Watch the Full Video Here: https://www.youtube.com/watch?v=wQg7BNmi8zs Vehicle detection has been a challenging part of building intelligent traffic management systems. Such systems are critical for...
In this video we will explore how you can perform tasks like vehicle detection using a simple but yet an effective approach of background-foreground subtraction. You will be learning about using background-foreground subtraction along with contour detection in OpenCV and how you tune different parameters to achieve better results.
In part two of the series, the contour manipulation techniques we are going to learn will enable us to perform some important tasks such as extracting the largest contour in an image, sorting contours in terms of size, extracting bounding box regions of a targeted contour, etc. These techniques also form the building blocks for building robust classical vision applications.
This simpler approach relies on a popular Computer Vision technique called Contour Detection. A handy technique that can save the day when dealing with vision problems such as the one above. Although not as generalizable as Deep Neural Networks, contour detection can prove robust under controlled circumstances, requiring minimum time investment and effort to build vision applications.
This video is a part of our upcoming Building Vision Applications with Contours and OpenCV course. In this video, I’ve covered all the basics of contours you need to know. You will learn how to detect and visualize contours, the various image pre-processing techniques required before detecting contours, and a lot more.
The course will be released in a couple of weeks on our site and will contain quizzes, assignments, and walkthroughs of high-level Jupyter notebooks which will teach you a variety of concepts.
Image classification is used to solve several Computer Vision problems; right from medical diagnoses, to surveillance systems, on to monitoring agricultural farms. There are innumerable...
In this article, you will learn how to build python-based gesture-controlled applications using AI. We will guide you all the way with step-by-step instructions. I’m sure you will have loads of fun...
This is a really descriptive and interesting tutorial, let me highlight what you will learn in this tutorial.
A Crystal Clear step by step tutorial on training a custom object detector.
A method to download videos and create a custom dataset out of that.
How to use the custom trained network inside the OpenCV DNN module so you can get rid of the TensorFlow framework.
Plus here are two things you will receive from the provided source code:
A Jupyter Notebook that automatically downloads and installs all the required things for you so you don’t have to step outside of that notebook.
A Colab version of the notebook that runs out of the box, just run the cells and train your own network.
I will stress this again that all of the steps are explained in a neat and digestible way. I’ve you ever plan to do Object Detection then this is one tutorial you don’t want to miss.
As mentioned, by downloading the Source Code you will get 2 versions of the notebook: a local version and a colab version.
So first we’re going to see a complete end to end pipeline for training a custom object detector on our data and then we will use it in the OpenCV DNN module so we can get rid of the heavy Tensorflow framework for deployment. We have already discussed the advantages of using the final trained model in OpenCV instead of Tensorflow in my previous post.
Today’s post is the 3rd tutorial in our 3 part Deep Learning with OpenCV series. All three posts are titled as:
Deep Learning with OpenCV DNN Module, A Comprehensive Guide
Training a Custom Image Classifier with OpenCV, Converting to ONNX, and using it in OpenCV DNN module.
Training a Custom Object Detector with Tensorflow and using it with OpenCV DNN (This Post)