Data Augmentation Compilation with Python and OpenCV

Data Augmentation Compilation with Python and OpenCV

Prevent Your Model from Overfitting .Data Augmentation Compilation with Python and OpenCV

Data augmentation is a technique to increase the diversity of dataset without an effort to collect any more real data but still help improve your model accuracy and prevent the model from overfitting. In this post, you will learn to implement the most popular and efficient data augmentation procedures for object detection task using Python and OpenCV.

The set of data augmentation methods that are about to be introduced includes:

  1. Random Crop
  2. Cutout
  3. ColorJitter
  4. Adding Noise
  5. Filtering

Firstly, let’s import several libraries and prepare some necessary subroutines before going ahead.

The below image is used as a sample image during this post.

Image for post

Image: tr03–14–18–1-FRONT.jpg in WAYMO Dataset

Random Crop

Random Crop selects randomly a region and crops it out to make a new data sample, the cropped region should have the same width/height ratio as the original image to maintain the shapes of objects.

Image for post

object-detection python opencv

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