https://github.com/codebox/image_augmentor

Data augmentation tool for images
https://github.com/codebox/image_augmentor

data-augmentation image-augmentor machine-learning

Last synced: 8 days ago
JSON representation

Data augmentation tool for images

• Host: GitHub
• URL: https://github.com/codebox/image_augmentor
• Owner: codebox
• Created: 2016-09-11T12:48:47.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2023-10-03T21:37:59.000Z (about 1 year ago)
• Last Synced: 2024-08-02T06:19:20.289Z (3 months ago)
• Topics: data-augmentation, image-augmentor, machine-learning
• Language: Python
• Homepage: https://codebox.net/pages/image-augmentation-with-python
• Size: 16.6 KB
• Stars: 442
• Watchers: 10
• Forks: 156
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• Awesome-Object-Detections - Image Augmentor

README

        
## Image Augmentor

This is a simple data augmentation tool for image files, intended for use with machine learning data sets.

The tool scans a directory containing image files, and generates new images by performing a specified set of

augmentation operations on each file that it finds. This process multiplies the number of training examples that can

be used when developing a neural network, and should significantly improve the resulting network's performance,

particularly when the number of training examples is relatively small.

Run the utility from the command-line as follows:

    python main.py    ...

The `` argument should be the path to a directory containing the image files to be augmented.

The utility will search the directory recursively for files with any of the following extensions:

`jpg, jpeg, bmp, png`.

The `transform` arguments determine what types of augmentation operations will be performed,

using the codes listed in the table below:

|Code|Description|Example Values|

|---|---|------|

|`fliph`|Horizontal Flip|`fliph`|

|`flipv`|Vertical Flip|`flipv`|

|`noise`|Adds random noise to the image|`noise_0.01`,`noise_0.5`|

|`rot`|Rotates the image by the specified amount|`rot_90`,`rot_-45`|

|`trans`|Shifts the pixels of the image by the specified amounts in the x and y directions|`trans_20_10`,`trans_-10_0`|

|`zoom`|Zooms into the specified region of the image, performing stretching/shrinking as necessary|`zoom_0_0_20_20`,`zoom_-10_-20_10_10`|

|`blur`|Blurs the image by the specified amount|`blur_1.5`|

Each transform argument results in one additional output image being generated for each input image.

An argument may consist of one or more augmentation operations. Multiple operations within a single argument

must be separated by commas, and the order in which the operations are performed will match the order in which they

are specified within the argument.

### Examples

Produce 2 output images for each input image, one of which is flipped horizontally, and one of which is flipped vertically:

    python main.py ./my_images fliph flipv

Produce 1 output image for each input image, by first rotating the image by 90° and then flipping it horizontally:

    python main.py ./my_images rot_90,fliph

### Operations

#### Horizontal Flip

Mirrors the image around a vertical line running through its center

    python main.py ./my_images fliph

       

#### Vertical Flip

Mirrors the image around a horizontal line running through its center

    python main.py ./my_images flipv

       

#### Noise

Adds random noise to the image. The amount of noise to be added is specified by a floating-point numeric value that is included

in the transform argument, the numeric value must be greater than 0.

    python main.py ./my_images noise_0.01 noise_0.02 noise_0.05

                     

#### Rotate

Rotates the image. The angle of rotation is specified by an integer value that is included in the transform argument

    python main.py ./my_images rot_90 rot_180 rot_-90

                     

#### Translate

Performs a translation on the image. The size of the translation in the x and y directions are specified by integer values that

are included in the transform argument

    python main.py ./my_images trans_20_20 trans_0_100

              

#### Zoom/Stretch

Zooms in (or out) to a particular area of the image. The top-left and bottom-right coordinates of the target region are

specified by integer values included in the transform argument. By specifying a target region with an aspect ratio that

differs from that of the source image, stretching transformations can be performed.

    python main.py ./my_images zoom_150_0_300_150 zoom_0_50_300_150 zoom_200_0_300_300

                     

#### Blur

Blurs the image. The amount of blurring is specified by a floating-point value included in the transform argument.

    python main.py ./my_images blur_1.0 blur_2.0 blur_4.0