mmr/tkDNN
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Shelfnet works, also visualization. Postprocessing need to be parallelized

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Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
This commit is contained in:
Micaela Verucchi
2020-06-23 20:01:47 +02:00
parent 94e558003d
commit 082920f3f5
12 changed files with 366 additions and 20 deletions
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src/NetworkViz.cpp
+7 -8
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@@ -6,23 +6,22 @@
namespace tk { namespace dnn {
cv::Mat vizFloat2colorMap(cv::Mat map) {
cv::Mat vizFloat2colorMap(cv::Mat map,double min, double max) {
if(min == 0 && max == 0)
cv::minMaxIdx(map, &min, &max);
double min;
double max;
cv::minMaxIdx(map, &min, &max);
cv::Mat adjMap;
// expand your range to 0..255. Similar to histEq();
map.convertTo(adjMap,CV_8UC1, 255 / (max-min), -min);
//return adjMap;
cv::Mat falseColorsMap;
applyColorMap(adjMap, falseColorsMap, cv::COLORMAP_HOT);
applyColorMap(adjMap, falseColorsMap, cv::COLORMAP_VIRIDIS);
return falseColorsMap;
}
cv::Mat vizData2Mat(dnnType *dataInput, tk::dnn::dataDim_t dim, int imgdim) {
cv::Mat vizData2Mat(dnnType *dataInput, tk::dnn::dataDim_t dim, int imgdim, double min, double max) {
dnnType *data = nullptr;
// copy to CPU
@@ -38,7 +37,7 @@ cv::Mat vizData2Mat(dnnType *dataInput, tk::dnn::dataDim_t dim, int imgdim) {
cv::Mat grid = cv::Mat(gridSize, CV_8UC3, cv::Scalar(0));
for(int i=0; i<dim.c;i++) {
cv::Mat raw = vizFloat2colorMap(cv::Mat(cv::Size(dim.w, dim.h),CV_32FC1, data + dim.w*dim.h*i));
cv::Mat raw = vizFloat2colorMap(cv::Mat(cv::Size(dim.w, dim.h),CV_32FC1, data + dim.w*dim.h*i), min, max);
int r = i / gridDim;
int c = i - r * gridDim;
raw.copyTo(grid.rowRange(r*dim.h, r*dim.h + dim.h).colRange(c*dim.w, c*dim.w + dim.w));