def __init__(self, data, dataset, batch_size, shape_rgb, shape_depth, is_flip=False, is_addnoise=False, is_erase=False): self.data = data self.dataset = dataset self.policy = BasicPolicy(color_change_ratio=0.50, mirror_ratio=0.50, flip_ratio=0.0 if not is_flip else 0.2, add_noise_peak=0 if not is_addnoise else 20, erase_ratio=-1.0 if not is_erase else 0.5) self.batch_size = batch_size self.shape_rgb = shape_rgb self.shape_depth = shape_depth self.maxDepth = 1000.0 from sklearn.utils import shuffle self.dataset = shuffle(self.dataset, random_state=0) self.N = len(self.dataset)
def __init__(self, root, dataset, batch_size, shape_rgb, shape_depth, locations=False, is_flip=False, is_addnoise=False, is_erase=False, is_skip_policy=False): self.root = root self.shape_rgb = [ (min(shape_rgb), max(shape_rgb)), (max(shape_rgb), min(shape_rgb)), ] self.shape_depth = [ (min(shape_depth), max(shape_depth)), (max(shape_depth), min(shape_depth)), ] self.dataset = [{ datum: pickle.load( open(os.path.join(dataset, orientation, datum + '_MD.p'), 'rb')) for datum in ["imgs", "targets"] } for orientation in ["landscape", "portrait"]] self.policy = BasicPolicy(color_change_ratio=0.50, mirror_ratio=0.50, flip_ratio=0.0 if not is_flip else 0.2, add_noise_peak=0 if not is_addnoise else 20, erase_ratio=-1.0 if not is_erase else 0.5) self.batch_size = batch_size self.N = [len(dataset["imgs"]) for dataset in self.dataset] self.is_skip_policy = is_skip_policy self.locations = locations
def __init__(self, data, dataset, batch_size, shape_rgb, shape_output, is_flip=False, is_addnoise=False, is_erase=False): self.data = data self.dataset = dataset self.policy = BasicPolicy(color_change_ratio=0.50, mirror_ratio=0.50, flip_ratio=0.0 if not is_flip else 0.2, add_noise_peak=0 if not is_addnoise else 20, erase_ratio=-1.0 if not is_erase else 0.5) self.batch_size = batch_size self.shape_rgb = shape_rgb self.shape_output = shape_output #Shuffling dataset as continous frames can lead to overfitting from sklearn.utils import shuffle self.dataset = shuffle(self.dataset, random_state=0) self.N = len(self.dataset)
def __init__(self, data_root, data_paths, batch_size, shape_depth, channels=5, is_flip=False, is_addnoise=False, is_erase=False, dont_interpolate=False): self.data_root = data_root self.dataset = data_paths self.policy = BasicPolicy(color_change_ratio=0.50, mirror_ratio=0.50, flip_ratio=0.0 if not is_flip else 0.2, add_noise_peak=0 if not is_addnoise else 20, erase_ratio=-1.0 if not is_erase else 0.5) self.batch_size = batch_size self.channels = channels self.shape_rgbd = (batch_size, 480, 640, self.channels) self.shape_depth = shape_depth self.minDepth = settings.MIN_DEPTH * settings.DEPTH_SCALE #cm self.maxDepth = settings.MAX_DEPTH * settings.DEPTH_SCALE #cm self.dont_interpolate = dont_interpolate from sklearn.utils import shuffle self.dataset = shuffle(self.dataset, random_state=0) self.N = len(self.dataset)
def __init__(self, data, dataset, batch_size, shape_rgb, shape_depth, is_flip=False, is_addnoise=False, is_erase=False, is_skip_policy=False): self.data = data self.dataset = dataset self.policy = BasicPolicy( color_change_ratio=0.50, mirror_ratio=0.50, flip_ratio=0.0 if not is_flip else 0.2, add_noise_peak=0 if not is_addnoise else 20, erase_ratio=-1.0 if not is_erase else 0.5) self.batch_size = batch_size self.shape_rgb = shape_rgb self.shape_depth = shape_depth self.maxDepth = 1000.0 self.N = len(self.dataset) self.is_skip_policy = is_skip_policy
def __init__(self, data_path, min_depth, max_depth, batch_size, image_shape, depth_shape, n_channels, is_augment=False, shuffle=False, mode="train", is_flip=False, is_addnoise=False, is_erase=False, is_scale=False, is_deconv=False, select_label_mode=None, scale_value=None): self.data_path = data_path self.batch_size = batch_size self.image_shape = image_shape self.depth_shape = depth_shape self.n_channels = n_channels self.shuffle = shuffle self.is_augment = is_augment self.min_depth = min_depth self.max_depth = max_depth self.is_deconv = is_deconv self.scale_value = scale_value self.select_label_mode = select_label_mode self.policy = BasicPolicy(image_shape=image_shape, color_change_ratio=0.50, mirror_ratio=0.50, flip_ratio=0.0 if not is_flip else 0.2, add_noise_peak=0 if not is_addnoise else 20, erase_ratio=-1.0 if not is_erase else 0.5, scale_ratio=-1 if not is_scale else 0.2) train_csv_path = os.path.join(self.data_path, "data/nyu2_train.csv") valid_csv_path = os.path.join(self.data_path, "data/nyu2_test.csv") if mode == "train": self.imagePath_labelPath_list = self.read_csv(train_csv_path) np.random.shuffle(self.imagePath_labelPath_list) self.on_epoch_end() else: self.imagePath_labelPath_list = self.read_csv(valid_csv_path)