Computer vision: OpenCV Fundamentals using Python

Hi There!

Welcome to my new course, OpenCV Fundamentals using Python. This is the first course in my Computer Vision series. 

Let's see what are the interesting topics included in this course. At first, we will have an overview of computer vision and the amazing OpenCV, the open-source computer vision library.

After that, we are ready to proceed with preparing our computer for installing OpenCV and later will proceed with installing OpenCV itself. Then we will try a one-liner code to check if everything is working fine.

When I say this course is for complete beginners, I really mean it. Because even if you are coming from a non-Python background, the next few sessions and examples will help you get the basic Python programming skills to proceed with the rest of the sessions. The topics include Python assignment, flow control, functions, and data structures. 

Now we are all set to proceed with Python computer vision exercises.  But before that, we need to learn the theory of how a digital image is organized. Concept of pixels, color and grey scale channels, color codes, etc.

Then we will write our first OpenCV program, in which we will simply load and display an image from our computer, and we will write a greyscale version of this image back to our computer itself. 

As you already know the basic building block of a digital image is pixels, we will use the power of OpenCV to manipulate the individual pixels of an image and modify it.

Later in the next session, we will use a similar technique to select a collective area of pixels and manipulate it by trying to change color and also get the properties of the image.

Hope you know that there are 3 color channels in a color image and a single one in a greyscale image. We will try to separate and extract those color channels and later try to merge them back to form the original image. 

Color spaces, unlike the color channels, are the way colors are organized in an image. In the next session, we will explore the popular color spaces and will do exercises that switch an image between different color spaces.

In the next session, we will use OpenCV to create and draw simple geometric shapes like lines, rectangles, circles, ellipses, polygons, etc. into an image canvas. We will also try to insert text into the canvas.

Then we will try some morphological transformations to our image, which include erosion, which erodes the pixels, and dilation, which will expand the pixels, Opening transformation for white noise removal, and closing for black point noise removal. Then gradient transformation and finally the top hat and black hat morphological image transformations.

After that, we will try the geometric transformations, which include scaling or resizing the image, then translating or place shifting the image, flipping or changing sides, rotating the image by fixing an axis, and cropping the image to extract the region of interest.

In the coming two sessions, we will try the basic arithmetic and logical operations between two images. We will try to do the addition and subtraction operations between two images. We will also try the AND, OR, XOR, and NOT binary bitwise operations for two images and check the results obtained.

Later we will go ahead with Image masking, which is a technique of covering the unwanted areas of the image and displaying only the region of interest.

And after that, we will try image-smoothing techniques. At first, we will use our own filter to do a custom smoothing of the image, and later built-in filters using algorithms like Gaussian smoothing, average smoothing, median, and finally bilateral smoothing.

Then we will see an advanced technique called thresholding, which is very useful in preprocessing and preparing the image for computer vision algorithms. We will do exercises to demonstrate simple thresholding, Otsu thresholding, and adaptive thresholding. 

Then we will check an interesting image color intensity plotting technique called histograms. We will plot a histogram and learn how we can analyze the histogram to predict the nature of the image.

By using this histogram and adjusting the values based on it, we can enhance the contrast of dull-looking images. We will explore the technique called histogram equalization.

Image pyramids are different-sized images generated and stacked one on top of the other. We will explore how we can use openCV methods to generate image pyramids.

For us humans, it's an easy task to find an object in a scene and find the edges of it. For computers, it's not that easy. We will explore the openCV functions that enable us to find the edges using the Canny edge detection.

As we know from computers, an image is just a collection of numbers. To find the edges, gradients or the pattern of intensity change of colors should be found. We will use the gradient detection function of OpenCV to do that. 

Then, finally, we will draw contours along the different objects in an image with the help of the above-mentioned techniques and try to count the number of objects available in the scene.

That's all about the basics. The code and the images used in this course have been uploaded and shared in a folder. I will include the link to download them in the last session or in the resource section of this course. You are free to use the code in your projects with no questions asked.

So that's all for now, See you soon in the classroom. Happy learning and have a great time.