OpenCV  4.10.0-dev
Open Source Computer Vision
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samples/dnn/classification.cpp

Check the corresponding tutorial for more details

#include <fstream>
#include <sstream>
#include <iostream>
#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 }"
"{ input i | | Path to input image or video file. Skip this argument to capture frames from a camera.}"
"{ initial_width | 0 | Preprocess input image by initial resizing to a specific width.}"
"{ initial_height | 0 | Preprocess input image by initial resizing to a specific height.}"
"{ std | 0.0 0.0 0.0 | Preprocess input image by dividing on a standard deviation.}"
"{ crop | false | Preprocess input image by center cropping.}"
"{ framework f | | Optional name of an origin framework of the model. Detect it automatically if it does not set. }"
"{ needSoftmax | false | Use Softmax to post-process the output of the net.}"
"{ classes | | Optional path to a text file with names of classes. }"
"{ 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, "
"4: VKCOM, "
"5: CUDA, "
"6: WebNN }"
"{ target | 0 | Choose one of target computation devices: "
"0: CPU target (by default), "
"1: OpenCL, "
"2: OpenCL fp16 (half-float precision), "
"3: VPU, "
"4: Vulkan, "
"6: CUDA, "
"7: CUDA fp16 (half-float preprocess) }";
using namespace cv;
using namespace dnn;
std::vector<std::string> classes;
int main(int argc, char** argv)
{
CommandLineParser parser(argc, argv, keys);
const std::string modelName = parser.get<String>("@alias");
const std::string zooFile = parser.get<String>("zoo");
keys += genPreprocArguments(modelName, zooFile);
parser = CommandLineParser(argc, argv, keys);
parser.about("Use this script to run classification deep learning networks using OpenCV.");
if (argc == 1 || parser.has("help"))
{
parser.printMessage();
return 0;
}
int rszWidth = parser.get<int>("initial_width");
int rszHeight = parser.get<int>("initial_height");
float scale = parser.get<float>("scale");
Scalar mean = parser.get<Scalar>("mean");
Scalar std = parser.get<Scalar>("std");
bool swapRB = parser.get<bool>("rgb");
bool crop = parser.get<bool>("crop");
int inpWidth = parser.get<int>("width");
int inpHeight = parser.get<int>("height");
String model = findFile(parser.get<String>("model"));
String config = findFile(parser.get<String>("config"));
String framework = parser.get<String>("framework");
int backendId = parser.get<int>("backend");
int targetId = parser.get<int>("target");
bool needSoftmax = parser.get<bool>("needSoftmax");
std::cout<<"mean: "<<mean<<std::endl;
std::cout<<"std: "<<std<<std::endl;
// 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);
}
}
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 image classification in OpenCV";
namedWindow(kWinName, WINDOW_NORMAL);
if (parser.has("input"))
cap.open(parser.get<String>("input"));
else
cap.open(0);
// Process frames.
Mat frame, blob;
while (waitKey(1) < 0)
{
cap >> frame;
if (frame.empty())
{
waitKey();
break;
}
if (rszWidth != 0 && rszHeight != 0)
{
resize(frame, frame, Size(rszWidth, rszHeight));
}
blobFromImage(frame, blob, scale, Size(inpWidth, inpHeight), mean, swapRB, crop);
// Check std values.
if (std.val[0] != 0.0 && std.val[1] != 0.0 && std.val[2] != 0.0)
{
// Divide blob by std.
divide(blob, std, blob);
}
net.setInput(blob);
// double t_sum = 0.0;
// double t;
int classId;
double confidence;
cv::TickMeter timeRecorder;
timeRecorder.reset();
Mat prob = net.forward();
double t1;
timeRecorder.start();
prob = net.forward();
timeRecorder.stop();
t1 = timeRecorder.getTimeMilli();
timeRecorder.reset();
for(int i = 0; i < 200; i++) {
timeRecorder.start();
prob = net.forward();
timeRecorder.stop();
Point classIdPoint;
minMaxLoc(prob.reshape(1, 1), 0, &confidence, 0, &classIdPoint);
classId = classIdPoint.x;
// Put efficiency information.
// std::vector<double> layersTimes;
// double freq = getTickFrequency() / 1000;
// t = net.getPerfProfile(layersTimes) / freq;
// t_sum += t;
}
if (needSoftmax == true)
{
float maxProb = 0.0;
float sum = 0.0;
Mat softmaxProb;
maxProb = *std::max_element(prob.begin<float>(), prob.end<float>());
cv::exp(prob-maxProb, softmaxProb);
sum = (float)cv::sum(softmaxProb)[0];
softmaxProb /= sum;
Point classIdPoint;
minMaxLoc(softmaxProb.reshape(1, 1), 0, &confidence, 0, &classIdPoint);
classId = classIdPoint.x;
}
std::string label = format("Inference time of 1 round: %.2f ms", t1);
std::string label2 = format("Average time of 200 rounds: %.2f ms", timeRecorder.getTimeMilli()/200);
putText(frame, label, Point(0, 15), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
putText(frame, label2, Point(0, 35), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
// Print predicted class.
label = format("%s: %.4f", (classes.empty() ? format("Class #%d", classId).c_str() :
classes[classId].c_str()),
confidence);
putText(frame, label, Point(0, 55), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0));
imshow(kWinName, frame);
}
return 0;
}
Designed for command line parsing.
Definition utility.hpp:832
T get(const String &name, bool space_delete=true) const
Access arguments by name.
Definition utility.hpp:898
void about(const String &message)
Set the about message.
void printErrors() const
Print list of errors occurred.
void printMessage() const
Print help message.
bool has(const String &name) const
Check if field was provided in the command line.
bool check() const
Check for parsing errors.
n-dimensional dense array class
Definition mat.hpp:812
Mat reshape(int cn, int rows=0) const
Changes the shape and/or the number of channels of a 2D matrix without copying the data.
MatIterator_< _Tp > end()
Returns the matrix iterator and sets it to the after-last matrix element.
MatIterator_< _Tp > begin()
Returns the matrix iterator and sets it to the first matrix element.
_Tp x
x coordinate of the point
Definition types.hpp:201
Template class for specifying the size of an image or rectangle.
Definition types.hpp:335
a Class to measure passing time.
Definition utility.hpp:295
void start()
starts counting ticks.
Definition utility.hpp:304
void stop()
stops counting ticks.
Definition utility.hpp:310
void reset()
resets internal values.
Definition utility.hpp:374
double getTimeMilli() const
returns passed time in milliseconds.
Definition utility.hpp:333
Class for video capturing from video files, image sequences or cameras.
Definition videoio.hpp:731
virtual bool open(const String &filename, int apiPreference=CAP_ANY)
Opens a video file or a capturing device or an IP video stream for video capturing.
Scalar sum(InputArray src)
Calculates the sum of array elements.
std::string String
Definition cvstd.hpp:151
#define CV_Error(code, msg)
Call the error handler.
Definition base.hpp:335
#define CV_Assert(expr)
Checks a condition at runtime and throws exception if it fails.
Definition base.hpp:359
int main(int argc, char *argv[])
Definition highgui_qt.cpp:3
"black box" representation of the file storage associated with a file on disk.
Definition core.hpp:102
STL namespace.