使用OpenCV实现Halcon算法（6）dyn

算法（6）dyn"/>

使用OpenCV实现Halcon算法（6）dyn

先看halcon算子的使用：

read_image (Image,'photometric_stereo/embossed_01')
mean_image (Image,ImageMean,60,60)
dyn_threshold (Image, ImageMean, RegionDynThresh, 15, 'not_equal')

再看OpenCV的实现：

void CImagePreprocessing::dynamic_threshold_referHalcon(cv::Mat &frame_gray, int ksize, int offset) //仿Halcon
{cv::Mat srcMean;cv::Mat binary1;cv::Mat binary2;//均值滤波blur(frame_gray, srcMean, cv::Size(9, 9));//动态阈值binary1 = cv::Mat::zeros(frame_gray.size(), CV_8UC1);_HalconDynThreshold(frame_gray, srcMean, binary1, offset, Equal);
}void CImagePreprocessing::_HalconDynThreshold(cv::Mat &src, cv::Mat &srcMean, cv::Mat &result, int offset, int LightDark)
{//使用Opencv实现Halcon中的动态阈值//src是原图,灰度图//srcMean是平滑滤波之后的图//最好不要把Offset这个变量设置为0，因为这样会导致最后找到太多很小的regions，而这基本上都是噪声。//所以这个值最好是在5-40之间，值选择的越大，提取出来的regions就会越小。int r = src.rows; //高int c = src.cols; //宽int Value = 0;for (int i = 0; i < r; i++){uchar *datasrc = src.ptr<uchar>(i); //指针访问图像像素uchar *datasrcMean = srcMean.ptr<uchar>(i);uchar *dataresult = result.ptr<uchar>(i);for (int j = 0; j < c; j++){switch (LightDark){case Light:Value = datasrc[j] - datasrcMean[j];if (Value >= offset){dataresult[j] = 255;}break;case Dark:Value = datasrcMean[j] - datasrc[j];if (Value >= offset){dataresult[j] = 255;}break;case Equal:Value = datasrc[j] - datasrcMean[j];if (Value >= -offset && Value <= offset){dataresult[j] = 255;}break;case Not_equal:Value = datasrc[j] - datasrcMean[j];if (Value < -offset || Value > offset){dataresult[j] = 255;}break;default:break;}}}
}