Skip to main content

CVAugmentor

Introduction

This is a simple tool to augment images and videos for computer vision tasks.

Available augmentations are: no_augmentation, flip, zoom, rotate, shear, grayscale, hue, saturation, brightness, exposure, blur, noise, cutout, negative.

Installation

You can install the package using pip:

pip install CVAugmentor

Usage

For a detailed usage guide, please refer to the documentation.

Single Image Augmentation

# Importing the libraries
from CVAugmentor import Augmentations as aug
from CVAugmentor import Pipeline


# Define the augmentations
augmentations = {
    "zoom": aug.zoom(),
    "flip": aug.flip(),
}

# Create a Pipeline object
p = Pipeline()

# Augment the image
p.augment(input_path="path/to/input_image", output_path="path/to/output_image", target="image", process_type="single", mode="singular", augmentations=augmentations, verbose=True, warn_verbose=True)

Single Video Augmentation

# Importing the libraries
from CVAugmentor import Augmentations as aug
from CVAugmentor import Pipeline


# Define the augmentations
augmentations = {
    "zoom": aug.zoom(),
    "flip": aug.flip(),
}

# Create a Pipeline object
p = Pipeline()

# Augment the video
p.augment(input_path="path/to/input_video", output_path="path/to/output_video", target="video", process_type="single", mode="singular", augmentations=augmentations, verbose=True, warn_verbose=True)

Augmenting Multiple Images

# Importing the libraries
from CVAugmentor import Augmentations as aug
from CVAugmentor import Pipeline


# Define the augmentations
augmentations = {
    "zoom": aug.zoom(),
    "flip": aug.flip(),
}

# Create a Pipeline object
p = Pipeline()

# Augment the images
p.augment(input_path="path/to/input_images", output_path="path/to/output_images", target="image", process_type="batch", mode="singular", augmentations=augmentations, verbose=True, warn_verbose=True)

Augmenting Multiple Videos

# Importing the libraries
from CVAugmentor import Augmentations as aug
from CVAugmentor import Pipeline


# Define the augmentations
augmentations = {
    "zoom": aug.zoom(),
    "flip": aug.flip(),
}

# Create a Pipeline object
p = Pipeline()

# Augment the videos
p.augment(input_path="path/to/input_videos", output_path="path/to/output_videos", target="video", process_type="batch", mode="singular", augmentations=augmentations, verbose=True, warn_verbose=True)

License

This work is licensed under an MIT License.

Metadata

Release files for cvaugmentor 1.0.10

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for cvaugmentor 1.0.10
File Size Uploaded
CVAugmentor-1.0.10.tar.gz 11.0 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for cvaugmentor 1.0.10
File Interpreter ABI Platform
CVAugmentor-1.0.10-py3-none-any.whl Python 3 none any Details

Total release size: 25.3 kB

Release files / CVAugmentor-1.0.10.tar.gz

Download URL CVAugmentor-1.0.10.tar.gz
Size 11.0 kB
Tags Source
SHA-256 checksum
How to use checksums
790ab28aae9892ca6d8510273e9b410d0b4f7a8f139ff2decdb32d476e113524
BLAKE2b-256 checksum
How to use checksums
dcd11ab4f2ad1198c36e3ff1e0419e5e8ffc22e1404ab438c9550343671d752f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.9.16

Release files / CVAugmentor-1.0.10-py3-none-any.whl

Download URL CVAugmentor-1.0.10-py3-none-any.whl
Size 14.3 kB
Tags Python 3
SHA-256 checksum
How to use checksums
bfa179a95960e1f673b217fa457c7e7d3a5195d0ed6a52de753a29ad1a050b8e
BLAKE2b-256 checksum
How to use checksums
4a5acddf45d964e55c8d34efc635bceeb38da37f31a0982b9fa08ddf3fb44bf7
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.2 CPython/3.9.16

Release history Release notifications | RSS feed

2.0.0

2 release files

1.1.2

2 release files

1.1.1

2 release files

1.1.0

2 release files

This release

1.0.10 This release

2 release files

1.0.9

2 release files

1.0.8

2 release files

1.0.7

2 release files

1.0.6

2 release files

1.0.5

2 release files

1.0.4

2 release files

1.0.3

2 release files

1.0.2

2 release files

1.0.1

2 release files

1.0.0

2 release files

0.0.1

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page