Your ad could be here - Reach CV/ML engineers

Contact for advertising

Interested in

advertising?

Contact us
Lib Comparison
FAQ
showcasepytorch classificationpytorch semantic segmentationexample bboxesexample keypointsmigrating from torchvision to albumentationsexampleexample bboxes2example chromatic aberrationexample d4example documentsexample domain adaptationexample gridshuffleexample hfhubexample kaggle saltexample mosaicexample multi targetexample OverlayElementsexample textimageexample weather transformsexample xymaskingreplayserialization
API Reference
%matplotlib inline

RandomGridShuffle 🔗

This transformation divides the image into a grid and then permutes these grid cells based on a random mapping.

It could be useful when only micro features are important for the model, and memorizing the global structure could be harmful.

For example:

  • Identifying the type of cell phone used to take a picture based on micro artifacts generated by phone post-processing algorithms, rather than the semantic features of the photo. See more at https://ieeexplore.ieee.org/abstract/document/8622031
  • Identifying stress, glucose, hydration levels based on skin images.
import albumentations as A
import cv2
from matplotlib import pyplot as plt

/opt/homebrew/Caskroom/miniconda/base/envs/albumentations_examples/lib/python3.9/site-packages/tqdm/auto.py:21: 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

import json
KEYPOINT_COLOR = (0, 255, 0)
def vis_keypoints(image, keypoints, color=KEYPOINT_COLOR, diameter=3):
    image = image.copy()
    for x, y in keypoints:
        cv2.circle(image, (int(x), int(y)), diameter, color, -1)
    return image
def visualize(image, mask, keypoints):
    # Create a copy of the image to draw on
    img = image.copy()
 
    # Apply keypoints if provided
    if keypoints:
        img = vis_keypoints(img, keypoints)
 
    # Setup plot
    fig, ax = plt.subplots(1, 2, figsize=(10, 5))
 
    # Show the image with annotations
    ax[0].imshow(img)
    ax[0].axis("off")
 
    # Show the mask
    ax[1].imshow(mask, cmap="gray")
    ax[1].axis("off")
 
    plt.tight_layout()
    plt.show()
with open("../data/road_labels.json") as f:
    labels = json.load(f)
keypoints = labels["keypoints"]
image = cv2.imread("../data/road.jpeg", cv2.IMREAD_COLOR_RGB)
 
mask = cv2.imread("../data/road.png", 0)
visualize(image, mask, keypoints)

png

transform = A.Compose([A.RandomGridShuffle(grid=(2, 2), p=1)], keypoint_params=A.KeypointParams(format="xy"))
transformed = transform(image=image, keypoints=keypoints, mask=mask)
visualize(transformed["image"], transformed["mask"], transformed["keypoints"])

png

transform = A.Compose(
    [A.RandomGridShuffle(grid=(3, 3), p=1)],
    keypoint_params=A.KeypointParams(format="xy"),
    seed=137,
    strict=True,
)
transformed = transform(image=image, keypoints=keypoints, mask=mask)
visualize(transformed["image"], transformed["mask"], transformed["keypoints"])

png

transform = A.Compose(
    [A.RandomGridShuffle(grid=(5, 7), p=1)],
    keypoint_params=A.KeypointParams(format="xy"),
    seed=137,
    strict=True,
)
transformed = transform(image=image, keypoints=keypoints, mask=mask)
visualize(transformed["image"], transformed["mask"], transformed["keypoints"])

png