Detailed explanation of the computer vision library opencv in Python

Computer vision is a branch of artificial intelligence that is dedicated to using computers to simulate and realize the functions of the human visual system. In the context of the development of modern science and technology, computer vision is widely used in various fields, such as robotics, security, medical care, autonomous driving, etc.

OpenCV (Open Source Computer Vision) is a cross-platform open source computer vision library initiated and maintained by Intel Corporation. It provides rich image processing and computer vision functions, including image detection, feature extraction, target tracking, face recognition, etc. The popularity of Python in the fields of machine learning and data science has also made the version of OpenCV in Python attract much attention. This article will introduce the use and specific implementation of OpenCV in Python.

Install OpenCV

Before using OpenCV, you need to install the relevant libraries and dependencies. In Python, you can use the pip command to install.

pip install opencv-python

pip install opencv-contrib-python

These two commands install the basic version and the extended version of OpenCV respectively. The extended version includes some basic Features not available in version.

Reading and displaying images

The first step in using OpenCV to process images is to read the image into the program. OpenCV provides the cv2.imread() function to read images. The read images are stored in the form of NumPy arrays.

import cv2

img = cv2.imread('example.jpg')

After reading the image, you can use the cv2.imshow() function to display the image . This function takes two parameters, the first parameter is the name of the window, and the second parameter is the image to be displayed.

cv2.imshow('example', img)

Use the cv2.waitKey() function to wait for the user to press any key, and the window will automatically close.

cv2.waitKey(0)

The size of the image can be obtained using img.shape, which returns a triplet (height, width, depth), where height represents the height of the image, Width represents the width of the image, and depth represents the number of bits per pixel. The depth is 3 if the image is a color image and 1 for a grayscale image.

Image processing and operation

OpenCV provides a wealth of image processing and operation functions. Among them, the simplest is grayscale processing, which converts color images into grayscale images. This requires the use of the cv2.cvtColor() function.

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

cv2.COLOR_BGR2GRAY is defined in grayscale processing, where cv2 is the name of the OpenCV module. OpenCV's built-in image format is BGR, not the common RGB, so the order of its parameters is BGR.

In addition to grayscale processing, there are also some commonly used operation functions, such as adjusting image size, rotation, translation, etc.

Image scaling operation

resized = cv2.resize(img, (600, 600), interpolation=cv2.INTER_AREA)

Image flipping operation

flipped = cv2.flip(img, -1)

Image rotation operation

(rows, cols) = img.shape[:2]
M = cv2.getRotationMatrix2D ((cols / 2, rows / 2), 45, 1)
rotated = cv2.warpAffine(img, M, (cols, rows))

Feature extraction and detection of images

In addition to basic image processing and operations, OpenCV also provides various feature extraction and detection functions. These functions can detect and extract features in images, such as corners, lines, and contours.

Corner detection is a technology widely used in the fields of machine vision and computer vision. It can be used for applications such as image matching, target tracking, and three-dimensional reconstruction. OpenCV provides the cv2.goodFeaturesToTrack() function to complete corner detection.

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
corners = cv2.goodFeaturesToTrack(gray, 100, 0.01, 10)
corners = np.int0(corners)

Among them, the parameter gray is the grayscale processed image, the parameter 100 represents the number of detected corner points, the parameter 0.01 is the qualityLevel, which is used to represent the corner quality factor, and the parameter 10 is minDistance, which represents the minimum distance between the two corner points. distance.

In addition, there are other feature detection methods, such as SIFT, SURF and ORB.

Conclusion

OpenCV is a powerful computer vision library that is widely used in various fields. In Python, image processing and computer vision tasks can be easily implemented using OpenCV. This article introduces the basic usage and common operations of OpenCV in Python. I hope it will be helpful to readers.

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