通过C#来实现,二张JPG图片请问如何对比它的相似度，例如：90% ,100% ,60%？

二张JPG图片请问如何对比它的相似度，例如：90% ,100% ,,60%？

我给图片，请看一下，是二张监控图片，请问相似度如何计算？通过C#来实现。

c# 的话 用OpenCVSharp 会简单些

 float Similar(Mat src, Mat src2)
        {
            Mat gray1 = new Mat(src.Size(), src.Type()),
                gray2 = new Mat(src2.Size(), src2.Type());
            Cv2.CvtColor(src, gray1, ColorConversionCodes.BGR2GRAY);
            Cv2.CvtColor(src2, gray2, ColorConversionCodes.BGR2GRAY);

            var size = new OpenCvSharp.Size(512, 512);
            using (var scaledImg1 = gray1.Resize(size))
            using (var scaledImg2 = gray2.Resize(size))
            {
                Cv2.Threshold(scaledImg1, scaledImg1, 128, 255, ThresholdTypes.BinaryInv);
                Cv2.Threshold(scaledImg2, scaledImg2, 128, 255, ThresholdTypes.BinaryInv);
                Mat res = new Mat(size, scaledImg1.Type());
                Cv2.Absdiff(scaledImg1, scaledImg2, res);
                var all = (float)scaledImg1.Sum();
                var result = (float)res.Sum();
                return (1 - result / all);
            }
        }

拿走不谢：

using  System;
using  System.IO;
using  System.Drawing;
 
namespace  SimilarPhoto
{
     class  SimilarPhoto
     {
         Image SourceImg;
 
         public  SimilarPhoto( string  filePath)
         {
             SourceImg = Image.FromFile(filePath);
         }
 
         public  SimilarPhoto(Stream stream)
         {
             SourceImg = Image.FromStream(stream);
         }
 
         public  String GetHash()
         {
             Image image = ReduceSize();
             Byte[] grayValues = ReduceColor(image);
             Byte average = CalcAverage(grayValues);
             String reslut = ComputeBits(grayValues, average);
             return  reslut;
         }
 
         // Step 1 : Reduce size to 8*8
         private  Image ReduceSize( int  width = 8,  int  height = 8)
         {
             Image image = SourceImg.GetThumbnailImage(width, height, () => {  return  false ; }, IntPtr.Zero);
             return  image;
         }
 
         // Step 2 : Reduce Color
         private  Byte[] ReduceColor(Image image)
         {
             Bitmap bitMap =  new  Bitmap(image);
             Byte[] grayValues =  new  Byte[image.Width * image.Height];
 
             for ( int  x = 0; x<image.Width; x++)
                 for  ( int  y = 0; y < image.Height; y++)
                 {
                     Color color = bitMap.GetPixel(x, y);
                     byte  grayValue = ( byte )((color.R * 30 + color.G * 59 + color.B * 11) / 100);
                     grayValues[x * image.Width + y] = grayValue;
                 }
             return  grayValues;
         }
 
         // Step 3 : Average the colors
         private  Byte CalcAverage( byte [] values)
         {
             int  sum = 0;
             for  ( int  i = 0; i < values.Length; i++)
                 sum += ( int )values[i];
             return  Convert.ToByte(sum / values.Length);
         }
 
         // Step 4 : Compute the bits
         private  String ComputeBits( byte [] values,  byte  averageValue)
         {
             char [] result =  new  char [values.Length];
             for  ( int  i = 0; i < values.Length; i++)
             {
                 if  (values[i] < averageValue)
                     result[i] =  '0' ;
                 else
                     result[i] =  '1' ;
             }
             return  new  String(result);
         }
 
         // Compare hash
         public  static  Int32 CalcSimilarDegree( string  a,  string  b)
         {
             if  (a.Length != b.Length)
                 throw  new  ArgumentException();
             int  count = 0;
             for  ( int  i = 0; i < a.Length; i++)
             {
                 if  (a[i] != b[i])
                     count++;
             }
             return  count;
         }
     }
}

看一下https://blog.csdn.net/jiangxinyu/article/details/7968139

不用sift吗？

phash相似度

看看这个怎么样：

public Bitmap Resize(string imageFile, string newImageFile)

{undefined

img = Image.FromFile(imageFile); 

Bitmap imgOutput = new Bitmap(img, 256, 256);

imgOutput.Save(newImageFile, System.Drawing.Imaging.ImageFormat.Jpeg);

imgOutput.Dispose();

return (Bitmap)Image.FromFile(newImageFile);

}
public int[] GetHisogram(Bitmap img)

{undefined

BitmapData data = img.LockBits(new System.Drawing.Rectangle( 0 , 0 , img.Width , img.Height ),ImageLockMode.ReadWrite , PixelFormat.Format24bppRgb );

int[ ] histogram = new int[ 256 ];

unsafe

{undefined

byte* ptr = ( byte* )data.Scan0;

int remain = data.Stride - data.Width * 3;

for( int i = 0 ; i < histogram.Length ; i ++ )

histogram[ i ] = 0;

for( int i = 0 ; i < data.Height ; i ++ )

{undefined

for( int j = 0 ; j < data.Width ; j ++ )

{undefined

int mean = ptr[ 0 ] + ptr[ 1 ] + ptr[ 2 ];

mean /= 3;

histogram[ mean ] ++;

ptr += 3;

}

ptr += remain;

}

}

img.UnlockBits( data );

return histogram;

}
//计算相减后的绝对值

private float GetAbs(int firstNum, int secondNum)

{undefined

float abs = Math.Abs((float)firstNum - (float)secondNum);

float result = Math.Max(firstNum, secondNum);

if (result == 0)

result = 1;

return abs / result;

}

//最终计算结果

public float GetResult(int[] firstNum,int[] scondNum)

{undefined

if (firstNum.Length != scondNum.Length)

{undefined

return 0;

}

else

{undefined

float result = 0;

int j = firstNum.Length;

for (int i = 0; i < j; i++)

{undefined

result += 1 - GetAbs(firstNum[i], scondNum[i]);

Console.WriteLine(i + "----" + result);

}

return result/j;

}

}