Category: Tutorial

  • Setting up the OpenMV Cam

    Few weeks back, we talked about OpenMV – the small embedded computer vision module with a built-in camera, that can be programmed to perform various vision tasks. It gives you the ability to bring computer vision in to your embedded projects. After I first read about it, I was eager to get my hands on…

  • Track any object in a video with Dlib Correlation Trackers

    Training an object detector is bit of a complicated task. You need to have a proper training dataset with the relevant bounding boxes, and then use something like a HOG feature extractor with a SVM classifier for the detection – such as the Dlib Object Detection classes (link). But that’s a lot of work if…

  • Using Data Augmentations in Keras

    When I did the article on Using Bottleneck Features for Multi-Class Classification in Keras and TensorFlow, a few of you asked about using data augmentation in the model. So, I decided to do few articles experimenting various data augmentations on a bottleneck model. As a start, here’s a quick tutorial explaining what data augmentation is, and…

  • Visualizing the Convolutional Filters of the LeNet Model

    First of all, Happy New Year to you all! We have a great year ahead. And, let’s start it with something interesting. We’ve talked about how Convolutional Neural Networks (CNNs) are able to learn complex features from input procedurally through convolutional filters in each layer. But, how does a convolutional filter really look like? In…

  • Using Bottleneck Features for Multi-Class Classification in Keras and TensorFlow

    Training an Image Classification model – even with Deep Learning – is not an easy task. In order to get sufficient accuracy, without overfitting requires a lot of training data. If you try to train a deep learning model from scratch, and hope build a classification system with similar level of capability of an ImageNet-level…

  • Snapchat like Image Overlays with Dlib, OpenCV, and Python

    You’re probably familiar with Snapchat, and it’s filters feature where you can put some cool and funny image overlays on your face images. As computer vision enthusiasts, we typically look at applications like these, and try to understand how it’s done, and whether we can build something similar. It turns out, we can make our…

  • Wink Detection using Dlib and OpenCV

    A couple of weeks ago, I was going through a tutorial for eye blink detection by Adrian at PyImageSearch. It was an excellent tutorial, which explained the use of Eye Aspect Ratio (EAR) in order to detect when an eye gets closed. Then, few weeks back, I was having a chat with Shirish Ranade, a…

  • Visualizing Keras Models – Updated

    About 2 months back, I did a post on how you can visualize the structure of a Keras model. As I mentioned, when the machine learning (or deep learning) model you’re building is complex, then it may be easier to understand it if you can see a visual representation of it. I showed you how…

  • Extracting individual Facial Features from Dlib Face Landmarks

    If you remember, in my last post on Dlib, I showed how to get the Face Landmark Detection feature of Dlib working with OpenCV. We saw how to use the pre-trained 68 facial landmark model that comes with Dlib with the shape predictor functionality of Dlib, and then to convert the output of into a…