My actions before raising this issue

• Read/searched the docs
• Searched past issues

In some cases, raw images are hard to annotated due to their contrasts, brightness, etc. It is great to see that CVAT has already integrated the Histogram equalization with OpenCV tools in the GUI. However, the default cv.equalizeHist function may work well as it considers the global contrast of the image. This can be improved by using the CLAHE (Contrast Limited Adaptive Histogram Equalization) algo which exists already in OpenCV.

Expected Behaviour

Using CLAHE to perform histogram equalization.

Current Behaviour

Using cv.equalizeHist function to permform histogram equalization. #3447

Possible Solution

Replace cv.equalizeHist by cv.CLAHE(clipLimit, tileGridSize) and cv.CLAHE().apply().

Where clipLimit and titleGridSize may be adjusted by adding a Trackbar.

Steps to Reproduce (for bugs)

Context

Thanks for consideration!

Your Environment

• Git hash commit (git log -1):
• Docker version docker version (e.g. Docker 17.0.05):
• Are you using Docker Swarm or Kubernetes?
• Operating System and version (e.g. Linux, Windows, MacOS):
• Code example or link to GitHub repo or gist to reproduce problem:
• Other diagnostic information / logs:
  Logs from `cvat` container