pytutorial/pytorch/augmentation

Initial Image:

import cv2
import matplotlib.pyplot as plt

filename: str = "data_augmentation_test_image.jpg"

original_image = cv2.imread(filename)

plt.imshow(original_image)
plt.show()

original_image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)

plt.imshow(original_image, cmap="gray")
plt.show()

import numpy as np

original_image = cv2.imread(filename, cv2.IMREAD_COLOR)

# "Convert" from BlueGreenRed (BGR) to RGB (RedGreenBlue)
# This is a flip in the third dimension.
original_image = np.flip(original_image, axis=2)
plt.imshow(original_image)
plt.show()

Into PyTorch

import torch

torch_image = torch.tensor(
    np.moveaxis(original_image.astype(dtype=np.float32) / 255.0, 2, 0)
)
print(torch_image.shape) # -> torch.Size([3, 1200, 1600])

Pad

import torchvision as tv

pad_transform = tv.transforms.Pad(padding=(50, 100), fill=0.5)
new_image = pad_transform(torch_image)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

Resize

resize_transform = tv.transforms.Resize(size=(50, 100))
new_image = resize_transform(torch_image)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

CenterCrop

center_crop_transform = tv.transforms.CenterCrop(size=(250, 200))
new_image = center_crop_transform(torch_image)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

FiveCrop

position = (1, 3, 7, 9, 5)
five_crop_transform = tv.transforms.FiveCrop(size=(250, 200))
new_image = five_crop_transform(torch_image)

for i, p in enumerate(position):
    plt.subplot(3, 3, p)
    plt.imshow(np.moveaxis(new_image[i].detach().numpy(), 0, 2))

plt.show()

Grayscale

gray_transform = tv.transforms.Grayscale()
new_image = gray_transform(torch_image)
plt.imshow(new_image.squeeze().detach().numpy(), cmap="gray")
plt.show()

ColorJitter

color_jitter_transform = tv.transforms.ColorJitter(brightness=0.75, hue=0.5)
for i in range(1, 10):
    new_image = color_jitter_transform(torch_image)
    plt.subplot(3, 3, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

Gaussian Blur

gauss_transform = tv.transforms.GaussianBlur(kernel_size=(101, 101), sigma=(0.1, 10))
new_image = gauss_transform(torch_image)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

Random Perspective

random_perspective_transform = tv.transforms.RandomPerspective(
    distortion_scale=0.6, p=1.0
)
for i in range(1, 10):
    new_image = random_perspective_transform(torch_image)
    plt.subplot(3, 3, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

Random Rotation

random_rotation_transform = tv.transforms.RandomRotation(degrees=(0, 180))
for i in range(1, 10):
    new_image = random_rotation_transform(torch_image)
    plt.subplot(3, 3, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

Random Affine

random_affine_transform = tv.transforms.RandomAffine(degrees=(0, 180))
for i in range(1, 10):
    new_image = random_affine_transform(torch_image)
    plt.subplot(3, 3, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

Random Crop

random_crop_transform = tv.transforms.RandomCrop(size=(250, 200))
for i in range(1, 10):
    new_image = random_crop_transform(torch_image)
    plt.subplot(3, 3, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

Random Invert

random_invert_transform = tv.transforms.RandomInvert(p=0.5)
for i in range(1, 3):
    new_image = random_invert_transform(torch_image)
    plt.subplot(2, 1, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

Random Posterize

for i in range(1, 5):
    random_posterize_transform = tv.transforms.RandomPosterize(bits=i, p=1.0)
    new_image = random_posterize_transform((torch_image * 255).type(dtype=torch.uint8))
    plt.subplot(2, 2, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

Random Solarize

random_solarize_transform = tv.transforms.RandomSolarize(threshold=0.5)
new_image = random_solarize_transform(torch_image)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

Random Adjust Sharpness

random_sharpness_transform = tv.transforms.RandomAdjustSharpness(
    sharpness_factor=50, p=1.0
)
new_image = random_sharpness_transform(torch_image)
plt.subplot(1, 2, 1)
plt.imshow(np.moveaxis(torch_image.detach().numpy(), 0, 2))
plt.subplot(1, 2, 2)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

Random Autocontrast

random_autocontrast_transform = tv.transforms.RandomAutocontrast(p=1.0)

new_image = random_autocontrast_transform(torch_image)
plt.subplot(1, 2, 1)
plt.imshow(np.moveaxis(torch_image.detach().numpy(), 0, 2))
plt.subplot(1, 2, 2)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

Random Erasing

random_erasing_transform = tv.transforms.RandomErasing(p=1.0)

new_image = random_erasing_transform(torch_image)
plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

Auto Augment

random_auto1_transform = tv.transforms.AutoAugment(
    tv.transforms.AutoAugmentPolicy.CIFAR10
)
for i in range(1, 10):
    new_image = random_auto1_transform((torch_image * 255).type(dtype=torch.uint8))
    plt.subplot(3, 3, i)
    plt.imshow(np.moveaxis(new_image.detach().numpy(), 0, 2))
plt.show()

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