Category: Image Processing

  • Using model.fit() instead of fit_generator() with Data Generators – TF.Keras

    Using model.fit() instead of fit_generator() with Data Generators – TF.Keras

    If you have been using data generators in Keras, such as ImageDataGenerator for augment and load the input data, then you would be familiar with the using the *_generator() methods (fit_generator(), evaluate_generator(), etc.) to pass the generators when trainning the model.  But recently, if you have switched to TensorFlow 2.1 or later (and tf.keras), you might…

  • 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,…

  • 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…

  • 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…

  • 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…