Source code for niftynet.contrib.csv_reader.sampler_csv_rows

from niftynet.engine.image_window_dataset import ImageWindowDataset
from niftynet.engine.image_window import N_SPATIAL, LOCATION_FORMAT

[docs]class ImageWindowDatasetCSV(ImageWindowDataset): """ Extending the default sampler to include csv data """ def __init__(self, reader, csv_reader=None, window_sizes=None, batch_size=10, windows_per_image=1, shuffle=True, queue_length=10, epoch=-1, smaller_final_batch_mode='pad', name='random_vector_sampler'): self.csv_reader = csv_reader ImageWindowDataset.__init__( self, reader=reader, window_sizes=window_sizes, batch_size=batch_size, windows_per_image=windows_per_image, shuffle=shuffle, queue_length=queue_length, epoch=epoch, smaller_final_batch_mode=smaller_final_batch_mode, name=name)
[docs] def layer_op(self, idx=None): """ Generating each image as a window. Overriding this function to create new image sampling strategies. This function should either yield a dictionary (for single window per image):: yield a dictionary { 'image_name': a numpy array, 'image_name_location': (image_id, x_start, y_start, z_start, x_end, y_end, z_end) } or return a dictionary (for multiple windows per image):: return a dictionary: { 'image_name': a numpy array, 'image_name_location': [n_samples, 7] } where the 7-element location vector encode the image_id, starting and ending coordinates of the image window. Following the same notation, the dictionary can be extended to multiple modalities; the keys will be:: {'image_name_1', 'image_name_location_1', 'image_name_2', 'image_name_location_2', ...} :param idx: image_id used to load the image at the i-th row of the input :return: a image data dictionary """ # dataset: from a window generator # assumes self.window.n_samples == 1 # the generator should yield one window at each iteration assert self.window.n_samples == 1, \ 'image_window_dataset.layer_op() requires: ' \ 'windows_per_image should be 1.' image_id, image_data, _ = self.reader(idx=idx) for mod in list(image_data): spatial_shape = image_data[mod].shape[:N_SPATIAL] coords = self.dummy_coordinates(image_id, spatial_shape, 1) image_data[LOCATION_FORMAT.format(mod)] = coords image_data[mod] = image_data[mod][np.newaxis, ...] if self.csv_reader is not None: _, label_data, _ = self.csv_reader(idx=image_id) image_data['label'] = label_data['label'] image_data['label_location'] = image_data['image_location'] return image_data
@property def tf_shapes(self): """ returns a dictionary of sampler output tensor shapes """ assert self.window, 'Unknown output shapes: self.window not initialised' shape_dict = self.window.tf_shapes if self.csv_reader is not None: shape_dict.update(self.csv_reader.tf_shapes) return shape_dict @property def tf_dtypes(self): """ returns a dictionary of sampler output tensorflow dtypes """ assert self.window, 'Unknown output shapes: self.window not initialised' shape_dict = self.window.tf_dtypes if self.csv_reader is not None: shape_dict.update(self.csv_reader.tf_dtypes) return shape_dict