samples/dnn/segmentation.cpp


#include <fstream>
#include <sstream>
#include < opencv2/dnn.hpp >
#include "common.hpp"
std::string keys =
"{ help h | | Print help message. }"
"{ @alias | | An alias name of model to extract preprocessing parameters from models.yml file. }"
"{ zoo | models.yml | An optional path to file with preprocessing parameters }"
"{ device | 0 | camera device number. }"
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
"{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
"{ classes | | Optional path to a text file with names of classes. }"
"{ colors | | Optional path to a text file with colors for an every class. "
"An every color is represented with three values from 0 to 255 in BGR channels order. }"
"{ backend | 0 | Choose one of computation backends: "
"0: automatically (by default), "
"1: Halide language (http://halide-lang.org/), "
"2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
"3: OpenCV implementation }"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU }" ;
using namespace cv ;
using namespace dnn;
std::vector<std::string> classes;
std::vector<Vec3b> colors;
void showLegend();
void colorizeSegmentation( const Mat &score, Mat &segm);
int main( int argc, char ** argv)
{
CommandLineParser parser(argc, argv, keys);
const std::string modelName = parser.get< 字符串 >( "@alias" );
const std::string zooFile = parser.get< 字符串 >( "zoo" );
keys += genPreprocArguments(modelName, zooFile);
parser = CommandLineParser (argc, argv, keys);
parser.about( "Use this script to run semantic segmentation deep learning networks using OpenCV." );
if (argc == 1 || parser.has( "help" ))
{
parser.printMessage();
return 0;
}
float scale = parser.get< float >( "scale" );
Scalar mean = parser.get< Scalar >( "mean" );
bool swapRB = parser.get< bool >( "rgb" );
int inpWidth = parser.get< int >( "width" );
int inpHeight = parser.get< int >( "height" );
字符串 model = findFile (parser.get< 字符串 >( "model" ));
字符串 config = findFile (parser.get< 字符串 >( "config" ));
字符串 framework = parser.get< 字符串 >( "framework" );
int backendId = parser.get< int >( "backend" );
int targetId = parser.get< int >( "target" );
// Open file with classes names.
if (parser.has( "classes" ))
{
std::string file = parser.get< 字符串 >( "classes" );
std::ifstream ifs(file.c_str());
if (!ifs.is_open())
CV_Error ( Error::StsError , "File " + file + " not found" );
std::string line ;
while (std::getline(ifs, line))
{
classes.push_back(line);
}
}
// Open file with colors.
if (parser.has( "colors" ))
{
std::string file = parser.get< 字符串 >( "colors" );
std::ifstream ifs(file.c_str());
if (!ifs.is_open())
CV_Error ( Error::StsError , "File " + file + " not found" );
std::string line ;
while (std::getline(ifs, line))
{
std::istringstream colorStr(line.c_str());
Vec3b color;
for ( int i = 0; i < 3 && !colorStr.eof(); ++i)
colorStr >> color[i];
colors.push_back(color);
}
}
if (!parser.check())
{
parser.printErrors();
return 1;
}
CV_Assert (!model.empty());
Net net = readNet (model, config, framework);
net.setPreferableBackend(backendId);
net.setPreferableTarget(targetId);
// Create a window
static const std::string kWinName = "Deep learning semantic segmentation in OpenCV" ;
if (parser.has( "input" ))
cap. open (parser.get< 字符串 >( "input" ));
else
cap. open (parser.get< int >( "device" ));
// Process frames.
Mat frame, blob;
while ( waitKey (1) < 0)
{
cap >> frame;
if (frame.empty())
{
break ;
}
blobFromImage (frame, blob, scale, Size (inpWidth, inpHeight), mean, swapRB, false );
net.setInput(blob);
Mat score = net.forward();
Mat segm;
colorizeSegmentation(score, segm);
resize (segm, segm, frame. size (), 0, 0, INTER_NEAREST );
addWeighted (frame, 0.1, segm, 0.9, 0.0, frame);
// Put efficiency information.
std::vector<double> layersTimes;
double freq = getTickFrequency () / 1000;
double t = net.getPerfProfile(layersTimes) / freq;
std::string label = format( "Inference time: %.2f ms" , t);
putText (frame, label, Point (0, 15), FONT_HERSHEY_SIMPLEX , 0.5, Scalar (0, 255, 0));
imshow (kWinName, frame);
if (!classes.empty())
showLegend();
}
return 0;
}
void colorizeSegmentation( const Mat &score, Mat &segm)
{
const int rows = score. size [2];
const int cols = score. size [3];
const int chns = score. size [1];
if (colors.empty())
{
// Generate colors.
colors.push_back( Vec3b ());
for ( int i = 1; i < chns; ++i)
{
Vec3b color;
for ( int j = 0; j < 3; ++j)
color[j] = (colors[i - 1][j] + rand() % 256) / 2;
colors.push_back(color);
}
}
else if (chns != ( int )colors.size())
{
CV_Error ( Error::StsError , format( "Number of output classes does not match "
"number of colors (%d != %zu)" , chns, colors.size()));
}
Mat maxCl = Mat::zeros (rows, cols, CV_8UC1 );
Mat maxVal(rows, cols, CV_32FC1 , score. data );
for ( int ch = 1; ch < chns; ch++)
{
for ( int row = 0; row < rows; row++)
{
const float *ptrScore = score. ptr < float >(0, ch, row);
uint8_t *ptrMaxCl = maxCl. ptr < uint8_t >(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] = ( uchar )ch;
}
}
}
}
segm. create (rows, cols, CV_8UC3 );
for ( int row = 0; row < rows; row++)
{
const uchar *ptrMaxCl = maxCl. ptr < uchar >(row);
Vec3b *ptrSegm = segm. ptr < Vec3b >(row);
for ( int col = 0; col < cols; col++)
{
ptrSegm[col] = colors[ptrMaxCl[col]];
}
}
}
void showLegend()
{
static const int kBlockHeight = 30;
static Mat legend;
if (legend. empty ())
{
const int numClasses = (int)classes.size();
if (( int )colors.size() != numClasses)
{
CV_Error ( Error::StsError , format( "Number of output classes does not match "
"number of labels (%zu != %zu)" , colors.size(), classes.size()));
}
legend. create (kBlockHeight * numClasses, 200, CV_8UC3 );
for ( int i = 0; i < numClasses; i++)
{
Mat block = legend. rowRange (i * kBlockHeight, (i + 1) * kBlockHeight);
块。 setTo (colors[i]);
putText (block, classes[i], Point (0, kBlockHeight / 2), FONT_HERSHEY_SIMPLEX , 0.5, Vec3b (255, 255, 255));
}
imshow ( "Legend" , legend);
}
}