Gain insights into OpenCV with C++. Delve into image editing, machine learning functions, and practical applications like object detection. Discover real-world projects to elevate your programming skills.

open_cv_cpp.tar.gz

OpenCV

OpenCV-GUI

OpenCV is extensively used in the business world for various applications, including facial recognition, object detection, and image and video manipulation. 

In this course, you'll learn the basics of OpenCV—an open-source machine learning library. You'll begin with an overview of the OpenCV library before moving on to practical applications like viewing images, videos, and webcam input. Next, you'll learn how to edit images with tools like decolorizing, cropping, resizing, and captioning. You'll dive into machine learning functions like edge detection, contour detection, corner detection, and face detection. Finally, you'll work on practical projects like document scanners and object trackers. 

By the end of this course, you’ll have a solid understanding of OpenCV and machine learning with C++. You'll be able to create functional applications using computer vision principles, preparing you for real-world projects. Taking this course will allow you to take your programming skills to the next level.

Learning OpenCV from Scratch with C++

In this lesson, we’ll learn to detect contours, which will help us find different types of shapes in our image. This can be used for motion detection, object detection, and background removal, among other things.

First, we need to convert our image to grayscale. This is really important because it helps us correctly find the edges. It also helps in simplifying algorithms and eliminates the complexity of the computational requirements. We use the edge detection algorithm to detect edges and the retrieval algorithm to detect shapes.
```
cv::cvtColor(img, src_gray, COLOR_BGR2GRAY);
```
To convert an image to a Canny image, we use the `Canny()` function of the OpenCV library. This function requires four parameters: 

- The first parameter is the input image.
- The second parameter is the output image. 
- The third parameter is the minimum value of the intensity gradient.
- The fourth parameter is the maximum value of the intensity gradient. 
```
cv::Canny( src_gray, canny_img, 50, 50);
```
This function converts the image to black and white, highlighting the edges of the contours. It makes the work of the contour retrieval algorithm easier.

# Detect shapes

To detect shapes, we use the `findContours()` function of the OpenCV library. This function requires five parameters, which are listed below:
 
- The first parameter is the Canny image.
- The second parameter is the variable `contours`, which will store the shapes detected. We use this value to draw on those shapes later on.
- The third parameter is the variable `hierarchy` that stores the hierarchy of the shapes detected. 
- The fourth parameter is the mode to find the contours. We use `RETR_EXTERNAL` to find the external contours. It's one of the modes of the contour retrieval algorithm and is used to retrieve only the outer contours. 
- The fifth parameter is the contour approximation method. For this, we use `CHAIN_APPROX_NONE`. It stores all the contours found by our retrieval algorithm.

```
std::vector<std::vector<cv::Point>> contours;
std::vector<cv::Vec4i> hierarchy;
cv::findContours(canny_img, contours, hierarchy, cv::RETR_EXTERNAL, cv::CHAIN_APPROX_SIMPLE);
```
We've initialized the variables, such as `contours` and `hierarchy`, that we're going to use. We've initialized `contours` as an array of `Point`, a point in 2D space. We've also initialized `hierarchy` as an array of `Vec4i`, a vector with four dimensions. Then we'll use the `findContour()` function.
 
The retrieval algorithms look for the edges in the Canny image to detect the shapes.

# Draw contours
To draw the contours, we use the `drawContours()` function of the OpenCV library. The syntax of this function is:
```
cv::drawContours(img, contours, -1, Scalar(255, 0, 255), 2);
```

We need to give five parameters to the function. They are listed below:

- `img` is the image on which we want to draw contours.
- `contours` are the contours that we detected using the `findContours()` function.
- `-1` is the contour index. It's the number of contours we want to draw. For example, if we only want to draw the third contour, we give the value `2`. In this example, we enter the value `-1` because we want to draw all the contours.
- The fourth parameter is the scalar value of the color to be given while drawing.
- The fifth parameter is the thickness of the drawing.












Learn to detect different types of shapes.

Introduction to C++ OpenCV

Reading Media

Editing Images

Reading and Editing Images with OpenCV

Advanced Editing

Detection

Assessment on Advanced Editing and Detection

Real-Life Projects

Conclusion

Appendix

Contour Detection