void ofxImagePreProcess::update(const cv::UMat & _src, cv::UMat & dst){
cv::UMat src = _src.clone();
// - - - - convert into Gray
cvtColor( src, src, COLOR_BGR2GRAY );
// - - - - filter
//cv::bilateralFilter(camerauMat, mUMat, 7, 100, 7);
cv::Size kernel = cv::Size(5,5);
cv::boxFilter(src, mUMat, -1, kernel);
//- - - - - get "detials image"
cv::absdiff(src, mUMat, difMat);
//- - - multiply
cv::multiply(difMat, 20, difMat);
// - - - -temporal filtering
if(isInitial)
{
tempFilteredDifMat = difMat.clone();
cv::multiply(tempFilteredDifMat, 1, tempFilteredDifMat);
isInitial = false;
}else{
cv::addWeighted(tempFilteredDifMat,0.7,difMat,0.7,0.0, tempFilteredDifMat);
}
//- - - - then blur it.
cv::Size Blurkernel = cv::Size(55,55);
cv::blur(tempFilteredDifMat, regionMask, Blurkernel);
// - - - then Thresh it.
cv::threshold(regionMask, regionMask, 50, 255, THRESH_BINARY_INV);
// - - - create masked image from src
cv::UMat mask;
mMaskOperated = src.clone();
cv::multiply(regionMask, 1.0/255.0, mask);
cv::multiply(mMaskOperated, mask, mMaskOperated);
// - - - expand dynamic range
// TODO parameters
cv::subtract(mMaskOperated, 200, mMaskOperatedAndDym);
cv::multiply(mMaskOperatedAndDym, 255.0 / (255.0 - 200) , mMaskOperatedAndDym);
cv::subtract(src, mMaskOperatedAndDym, dst);
}
cv::UMat UBMS::getAttentionMap(const cv::UMat& bm, int dilation_width_1, bool toNormalize, bool handle_border) {
using namespace std::chrono;
cv::UMat ret = bm.clone();
int jump;
if (handle_border) {
cv::Mat cpuRet = ret.getMat(cv::ACCESS_FAST);
for (int i = 0; i < bm.rows; i++) {
jump = BMS_RNG.uniform(0.0, 1.0)>0.99 ? BMS_RNG.uniform(5, 25) : 0;
if (cpuRet.at<uchar>(i, 0 + jump) != 1) {
cv::floodFill(ret, cv::Point(0 + jump, i), cv::Scalar(1), 0, cv::Scalar(0), cv::Scalar(0), 8);
//cpuRet = ret.getMat(cv::ACCESS_READ);
}
jump = BMS_RNG.uniform(0.0, 1.0)>0.99 ? BMS_RNG.uniform(5, 25) : 0;
if (cpuRet.at<uchar>(i, bm.cols - 1 - jump) != 1) {
cv::floodFill(ret, cv::Point(bm.cols - 1 - jump, i), cv::Scalar(1), 0, cv::Scalar(0), cv::Scalar(0), 8);
//cpuRet = ret.getMat(cv::ACCESS_READ);
}
}
for (int j = 0; j < bm.cols; j++) {
jump = BMS_RNG.uniform(0.0, 1.0)>0.99 ? BMS_RNG.uniform(5, 25) : 0;
if (cpuRet.at<uchar>(0 + jump, j) != 1) {
cv::floodFill(ret, cv::Point(j, 0 + jump), cv::Scalar(1), 0, cv::Scalar(0), cv::Scalar(0), 8);
//cpuRet = ret.getMat(cv::ACCESS_READ);
}
jump = BMS_RNG.uniform(0.0, 1.0)>0.99 ? BMS_RNG.uniform(5, 25) : 0;
if (cpuRet.at<uchar>(bm.rows - 1 - jump, j) != 1) {
cv::floodFill(ret, cv::Point(j, bm.rows - 1 - jump), cv::Scalar(1), 0, cv::Scalar(0), cv::Scalar(0), 8);
//cpuRet = ret.getMat(cv::ACCESS_READ);
}
}
}
else {
cv::Mat cpuRet = ret.getMat(cv::ACCESS_FAST);
for (int i = 0; i < bm.rows; i++) {
if (cpuRet.at<uchar>(i, 0) != 1) {
cv::floodFill(ret, cv::Point(0, i), cv::Scalar(1), 0, cv::Scalar(0), cv::Scalar(0), 8);
//cpuRet = ret.getMat(cv::ACCESS_READ);
}
if (cpuRet.at<uchar>(i, bm.cols - 1) != 1) {
cv::floodFill(ret, cv::Point(bm.cols - 1, i), cv::Scalar(1), 0, cv::Scalar(0), cv::Scalar(0), 8);
//cpuRet = ret.getMat(cv::ACCESS_READ);
}
}
for (int j = 0; j < bm.cols; j++) {
if (cpuRet.at<uchar>(0, j) != 1) {
cv::floodFill(ret, cv::Point(j, 0), cv::Scalar(1), 0, cv::Scalar(0), cv::Scalar(0), 8);
//cpuRet = ret.getMat(cv::ACCESS_READ);
}
if (cpuRet.at<uchar>(bm.rows - 1, j) != 1) {
cv::floodFill(ret, cv::Point(j, bm.rows - 1), cv::Scalar(1), 0, cv::Scalar(0), cv::Scalar(0), 8);
//cpuRet = ret.getMat(cv::ACCESS_READ);
}
}
}
cv::bitwise_xor(ret, cv::Scalar(1), ret);
cv::UMat map1, map2;
cv::bitwise_and(ret, bm, map1);
cv::UMat notBM;
cv::subtract(cv::Scalar(1), bm, notBM);
cv::bitwise_and(ret, notBM, map2);
if (dilation_width_1 > 0) {
for (int k = 0; k < (dilation_width_1 - 1) / 2; ++k) {
dilate(map1, map1, cv::UMat(), cv::Point(-1, -1), 3);
dilate(map2, map2, cv::UMat(), cv::Point(-1, -1), 3);
}
}
map1.convertTo(map1, CV_32FC1);
map2.convertTo(map2, CV_32FC1);
bmsNormalize(map1);
bmsNormalize(map2);
cv::add(map1, map2, map1);
return map1;
}
