Feature Request: Blur Detection Using Laplacian (CV2) | Python

[Sunny1198]

Is your feature request related to a problem? Please describe. In many image-based applications, such as computer vision and image processing, the quality of images plays a crucial role. Blurry images can degrade the performance of downstream tasks like object detection, classification, or industrial inspection. There is a need for a reliable method to automatically detect and quantify blur in images before processing them further.

Describe the solution you'd like I propose implementing a blur detection feature that uses the Laplacian operator to measure the amount of blur in an image. By calculating the variance of the Laplacian of an image, we can quantify the sharpness or blur level, and based on a threshold value, classify an image as "blurry" or "sharp." This solution would be efficient and suitable for real-time applications.

Describe alternatives you've considered

1. FFT-based methods: Detect blur using frequency domain analysis, but these are computationally more intensive than the Laplacian method.
2. Edge detection methods: Sobel or Canny edge detection can also identify blurred images, but they are less effective in capturing subtle blur variations compared to the Laplacian operator.
3. Machine learning approaches: Train a model to classify blurred vs. sharp images, but this requires labeled datasets and is more complex to implement.

Additional context Implementing this feature would help in identifying poor-quality images early in the processing pipeline, improving the performance of subsequent image processing tasks. It's lightweight, easy to implement, and can be run in real-time applications such as image classification, industrial inspection, or photography.

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