Read, Display and Save Image with OpenCV
A wide range of tools is provided by OpenCV, an open-source library for computer vision and image processing. It is essential for reading, displaying, and saving images because it provides a simple and efficient way to perform these tasks without writing a lot of code from scratch.
When reading images, OpenCV provides a set of functions that can read image files in various formats, such as JPEG, PNG, and BMP, and then convert them into a format that can be easily manipulated using the library’s image processing functions. This makes it easy to work with images in different file formats and eliminates the need to write many codes to handle different file types.
When displaying images, OpenCV provides the “cv2.imshow” function, which can be used to display the image on the screen in a window. This function takes care of the low-level details of displaying the image, such as creating the window and handling user input and makes it easy to display images without having to write a lot of code.
When saving images, OpenCV provides the “cv2.imwrite” function, which can be used to save the image data to a file on a disk. This function takes care of the details of writing the image data to the file in the specified format, such as handling file compression and different file types. This makes it easy to save images in different file formats without writing much code.
OpenCV is necessary for reading, displaying, and saving images because it provides a simple and efficient way to perform these tasks and eliminates the need to write a lot of code from scratch. Instead of spending a lot of time on the basics, developers can focus on the more complex aspects of image processing and computer vision.
Features of OpenCV
OpenCV (Open Source Computer Vision) is a powerful and widely-used library for computer vision and image processing. Some of its key features include:
1. Image processing: OpenCV provides a wide range of image processing functions, such as thresholding, filtering, and geometric transformations, which can be used to manipulate images and extract information from them.
2. Computer vision: OpenCV provides a wide range of computer vision algorithms, such as object detection, face recognition, and optical flow, which can be used to analyze images and extract information about the scene.
3. Camera calibration and 3D reconstruction: OpenCV provides functions for calibrating cameras and reconstructing 3D scenes from multiple images, which can be used to create 3D models of real-world scenes.
4. Feature detection and matching: OpenCV provides functions for detecting and matching features in images, such as SIFT, SURF, and ORB, which can be used for image registration, object recognition, and tracking.
5. Machine learning: OpenCV provides interfaces for machine learning libraries such as TensorFlow and Caffe, which can be used to train models for image classification, object detection, and other computer vision tasks.
6. Platform-independent: OpenCV is written in C++ and has interfaces for multiple languages such as Python, Java and MATLAB, making it platform-independent.
Read image with OpenCV
Reading an image in OpenCV refers to the process of loading an image file (such as a JPEG, PNG, or BMP) into the program and converting it into a format that can be manipulated and analyzed using OpenCV’s image processing and computer vision functions. This typically involves converting the image data into a NumPy array, which can then be manipulated using functions such as thresholding, filtering, and geometric transformations.
import cv2
# Load the image
img = cv2.imread('image.jpg')
# Check if the image was successfully loaded
if img is None:
print("Error: Could not load image")
exit()
# Display the image
cv2.imshow('Image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
Display image with OpenCV
Displaying an image in OpenCV refers to the process of showing the image on the screen using the OpenCV library. After reading the image and manipulating it using OpenCV’s image processing functions, the image can be displayed using the “cv2.imshow” function. This function takes in two parameters: the name of the window in which the image should be displayed, and the image data itself (stored as a NumPy array). Once the image is displayed, the program will wait for a key press and then close the window.
import cv2
# Load the image
img = cv2.imread("image.jpg")
# Display the image
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
Save image with OpenCV
Saving an image in OpenCV refers to the process of storing the image data to a file on disk. This can be done using the “cv2.imwrite” function, which takes in two parameters: the file path and the image data to be saved. The image data should be in the form of a NumPy array, and the file path should include the desired file format (such as .jpg, .png, etc.). This function allows you to save the image in different file formats as per requirement. This is useful for storing the image for later use, or for saving the result of image processing operations.
import cv2
# Load the image
img = cv2.imread("image.jpg")
# Save the image
cv2.imwrite("output_image.jpg", img)
Conclusion
In this TechVidvan article, we conclude that OpenCV is a powerful and widely-used library for computer vision and image processing that provides various tools for working with images. It is beneficial for reading, displaying, and saving images, providing simple and efficient functions for performing these tasks.
The library can read images in various formats, display them on the screen, and save them to a file on disk, making it easy for developers to work with images without writing a lot of code from scratch. This allows developers to focus on the more complex aspects of image processing and computer vision, making OpenCV an essential tool for anyone working with images.