" The OpenCV Development Notes (V): OpenCV reading operation Camera " may be an image captured by the camera contrast, brightness, adjust the exposure or the like, but require camera hardware itself supports, and Benpian directly to image processing.

Demo demonstration effect

Contrast

Contrast refers to the measurement of different brightness levels of light and dark areas in an image between the brightest and the darkest black and white, the greater the difference in range represents a higher contrast, the smaller the difference the smaller the range representative of comparison, a good contrast ratio 120 : 1 can be easily displayed vivid, rich color, when the ratio up to 300: 1, can support the color of each floor. But the plight of suffering and the ratio of the same brightness, today there is no effective and fair set of criteria to measure the ratio, so the best way to identify or rely on the user's eyes.

brightness

Brightness refers to the brightness of the screen, the unit is candela per square meter (cd / m2) also known as nits, that is, per square meter per candlelight. Current methods to improve display brightness, there are two, one is to improve the rate of light through the LCD panel; the other is to increase the brightness of the background light.

Converted to RGB luminance operation value is a fixed value for each added.

Operator calculates the contrast and brightness of formula

The concept of child count

General image processing operator (we understand a calculation formula) is a function that takes one or more input images, and to generate an output image.

The following is the general form of the operator:

g (x) = h (f (x)) or g (x) = h (f0 (x) ...... fn (x))

Contrast and brightness formula

g (x, y) = f (x, y) * alpha + beta;

Wherein: f (x, y) the representative color value of the original pixel image at a certain point; Alpha denotes the contrast value; Beta representative luminance value;

Here as in the actual development process, according to the calculation method requires the actual contrast and brightness adjustments:

g (x, y) = f (x, y) * (alpha / 100.0f) + beta

The contrast range of 0 to 400 (I selected), the denominator of 100.0f;

Luminance range -255 to 255 (I selected), no denominator (the denominator can be considered as 1.0f);

Source Demo

void OpenCVManager::testContrastAndBrightness()
{
    cv::Mat mat1;
    QString fileName1 = "E:/qtProject/openCVDemo/openCVDemo/modules/openCVManager/images/1.jpg";
    mat1 = cv::imread(fileName1.toStdString());

    cv::String windowName = "openCVDemo v1.8.0";
    cvui::init(windowName);

    if(!mat1.data)
    {
        qDebug() << __FILE__ << __LINE__
                 << "Failed to load image:" << fileName1;
        return;
    }
    // 增强对比度
    float r;
    float g;
    float b;
    cv::Mat dstMat;
    dstMat = cv::Mat::zeros(mat1.size(), mat1.type());
    cv::Mat windowMat = cv::Mat(cv::Size(dstMat.cols * 2, dstMat.rows), CV_8UC3);
    int alpha = 100;    // 小于1，则降低对比度
    int beta = 0;     // 负数，则降低亮度
    cvui::window(windowMat, dstMat.cols, 0, dstMat.cols, dstMat.rows, "settings");
    while(true)
    {
        windowMat = cv::Scalar(0, 0, 0);
        cvui::printf(windowMat, 375, 40, "contrast");
        cvui::trackbar(windowMat, 375, 50, 165, &alpha, 0, 400);
        cvui::printf(windowMat, 375, 100, "brightness");
        cvui::trackbar(windowMat, 375, 110, 165, &beta, -255, 255);
#if 1
        for(int row = 0; row < mat1.rows; row++)
        {
            for(int col = 0; col < mat1.cols; col++)
            {
                b = mat1.at<cv::Vec3b>(row, col)[0];
                g = mat1.at<cv::Vec3b>(row, col)[1];
                r = mat1.at<cv::Vec3b>(row, col)[2];
#if 0
                // 改变背景色
                if(r > 200 && g > 200 && b > 200)
                {
                    r = 25/4.0;
                    g = 246.0f;
                    b = 197.0f;
                    dstMat.at<cv::Vec3b>(row, col)[0] = b;
                    dstMat.at<cv::Vec3b>(row, col)[1] = g;
                    dstMat.at<cv::Vec3b>(row, col)[2] = r;
                }
#endif
#if 1
                // 对比度、亮度计算公式 cv::saturate_cast<uchar>(value)：防止溢出
                dstMat.at<cv::Vec3b>(row, col)[0] = cv::saturate_cast<uchar>(b * alpha / 100.0f + beta);
                dstMat.at<cv::Vec3b>(row, col)[1] = cv::saturate_cast<uchar>(g * alpha / 100.0f + beta);
                dstMat.at<cv::Vec3b>(row, col)[2] = cv::saturate_cast<uchar>(r * alpha / 100.0f + beta);
#else
                // 对比度、亮度计算公式 cv::saturate_cast<uchar>(value)：防止溢出
                dstMat.at<cv::Vec3b>(row, col)[0] = cv::saturate_cast<uchar>(b * alpha / 100.0f + beta);
                dstMat.at<cv::Vec3b>(row, col)[1] = cv::saturate_cast<uchar>(g * alpha / 100.0f + beta);
                dstMat.at<cv::Vec3b>(row, col)[2] = cv::saturate_cast<uchar>(r * alpha / 100.0f + beta);
#endif
            }
        }
//        cvui::trackbar(windowMat, dstMat.cols, dstMat.rows / 3, dstMat.cols, &?alpha, 0, 100, );
        cv::Mat imageMat = windowMat(cv::Range(0, dstMat.rows), cv::Range(0, dstMat.cols));
        cv::addWeighted(imageMat, 0.0, dstMat, 1.0, 0.0f, imageMat);
#endif
        cvui::update();
        cv::imshow(windowName, windowMat);
        if(cv::waitKey(25) == 27)
        {
            break;
        }
    }
}