in the Precision, and vice-versa for the Recall.
Parameters
----------
pred : np.ndarray of float, arbitrary shape
The prediction values, expressed as probability of observing a boundary
(i.e. a voxel with label 1).
boundary : np.ndarray of int, same shape as pred
The true boundary map. 1 indicates boundary, 0 indicates non-boundary.
margin : int, optional
The number of dilations that define the margin. default: 2.
connectivity : {1, ..., pred.ndim}, optional
The morphological voxel connectivity (defined as in SciPy) for the
dilation step.
Returns
-------
ts, pred, rec : np.ndarray of float, shape `(len(np.unique(pred)+1),)`
The prediction value thresholds corresponding to each precision and
recall value, the precision values, and the recall values.
"""
struct = nd.generate_binary_structure(boundary.ndim, connectivity)
gtd = nd.binary_dilation(boundary, struct, margin)
struct_m = nd.iterate_structure(struct, margin)
pred_dil = nd.grey_dilation(pred, footprint=struct_m)
missing = np.setdiff1d(np.unique(pred), np.unique(pred_dil))
for m in missing:
pred_dil.ravel()[np.flatnonzero(pred == m)[0]] = m
prec, _, ts = precision_recall_curve(gtd.ravel(), pred.ravel())
_, rec, _ = precision_recall_curve(boundary.ravel(), pred_dil.ravel())
return list(zip(ts, prec, rec))
def solvePoissonEq(self, inst, I1, I2, iOutItr=0):
if self.PoissonSolver == 'fft':
'''Poisson Solver using an FFT
'''
# this is the only place iOutItr is used.
cliplevel = self.sumclipSequence[iOutItr]
aperturePixelSize = \
(inst.apertureDiameter *
inst.sensorFactor / inst.sensorSamples)
v, u = np.mgrid[
-0.5 / aperturePixelSize:(0.5) / aperturePixelSize:
1 / self.padDim / aperturePixelSize,
-0.5 / aperturePixelSize:(0.5) / aperturePixelSize:
1 / self.padDim / aperturePixelSize]
if self.debugLevel >= 3:
print('iOuter=%d, cliplevel=%4.2f' % (iOutItr, cliplevel))
print(v.shape)
u2v2 = -4 * (np.pi**2) * (u * u + v * v)
# Set origin to Inf and 0 to result in 0 at origin after filtering
ctrIdx = np.floor(self.padDim / 2)
u2v2[ctrIdx, ctrIdx] = np.inf
self.createSignal(inst, I1, I2, cliplevel)
# find the indices for a ring of pixels
# just ouside and just inside the
# aperture for use in setting dWdn = 0
struct = ndimage.generate_binary_structure(2, 1)
struct = ndimage.iterate_structure(struct, self.boundaryT)
# print struct
ApringOut = np.logical_xor(ndimage.morphology.binary_dilation(
self.pMask, structure=struct), self.pMask).astype(int)
ApringIn = np.logical_xor(ndimage.morphology.binary_erosion(
self.pMask, structure=struct), self.pMask).astype(int)
bordery, borderx = np.nonzero(ApringOut)
if (self.compMode == 'zer'):
zc = np.zeros((self.numTerms, self.innerItr))
# print "ZC ONE",zc.shape
# **************************************************************
# initial BOX 3 - put signal in boundary (since there's no existing
# Sestimate, S just equals self.S
S = self.S.copy()
for jj in range(int(self.innerItr)):
# *************************************************************
# BOX 4 - forward filter: forward FFT, divide by u2v2, inverse
# FFT
SFFT = np.fft.fftshift(np.fft.fft2(np.fft.fftshift(S)))
# print SFFT.shape, u2v2.shape
W = np.fft.fftshift(np.fft.irfft2(np.fft.fftshift(SFFT / u2v2),
s=S.shape))
# *************************************************************
# BOX 5 - Wavefront estimate
# (includes zeroing offset & masking to the aperture size)
West = tools.extractArray(W, inst.sensorSamples)
# print "WEST", West.shape, W.shape
offset = West[self.pMask == 1].mean()
West = West - offset
West[self.pMask == 0] = 0
if (self.compMode == 'zer'):
zc[:, jj] = tools.ZernikeMaskedFit(
West, inst.xSensor, inst.ySensor,
self.numTerms, self.pMask, self.zobsR)
# ************************************************************
# BOX 6 - set dWestimate/dn = 0 around boundary
WestdWdn0 = West.copy()
# do a 3x3 average around each border pixel,
# including only those pixels inside the aperture
for ii in range(len(borderx)):
reg = West[borderx[ii] - self.boundaryT:
borderx[ii] + self.boundaryT + 1,
bordery[ii] - self.boundaryT:
bordery[ii] + self.boundaryT + 1]
intersectIdx = ApringIn[borderx[ii] - self.boundaryT:
borderx[ii] + self.boundaryT + 1,
bordery[ii] - self.boundaryT:
bordery[ii] + self.boundaryT + 1]
WestdWdn0[borderx[ii], bordery[ii]] =\
reg[np.nonzero(intersectIdx)].mean()
# ***********************************************************
# BOX 7 - Take Laplacian to find sensor signal estimate
Wxx = np.zeros((inst.sensorSamples, inst.sensorSamples))
Wyy = np.zeros((inst.sensorSamples, inst.sensorSamples))
Wt = WestdWdn0.copy()
Wxx[:, 1:-1] = (Wt[:, 0:-2] - 2 * Wt[:, 1:-1] + Wt[:, 2:]) /\
aperturePixelSize**2
Wyy[1:-1, :] = (Wt[0:-2, :] - 2 * Wt[1:-1, :] + Wt[2:, :]) /\
aperturePixelSize**2
del2W = Wxx + Wyy
Sest = tools.padArray(del2W, self.padDim)
# ********************************************************
# BOX 3 - Put signal back inside boundary,
# leaving the rest of Sestimate
Sest[self.pMaskPad == 1] = self.S[self.pMaskPad == 1]
S = Sest
self.West = West.copy()
if (self.compMode == 'zer'):
self.zc = zc
elif self.PoissonSolver == 'exp':
self.getdIandI(I1, I2)
xSensor = inst.xSensor * self.cMask
ySensor = inst.ySensor * self.cMask
F = np.zeros(self.numTerms)
dZidx = np.zeros((self.numTerms, inst.sensorSamples,
inst.sensorSamples))
dZidy = dZidx.copy()
aperturePixelSize = \
(inst.apertureDiameter *
inst.sensorFactor / inst.sensorSamples)
zcCol = np.zeros(self.numTerms)
for i in range(int(self.numTerms)):
zcCol[i] = 1
# we integrate, instead of decompose, integration is faster.
# Also, decomposition is ill-defined on m.cMask.
# Using m.pMask, the two should give same results.
if (self.zobsR > 0):
F[i] = np.sum(np.sum(
self.dI * tools.ZernikeAnnularEval(
zcCol, xSensor, ySensor,
self.zobsR))) * aperturePixelSize**2
dZidx[i, :, :] = tools.ZernikeAnnularGrad(
zcCol, xSensor, ySensor, self.zobsR, 'dx')
dZidy[i, :, :] = tools.ZernikeAnnularGrad(
zcCol, xSensor, ySensor, self.zobsR, 'dy')
else:
F[i] = np.sum(np.sum(
self.dI * tools.ZernikeEval(
zcCol, xSensor, ySensor))) * aperturePixelSize**2
dZidx[i, :, :] = tools.ZernikeGrad(
zcCol, xSensor, ySensor, 'dx')
dZidy[i, :, :] = tools.ZernikeGrad(
zcCol, xSensor, ySensor, 'dy')
zcCol[i] = 0
self.Mij = np.zeros((self.numTerms, self.numTerms))
for i in range(self.numTerms):
for j in range(self.numTerms):
self.Mij[i, j] = aperturePixelSize**2 /\
(inst.apertureDiameter / 2)**2 * \
np.sum(np.sum(
self.image *
(dZidx[i, :, :].squeeze() *
dZidx[j, :, :].squeeze() +
dZidy[i, :, :].squeeze() *
dZidy[j, :, :].squeeze())))
dz = 2 * inst.focalLength * \
(inst.focalLength - inst.offset) / inst.offset
self.zc = np.zeros(self.numTerms)
idx = [x - 1 for x in self.ZTerms]
# phi in GN paper is phase, phi/(2pi)*lambda=W
zc_tmp = np.dot(np.linalg.pinv(self.Mij[:, idx][idx]), F[idx]) / dz
self.zc[idx] = zc_tmp
if (self.zobsR > 0):
self.West = tools.ZernikeAnnularEval(
np.concatenate(([0, 0, 0], self.zc[3:])),
xSensor, ySensor, self.zobsR)
else:
self.West = tools.ZernikeEval(
np.concatenate(([0, 0, 0], self.zc[3:])),
xSensor, ySensor)
def compensate(self, inst, algo, zcCol, model):
"""Calculate the image compensated from the affection of wavefront.
Parameters
----------
inst : Instrument
Instrument to use.
algo : Algorithm
Algorithm to solve the Poisson's equation. It can by done by the
fast Fourier transform or serial expansion.
zcCol : numpy.ndarray
Coefficients of wavefront.
model : str
Optical model. It can be "paraxial", "onAxis", or "offAxis".
Raises
------
RuntimeError
input:size zcCol in compensate needs to be a numTerms row column
vector.
"""
# Check the condition of inputs
numTerms = algo.getNumOfZernikes()
if ((zcCol.ndim == 1) and (len(zcCol) != numTerms)):
raise RuntimeError("input:size",
"zcCol in compensate needs to be a %d row column vector. \n" % numTerms)
# Dimension of image
sm, sn = self._image.getImg().shape
# Dimenstion of projected image on focal plane
projSamples = sm
# Let us create a look-up table for x -> xp first.
luty, lutx = np.mgrid[-(projSamples/2 - 0.5):(projSamples/2 + 0.5),
-(projSamples/2 - 0.5):(projSamples/2 + 0.5)]
sensorFactor = inst.getSensorFactor()
lutx = lutx/(projSamples/2/sensorFactor)
luty = luty/(projSamples/2/sensorFactor)
# Set up the mapping
lutxp, lutyp, J = self._aperture2image(inst, algo, zcCol, lutx, luty,
projSamples, model)
show_lutxyp = self._showProjection(lutxp, lutyp, sensorFactor,
projSamples, raytrace=False)
if (np.all(show_lutxyp <= 0)):
self.caustic = True
return
# Calculate the weighting center (x, y) and radius
realcx, realcy = self._image.getCenterAndR_ef()[0:2]
# Extend the dimension of image by 20 pixel in x and y direction
show_lutxyp = padArray(show_lutxyp, projSamples+20)
# Get the binary matrix of image on pupil plane if raytrace=False
struct0 = generate_binary_structure(2, 1)
struct = iterate_structure(struct0, 4)
struct = binary_dilation(struct, structure=struct0, iterations=2).astype(int)
show_lutxyp = binary_dilation(show_lutxyp, structure=struct)
show_lutxyp = binary_erosion(show_lutxyp, structure=struct)
# Extract the region from the center of image and get the original one
show_lutxyp = extractArray(show_lutxyp, projSamples)
# Calculate the weighting center (x, y) and radius
projcx, projcy = self._image.getCenterAndR_ef(image=show_lutxyp.astype(float))[0:2]
# Shift the image to center of projection on pupil
# +(-) means we need to move image to the right (left)
shiftx = projcx - realcx
# +(-) means we need to move image upward (downward)
shifty = projcy - realcy
self._image.updateImage(np.roll(self._image.getImg(), int(np.round(shifty)), axis=0))
self._image.updateImage(np.roll(self._image.getImg(), int(np.round(shiftx)), axis=1))
# Construct the interpolant to get the intensity on (x', p') plane
# that corresponds to the grid points on (x,y)
yp, xp = np.mgrid[-(sm/2 - 0.5):(sm/2 + 0.5), -(sm/2 - 0.5):(sm/2 + 0.5)]
xp = xp/(sm/2/sensorFactor)
yp = yp/(sm/2/sensorFactor)
# Put the NaN to be 0 for the interpolate to use
lutxp[np.isnan(lutxp)] = 0
lutyp[np.isnan(lutyp)] = 0
# Construct the function for interpolation
ip = RectBivariateSpline(yp[:, 0], xp[0, :], self._image.getImg(), kx=1, ky=1)
# Construct the projected image by the interpolation
lutIp = np.zeros(lutxp.shape[0]*lutxp.shape[1])
for ii, (xx, yy) in enumerate(zip(lutxp.ravel(), lutyp.ravel())):
lutIp[ii] = ip(yy, xx)
lutIp = lutIp.reshape(lutxp.shape)
# Calaculate the image on focal plane with compensation based on flux
# conservation
# I(x, y)/I'(x', y') = J = (dx'/dx)*(dy'/dy) - (dx'/dy)*(dy'/dx)
self._image.updateImage(lutIp * J)
if (self.defocalType == DefocalType.Extra):
self._image.updateImage(np.rot90(self._image.getImg(), k=2))
# Put NaN to be 0
holdedImg = self._image.getImg()
holdedImg[np.isnan(holdedImg)] = 0
self._image.updateImage(holdedImg)
# Check the compensated image has the problem or not.
# The negative value means the over-compensation from wavefront error
if (np.any(self._image.getImg() < 0) and np.all(self.image0 >= 0)):
print("WARNING: negative scale parameter, image is within caustic, zcCol (in um)=\n")
self.caustic = True
# Put the overcompensated part to be 0
holdedImg = self._image.getImg()
holdedImg[holdedImg < 0] = 0
self._image.updateImage(holdedImg)
def _run_interface(self, runtime):
from scipy import ndimage as sim
fmap_nii = nb.load(self.inputs.in_file)
data = np.squeeze(fmap_nii.get_data().astype(np.float32))
# Despike / denoise (no-mask)
if self.inputs.despike:
data = _despike2d(data, self.inputs.despike_threshold)
mask = None
if isdefined(self.inputs.in_mask):
masknii = nb.load(self.inputs.in_mask)
mask = masknii.get_data().astype(np.uint8)
# Dilate mask
if self.inputs.mask_erode > 0:
struc = sim.iterate_structure(sim.generate_binary_structure(3, 2), 1)
mask = sim.binary_erosion(
mask, struc,
iterations=self.inputs.mask_erode
).astype(np.uint8) # pylint: disable=no-member
self._results['out_file'] = genfname(self.inputs.in_file, suffix='enh')
datanii = nb.Nifti1Image(data, fmap_nii.affine, fmap_nii.header)
if self.inputs.unwrap:
data = _unwrap(data, self.inputs.in_magnitude, mask)
self._results['out_unwrapped'] = genfname(self.inputs.in_file, suffix='unwrap')
nb.Nifti1Image(data, fmap_nii.affine, fmap_nii.header).to_filename(
self._results['out_unwrapped'])
if not self.inputs.bspline_smooth:
datanii.to_filename(self._results['out_file'])
return runtime
else:
from fmriprep.utils import bspline as fbsp
from statsmodels.robust.scale import mad
# Fit BSplines (coarse)
bspobj = fbsp.BSplineFieldmap(datanii, weights=mask,
njobs=self.inputs.njobs)
bspobj.fit()
smoothed1 = bspobj.get_smoothed()
# Manipulate the difference map
diffmap = data - smoothed1.get_data()
sderror = mad(diffmap[mask > 0])
LOGGER.info('SD of error after B-Spline fitting is %f', sderror)
errormask = np.zeros_like(diffmap)
errormask[np.abs(diffmap) > (10 * sderror)] = 1
errormask *= mask
nslices = 0
try:
errorslice = np.squeeze(np.argwhere(errormask.sum(0).sum(0) > 0))
nslices = errorslice[-1] - errorslice[0]
except IndexError: # mask is empty, do not refine
pass
if nslices > 1:
diffmapmsk = mask[..., errorslice[0]:errorslice[-1]]
diffmapnii = nb.Nifti1Image(
diffmap[..., errorslice[0]:errorslice[-1]] * diffmapmsk,
datanii.affine, datanii.header)
bspobj2 = fbsp.BSplineFieldmap(diffmapnii, knots_zooms=[24., 24., 4.],
njobs=self.inputs.njobs)
bspobj2.fit()
smoothed2 = bspobj2.get_smoothed().get_data()
final = smoothed1.get_data().copy()
final[..., errorslice[0]:errorslice[-1]] += smoothed2
else:
final = smoothed1.get_data()
nb.Nifti1Image(final, datanii.affine, datanii.header).to_filename(
self._results['out_file'])
return runtime
def _solvePoissonEq(self, I1, I2, iOutItr=0):
"""Solve the Poisson's equation by Fourier transform (differential) or
serial expansion (integration).
There is no convergence for fft actually. Need to add the difference
comparison and X-alpha method. Need to discuss further for this.
Parameters
----------
I1 : Image
Intra- or extra-focal image.
I2 : Image
Intra- or extra-focal image.
iOutItr : int, optional
ith number of outer loop iteration which is important in "fft"
algorithm. (the default is 0.)
Returns
-------
numpy.ndarray
Coefficients of normal/ annular Zernike polynomials.
numpy.ndarray
Estimated wavefront.
"""
# Calculate the aperature pixel size
apertureDiameter = self._inst.getApertureDiameter()
sensorFactor = self._inst.getSensorFactor()
dimOfDonut = self._inst.getDimOfDonutOnSensor()
aperturePixelSize = apertureDiameter*sensorFactor/dimOfDonut
# Calculate the differential Omega
dOmega = aperturePixelSize**2
# Solve the Poisson's equation based on the type of algorithm
numTerms = self.getNumOfZernikes()
zobsR = self.getObsOfZernikes()
PoissonSolver = self.getPoissonSolverName()
if (PoissonSolver == "fft"):
# Use the differential method by fft to solve the Poisson's
# equation
# Parameter to determine the threshold of calculating I0.
sumclipSequence = self.getSignalClipSequence()
cliplevel = sumclipSequence[iOutItr]
# Generate the v, u-coordinates on pupil plane
padDim = self.getFftDimension()
v, u = np.mgrid[
-0.5/aperturePixelSize: 0.5/aperturePixelSize: 1./padDim/aperturePixelSize,
-0.5/aperturePixelSize: 0.5/aperturePixelSize: 1./padDim/aperturePixelSize]
# Show the threshold and pupil coordinate information
if (self.debugLevel >= 3):
print("iOuter=%d, cliplevel=%4.2f" % (iOutItr, cliplevel))
print(v.shape)
# Calculate the const of fft:
# FT{Delta W} = -4*pi^2*(u^2+v^2) * FT{W}
u2v2 = -4 * (np.pi**2) * (u*u + v*v)
# Set origin to Inf to result in 0 at origin after filtering
ctrIdx = int(np.floor(padDim/2.0))
u2v2[ctrIdx, ctrIdx] = np.inf
# Calculate the wavefront signal
Sini = self._createSignal(I1, I2, cliplevel)
# Find the just-outside and just-inside indices of a ring in pixels
# This is for the use in setting dWdn = 0
boundaryT = self.getBoundaryThickness()
struct = generate_binary_structure(2, 1)
struct = iterate_structure(struct, boundaryT)
ApringOut = np.logical_xor(binary_dilation(self.pMask, structure=struct),
self.pMask).astype(int)
ApringIn = np.logical_xor(binary_erosion(self.pMask, structure=struct),
self.pMask).astype(int)
bordery, borderx = np.nonzero(ApringOut)
# Put the signal in boundary (since there's no existing Sestimate,
# S just equals self.S as the initial condition of SCF
S = Sini.copy()
for jj in range(self.getNumOfInnerItr()):
# Calculate FT{S}
SFFT = np.fft.fftshift(np.fft.fft2(np.fft.fftshift(S)))
# Calculate W by W=IFT{ FT{S}/(-4*pi^2*(u^2+v^2)) }
W = np.fft.fftshift(np.fft.irfft2(np.fft.fftshift(SFFT/u2v2), s=S.shape))
# Estimate the wavefront (includes zeroing offset & masking to
# the aperture size)
# Take the estimated wavefront
West = extractArray(W, dimOfDonut)
# Calculate the offset
offset = West[self.pMask == 1].mean()
West = West - offset
West[self.pMask == 0] = 0
# Set dWestimate/dn = 0 around boundary
WestdWdn0 = West.copy()
# Do a 3x3 average around each border pixel, including only
# those pixels inside the aperture
for ii in range(len(borderx)):
reg = West[borderx[ii] - boundaryT:
borderx[ii] + boundaryT + 1,
bordery[ii] - boundaryT:
bordery[ii] + boundaryT + 1]
intersectIdx = ApringIn[borderx[ii] - boundaryT:
borderx[ii] + boundaryT + 1,
bordery[ii] - boundaryT:
bordery[ii] + boundaryT + 1]
WestdWdn0[borderx[ii], bordery[ii]] = \
reg[np.nonzero(intersectIdx)].mean()
# Take Laplacian to find sensor signal estimate (Delta W = S)
del2W = laplace(WestdWdn0)/dOmega
# Extend the dimension of signal to the order of 2 for "fft" to
# use
Sest = padArray(del2W, padDim)
# Put signal back inside boundary, leaving the rest of
# Sestimate
Sest[self.pMaskPad == 1] = Sini[self.pMaskPad == 1]
# Need to recheck this condition
S = Sest
# Define the estimated wavefront
# self.West = West.copy()
# Calculate the coefficient of normal/ annular Zernike polynomials
if (self.getCompensatorMode() == "zer"):
xSensor, ySensor = self._inst.getSensorCoor()
zc = ZernikeMaskedFit(West, xSensor, ySensor, numTerms,
self.pMask, zobsR)
else:
zc = np.zeros(numTerms)
elif (PoissonSolver == "exp"):
# Use the integration method by serial expansion to solve the
# Poisson's equation
# Calculate I0 and dI
I0, dI = self._getdIandI(I1, I2)
# Get the x, y coordinate in mask. The element outside mask is 0.
xSensor, ySensor = self._inst.getSensorCoor()
xSensor = xSensor * self.cMask
ySensor = ySensor * self.cMask
# Create the F matrix and Zernike-related matrixes
F = np.zeros(numTerms)
dZidx = np.zeros((numTerms, dimOfDonut, dimOfDonut))
dZidy = dZidx.copy()
zcCol = np.zeros(numTerms)
for ii in range(int(numTerms)):
# Calculate the matrix for each Zk related component
# Set the specific Zk cofficient to be 1 for the calculation
zcCol[ii] = 1
F[ii] = np.sum(dI*ZernikeAnnularEval(zcCol, xSensor, ySensor, zobsR))*dOmega
dZidx[ii, :, :] = ZernikeAnnularGrad(zcCol, xSensor, ySensor, zobsR, "dx")
dZidy[ii, :, :] = ZernikeAnnularGrad(zcCol, xSensor, ySensor, zobsR, "dy")
# Set the specific Zk cofficient back to 0 to avoid interfering
# other Zk's calculation
zcCol[ii] = 0
# Calculate Mij matrix, need to check the stability of integration
# and symmetry later
Mij = np.zeros([numTerms, numTerms])
for ii in range(numTerms):
for jj in range(numTerms):
Mij[ii, jj] = np.sum(I0*(dZidx[ii, :, :].squeeze()*dZidx[jj, :, :].squeeze() +
dZidy[ii, :, :].squeeze()*dZidy[jj, :, :].squeeze()))
Mij = dOmega/(apertureDiameter/2.)**2 * Mij
# Calculate dz
focalLength = self._inst.getFocalLength()
offset = self._inst.getDefocalDisOffset()
dz = 2*focalLength*(focalLength-offset)/offset
# Define zc
zc = np.zeros(numTerms)
# Consider specific Zk terms only
idx = (self.getZernikeTerms() - 1).tolist()
# Solve the equation: M*W = F => W = M^(-1)*F
zc_tmp = np.linalg.lstsq(Mij[:, idx][idx], F[idx], rcond=None)[0]/dz
zc[idx] = zc_tmp
# Estimate the wavefront surface based on z4 - z22
# z0 - z3 are set to be 0 instead
West = ZernikeAnnularEval(np.concatenate(([0, 0, 0], zc[3:])),
xSensor, ySensor, zobsR)
return zc, West
# <codecell>
a = imread('cam1.10000').astype(np.ubyte)
# <codecell>
imshow(a,cmap=cm.gray)
# <codecell>
b = where(a>50,1,0).astype(np.ubyte)
# <codecell>
struct = array([[1, 1, 1], [1, 1, 1], [1, 1, 1]])
struct = ndimage.iterate_structure(struct,2)
c = ndimage.binary_dilation(b,structure=struct,iterations=5)
c = ndimage.binary_fill_holes(c,structure=struct)
# <codecell>
imshow(np.c_[b,c],cmap=cm.gray)
# <codecell>
n = 10
l = 256
img = ndimage.gaussian_filter(a, sigma=l/(4.*n))
# mask = (im > im.mean()).astype(np.float)
# mask += 0.1 * im
# img = mask + 0.2*np.random.randn(*mask.shape)
def compensate(self, inst, algo, zcCol, oversample, model):
if ((zcCol.ndim == 1) and (len(zcCol) != algo.numTerms)):
raise Exception(
'input:size', 'zcCol in compensate needs to be a %d \
row column vector\n' % algo.numTerms)
sm, sn = self.image.shape
projSamples = sm * oversample
# Let us create a look-up table for x -> xp first.
luty, lutx = np.mgrid[
-(projSamples / 2 - 0.5):(projSamples / 2 + 0.5),
-(projSamples / 2 - 0.5):(projSamples / 2 + 0.5)]
lutx = lutx / (projSamples / 2 / inst.sensorFactor)
luty = luty / (projSamples / 2 / inst.sensorFactor)
# set up the mapping
lutxp, lutyp, J = aperture2image(
self, inst, algo, zcCol, lutx, luty, projSamples, model)
# print "J",J.shape
show_lutxyp = showProjection(
lutxp, lutyp, inst.sensorFactor, projSamples, 0)
if (np.all(show_lutxyp<=0)):
self.caustic = 1
return
realcx, realcy, tmp = getCenterAndR_ef(self.image)
show_lutxyp = padArray(show_lutxyp, projSamples + 20)
struct0 = ndimage.generate_binary_structure(2, 1)
struct = ndimage.iterate_structure(struct0, 4)
struct = ndimage.morphology.binary_dilation(struct, structure=struct0)
struct = ndimage.morphology.binary_dilation(
struct, structure=struct0).astype(int)
show_lutxyp = ndimage.morphology.binary_dilation(
show_lutxyp, structure=struct)
show_lutxyp = ndimage.morphology.binary_erosion(
show_lutxyp, structure=struct)
show_lutxyp = extractArray(show_lutxyp, projSamples)
projcx, projcy, tmp = getCenterAndR_ef(show_lutxyp.astype(float))
projcx = projcx / (oversample)
projcy = projcy / (oversample)
# +(-) means we need to move image to the right (left)
shiftx = (projcx - realcx)
# +(-) means we need to move image upward (downward)
shifty = (projcy - realcy)
self.image = np.roll(self.image, int(np.round(shifty)), axis=0)
self.image = np.roll(self.image, int(np.round(shiftx)), axis=1)
# let's construct the interpolant,
# to get the intensity on (x',p') plane
# that corresponds to the grid points on (x,y)
yp, xp = np.mgrid[-(sm / 2 - 0.5):(sm / 2 + 0.5), -
(sm / 2 - 0.5):(sm / 2 + 0.5)]
xp = xp / (sm / 2 / inst.sensorFactor)
yp = yp / (sm / 2 / inst.sensorFactor)
# xp = reshape(xp,sm^2,1);
# yp = reshape(yp,sm^2,1);
# self.image = reshape(self.image,sm^2,1);
#
# FIp = TriScatteredInterp(xp,yp,self.image,'nearest');
# lutIp = FIp(lutxp, lutyp);
lutxp[np.isnan(lutxp)] = 0
lutyp[np.isnan(lutyp)] = 0
# lutIp=interp2(xp,yp,self.image,lutxp,lutyp)
# print xp.shape, yp.shape, self.image.shape
# print lutxp.ravel()
# print xp[:,0],yp[0,:]
ip = interpolate.RectBivariateSpline(
yp[:, 0], xp[0, :], self.image, kx=1, ky=1)
# ip = interpolate.interp2d(xp, yp, self.image)
# ip = interpolate.interp2d(xp, yp, self.image)
# print lutxp.shape, lutyp.shape
# lutIp = ip(0.5, -0.5)
# print lutIp, 'lutIp1'
# lutIp = ip([-0.1],[-0.1])
# print lutIp, 'lutIp2'
# lutIp = ip(np.array(0.5,-0.1), np.array(-0.5, -0.1))
# print lutIp, 'lutIp12',lutxp.ravel()[0:10]
lutIp = np.zeros(lutxp.shape[0] * lutxp.shape[1])
for i, (xx, yy) in enumerate(zip(lutxp.ravel(), lutyp.ravel())):
lutIp[i] = ip(yy, xx)
lutIp = lutIp.reshape(lutxp.shape)
self.image = lutIp * J
if (self.type == 'extra'):
self.image = np.rot90(self.image, k=2)
# if we want the compensator to drive down tip-tilt
# self.image = offsetImg(-shiftx, -shifty, self.image);
# self.image=circshift(self.image,[round(-shifty) round(-shiftx)]);
self.image[np.isnan(self.image)] = 0
# self.image < 0 will not be physical, remove that region
# x(self.image<0) = NaN;
self.caustic = 0
if (np.any(self.image<0) and np.all(self.image0>=0)):
print(
'WARNING: negative scale parameter, \
image is within caustic, zcCol (in um)=\n')
# for i in range(len(zcCol)):
# print zcCol[i]
# print('%5.2f '%(zcCol[i]*1.e6))
# print('\n');
self.caustic = 1
self.image[self.image < 0] = 0
if (oversample > 1):
self.downResolution(self, oversample, sm, sn)
def _filter_struc(size):
base = sn.generate_binary_structure(2, 1)
struc = sn.iterate_structure(base, size)
return struc
# pare down to set of slices that are of interest (optional)
if len(args.evalSliceExpr):
evalSlices = eval(args.evalSliceExpr)
X = X[evalSlices]
# preprocessing. This includes volume normalization (optional) and thresholding (optional)
selectedPixels = numpy.logical_and(args.xLowerBound <= X, X <= args.xUpperBound)
# Note: I observed strange behavior when running the erosion
# operator on the entire tensor in a single call. So for now,
# I'll do this a slice at a time until I can figure out what the
# situation is with the tensor.
if args.threshDilationKernel > 0:
kernel = ndimage.generate_binary_structure(2,1)
kernel = ndimage.iterate_structure(kernel, args.threshDilationKernel).astype(int)
for ii in range(selectedPixels.shape[0]):
selectedPixels[ii,:,:] = ndimage.binary_dilation(selectedPixels[ii,:,:], structure=kernel, iterations=1)
else:
print '[em_evaluate]: no threshold dilation will be applied'
if args.threshErosionKernel > 0:
kernel = ndimage.generate_binary_structure(2,1)
kernel = ndimage.iterate_structure(kernel, args.threshErosionKernel).astype(int)
for ii in range(selectedPixels.shape[0]):
selectedPixels[ii,:,:] = ndimage.binary_erosion(selectedPixels[ii,:,:], structure=kernel, iterations=1)
else:
print '[em_evaluate]: no threshold erosion will be applied'
lowerPixels = numpy.logical_and(numpy.logical_not(selectedPixels), X < args.xLowerBound)
upperPixels = numpy.logical_and(numpy.logical_not(selectedPixels), X > args.xUpperBound)
