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++

We might have used different features in our cameras or photo editing tools, such as making the images blurry. These features make our images more suitable for different contexts.

# Making an image blurry

We have to use the `GaussianBlur()` method to make an image blurry. We need to give four parameters to this function: 
- The original image
- The blurred image
- Kernel size
- SigmaX
```
cv::Size kernelSize = Size(7,7)
int sigmaX = 0
cv::GaussianBlur(img, blurredImg, kernelSize, sigmaX); 
```
>**Note:**  We need to read the image as `img` using the `imread` function and declare header files and namespaces.

In the code above, `sigmaX` represents the kernel's standard deviation in the x-direction.

# Kernel size
Here, the `kernelSize` is the size of the window where the blur is applied. It should be in odd numbers. This will make the previous layer's pixels similar to the output pixel. If we use even numbers, we might suffer from aliasing errors. Increasing the number will increase the blurriness of the image. 

Let's try blurring the image with a different `kernelSize` and check out the differences. 



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

Making Images Blurry

Making an image blurry