Augmentation Gallery

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 numpy as np
from timeit import default_timer as timer
import numpy as np
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)

# twists colors of the image
augmentations["hsv"] = \
    lambda images: fn.hsv(images, hue=45., saturation=0.2)

# places the image on a larger canvas
augmentations["paste"] = \
    lambda images: fn.paste(images, ratio=2., fill_value=(55, 155, 155), paste_x=.1, paste_y=.7)

# param flips the image
augmentations["horizontal flip"] = \
    lambda images: fn.flip(images, vertical=0, horizontal=1)

augmentations["vertical flip"] = \
    lambda images: fn.flip(images, vertical=1, horizontal=0)


@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=11122)
pipe.build()

pipe_out = pipe.run()

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)