ios判断图片相似度，排版瞎了

http://bbs.125.la/thread-13628690-1-1.html

这个也是从别的地方看来的算法。
经验：第五步，计算哈希值
扯淡的，就是用获得的010101这样的字符串互相比较。

下面一步一步来：
第一步，缩小尺寸。将图片缩小到8x8的尺寸，总共64个像素。这一步的作用是去除图片的细节，只保留结构、明暗等基本信息，摒弃不同尺寸、比例带来的图片差异。

-(UIImage * ) OriginImage:(UIImage **)image scaleToSize:(CGSize)size
{
    UIGraphicsBeginImageContext(size);  //size 为CGSize类型，即你所需要的图片尺寸
    [image drawInRect:CGRectMake(0, 0, size.width, size.height)];
    UIImage* scaledImage = UIGraphicsGetImageFromCurrentImageContext();
    UIGraphicsEndImageContext();
    return scaledImage;   //返回的就是已经改变的图片
}

第二步，简化色彩。将缩小后的图片，转为64级灰度。也就是说，所有像素点总共只有64种颜色。

-(UIImage*)getGrayImage:(UIImage*)sourceImage
{
    int width = sourceImage.size.width;
    int height = sourceImage.size.height;
    CGColorSpaceRef colorSpace = CGColorSpaceCreateDeviceGray();
    CGContextRef context = CGBitmapContextCreate (nil,width,height,8,0,colorSpace,kCGImageAlphaNone);
    CGColorSpaceRelease(colorSpace);
    if (context == NULL) {
        return nil;
    }
    CGContextDrawImage(context,CGRectMake(0, 0, width, height), sourceImage.CGImage);
    UIImage *grayImage = [UIImage imageWithCGImage:CGBitmapContextCreateImage(context)];
    CGContextRelease(context);
    return grayImage;
}

第三步，计算平均值。计算所有64个像素的灰度平均值。
以下代码来自github

-(unsigned char*) grayscalePixels:(UIImage *) image
{
    // The amount of bits per pixel, in this case we are doing grayscale so 1 byte = 8 bits
#define BITS_PER_PIXEL 8
    // The amount of bits per component, in this it is the same as the bitsPerPixel because only 1 byte represents a pixel
#define BITS_PER_COMPONENT (BITS_PER_PIXEL)
    // The amount of bytes per pixel, not really sure why it asks for this as well but it's basically the bitsPerPixel divided by the bits per component (making 1 in this case)
#define BYTES_PER_PIXEL (BITS_PER_PIXEL/BITS_PER_COMPONENT)
    
    // Define the colour space (in this case it's gray)
    CGColorSpaceRef colourSpace = CGColorSpaceCreateDeviceGray();
    
    // Find out the number of bytes per row (it's just the width times the number of bytes per pixel)
    size_t bytesPerRow = image.size.width * BYTES_PER_PIXEL;
    // Allocate the appropriate amount of memory to hold the bitmap context
    unsigned char* bitmapData = (unsigned char*) malloc(bytesPerRow*image.size.height);
    
    // Create the bitmap context, we set the alpha to none here to tell the bitmap we don't care about alpha values
    CGContextRef context = CGBitmapContextCreate(bitmapData,image.size.width,image.size.height,BITS_PER_COMPONENT,bytesPerRow,colourSpace,kCGImageAlphaNone);
    
    // We are done with the colour space now so no point in keeping it around
    CGColorSpaceRelease(colourSpace);
    
    // Create a CGRect to define the amount of pixels we want
    CGRect rect = CGRectMake(0.0,0.0,image.size.width,image.size.height);
    // Draw the bitmap context using the rectangle we just created as a bounds and the Core Graphics Image as the image source
    CGContextDrawImage(context,rect,image.CGImage);
    // Obtain the pixel data from the bitmap context
    unsigned char* pixelData = (unsigned char*)CGBitmapContextGetData(context);
    
    // Release the bitmap context because we are done using it
    CGContextRelease(context);
    
    return pixelData;
#undef BITS_PER_PIXEL
#undef BITS_PER_COMPONENT
}

返回就是0101字符串了

-(NSString *) myHash:(UIImage *) img
{
    unsigned char* pixelData = [self grayscalePixels:img];
    
    int total = 0;
    int ave = 0;
    for (int i = 0; i < img.size.height; i++) {
        for (int j = 0; j < img.size.width; j++) {
            total += (int)pixelData[(i*((int)img.size.width))+j];
        }
    }
    ave = total/64;
    NSMutableString *result = [[NSMutableString alloc] init];
    for (int i = 0; i < img.size.height; i++) {
        for (int j = 0; j < img.size.width; j++) {
            int a = (int)pixelData[(i*((int)img.size.width))+j];
            if(a >= ave)
            {
                [result appendString:@"1"];
            }
            else
            {
                [result appendString:@"0"];
            }
        }
    }
    return result;
}

第五步，计算哈希值。将上一步的比较结果，组合在一起，就构成了一个64位的整数，这就是这张图片的指纹。组合的次序并不重要，只要保证所有图片都采用同样次序就行了。得到指纹以后，就可以对比不同的图片，看看64位中有多少位是不一样的。在理论上，这等同于计算"汉明距离"
（Hamming distance）。如果不相同的数据位不超过5，就说明两张图片很相似；如果大于10，就说明这是两张不同的图片。

这里在文章开头说过了，这里不是计算哈希值。上面得到的01010字符串就是这个图片的指纹了。比较就行了。
以下是两个图片的对比
0111111011110011111100111110000111000001100000011110001101111010
1111111111110001111000011110000111000001100000011110000111111011

然后算一下不一样的，排序。结束～～！