Flashy, ravey and state-of-the-art image augmentations to boost the performance of deep convolutional neural networks
Project description
Discolight
Flashy, ravey and state-of-the-art image augmentations to boost the performance of deep convolutional neural networks
At a Glance
- The library works with images in
HxWxC
(channels last) format. - Based on numpy, OpenCV, paperspace, imgaug and albumentation.
- Diversified set of augmentations available.
- Supports python 3.6-3.8
- Supports transformations on images, and bounding boxes.
- The library works with images in
HxWxC
format.
Setup
The easiest way to get started is to install the discolight
package
via pip:
$ pip install discolight
Alternatively, if you have cloned this repository, you can run pip on the contents of the repository folder:
$ pip install .
Supported Augmentations
Sample Image |
Augmented Image |
Sample Image (Bounding Boxes) |
Augmented Image (Bounding Boxes) |
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ColorTemperature
Changes the color temperature of the input image. |
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GaussianNoise
Add gaussian noise to the given image. |
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GrayScale
Return a grayscale version of the given image. |
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HorizontalFlip
Horizontally flips the given image. |
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ImageCompression
Apply a compression effect to the given image. |
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MotionBlur
Add motionblur to a given image. |
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RandomCrop
Randomly crops the given image. |
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RandomEraser
Randomly erase a rectangular area in the given image. |
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RandomHSV
Randomly shift the color space of the given image. |
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RandomRotate
Randomly rotate the given image. |
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Rotate
Rotate the given image. |
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SaltAndPepperNoise
Add salt and pepper or RGB noise to the given image. |
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Scale
Scale the given image. |
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Sepia
Returns a given image passed through the sepia filter. |
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Sequence
Perform a sequence of augmentations on the given image. |
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Shear
Horizontally shear the given image. |
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Translate
Translate the given image. |
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VerticalFlip
Vertically flip the given image. |
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Generating Augmented Images on the Command Line
To augment a set of images with annotations, you must first prepare your input data and prepare YAML configuration file to describe the augmentations you want to perform:
input:
images:
loader: Directory
options:
directory: ./sample_images # The base path of where the images are stored
annotations:
loader: FourCornersCSV
options:
annotations_file: ./sample_images/annotations.csv # The CSV file with annotations
normalized: true
output:
images:
writer: Directory
options:
directory: ./output_folder
annotations:
writer: FourCornersCSV
options:
annotations_file: ./output_folder/aug_annotations.csv
normalized: true
augmentations:
- name: GrayScale
options:
probs: 0.5
- name: Rotate
options:
probs: 0.7
angle: 35
- name: GaussianNoise
options:
probs: 0.9
- name: Shear
options: {}
save-original: true # Whether to save the original images to the output folder
save-bbox: true # Whether bounding boxes should be drawn on the augmented images
The image and annotation loaders specify how the images and
annotations should be loaded. The FourCornersCSV
(so-named because
it expects the bounding box to be specified with four coordinates)
loader expects an input CSV file in the following format:
image_name,x_min,y_min,x_max,y_max,label
wheat1.jpg,0,0.799805,0.037109,0.94043,1
wheat1.jpg,0.064453,0.892578,0.181641,0.949219,1
...
The chosen image loader will be used to load the images specified
under the image_name
column (for example, wheat1.jpg
). In this
case, the Directory
image loader will fetch the images from the
specified folder. For more information on available image and
annotation loaders, see .
Now you can run Discolight:
$ discolight generate configuration.yml
You can also pass your YAML configuration on standard input:
$ cat configuration.yml | discolight generate
After generation is complete, the augmented images and annotations
will be written out according to your configuration options. Here, we
choose to save the annotations to a CSV file using the same
four-corners format, and the augmented images will appear in the
output_folder/
directory.
Library Functionality
In addition to invoking Discolight from the command line, you can also invoke it as a library. First, construct the augmentation you want to apply to an image. For example, here is a simple augmentation sequence:
from discolight.disco import disco
from discolight.annoations import BoundingBox
from discolight.util.image import load_image, save_image
seq = disco.Sequence(augmentations=[
disco.GaussianNoise(),
disco.RandomEraser(),
disco.Rotate(angle=35)
])
Then, load the image and specify your annotations:
image = load_image("path/to/image.jpg")
annotations = [
# BoundingBox(x_min, y_min, x_max, y_max, class_idx)
BoundingBox(0, 819, 38, 963, 1),
BoundingBox(66, 914, 186, 972, 1),
BoundingBox(52, 526, 126, 645, 1),
]
Now, you can invoke seq
as a function to apply your augmentation:
aug_img, aug_annotations = seq(image, annotations)
If you have no annotations, you can also apply seq
only to an image:
aug_img = seq(image)
To save the augmented image, use the provided save_image
function. You can optionally pass the augmented annotations (also an
array of BoundingBox
objects) to be drawn in red over the augmented
image:
save_image("aug-with-bboxes.jpg", aug_img, annotations=aug_annotations)
save_image("aug-no-bboxes.jpg", aug_img)
If you prefer to perform additional operations on the image after augmentation, augmented images are OpenCV images in RGB format.
Development
To learn more about how to develop Discolight (e.g., adding additional augmentatations), see the guide in https://github.com/datature/discolight/blob/master/doc/develop.md.
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