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; } } } }