shoumikchow

Hi! This exists as a standalone library which means you don't have to go through the trouble of cloning the tensorflow/models repository and using the specific functions you need. Moreover, I'd argue this code is easier to follow (ymmv) than Tensorflow's and it would be easier to debug if the user needs to make modifications themselves. Additionally, this library also gives you some extra visualizations you can use on your bounding boxes.

Thanks for the comment! I'll look into it and aim for the next major point release.

Interesting idea! I'll have to do some studying as to how to make that happen, so I doubt I'll be adding that option anytime soon but I'll be adding it to the icebox.

Thank you for the comment!

Thanks for the constructive comment! I'll try and see if it is at all possible to implement it in Pillow and maybe re-implement it in a major release, unless it is terribly convoluted.

You are correct that you do not need this library to draw bounding boxes with a label. You can use OpenCV or Pillow to draw it.

However, positioning the label to be exactly above the bounding box can be a little finicky. This just takes care of the math that you'd have to do to place it right above the box. I agree that I am not doing something revolutionary with the math here, but these functions are something that I've had to use over and over again and thought it would be nice to package the whole thing. This library abstracts everything behind two main functions.

There are also a few different visualizations that you can use.

Personally, I've always used OpenCV with all object detection and object recognition tasks I've worked on to take advantage of the many CV functions it provides. So it was only natural for me to use OpenCV since it wouldn't require an additional dependency like Pillow.

I could've definitely used Pillow, but is there any tangible advantage to using Pillow over OpenCV?