def process_empty_map(self, map_feature): # In the returned empty map, 1 represents empty space empty_map = np.atleast_3d(decode_image(map_feature)) ones, zeros = np.ones_like(empty_map), np.zeros_like(empty_map) empty_map = np.where(np.greater(empty_map, 1), ones, zeros) empty_map = np.transpose(empty_map, axes=[1, 0, 2]) return empty_map
def raw_images_to_array(images): """ Decode and normalize multiple images from tfrecord data :param images: list of images encoded as a png in a string :return: a numpy array of size (N, 56, 56, channels), normalized for training """ image_list = [] for image_str in images: image = decode_image(image_str, (56, 56)) image = scale_observation(np.atleast_3d(image.astype(np.float32))) image = remove_depth_noise(image) image_list.append(image) return np.stack(image_list, axis=0)
def process_roomtype_map(self, map_feature): output = np.atleast_3d(decode_image(map_feature)) # transpose and invert output = np.transpose(output, axes=[1, 0, 2]) return output
def process_roomid_map(self, roomidmap_feature): # this is not transposed, unlike other maps roomidmap = np.atleast_3d(decode_image(roomidmap_feature)) return roomidmap
def process_door_map(self, map_feature): wall_map = np.atleast_3d(decode_image(map_feature)) wall_map = np.transpose(wall_map, axes=[1, 0, 2]) wall_map = wall_map.astype(np.float32) * (1.0 / 255.0) return wall_map