Category: Computer Vision

  • Installing OpenCV got easier

    Installing OpenCV got easier

    OpenCV is undoubtedly the unmatched de facto standard library for computer vision. Not only does it provide a near complete set of vision algorithms, the set of primitive graphics functions it provides to manipulate images makes it essential to many of our projects. OpenCV being used when building a Keras CNN model Installing the latest version…

  • Using Multiple Cameras with OpenCV

    As you know, OpenCV is capable of reading from any connected camera in your system, whether it’s a built-in webcam (in a laptop) or a USB connected one. But what if, you wanted to read from more than one cam at the same time? Can OpenCV handle it? OpenCV accessing 2 cameras at once Yes,…

  • 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…

  • Embedded Computer Vision with OpenMV

    Have you ever wanted to put computer vision into an embedded device? But wasn’t sure whether it’s possible to cram in a computer vision algorithm into a small hardware device? Well, that’s what the OpenMV project is all about. OpenMV is a programmable embedded device, with a built-in camera, that you can program with variety…

  • 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…

  • 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…

  • Installing OpenCV from source on Anaconda Python on Ubuntu 16.10

    I recently switched to Linux for my Machine Learning experiments, and I did a post on How to install Keras and Anaconda Python on Ubuntu 16.10. Now, I wanted to install OpenCV on Ubuntu also. Since OpenCV does not have a pre-built package for Linux, it meant I had to compile OpenCV from source. OpenCV…

  • Getting Dlib Face Landmark Detection working with OpenCV

    Dlib has excellent Face Detection and Face Landmark Detection algorithms built-in. Its face detection is based on Histogram of Oriented Gradients (HOG) feature combined with a linear classifier, on a sliding window detection scheme (Ref. http://dlib.net/) and it provides pre-trained models for face landmark detection. It also provides handy utility functions like dlib.get_frontal_face_detector() to make…