https://github.com/jbohnslav/opencv_transforms

OpenCV implementation of Torchvision's image augmentations
https://github.com/jbohnslav/opencv_transforms

Last synced: 2 months ago
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OpenCV implementation of Torchvision's image augmentations

• Host: GitHub
• URL: https://github.com/jbohnslav/opencv_transforms
• Owner: jbohnslav
• License: mit
• Created: 2018-08-30T17:43:11.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2021-05-03T14:25:06.000Z (about 3 years ago)
• Last Synced: 2024-03-25T00:02:07.398Z (3 months ago)
• Language: Python
• Homepage:
• Size: 1.7 MB
• Stars: 377
• Watchers: 14
• Forks: 45
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-stars - jbohnslav/opencv_transforms - OpenCV implementation of Torchvision's image augmentations (Python)
• Awesome-pytorch-list - opencv_transforms
• Awesome-pytorch-list-CNVersion - opencv_transforms

README

        
# opencv_transforms

This repository is intended as a faster drop-in replacement for [Pytorch's Torchvision augmentations](https://github.com/pytorch/vision/). This repo uses OpenCV for fast image augmentation for PyTorch computer vision pipelines. I wrote this code because the Pillow-based Torchvision transforms was starving my GPU due to slow image augmentation.

## Requirements

* A working installation of OpenCV. **Tested with OpenCV version 3.4.1, 4.1.0**

* Tested on Windows 10 and Ubuntu 18.04. There is evidence that OpenCV doesn't work well with multithreading on Linux / MacOS, for example `num_workers >0` in a pytorch `DataLoader`. I haven't run into this issue yet. 

## Installation

opencv_transforms is now a pip package! Simply use

* `pip install opencv_transforms`

## Usage

**Breaking change! Please note the import syntax!** 

* `from opencv_transforms import transforms`

* From here, almost everything should work exactly as the original `transforms`.

#### Example: Image resizing 

```python

import numpy as np

image = np.random.randint(low=0, high=255, size=(1024, 2048, 3))

resize = transforms.Resize(size=(256,256))

image = resize(image)

```

Should be 1.5 to 10 times faster than PIL. See benchmarks

## Performance

* Most transformations are between 1.5X and ~4X faster in OpenCV. Large image resizes are up to 10 times faster in OpenCV.

* To reproduce the following benchmarks, download the [Cityscapes dataset](https://www.cityscapes-dataset.com/). 

* An example benchmarking file can be found in the notebook **bencharming_v2.ipynb** I wrapped the Cityscapes default directories with a HDF5 file for even faster reading. 

![resize](benchmarks/benchmarking_Resize.png)

![random crop](benchmarks/benchmarking_Random_crop_quarter_size.png)

![change brightness](benchmarks/benchmarking_Color_brightness_only.png)

![change brightness and contrast](benchmarks/benchmarking_Color_constrast_and_brightness.png)

![change contrast only](benchmarks/benchmarking_Color_contrast_only.png)

![random horizontal flips](benchmarks/benchmarking_Random_horizontal_flip.png)

The changes start to add up when you compose multiple transformations together.

![composed transformations](benchmarks/benchmarking_Resize_flip_brightness_contrast_rotate.png)

## TODO

- [x] Initial commit with all currently implemented torchvision transforms

- [x] Cityscapes benchmarks

- [ ] Make the `resample` flag on `RandomRotation`, `RandomAffine` actually do something

- [ ] Speed up augmentation in saturation and hue. Currently, fastest way is to convert to a PIL image, perform same augmentation as Torchvision, then convert back to np.ndarray