opencv/opencv_contrib
1
0
Fork 0
mirror of https://github.com/opencv/opencv_contrib.git synced 2025-10-22 07:31:26 +08:00
Code Issues Releases Wiki Activity

Network initialized successfully

Browse Source
This commit is contained in:
Anna Petrovicheva
2016-07-20 12:27:31 +03:00
parent b65a84fac0
commit 355d07ecef
14 changed files with 274 additions and 210 deletions
Download Patch File Download Diff File Expand all files Collapse all files

106
modules/dnn/samples/ssd_object_detection.cpp
View File

@@ -9,48 +9,46 @@ using namespace cv::dnn;
#include <cstdlib>
using namespace std;
static const string fcnType = "fcn8s";
//static void colorizeSegmentation(dnn::Blob& score,
// const vector<cv::Vec3b>& colors,
// cv::Mat& segm)
//{
// const int rows = score.rows();
// const int cols = score.cols();
// const int chns = score.channels();
static void colorizeSegmentation(dnn::Blob& score,
const vector<cv::Vec3b>& colors,
cv::Mat& segm)
{
const int rows = score.rows();
const int cols = score.cols();
const int chns = score.channels();
// cv::Mat maxCl(rows, cols, CV_8UC1);
// cv::Mat maxVal(rows, cols, CV_32FC1);
// for (int ch = 0; ch < chns; ch++)
// {
// for (int row = 0; row < rows; row++)
// {
// const float* ptrScore = score.ptrf(0, ch, row);
// uchar* ptrMaxCl = maxCl.ptr<uchar>(row);
// float* ptrMaxVal = maxVal.ptr<float>(row);
// for (int col = 0; col < cols; col++)
// {
// if (ptrScore[col] > ptrMaxVal[col])
// {
// ptrMaxVal[col] = ptrScore[col];
// ptrMaxCl[col] = ch;
// }
// }
// }
// }
cv::Mat maxCl(rows, cols, CV_8UC1);
cv::Mat maxVal(rows, cols, CV_32FC1);
for (int ch = 0; ch < chns; ch++)
{
for (int row = 0; row < rows; row++)
{
const float* ptrScore = score.ptrf(0, ch, row);
uchar* ptrMaxCl = maxCl.ptr<uchar>(row);
float* ptrMaxVal = maxVal.ptr<float>(row);
for (int col = 0; col < cols; col++)
{
if (ptrScore[col] > ptrMaxVal[col])
{
ptrMaxVal[col] = ptrScore[col];
ptrMaxCl[col] = ch;
}
}
}
}
segm.create(rows, cols, CV_8UC3);
for (int row = 0; row < rows; row++)
{
const uchar* ptrMaxCl = maxCl.ptr<uchar>(row);
cv::Vec3b* ptrSegm = segm.ptr<cv::Vec3b>(row);
for (int col = 0; col < cols; col++)
{
ptrSegm[col] = colors[ptrMaxCl[col]];
}
}
}
// segm.create(rows, cols, CV_8UC3);
// for (int row = 0; row < rows; row++)
// {
// const uchar* ptrMaxCl = maxCl.ptr<uchar>(row);
// cv::Vec3b* ptrSegm = segm.ptr<cv::Vec3b>(row);
// for (int col = 0; col < cols; col++)
// {
// ptrSegm[col] = colors[ptrMaxCl[col]];
// }
// }
//}
const char* about = "This sample uses Single-Shot Detector to detect objects "
"from camera\n"; // TODO: link
@@ -111,6 +109,7 @@ int main(int argc, char** argv)
return 0;
}
size_t i = 0;
for (;; )
{
Mat frame;
@@ -118,12 +117,15 @@ int main(int argc, char** argv)
if (frame.empty())
break;
//! [Prepare blob]
resize(frame, frame, Size(300, 300)); //SSD accepts 300x300 RGB-images
dnn::Blob inputBlob = dnn::Blob(frame); //Convert Mat to dnn::Blob image
//! [Prepare blob]
std::ostringstream stream;
stream << "folder/" << i << ".jpg";
imwrite(stream.str(), frame);
//! [Set input blob]
net.setBlob(".data", inputBlob); //set the network input
//! [Set input blob]
@@ -132,20 +134,20 @@ int main(int argc, char** argv)
net.forward(); //compute output
//! [Make forward pass]
//! [Gather output]
dnn::Blob detection = net.getBlob("detection_out");
// //! [Gather output]
// dnn::Blob detection = net.getBlob("detection_out");
// cv::Mat colorize;
// colorizeSegmentation(score, colors, colorize);
// cv::Mat show;
// cv::addWeighted(img, 0.4, colorize, 0.6, 0.0, show);
// cv::imshow("show", show);
// cv::waitKey(0);
// return 0;
// // cv::Mat colorize;
// // colorizeSegmentation(score, colors, colorize);
// // cv::Mat show;
// // cv::addWeighted(img, 0.4, colorize, 0.6, 0.0, show);
// // cv::imshow("show", show);
// // cv::waitKey(0);
// // return 0;
imshow("frame", frame);
if (waitKey(1) == 27)
break; // stop capturing by pressing ESC
// imshow("frame", frame);
// if (waitKey(1) == 27)
// break; // stop capturing by pressing ESC
}
camera.release();