Python OpenCV

Introduction to Python OpenCV

Python OpenCV is an open-source library that helps users to process images and video streams through computer vision algorithms. Developed by Intel, the library allows the developers to write codes in C++ and Python.

Python OpenCV provides a vast set of functions and tools that help in processing the images and videos with ease. In this article, we dive deeper into the concepts and features of Python OpenCV.

Installation of Python OpenCV

Before we start using Python OpenCV, we need to install it. The following steps will guide you through the process of installing Python OpenCV using Anaconda:

Step 1: Install Anaconda

The first step in installing Python OpenCV is to download and install Anaconda Python. Anaconda is a data science platform that comes pre-installed with most of the libraries used in computer vision and other fields.

Step 2: Create an Anaconda Environment

The next step is to create an Anaconda environment. To create an environment, open Anaconda prompt and run the following command:

conda create --name opencv_env python=3.7

This will create a new environment named “opencv_env” with Python 3.7 version.

Step 3: Install OpenCV

To install OpenCV, run the following command:

conda install -c anaconda opencv

This command will install OpenCV in the environment.

Working with Images using Python OpenCV

Python OpenCV provides a vast range of functions for image processing. Here are some essential functions you need to know to work with images:

Reading, Displaying and Saving Images

To read an image in Python OpenCV, we use the `cv2.imread()` function. Here’s an example code snippet:

import cv2
image = cv2.imread('image.jpg')
cv2.imshow('Image', image)
cv2.waitKey(0) # waits until a key is pressed
cv2.destroyAllWindows()

The above code reads an image named “image.jpg” and displays it using the `cv2.imshow()` function. We use the `cv2.waitKey()` function to wait for a key press, and then we use the `cv2.destroyAllWindows()` function to close the window.

To save an image, we use the `cv2.imwrite()` function. Here’s an example code snippet:

cv2.imwrite('new_image.jpg', image)

The above code saves the image with the name “new_image.jpg.”

Converting Color Spaces

Python OpenCV provides various functions to convert an image from one color space to another. The `cv2.cvtColor()` function is used to perform this function. Here’s an example code snippet:

gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow('Grayscale Image', gray_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

The above code converts the original image to grayscale using the `cv2.COLOR_BGR2GRAY` parameter.

Resizing Images

To resize an image, we use the `cv2.resize()` function. Here’s an example code snippet:

resized_image = cv2.resize(image, (500, 500))
cv2.imshow('Resized Image', resized_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

The above code resizes the original image to 500×500 pixels.

Working with Video Streams using Python OpenCV

Python OpenCV provides various functions to work with video streams. Here are some essential functions you need to know to work with video streams:

Reading, Playing and Saving Videos

To read a video stream, we use the `cv2.VideoCapture()` function. Here’s an example code snippet:

import cv2
video = cv2.VideoCapture('video.mp4')
while True:
    ret, frame = video.read()
    if ret == False:
        break
    cv2.imshow('Video', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
video.release()
cv2.destroyAllWindows()

The above code reads the video stream named “video.mp4” and plays it using the `cv2.imshow()` function. We use the `cv2.waitKey()` function to wait for a key press, and then we use the `cv2.destroyAllWindows()` function to close the window.

To save a video stream, we use the `cv2.VideoWriter()` function. Here’s an example code snippet:

import cv2
video = cv2.VideoCapture('video.mp4')
fourcc = cv2.VideoWriter_fourcc(*'XVID')
output = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))
while True:
    ret, frame = video.read()
    if ret == False:
        break
    output.write(frame)
video.release()
output.release()
cv2.destroyAllWindows()

The above code reads the video stream named “video.mp4,” saves it using the `cv2.VideoWriter()` function with the name “output.avi.”

Working with Webcam Video Streams

To work with a webcam video stream, we use the `cv2.VideoCapture()` function with the parameter set to 0. Here’s an example code snippet:

import cv2
camera = cv2.VideoCapture(0)
while True:
    _, frame = camera.read()
    cv2.imshow('Webcam', frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
camera.release()
cv2.destroyAllWindows()

The above code reads the video stream from the camera and plays it using the `cv2.imshow()` function.

Conclusion

Python OpenCV provides a vast range of functions and tools that help in processing images and video streams with ease. In this article, we have discussed various functions and tools that are essential to know while working with Python OpenCV. Using these tools, we can process images and video streams with ease and efficiency.

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