Detailed examples of implementing face recognition function based on Go+OpenCV

introduction

OpenCV is a powerful computer vision library that provides rich image processing and computer vision algorithms. Installing OpenCV on Mac can be done with Homebrew for simple and quick installation. Once the installation is complete, we can use Go's OpenCV binding library to implement computer vision tasks such as face recognition.

This article will introduce you to the steps to install OpenCV on your Mac and demonstrate how to use Go's OpenCV binding library for face recognition. By reading this article, you will learn how to configure OpenCV's environment and use the Go programming language for image processing and computer vision tasks.

1. Install the binding library of OpenCV and Go

Installing OpenCV on Mac can use Homebrew for quick installation, and you also need to manually download the XML classifier file of OpenCV. We can configure the OpenCV environment by setting the environment variable PKG_CONFIG_PATH.

Install OpenCV on Mac

Installing OpenCV on Mac can be installed using Homebrew or manually compiled. Here are the steps to install OpenCV using Homebrew:

1.1 Install Homebrew: If we have not installed Homebrew, we can run the following command in the terminal to install Homebrew:

/bin/bash -c "$(curl -fsSL /Homebrew/install/HEAD/)"

1.2 Installing OpenCV: Using Homebrew to install OpenCV is very simple, you only need to run the following command in the terminal:

brew install opencv

1.3 Configure the PKG_CONFIG_PATH environment variable: After the installation is completed, we need to add the installation path of OpenCV to the PKG_CONFIG_PATH environment variable. Run the following command to add the pkgconfig directory of OpenCV to the environment variable:

export PKG_CONFIG_PATH="/usr/local/opt/opencv@4/lib/pkgconfig:$PKG_CONFIG_PATH"

Please note that the above command assumes that we are using the default installation path of Homebrew. If the location where we install OpenCV is different, please adjust the value of PKG_CONFIG_PATH accordingly.

1.4 Verify the installation: After completing the above steps, we can verify that OpenCV is installed correctly by running the following command:

pkg-config --cflags --libs opencv4

If there is no error and the output contains information about OpenCV, it means that OpenCV has been successfully installed and configured.

2. Use Go for face recognition

After installing the binding libraries of OpenCV and Go, we can use the Go programming language to achieve face recognition. We will demonstrate how to load a face recognition classifier file, load an image, convert an image to a grayscale image, detect a face, and draw a rectangular box on the image to mark the face.

• Install the binding library for OpenCV and Go:

  • First, we need to install OpenCV itself. We can access the OpenCV official websiteOpenCV - Open Computer Vision Library
  • Next, we can use the following command to install Go's OpenCV binding library:

go get -u /x/gocv

• downloadhaarcascade_frontalface_default.xmldocument:

  • haarcascade_frontalface_default.xmlIt is a cascading classifier file of OpenCV for face detection.
  • We can use the GitHub repository from OpenCV (OpenCV · GitHub）
  • After downloading, pleasehaarcascade_frontalface_default.xmlThe file is saved in our Golang project directory or under the specified path.

• Loading in our Golang codehaarcascade_frontalface_default.xmldocument:

  • In our Golang code, make sure to load with the correct file pathhaarcascade_frontalface_default.xmldocument. For example, if the file is in the same directory as our Golang file, you can use the relative path to load it.

4. Use go mod to initialize a project directory

.
├──
├──
├── haarcascade_frontalface_default.xml
└──

File encoding

In this code, we first import/x/gocvPackage, this package is an OpenCV binding library for Go language. Then, we useThe function loads the face recognition classifier file "haarcascade_frontalface_default.xml". If the load fails, we output an error message and terminate the program.

Since the face recognition classifier file is a model file used to detect faces, we need to load this file before using OpenCV for face recognition.

6. Through the above steps, we have basically learned about an implementation process. The following is the complete file

The complete code is as follows:

package main
import (
	"fmt"
	"/x/gocv"
	"image/color"
)
func main() {
	// Step 1: Turn on the camera device	webcam, err := (0)
	if err != nil {
		("Failed to turn on the camera device:", err)
		return
	}
	defer ()
	// Step 2: Load the face recognition classifier	classifier := ()
	defer ()
	if !("haarcascade_frontalface_default.xml") {
		("Collection of classifier file failed")
		return
	}
	// Step 3: Create a window for displaying the image	window := ("Face Detection")
	defer ()
	img := ()
	defer ()
	for {
		// Step 4: Read image frames from the camera		if ok := (&img); !ok || () {
			("Unable to read image frames from camera")
			break
		}
		// Step 5: Convert the image to a grayscale image, because face recognition is usually done on a grayscale image		gray := ()
		defer ()
		(img, &gray, )
		// Step 6: Detect faces		rects := (gray)
		("%d face detected\n", len(rects))
		// Step 7: Draw the bounding box of faces on the image		for _, r := range rects {
			(&img, r, {0, 255, 0, 0}, 2)
		}
		// Step 8: Display the image		(img)
		// Step 9: Wait for the user to press the ESC key to exit		if (1) == 27 {
			break
		}
	}
}

illustrate:

• Step 1: We useFunction to turn on the camera device,0Indicates the use of the default camera.
• Step 2: We useFunction creates a face recognition classifier and usesMethod loadinghaarcascade_frontalface_default.xmlClassifier file.
• Step 3: We useThe function creates a window named "Face Detection" to display the image.
• Step 4: We useMethod reads image frames from the camera and checks whether the image is successfully read.
• Step 5: We useFunctions convert images to grayscale images, because face recognition is usually done on grayscale images.
• Step 6: We useMethods detect faces and obtain rectangular areas of faces in the image.
• Step 7: We useFunction draws the bounding box of the face on the image to mark the face position.
• Step 8: We useMethod displays the marked image in the window.
• Step 9: We useMethod waits for the user to press the ESC key, and exits the program if the ESC key is pressed.

Summarize

The above code demonstrates the simple face recognition task using the OpenCV binding library in Go language. By loading the face recognition classifier file and image, convert the image into a grayscale image, and use the classifier to detect the face, and finally draw a rectangular box on the original image to mark the face. Face recognition is one of the important applications in the field of computer vision and can be applied to scenes such as face recognition login, face expression recognition, face tracking, etc. The combination of OpenCV and Go makes image processing and computer vision tasks simple and powerful. I hope this article will be helpful to your study and practice in the field of computer vision!

The above is a detailed example of the detailed example of the face recognition function based on Go+OpenCV. For more information about Go OpenCV’s face recognition, please pay attention to my other related articles!