def _read_data(self, dataset_root_dir):
dataset_dir = self.dataset_dir(dataset_root_dir)
positive_file_name = self._positive_file_name(dataset_dir)
part_file_name = self._part_file_name(dataset_dir)
negative_file_name = self._negative_file_name(dataset_dir)
image_list_file_name = self._image_list_file_name(dataset_dir)
tensorflow_file_names = [positive_file_name, part_file_name, negative_file_name, image_list_file_name]
positive_ratio = 1.0/6
part_ratio = 1.0/6
landmark_ratio = 1.0/6
negative_ratio = 3.0/6
positive_batch_size = int(np.ceil(self._batch_size*positive_ratio))
part_batch_size = int(np.ceil(self._batch_size*part_ratio))
negative_batch_size = int(np.ceil(self._batch_size*negative_ratio))
landmark_batch_size = int(np.ceil(self._batch_size*landmark_ratio))
batch_sizes = [positive_batch_size, part_batch_size, negative_batch_size, landmark_batch_size]
self._number_of_samples = 0
for d in tensorflow_file_names:
self._number_of_samples += sum(1 for _ in tf.python_io.tf_record_iterator(d))
image_size = self.network_size()
tensorflow_dataset = TensorFlowDataset()
return(tensorflow_dataset.read_tensorflow_files(tensorflow_file_names, batch_sizes, image_size))
def _read_data(self, dataset_root_dir): dataset_dir = self.dataset_dir(dataset_root_dir) tensorflow_file_name = self._image_list_file_name(dataset_dir) self._number_of_samples = 0 self._number_of_samples = sum(1 for _ in tf.python_io.tf_record_iterator(tensorflow_file_name)) image_size = self.network_size() tensorflow_dataset = TensorFlowDataset() return(tensorflow_dataset.read_tensorflow_file(tensorflow_file_name, self._batch_size, image_size))
def _generate_dataset(self, target_root_dir):
tensorflow_dataset = TensorFlowDataset()
print('Generating TensorFlow dataset for positive images.')
if (not tensorflow_dataset.generate(
SimpleFaceDataset.positive_file_name(target_root_dir),
target_root_dir, 'positive')):
print('Error generating TensorFlow dataset for positive images.')
return (False)
print('Generated TensorFlow dataset for positive images.')
print('Generating TensorFlow dataset for partial images.')
if (not tensorflow_dataset.generate(
SimpleFaceDataset.part_file_name(target_root_dir),
target_root_dir, 'part')):
print('Error generating TensorFlow dataset for partial images.')
return (False)
print('Generated TensorFlow dataset for partial images.')
print('Generating TensorFlow dataset for negative images.')
if (not tensorflow_dataset.generate(
SimpleFaceDataset.negative_file_name(target_root_dir),
target_root_dir, 'negative')):
print('Error generating TensorFlow dataset for negative images.')
return (False)
print('Generated TensorFlow dataset for negative images.')
print('Generating TensorFlow dataset for landmark images.')
if (not tensorflow_dataset.generate(
self._image_list_file_name(target_root_dir), target_root_dir,
'image_list')):
print('Error generating TensorFlow dataset for landmark images.')
return (False)
print('Generated TensorFlow dataset for landmark images.')
return (True)
def _image_list_file_name(self, dataset_dir):
image_list_file_name = TensorFlowDataset.tensorflow_file_name(
dataset_dir, 'image_list')
return (image_list_file_name)
def _negative_file_name(self, dataset_dir):
negative_file_name = TensorFlowDataset.tensorflow_file_name(
dataset_dir, 'negative')
return (negative_file_name)
def _part_file_name(self, dataset_dir):
part_file_name = TensorFlowDataset.tensorflow_file_name(
dataset_dir, 'part')
return (part_file_name)
def _positive_file_name(self, dataset_dir):
positive_file_name = TensorFlowDataset.tensorflow_file_name(
dataset_dir, 'positive')
return (positive_file_name)
def _generate_dataset(self, target_root_dir): tensorflow_dataset = TensorFlowDataset() if(not tensorflow_dataset.generate(self._image_list_file_name(target_root_dir), target_root_dir, 'image_list')): return(False) return(True)