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Added im2row, tiny optimiziations

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This commit is contained in:
arrybn
2016-12-23 10:56:01 +03:00
parent 6891d7788f
commit 310a096b46
6 changed files with 223 additions and 48 deletions
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112
modules/dnn/samples/torch_enet.cpp
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@@ -20,13 +20,14 @@ const String keys =
"https://www.dropbox.com/sh/dywzk3gyb12hpe5/AAD5YkUa8XgMpHs2gCRgmCVCa }"
"{model m || path to Torch .net model file (model_best.net) }"
"{image i || path to image file }"
"{i_blob | .0 | input blob name) }"
"{o_blob || output blob name) }"
"{c_names c || path to file with classnames for channels (categories.txt) }"
"{c_names c || path to file with classnames for channels (optional, categories.txt) }"
"{result r || path to save output blob (optional, binary format, NCHW order) }"
"{show s || whether to show all output channels or not}"
;
std::vector<String> readClassNames(const char *filename);
static void colorizeSegmentation(Blob &score, Mat &segm,
Mat &legend, vector<String> &classNames);
int main(int argc, char **argv)
{
@@ -40,8 +41,6 @@ int main(int argc, char **argv)
String modelFile = parser.get<String>("model");
String imageFile = parser.get<String>("image");
String inBlobName = parser.get<String>("i_blob");
String outBlobName = parser.get<String>("o_blob");
if (!parser.check())
{
@@ -78,7 +77,7 @@ int main(int argc, char **argv)
//! [Initialize network]
//! [Prepare blob]
Mat img = imread(imageFile);
Mat img = imread(imageFile), input;
if (img.empty())
{
std::cerr << "Can't read image from the file: " << imageFile << std::endl;
@@ -91,15 +90,15 @@ int main(int argc, char **argv)
resize(img, img, inputImgSize); //Resize image to input size
if(img.channels() == 3)
cv::cvtColor(img, img, cv::COLOR_BGR2RGB);
cv::cvtColor(img, input, cv::COLOR_BGR2RGB);
img.convertTo(img, CV_32F, 1/255.0);
input.convertTo(input, CV_32F, 1/255.0);
dnn::Blob inputBlob = dnn::Blob::fromImages(img); //Convert Mat to dnn::Blob image batch
dnn::Blob inputBlob = dnn::Blob::fromImages(input); //Convert Mat to dnn::Blob image batch
//! [Prepare blob]
//! [Set input blob]
net.setBlob(inBlobName, inputBlob); //set the network input
net.setBlob("", inputBlob); //set the network input
//! [Set input blob]
cv::TickMeter tm;
@@ -112,7 +111,8 @@ int main(int argc, char **argv)
tm.stop();
//! [Gather output]
dnn::Blob prob = net.getBlob(outBlobName); //gather output of "prob" layer
dnn::Blob prob = net.getBlob(net.getLayerNames().back()); //gather output of "prob" layer
Mat& result = prob.matRef();
@@ -129,25 +129,27 @@ int main(int argc, char **argv)
std::cout << "Output blob shape " << shape << std::endl;
std::cout << "Inference time, ms: " << tm.getTimeMilli() << std::endl;
std::vector<String> classNames;
if(!classNamesFile.empty()) {
classNames = readClassNames(classNamesFile.c_str());
if (classNames.size() > prob.channels())
classNames = std::vector<String>(classNames.begin() + classNames.size() - prob.channels(),
classNames.end());
if (parser.has("show"))
{
std::vector<String> classNames;
if(!classNamesFile.empty()) {
classNames = readClassNames(classNamesFile.c_str());
if (classNames.size() > prob.channels())
classNames = std::vector<String>(classNames.begin() + classNames.size() - prob.channels(),
classNames.end());
}
Mat segm, legend;
colorizeSegmentation(prob, segm, legend, classNames);
Mat show;
addWeighted(img, 0.2, segm, 0.8, 0.0, show);
imshow("Result", show);
if(classNames.size())
imshow("Legend", legend);
waitKey();
}
for(int i_c = 0; i_c < prob.channels(); i_c++) {
ostringstream convert;
convert << "Channel #" << i_c;
if(classNames.size() == prob.channels())
convert << ": " << classNames[i_c];
imshow(convert.str().c_str(), prob.getPlane(0, i_c));
}
waitKey();
return 0;
} //main
@@ -174,3 +176,57 @@ std::vector<String> readClassNames(const char *filename)
fp.close();
return classNames;
}
static void colorizeSegmentation(Blob &score, Mat &segm, Mat &legend, vector<String> &classNames)
{
const int rows = score.rows();
const int cols = score.cols();
const int chns = score.channels();
vector<Vec3i> colors;
RNG rng(12345678);
cv::Mat maxCl(rows, cols, CV_8UC1);
cv::Mat maxVal(rows, cols, CV_32FC1);
for (int ch = 0; ch < chns; ch++)
{
colors.push_back(Vec3i(rng.uniform(0, 256), rng.uniform(0, 256), rng.uniform(0, 256)));
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]];
}
}
if (classNames.size() == colors.size())
{
int blockHeight = 30;
legend.create(blockHeight*classNames.size(), 200, CV_8UC3);
for(int i = 0; i < classNames.size(); i++)
{
cv::Mat block = legend.rowRange(i*blockHeight, (i+1)*blockHeight);
block = colors[i];
putText(block, classNames[i], Point(0, blockHeight/2), FONT_HERSHEY_SIMPLEX, 0.5, Scalar());
}
}
}