【iOS开发】图像处理之获取RGB

本文详细介绍了雾霾检测机的工作原理及实现过程,通过获取图像的RGB值并转换为灰度值,进而计算每个像素点的梯度,最终确定清晰度指数。文中还涉及到图像处理算法,包括使用Sobel算子进行边缘检测。

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截取自雾霾检测机
-(float) returnGrayVale:(UInt8*)buffer withBytesPerRow:(size_t)bytesPerRow withX:(int)x withY:(int)y{
    UInt8*  tmp;
    tmp = buffer + y * bytesPerRow + x * 4; // RGBAの4つ値をもっているので、1ピクセルごとに*4してずらす
    
    // 获取图像的RGB
    UInt8 red,green,blue;
    red = *(tmp + 0);
    green = *(tmp + 1);
    blue = *(tmp + 2);
    
    //获取灰度值
    float grayValue = [self returnGrayValueWithRGB:red withGreen:green withBlue:blue];
    return grayValue;
}


-(float)ClearIndexBySobel:(UIImage*)sourceImage fromX:(int)Xpoint fromY:(int)Ypoint zoneWidth:(int)Width zoneHeihgt:(int)Height{
    
    int imageWidth = sourceImage.size.width;
    int imageHeight = sourceImage.size.height;
    
    //防止越界处理
    if (Xpoint<=0 || Xpoint>=imageWidth-1 || Ypoint<=0 || Ypoint>=imageHeight-1) {
        return -1;
    }
    
    
    //获取ref对象
    CGImageRef  imageRef;
    imageRef = sourceImage.CGImage;
    
    // 一行有多少个字节
    size_t                  bytesPerRow;
    bytesPerRow = CGImageGetBytesPerRow(imageRef);
    
    CGDataProviderRef   dataProvider;
    dataProvider = CGImageGetDataProvider(imageRef);
    
    CFDataRef   data;
    UInt8*      buffer;
    data = CGDataProviderCopyData(dataProvider);
    buffer = (UInt8*)CFDataGetBytePtr(data);

    //循环计算区域像素点获得总的梯度值
    float sum_grad = 0;
    int count = 0;
    for (int y = Ypoint; y < Ypoint + Height; y++) {
        for (int x = Xpoint; x < Xpoint + Width; x++) {
            
            //计算周边8个点的灰度值
            float gray_00 = [self returnGrayVale:buffer withBytesPerRow:bytesPerRow withX:x-1 withY:y-1];
            float gray_01 = [self returnGrayVale:buffer withBytesPerRow:bytesPerRow withX:x-1 withY:y];
            float gray_02 = [self returnGrayVale:buffer withBytesPerRow:bytesPerRow withX:x-1 withY:y+1];
            
            float gray_10 = [self returnGrayVale:buffer withBytesPerRow:bytesPerRow withX:x withY:y-1];
            float gray_12 = [self returnGrayVale:buffer withBytesPerRow:bytesPerRow withX:x withY:y+1];
            
            float gray_20 = [self returnGrayVale:buffer withBytesPerRow:bytesPerRow withX:x+1 withY:y-1];
            float gray_21 = [self returnGrayVale:buffer withBytesPerRow:bytesPerRow withX:x+1 withY:y];
            float gray_22 = [self returnGrayVale:buffer withBytesPerRow:bytesPerRow withX:x+1 withY:y+1];
            

            //计算梯度值
            float grad_x = gray_00-gray_02+2*gray_10-2*gray_12+gray_20-gray_22;
            float grad_y = gray_00+2*gray_01+gray_02-gray_20-2*gray_21-gray_22;
            
            float grad=sqrt(grad_x*grad_x+grad_y*grad_y);
            sum_grad +=grad;
            count++;
        }
    }
    
    if(count<=0){
        return 0;
    }
    
    float clearIndex = sum_grad / count;
    
    dataProvider = nil;
    data = nil;
    buffer = nil;
    
    return clearIndex;
}
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