def __get_numslices(self):
ts = time.clock()
# nums_slice = np.zeros(len(self.images_file_train), dtype='int')
# for i, image_file in enumerate(self.images_file_train):
# image_path = os.path.join(self.data_dir, image_file)
# images = np.load(image_path)
# nums_slice[i] = len(images)
# print(nums_slice)
# print(np.sum(nums_slice))
nums_slice = [41,96,276,44,177,168,183,167,186,133,189,90,226,64,181,89,241,59,37,276,110,123,119,111,250, \
77,191,194,104,215,98,170,75,277,186,76,229,99,219,173,69,239,36,260,280,112,299,258,59,73, \
189,29,176,116,189,233,214,179,89,92,205,118,232,56,67,170,113,169,189,132,192,139,114,194, \
113,59,232,179,83,113,64,266,58,292,193,46,116,175,98,122,234,187,251,85,79,115,61,120,113, \
118,215,50,37,263,56,259,215,241,248,91,149,201,79,29,198,227,112]
# nums_slice = [41, 96, 276, 44, 177, 168, 183, 167, 186, 133, 189, 90]
print("run time of importing all data: ",
cvtSecond2HMS(time.clock() - ts))
return nums_slice
def __get_numslices(self):
ts = time.clock()
category = self.data_dir.split('/')[-1]
info_file = 'cache/{0}/datasets/info.npy'.format(category)
mkdirInCache(info_file)
if os.path.isfile(info_file):
self.images_file_train, nums_slice, self.images_file_test = np.load(
info_file)
else:
nums_slice = np.zeros(len(self.images_file_train), dtype='int')
for i, image_file in enumerate(self.images_file_train):
image_path = os.path.join(self.data_dir, image_file)
images = self._load_image(image_path)
nums_slice[i] = len(images)
np.save(
info_file,
(self.images_file_train, nums_slice, self.images_file_test))
# print(nums_slice)
# print(np.sum(nums_slice))
# home
# nums_slice = [41,96,276,44,177,168,183,167,186,133,189,90,226,64,181,89,241,59,37,276,110,123,119,111,250, \
# 77,191,194,104,215,98,170,75,277,186,76,229,99,219,173,69,239,36,260,280,112,299,258,59,73, \
# 189,29,176,116,189,233,214,179,89,92,205,118,232,56,67,170,113,169,189,132,192,139,114,194, \
# 113,59,232,179,83,113,64,266,58,292,193,46,116,175,98,122,234,187,251,85,79,115,61,120,113, \
# 118,215,50,37,263,56,259,215,241,248,91,149,201,79,29,198,227,112]
# nums_slice = [41, 96, 276, 44, 177, 168, 183, 167, 186, 133, 189, 90]
# office
# nums_slice = [112, 36, 292, 132, 36, 241, 194, 59, 110, 56, 241, 194]
# nums_slice = [112, 36]
case_num = len(nums_slice)
self.images_file_train, self.images_file_test, self.masks_file_train, self.masks_file_test = \
self.images_file_train[:case_num], self.images_file_test[:case_num], self.masks_file_train[
:case_num], self.masks_file_test[
:case_num]
print("run time of importing {0} data is {1}".format(
np.sum(nums_slice), cvtSecond2HMS(time.clock() - ts)))
return nums_slice
modelcheckpoint='cache/breast/model/unet_gen_448_448_padecho.hdf5',
batch_size=16,
is_datagen=True,
images_npy='cache/breast/datasets/images_pad_echo_448_448_tf.npy',
masks_npy='cache/breast/datasets/masks_pad_echo_448_448_tf.npy')
if __name__ == '__main__':
# print_env()
ts = time.clock()
# (X_test, y_test, predicts) = run_unet_gen(istrain=False) # dice = 86.6
(X_test, y_test,
predicts) = run_unet_gen_448_448(istrain=True) # dice = 84.7
# (X_test, y_test, predicts) = run_unet_gen_448_448_echo(istrain=False) # dice = 80.25
# (X_test, y_test, predicts) = run_unet_gen_448_448_padecho(istrain=False) # dice = 80.1
print("total process time: %s" % cvtSecond2HMS(time.clock() - ts))
#
# for i in range(0, 1):
# showImages(X_test[i, :, :, 0], y_test[i, :, :, 0], predicts[i, :, :, 0])
#
# # for (image, mask, predict) in zip(X_test, y_test, predicts):
# # seg.show(image[0, :, :], mask[0, :, :], predict[0, :, :])
#
# plt.show()