Bounding boxes and distortion

 This week I was mainly focused on the ways to distort bounding boxes. Unfortunately, albuminations do not provide the functionality of distorting bounding-boxes. That is why I have had to experiment with different approach.


Changing nothing

The very first approach I have used was simple: I have just distorted the image, without changing the bounding boxes. And actually, it did work (but for objects that are in the center only). 

Distorting coordinates

I was using an approach from this post on OpenCV blog. It suggest a way to redistort points. Actually, it is better than changing nothing.


Ellipse

Another approach was to simply change coordinates to the form of an ellipse (x and y of the center + radii). This approach was proposed in this paper.  In the paper, this approach has beaten the original box method, but I believe that to use it in the project, manual annotations are required.

Comments

Post a Comment

Popular posts from this blog

Summing up my GSoC experience

Image
 Introduction Once again, I am Daniil Orel from Kazakhstan (Nur-Sultan / Astana city). This summer I was working with LibreHealth organisation for an amazing GSoC project. Few words about my host organisation LibreHealth is a  collaborative community, which supports free open source software projects in HealthTech. This organisation has multiple projects, but I was working towards the radiology project. My project Several medical procedures in surgery or interventional radiology are recorded as videos used for review, training, and quality monitoring. These videos have at least three interesting artifacts - anatomical structures such as organs, tumors, tissues, etc.; medical equipment, and medical information overlaid that describes the patient. It will be immensely helpful for review and search purposes if these can be identified and automatically labeled in the videos. My task was to develop ML models which can process videos and that are suitable for VR environment. As a result, I h
Read more

How can we "simulate" VR?

     Having the proper data is important when it comes to developing an ML/DL application. In some cases, the amount of data is limited due to some reasons. For example, if we are trying to detect a rare disease, collecting 1KK samples could be simply impossible. When collecting a proper dataset is too expensive, we cannot afford it as well. What shall we do in such a case?     The answer is simple: we have to generate data. Generative Adversarial Networks  are said to be one of the ways to synthetically generate the data. However, it requires at least some data from the domain of interest to generate new data. In this case augmentations could help.  Data augmentation is the artificial creation of training data for machine learning by transformations (c) It means, that we can change the original data to increase the training sample and / or apply some transformations to another dataset to get something in the domain of our interest. The second technique can be used, for example, to add
Read more

Surgical tools detection

Image
 This week I was training the Surgical Tools Detection model. The dataset was taken from here . To train the model and prepare it for inference I was using Roboflow. Roboflow  is a robust web-platform for collecting datasets, training models and running them into production. Training I have split the data on 69/21/10 for train, val and test sets. Graphs with training metrics is shown below Train / val metrics To be more precise, on test set the model has 93.8% precision with 90% recall and 96.4% mAP.  It is considered as a high performance and means that the model can be used in production. Inference Roboflow itself provides multiple ways for inference. I could use web-API method (requests are sent to the server and the model sends back predictions), but to make it more flexible, I prefer python-package method. It can be easily imported to the main project of LibreHealth and run.
Read more