How to convert multiple images into segmentation and image fusion using Golang
Overview:
In this article, we will show how to use Golang programming language to Convert multiple images to segmentation and image fusion. We will use Golang's image processing library and simple algorithms to implement this process. By converting multiple pictures into different parts of an image and then blending them together, we can create a new interesting and unique image.
Step 1: Import the required libraries
First, we need to import Golang’s image processing library and other required libraries. In our code, we will use the image and os libraries.
package main
import (
"fmt"
"image"
_ "image/jpeg"
"image/png"
"os"
)Step 2: Load multiple images
Next, we need to load multiple images. We can use Golang's image.Decode function to load image files.
func loadImage(path string) (image.Image, error) {
file, err := os.Open(path)
if err != nil {
return nil, err
}
defer file.Close()
img, _, err := image.Decode(file)
if err != nil {
return nil, err
}
return img, nil
}
func loadImages(paths []string) ([]image.Image, error) {
var images []image.Image
for _, path := range paths {
img, err := loadImage(path)
if err != nil {
return nil, err
}
images = append(images, img)
}
return images, nil
}Step 3: Split the image
Next, we will implement a function to split the image into multiple parts. We can use Golang's image library to get the width and height of the image and split it into equal-sized parts as needed.
func splitImage(img image.Image, rows, cols int) [][]image.Image {
bounds := img.Bounds()
width := bounds.Max.X - bounds.Min.X
height := bounds.Max.Y - bounds.Min.Y
cellWidth := width / cols
cellHeight := height / rows
var splitImages [][]image.Image
for row := 0; row < rows; row++ {
var rowImages []image.Image
for col := 0; col < cols; col++ {
x := bounds.Min.X + col*cellWidth
y := bounds.Min.Y + row*cellHeight
r := image.Rect(x, y, x+cellWidth, y+cellHeight)
subImage := imaging.Crop(img, r)
rowImages = append(rowImages, subImage)
}
splitImages = append(splitImages, rowImages)
}
return splitImages
}Step 4: Image Fusion
Finally, we will implement a function to fuse the segmented images together. In this example, we will use a simple algorithm to accumulate the pixel values at each moment and average the results.
func mergeImages(images [][]image.Image) image.Image {
rows := len(images)
cols := len(images[0])
cellWidth := images[0][0].Bounds().Dx()
cellHeight := images[0][0].Bounds().Dy()
merged := image.NewRGBA(image.Rect(0, 0, cellWidth*cols, cellHeight*rows))
for row := 0; row < rows; row++ {
for col := 0; col < cols; col++ {
x := col * cellWidth
y := row * cellHeight
subImage := images[row][col]
rect := image.Rect(x, y, x+cellWidth, y+cellHeight)
draw.Draw(merged, rect, subImage, image.Point{}, draw.Over)
}
}
return merged
}Step 5: Full Code Example
Below is a complete code example that demonstrates how to convert multiple images into segmentation and image fusion.
package main
import (
"fmt"
"image"
_ "image/jpeg"
"image/png"
"os"
)
func loadImage(path string) (image.Image, error) {
file, err := os.Open(path)
if err != nil {
return nil, err
}
defer file.Close()
img, _, err := image.Decode(file)
if err != nil {
return nil, err
}
return img, nil
}
func loadImages(paths []string) ([]image.Image, error) {
var images []image.Image
for _, path := range paths {
img, err := loadImage(path)
if err != nil {
return nil, err
}
images = append(images, img)
}
return images, nil
}
func splitImage(img image.Image, rows, cols int) [][]image.Image {
bounds := img.Bounds()
width := bounds.Max.X - bounds.Min.X
height := bounds.Max.Y - bounds.Min.Y
cellWidth := width / cols
cellHeight := height / rows
var splitImages [][]image.Image
for row := 0; row < rows; row++ {
var rowImages []image.Image
for col := 0; col < cols; col++ {
x := bounds.Min.X + col*cellWidth
y := bounds.Min.Y + row*cellHeight
r := image.Rect(x, y, x+cellWidth, y+cellHeight)
subImage := imaging.Crop(img, r)
rowImages = append(rowImages, subImage)
}
splitImages = append(splitImages, rowImages)
}
return splitImages
}
func mergeImages(images [][]image.Image) image.Image {
rows := len(images)
cols := len(images[0])
cellWidth := images[0][0].Bounds().Dx()
cellHeight := images[0][0].Bounds().Dy()
merged := image.NewRGBA(image.Rect(0, 0, cellWidth*cols, cellHeight*rows))
for row := 0; row < rows; row++ {
for col := 0; col < cols; col++ {
x := col * cellWidth
y := row * cellHeight
subImage := images[row][col]
rect := image.Rect(x, y, x+cellWidth, y+cellHeight)
draw.Draw(merged, rect, subImage, image.Point{}, draw.Over)
}
}
return merged
}
func main() {
paths := []string{"image1.jpg", "image2.jpg", "image3.jpg"}
images, err := loadImages(paths)
if err != nil {
fmt.Println("Failed to load images:", err)
return
}
rows := 2
cols := 2
splitImages := splitImage(images[0], rows, cols)
merged := mergeImages(splitImages)
output, err := os.Create("output.png")
if err != nil {
fmt.Println("Failed to create output file:", err)
return
}
defer output.Close()
err = png.Encode(output, merged)
if err != nil {
fmt.Println("Failed to encode output file:", err)
return
}
fmt.Println("Image conversion and merging is done!")
}Summary:
The above are the steps and code examples for converting multiple images into segmentation and image fusion using Golang. By using Golang's image processing library and simple algorithms, we can easily implement this process. You can adjust the segmentation and fusion parameters as needed to create images of different forms and styles. Hope this article helps you!
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