Augmentation Gallery — NVIDIA DALI (original) (raw)

This example showcases different augmentations possible with DALI.

DALI_EXTRA_PATH environment variable should point to the place where data from DALI extra repository is downloaded. Please make sure that the proper release tag is checked out.

from nvidia.dali import pipeline_def import nvidia.dali.fn as fn import nvidia.dali.types as types import matplotlib.pyplot as plt import math import os.path

test_data_root = os.environ["DALI_EXTRA_PATH"] db_folder = os.path.join(test_data_root, "db", "lmdb")

augmentations = {}

input is sampled randomly for output pixel's neighbourhood

augmentations["erase"] = lambda images: fn.erase( images, anchor=[0.3, 0.2], shape=[0.5, 0.6], normalized_anchor=True, normalized_shape=True, axis_names="HW", )

augmentations["slice"] = lambda images: fn.slice( images, rel_start=[0.3, 0.2], rel_shape=[0.5, 0.6], axis_names="HW" )

transforms sampling coordinates to produce wavy patterns

augmentations["water"] = lambda images: fn.water(images)

applies fisheye distortion

augmentations["sphere"] = lambda images: fn.sphere(images)

rotates the image, enlarging the canvas

augmentations["rotate"] = lambda images: fn.rotate( images, angle=30, interp_type=types.INTERP_LINEAR, fill_value=0 )

applies affine transform

augmentations["warp_affine"] = lambda images: fn.warp_affine( images, matrix=[1.0, 0.8, 0.0, 0.0, 1.2, 0.0], interp_type=types.INTERP_LINEAR, )

manipulates brightness and contrast of the image

augmentations["brightness_contrast"] = lambda images: fn.brightness_contrast( images, brightness=0.5, contrast=1.5 )

Generate JPEG compression artifacts

augmentations["jpeg compression distortion"] = ( lambda images: fn.jpeg_compression_distortion(images, quality=5) )

twists colors of the image

augmentations["hsv"] = lambda images: fn.hsv(images, hue=45.0, saturation=0.2)

places the image on a larger canvas

augmentations["paste"] = lambda images: fn.paste( images, ratio=2.0, fill_value=(55, 155, 155), paste_x=0.1, paste_y=0.7 )

param flips the image

augmentations["vertical and horizontal flip"] = lambda images: fn.flip( images, vertical=1, horizontal=1 )

@pipeline_def() def augmentation_gallery_pipe(path): encoded, labels = fn.readers.caffe(path=path, random_shuffle=True) images = fn.decoders.image(encoded, device="mixed", output_type=types.RGB) return tuple( [labels, images] + [aug(images) for aug in augmentations.values()] )

pipe = augmentation_gallery_pipe( path=db_folder, batch_size=32, num_threads=2, device_id=0, seed=10 ) pipe.build()

n = 5 from synsets import imagenet_synsets import matplotlib.gridspec as gridspec

len_outputs = len(pipe_out) - 1 augmentations = ["original"] + list(augmentations.keys()) fig = plt.figure(figsize=(16, 16)) plt.suptitle(imagenet_synsets[pipe_out[0].at(n)[0]], fontsize=16) columns = 4 rows = int(math.ceil(len_outputs / columns)) gs = gridspec.GridSpec(rows, columns) for i in range(len_outputs): plt.subplot(gs[i]) plt.axis("off") plt.title(augmentations[i]) pipe_out_cpu = pipe_out[1 + i].as_cpu() img_chw = pipe_out_cpu.at(n) plt.imshow((img_chw) / 255.0)