Getting Started with OpenCV in Go Programming Language

OpenCV is a widely used computer vision library, which provides a set of tools for image and video processing, feature extraction, object detection and more. In this tutorial, we will explore how to use OpenCV with Go programming language.

Prerequisites

Before we get started, you will need to have the following tools and libraries installed on your system:

• Go programming language
• OpenCV library
• GoCV library

You can install OpenCV and GoCV by following the instructions on the official websites:

• OpenCV: https://opencv.org/
• GoCV: https://gocv.io/

Getting Started

Let's start by creating a new Go program that imports the GoCV library:

package main

import (
	"gocv.io/x/gocv"
)

func main() {
	// Your code here
}

Now we can start working with OpenCV in our program.

Loading and Displaying Images

The first thing we will do is load an image from disk and display it on the screen. Here's how:

package main

import (
	"gocv.io/x/gocv"
)

func main() {
	// Load an image from disk
	img := gocv.IMRead("image.jpg", gocv.IMReadColor)

	// Create a window to display the image
	window := gocv.NewWindow("Image")

	// Display the image in the window
	window.IMShow(img)

	// Wait for a key press to close the window
	window.WaitKey(0)
}

In this code, we use the IMRead function from the GoCV library to load an image from disk. We then create a new window using the NewWindow function and display the image in the window using the IMShow function. Finally, we use the WaitKey function to wait for a key press to close the window.

Image Processing

Now that we can load and display images, let's explore some image processing techniques that OpenCV provides.

Converting Images to Grayscale

One common technique in image processing is to convert images to grayscale. Here's how to do it in Go using OpenCV:

package main

import (
	"gocv.io/x/gocv"
)

func main() {
	// Load an image from disk
	img := gocv.IMRead("image.jpg", gocv.IMReadColor)

	// Convert the image to grayscale
	gray := gocv.NewMat()
	defer gray.Close()

	gocv.CvtColor(img, &gray, gocv.ColorBGRToGray)

	// Create a window to display the grayscale image
	window := gocv.NewWindow("Grayscale Image")

	// Display the grayscale image in the window
	window.IMShow(gray)

	// Wait for a key press to close the window
	window.WaitKey(0)
}

In this code, we use the CvtColor function from the GoCV library to convert the image from color to grayscale. We then create a new window and display the grayscale image in the window.

Detecting Faces

Another common technique in computer vision is to detect faces in images. OpenCV provides a pre-trained face detection model that we can use to detect faces. Here's how:

package main

import (
	"gocv.io/x/gocv"
)

func main() {
	// Load an image from disk
	img := gocv.IMRead("image.jpg", gocv.IMReadColor)

	// Load the pre-trained face detection model
	classifier := gocv.NewCascadeClassifier()
	defer classifier.Close()

	classifier.Load("haarcascade_frontalface_default.xml")

	// Detect faces in
the image
rects := classifier.DetectMultiScale(img)
// Draw a rectangle around each face
for _, r := range rects {
	gocv.Rectangle(img, r, color.RGBA{0, 255, 0, 0}, 2)
}

// Create a window to display the image with faces
window := gocv.NewWindow("Faces Detected")

// Display the image with faces in the window
window.IMShow(img)

// Wait for a key press to close the window
window.WaitKey(0)
}

In this code, we first load an image from disk. We then load the pre-trained face detection model using the `NewCascadeClassifier` function. We use the `DetectMultiScale` function to detect faces in the image and store the result in a slice of rectangles. Finally, we loop through the rectangles and draw a green rectangle around each face using the `Rectangle` function.

Conclusion

In this tutorial, we explored how to work with OpenCV in Go programming language. We learned how to load and display images, convert images to grayscale, and detect faces in images. These are just a few examples of what OpenCV can do, and there are many more image processing techniques that you can explore.