Description:-
Computer vision is a field of artificial intelligence that focuses on developing algorithms to process and interpret digital images. It enables machines to recognize objects, interpret images, and understand their environment. Computer vision systems are used in a variety of applications, such as autonomous vehicles, medical image analysis, and robotics.
A lot of research has been carried out in this field of computer vision for decades. Some of the most important research in this field includes object recognition, image segmentation, image recognition, object tracking, facial recognition, 3D object modeling, and optical character recognition.
The processing of images has now become faster and more efficient. Thanks to cloud technologies and powerful GPUs! With the ever-evolving technology, the interface “computer vision” between machines and humans is bound to gain attention in the coming days.
Open Source Computer Vision Library commonly known as OpenCV is an open source library of programming functions mainly aimed at real-time computer vision. It is used for image processing, video capture and analysis, object detection, and recognition, and can be used in various applications such as robotics, mobile phones, and large-scale industrial systems. OpenCV can be used to develop applications that include facial recognition and gesture recognition.
A basic understanding of Computer Vision and OpenCV helps newbies enter into the field of AI but there are various challenges that one might face in understanding the practical application of computer vision. To ease down the process of getting acquainted with Computer Vision and OpenCV, we have come up with a stepwise guide to use simple functions of OpenCV to read, write and display images on the screen. Also, this guide will help you learn how to do face detection and edge detection.
So, without further ado, let’s begin!
A Stepwise guide to Read, Write and Display Images
Step 1. Library Installation
Install OpenCV library with the pip command of python.
Step 2. Import necessary libraries.
Step 3. Now, read images from CV2.
i. Read image from CV2.imread() function.
ii. Now, split images into three parts i.e. RGB.
iii. Show the split and original image with the help of Imshow() function
Output:-
Vi. Lastly, merge all images using the merge function.
A Stepwise guide to Draw Functions in OpenCV
Step 1. Import necessary libraries.
Step 2. Null image
Create a null image with help of numpy array.
Step 3. Null image subplot with
Create subplots with matplotlib for null images and drawn images.
Step 4. Draw line
Now, draw a line with the help of line function of the OpenCV library.
Output:-
Step 5. Draw Rectangle
After drawing a line, make a rectangle on a 100, 100 x, y axis with 300 x 300 height and width.
Output:-
A Stepwise guide to Canny Edge Detection
Step 1. Import necessary libraries.
Step 2. Use haar casecade classifire function
Download haar cascade file from web. Put this XML file with cascade classifier function as the attribute of function.
Step 3.
Make conditional statement to raise an error if haar cascade file is not available
Step 4. Using camera read real time video capture.
Step 5. Capture real-time frames with While Loop.
Step 6. Gray Scale Conversion
Now, resize the frame and convert frame into Grayscale.
Step 7. Draw Rectangle frame on detected face
Step 8. Now, release the video capture object.
Output:-
Conclusion:-
I hope this article helps you understand the fundamentals of OpenCV and face recognition in images and real-time video capture. If you have any other questions regarding OpenCV or face recognition in images and real-time video capture, feel free to ask in the comments section below.
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