Patch Sequential

In this tutorial we will show how we can quickly perform patch processing using the features provided by the kornia.augmentation.PatchSequential API.

Install and get data

We install Kornia and some dependencies, and download a simple data sample

%%capture
!wget https://tinypng.com/images/social/website.jpg -O panda.jpg

from matplotlib import pyplot as plt
import numpy as np
import torch
import cv2

from kornia import augmentation as K
from kornia.augmentation import PatchSequential, ImageSequential
from kornia.geometry import bbox_to_mask
from kornia.utils import image_to_tensor, tensor_to_image
from torchvision.transforms import transforms

to_tensor = transforms.ToTensor()
to_pil = transforms.ToPILImage()


def plot_resulting_image(img, bbox, keypoints, mask):
    img = img * mask
    img_draw = cv2.polylines(np.array(to_pil(img)), bbox.numpy(), isClosed=True, color=(255, 0, 0))
    for k in keypoints[0]:
        img_draw = cv2.circle(img_draw, tuple(k.numpy()[:2]), radius=6, color=(255, 0, 0), thickness=-1)
    return img_draw

img = cv2.imread("panda.jpg", cv2.IMREAD_COLOR)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
h, w = img.shape[:2]

img_tensor = image_to_tensor(img).float() / 255.
plt.imshow(img); plt.axis('off');

/home/docs/checkouts/readthedocs.org/user_builds/kornia-tutorials/envs/latest/lib/python3.7/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

Patch Augmentation Sequential with patchwise_apply=True

patchwise_apply is a feature that used to define unique processing pipeline for each patch location. If patchwise_apply=True, the number of pipelines defined must be as same as the number of patches in an image.

pseq = PatchSequential(
    ImageSequential(
        K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
        K.RandomPerspective(0.2, p=0.5),
        K.RandomSolarize(0.1, 0.1, p=0.5),
    ),
    K.RandomAffine(15, [0.1, 0.1], [0.7, 1.2], [0., 20.], p=0.5),
    K.RandomPerspective(0.2, p=0.5),
    ImageSequential(
        K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
        K.RandomPerspective(0.2, p=0.5),
        K.RandomSolarize(0.1, 0.1, p=0.5),
    ),
    K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
    K.RandomAffine(15, [0.1, 0.1], [0.7, 1.2], [0., 20.], p=0.5),
    K.RandomPerspective(0.2, p=0.5),
    K.RandomSolarize(0.1, 0.1, p=0.5),
    K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
    K.RandomAffine(15, [0.1, 0.1], [0.7, 1.2], [0., 20.], p=0.5),
    ImageSequential(
        K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
        K.RandomPerspective(0.2, p=0.5),
        K.RandomSolarize(0.1, 0.1, p=0.5),
    ),
    K.RandomSolarize(0.1, 0.1, p=0.5),
    K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5),
    K.RandomAffine(15, [0.1, 0.1], [0.7, 1.2], [0., 20.], p=0.5),
    K.RandomPerspective(0.2, p=0.5),
    K.RandomSolarize(0.1, 0.1, p=0.5),
    patchwise_apply=True,
    same_on_batch=True,
)
out_tensor = pseq(img_tensor[None].repeat(2, 1, 1, 1))
to_pil(torch.cat([out_tensor[0], out_tensor[1]], dim=2))

Patch Augmentation Sequential with patchwise_apply=False

If patchwise_apply=False, all the args will be combined and applied as one pipeline for each patch.

pseq = PatchSequential(
    K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.75),
    K.RandomElasticTransform(alpha=(4., 4.)),
    patchwise_apply=False,
    same_on_batch=False
)
out_tensor = pseq(img_tensor[None].repeat(2, 1, 1, 1))
to_pil(torch.cat([out_tensor[0], out_tensor[1]], dim=2))