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OpenCV在iOS端的集成及Mat和UIImage互相转化（附源码）

废话不多说直接上代码，伸手党福利：
代码中记得引入头文件及命名空间

#import <opencv2/opencv.hpp>

#import "CVTools.h" //对应的.h文件  命名空间在自己的.h文件后定义

using namespace cv;
using namespace std;

下面的处理方法统一对使用最多的8bit图片处理，如果是16bit的图片需要修改对应的格式，在使用的方法内需要改动的如下:
255 调整为 65535
Vec4b 调整为 Vec4w
uchar 调整为 ushort
UCHAR_MAX 调整为 USHRT_MAX

1.亮度

//调整亮度
+(UIImage *)brightInPutImage:(UIImage *)inputImage value:(float)beta{
    Mat g_srcImage = [CVTools cvMatFromUIImage:inputImage];
    if(g_srcImage.empty()){
        return nil;
    }
    Mat g_dstImage=Mat::zeros(g_srcImage.size(),g_srcImage.type());
    g_srcImage.convertTo(g_dstImage, -1, 1, beta);
    
    return [CVTools UIImageFromCVMat:g_dstImage];
}

2.对比度

//调整对比度
+(UIImage *)contrasInPutImage:(UIImage *)inputImage value:(CGFloat)alpha{
    Mat g_srcImage = [CVTools cvMatFromUIImage:inputImage];
    if(g_srcImage.empty()){
        return nil;
    }
    Mat g_dstImage=Mat::zeros(g_srcImage.size(),g_srcImage.type());
    g_srcImage.convertTo(g_dstImage, -1, alpha, 1);
    return [CVTools UIImageFromCVMat:g_dstImage];
}

3.曝光

曝光调节的方法是是对伽马参数进行调节来啊达到调整曝光的目的

//调节曝光
+(UIImage *)gammaInPutImage:(UIImage *)inputImage value:(CGFloat)value{
    
    Mat g_srcImage =  [CVTools cvMatFromUIImage:inputImage];
    
    double gamma_c = 1;
    double gamma_g = value;
    int height=g_srcImage.rows;
    int width=g_srcImage.cols;
    
    double val;
    Mat g_dstImage=Mat::zeros(g_srcImage.size(),g_srcImage.type());
    for (int row=0; row<height; row++) {
        for (int col=0; col<width; col++) {
            
            for (int c = 0; c < 3; c++){
                val = (double)g_srcImage.at<cv::Vec4b>(row, col)[c] / UCHAR_MAX;
                g_dstImage.at<cv::Vec4b>(row, col)[c] =  saturate_cast<uchar>((uchar)(pow(val / gamma_c, 1 / gamma_g) * UCHAR_MAX));
            }
            g_dstImage.at<cv::Vec4b>(row, col)[3] = g_srcImage.at<cv::Vec4b>(row, col)[3];
        }
    }
    
    return [CVTools UIImageFromCVMat:g_dstImage];
}

4.高光

//调节高光
+(UIImage *)highLightInPutImage:(UIImage *)inputImage value:(CGFloat)light{
    
    Mat input =  [CVTools cvMatFromUIImage:inputImage];
    // 生成灰度图
    cv::Mat gray = cv::Mat::zeros(input.size(), CV_32FC1);
    cv::Mat f = input.clone();
    f.convertTo(f, CV_32FC3);
    vector<cv::Mat> pics;
    split(f, pics);
    gray = 0.299f*pics[2] + 0.587*pics[2] + 0.114*pics[0];
    gray = gray / 255.0;
    
    // 确定高光区
    cv::Mat thresh = cv::Mat::zeros(gray.size(), gray.type());
    thresh = gray.mul(gray);
    // 取平均值作为阈值
    Scalar t = mean(thresh);
    cv::Mat mask = cv::Mat::zeros(gray.size(), CV_16UC1);
    mask.setTo(255.0, thresh >= t[0]);
    
    // 参数设置
    int max = 4;
    float bright = light / 100.0f / max;
    float mid = 1.0f + max * bright;
    
    // 边缘平滑过渡
    cv::Mat midrate = cv::Mat::zeros(input.size(), CV_32FC1);
    cv::Mat brightrate = cv::Mat::zeros(input.size(), CV_32FC1);
    for (int i = 0; i < input.rows; ++i)
    {
        uchar *m = mask.ptr<uchar>(i);
        float *th = thresh.ptr<float>(i);
        float *mi = midrate.ptr<float>(i);
        float *br = brightrate.ptr<float>(i);
        for (int j = 0; j < input.cols; ++j)
        {
            if (m[j] == 255.0)
            {
                mi[j] = mid;
                br[j] = bright;
            }
            else {
                mi[j] = (mid - 1.0f) / t[0] * th[j] + 1.0f;
                br[j] = (1.0f / t[0] * th[j])*bright;
            }
        }
    }
    
    // 高光提亮，获取结果图
    cv::Mat result = cv::Mat::zeros(input.size(), input.type());
    for (int i = 0; i < input.rows; i++)
    {
        float *mi = midrate.ptr<float>(i);
        float *br = brightrate.ptr<float>(i);
        for (int j = 0; j < input.cols; j++)
        {
            for (int k = 0; k < 3; k++)
            {
                float temp = pow(float(input.at<cv::Vec4b>(i,j)[k]) / UCHAR_MAX, 1.0f / mi[j])*(1.0 / (1 - br[j]));
                if (temp > 1.0f)temp = 1.0f;
                if (temp < 0.0f) temp = 0.0f;
                uchar utemp = uchar(UCHAR_MAX*temp);
                result.at<cv::Vec4b>(i,j)[k] = utemp;
            }
            result.at<cv::Vec4b>(i,j)[3] = input.at<cv::Vec4b>(i,j)[3];
        }
    }
    
    return  [CVTools UIImageFromCVMat:result];
    
}

5.阴影

// 调节阴影
/// - Parameters:
///   - inputImage: inputImage
///   - value: value
+(UIImage *)shadowInPutImage:(UIImage *)inputImage value:(CGFloat)light{
    
    Mat input =  [CVTools cvMatFromUIImage:inputImage];
    // 生成灰度图
    cv::Mat gray = cv::Mat::zeros(input.size(), CV_32FC1);
    cv::Mat f = input.clone();
    f.convertTo(f, CV_32FC3);
    vector<cv::Mat> pics;
    split(f, pics);
    gray = 0.299f*pics[2] + 0.587*pics[2] + 0.114*pics[0];
    gray = gray / 255.0;
    
    // 确定阴影区
    cv::Mat thresh = cv::Mat::zeros(gray.size(), gray.type());
    thresh = (1.0f - gray).mul(1.0f - gray);
    // 取平均值作为阈值
    Scalar t = mean(thresh);
    cv::Mat mask = cv::Mat::zeros(gray.size(), CV_16UC1);
    mask.setTo(35535, thresh >= t[0]);
    
    // 参数设置
    int max = 4;
    float bright = light / 100.0f / max;
    float mid = 1.0f + max * bright;
    
    // 边缘平滑过渡
    cv::Mat midrate = cv::Mat::zeros(input.size(), CV_32FC1);
    cv::Mat brightrate = cv::Mat::zeros(input.size(), CV_32FC1);
    for (int i = 0; i < input.rows; ++i)
    {
        uchar *m = mask.ptr<uchar>(i);
        float *th = thresh.ptr<float>(i);
        float *mi = midrate.ptr<float>(i);
        float *br = brightrate.ptr<float>(i);
        for (int j = 0; j < input.cols; ++j)
        {
            if (m[j] == 255.0)
            {
                mi[j] = mid;
                br[j] = bright;
            }
            else {
                mi[j] = (mid - 1.0f) / t[0] * th[j]+ 1.0f;
                br[j] = (1.0f / t[0] * th[j])*bright;
            }
        }
    }
    
    // 阴影提亮，获取结果图
    cv::Mat result = cv::Mat::zeros(input.size(), input.type());
    for (int i = 0; i < input.rows; ++i)
    {
        float *mi = midrate.ptr<float>(i);
        float *br = brightrate.ptr<float>(i);
        for (int j = 0; j < input.cols; ++j)
        {
            for (int k = 0; k < 3; ++k)
            {
                float temp = pow(float(input.at<cv::Vec4b>(i,j)[k]) / UCHAR_MAX, 1.0f / mi[j])*(1.0 / (1 - br[j]));
                if (temp > 1.0f)
                    temp = 1.0f;
                if (temp < 0.0f)
                    temp = 0.0f;
                uchar utemp = uchar(UCHAR_MAX*temp);
                result.at<cv::Vec4b>(i,j)[k] = utemp;
            }
            result.at<cv::Vec4b>(i,j)[3] = input.at<cv::Vec4b>(i,j)[3];
        }
    }
    
    return [CVTools UIImageFromCVMat:result];
    
}

6.饱和度

将RGB颜色空间转换为HLS颜色空间，HLS空间三个分量分别是色相（H）、亮度（L）、饱和度（S），通过调整S通道的数值来达到修改色相的目的，代码中有8位转32位浮点数的操作，是因为在iOS端Opencv只支持处理8位或者32位的浮点数，因项目中使用的是16位的图片，所以需要转成32位的归一化的浮点数格式来进行处理，如果是8位图片，可以考虑去除掉该转换。

//调节饱和度
+(UIImage *)saturationInPutImage:(UIImage *)inputImage value:(CGFloat)value{
    cv::Mat originImgMat = [CVTools cvMatFromUIImage:inputImage];
    
    //u8 to 32f
    cv::Mat folatImgMat;
    originImgMat.convertTo(folatImgMat,CV_32FC4,1/255.0);

    //32f to hls
    cv::Mat hlsMat = Mat(folatImgMat.size(),CV_32FC3);
    cvtColor(folatImgMat, hlsMat, COLOR_RGB2HLS);

    //处理S通道
    for (int row=0; row < originImgMat.rows; row++) {
        for (int col=0; col < originImgMat.cols; col++) { //hlsMat.at<Vec3f>(row,col)[1]
            CGFloat sValue = hlsMat.at<Vec3f>(row,col)[2] + value;
            if(sValue < 0){
                sValue = 0;
            }
            if(sValue > 1){
                sValue = 1;
            }
            hlsMat.at<Vec3f>(row,col)[2] = sValue;
        }
    }

    //hls to 32f
    cv::Mat hlsFloatMat = Mat(folatImgMat.size(),CV_32FC4);
    cvtColor(hlsMat, hlsFloatMat, COLOR_HLS2RGB);

    //32f to u8
    cv::Mat reslutMat = Mat(folatImgMat.size(),CV_8UC4);
    hlsFloatMat.convertTo(reslutMat,CV_8UC4,255.0);
    
    return [CVTools UIImageFromCVMat:reslutMat];
}

7.色温

增加红绿通道，则颜色偏暖；减少红绿通道，则颜色偏冷，以达到修改色温的目的

// 调节色温
+(UIImage *)colorTemperatureInPutImage:(UIImage *)inputImage value:(CGFloat)value{
    Mat input =  [CVTools cvMatFromUIImage:inputImage];
    //    cv::Mat result = input.clone();
    Mat result=Mat::zeros(input.size(),input.type());
    
    int row = input.rows;
    int col = input.cols;
    for (int i = 0; i < row; i++)
    {
        for (int j = 0; j < col; j++)
        {
            result.at<cv::Vec4b>(i, j)[0] = saturate_cast<uchar>(input.at<cv::Vec4b>(i, j)[0] + value*1.3);
            result.at<cv::Vec4b>(i, j)[1] = saturate_cast<uchar>(input.at<cv::Vec4b>(i, j)[1] + value*1.3 );
            result.at<cv::Vec4b>(i, j)[2] = saturate_cast<uchar>(input.at<cv::Vec4b>(i, j)[2]);
            //处理alpha通道
            result.at<Vec4b>(i,j)[3] = input.at<cv::Vec4b>(i,j)[3];
        }
    }
    return [CVTools UIImageFromCVMat:result];
}

8.色相

与调整饱和度逻辑类似，将RGB颜色空间转换为HLS颜色空间，HLS空间三个分量分别是色相（H）、亮度（L）、饱和度（S），通过调整H通道的数值来达到修改色相的目的。

/// 调节色相
+(UIImage *)hueInPutImage:(UIImage *)inputImage value:(CGFloat)value{
    cv::Mat originImgMat = [CVTools cvMatFromUIImage:inputImage];
    
    //u8 to 32f
    cv::Mat folatImgMat;
    originImgMat.convertTo(folatImgMat,CV_32FC4,1/255.0);

    //32f to hls
    cv::Mat hlsMat = Mat(folatImgMat.size(),CV_32FC3);
    cvtColor(folatImgMat, hlsMat, COLOR_RGB2HLS);

    //处理H通道
    for (int row=0; row < originImgMat.rows; row++) {
        for (int col=0; col < originImgMat.cols; col++) { //hlsMat.at<Vec3f>(row,col)[1]
            CGFloat hue = hlsMat.at<Vec3f>(row,col)[0] + value;
            if(hue < 0){
                hue = 0;
            }
            if(hue > 360){
                hue = 360;
            }
            hlsMat.at<Vec3f>(row,col)[0] = hue;
        }
    }

    //hls to 32f
    cv::Mat hlsFloatMat = Mat(folatImgMat.size(),CV_32FC4);
    cvtColor(hlsMat, hlsFloatMat, COLOR_HLS2RGB);

    //32f to u8
    cv::Mat reslutMat = Mat(folatImgMat.size(),CV_8UC4);
    hlsFloatMat.convertTo(reslutMat,CV_8UC4,255.0);
    
    return [CVTools UIImageFromCVMat:reslutMat];
}

代码中用到的cvMatFromUIImage方法及UIImageFromCVMat方法可移步OpenCV在iOS端的集成及Mat和UIImage互相转化（附源码）
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