def raw_reader(path, i, lung=True):
"""
:param path: path to mhd file
i: the id of slice in raw file
lung=True: lung windowing
lung=False: bone,tissue,muscle windowing
:return: a 3 channel numpy array
"""
# file_name = tool_packages.get_filename(path)
img_set, origin, spacing = tool_packages.raw_image_reader(path)
masked_img = []
if i == 0:
masked_img.append(img_set[0])
masked_img.append(img_set[0])
masked_img.append(img_set[1])
elif i == len(img_set) - 1:
masked_img.append(img_set[-2])
masked_img.append(img_set[-1])
masked_img.append(img_set[-1])
else:
for j in range(i - 1, i + 2, 1):
image = np.squeeze(img_set[j])
masked_img.append(image)
masked_img, _slice_num = img_seg.image_segmentor(np.array(masked_img),
lung)
three_channels = np.array(masked_img).transpose((1, 2, 0))
return three_channels
def segmentation_interface(mhd_dir, out_dir, button):
mhd_path_list = tool_packages.get_path(mhd_dir, 'mhd')
length = len(mhd_path_list)
count = 0
for path in mhd_path_list:
# a = time.time()
img_set, _, __ = tool_packages.raw_image_reader(path)
file_name = tool_packages.get_filename(path)
np_array, slicenum = image_segmentor(img_set, button=button)
tool_packages.raw_image_writer(np_array, out_dir + file_name + '.mhd')
# b = time.time()
# print('elapse: ' + str(b-a))
# print('slices:' + str(slicenum))
count += 1
print('file processed: ' + str(count) + '/' + str(length))
# print(samples)
return None
def file_parser_interface(mhdfile_path_list, out_path, csv_handle):
"""
:Function a raw format file parser, to generate npy files and corresponding labels for dl training
:param mhdfile_path_list: a list of mhd file path
anno_path: path to the annotation file
:return: None
"""
length = len(mhdfile_path_list)
count = 0
for path in mhdfile_path_list:
file_name = tool_packages.get_filename(path)
img_set, origin, spacing = tool_packages.raw_image_reader(path)
slice_num, width, height = img_set.shape # can be optimized HERE
# param1 = 1
# if width != 512:
# param1 = label_normalization_parameter(width)
for i in range(slice_num):
label_parser(out_path + file_name, i, path, slice_num - 1,
csv_handle)
count += 1
print('file processed: ' + str(count) + '/' + str(length))
plt visualization below
"""
# plt.figure()
# plt.subplot(2, 2, 1), plt.imshow(image, 'gray'), plt.title('original')
# plt.subplot(2, 2, 2), plt.imshow(result_, 'gray'), plt.title('result')
# plt.subplot(2, 2, 3), plt.imshow(opening, 'gray'), plt.title('opening')
# plt.subplot(2, 2, 4), plt.imshow(lca, 'gray'), plt.title('lca')
# plt.show()
if img_set.shape[1] != 512:
img_resize = cv2.resize(result_, (512, 512))
rt.append(img_resize)
else:
rt.append(result_)
return rt, slice_num
if __name__ == '__main__':
samples = '../chestCT_round1/test/320831.mhd'
img_set, _, __ = tool_packages.raw_image_reader(samples)
file_name = tool_packages.get_filename(samples)
result, s_num = image_segmentor(
img_set, True) # true->lung. false->muscle, tissue, bone
# plt.figure()
# plt.subplot(2, 2, 1),
# plt.imshow(result[10], 'gray'), plt.title('test1')
# plt.subplot(2, 2, 2), plt.imshow(result[5], 'gray'), plt.title('test2')
# plt.subplot(2, 2, 3), plt.imshow(result[10], 'gray'), plt.title('test3')
# plt.subplot(2, 2, 4), plt.imshow(result[15], 'gray'), plt.title('test4')
# plt.show()
tool_packages.write_csv(out_path, [
line[0], min_coord[0], min_coord[1], min_coord[2],
max_coord[0], max_coord[1], max_coord[2], line[7]
])
return None
def label_normalization_parameter(w):
coefficient = 512 / int(w)
return coefficient
if __name__ == '__main__':
pass
img_set, origin, spacing = tool_packages.raw_image_reader(
'../chestCT_round1/test/318818.mhd')
slices, width, height = img_set.shape
name = tool_packages.get_filename('chestCT_round1/test/318818.mhd')
# csv_file = tool_packages.read_csv('../do_not_git/chestCT_round1_annotation.csv')
annotation_path_world = '../do_not_git/chestCT_round1_annotation.csv'
annotation_path_pixel = '../chestCT_round1/test.csv'
if os.path.exists(annotation_path_pixel):
print('the pixel coordinates file exists')
csv_file_handle = tool_packages.read_csv(annotation_path_pixel)
else:
print('generating pixel coordinates file')
annotation_csv_translator(annotation_path_world, annotation_path_pixel)
print('done')
csv_file_handle = tool_packages.read_csv(annotation_path_pixel)
file_parser_interface(['../chestCT_round1/test/318818.mhd'], '',