From Zero to Mastery in Computer Vision, Deep Learning & Image Processing, A Step by Step learning path for you.
How to Get Started with OpenCV & Image Processing
Follow these steps to learn more about OpenCV and the fundamentals of Image Processing.
1. OpenCV Crash Course
In this Crash Course you will learn about the basics of OpenCV Library.
Now what is Open CV?
OpenCV (Open Source Computer Vision ) is the biggest library for Computer Vision which contains more than 2500 optimized algorithms that can be used for several purposes.
1- Familiarity with Python language
2- Python installed in your system.
2- OpenCV tips and tricks
Here in this video some very interesting information about the opencv are discussed :
How to navigate the opencv docs to find what you’re looking for
- How to get details regarding any OpenCV function
- The differences between the C++ and python version of OpenCV and which one you should work with
- Pip installation of OpenCV vs Source installation
- Where to ask questions regarding OpenCV when you’re stuck
It tells us how to install OpenCV from Source in Windows 10.
But if this is your first time dealing with OpenCV then it is highly recommended that you just install OpenCV with pip by doing :
pip install opencv-contrib-python
There are 2 methods that you can use to do the installation :
- Installation with Package Manager (pip/conda)
- Installation from Source
Both have there own advantages and disadvantages.
For the complete article click here : Installation of OpenCV in Windows 10
This is about a popular Computer Vision technique called Contour Detection. A handy technique that can save the day when dealing with vision problems.
Here in the first lecture we will discuss the following parts :
- What is contour
Visualizing the Contours detected
Pre-processing images For Contour Detection
- Edge Based Pre-processing For the full lecture go to this link: Contour detection Part 1
5- Contours Lecture 2
In this second part 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
- For the complete article click: Contour Manipulation part 2
6- Contours Lecture 3
Now in the third part of this series, we will be learning about analyzing contours.We will also explore how you can identify different properties of contours to retrieve useful information.
7- Vehcile Detection
In this video it explains how you can perform tasks like vehicle detection . You will be learning about using background-foreground subtraction along with contour detection in OpenCV.
Complete article : Vehicle detection
OpenCV 102: Go beyond the Basics & Building Interesting Applications
This tutorial can be split into 4 parts:
- Accessing the Live stream from your phone to OpenCV.
- Learning how to use the Twilio API to send Alert messages.
- Building a Motion Detector with Background Subtraction and Contour detection.
- Making the Final Application
For the full post click :Building a smart intruder detection system with opencv and your phone
2- Virtual Pen.
In this post, you will learn how to create your own Virtual Pen & also a virtual eraser.
Following is the breakdown of each step of our application:
- Step 1: Find the color range of the target object and save it.
- Step 2: Apply the correct morphological operations to reduce noise in the video
- Step 3: Detect and track the colored object with contour detection.
- Step 4: Find the object’s x,y location coordinates to draw on the screen.
- Step 5: Add a Wiper functionality to wipe off the whole screen.
- Step 6: Add an Eraser Functionality to erase parts of the drawing
Link for the complete post : Creating a virtual pen and eraser with opencv
This post helps you to play Rock,Paper and Scissor. Following are the steps that we need to follow :
Step 1: Gather Data, for rock, paper scissor classes.
Step 2: (Optional) Visualize the Data.
Step 3: Preprocess Data and Split it.
Step 4: Prepare Our Model for Transfer Learning.
Step 5: Train Our Model.
Step 6: Check our Accuracy, Loss graphs & save the model.
Step 7: Test on Live Webcam Feed.
Step 8: Create the Final Application.
For complete tutorial : Playing rock paper scissors with AI
Mediapipe: Learn to Build Cool Real World Applications
1- Building 4 Applications Using Real-Time Selfie Segmentation in Python
In this tutorial, you’ll learn how to do Real-Time Selfie Segmentation using Mediapipe in Python and then build the following 4 applications.
- Background Removal/Replacement
- Background Blur
- Background Desaturation
- Convert Image to Transparent PNG
In the first part of this post, we’ll understand the problem of image segmentation and its types. The task of image Segmentation can be divided into several categories.
- Semantic Segmentation.
- Instance Segmentation
- Panoptic Segmentation
- Saliency Detection
For complete article : Building 4 Applications Using Real-Time Selfie Segmentation in Python