def make_pipeline(imageset_path, output_dir):
"""returns an augmentation pipeline for a given image set"""
p = Pipeline(imageset_path, output_dir)
p.random_distortion(probability=0.7,
grid_width=4,
grid_height=4,
magnitude=8)
p.flip_left_right(probability=0.5)
p.flip_top_bottom(probability=0.5)
p.zoom(probability=0.3, min_factor=1.1, max_factor=1.4)
p.rotate(probability=0.5, max_left_rotation=10, max_right_rotation=10)
return p
def create_aug_pipeline_train(input_size):
"""Image Augmentation Pipeline for Training Set."""
p_train = Pipeline()
# Random crop
p_train.add_operation(CropPercentageRange(probability=1, min_percentage_area=0.8, max_percentage_area=1, centre=False))
# Rotate the image by either 90, 180, or 270 degrees randomly
p_train.rotate_random_90(probability=0.5)
# Flip the image along its vertical axis
p_train.flip_top_bottom(probability=0.5)
# Flip the image along its horizontal axis
p_train.flip_left_right(probability=0.5)
# Random change brightness of the image
p_train.random_brightness(probability=0.5, min_factor=0.9, max_factor=1.1)
# Random change saturation of the image
p_train.random_color(probability=0.5, min_factor=0.9, max_factor=1.1)
# Resize the image to the desired input size of the model
p_train.resize(probability=1, width=input_size[0], height=input_size[1])
return p_train
