def display_all(self):
plt.imshow(self.image_slice, cmap = plt.cm.gray)
plt.show()
plt.imshow(self.label_slice, cmap=plt.cm.gray)
plt.show()
print("Masked version: ")
mask = np.where(self.label_slice == 0, self.label_slice,
self.image_slice)
plt.imshow(mask, cmap=plt.cm.gray)
plt.show()
show_eigenpatches(self.eigens_mask)
plot_gabor(self.eigens_mask)
def display_all(self):
plt.imshow(self.image_slice, cmap=plt.cm.gray)
plt.show()
plt.imshow(self.label_slice, cmap=plt.cm.gray)
plt.show()
print("Masked version: ")
mask = np.where(self.label_slice == 0, self.label_slice,
self.image_slice)
plt.imshow(mask, cmap=plt.cm.gray)
plt.show()
show_eigenpatches(self.eigens_mask)
plot_gabor(self.eigens_mask)
def save_report(self, path):
save_fn = path + os.path.basename(self.filename)
eigen_fn = show_eigenpatches(self.eigens_mask, save_fn)
gabor_fn = plot_gabor(self.eigens_mask, save_fn)
# base pdf
plt.figure(figsize=(20, 28))
plt.suptitle('Report: Slice ' + str(self.slice_no) + ' for ' +
self.filename + '\n',
fontsize=48)
# prepare the mask
mask = np.where(self.label_slice == 0, self.label_slice,
self.image_slice)
# Saves a summary of the slice information as pdf
with PdfPages(save_fn + '.pdf') as pdf:
plt.subplot(2, 3, 1)
plt.imshow(self.image_slice, cmap=plt.cm.gray)
plt.xticks(())
plt.yticks(())
plt.title('Original Image', fontsize=24)
plt.subplot(2, 3, 2)
plt.imshow(mask, cmap=plt.cm.gray)
plt.xticks(())
plt.yticks(())
plt.title('Mask', fontsize=24)
plt.subplot(2, 3, 3)
plt.imshow(plt.imread(eigen_fn))
plt.xticks(())
plt.yticks(())
plt.title('Eigenpatches', fontsize=24)
plt.subplot(2, 1, 2)
plt.imshow(plt.imread(gabor_fn))
plt.xticks(())
plt.yticks(())
plt.title('Gabor responses', fontsize=24)
pdf.savefig()
plt.close()
def save_report(self, path):
save_fn = path + os.path.basename(self.filename)
eigen_fn = show_eigenpatches(self.eigens_mask, save_fn)
gabor_fn = plot_gabor(self.eigens_mask, save_fn)
# base pdf
plt.figure(figsize=(20, 28))
plt.suptitle('Report: Slice ' + str(self.slice_no) +
' for ' + self.filename + '\n', fontsize=48)
# prepare the mask
mask = np.where(self.label_slice == 0, self.label_slice,
self.image_slice)
# Saves a summary of the slice information as pdf
with PdfPages(save_fn + '.pdf') as pdf:
plt.subplot(2, 3, 1)
plt.imshow(self.image_slice, cmap = plt.cm.gray)
plt.xticks(())
plt.yticks(())
plt.title('Original Image', fontsize=24)
plt.subplot(2, 3, 2)
plt.imshow(mask, cmap = plt.cm.gray)
plt.xticks(())
plt.yticks(())
plt.title('Mask', fontsize=24)
plt.subplot(2, 3, 3)
plt.imshow(plt.imread(eigen_fn))
plt.xticks(())
plt.yticks(())
plt.title('Eigenpatches', fontsize=24)
plt.subplot(2, 1, 2)
plt.imshow(plt.imread(gabor_fn))
plt.xticks(())
plt.yticks(())
plt.title('Gabor responses', fontsize=24)
pdf.savefig()
plt.close()
def process(filename, filename_label, slice_no):
# Grab the image
image_slice, orientation_slice = get_nifti_slice(filename, slice_no)
if SHOW_IMG:
plt.imshow(image_slice, cmap = plt.cm.gray)
plt.show()
# Grab the labels
label_slice, orientation_label = get_nrrd_data(filename_label, slice_no)
if SHOW_IMG:
plt.imshow(label_slice, cmap=plt.cm.gray)
plt.show()
# Show the mask
if SHOW_IMG:
print("Masked version: ")
mask = np.where(label_slice == 0, label_slice, image_slice)
plt.imshow(mask, cmap=plt.cm.gray)
plt.show()
# Extract patches in ROI
patches_mask, patches_nonmask = extract_roi_patches(image_slice,
label_slice,
PATCH_SIZE)
# Get the decomposed patches
eigens_mask = get_eigenpatches(patches_mask, PATCH_SIZE, MAX_EIGEN)
eigens_nonmask = get_eigenpatches(patches_nonmask, PATCH_SIZE, MAX_EIGEN)
# Show the eigens, if you want
if SHOW_IMG:
show_eigenpatches(eigens_mask)
# Generate Gabor Kernels
kernels = generate_kernels()
# Show the Gabors
if SHOW_IMG:
plot_gabor(eigens_mask)
# Store all the features and Gabor responses
all_features_mask = []
all_powers_mask = []
complete_features_mask = []
all_features_nonmask = []
all_powers_nonmask = []
complete_features_nonmask = []
for eigen in eigens_mask:
all_features_mask.append(compute_feats(eigen, kernels))
all_powers_mask.append(compute_powers(eigen, kernels))
for eigen in eigens_nonmask:
all_features_nonmask.append(compute_feats(eigen, kernels))
all_powers_nonmask.append(compute_powers(eigen, kernels))
# for patch in patches_mask:
# complete_features_mask.append(compute_feats(patch, kernels))
#
# for patch in patches_nonmask:
# complete_features_nonmask.append(compute_feats(patch, kernels))
return SliceInfo(filename, slice_no, image_slice, orientation_slice,
label_slice, orientation_label, kernels,
patches_mask, eigens_mask,
all_features_mask, all_powers_mask,
patches_nonmask, eigens_nonmask,
all_features_nonmask, all_powers_nonmask)
def process(filename, filename_label, slice_no):
# Grab the image
image_slice, orientation_slice = get_nifti_slice(filename, slice_no)
image_slice = normalise(image_slice)
if SHOW_IMG:
plt.imshow(image_slice, cmap=plt.cm.gray)
plt.show()
# Grab the labels
label_slice, orientation_label = get_nrrd_data(filename_label, slice_no)
#if SHOW_IMG:
# plt.imshow(label_slice, cmap=plt.cm.gray)
# plt.show()
# Show the mask
if SHOW_IMG:
print("Masked version: ")
mask = np.where(label_slice == 0, label_slice, image_slice)
plt.imshow(mask, cmap=plt.cm.gray)
plt.show()
# Extract patches in ROI
patches_mask, patches_nonmask = extract_roi_patches(
image_slice, label_slice, PATCH_SIZE)
# Get the decomposed patches
eigens_mask = get_randoms(patches_mask, MAX_EIGEN)
eigens_nonmask = get_randoms(patches_nonmask, MAX_EIGEN)
# Show the eigens, if you want
if SHOW_IMG:
show_eigenpatches(eigens_mask)
# Generate Gabor Kernels
kernels = generate_kernels()
# Show the Gabors
if SHOW_IMG:
plot_gabor(eigens_mask)
# Store all the features and Gabor responses
all_features_mask = []
all_powers_mask = []
all_features_nonmask = []
all_powers_nonmask = []
for eigen in eigens_mask:
all_features_mask.append(compute_feats(eigen, kernels))
all_powers_mask.append(compute_powers(eigen, kernels))
for eigen in eigens_nonmask:
all_features_nonmask.append(compute_feats(eigen, kernels))
all_powers_nonmask.append(compute_powers(eigen, kernels))
return SliceInfo(filename, slice_no, image_slice, orientation_slice,
label_slice, orientation_label, kernels, patches_mask,
eigens_mask, all_features_mask, all_powers_mask,
patches_nonmask, eigens_nonmask, all_features_nonmask,
all_powers_nonmask)
