在C＃中比较两个图像的算法 [英] Algorithm to compare two images in C#

问题描述

我正在用C＃编写一个工具来查找重复的图像。目前我创建了文件的MD5校验和并对其进行比较。

不幸的是，我的图片可以是

• 旋转90度


• 具有不同的维度（小图像具有相同的内容）
  
• 具有不同的压缩或文件类型（例如jpeg文物，见下文）
  

解决这个问题的最佳方法是什么问题？

解决方案

这里有一个简单的方法，用256位图像哈希（MD5有128位） b
$ b

1. 将图片大小调整为<​​strong> 16x16 像素
   

2. 减少颜色为黑色 / 白色
（在此控制台输出中等于 true / false ）

GetPixel 以获得更多表现

使用 exeif-thumbnail 而不是阅读整个图像以提高性能

而不是设置 0.5f 在光线和黑暗之间有所不同 - 使用全部256个像素的不同的中值亮度。否则，暗/暗图像被假定为相同，并且它可以检测亮度发生变化的图像。

如果您需要

a / 16471790/1315444>快速计算，使用 bool [] 或 List< bool> if你需要存储大量哈希以节省内存，使用 Bitarray ，因为布尔值不会存储在一个位中，它需要一个 byte ！

I'm writing a tool in C# to find duplicate images. Currently i create a MD5 checksum of the files and compare those.

Unfortunately my images can be

• rotated by 90 degrees
• have different dimensions (smaller image with same content)
• have different compressions or filetypes (e.g. jpeg artifacts, see below)

what would be the best approach to solve this problem?

解决方案

Here is a simple approach with a 256 bit image-hash (MD5 has 128 bit)

1. resize the picture to 16x16 pixel

2. reduce colors to black/white (which equals true/false in this console output)

3. read the boolean values into List<bool> - this is the hash

Code:

public static List<bool> GetHash(Bitmap bmpSource)
{
    List<bool> lResult = new List<bool>();         
    //create new image with 16x16 pixel
    Bitmap bmpMin = new Bitmap(bmpSource, new Size(16, 16));
    for (int j = 0; j < bmpMin.Height; j++)
    {
        for (int i = 0; i < bmpMin.Width; i++)
        {
            //reduce colors to true / false                
            lResult.Add(bmpMin.GetPixel(i, j).GetBrightness() < 0.5f);
        }             
    }
    return lResult;
}

I know, GetPixel is not that fast but on a 16x16 pixel image it should not be the bottleneck.

4. compare this hash to hash values from other images and add a tolerance.(number of pixels that can differ from the other hash)

Code:

List<bool> iHash1 = GetHash(new Bitmap(@"C:\mykoala1.jpg"));
List<bool> iHash2 = GetHash(new Bitmap(@"C:\mykoala2.jpg"));

//determine the number of equal pixel (x of 256)
int equalElements = iHash1.Zip(iHash2, (i, j) => i == j).Count(eq => eq);

So this code is able to find equal images with:

• different file formats (e.g. jpg, png, bmp)
• rotation (90, 180, 270), horizontal /vertical flip - by changing the iteration order of i and j
• different dimensions (same aspect is required)
• different compression (tolerance is required in case of quality loss like jpeg artifacts) - you can accept a 99% equality to be the same image and 50% to be a different one.
• colored changed to geyscaled and the other way round (because brightness is independent of color)

Update / Improvements:

after using this method for a while I noticed a few improvements that can be done

• replacing GetPixel for more performance
• using the exeif-thumbnail instead of reading the whole image for a performance improvement
• instead of setting 0.5f to differ between light and dark - use the distinct median brightness of all 256 pixels. Otherwise dark/light images are assumed to be the same and it enables to detect images which have a changed brightness.
• if you need fast calculations, use bool[] or List<bool> if you need to store a lot hashes with the need to save memory, use a Bitarray because a Boolean isn't stored in a bit, it takes a byte!

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