更多详细信息请看：OpenCV专栏：翟天保Steven
一、多功能色彩调整
1.1、亮度
//--------------------------------------------------------------------------------
// 亮度与对比度
cv::Mat Brightness(cv::Mat src, float brightness, int contrast)
{
cv::Mat dst;
dst = cv::Mat::zeros(src.size(), src.type()); //新建空白模板：大小/类型与原图像一致，像素值全0。
int height = src.rows; //获取图像高度
int width = src.cols; //获取图像宽度
float alpha = brightness; //亮度（0~1为暗，1~正无穷为亮）
float beta = contrast; //对比度
cv::Mat template1;
src.convertTo(template1, CV_32F); //将CV_8UC1转换为CV32F1数据格式。
for (int row = 0; row < height; row++)
{
for (int col = 0; col < width; col++)
{
if (src.channels() == 3)
{
float b = template1.at<cv::Vec3f>(row, col)[0]; //获取通道的像素值（blue）
float g = template1.at<cv::Vec3f>(row, col)[1]; //获取通道的像素值（green）
float r = template1.at<cv::Vec3f>(row, col)[2]; //获取通道的像素值（red）
//cv::saturate_cast<uchar>(vaule)：需注意，value值范围必须在0~255之间。
dst.at<cv::Vec3b>(row, col)[0] = cv::saturate_cast<uchar>(b * alpha + beta); //修改通道的像素值（blue）
dst.at<cv::Vec3b>(row, col)[1] = cv::saturate_cast<uchar>(g * alpha + beta); //修改通道的像素值（green）
dst.at<cv::Vec3b>(row, col)[2] = cv::saturate_cast<uchar>(r * alpha + beta); //修改通道的像素值（red）
}
else if (src.channels() == 1)
{
float v = src.at<uchar>(row, col); //获取通道的像素值（单）
dst.at<uchar>(row, col) = cv::saturate_cast<uchar>(v * alpha + beta); //修改通道的像素值（单）
//saturate_cast<uchar>：主要是为了防止颜色溢出操作。如果color<0，则color等于0；如果color>255，则color等于255。
}
}
}
return dst;
}
1.2、对比度
//--------------------------------------------------------------------------------
// 亮度与对比度
cv::Mat Brightness(cv::Mat src, float brightness, int contrast)
{
cv::Mat dst;
dst = cv::Mat::zeros(src.size(), src.type()); //新建空白模板：大小/类型与原图像一致，像素值全0。
int height = src.rows; //获取图像高度
int width = src.cols; //获取图像宽度
float alpha = brightness; //亮度（0~1为暗，1~正无穷为亮）
float beta = contrast; //对比度
cv::Mat template1;
src.convertTo(template1, CV_32F); //将CV_8UC1转换为CV32F1数据格式。
for (int row = 0; row < height; row++)
{
for (int col = 0; col < width; col++)
{
if (src.channels() == 3)
{
float b = template1.at<cv::Vec3f>(row, col)[0]; //获取通道的像素值（blue）
float g = template1.at<cv::Vec3f>(row, col)[1]; //获取通道的像素值（green）
float r = template1.at<cv::Vec3f>(row, col)[2]; //获取通道的像素值（red）
//cv::saturate_cast<uchar>(vaule)：需注意，value值范围必须在0~255之间。
dst.at<cv::Vec3b>(row, col)[0] = cv::saturate_cast<uchar>(b * alpha + beta); //修改通道的像素值（blue）
dst.at<cv::Vec3b>(row, col)[1] = cv::saturate_cast<uchar>(g * alpha + beta); //修改通道的像素值（green）
dst.at<cv::Vec3b>(row, col)[2] = cv::saturate_cast<uchar>(r * alpha + beta); //修改通道的像素值（red）
}
else if (src.channels() == 1)
{
float v = src.at<uchar>(row, col); //获取通道的像素值（单）
dst.at<uchar>(row, col) = cv::saturate_cast<uchar>(v * alpha + beta); //修改通道的像素值（单）
//saturate_cast<uchar>：主要是为了防止颜色溢出操作。如果color<0，则color等于0；如果color>255，则color等于255。
}
}
}
return dst;
}
1.3、饱和度
//--------------------------------------------------------------------------------
// 饱和度
cv::Mat Saturation(cv::Mat src, int saturation)
{
float Increment = saturation * 1.0f / 100;
cv::Mat temp = src.clone();
int row = src.rows;
int col = src.cols;
for (int i = 0; i < row; ++i)
{
uchar *t = temp.ptr<uchar>(i);
uchar *s = src.ptr<uchar>(i);
for (int j = 0; j < col; ++j)
{
uchar b = s[3 * j];
uchar g = s[3 * j + 1];
uchar r = s[3 * j + 2];
float max = max3(r, g, b);
float min = min3(r, g, b);
float delta, value;
float L, S, alpha;
delta = (max - min) / 255;
if (delta == 0)
continue;
value = (max + min) / 255;
L = value / 2;
if (L < 0.5)
S = delta / value;
else
S = delta / (2 - value);
if (Increment >= 0)
{
if ((Increment + S) >= 1)
alpha = S;
else
alpha = 1 - Increment;
alpha = 1 / alpha - 1;
t[3 * j + 2] =static_cast<uchar>( r + (r - L * 255) * alpha);
t[3 * j + 1] = static_cast<uchar>(g + (g - L * 255) * alpha);
t[3 * j] = static_cast<uchar>(b + (b - L * 255) * alpha);
}
else
{
alpha = Increment;
t[3 * j + 2] = static_cast<uchar>(L * 255 + (r - L * 255) * (1 + alpha));
t[3 * j + 1] = static_cast<uchar>(L * 255 + (g - L * 255) * (1 + alpha));
t[3 * j] = static_cast<uchar>(L * 255 + (b - L * 255) * (1 + alpha));
}
}
}
return temp;
}
1.4、高光
//--------------------------------------------------------------------------------
// 高光
cv::Mat HighLight(cv::Mat src, int highlight)
{
// 生成灰度图
cv::Mat gray = cv::Mat::zeros(src.size(), CV_32FC1);
cv::Mat f = src.clone();
f.convertTo(f, CV_32FC3);
std::vector<cv::Mat> pics;
split(f, pics);
gray = 0.299f*pics[2] + 0.587*pics[2] + 0.114*pics[0];
gray = gray / 255.f;
// 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
cv::Scalar t = mean(thresh);
cv::Mat mask = cv::Mat::zeros(gray.size(), CV_8UC1);
mask.setTo(255, thresh >= t[0]);
// 参数设置
int max = 4;
float bright = highlight / 100.0f / max;
float mid = 1.0f + max * bright;
// 边缘平滑过渡
cv::Mat midrate = cv::Mat::zeros(src.size(), CV_32FC1);
cv::Mat brightrate = cv::Mat::zeros(src.size(), CV_32FC1);
for (int i = 0; i < src.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 < src.cols; ++j)
{
if (m[j] == 255)
{
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(src.size(), src.type());
for (int i = 0; i < src.rows; ++i)
{
float *mi = midrate.ptr<float>(i);
float *br = brightrate.ptr<float>(i);
uchar *in = src.ptr<uchar>(i);
uchar *r = result.ptr<uchar>(i);
for (int j = 0; j < src.cols; ++j)
{
for (int k = 0; k < 3; ++k)
{
float temp = pow(float(in[3 * j + k]) / 255.f, 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(255*temp);
r[3 * j + k] = utemp;
}
}
}
return result;
}
1.5、暖色调
//--------------------------------------------------------------------------------
// 暖色调
cv::Mat ColorTemperature(cv::Mat src, int warm)
{
cv::Mat result = src.clone();
int row = src.rows;
int col = src.cols;
int level = warm/2;
for (int i = 0; i < row; ++i)
{
uchar* a = src.ptr<uchar>(i);
uchar* r = result.ptr<uchar>(i);
for (int j = 0; j < col; ++j)
{
int R,G,B;
// R通道
R = a[j * 3 + 2];
R = R + level;
if (R > 255) {
r[j * 3 + 2] = 255;
}
else if (R < 0) {
r[j * 3 + 2] = 0;
}
else {
r[j * 3 + 2] = R;
}
// G通道
G = a[j * 3 + 1];
G = G + level;
if (G > 255) {
r[j * 3 + 1] = 255;
}
else if (G < 0) {
r[j * 3 + 1] = 0;
}
else {
r[j * 3 + 1] = G;
}
// B通道
B = a[j * 3];
B = B - level;
if (B > 255) {
r[j * 3] = 255;
}
else if (B < 0) {
r[j * 3] = 0;
}
else {
r[j * 3] = B;
}
}
}
return result;
}
1.6、阴影
//--------------------------------------------------------------------------------
// 阴影
cv::Mat Shadow(cv::Mat src, int shadow)
{
// 生成灰度图
cv::Mat gray = cv::Mat::zeros(src.size(), CV_32FC1);
cv::Mat f = src.clone();
f.convertTo(f, CV_32FC3);
std::vector<cv::Mat> pics;
split(f, pics);
gray = 0.299f*pics[2] + 0.587*pics[2] + 0.114*pics[0];
gray = gray / 255.f;
// 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
cv::Scalar t = mean(thresh);
cv::Mat mask = cv::Mat::zeros(gray.size(), CV_8UC1);
mask.setTo(255, thresh >= t[0]);
// 参数设置
int max = 4;
float bright = shadow / 100.0f / max;
float mid = 1.0f + max * bright;
// 边缘平滑过渡
cv::Mat midrate = cv::Mat::zeros(src.size(), CV_32FC1);
cv::Mat brightrate = cv::Mat::zeros(src.size(), CV_32FC1);
for (int i = 0; i < src.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 < src.cols; ++j)
{
if (m[j] == 255)
{
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(src.size(), src.type());
for (int i = 0; i < src.rows; ++i)
{
float *mi = midrate.ptr<float>(i);
float *br = brightrate.ptr<float>(i);
uchar *in = src.ptr<uchar>(i);
uchar *r = result.ptr<uchar>(i);
for (int j = 0; j < src.cols; ++j)
{
for (int k = 0; k < 3; ++k)
{
float temp = pow(float(in[3 * j + k]) / 255.f, 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(255*temp);
r[3 * j + k] = utemp;
}
}
}
return result;
}
1.7、漫画效果
//--------------------------------------------------------------------------------
// 漫画效果
cv::Mat Cartoon(cv::Mat src, double clevel, int d, double sigma, int size)
{
// 中值滤波
cv::Mat m;
cv::medianBlur(src, m, 7);
// 提取轮廓
cv::Mat c;
clevel = cv::max(40., cv::min(80., clevel));
cv::Canny(m, c, clevel, clevel *3);
// 轮廓膨胀加深
cv::Mat k = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
cv::dilate(c, c, k);
// 反转
c = c / 255;
c = 1 - c;
// 类型转化
cv::Mat cf;
c.convertTo(cf, CV_32FC1);
// 均值滤波
cv::blur(cf, cf, cv::Size(5, 5));
// 双边滤波
cv::Mat srcb;
d = cv::max(0, cv::min(10, d));
sigma = cv::max(10., cv::min(250., sigma));
cv::bilateralFilter(src, srcb, d, sigma, sigma);
size = cv::max(10, cv::min(25, size));
cv::Mat temp = srcb / size;
temp = temp * size;
// 通道合并
cv::Mat c3;
cv::Mat cannyChannels[] = { cf, cf, cf };
cv::merge(cannyChannels, 3, c3);
// 类型转化
cv::Mat tempf;
temp.convertTo(tempf, CV_32FC3);
// 图像相乘
cv::multiply(tempf, c3, tempf);
// 类型转化
tempf.convertTo(temp, CV_8UC3);
return temp;
}
1.8、白平衡-灰度世界
//--------------------------------------------------------------------------------
// 白平衡-灰度世界
cv::Mat WhiteBalcane_Gray(cv::Mat src)
{
cv::Mat result = src.clone();
if (src.channels() != 3)
{
std::cout << "The number of image channels is not 3." << std::endl;
return result;
}
// 通道分离
std::vector<cv::Mat> Channel;
cv::split(src, Channel);
// 计算通道灰度值均值
double Bm = cv::mean(Channel[0])[0];
double Gm = cv::mean(Channel[1])[0];
double Rm = cv::mean(Channel[2])[0];
double Km = (Bm + Gm + Rm) / 3;
// 通道灰度值调整
Channel[0] *= Km / Bm;
Channel[1] *= Km / Gm;
Channel[2] *= Km / Rm;
// 合并通道
cv::merge(Channel, result);
return result;
}
1.9、白平衡-完美反射
//--------------------------------------------------------------------------------
// 白平衡-完美反射
cv::Mat WhiteBalcane_PRA(cv::Mat src)
{
cv::Mat result = src.clone();
if (src.channels() != 3)
{
std::cout << "The number of image channels is not 3." << std::endl;
return result;
}
// 通道分离
std::vector<cv::Mat> Channel;
cv::split(src, Channel);
// 定义参数
int row = src.rows;
int col = src.cols;
int RGBSum[766] = { 0 };
uchar maxR, maxG, maxB;
// 计算单通道最大值
for (int i = 0; i < row; ++i)
{
uchar *b = Channel[0].ptr<uchar>(i);
uchar *g = Channel[1].ptr<uchar>(i);
uchar *r = Channel[2].ptr<uchar>(i);
for (int j = 0; j < col; ++j)
{
int sum = b[j] + g[j] + r[j];
RGBSum[sum]++;
maxB = cv::max(maxB, b[j]);
maxG = cv::max(maxG, g[j]);
maxR = cv::max(maxR, r[j]);
}
}
// 计算最亮区间下限T
int T = 0;
int num = 0;
int K = static_cast<int>(row * col * 0.1);
for (int i = 765; i >= 0; --i)
{
num += RGBSum[i];
if (num > K)
{
T = i;
break;
}
}
// 计算单通道亮区平均值
double Bm = 0.0, Gm = 0.0, Rm = 0.0;
int count = 0;
for (int i = 0; i < row; ++i)
{
uchar *b = Channel[0].ptr<uchar>(i);
uchar *g = Channel[1].ptr<uchar>(i);
uchar *r = Channel[2].ptr<uchar>(i);
for (int j = 0; j < col; ++j)
{
int sum = b[j] + g[j] + r[j];
if (sum > T)
{
Bm += b[j];
Gm += g[j];
Rm += r[j];
count++;
}
}
}
Bm /= count;
Gm /= count;
Rm /= count;
// 通道调整
Channel[0] *= maxB / Bm;
Channel[1] *= maxG / Gm;
Channel[2] *= maxR / Rm;
// 合并通道
cv::merge(Channel, result);
return result;
}
1.10、浮雕
//--------------------------------------------------------------------------------
// 浮雕
cv::Mat Relief(cv::Mat src)
{
CV_Assert(src.channels() == 3);
int row = src.rows;
int col = src.cols;
cv::Mat temp = src.clone();
for (int i = 1; i < row-1; ++i)
{
uchar *s1 = src.ptr<uchar>(i - 1);
uchar *s2 = src.ptr<uchar>(i + 1);
uchar *t = temp.ptr<uchar>(i);
for (int j = 1; j < col-1; ++j)
{
for (int k = 0; k < 3; ++k)
{
int RGB = s1[3 * (j - 1) + k] - s2[3 * (j + 1) + k] + 128;
if (RGB < 0)RGB = 0;
if (RGB > 255)RGB = 255;
t[3*j+k] =(uchar)RGB;
}
}
}
return temp;
}
1.11、羽化
//--------------------------------------------------------------------------------
// 羽化
cv::Mat Eclosion(cv::Mat src, cv::Point center, float level)
{
if (level > 0.9)
level = 0.9f;
float diff = (1-level) * (src.rows / 2 * src.rows / 2 + src.cols / 2 * src.cols / 2);
cv::Mat result = src.clone();
for (int i = 0; i < result.rows; ++i)
{
for (int j = 0; j < result.cols; ++j)
{
float dx = float(center.x - j);
float dy = float(center.y - i);
float ra = dx * dx + dy * dy;
float m = ((ra-diff) / diff * 255)>0? ((ra - diff) / diff * 255):0;
int b = result.at<cv::Vec3b>(i, j)[0];
int g = result.at<cv::Vec3b>(i, j)[1];
int r = result.at<cv::Vec3b>(i, j)[2];
b = (int)(b+ m);
g = (int)(g + m);
r = (int)(r + m);
result.at<cv::Vec3b>(i, j)[0] = (b > 255 ? 255 : (b < 0 ? 0 : b));
result.at<cv::Vec3b>(i, j)[1] = (g > 255 ? 255 : (g < 0 ? 0 : g));
result.at<cv::Vec3b>(i, j)[2] = (r > 255 ? 255 : (r < 0 ? 0 : r));
}
}
return result;
}
1.12、锐化
//--------------------------------------------------------------------------------
// 锐化
cv::Mat Sharpen(cv::Mat input, int percent, int type)
{
cv::Mat result;
cv::Mat s = input.clone();
cv::Mat kernel;
switch (type)
{
case 0:
kernel = (cv::Mat_<int>(3, 3) <<
0, -1, 0,
-1, 4, -1,
0, -1, 0
);
case 1:
kernel = (cv::Mat_<int>(3, 3) <<
-1, -1, -1,
-1, 8, -1,
-1, -1, -1
);
default:
kernel = (cv::Mat_<int>(3, 3) <<
0, -1, 0,
-1, 4, -1,
0, -1, 0
);
}
cv::filter2D(s, s, s.depth(), kernel);
result = input + s * 0.01 * percent;
return result;
}
1.13、颗粒感
文章来源：https://www.toymoban.com/news/detail-757543.html
//--------------------------------------------------------------------------------
// 颗粒感
cv::Mat Grainy(cv::Mat src, int level)
{
int row = src.rows;
int col = src.cols;
if (level > 100)
level = 100;
if (level < 0)
level = 0;
cv::Mat result = src.clone();
for (int i = 0; i < row; ++i)
{
uchar *t = result.ptr<uchar>(i);
for (int j = 0; j < col; ++j)
{
for (int k = 0; k < 3; ++k)
{
int temp = t[3 * j + k];
temp += ((rand() % (2 * level)) - level);
if (temp < 0)temp = 0;
if (temp > 255)temp = 255;
t[3 * j + k] = temp;
}
}
}
return result;
}
二、实战案例
2.1、主函数
文章来源地址https://www.toymoban.com/news/detail-757543.html
#include<opencv2\opencv.hpp>
//using namespace cv;
//using namespace std;
#define max2(a,b) (a>b?a:b)
#define max3(a,b,c) (a>b?max2(a,c):max2(b,c))
#define min2(a,b) (a<b?a:b)
#define min3(a,b,c) (a<b?min2(a,c):min2(b,c))
//函数申明
cv::Mat Brightness(cv::Mat src, float brightness, int contrast); //亮度+对比度。
cv::Mat Saturation(cv::Mat src, int saturation); //饱和度
cv::Mat HighLight(cv::Mat src, int highlight); //高光
cv::Mat ColorTemperature(cv::Mat src, int warm); //暖色调
cv::Mat Shadow(cv::Mat src, int shadow); //阴影
cv::Mat Sharpen(cv::Mat input, int percent, int type); //图像锐化
cv::Mat Grainy(cv::Mat src, int level); //颗粒感
cv::Mat Cartoon(cv::Mat src, double clevel, int d, double sigma, int size); //漫画效果
cv::Mat WhiteBalcane_PRA(cv::Mat src); //白平衡-完美反射算法（效果偏白）
cv::Mat WhiteBalcane_Gray(cv::Mat src); //白平衡-灰度世界算法（效果偏蓝）
cv::Mat Relief(cv::Mat src); //浮雕
cv::Mat Eclosion(cv::Mat src, cv::Point center, float level); //羽化
int main(int argc, char* argv[])
{
//（1）读取图像
std::string img_path = "test.jpg";
cv::Mat src = cv::imread(img_path, 1);
//（2）判断图像是否读取成功
if (!src.data)
{
std::cout << "can't read image!" << std::endl;
return -1;
}
float brightness_value = 1; //[0, 10] 亮度。暗~亮：[0, 1] ~ [1, 10]
int contrast_value = 0; //[-100, 100] 对比度。
int saturation_value = 0; //[-100, 100] 饱和度。
int highlight_value = 0; //[-100, 100] 高光。
int warm_value = 0; //[-100, 100] 暖色调。
int shadow_value = 0; //[-100, 100] 阴影。
int sharpen_value = 0; //[-100, 100] 锐化。[-1000000, 1000000]
int grainy_value = 0; //[0, 100] 颗粒感。
int eclosion_flag = 0; //[0, 1] 羽化。
int cartoon_flag = 0; //[0, 1] 漫画效果。clevel阈值40-80，d阈值0-10，sigma阈值10-250，size阈值10-25
int reflect_flag = 0; //[0, 1] 白平衡-完美反射。
int world_flag = 0; //[0, 1] 白平衡-灰度世界。
int relief_flag = 0; //[0, 1] 浮雕。
cv::Mat dst = src.clone();
if (brightness_value != 1)
dst = Brightness(dst, brightness_value, 0);
if (contrast_value != 0)
dst = Brightness(dst, 1, contrast_value);
if (saturation_value != 0)
dst = Saturation(dst, saturation_value);
if (highlight_value != 0)
dst = HighLight(dst, highlight_value);
if (warm_value != 0)
dst = ColorTemperature(dst, warm_value);
if (shadow_value != 0)
dst = Shadow(dst, shadow_value);
if (sharpen_value != 0)
dst = Sharpen(dst, sharpen_value, 0);
if (grainy_value != 0)
dst = Grainy(dst, grainy_value);
if (cartoon_flag != 0)
dst = Cartoon(dst, 80, 5, 150, 20); //clevel阈值40-80，d阈值0-10，sigma阈值10-250，size阈值10-25说
if (reflect_flag != 0)
dst = WhiteBalcane_PRA(dst);
if (world_flag != 0)
dst = WhiteBalcane_Gray(dst);
if (relief_flag != 0)
dst = Relief(dst);
if (eclosion_flag != 0)
dst = Eclosion(dst, cv::Point(src.cols / 2, src.rows / 2), 0.8f);
//（4）显示图像
cv::imshow("src", src);
cv::imshow("锐化", dst);
cv::waitKey(0); //等待用户任意按键后结束暂停功能
return 0;
}
2.2、函数定义
//--------------------------------------------------------------------------------
//调整对比度与亮度
cv::Mat Brightness(cv::Mat src, float brightness, int contrast)
{
cv::Mat dst;
dst = cv::Mat::zeros(src.size(), src.type()); //新建空白模板：大小/类型与原图像一致，像素值全0。
int height = src.rows; //获取图像高度
int width = src.cols; //获取图像宽度
float alpha = brightness; //亮度（0~1为暗，1~正无穷为亮）
float beta = contrast; //对比度
cv::Mat template1;
src.convertTo(template1, CV_32F); //将CV_8UC1转换为CV32F1数据格式。
for (int row = 0; row < height; row++)
{
for (int col = 0; col < width; col++)
{
if (src.channels() == 3)
{
float b = template1.at<cv::Vec3f>(row, col)[0]; //获取通道的像素值（blue）
float g = template1.at<cv::Vec3f>(row, col)[1]; //获取通道的像素值（green）
float r = template1.at<cv::Vec3f>(row, col)[2]; //获取通道的像素值（red）
//cv::saturate_cast<uchar>(vaule)：需注意，value值范围必须在0~255之间。
dst.at<cv::Vec3b>(row, col)[0] = cv::saturate_cast<uchar>(b * alpha + beta); //修改通道的像素值（blue）
dst.at<cv::Vec3b>(row, col)[1] = cv::saturate_cast<uchar>(g * alpha + beta); //修改通道的像素值（green）
dst.at<cv::Vec3b>(row, col)[2] = cv::saturate_cast<uchar>(r * alpha + beta); //修改通道的像素值（red）
}
else if (src.channels() == 1)
{
float v = src.at<uchar>(row, col); //获取通道的像素值（单）
dst.at<uchar>(row, col) = cv::saturate_cast<uchar>(v * alpha + beta); //修改通道的像素值（单）
//saturate_cast<uchar>：主要是为了防止颜色溢出操作。如果color<0，则color等于0；如果color>255，则color等于255。
}
}
}
return dst;
}
//--------------------------------------------------------------------------------
// 饱和度
cv::Mat Saturation(cv::Mat src, int saturation)
{
float Increment = saturation * 1.0f / 100;
cv::Mat temp = src.clone();
int row = src.rows;
int col = src.cols;
for (int i = 0; i < row; ++i)
{
uchar *t = temp.ptr<uchar>(i);
uchar *s = src.ptr<uchar>(i);
for (int j = 0; j < col; ++j)
{
uchar b = s[3 * j];
uchar g = s[3 * j + 1];
uchar r = s[3 * j + 2];
float max = max3(r, g, b);
float min = min3(r, g, b);
float delta, value;
float L, S, alpha;
delta = (max - min) / 255;
if (delta == 0)
continue;
value = (max + min) / 255;
L = value / 2;
if (L < 0.5)
S = delta / value;
else
S = delta / (2 - value);
if (Increment >= 0)
{
if ((Increment + S) >= 1)
alpha = S;
else
alpha = 1 - Increment;
alpha = 1 / alpha - 1;
t[3 * j + 2] =static_cast<uchar>( r + (r - L * 255) * alpha);
t[3 * j + 1] = static_cast<uchar>(g + (g - L * 255) * alpha);
t[3 * j] = static_cast<uchar>(b + (b - L * 255) * alpha);
}
else
{
alpha = Increment;
t[3 * j + 2] = static_cast<uchar>(L * 255 + (r - L * 255) * (1 + alpha));
t[3 * j + 1] = static_cast<uchar>(L * 255 + (g - L * 255) * (1 + alpha));
t[3 * j] = static_cast<uchar>(L * 255 + (b - L * 255) * (1 + alpha));
}
}
}
return temp;
}
//--------------------------------------------------------------------------------
// 高光
cv::Mat HighLight(cv::Mat src, int highlight)
{
// 生成灰度图
cv::Mat gray = cv::Mat::zeros(src.size(), CV_32FC1);
cv::Mat f = src.clone();
f.convertTo(f, CV_32FC3);
std::vector<cv::Mat> pics;
split(f, pics);
gray = 0.299f*pics[2] + 0.587*pics[2] + 0.114*pics[0];
gray = gray / 255.f;
// 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
cv::Scalar t = mean(thresh);
cv::Mat mask = cv::Mat::zeros(gray.size(), CV_8UC1);
mask.setTo(255, thresh >= t[0]);
// 参数设置
int max = 4;
float bright = highlight / 100.0f / max;
float mid = 1.0f + max * bright;
// 边缘平滑过渡
cv::Mat midrate = cv::Mat::zeros(src.size(), CV_32FC1);
cv::Mat brightrate = cv::Mat::zeros(src.size(), CV_32FC1);
for (int i = 0; i < src.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 < src.cols; ++j)
{
if (m[j] == 255)
{
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(src.size(), src.type());
for (int i = 0; i < src.rows; ++i)
{
float *mi = midrate.ptr<float>(i);
float *br = brightrate.ptr<float>(i);
uchar *in = src.ptr<uchar>(i);
uchar *r = result.ptr<uchar>(i);
for (int j = 0; j < src.cols; ++j)
{
for (int k = 0; k < 3; ++k)
{
float temp = pow(float(in[3 * j + k]) / 255.f, 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(255*temp);
r[3 * j + k] = utemp;
}
}
}
return result;
}
//--------------------------------------------------------------------------------
// 暖色调
cv::Mat ColorTemperature(cv::Mat src, int warm)
{
cv::Mat result = src.clone();
int row = src.rows;
int col = src.cols;
int level = warm/2;
for (int i = 0; i < row; ++i)
{
uchar* a = src.ptr<uchar>(i);
uchar* r = result.ptr<uchar>(i);
for (int j = 0; j < col; ++j)
{
int R,G,B;
// R通道
R = a[j * 3 + 2];
R = R + level;
if (R > 255)
{
r[j * 3 + 2] = 255;
}
else if (R < 0)
{
r[j * 3 + 2] = 0;
}
else
{
r[j * 3 + 2] = R;
}
// G通道
G = a[j * 3 + 1];
G = G + level;
if (G > 255)
{
r[j * 3 + 1] = 255;
}
else if (G < 0)
{
r[j * 3 + 1] = 0;
}
else
{
r[j * 3 + 1] = G;
}
// B通道
B = a[j * 3];
B = B - level;
if (B > 255)
{
r[j * 3] = 255;
}
else if (B < 0)
{
r[j * 3] = 0;
}
else {
r[j * 3] = B;
}
}
}
return result;
}
//--------------------------------------------------------------------------------
// 阴影
cv::Mat Shadow(cv::Mat src, int shadow)
{
// 生成灰度图
cv::Mat gray = cv::Mat::zeros(src.size(), CV_32FC1);
cv::Mat f = src.clone();
f.convertTo(f, CV_32FC3);
std::vector<cv::Mat> pics;
split(f, pics);
gray = 0.299f*pics[2] + 0.587*pics[2] + 0.114*pics[0];
gray = gray / 255.f;
// 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
cv::Scalar t = mean(thresh);
cv::Mat mask = cv::Mat::zeros(gray.size(), CV_8UC1);
mask.setTo(255, thresh >= t[0]);
// 参数设置
int max = 4;
float bright = shadow / 100.0f / max;
float mid = 1.0f + max * bright;
// 边缘平滑过渡
cv::Mat midrate = cv::Mat::zeros(src.size(), CV_32FC1);
cv::Mat brightrate = cv::Mat::zeros(src.size(), CV_32FC1);
for (int i = 0; i < src.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 < src.cols; ++j)
{
if (m[j] == 255)
{
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(src.size(), src.type());
for (int i = 0; i < src.rows; ++i)
{
float *mi = midrate.ptr<float>(i);
float *br = brightrate.ptr<float>(i);
uchar *in = src.ptr<uchar>(i);
uchar *r = result.ptr<uchar>(i);
for (int j = 0; j < src.cols; ++j)
{
for (int k = 0; k < 3; ++k)
{
float temp = pow(float(in[3 * j + k]) / 255.f, 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(255*temp);
r[3 * j + k] = utemp;
}
}
}
return result;
}
//--------------------------------------------------------------------------------
// 漫画效果
cv::Mat Cartoon(cv::Mat src, double clevel, int d, double sigma, int size)
{
//（1）中值滤波
cv::Mat m;
cv::medianBlur(src, m, 7);
//（2）提取轮廓
cv::Mat c;
clevel = cv::max(40., cv::min(80., clevel));
cv::Canny(m, c, clevel, clevel *3);
//（3）轮廓膨胀
cv::Mat k = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
cv::dilate(c, c, k);
//（4）图像反转
c = c / 255;
c = 1 - c;
//（5）均值滤波
cv::Mat cf;
c.convertTo(cf, CV_32FC1); // 类型转换
cv::blur(cf, cf, cv::Size(5, 5));
//（6）双边滤波
cv::Mat srcb;
d = cv::max(0, cv::min(10, d));
sigma = cv::max(10., cv::min(250., sigma));
cv::bilateralFilter(src, srcb, d, sigma, sigma);
size = cv::max(10, cv::min(25, size));
cv::Mat temp = srcb / size;
temp = temp * size;
//（7）通道合并
cv::Mat c3;
cv::Mat cannyChannels[] = { cf, cf, cf };
cv::merge(cannyChannels, 3, c3);
//（8）图像相乘
cv::Mat tempf;
temp.convertTo(tempf, CV_32FC3); // 类型转换
cv::multiply(tempf, c3, tempf);
tempf.convertTo(temp, CV_8UC3); // 类型转换
return temp;
}
//--------------------------------------------------------------------------------
// 白平衡-灰度世界
cv::Mat WhiteBalcane_Gray(cv::Mat src)
{
//（1）3通道处理
cv::Mat result = src.clone();
if (src.channels() != 3)
{
std::cout << "The number of image channels is not 3." << std::endl;
return result;
}
//（2）通道分离
std::vector<cv::Mat> Channel;
cv::split(src, Channel);
//（3）计算通道灰度值均值
double Bm = cv::mean(Channel[0])[0];
double Gm = cv::mean(Channel[1])[0];
double Rm = cv::mean(Channel[2])[0];
double Km = (Bm + Gm + Rm) / 3;
//（4）通道灰度值调整
Channel[0] *= Km / Bm;
Channel[1] *= Km / Gm;
Channel[2] *= Km / Rm;
//（5）通道合并
cv::merge(Channel, result);
return result;
}
//--------------------------------------------------------------------------------
// 白平衡-完美反射
cv::Mat WhiteBalcane_PRA(cv::Mat src)
{
//（1）3通道处理
cv::Mat result = src.clone();
if (src.channels() != 3)
{
std::cout << "The number of image channels is not 3." << std::endl;
return result;
}
//（2）通道分离
std::vector<cv::Mat> Channel;
cv::split(src, Channel);
//（3）计算单通道最大值
int row = src.rows;
int col = src.cols;
int RGBSum[766] = { 0 };
uchar maxR, maxG, maxB;
for (int i = 0; i < row; ++i)
{
uchar *b = Channel[0].ptr<uchar>(i);
uchar *g = Channel[1].ptr<uchar>(i);
uchar *r = Channel[2].ptr<uchar>(i);
for (int j = 0; j < col; ++j)
{
int sum = b[j] + g[j] + r[j];
RGBSum[sum]++;
maxB = cv::max(maxB, b[j]);
maxG = cv::max(maxG, g[j]);
maxR = cv::max(maxR, r[j]);
}
}
//（4）计算最亮区间下限T
int T = 0;
int num = 0;
int K = static_cast<int>(row * col * 0.1);
for (int i = 765; i >= 0; --i)
{
num += RGBSum[i];
if (num > K)
{
T = i;
break;
}
}
//（5）计算单通道亮区平均值
double Bm = 0.0, Gm = 0.0, Rm = 0.0;
int count = 0;
for (int i = 0; i < row; ++i)
{
uchar *b = Channel[0].ptr<uchar>(i);
uchar *g = Channel[1].ptr<uchar>(i);
uchar *r = Channel[2].ptr<uchar>(i);
for (int j = 0; j < col; ++j)
{
int sum = b[j] + g[j] + r[j];
if (sum > T)
{
Bm += b[j];
Gm += g[j];
Rm += r[j];
count++;
}
}
}
Bm /= count;
Gm /= count;
Rm /= count;
//（6）通道调整
Channel[0] *= maxB / Bm;
Channel[1] *= maxG / Gm;
Channel[2] *= maxR / Rm;
//（7）通道合并
cv::merge(Channel, result);
return result;
}
//--------------------------------------------------------------------------------
// 浮雕
cv::Mat Relief(cv::Mat src)
{
CV_Assert(src.channels() == 3);
int row = src.rows;
int col = src.cols;
cv::Mat temp = src.clone();
for (int i = 1; i < row-1; ++i)
{
uchar *s1 = src.ptr<uchar>(i - 1);
uchar *s2 = src.ptr<uchar>(i + 1);
uchar *t = temp.ptr<uchar>(i);
for (int j = 1; j < col-1; ++j)
{
for (int k = 0; k < 3; ++k)
{
int RGB = s1[3 * (j - 1) + k] - s2[3 * (j + 1) + k] + 128;
if (RGB < 0)RGB = 0;
if (RGB > 255)RGB = 255;
t[3*j+k] =(uchar)RGB;
}
}
}
return temp;
}
//--------------------------------------------------------------------------------
// 羽化
cv::Mat Eclosion(cv::Mat src, cv::Point center, float level)
{
if (level > 0.9)
level = 0.9f;
float diff = (1-level) * (src.rows / 2 * src.rows / 2 + src.cols / 2 * src.cols / 2);
cv::Mat result = src.clone();
for (int i = 0; i < result.rows; ++i)
{
for (int j = 0; j < result.cols; ++j)
{
float dx = float(center.x - j);
float dy = float(center.y - i);
float ra = dx * dx + dy * dy;
float m = ((ra-diff) / diff * 255)>0? ((ra - diff) / diff * 255):0;
int b = result.at<cv::Vec3b>(i, j)[0];
int g = result.at<cv::Vec3b>(i, j)[1];
int r = result.at<cv::Vec3b>(i, j)[2];
b = (int)(b+ m);
g = (int)(g + m);
r = (int)(r + m);
result.at<cv::Vec3b>(i, j)[0] = (b > 255 ? 255 : (b < 0 ? 0 : b));
result.at<cv::Vec3b>(i, j)[1] = (g > 255 ? 255 : (g < 0 ? 0 : g));
result.at<cv::Vec3b>(i, j)[2] = (r > 255 ? 255 : (r < 0 ? 0 : r));
}
}
return result;
}
//--------------------------------------------------------------------------------
// 锐化
cv::Mat Sharpen(cv::Mat input, int percent, int type)
{
cv::Mat result;
cv::Mat s = input.clone();
cv::Mat kernel;
switch (type)
{
case 0:
kernel = (cv::Mat_<int>(3, 3) <<
0, -1, 0,
-1, 4, -1,
0, -1, 0
);
case 1:
kernel = (cv::Mat_<int>(3, 3) <<
-1, -1, -1,
-1, 8, -1,
-1, -1, -1
);
default:
kernel = (cv::Mat_<int>(3, 3) <<
0, -1, 0,
-1, 4, -1,
0, -1, 0
);
}
cv::filter2D(s, s, s.depth(), kernel);
result = input + s * 0.01 * percent;
return result;
}
//--------------------------------------------------------------------------------
// 颗粒感
cv::Mat Grainy(cv::Mat src, int level)
{
int row = src.rows;
int col = src.cols;
if (level > 100)
level = 100;
if (level < 0)
level = 0;
cv::Mat result = src.clone();
for (int i = 0; i < row; ++i)
{
uchar *t = result.ptr<uchar>(i);
for (int j = 0; j < col; ++j)
{
for (int k = 0; k < 3; ++k)
{
int temp = t[3 * j + k];
temp += ((rand() % (2 * level)) - level);
if (temp < 0)temp = 0;
if (temp > 255)temp = 255;
t[3 * j + k] = temp;
}
}
}
return result;
}
到了这里，关于Opencv C++图像处理：亮度+对比度+饱和度+高光+暖色调+阴影+漫画效果+白平衡+浮雕+羽化+锐化+颗粒感的文章就介绍完了。如果您还想了解更多内容，请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章，希望大家以后多多支持TOY模板网！
