def ImageInMultiROIOut2D(root_folder,
input_shape,
batch_size=8,
hierarchical_level=0,
augment_param={}):
input_number, output_number = _GetInputOutputNumber(root_folder)
assert (output_number == 1)
case_list = os.listdir(root_folder)
input_list = [[] for index in range(input_number)]
output_list = [[] for index in range(hierarchical_level + 1)]
param_generator = AugmentParametersGenerator()
augmentor = DataAugmentor2D()
while True:
shuffle(case_list)
for case in case_list:
case_path = os.path.join(root_folder, case)
if not case_path.endswith('.h5'):
continue
input_data_list, output_data_list = [], []
file = h5py.File(case_path, 'r')
for input_number_index in range(input_number):
temp_data = np.asarray(file['input_' +
str(input_number_index)])
if temp_data.ndim == 2:
temp_data = temp_data[..., np.newaxis]
input_data_list.append(temp_data)
one_roi = np.asarray(file['output_0'])
param_generator.RandomParameters(augment_param)
augmentor.SetParameter(param_generator.GetRandomParametersDict())
input_data_list = _AugmentDataList2D(input_data_list, augmentor)
one_roi = _AugmentDataList2D([one_roi], augmentor)[0]
input_data_list = _CropDataList2D(input_data_list, input_shape)
one_roi = _CropDataList2D([one_roi], input_shape)[0]
one_roi_list = [one_roi]
for index in np.arange(1, hierarchical_level + 1):
temp_roi = deepcopy(
cv2.resize(one_roi,
(one_roi.shape[0] // np.power(2, index),
one_roi.shape[1] // np.power(2, index)),
interpolation=cv2.INTER_LINEAR))
one_roi_list.insert(0, temp_roi[..., np.newaxis])
_AddOneSample(input_list, input_data_list)
_AddOneSample(output_list, one_roi_list)
if len(input_list[0]) >= batch_size:
inputs = _MakeKerasFormat(input_list)
outputs = _MakeKerasFormat(output_list)
yield inputs, outputs
input_list = [[] for index in range(input_number)]
output_list = [[] for index in range(hierarchical_level + 1)]
def dataAug(data, label):
random_params = {'shear': 0.1, 'shift_x': 10, 'shift_y': 10, 'rotate_z_angle': 5, 'stretch_x': 0.1, 'stretch_y': 0.1}
param_generator = AugmentParametersGenerator()
aug_generator = DataAugmentor3D()
while True:
param_generator.RandomParameters(random_params)
aug_generator.SetParameter(param_generator.GetRandomParametersDict())
data = aug_generator.Execute(source_data=data, interpolation_method='linear', is_clear=False)
label = aug_generator.Execute(source_data=label, interpolation_method='nearest', is_clear=False)
return data, label
def ImageInImageOut2D(root_folder, input_shape, batch_size=8, augment_param={}):
input_number, output_number = _GetInputOutputNumber(root_folder)
case_list = os.listdir(root_folder)
input_list = [[] for index in range(input_number)]
output_list = [[] for index in range(output_number)]
param_generator = AugmentParametersGenerator()
augmentor = DataAugmentor2D()
crop = ImageProcess2D()
while True:
shuffle(case_list)
for case in case_list:
case_path = os.path.join(root_folder, case)
if not case_path.endswith('.h5'):
continue
input_data_list, output_data_list = [], []
try:
file = h5py.File(case_path, 'r')
for input_number_index in range(input_number):
temp_data = np.asarray(file['input_' + str(input_number_index)])
if temp_data.ndim == 2:
temp_data = temp_data[..., np.newaxis]
input_data_list.append(temp_data)
for output_number_index in range(output_number):
temp_data = np.asarray(file['output_' + str(output_number_index)])
if temp_data.ndim == 2:
temp_data = temp_data[..., np.newaxis]
output_data_list.append(temp_data)
file.close()
except Exception as e:
print(case_path)
print(e.__str__())
continue
param_generator.RandomParameters(augment_param)
augmentor.SetParameter(param_generator.GetRandomParametersDict())
input_data_list = _AugmentDataList2D(input_data_list, augmentor)
output_data_list = _AugmentDataList2D(output_data_list, augmentor)
input_data_list = crop.CropDataList2D(input_data_list, input_shape)
output_data_list = crop.CropDataList2D(output_data_list, input_shape)
_AddOneSample(input_list, input_data_list)
_AddOneSample(output_list, output_data_list)
if len(input_list[0]) >= batch_size:
inputs = _MakeKerasFormat(input_list)
outputs = _MakeKerasFormat(output_list)
return inputs, outputs
def AugmentScript():
from MeDIT.SaveAndLoad import LoadNiiData
from MeDIT.Visualization import Imshow3DArray, DrawBoundaryOfBinaryMask
from MeDIT.Normalize import Normalize01
import numpy as np
import time
t2_image, _, t2 = LoadNiiData(r'H:/data/TZ roi/BIAN ZHONG BEN/t2.nii',
dtype=np.float32)
roi_image, _, roi = LoadNiiData(
r'H:/data/TZ roi/BIAN ZHONG BEN/prostate_roi_TrumpetNet.nii',
dtype=np.uint8)
Imshow3DArray(t2, ROI=roi)
t2_slice = t2[..., 10]
roi_slice = roi[..., 10]
# DrawBoundaryOfBinaryMask(t2_slice, roi_slice)
import matplotlib.pyplot as plt
# plt.imshow(np.concatenate((Normalize01(t2_slice), Normalize01(roi_slice)), axis=1), cmap='gray')
# plt.show()
from MeDIT.DataAugmentor import DataAugmentor2D, AugmentParametersGenerator
param_dict = {
'stretch_x': 0.1,
'stretch_y': 0.1,
'shear': 0.1,
'rotate_z_angle': 20,
'horizontal_flip': True
}
augment_generator = AugmentParametersGenerator()
augmentor = DataAugmentor2D()
while True:
augment_generator.RandomParameters(param_dict)
transform_param = augment_generator.GetRandomParametersDict()
print(transform_param)
augment_t2 = augmentor.Execute(t2_slice,
aug_parameter=transform_param,
interpolation_method='linear')
augment_roi = augmentor.Execute(roi_slice,
aug_parameter=transform_param,
interpolation_method='linear')
def ImageInImageOut2D(root_folder, input_shape, batch_size=8, augment_param={}):
input_number, output_number = GetInputOutputNumber(root_folder)
case_list = os.listdir(root_folder)
input_list = [[] for index in range(input_number)]
output_list = [[] for index in range(output_number)]
param_generator = AugmentParametersGenerator()
augmentor = DataAugmentor2D()
while True:
shuffle(case_list)
for case in case_list:
case_path = os.path.join(root_folder, case)
if not case_path.endswith('.h5'):
continue
input_data_list, output_data_list = [], []
file = h5py.File(case_path, 'r')
for input_number_index in range(input_number):
input_data_list.append(file['input_' + str(input_number_index)])
for output_number_index in range(output_number):
output_data_list.append(file['output_' + str(output_number_index)])
param_generator.RandomParameters(augment_param)
augmentor.SetParameter(param_generator.GetRandomParametersDict())
input_data_list = AugmentDataList2D(input_data_list, augmentor)
output_data_list = AugmentDataList2D(output_data_list, augmentor)
input_data_list = CropDataList2D(input_data_list, input_shape)
output_data_list = CropDataList2D(output_data_list, input_shape)
AddOneSample(input_list, input_data_list)
AddOneSample(output_list, output_data_list)
if len(input_list[0]) >= batch_size:
inputs = MakeKerasFormat(input_list)
outputs = MakeKerasFormat(output_list)
yield inputs, outputs
input_list = [[] for index in range(input_number)]
output_list = [[] for index in range(output_number)]
def GenerateMultiInputOneOutput_From2DMultiSliceTo2D(root_folder,
input_shape,
batch_size=8,
augment_config='',
is_yield=True):
if augment_config:
with open(augment_config, 'r') as file:
random_params = json.load(file)
param_generator = AugmentParametersGenerator()
aug_generator = DataAugmentor2D()
case_list = os.listdir(root_folder)
shuffle(case_list)
one_path = os.path.join(root_folder, case_list[0])
info = LoadH5InfoForGenerate(one_path)
if info['input_number'] <= 1:
print('Need Multi Input ', one_path)
return
input_list = [[] for temp in range(info['input_number'])]
one_output = LoadH5(one_path, tag='output_0')
output_array = np.zeros(
(batch_size, input_shape[0], input_shape[1], one_output.shape[-1]))
current_batch = 0
while True:
shuffle(case_list)
for case in case_list:
case_path = os.path.join(root_folder, case)
if augment_config:
param_generator.RandomParameters(random_params)
aug_generator.SetParameter(
param_generator.GetRandomParametersDict())
for input_number in range(info['input_number']):
data = LoadH5(case_path, tag='input_{:d}'.format(input_number))
crop_data = np.zeros(
(input_shape[0], input_shape[1], data.shape[-1]))
for slice_index in range(data.shape[-1]):
if augment_config:
aug_data = aug_generator.Execute(
data[..., slice_index],
interpolation_method='linear')
crop_data[..., slice_index] = ExtractPatch(
aug_data, patch_size=input_shape)[0]
else:
crop_data[..., slice_index] = ExtractPatch(
data[..., slice_index], patch_size=input_shape)[0]
input_list[input_number].append(crop_data)
output_data = LoadH5(case_path, tag='output_0')
if augment_config:
aug_data = aug_generator.Execute(np.squeeze(output_data),
interpolation_method='linear')
output_array[current_batch, ...,
0] = ExtractPatch(aug_data,
patch_size=input_shape)[0]
else:
output_array[current_batch, ...,
0] = ExtractPatch(np.squeeze(output_data),
patch_size=input_shape)[0]
current_batch += 1
if current_batch >= batch_size:
input_list = [np.asarray(temp) for temp in input_list]
return input_list, output_array
current_batch = 0
# yield input_list, output_array
input_list = [[] for temp in range(info['input_number'])]
output_array = np.zeros((batch_size, input_shape[0],
input_shape[1], one_output.shape[-1]))
def AugmentTrain(train_folder, batch_size):
file_list = os.listdir(train_folder)
image_list = []
label_list = []
for i in range(len(file_list)):
# path
data_path = os.path.join(train_folder, file_list[i])
h5_file = h5py.File(data_path, 'r')
image = np.asarray(h5_file['input_0'], dtype=np.float32)
label = np.asarray(h5_file['output_0'], dtype=np.uint8)
# augmentation param
param_dict = {
'stretch_x': 0.1,
'stretch_y': 0.1,
'shear': 0.1,
'rotate_z_angle': 20,
'horizontal_flip': True
}
# 设增强函数为augment_generator
augment_generator = AugmentParametersGenerator()
# 2D的数据增强
augmentor = DataAugmentor2D()
augment_generator.RandomParameters(param_dict)
transform_param = augment_generator.GetRandomParametersDict()
augment_t2 = augmentor.Execute(image,
aug_parameter=transform_param,
interpolation_method='linear')
augment_roi = augmentor.Execute(label,
aug_parameter=transform_param,
interpolation_method='linear')
# cut
if np.shape(augment_t2) == (440, 440):
cropImage = image[100:340, 100:340]
cropRoi = label[100:340, 100:340]
cropaugmentImage = augment_t2[100:340, 100:340]
cropaugmentRoi = augment_roi[100:340, 100:340]
else:
cropImage = image[60:300, 60:300]
cropRoi = label[60:300, 60:300]
cropaugmentImage = augment_t2[60:300, 60:300]
cropaugmentRoi = augment_roi[60:300, 60:300]
# one_hot
roi_onehot = to_categorical(cropRoi)
augment_roi_onehot = to_categorical(cropaugmentRoi)
# show
# plt.imshow(np.concatenate((Normalize01(cropImage), Normalize01(cropRoi)), axis=1), cmap='gray')
# plt.show()
# plt.imshow(np.concatenate((Normalize01(cropaugmentImage), Normalize01(cropaugmentRoi)), axis=1), cmap='gray')
# plt.show()
reshape = (240, 240, 1)
cropImage = cropImage.reshape(reshape)
cropaugmentImage = cropaugmentImage.reshape(reshape)
# add data into list
image_list.append(cropImage)
label_list.append(roi_onehot)
image_list.append(cropaugmentImage)
label_list.append(augment_roi_onehot)
if len(image_list) >= batch_size:
yield np.asarray(image_list), np.asarray(label_list)
image_list = []
label_list = []
def _ImageInImageOut3D(root_folder,
input_shape,
batch_size=8,
augment_param={},
batch_rate=None):
input_number, output_number = _GetInputOutputNumber(root_folder)
case_list = os.listdir(root_folder)
input_list = [[] for index in range(input_number)]
output_list = [[] for index in range(output_number)]
param_generator = AugmentParametersGenerator()
augmentor = DataAugmentor3D()
while True:
shuffle(case_list)
if batch_rate:
expected_cate_num = np.array([
int(round(batch_size * batch_rate[0])),
int(round(batch_size * batch_rate[1])),
int(round(batch_size * batch_rate[2]))
])
for case in case_list:
case_path = os.path.join(root_folder, case)
# print(case_path)
if not case_path.endswith('.h5'):
continue
if batch_rate:
f = h5py.File(case_path)
label = f['output_0'][:]
cate = np.where(label == np.max(label))[0]
f.close()
if expected_cate_num[cate] <= 0:
continue
else:
expected_cate_num[cate] -= 1
input_data_list, output_data_list = [], []
try:
file = h5py.File(case_path, 'r')
for input_number_index in range(input_number):
temp_data = np.asarray(file['input_' +
str(input_number_index)])
if temp_data.ndim == 3:
temp_data = temp_data[..., np.newaxis]
input_data_list.append(temp_data)
for output_number_index in range(output_number):
temp_data = np.asarray(file['output_' +
str(output_number_index)])
if temp_data.ndim == 3:
temp_data = temp_data[..., np.newaxis]
output_data_list.append(temp_data)
file.close()
except Exception as e:
print(e.__str__())
continue
param_generator.RandomParameters(augment_param)
augmentor.SetParameter(param_generator.GetRandomParametersDict())
input_data_list = _AugmentDataList3D(input_data_list, augmentor)
input_data_list = _CropDataList3D(input_data_list, input_shape)
_AddOneSample(input_list, input_data_list)
_AddOneSample(output_list, output_data_list)
if len(input_list[0]) >= batch_size:
inputs = _MakeKerasFormat(input_list)
outputs = _MakeKerasFormat(output_list)
# inputs = np.transpose(np.asarray(inputs), [1,0,2,3,4,5])
# outputs = np.asarray(outputs)
# return inputs, outputs
yield inputs, outputs
input_list = [[] for index in range(input_number)]
output_list = [[] for index in range(output_number)]