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.
This video is the third and final part of our mini-series Contour Detection 101. Since we have already learned to detect and manipulate contours in previous parts, in this video, I’ve covered Contour Analysis which will make you capable of detecting and recognizing objects in images, and videos and build some interesting applications like Real-time Shape detection.
In our upcoming course named Building Applications with Contours in OpenCV, I am gonna teach you to build Computer Vision applications using the concepts you have learned in the mini-series. The course will be released soon on our site so stay tuned for it.
This video is part two of our mini-series Contour Detection 101. Detecting contours will not be enough to build contours based applications so, in this video, I’ve covered contours manipulations like extracting the largest contour, sorting contours according to their sizes, drawing rectangles and convex hulls around them and a lot more.
This mini-series is a part of our upcoming course named Building Vision Applications with Contours and OpenCV which will be released in a couple of weeks on our website.
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.
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)