def check_multiphase_tripleslice(slice_dir, save_dir, phase_names,
target_phase_name):
def is_eauql(list_1, list_2):
if len(list_1) != len(list_2):
return False
list_1 = sorted(list_1)
list_2 = sorted(list_2)
for ele_1, ele_2 in zip(list_1, list_2):
if ele_1 != ele_2:
return False
return True
total_phase_name = ''.join(phase_names)
total_phase_name += '_tripleslice'
mhd_images = []
mask_images = []
for phase_name in phase_names:
mhd_image_path = os.path.join(slice_dir,
'Image_' + phase_name + '.mhd')
mhd_mask_path = os.path.join(slice_dir, 'Mask_' + phase_name + '.mhd')
mhd_image = read_mhd_image(mhd_image_path)
mask_image = read_mhd_image(mhd_mask_path)
mhd_image = np.asarray(np.squeeze(mhd_image), np.float32)
mhd_image = np.transpose(mhd_image, axes=[1, 2, 0])
_, _, depth_image = np.shape(mhd_image)
if depth_image == 2:
mhd_image = np.concatenate([
mhd_image,
np.expand_dims(
mhd_image[:, :,
np.argmax(
np.sum(np.sum(mask_image, axis=1), axis=1))],
axis=2)
],
axis=2)
print('Error')
mhd_images.append(mhd_image)
mask_images.append(mask_image)
nc_unique = np.unique(mask_images[0])
art_unique = np.unique(mask_images[1])
pv_unique = np.unique(mask_images[2])
if is_eauql(nc_unique, art_unique) and is_eauql(art_unique, pv_unique):
print('OK ', slice_dir)
else:
print('Error ', slice_dir, nc_unique, art_unique, pv_unique)
def _read_liver_mask(mask_path, size=16):
liver_mask = np.squeeze(read_mhd_image(mask_path))
min_xs, min_ys, max_xs, max_ys = bounding_box(liver_mask)
center_x = min_xs + (max_xs - min_xs) // 2
center_y = min_ys + (max_ys - min_ys) // 2
new_min_xs = center_x - size // 2
new_max_xs = center_x + size // 2
new_min_ys = center_y - size // 2
new_max_ys = center_y + size // 2
return new_min_xs, new_min_ys, new_max_xs, new_max_ys
def convertMHD2NPY(dataset_dir, stage_name):
def _get_filled_mask(mask):
from medicalImage import fill_region
mask = np.asarray(mask == 1, np.uint8)
mask = fill_region(mask)
xs, ys = np.where(mask == 1)
return mask, np.min(xs), np.min(ys), np.max(xs), np.max(ys)
stage_dir = os.path.join(dataset_dir, stage_name)
slice_names = os.listdir(stage_dir)
labels = []
imgs = []
output_shape = (224, 224)
for slice_name in slice_names:
if slice_name.startswith('.DS'):
continue
print slice_name
slice_dir = os.path.join(stage_dir, slice_name)
nc_img_path = glob(os.path.join(slice_dir, 'NC_Image*.mhd'))[0]
art_img_path = glob(os.path.join(slice_dir, 'ART_Image*.mhd'))[0]
pv_img_path = glob(os.path.join(slice_dir, 'PV_Image*.mhd'))[0]
nc_img = np.squeeze(read_mhd_image(nc_img_path))
art_img = np.squeeze(read_mhd_image(art_img_path))
pv_img = np.squeeze(read_mhd_image(pv_img_path))
nc_mask_path = glob(os.path.join(slice_dir, 'NC_Mask*.mhd'))[0]
art_mask_path = glob(os.path.join(slice_dir, 'ART_Mask*.mhd'))[0]
pv_mask_path = glob(os.path.join(slice_dir, 'PV_Mask*.mhd'))[0]
nc_mask = np.squeeze(read_mhd_image(nc_mask_path))
art_mask = np.squeeze(read_mhd_image(art_mask_path))
pv_mask = np.squeeze(read_mhd_image(pv_mask_path))
nc_mask, nc_min_xs, nc_min_ys, nc_max_xs, nc_max_ys = _get_filled_mask(
nc_mask)
art_mask, art_min_xs, art_min_ys, art_max_xs, art_max_ys = _get_filled_mask(
art_mask)
pv_mask, pv_min_xs, pv_min_ys, pv_max_xs, pv_max_ys = _get_filled_mask(
pv_mask)
nc_img[nc_mask != 1] = 0
art_img[art_mask != 1] = 0
pv_img[pv_mask != 1] = 0
nc_img = nc_img[nc_min_xs:nc_max_xs, nc_min_ys:nc_max_ys]
art_img = art_img[art_min_xs:art_max_xs, art_min_ys:art_max_ys]
pv_img = pv_img[pv_min_xs:pv_max_xs, pv_min_ys:pv_max_ys]
nc_img = np.asarray(nc_img, np.float32)
art_img = np.asarray(art_img, np.float32)
pv_img = np.asarray(pv_img, np.float32)
nc_img = cv2.resize(nc_img, output_shape)
art_img = cv2.resize(art_img, output_shape)
pv_img = cv2.resize(pv_img, output_shape)
img = np.concatenate([
np.expand_dims(nc_img, axis=2),
np.expand_dims(art_img, axis=2),
np.expand_dims(pv_img, axis=2)
],
axis=2)
imgs.append(img)
labels.append(int(os.path.basename(slice_dir)[-1]))
imgs = np.asarray(imgs)
labels = np.asarray(labels)
print np.shape(imgs), np.shape(labels)
np.save(os.path.join(dataset_dir, stage_name + '_imgs.npy'), imgs)
np.save(os.path.join(dataset_dir, stage_name + '_labels.npy'), labels)
def dicom2jpg_multiphase_tripleslice(slice_dir, save_dir, phase_names,
target_phase_name):
'''
前置条件：已经将dicom格式的数据转为成MHD格式，并且已经提出了slice，一个mhd文件只包含了三个slice
针对每个phase提取三个slice(all), 但是mask还是只提取一个
保存的格式是name_nc.jpg, name_art.jpg, name_pv.jpg
:param slice_dir:
:param save_dir:
:param phase_names:
:param target_phase_name:
:return:
'''
total_phase_name = ''.join(phase_names)
total_phase_name += '_tripleslice'
target_phase_mask = None
mhd_images = []
for phase_name in phase_names:
mhd_image_path = os.path.join(slice_dir,
'Image_' + phase_name + '.mhd')
mhd_mask_path = os.path.join(slice_dir, 'Mask_' + phase_name + '.mhd')
mhd_image = read_mhd_image(mhd_image_path)
mask_image = read_mhd_image(mhd_mask_path)
mhd_image = np.asarray(np.squeeze(mhd_image), np.float32)
mhd_image = np.transpose(mhd_image, axes=[1, 2, 0])
if phase_name == target_phase_name:
target_phase_mask = mask_image
_, _, depth_image = np.shape(mhd_image)
if depth_image == 2:
mhd_image = np.concatenate([
mhd_image,
np.expand_dims(
mhd_image[:, :,
np.argmax(
np.sum(np.sum(mask_image, axis=1), axis=1))],
axis=2)
],
axis=2)
print('Error')
mhd_images.append(mhd_image)
# mhd_image = np.expand_dims(mhd_image, axis=2)
# mhd_image = np.concatenate([mhd_image, mhd_image, mhd_image], axis=2)
mhd_image = np.concatenate(mhd_images, axis=-1)
mask_image = target_phase_mask
mask_image = np.asarray(np.squeeze(mask_image), np.uint8)
max_v = 300.
min_v = -350.
mhd_image[mhd_image > max_v] = max_v
mhd_image[mhd_image < min_v] = min_v
print(np.mean(mhd_image, dtype=np.float32))
mhd_image -= np.mean(mhd_image)
min_v = np.min(mhd_image)
max_v = np.max(mhd_image)
interv = max_v - min_v
mhd_image = (mhd_image - min_v) / interv
file_name = os.path.basename(slice_dir)
dataset_name = os.path.basename(os.path.dirname(slice_dir))
print('the shape of mhd_image is ', np.shape(mhd_image), np.min(mhd_image),
np.max(mhd_image))
print('the shape of mask_image is ', np.shape(mask_image))
min_xs, min_ys, max_xs, max_ys, names, masks = extract_bboxs_from_mask(
mask_image, os.path.join(slice_dir, 'tumor_types'))
for mask, name in zip(masks, names):
print name, np.shape(mask)
def convert2jpg_multiphase_with_liver(slice_dir, stage_name, save_dir):
if os.path.basename(slice_dir).startswith('.DS'):
return
def preprocessing_mhd(image, windows=[-350, 300]):
image = np.asarray(image, np.float32)
image[image < windows[0]] = windows[0]
image[image > windows[1]] = windows[1]
# image = image - np.mean(image)
min_v = np.min(image)
max_v = np.max(image)
interv = max_v - min_v
image = (image - min_v) / interv
return image * 255.0
def bounding_box(mask):
xs, ys = np.where(mask == 1)
return np.min(xs), np.min(ys), np.max(xs), np.max(ys)
def _get_liver_path(cur_slice_dir, phase_name):
paths = glob(
os.path.join(cur_slice_dir,
str(phase_name).lower() + '_liver_mask.mhd'))
if len(paths) == 1:
return paths[0]
paths = glob(
os.path.join(cur_slice_dir,
str(phase_name).upper() + '_Liver_Mask.mhd'))
if len(paths) == 1:
return paths[0]
print('Error ', cur_slice_dir, phase_name)
assert False
def _read_liver_mask(mask_path, size=16):
liver_mask = np.squeeze(read_mhd_image(mask_path))
min_xs, min_ys, max_xs, max_ys = bounding_box(liver_mask)
center_x = min_xs + (max_xs - min_xs) // 2
center_y = min_ys + (max_ys - min_ys) // 2
new_min_xs = center_x - size // 2
new_max_xs = center_x + size // 2
new_min_ys = center_y - size // 2
new_max_ys = center_y + size // 2
return new_min_xs, new_min_ys, new_max_xs, new_max_ys
patient_name = os.path.basename(slice_dir).split('_')[0]
cur_save_dir = os.path.join(save_dir, stage_name,
os.path.basename(slice_dir))
print cur_save_dir
window_center_dict = get_window_center_info()
if not os.path.exists(cur_save_dir):
os.makedirs(cur_save_dir)
nc_image_paths = glob(os.path.join(slice_dir, '%s_Image*.mhd' % 'NC'))
art_image_paths = glob(os.path.join(slice_dir, '%s_Image*.mhd' % 'ART'))
pv_image_paths = glob(os.path.join(slice_dir, '%s_Image*.mhd' % 'PV'))
nc_mask_paths = glob(os.path.join(slice_dir, '%s_Mask*.mhd' % 'NC'))
art_mask_paths = glob(os.path.join(slice_dir, '%s_Mask*.mhd' % 'ART'))
pv_mask_paths = glob(os.path.join(slice_dir, '%s_Mask*.mhd' % 'PV'))
# for nc_mask_path in nc_mask_paths:
# os.remove(nc_mask_path)
# for art_mask_path in art_mask_paths:
# os.remove(art_mask_path)
# for pv_mask_path in pv_mask_paths:
# os.remove(pv_mask_path)
# if len(nc_image_paths) == 1 and len(art_image_paths) == 1 and len(pv_image_paths) == 1:
# print('OK', slice_dir)
# else:
# print('Error', slice_dir, len(nc_image_paths), len(art_image_paths), len(pv_image_paths), nc_image_paths)
#
# if len(nc_mask_paths) == 1 and len(art_mask_paths) == 1 and len(pv_mask_paths) == 1:
# print('OK', slice_dir)
# else:
# print('Error', slice_dir, len(nc_mask_paths), len(art_mask_paths), len(pv_mask_paths), nc_mask_paths)
if len(nc_image_paths) == 0:
print slice_dir
assert False
nc_image_path = nc_image_paths[0]
art_image_path = art_image_paths[0]
pv_image_path = pv_image_paths[0]
nc_mask_path = nc_mask_paths[0]
art_mask_path = art_mask_paths[0]
pv_mask_path = pv_mask_paths[0]
nc_liver_mask_path = _get_liver_path(slice_dir, 'nc')
art_liver_mask_path = _get_liver_path(slice_dir, 'art')
pv_liver_mask_path = _get_liver_path(slice_dir, 'pv')
nc_liver_min_xs, nc_liver_min_ys, nc_liver_max_xs, nc_liver_max_ys = _read_liver_mask(
nc_liver_mask_path)
art_liver_min_xs, art_liver_min_ys, art_liver_max_xs, art_liver_max_ys = _read_liver_mask(
art_liver_mask_path)
pv_liver_min_xs, pv_liver_min_ys, pv_liver_max_xs, pv_liver_max_ys = _read_liver_mask(
pv_liver_mask_path)
nc_image = np.squeeze(read_mhd_image(nc_image_path))
art_image = np.squeeze(read_mhd_image(art_image_path))
pv_image = np.squeeze(read_mhd_image(pv_image_path))
nc_mask = np.asarray(
np.squeeze(read_mhd_image(nc_mask_path)) == 1, np.uint8)
art_mask = np.asarray(
np.squeeze(read_mhd_image(art_mask_path)) == 1, np.uint8)
pv_mask = np.asarray(
np.squeeze(read_mhd_image(pv_mask_path)) == 1, np.uint8)
height_nc, width_nc, perimeter_nc, area_nc, circle_metric_nc, edge_nc = calculate_mask_attributes(
nc_mask)
with open(os.path.join(cur_save_dir, 'NC_attributes.txt'),
'w') as nc_attr_file:
line = '%d %d %d %d %.6f\n' % (height_nc, width_nc, perimeter_nc,
area_nc, circle_metric_nc)
nc_attr_file.writelines([line])
nc_attr_file.close()
height_art, width_art, perimeter_art, area_art, circle_metric_art, edge_art = calculate_mask_attributes(
art_mask)
with open(os.path.join(cur_save_dir, 'ART_attributes.txt'),
'w') as art_attr_file:
line = '%d %d %d %d %.6f\n' % (height_art, width_art, perimeter_art,
area_art, circle_metric_art)
art_attr_file.writelines([line])
art_attr_file.close()
height_pv, width_pv, perimeter_pv, area_pv, circle_metric_pv, edge_pv = calculate_mask_attributes(
pv_mask)
with open(os.path.join(cur_save_dir, 'PV_attributes.txt'),
'w') as pv_attr_file:
line = '%d %d %d %d %.6f\n' % (height_pv, width_pv, perimeter_pv,
area_pv, circle_metric_pv)
pv_attr_file.writelines([line])
pv_attr_file.close()
# print('edge_nc: ', np.min(edge_nc), np.max(edge_nc), np.unique(edge_nc))
# print(height_nc, width_nc, perimeter_nc, area_nc, circle_metric_nc, cur_save_dir[-1])
# cv2.imwrite(os.path.join(cur_save_dir, 'NC_EDGE.jpg'), edge_nc)
if patient_name in window_center_dict.keys():
cur_window_center = window_center_dict[patient_name]
else:
cur_window_center = [-70, 180, -70, 180, -70, 180]
# print('cur_window_center is ', cur_window_center)
nc_image = preprocessing_mhd(nc_image,
[cur_window_center[0], cur_window_center[1]])
art_image = preprocessing_mhd(art_image,
[cur_window_center[2], cur_window_center[3]])
pv_image = preprocessing_mhd(pv_image,
[cur_window_center[4], cur_window_center[5]])
nc_min_xs, nc_min_ys, nc_max_xs, nc_max_ys = bounding_box(nc_mask)
art_min_xs, art_min_ys, art_max_xs, art_max_ys = bounding_box(art_mask)
pv_min_xs, pv_min_ys, pv_max_xs, pv_max_ys = bounding_box(pv_mask)
nc_roi = nc_image[nc_min_xs:nc_max_xs, nc_min_ys:nc_max_ys]
art_roi = art_image[art_min_xs:art_max_xs, art_min_ys:art_max_ys]
pv_roi = pv_image[pv_min_xs:pv_max_xs, pv_min_ys:pv_max_ys]
nc_liver = nc_image[nc_liver_min_xs:nc_liver_max_xs,
nc_liver_min_ys:nc_liver_max_ys]
art_liver = art_image[art_liver_min_xs:art_liver_max_xs,
art_liver_min_ys:art_liver_max_ys]
pv_liver = pv_image[pv_liver_min_xs:pv_liver_max_xs,
pv_liver_min_ys:pv_liver_max_ys]
nc_mean_liver = np.mean(nc_liver)
art_mean_liver = np.mean(art_liver)
pv_mean_liver = np.mean(pv_liver)
compared_nc_roi = nc_roi - nc_mean_liver
compared_art_roi = art_roi - art_mean_liver
compared_pv_roi = pv_roi - pv_mean_liver
compared_min = np.min([
np.min(compared_nc_roi),
np.min(compared_art_roi),
np.min(compared_pv_roi)
])
compared_max = np.max([
np.max(compared_nc_roi),
np.max(compared_art_roi),
np.max(compared_pv_roi)
])
compared_nc_roi = (compared_nc_roi - compared_min) / (compared_max -
compared_min)
compared_art_roi = (compared_art_roi - compared_min) / (compared_max -
compared_min)
compared_pv_roi = (compared_pv_roi - compared_min) / (compared_max -
compared_min)
compared_nc_roi *= 255.0
compared_art_roi *= 255.0
compared_pv_roi *= 255.0
# compared_nc_roi = np.asarray(compared_nc_roi * 255.0, np.uint8)
# compared_art_roi = np.asarray(compared_art_roi * 255.0, np.uint8)
# compared_pv_roi = np.asarray(compared_pv_roi * 255.0, np.uint8)
# nc_roi = np.concatenate(
# [np.expand_dims(nc_roi, axis=2), np.expand_dims(nc_roi, axis=2), np.expand_dims(nc_roi, axis=2)], axis=2)
# art_roi = np.concatenate(
# [np.expand_dims(art_roi, axis=2), np.expand_dims(art_roi, axis=2), np.expand_dims(art_roi, axis=2)], axis=2)
# pv_roi = np.concatenate(
# [np.expand_dims(pv_roi, axis=2), np.expand_dims(pv_roi, axis=2), np.expand_dims(pv_roi, axis=2)], axis=2)
# 将所有的病灶resize到最大的那个病灶上面
nc_h, nc_w = np.shape(nc_roi)
art_h, art_w = np.shape(art_roi)
pv_h, pv_w = np.shape(pv_roi)
nc_size = nc_h * nc_w
art_size = art_h * art_w
pv_size = pv_h * pv_w
if pv_size >= nc_size and pv_size >= art_size:
target_h = pv_h
target_w = pv_w
elif art_size >= nc_size and art_size >= pv_size:
target_h = art_h
target_w = art_w
elif nc_size >= art_size and nc_size >= pv_size:
target_h = nc_h
target_w = nc_w
nc_roi_resized = cv2.resize(nc_roi, (target_h, target_w))
art_roi_resized = cv2.resize(art_roi, (target_h, target_w))
pv_roi_resized = cv2.resize(pv_roi, (target_h, target_w))
nc_liver_resized = cv2.resize(nc_liver, (target_h, target_w))
art_liver_resized = cv2.resize(art_liver, (target_h, target_w))
pv_liver_resized = cv2.resize(pv_liver, (target_h, target_w))
nc_roi_compared_resized = np.asarray(
cv2.resize(compared_nc_roi, (target_h, target_w)), np.uint8)
art_roi_compared_resized = np.asarray(
cv2.resize(compared_art_roi, (target_h, target_w)), np.uint8)
pv_roi_compared_resized = np.asarray(
cv2.resize(compared_pv_roi, (target_h, target_w)), np.uint8)
nc_roi_final = np.concatenate([
np.expand_dims(nc_roi_resized, axis=2),
np.expand_dims(nc_liver_resized, axis=2),
np.expand_dims(nc_roi_compared_resized, axis=2)
],
axis=2)
art_roi_final = np.concatenate([
np.expand_dims(art_roi_resized, axis=2),
np.expand_dims(art_liver_resized, axis=2),
np.expand_dims(art_roi_compared_resized, axis=2)
],
axis=2)
pv_roi_final = np.concatenate([
np.expand_dims(pv_roi_resized, axis=2),
np.expand_dims(pv_liver_resized, axis=2),
np.expand_dims(pv_roi_compared_resized, axis=2)
],
axis=2)
patch_size = 5
nc_patches, art_patches, pv_patches = extract_patches(
nc_roi_final, art_roi_final, pv_roi_final, patch_size)
nc_roi_save_path = os.path.join(cur_save_dir, 'NC.jpg')
art_roi_save_path = os.path.join(cur_save_dir, 'ART.jpg')
pv_roi_save_path = os.path.join(cur_save_dir, 'PV.jpg')
cv2.imwrite(nc_roi_save_path, nc_roi_final)
cv2.imwrite(art_roi_save_path, art_roi_final)
cv2.imwrite(pv_roi_save_path, pv_roi_final)
patch_dir = os.path.join(cur_save_dir, '%dx%d' % (patch_size, patch_size))
if not os.path.exists(patch_dir):
os.mkdir(patch_dir)
for idx, (nc_patch, art_patch,
pv_patch) in enumerate(zip(nc_patches, art_patches, pv_patches)):
nc_patch_save_path = os.path.join(patch_dir, '%d_NC.jpg' % idx)
art_patch_save_path = os.path.join(patch_dir, '%d_ART.jpg' % idx)
pv_patch_save_path = os.path.join(patch_dir, '%d_PV.jpg' % idx)
cv2.imwrite(nc_patch_save_path, nc_patch)
cv2.imwrite(art_patch_save_path, art_patch)
cv2.imwrite(pv_patch_save_path, pv_patch)
def convert2jpg_multiphase(slice_dir, stage_name, save_dir):
patient_name = os.path.basename(slice_dir).split('_')[0]
cur_save_dir = os.path.join(save_dir, stage_name,
os.path.basename(slice_dir))
window_center_dict = get_window_center_info()
if not os.path.exists(cur_save_dir):
os.makedirs(cur_save_dir)
nc_image_paths = glob(os.path.join(slice_dir, '%s_Image*.mhd' % 'NC'))
art_image_paths = glob(os.path.join(slice_dir, '%s_Image*.mhd' % 'ART'))
pv_image_paths = glob(os.path.join(slice_dir, '%s_Image*.mhd' % 'PV'))
nc_mask_paths = glob(os.path.join(slice_dir, '%s_Mask*.mhd' % 'NC'))
art_mask_paths = glob(os.path.join(slice_dir, '%s_Mask*.mhd' % 'ART'))
pv_mask_paths = glob(os.path.join(slice_dir, '%s_Mask*.mhd' % 'PV'))
# for nc_mask_path in nc_mask_paths:
# os.remove(nc_mask_path)
# for art_mask_path in art_mask_paths:
# os.remove(art_mask_path)
# for pv_mask_path in pv_mask_paths:
# os.remove(pv_mask_path)
# if len(nc_image_paths) == 1 and len(art_image_paths) == 1 and len(pv_image_paths) == 1:
# print('OK', slice_dir)
# else:
# print('Error', slice_dir, len(nc_image_paths), len(art_image_paths), len(pv_image_paths), nc_image_paths)
#
# if len(nc_mask_paths) == 1 and len(art_mask_paths) == 1 and len(pv_mask_paths) == 1:
# print('OK', slice_dir)
# else:
# print('Error', slice_dir, len(nc_mask_paths), len(art_mask_paths), len(pv_mask_paths), nc_mask_paths)
nc_image_path = nc_image_paths[0]
art_image_path = art_image_paths[0]
pv_image_path = pv_image_paths[0]
nc_mask_path = nc_mask_paths[0]
art_mask_path = art_mask_paths[0]
pv_mask_path = pv_mask_paths[0]
nc_image = np.squeeze(read_mhd_image(nc_image_path))
art_image = np.squeeze(read_mhd_image(art_image_path))
pv_image = np.squeeze(read_mhd_image(pv_image_path))
nc_mask = np.asarray(
np.squeeze(read_mhd_image(nc_mask_path)) == 1, np.uint8)
art_mask = np.asarray(
np.squeeze(read_mhd_image(art_mask_path)) == 1, np.uint8)
pv_mask = np.asarray(
np.squeeze(read_mhd_image(pv_mask_path)) == 1, np.uint8)
def preprocessing_mhd(image, windows=[-350, 300]):
image = np.asarray(image, np.float32)
image[image < windows[0]] = windows[0]
image[image > windows[1]] = windows[1]
# image = image - np.mean(image)
min_v = np.min(image)
max_v = np.max(image)
interv = max_v - min_v
image = (image - min_v) / interv
return image * 255.0
def bounding_box(mask):
xs, ys = np.where(mask == 1)
return np.min(xs), np.min(ys), np.max(xs), np.max(ys)
def calculate_mask_attributes(mask):
min_xs, min_ys, max_xs, max_ys = bounding_box(mask)
from medicalImage import fill_region
edge = cv2.Canny(mask, 1, 1)
perimeter = np.sum(edge >= 200)
area = np.sum(fill_region(mask)) * 1.0
circle_metric = 4 * np.pi * area / (perimeter * perimeter * 1.0)
return (max_xs -
min_xs), (max_ys -
min_ys), perimeter, area, circle_metric, edge
height_nc, width_nc, perimeter_nc, area_nc, circle_metric_nc, edge_nc = calculate_mask_attributes(
nc_mask)
with open(os.path.join(cur_save_dir, 'NC_attributes.txt'),
'w') as nc_attr_file:
line = '%d %d %d %d %.6f\n' % (height_nc, width_nc, perimeter_nc,
area_nc, circle_metric_nc)
nc_attr_file.writelines([line])
nc_attr_file.close()
height_art, width_art, perimeter_art, area_art, circle_metric_art, edge_art = calculate_mask_attributes(
art_mask)
with open(os.path.join(cur_save_dir, 'ART_attributes.txt'),
'w') as art_attr_file:
line = '%d %d %d %d %.6f\n' % (height_art, width_art, perimeter_art,
area_art, circle_metric_art)
art_attr_file.writelines([line])
art_attr_file.close()
height_pv, width_pv, perimeter_pv, area_pv, circle_metric_pv, edge_pv = calculate_mask_attributes(
pv_mask)
with open(os.path.join(cur_save_dir, 'PV_attributes.txt'),
'w') as pv_attr_file:
line = '%d %d %d %d %.6f\n' % (height_pv, width_pv, perimeter_pv,
area_pv, circle_metric_pv)
pv_attr_file.writelines([line])
pv_attr_file.close()
# print('edge_nc: ', np.min(edge_nc), np.max(edge_nc), np.unique(edge_nc))
# print(height_nc, width_nc, perimeter_nc, area_nc, circle_metric_nc, cur_save_dir[-1])
cv2.imwrite(os.path.join(cur_save_dir, 'NC_EDGE.jpg'), edge_nc)
if patient_name in window_center_dict.keys():
cur_window_center = window_center_dict[patient_name]
else:
cur_window_center = [-70, 180, -70, 180, -70, 180]
# print('cur_window_center is ', cur_window_center)
nc_image = preprocessing_mhd(nc_image,
[cur_window_center[0], cur_window_center[1]])
art_image = preprocessing_mhd(art_image,
[cur_window_center[2], cur_window_center[3]])
pv_image = preprocessing_mhd(pv_image,
[cur_window_center[4], cur_window_center[5]])
nc_min_xs, nc_min_ys, nc_max_xs, nc_max_ys = bounding_box(nc_mask)
art_min_xs, art_min_ys, art_max_xs, art_max_ys = bounding_box(art_mask)
pv_min_xs, pv_min_ys, pv_max_xs, pv_max_ys = bounding_box(pv_mask)
nc_roi = nc_image[nc_min_xs:nc_max_xs, nc_min_ys:nc_max_ys]
art_roi = art_image[art_min_xs:art_max_xs, art_min_ys:art_max_ys]
pv_roi = pv_image[pv_min_xs:pv_max_xs, pv_min_ys:pv_max_ys]
nc_roi = np.concatenate([
np.expand_dims(nc_roi, axis=2),
np.expand_dims(nc_roi, axis=2),
np.expand_dims(nc_roi, axis=2)
],
axis=2)
art_roi = np.concatenate([
np.expand_dims(art_roi, axis=2),
np.expand_dims(art_roi, axis=2),
np.expand_dims(art_roi, axis=2)
],
axis=2)
pv_roi = np.concatenate([
np.expand_dims(pv_roi, axis=2),
np.expand_dims(pv_roi, axis=2),
np.expand_dims(pv_roi, axis=2)
],
axis=2)
# 将所有的病灶resize到最大的那个病灶上面
nc_h, nc_w, _ = np.shape(nc_roi)
art_h, art_w, _ = np.shape(art_roi)
pv_h, pv_w, _ = np.shape(pv_roi)
nc_size = nc_h * nc_w
art_size = art_h * art_w
pv_size = pv_h * pv_w
if pv_size >= nc_size and pv_size >= art_size:
target_h = pv_h
target_w = pv_w
elif art_size >= nc_size and art_size >= pv_size:
target_h = art_h
target_w = art_w
elif nc_size >= art_size and nc_size >= pv_size:
target_h = nc_h
target_w = nc_w
nc_roi_resized = cv2.resize(nc_roi, (target_h, target_w))
art_roi_resized = cv2.resize(art_roi, (target_h, target_w))
pv_roi_resized = cv2.resize(pv_roi, (target_h, target_w))
patch_size = 5
nc_patches, art_patches, pv_patches = extract_patches(
nc_roi_resized, art_roi_resized, pv_roi_resized, patch_size)
nc_roi_save_path = os.path.join(cur_save_dir, 'NC.jpg')
art_roi_save_path = os.path.join(cur_save_dir, 'ART.jpg')
pv_roi_save_path = os.path.join(cur_save_dir, 'PV.jpg')
cv2.imwrite(nc_roi_save_path, nc_roi_resized)
cv2.imwrite(art_roi_save_path, art_roi_resized)
cv2.imwrite(pv_roi_save_path, pv_roi_resized)
patch_dir = os.path.join(cur_save_dir, '%dx%d' % (patch_size, patch_size))
if not os.path.exists(patch_dir):
os.mkdir(patch_dir)
for idx, (nc_patch, art_patch,
pv_patch) in enumerate(zip(nc_patches, art_patches, pv_patches)):
nc_patch_save_path = os.path.join(patch_dir, '%d_NC.jpg' % idx)
art_patch_save_path = os.path.join(patch_dir, '%d_ART.jpg' % idx)
pv_patch_save_path = os.path.join(patch_dir, '%d_PV.jpg' % idx)
cv2.imwrite(nc_patch_save_path, nc_patch)
cv2.imwrite(art_patch_save_path, art_patch)
cv2.imwrite(pv_patch_save_path, pv_patch)
def process_single_file(dataset_dir, slice_name):
cur_label = int(slice_name[-1])
cur_slice_dir = os.path.join(dataset_dir, slice_name)
nc_img_path = glob(os.path.join(cur_slice_dir, 'NC_Image*.mhd'))[0]
art_img_path = glob(os.path.join(cur_slice_dir, 'ART_Image*.mhd'))[0]
pv_img_path = glob(os.path.join(cur_slice_dir, 'PV_Image*.mhd'))[0]
print(nc_img_path, art_img_path, pv_img_path)
nc_img = np.squeeze(read_mhd_image(nc_img_path))
art_img = np.squeeze(read_mhd_image(art_img_path))
pv_img = np.squeeze(read_mhd_image(pv_img_path))
nc_mask_path = glob(os.path.join(cur_slice_dir, 'NC_Mask*.mhd'))[0]
art_mask_path = glob(os.path.join(cur_slice_dir, 'ART_Mask*.mhd'))[0]
pv_mask_path = glob(os.path.join(cur_slice_dir, 'PV_Mask*.mhd'))[0]
print(nc_mask_path, art_mask_path, pv_mask_path)
nc_mask = np.squeeze(read_mhd_image(nc_mask_path))
art_mask = np.squeeze(read_mhd_image(art_mask_path))
pv_mask = np.squeeze(read_mhd_image(pv_mask_path))
nc_mask, nc_min_xs, nc_min_ys, nc_max_xs, nc_max_ys = get_filled_mask(
nc_mask)
art_mask, art_min_xs, art_min_ys, art_max_xs, art_max_ys = get_filled_mask(
art_mask)
pv_mask, pv_min_xs, pv_min_ys, pv_max_xs, pv_max_ys = get_filled_mask(
pv_mask)
height_nc, width_nc, perimeter_nc, area_nc, circle_metric_nc, edge_nc = calculate_mask_attributes(
nc_mask)
nc_attr = [height_nc, width_nc, perimeter_nc, area_nc, circle_metric_nc]
height_art, width_art, perimeter_art, area_art, circle_metric_art, edge_art = calculate_mask_attributes(
art_mask)
art_attr = [
height_art, width_art, perimeter_art, area_art, circle_metric_art
]
height_pv, width_pv, perimeter_pv, area_pv, circle_metric_pv, edge_pv = calculate_mask_attributes(
pv_mask)
pv_attr = [height_pv, width_pv, perimeter_pv, area_pv, circle_metric_pv]
nc_liver_mask_path = get_liver_path(cur_slice_dir, 'nc')
art_liver_mask_path = get_liver_path(cur_slice_dir, 'art')
pv_liver_mask_path = get_liver_path(cur_slice_dir, 'pv')
print(nc_liver_mask_path, art_liver_mask_path, pv_liver_mask_path)
nc_liver_min_xs, nc_liver_min_ys, nc_liver_max_xs, nc_liver_max_ys = read_liver_mask(
nc_liver_mask_path)
art_liver_min_xs, art_liver_min_ys, art_liver_max_xs, art_liver_max_ys = read_liver_mask(
art_liver_mask_path)
pv_liver_min_xs, pv_liver_min_ys, pv_liver_max_xs, pv_liver_max_ys = read_liver_mask(
pv_liver_mask_path)
nc_liver = nc_img[nc_liver_min_xs:nc_liver_max_xs,
nc_liver_min_ys:nc_liver_max_ys]
art_liver = art_img[art_liver_min_xs:art_liver_max_xs,
art_liver_min_ys:art_liver_max_ys]
pv_liver = pv_img[pv_liver_min_xs:pv_liver_max_xs,
pv_liver_min_ys:pv_liver_max_ys]
# 非病灶区域置0
nc_roi_img = np.copy(nc_img)
art_roi_img = np.copy(art_img)
pv_roi_img = np.copy(pv_img)
nc_roi_img[nc_mask != 1] = 0
art_roi_img[art_mask != 1] = 0
pv_roi_img[pv_mask != 1] = 0
nc_roi_img = nc_roi_img[nc_min_xs:nc_max_xs, nc_min_ys:nc_max_ys]
art_roi_img = art_roi_img[art_min_xs:art_max_xs, art_min_ys:art_max_ys]
pv_roi_img = pv_roi_img[pv_min_xs:pv_max_xs, pv_min_ys:pv_max_ys]
nc_h, nc_w = np.shape(nc_roi_img)
art_h, art_w = np.shape(art_roi_img)
pv_h, pv_w = np.shape(pv_roi_img)
nc_size = nc_h * nc_w
art_size = art_h * art_w
pv_size = pv_h * pv_w
if pv_size >= nc_size and pv_size >= art_size:
target_h = pv_h
target_w = pv_w
elif art_size >= nc_size and art_size >= pv_size:
target_h = art_h
target_w = art_w
elif nc_size >= art_size and nc_size >= pv_size:
target_h = nc_h
target_w = nc_w
nc_roi_img = np.asarray(nc_roi_img, np.float32)
art_roi_img = np.asarray(art_roi_img, np.float32)
pv_roi_img = np.asarray(pv_roi_img, np.float32)
nc_liver = np.asarray(nc_liver, np.float32)
art_liver = np.asarray(art_liver, np.float32)
pv_liver = np.asarray(pv_liver, np.float32)
nc_roi_resized = cv2.resize(nc_roi_img, (target_h, target_w))
art_roi_resized = cv2.resize(art_roi_img, (target_h, target_w))
pv_roi_resized = cv2.resize(pv_roi_img, (target_h, target_w))
nc_liver_resized = cv2.resize(nc_liver, (target_h, target_w))
art_liver_resized = cv2.resize(art_liver, (target_h, target_w))
pv_liver_resized = cv2.resize(pv_liver, (target_h, target_w))
nc_roi_final = np.concatenate([
np.expand_dims(nc_roi_resized, axis=2),
np.expand_dims(nc_liver_resized, axis=2),
np.expand_dims(nc_roi_resized, axis=2)
],
axis=2)
art_roi_final = np.concatenate([
np.expand_dims(art_roi_resized, axis=2),
np.expand_dims(art_liver_resized, axis=2),
np.expand_dims(art_roi_resized, axis=2)
],
axis=2)
pv_roi_final = np.concatenate([
np.expand_dims(pv_roi_resized, axis=2),
np.expand_dims(pv_liver_resized, axis=2),
np.expand_dims(pv_roi_resized, axis=2)
],
axis=2)
print('nc mean is ', nc_roi_final.mean((0, 1)))
print('art mean is ', art_roi_final.mean((0, 1)))
print('pv mean is ', pv_roi_final.mean((0, 1)))
cur_nc_patches, cur_art_patches, cur_pv_patches = extract_patches(
nc_roi_final, art_roi_final, pv_roi_final, patch_size=5)
def run(dataset_dir,
output_dir,
name='voc_train',
shuffling=True,
stage_name='train'):
"""Runs the conversion operation.
Args:
dataset_dir: The dataset directory where the dataset is stored.
output_dir: Output directory.
"""
if not tf.gfile.Exists(dataset_dir):
tf.gfile.MakeDirs(dataset_dir)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# Dataset filenames, and shuffling.
cur_dataset_dir = os.path.join(dataset_dir, stage_name)
slice_names = os.listdir(cur_dataset_dir)
patch_size = 5
nc_rois = []
art_rois = []
pv_rois = []
nc_patches = []
art_patches = []
pv_patches = []
labels = []
nc_attrs = []
pv_attrs = []
art_attrs = []
for slice_name in slice_names:
if slice_name.startswith('.DS'):
continue
print slice_name
cur_label = int(slice_name[-1])
cur_slice_dir = os.path.join(cur_dataset_dir, slice_name)
nc_img_path = glob(os.path.join(cur_slice_dir, 'NC_Image*.mhd'))[0]
art_img_path = glob(os.path.join(cur_slice_dir, 'ART_Image*.mhd'))[0]
pv_img_path = glob(os.path.join(cur_slice_dir, 'PV_Image*.mhd'))[0]
nc_img = np.squeeze(read_mhd_image(nc_img_path))
art_img = np.squeeze(read_mhd_image(art_img_path))
pv_img = np.squeeze(read_mhd_image(pv_img_path))
nc_mask_path = glob(os.path.join(cur_slice_dir, 'NC_Mask*.mhd'))[0]
art_mask_path = glob(os.path.join(cur_slice_dir, 'ART_Mask*.mhd'))[0]
pv_mask_path = glob(os.path.join(cur_slice_dir, 'PV_Mask*.mhd'))[0]
nc_mask = np.squeeze(read_mhd_image(nc_mask_path))
art_mask = np.squeeze(read_mhd_image(art_mask_path))
pv_mask = np.squeeze(read_mhd_image(pv_mask_path))
nc_mask, nc_min_xs, nc_min_ys, nc_max_xs, nc_max_ys = get_filled_mask(
nc_mask)
art_mask, art_min_xs, art_min_ys, art_max_xs, art_max_ys = get_filled_mask(
art_mask)
pv_mask, pv_min_xs, pv_min_ys, pv_max_xs, pv_max_ys = get_filled_mask(
pv_mask)
height_nc, width_nc, perimeter_nc, area_nc, circle_metric_nc, edge_nc = calculate_mask_attributes(
nc_mask)
nc_attr = [
height_nc, width_nc, perimeter_nc, area_nc, circle_metric_nc
]
height_art, width_art, perimeter_art, area_art, circle_metric_art, edge_art = calculate_mask_attributes(
art_mask)
art_attr = [
height_art, width_art, perimeter_art, area_art, circle_metric_art
]
height_pv, width_pv, perimeter_pv, area_pv, circle_metric_pv, edge_pv = calculate_mask_attributes(
pv_mask)
pv_attr = [
height_pv, width_pv, perimeter_pv, area_pv, circle_metric_pv
]
nc_liver_mask_path = get_liver_path(cur_slice_dir, 'nc')
art_liver_mask_path = get_liver_path(cur_slice_dir, 'art')
pv_liver_mask_path = get_liver_path(cur_slice_dir, 'pv')
nc_liver_min_xs, nc_liver_min_ys, nc_liver_max_xs, nc_liver_max_ys = read_liver_mask(
nc_liver_mask_path)
art_liver_min_xs, art_liver_min_ys, art_liver_max_xs, art_liver_max_ys = read_liver_mask(
art_liver_mask_path)
pv_liver_min_xs, pv_liver_min_ys, pv_liver_max_xs, pv_liver_max_ys = read_liver_mask(
pv_liver_mask_path)
nc_liver = nc_img[nc_liver_min_xs:nc_liver_max_xs,
nc_liver_min_ys:nc_liver_max_ys]
art_liver = art_img[art_liver_min_xs:art_liver_max_xs,
art_liver_min_ys:art_liver_max_ys]
pv_liver = pv_img[pv_liver_min_xs:pv_liver_max_xs,
pv_liver_min_ys:pv_liver_max_ys]
# 非病灶区域置0
nc_roi_img = np.copy(nc_img)
art_roi_img = np.copy(art_img)
pv_roi_img = np.copy(pv_img)
nc_roi_img[nc_mask != 1] = 0
art_roi_img[art_mask != 1] = 0
pv_roi_img[pv_mask != 1] = 0
nc_roi_img = nc_roi_img[nc_min_xs:nc_max_xs, nc_min_ys:nc_max_ys]
art_roi_img = art_roi_img[art_min_xs:art_max_xs, art_min_ys:art_max_ys]
pv_roi_img = pv_roi_img[pv_min_xs:pv_max_xs, pv_min_ys:pv_max_ys]
nc_h, nc_w = np.shape(nc_roi_img)
art_h, art_w = np.shape(art_roi_img)
pv_h, pv_w = np.shape(pv_roi_img)
nc_size = nc_h * nc_w
art_size = art_h * art_w
pv_size = pv_h * pv_w
if pv_size >= nc_size and pv_size >= art_size:
target_h = pv_h
target_w = pv_w
elif art_size >= nc_size and art_size >= pv_size:
target_h = art_h
target_w = art_w
elif nc_size >= art_size and nc_size >= pv_size:
target_h = nc_h
target_w = nc_w
nc_roi_img = np.asarray(nc_roi_img, np.float32)
art_roi_img = np.asarray(art_roi_img, np.float32)
pv_roi_img = np.asarray(pv_roi_img, np.float32)
nc_liver = np.asarray(nc_liver, np.float32)
art_liver = np.asarray(art_liver, np.float32)
pv_liver = np.asarray(pv_liver, np.float32)
nc_roi_resized = cv2.resize(nc_roi_img, (target_h, target_w))
art_roi_resized = cv2.resize(art_roi_img, (target_h, target_w))
pv_roi_resized = cv2.resize(pv_roi_img, (target_h, target_w))
nc_liver_resized = cv2.resize(nc_liver, (target_h, target_w))
art_liver_resized = cv2.resize(art_liver, (target_h, target_w))
pv_liver_resized = cv2.resize(pv_liver, (target_h, target_w))
nc_roi_final = np.concatenate([
np.expand_dims(nc_roi_resized, axis=2),
np.expand_dims(nc_liver_resized, axis=2),
np.expand_dims(nc_roi_resized, axis=2)
],
axis=2)
art_roi_final = np.concatenate([
np.expand_dims(art_roi_resized, axis=2),
np.expand_dims(art_liver_resized, axis=2),
np.expand_dims(art_roi_resized, axis=2)
],
axis=2)
pv_roi_final = np.concatenate([
np.expand_dims(pv_roi_resized, axis=2),
np.expand_dims(pv_liver_resized, axis=2),
np.expand_dims(pv_roi_resized, axis=2)
],
axis=2)
print('nc mean is ', nc_roi_final.mean((0, 1)))
print('art mean is ', art_roi_final.mean((0, 1)))
print('pv mean is ', pv_roi_final.mean((0, 1)))
cur_nc_patches, cur_art_patches, cur_pv_patches = extract_patches(
nc_roi_final, art_roi_final, pv_roi_final, patch_size=patch_size)
nc_patches.extend(cur_nc_patches)
art_patches.extend(cur_art_patches)
pv_patches.extend(cur_pv_patches)
labels.extend([cur_label] * len(cur_pv_patches))
nc_rois.extend([nc_roi_final] * len(cur_nc_patches))
art_rois.extend([art_roi_final] * len(cur_art_patches))
pv_rois.extend([pv_roi_final] * len(cur_pv_patches))
nc_attrs.extend([nc_attr] * len(cur_nc_patches))
art_attrs.extend([art_attr] * len(cur_art_patches))
pv_attrs.extend([pv_attr] * len(cur_pv_patches))
if len(nc_rois) != len(nc_patches) or len(nc_rois) != len(
art_patches) or len(nc_patches) != len(art_patches) or len(
nc_rois) != len(pv_patches) or len(nc_rois) != len(
pv_rois):
print('the number is not equal')
assert False
nc_rois = np.asarray(nc_rois)
art_rois = np.asarray(art_rois)
pv_rois = np.asarray(pv_rois)
nc_patches = np.asarray(nc_patches)
art_patches = np.asarray(art_patches)
pv_patches = np.asarray(pv_patches)
nc_attrs = np.asarray(nc_attrs)
art_attrs = np.asarray(art_attrs)
pv_attrs = np.asarray(pv_attrs)
labels = np.asarray(labels)
nc_attrs /= config.MAX_ATTRS
art_attrs /= config.MAX_ATTRS
pv_attrs /= config.MAX_ATTRS
if shuffling:
random.seed(RANDOM_SEED)
idx = range(len(nc_rois))
random.shuffle(idx)
nc_rois = nc_rois[idx]
art_rois = art_rois[idx]
pv_rois = pv_rois[idx]
nc_patches = nc_patches[idx]
art_patches = art_patches[idx]
pv_patches = pv_patches[idx]
nc_attrs = nc_attrs[idx]
art_attrs = art_attrs[idx]
pv_attrs = pv_attrs[idx]
labels = labels[idx]
print(len(nc_rois), len(art_rois), len(pv_rois), len(nc_patches),
len(art_patches), len(pv_patches), len(nc_attrs), len(art_attrs),
len(pv_attrs), len(labels))
attrs = np.concatenate([nc_attrs, art_attrs, pv_attrs], axis=1)
print('the shape of nc_roi is ', np.shape(nc_rois))
print('nc_roi mean is ', compute_mean_img(nc_rois))
print('art_roi mean is ', compute_mean_img(art_rois))
print('pv_roi mean is ', compute_mean_img(pv_rois))
print('attrs shape is ', np.shape(attrs))
# Process dataset files.
i = 0
fidx = 0
while i < len(nc_rois):
# Open new TFRecord file.
tf_filename = _get_output_filename(output_dir, name, fidx)
with tf.python_io.TFRecordWriter(tf_filename) as tfrecord_writer:
j = 0
while i < len(nc_rois) and j < SAMPLES_PER_FILES:
sys.stdout.write('\r>> Converting image %d/%d' %
(i + 1, len(nc_rois)))
sys.stdout.flush()
nc_roi = nc_rois[i]
art_roi = art_rois[i]
pv_roi = pv_rois[i]
nc_patch = nc_patches[i]
art_patch = art_patches[i]
pv_patch = pv_patches[i]
label = labels[i]
attr = attrs[i]
_add_to_tfrecord(nc_roi, art_roi, pv_roi, nc_patch, art_patch,
pv_patch, attr, label, tfrecord_writer)
i += 1
j += 1
fidx += 1
# Finally, write the labels file:
# labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
# dataset_utils.write_label_file(labels_to_class_names, dataset_dir)
print('\nFinished converting the Pascal VOC dataset!')