mirror of
https://github.com/opencv/opencv.git
synced 2024-12-05 01:39:13 +08:00
247 lines
8.6 KiB
C++
247 lines
8.6 KiB
C++
#include <fstream>
|
|
#include <sstream>
|
|
|
|
#include <opencv2/dnn.hpp>
|
|
#include <opencv2/imgproc.hpp>
|
|
#include <opencv2/highgui.hpp>
|
|
|
|
const char* keys =
|
|
"{ help h | | Print help message. }"
|
|
"{ device | 0 | camera device number. }"
|
|
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
|
|
"{ model m | | Path to a binary file of model contains trained weights. "
|
|
"It could be a file with extensions .caffemodel (Caffe), "
|
|
".pb (TensorFlow), .t7 or .net (Torch), .weights (Darknet). }"
|
|
"{ config c | | Path to a text file of model contains network configuration. "
|
|
"It could be a file with extensions .prototxt (Caffe), .pbtxt (TensorFlow), .cfg (Darknet). }"
|
|
"{ 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. }"
|
|
"{ mean | | Preprocess input image by subtracting mean values. Mean values should be in BGR order and delimited by spaces. }"
|
|
"{ scale | 1 | Preprocess input image by multiplying on a scale factor. }"
|
|
"{ width | | Preprocess input image by resizing to a specific width. }"
|
|
"{ height | | Preprocess input image by resizing to a specific height. }"
|
|
"{ rgb | | Indicate that model works with RGB input images instead BGR ones. }"
|
|
"{ 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);
|
|
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");
|
|
String model = parser.get<String>("model");
|
|
String config = parser.get<String>("config");
|
|
String framework = parser.get<String>("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<String>("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<String>("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());
|
|
//! [Read and initialize network]
|
|
Net net = readNet(model, config, framework);
|
|
net.setPreferableBackend(backendId);
|
|
net.setPreferableTarget(targetId);
|
|
//! [Read and initialize network]
|
|
|
|
// Create a window
|
|
static const std::string kWinName = "Deep learning semantic segmentation in OpenCV";
|
|
namedWindow(kWinName, WINDOW_NORMAL);
|
|
|
|
//! [Open a video file or an image file or a camera stream]
|
|
VideoCapture cap;
|
|
if (parser.has("input"))
|
|
cap.open(parser.get<String>("input"));
|
|
else
|
|
cap.open(parser.get<int>("device"));
|
|
//! [Open a video file or an image file or a camera stream]
|
|
|
|
// Process frames.
|
|
Mat frame, blob;
|
|
while (waitKey(1) < 0)
|
|
{
|
|
cap >> frame;
|
|
if (frame.empty())
|
|
{
|
|
waitKey();
|
|
break;
|
|
}
|
|
|
|
//! [Create a 4D blob from a frame]
|
|
blobFromImage(frame, blob, scale, Size(inpWidth, inpHeight), mean, swapRB, false);
|
|
//! [Create a 4D blob from a frame]
|
|
|
|
//! [Set input blob]
|
|
net.setInput(blob);
|
|
//! [Set input blob]
|
|
//! [Make forward pass]
|
|
Mat score = net.forward();
|
|
//! [Make forward pass]
|
|
|
|
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 != %d)", 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 (%d != %d)", 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);
|
|
block.setTo(colors[i]);
|
|
putText(block, classes[i], Point(0, kBlockHeight / 2), FONT_HERSHEY_SIMPLEX, 0.5, Vec3b(255, 255, 255));
|
|
}
|
|
namedWindow("Legend", WINDOW_NORMAL);
|
|
imshow("Legend", legend);
|
|
}
|
|
}
|