def create_sliceinfo_w(images_fn,
labels_fn,
regint_fn,
kernels,
i,
slice_no=None):
# figure out the biggest slice
if slice_no == None:
slice_no = find_biggest_slice(path + labels_fn[i])
print("Creating Weighted Slice Info... {}/{}: Slice {}".format(
i + 1, len(images_fn), slice_no))
# get the slice, label, and associated orientations
slice_im, slice_im_or = get_nifti_slice(path + images_fn[i], slice_no)
slice_lb, slice_lb_or = get_nifti_slice(path + labels_fn[i], slice_no)
slice_ro, slice_ro_or = get_nifti_slice(path + regint_fn[i], slice_no)
# if crop, we crop the image down
if crop:
crop_x, crop_y = crop
start_x = slice_im.shape[0] / 2 - crop_x / 2
end_x = start_x + crop_x
start_y = slice_im.shape[1] / 2 - crop_y / 2
end_y = start_y + crop_y
slice_im = slice_im[start_x:end_x, start_y:end_y]
slice_lb = slice_lb[start_x:end_x, start_y:end_y]
slice_ro = slice_ro[start_x:end_x, start_y:end_y]
# figure out the principal patches
pc_payload = (slice_im, slice_lb)
patches_m_pc, patches_n_pc, vals_m, vals_n = create_pc_patches_w(
*pc_payload)
# compute gabor features for the patches
feats_m = []
feats_n = []
intens_m = []
intens_n = []
for patch in patches_m_pc:
feats_m.append(compute_feats(patch, kernels))
intens_m.append(compute_intens(patch))
for patch in patches_n_pc:
feats_n.append(compute_feats(patch, kernels))
intens_n.append(compute_intens(patch))
# package it into a SliceInfo object
si_payload = (images_fn[i], slice_no, slice_im, slice_im_or, slice_lb,
slice_lb_or, slice_ro, slice_ro_or, patches_m_pc,
patches_n_pc, feats_m, feats_n, intens_m, intens_n, vals_m,
vals_n)
return SliceInfo(*si_payload)
def create_sliceinfo_w(images_fn, labels_fn, regint_fn, kernels, i, slice_no=None):
# figure out the biggest slice
if slice_no == None:
slice_no = find_biggest_slice(path + labels_fn[i])
print("Creating Weighted Slice Info... {}/{}: Slice {}".format(i+1, len(images_fn), slice_no))
# get the slice, label, and associated orientations
slice_im, slice_im_or = get_nifti_slice(path + images_fn[i], slice_no)
slice_lb, slice_lb_or = get_nifti_slice(path + labels_fn[i], slice_no)
slice_ro, slice_ro_or = get_nifti_slice(path + regint_fn[i], slice_no)
# if crop, we crop the image down
if crop:
crop_x, crop_y = crop
start_x = slice_im.shape[0] / 2 - crop_x / 2
end_x = start_x + crop_x
start_y = slice_im.shape[1] / 2 - crop_y / 2
end_y = start_y + crop_y
slice_im = slice_im[start_x:end_x, start_y:end_y]
slice_lb = slice_lb[start_x:end_x, start_y:end_y]
slice_ro = slice_ro[start_x:end_x, start_y:end_y]
# figure out the principal patches
pc_payload = (slice_im, slice_lb)
patches_m_pc, patches_n_pc, vals_m, vals_n = create_pc_patches_w(*pc_payload)
# compute gabor features for the patches
feats_m = []
feats_n = []
intens_m = []
intens_n = []
for patch in patches_m_pc:
feats_m.append(compute_feats(patch, kernels))
intens_m.append(compute_intens(patch))
for patch in patches_n_pc:
feats_n.append(compute_feats(patch, kernels))
intens_n.append(compute_intens(patch))
# package it into a SliceInfo object
si_payload = (images_fn[i], slice_no,
slice_im, slice_im_or,
slice_lb, slice_lb_or,
slice_ro, slice_ro_or,
patches_m_pc, patches_n_pc,
feats_m, feats_n,
intens_m, intens_n,
vals_m, vals_n)
return SliceInfo(*si_payload)
filenames_label = []
for fn in os.listdir(path):
if "CTV" in fn:
filenames_label.append(path + fn)
else:
filenames.append(path + fn)
#filenames = [path + "anon_mr_150420_30sec.nii.gz"]
#filenames_label = [path + "anon_mr_150420_30sec.nii-label.nrrd.nii.gz"]
#slice_no = 128-105
slice_infos = []
for i, fn in enumerate(filenames):
slice_no = find_biggest_slice(filenames_label[i])
if DEBUG:
print("Processing: ", fn, ", Slice ", slice_no)
slice_info = process(fn, filenames_label[i], slice_no)
slice_infos.append(slice_info)
if REPORTS:
for sl in slice_infos:
sl.save_report("reports/")
else:
with open('slice_infos.pkl', 'rb') as f:
slice_infos = dill.load(f)
#if CLASSIFY:
# # we need to go through each slice and collect the masked features and
filenames_label = []
for fn in os.listdir(path):
if "CTV" in fn:
filenames_label.append(path + fn)
else:
filenames.append(path + fn)
#filenames = [path + "anon_mr_150420_30sec.nii.gz"]
#filenames_label = [path + "anon_mr_150420_30sec.nii-label.nrrd.nii.gz"]
#slice_no = 128-105
slice_infos = []
for i, fn in enumerate(filenames):
slice_no = find_biggest_slice(filenames_label[i])
if DEBUG:
print("Processing: ", fn, ", Slice ", slice_no)
slice_info = process(fn, filenames_label[i], slice_no)
slice_infos.append(slice_info)
if REPORTS:
for sl in slice_infos:
sl.save_report("reports/")
else:
with open('slice_infos.pkl', 'rb') as f:
slice_infos = dill.load(f)
if CLASSIFY:
# we need to go through each slice and collect the masked features and
