Video Augmentation Techniques for Deep Learning

This python library helps you with augmenting videos for your deep learning architectures. It converts input videos into a new, much larger set of slightly altered videos.

Original Video

Requirements and installation

Required packages: * numpy * PIL * scipy * skimage * OpenCV (i.e.

cv2

For installation, simply use

sudo pip install git+https://github.com/okankop/vidaug

git clone https://github.com/okankop/vidaug

python setup.py sdist && pip install dist/vidaug-0.1.tar.gz

Examples

A classical video classification with CNN using augmentations on videos. Train on batches of images and augment each batch via random crop, random crop and horizontal flip: ```python from vidaug import augmentors as va

sometimes = lambda aug: va.Sometimes(0.5, aug) # Used to apply augmentor with 50% probability seq = va.Sequential([ va.RandomCrop(size=(240, 180)), # randomly crop video with a size of (240 x 180) va.RandomRotate(degrees=10), # randomly rotates the video with a degree randomly choosen from [-10, 10]
sometimes(va.HorizontalFlip()) # horizontally flip the video with 50% probability ])

for batchidx in range(1000): # 'video' should be either a list of images from type of numpy array or PIL images video = loadbatch(batchidx) videoaug = seq(video) trainonvideo(video) ```

The videos below show examples for most augmentation techniques:

Augmentation Type	Augmented Video
Piecewise Affine Transform
Superpixel
Gausian Blur
Invert Color
Rondom Rotate
Random Resize
Translate
Center Crop
Horizontal Flip
Vertical Flip
Add
Multiply
Downsample
Upsample
Elastic Transformation
Salt
Pepper
Shear