Golang : Apply Histogram Equalization to color images

For this tutorial, we will learn how to convert color images to greyscale and then apply Histogram Equalization as a way to standardize the brightness and contrast. This is a step to pre-process the image for facial recognition purpose with Eigenface method.

An example output. Left most is the original and right most image is the final product.

Here you go!

 package main

 import (
  "fmt"
  "github.com/lazywei/go-opencv/opencv"
  "image"
  gif "image/gif"
  _ "image/jpeg"
  _ "image/png"
  "net/http"
  "os"
 )

 func errCheck(err error) {

  if err != nil {
 panic(err)
  }
 }

 func main() {

  if len(os.Args) != 2 {
 fmt.Printf("Usage : %s <imagefilename>\n", os.Args[0])
 os.Exit(0)
  }

  imageFileName := os.Args[1]

  // we will use Go's method of load images
  // instead of openCV.LoadImage
  // because we want to detect if the user supplies animated GIF or not
  imageFile, err := os.Open(imageFileName)

  errCheck(err)

  defer imageFile.Close()

  img, _, err := image.Decode(imageFile)
  errCheck(err)

  buffer := make([]byte, 512)
  imageFile.Seek(0, 0) // reset reader
  _, err = imageFile.Read(buffer)
  errCheck(err)

  filetype := http.DetectContentType(buffer)
  // check if image is GIF and if yes, check to see if it is animated GIF by
  // counting the LoopCount number
  fmt.Println("Analyzing image type : ", filetype)

  if filetype == "image/gif" {
 imageFile.Seek(0, 0)
 // warn if image is animated GIF
 gif, err := gif.DecodeAll(imageFile)
 errCheck(err)
 if gif.LoopCount != 0 {
 fmt.Println("Animated gif detected. Will only scan for faces in the 1st frame.")
 }
  }

  // convert Go's image.Image type to OpenCV's IplImage(Intel Image Processing Library)
  openCVImg := opencv.FromImage(img)
  defer openCVImg.Release()

  if openCVImg != nil {
 fmt.Println("Converting [" + imageFileName + "] to greyscale image......")

 w := openCVImg.Width()
 h := openCVImg.Height()

 // create an IplImage with 1 channel(grey)
 greyImg := opencv.CreateImage(w, h, opencv.IPL_DEPTH_8U, 1)
 defer greyImg.Release()

 // convert to greyscale
 opencv.CvtColor(openCVImg, greyImg, opencv.CV_BGR2GRAY)

 // from http://www.shervinemami.info/faceRecognition.html

 // make the image a fixed size
 // CV_INTER_CUBIC or CV_INTER_LINEAR is good for enlarging, and
 // CV_INTER_AREA is good for shrinking / decimation, but bad at enlarging.

 histoEqImg := opencv.CreateImage(w, h, opencv.IPL_DEPTH_8U, 1)
 defer histoEqImg.Release()
 histoEqImg = opencv.Resize(greyImg, w, h, opencv.CV_INTER_LINEAR)

 fmt.Println("Applying Histogram Equalization to [" + imageFileName + "]......")

 // standard brightness and contrast
 greyImg.EqualizeHist(histoEqImg)

 // save to file
 fmt.Println("Saving results to [grey.jpg] and [equalhisto.jpg]")
 opencv.SaveImage("./grey.jpg", greyImg, opencv.CV_IMWRITE_JPEG_QUALITY)
 opencv.SaveImage("./equalhisto.jpg", histoEqImg, opencv.CV_IMWRITE_JPEG_QUALITY)

  } else {
 panic("OpenCV FromImage error")
  }

 }

Happy coding and recognizing face with computer vision!

References:

http://www.shervinemami.info/faceRecognition.html

http://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.html

https://en.wikipedia.org/wiki/Eigenface