def generate_solid_cones(scales, m=1):
cones = np.zeros(mni_aseg.shape)
for i, (r_min, r_max) in enumerate(scales):
print('Creating sphere: {} - {} ...'.format((i + 1), (r_min, r_max)))
sc = solid_cone(radius=(r_min, r_max), center=centroid, m=m)
cones[np.where(sc)] = i + 1
return cones
# Calculate the inner and outer radius
# for all the spheres: scales
n_spheres = max_radius // (tk - overlap)
scales = [(i * (tk - overlap), ((i + 1) * tk) - (i * overlap))
for i in range(n_spheres)]
print('Number of scales: {} | Scales: {}'.format(len(scales), scales))
# Get centroid of MNI152
mni_aseg = nb.load(mni_file)
centroid = tuple(get_centroid(mni_aseg.get_data() > 0))
print('Centroid of MNI152: {}'.format(centroid))
spheres = np.zeros(mni_aseg.shape)
for i, (r_min, r_max) in enumerate(scales):
print('Creating sphere: {} ...'.format(i + 1))
sc = solid_cone(radius=(r_min, r_max), center=centroid)
spheres[np.where(sc)] = i + 1
# ==== INDEX CALCULATION ====
indexes = pd.DataFrame(columns=['scale', 'theta', 'phi', 'indexes'])
for i, z_angle in enumerate(range(-180, 180, ns)):
for j, x_angle in enumerate(range(0, 180, ns)):
print('Processing angles: ({}, {})'.format(z_angle, x_angle))
solid_ang_mask = rotate_vol(spheres,
angles=(x_angle, 0, z_angle)) # ROI
for i, (r_min, r_max) in enumerate(scales):
scale = '{}_{}'.format(r_min, r_max)
ix = np.where(solid_ang_mask == i + 1)
data = {
'scale': scale,
'theta': z_angle,
def process_image(folders):
# Scale definition
smin, smax, step = (0, 100, 5)
# Work directories
dataset_registered_folder = '/home/jullygh/sssilvar/Documents/workdir/'
results_folder = '/home/jullygh/sssilvar/Documents/results_radial_vid_optimized'
mni_dir = os.path.join(root, 'lib', 'templates', 'MNI152', 'aseg.mgz')
# Get template centroid
mni_aseg = nb.load(mni_dir).get_data()
centroid = tuple(get_centroid(mni_aseg > 0))
print('[ OK ] Centroid = {}'.format(centroid))
# Define scales (radial analysis)
scales = []
for i in range(smin, smax):
if i <= smax - step:
scales.append((i, i + step))
print('Scales to be analysed (%d scales):' % len(scales))
print(scales)
# Start processing the whole dataset
for folder in [folders]:
# Set of folders important in the processing pipeline
# aseg_file = os.path.join(subject_dir, 'aseg.mgz') # Segmentation volume per subject
subject_dir = os.path.join(dataset_registered_folder, folder)
brainmask_file = os.path.join(subject_dir, 'brainmask_reg.mgz')
subject_output_dir = os.path.join(results_folder, folder)
# Print info message
print('[ INFO ] Processing subject %s located in %s' % (folder, subject_dir))
# Try creating a folder for each subject
try:
os.mkdir(subject_output_dir)
os.mkdir(os.path.join(subject_output_dir, 'png'))
os.mkdir(os.path.join(subject_output_dir, 'raw'))
print('[ OK ] Folder created at: ' + subject_output_dir)
except OSError:
print('[ WARNING ] Folder already exists.')
# Load MRI image and aseg file
mgz = nb.load(brainmask_file)
img = mgz.get_data().astype(np.float32)
# Calculate the gradient: img_grad
gx, gy, gz = np.gradient(img)
img_grad = np.sqrt(gx ** 2 + gy ** 2 + gz ** 2)
# # Load aseg.mgz file: aseg, and centroid calculation: centroid
# mgz = nb.load(aseg_file)
# aseg = mgz.get_data()
# centroid = tuple(get_centroid(aseg > 0))
for n_scale, scale in enumerate(scales):
# Crete a solid angle from a scale: sa
r_min, r_max = scale
sa = solid_cone(radius=(r_min, r_max), center=centroid)
# Start go over the whole sphere (x_angle: [0, pi] and z_angle [-pi, pi])
img_2d = np.zeros([360, 180])
img_grad_2d = np.zeros_like(img_2d)
mask_sub, center = extract_sub_volume(sa, radius=(r_min, r_max), centroid=centroid)
vol_sub, _ = extract_sub_volume(img, radius=(r_min, r_max), centroid=centroid)
grad_sub, _ = extract_sub_volume(img_grad, radius=(r_min, r_max), centroid=centroid)
for i, z_angle in enumerate(range(-180, 180)):
for j, x_angle in enumerate(range(0, 180)):
# Create cone
solid_ang_mask = rotate_vol(mask_sub, angles=(x_angle, 0, z_angle))
# Get indexes where mask is 1
ix = np.where(solid_ang_mask == 1)
# Mask images
img_masked = vol_sub[ix]
grad_masked = grad_sub[ix]
# Number of voxels analyzed
n = np.sum(solid_ang_mask[ix])
# Set pixel in plane as the mean of the voxels analyzed
img_2d[i, j] = np.nan_to_num(img_masked.sum() / n)
img_grad_2d[i, j] = np.nan_to_num(grad_masked.sum() / n)
# print('[ SA ] Scale: %d of %d %s Ang: %s | Point (%d, %d) of (360/180) | Value: %f' %
# (n_scale + 1, len(scales), scale, (x_angle, z_angle), i, j, img_2d[i, j]))
print('Scale %d of %d' % (n_scale, len(scales)))
# Create results:
# 2 png files / 2 raw files
# Output filenames for intensity and gradients
png_intensity_fn = os.path.join(subject_output_dir, 'png',
'intensity_%03d_to_%03d_solid_angle_to_sphere.png' % (r_min, r_max))
raw_intensity_fn = os.path.join(subject_output_dir, 'raw',
'intensity_%03d_to_%03d_solid_angle_to_sphere.raw' % (r_min, r_max))
png_gradient_fn = os.path.join(subject_output_dir, 'png',
'gradient_%03d_to_%03d_solid_angle_to_sphere.png' % (r_min, r_max))
raw_gradient_fn = os.path.join(subject_output_dir, 'raw',
'gradient_%03d_to_%03d_solid_angle_to_sphere.raw' % (r_min, r_max))
# Save results (Intensity and gradients)
plt.imsave(png_intensity_fn, img_2d, cmap='gray')
img_2d.tofile(raw_intensity_fn)
# RAW output for gradients
plt.imsave(png_gradient_fn, img_grad_2d, cmap='gray')
img_grad_2d.tofile(raw_gradient_fn)
def process_image(folders):
# dataset_registered_folder = '/home/jullygh/sssilvar/Documents/workdir/'
# results_folder = '/home/jullygh/sssilvar/Documents/results_same_centroid'
# mni_file = join(root, 'param', 'FSL_MNI152_FreeSurferConformed_1mm.nii')
# Start processing the whole dataset
for folder in [folders]:
# Set of folders important in the processing pipeline
# aseg_file = join(subject_dir, 'aseg.mgz') # Segmentation volume per subject
subject_dir = join(dataset_registered_folder, folder)
brainmask_file = join(subject_dir, 'brainmask_reg.nii.gz')
subject_output_dir = join(results_folder, folder)
# Execute if file exists
if isfile(brainmask_file):
# Print info message
print('[ INFO ] Processing subject %s located in %s' %
(folder, subject_dir))
# Try creating a folder for each subject
try:
os.mkdir(subject_output_dir)
print('[ OK ] Folder created at: ' + subject_output_dir)
except OSError:
print('[ WARNING ] Folder already exists.')
# Load MRI image and aseg file
mgz = nb.load(brainmask_file)
img = mgz.get_data().astype(np.float)
# Calculate the gradient: img_grad
gx, gy, gz = np.gradient(img)
img_grad = np.sqrt(gx**2 + gy**2 + gz**2)
# # Load aseg.mgz file: aseg, and centroid calculation: centroid
# mgz = nb.load(aseg_file)
# aseg = mgz.get_data()
# centroid = tuple(get_centroid(aseg > 0))
scales = [
(0, 25),
(25, 50),
(50, 75),
(75, 100),
]
for n_scale, scale in enumerate(scales):
# Crete a solid angle from a scale: sa
r_min, r_max = scale
sa = solid_cone(radius=(r_min, r_max), center=centroid)
# Start go over the whole sphere (x_angle: [0, pi] and z_angle [-pi, pi])
img_2d = np.zeros([360, 180])
img_grad_2d = np.zeros_like(img_2d)
mask_sub, center = extract_sub_volume(sa,
radius=(r_min, r_max),
centroid=centroid)
vol_sub, _ = extract_sub_volume(img,
radius=(r_min, r_max),
centroid=centroid)
grad_sub, _ = extract_sub_volume(img_grad,
radius=(r_min, r_max),
centroid=centroid)
for i, z_angle in enumerate(range(-180, 180, 4)):
ti = time()
for j, x_angle in enumerate(range(0, 180, 4)):
solid_ang_mask = rotate_vol(mask_sub,
angles=(x_angle, 0,
z_angle))
img_masked = np.multiply(vol_sub, solid_ang_mask)
grad_masked = np.multiply(grad_sub, solid_ang_mask)
# Number of voxels analyzed
n = solid_ang_mask.sum()
# Set pixel in plane as the mean of the voxels analyzed
img_2d[i, j] = img_masked.sum() / n
img_grad_2d[i, j] = grad_masked.sum() / n
elapsed = time() - ti
print(
'[ SA ] Scale: %d %s Ang: %s | Point (%d, %d) of (360/180) | Value: %f | Time: %.2f'
% (n_scale + 1, scale,
(x_angle, z_angle), i, j, img_2d[i, j], elapsed))
# Create results:
# 2 png files / 2 raw files
# PNG output for intensities
img_filename = join(
subject_output_dir,
'intensity_%d_to_%d_solid_angle_to_sphere' %
(r_min, r_max))
plt.imsave(img_filename + '.png', img_2d, cmap='gray')
img_2d.tofile(img_filename + '.raw')
# RAW output for gradients
grad_filename = join(
subject_output_dir,
'gradient_%d_to_%d_solid_angle_to_sphere' % (r_min, r_max))
plt.imsave(grad_filename + '.png', img_grad_2d, cmap='gray')
img_grad_2d.tofile(grad_filename + '.raw')
else:
print('[ ERROR ] File {} was not found'.format(brainmask_file))
def process_image(folder):
subject_dir = join(dataset_registered_folder, folder)
brainmask_file = join(subject_dir, 'brainmask_reg.nii.gz')
subject_output_dir = join(results_folder, folder)
# Execute if file exists
if isfile(brainmask_file):
# Print info message
print('[ INFO ] Processing subject %s located in %s' % (folder, subject_dir))
# Try creating a folder for each subject
try:
os.mkdir(subject_output_dir)
print('[ OK ] Folder created at: ' + subject_output_dir)
except OSError:
print('[ WARNING ] Folder already exists.')
# Load MRI image and aseg file
mgz = nb.load(brainmask_file)
img = mgz.get_data().astype(np.float)
# Calculate the gradient: img_grad
gx, gy, gz = np.gradient(img)
img_grad = np.sqrt(gx ** 2 + gy ** 2 + gz ** 2)
scales = [
(0, 25),
(25, 50),
(50, 75),
(75, 100),
]
for n_scale, scale in enumerate(scales):
# Crete a solid angle from a scale: sa
r_min, r_max = scale
sa = solid_cone(radius=(r_min, r_max), center=centroid)
# Start go over the whole sphere (x_angle: [0, pi] and z_angle [-pi, pi])
img_2d = np.zeros([360, 180])
img_grad_2d = np.zeros_like(img_2d)
mask_sub, center = extract_sub_volume(sa, radius=(r_min, r_max), centroid=centroid)
vol_sub, _ = extract_sub_volume(img, radius=(r_min, r_max), centroid=centroid)
grad_sub, _ = extract_sub_volume(img_grad, radius=(r_min, r_max), centroid=centroid)
for i, z_angle in enumerate(range(0, 180, 8)):
ti = time()
rot_a = rotate(mask_sub, angle=z_angle, axes=(0,2), reshape=False)
for j, x_angle in enumerate(range(-270, 90, 8)):
solid_ang_mask = rotate(rot_a, angle=x_angle, axes=(0,1), reshape=False)
img_masked = np.multiply(vol_sub, solid_ang_mask)
grad_masked = np.multiply(grad_sub, solid_ang_mask)
# img_masked = vol_sub * solid_ang_mask
# grad_masked = grad_sub * solid_ang_mask
# Number of voxels analyzed
n = solid_ang_mask.sum()
# Set pixel in plane as the mean of the voxels analyzed
img_2d[j, i] = np.nan_to_num(img_masked.sum() / n)
img_grad_2d[j, i] = np.nan_to_num(grad_masked.sum() / n)
elapsed = time() - ti
print('[ SA ] Scale: %d %s Ang: %s | Point (%d, %d) of (360/180) | Value: %f | Time: %.2f' %
(n_scale + 1, scale, (x_angle, z_angle), j, i, img_2d[j, i], elapsed))
# Create results:
# 2 png files / 2 raw files
# PNG output for intensities
img_filename = join(subject_output_dir, 'intensity_%d_to_%d_solid_angle_to_sphere' % (r_min, r_max))
plt.imsave(img_filename + '.png', img_2d, cmap='gray')
img_2d.tofile(img_filename + '.raw')
# RAW output for gradients
grad_filename = join(subject_output_dir, 'gradient_%d_to_%d_solid_angle_to_sphere' % (r_min, r_max))
plt.imsave(grad_filename + '.png', img_grad_2d, cmap='gray')
img_grad_2d.tofile(grad_filename + '.raw')
else:
print('[ ERROR ] File {} was not found'.format(brainmask_file))
exit()
def process_image(folder, n_scale, scale):
# Set of folders important in the processing pipeline
subject_dir = join(dataset_registered_folder, folder)
brainmask_file = join(subject_dir, 'brainmask_reg.nii.gz')
subject_output_dir = join(results_folder, folder)
# Get radius range from scale
r_min, r_max = scale
# Declare image output filenames
intensities_file = join(subject_output_dir, 'intensity_%d_to_%d_solid_angle_to_sphere' % (r_min, r_max))
gradients_file = join(subject_output_dir, 'gradient_%d_to_%d_solid_angle_to_sphere' % (r_min, r_max))
sobel_file = join(subject_output_dir, 'sobel_%d_to_%d_solid_angle_to_sphere' % (r_min, r_max))
# Create a condition checking if mappings exist
images_missed = any([not isfile(f + '.raw') for f in [intensities_file, gradients_file]])
print('[ INFO ] Is any image missed?: {}'.format('Yes' if images_missed else 'No'))
# Execute if file exists
if isfile(brainmask_file) and images_missed:
# Print info message
print('[ INFO ] Processing subject %s located in %s' % (folder, subject_dir))
# Try creating a folder for each subject
try:
os.mkdir(subject_output_dir)
print('[ OK ] Folder created at: ' + subject_output_dir)
except OSError:
print('[ WARNING ] Folder {} already exists.'.format(subject_output_dir))
# Load MRI image and aseg file
mgz = nb.load(brainmask_file)
img = mgz.get_data().astype(np.float)
# Calculate the gradient: img_grad
gx, gy, gz = np.gradient(img)
img_grad = np.sqrt(gx ** 2 + gy ** 2 + gz ** 2)
# Extract edges with sobel
sobel_mode = 'reflect'
sobel_x = ndi.sobel(img, axis=0, mode=sobel_mode)
sobel_y = ndi.sobel(img, axis=1, mode=sobel_mode)
sobel_z = ndi.sobel(img, axis=2, mode=sobel_mode)
img_sobel = np.sqrt(sobel_x ** 2 + sobel_y ** 2 + sobel_z ** 2)
# Crete a solid angle from a scale: sa
sa = solid_cone(radius=(r_min, r_max), center=centroid)
# Start go over the whole sphere (x_angle: [0, pi] and z_angle [-pi, pi])
ns = 2 # TODO: Check if it's necessary to change it
img_2d = np.zeros([360 // ns, 180 // ns])
img_grad_2d = np.zeros_like(img_2d)
img_sobel_2d = np.zeros_like(img_2d)
# Extract only relevant volume
mask_sub, center = extract_sub_volume(sa, radius=(r_min, r_max), centroid=centroid)
vol_sub, _ = extract_sub_volume(img, radius=(r_min, r_max), centroid=centroid)
grad_sub, _ = extract_sub_volume(img_grad, radius=(r_min, r_max), centroid=centroid)
sobel_sub, _ = extract_sub_volume(img_sobel, radius=(r_min, r_max), centroid=centroid)
for i, z_angle in enumerate(range(-180, 180, ns)):
ti = time()
for j, x_angle in enumerate(range(0, 180, ns)):
solid_ang_mask = rotate_vol(mask_sub, angles=(x_angle, 0, z_angle))
img_masked = np.multiply(vol_sub, solid_ang_mask)
grad_masked = np.multiply(grad_sub, solid_ang_mask)
sobel_masked = np.multiply(sobel_sub, solid_ang_mask)
# Number of voxels analyzed
n = solid_ang_mask.sum()
# Set pixel in plane as the mean of the voxels analyzed
img_2d[i, j] = img_masked.sum() / n
img_grad_2d[i, j] = grad_masked.sum() / n
img_sobel_2d[i, j] = sobel_masked.sum() / n
elapsed = time() - ti
print('[ SA ] Scale: %d %s Ang: %s | Point (%d, %d) of (360/180) | Value: %f | Time: %.2f' %
(n_scale + 1, scale, (x_angle, z_angle), i, j, img_2d[i, j], elapsed))
# Create results:
# 2 png files / 2 raw files
# Image output for intensities
plt.imsave(intensities_file + '.png', img_2d, cmap='gray')
img_2d.tofile(intensities_file + '.raw')
# Image output for gradients
plt.imsave(gradients_file + '.png', img_grad_2d, cmap='gray')
img_grad_2d.tofile(gradients_file + '.raw')
# Image output for sobel
plt.imsave(sobel_file + '.png', img_sobel_2d, cmap='gray')
img_sobel_2d.tofile(sobel_file + '.raw')
else:
print('[ ERROR ] File {} was not found'.format(brainmask_file))
import time
from numpy import pi
import nibabel as nb
from scipy.ndimage.interpolation import rotate
from scipy.ndimage import affine_transform
import matplotlib.pyplot as plt
from lib.masks import solid_cone
from lib.visualization import show_mri
# Define the root folder
root = os.path.dirname(os.path.dirname(os.getcwd()))
if __name__ == '__main__':
a = solid_cone(radius=(33, 66))
print(a.shape)
cos_gamma = np.cos(pi / 8)
sin_gamma = np.sin(pi / 8)
rot_affine = np.array([[1, 0, 0, 0], [0, cos_gamma, -sin_gamma, 0],
[0, sin_gamma, cos_gamma, 0], [0, 0, 0, 1]])
rot_affine_3 = np.array([[cos_gamma, 0, sin_gamma, 0], [0, 1, 0, 0],
[-sin_gamma, 0, cos_gamma, 0], [0, 0, 0, 1]])
rot_affine_0 = np.array([[cos_gamma, 0, sin_gamma], [0, 1, 0],
[-sin_gamma, 0, cos_gamma]])
rot_affine_2 = np.array([[1, 0, 0, 0], [0, cos_gamma, -sin_gamma, 0],
[0, sin_gamma, cos_gamma, 0], [0, 0, 0, 1]])
