OpenCV: An Open-Source Image Recognition Powerhouse

Introduction

OpenCV stands as a towering testament to the power of open-source software. Developed and maintained by a community of passionate contributors, OpenCV has become the go-to library for everything from basic image manipulation to cutting-edge image recognition and machine learning applications. In this article, we’ll explore the essence of OpenCV, its features.

Getting started

Installation Python 3.X

Open Terminal/Command Prompt and type :
~ pip install opencv-python

How to read Images

1. Open PyCharm.
2. Import cv2.

Here, we import the OpenCV library, which provides various functions and tools for computer vision and image processing.

3. Displaying the Image:

This line displays the loaded image with a window title “Lena Soderberg”. The imshow function is responsible for showing the image.

4.Adding a Delay:

This line adds a delay to the image display. It waits for a key event indefinitely (0 milliseconds in this case) until a key is pressed. When any key is pressed, the window with the image will close. If you specify a positive integer, it would wait for that many milliseconds before closing the window automatically.

import cv2
# LOAD AN IMAGE USING 'IMREAD'
img = cv2.imread("Resources/lena.png")
# DISPLAY
cv2.imshow("Lena Soderberg”, img)
    cv2.waitKey(0)

Functions of OpenCV

Converting image to grayscale

1. Open PyCharm.
2. Import cv2.
3. Create variable to store image using imread() function.
4. To convert to grayscale use cv2.cvtColor() function

This line displays the grayscale image with the window title “Gray Image” using the cv2.imshow function.

5. Pass the parameter image location and COLOR_BGR2GRAY to convert.

6. You can use any integrated development environment (IDE) of your choice, whether it’s PyCharm, Visual Studio Code, Jupyter Notebook, or any other Python IDE that you prefer. Open your chosen IDE to start working with OpenCV

import cv2
img = cv2.imread("Resources/lena.png")
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow("Gray Image", imgGray)
cv2.waitKey(0)

Cropping Image

1. Import numpy and cv2.
2. Create two variables to store the height and width of the image.
3. Create two numpy arrays to store the coordinates.
4. First array — store the coordinates of the image to be cropped.
5. Second array — store the coordinates of the complete image.
6. Crop the image using getPerspective() and wrapPerspective() function.

image = cv2.imread("Assets/cards.jpg")width, height = 250, 350
point1 = np.float32([[111, 219], [287, 188], [154, 482], [352, 440]])
point2 = np.float32([[0, 0], [width, 0], [0, height], [width, height]])
matrix = cv2.getPerspectiveTransform(point1, point2)
Output = cv2.warpPerspective(image, matrix, (width, height))cv2.imshow("Image”, image)
    cv2.imshow("Output”, Output)
      cv2.waitKey(0)

Face detection

1. Open PyCharm.
2. Import cv2.
3. Create a variable to store cascade classifier (to learn more about cascade classifier click here.
4. Convert image to greyscale using cv2.cvtColor() function.
5. Detect face using detectMultiscale() function.
6. Draw a rectangle around the detected face.

import cv2
face_Cascade = cv2.CascadeClassifier("Resources/haarcascade_frontalface_default.xml")
image = cv2.imread('Resources/lena.png')
imgGray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)faces = face_Cascade.detectMultiScale(imgGray, 1.1, 4)
for (x, y, w, h) in faces:
  cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)
cv2.imshow("Result", image)
cv2.waitKey(0)

Applications:

1. Real-time object detection technology is used to apply image recognition and locate specific objects in video data or images, such as cars, humans, animals, and specific parts or equipment in industrial manufacturing.

2. Human pose and gesture recognition are used to interpret and understand the gestures of human beings through video analysis. Body, hand, or facial movements can be recognized and categorized to assign a pre-defined category.

Movement analysis is often part of pose estimation to analyze the body movements with reference key points (joints, limbs). Calculating the object pose provides a method to understand how the object is situated in a 3D space, for example, how it is rotated.

3. Face Recognition Automatic face recognition is used to identify humans by detecting a human face and matching it with a database based on detected facial features. The Face Recognizer of OpenCV provides a set of popular face recognition algorithms to use in real applications.

Conclusion:

OpenCV, the Open Source Computer Vision Library, stands as an indispensable open-source powerhouse for image recognition and computer vision.

It has evolved from a research project into a comprehensive toolkit for image processing and analysis. With a rich feature set, including image processing functions, computer vision algorithms, and machine learning integration, OpenCV has made its mark in various domains such as robotics, healthcare, surveillance, augmented reality, and the automotive industry.

As technology advances, OpenCV continues to be at the forefront, shaping innovations and enabling groundbreaking applications in artificial intelligence, augmented reality, and healthcare. In essence, OpenCV is not just a software library; it is a key enabler of the vast potential within the field of computer vision.