opencv/samples/dnn/ssd_mobilenet_object_detection.cpp
2017-08-01 12:30:27 +03:00

162 lines
5.7 KiB
C++

#include <opencv2/dnn.hpp>
#include <opencv2/dnn/shape_utils.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
using namespace cv;
using namespace cv::dnn;
#include <fstream>
#include <iostream>
#include <cstdlib>
using namespace std;
const size_t inWidth = 300;
const size_t inHeight = 300;
const float WHRatio = inWidth / (float)inHeight;
const float inScaleFactor = 0.007843f;
const float meanVal = 127.5;
const char* classNames[] = {"background",
"aeroplane", "bicycle", "bird", "boat",
"bottle", "bus", "car", "cat", "chair",
"cow", "diningtable", "dog", "horse",
"motorbike", "person", "pottedplant",
"sheep", "sofa", "train", "tvmonitor"};
const char* about = "This sample uses Single-Shot Detector "
"(https://arxiv.org/abs/1512.02325)"
"to detect objects on image.\n"
".caffemodel model's file is avaliable here: "
"https://github.com/chuanqi305/MobileNet-SSD/blob/master/MobileNetSSD_train.caffemodel\n";
const char* params
= "{ help | false | print usage }"
"{ proto | MobileNetSSD_300x300.prototxt | model configuration }"
"{ model | | model weights }"
"{ video | | video for detection }"
"{ out | | path to output video file}"
"{ min_confidence | 0.2 | min confidence }";
int main(int argc, char** argv)
{
cv::CommandLineParser parser(argc, argv, params);
if (parser.get<bool>("help"))
{
cout << about << endl;
parser.printMessage();
return 0;
}
String modelConfiguration = parser.get<string>("proto");
String modelBinary = parser.get<string>("model");
//! [Initialize network]
dnn::Net net = readNetFromCaffe(modelConfiguration, modelBinary);
//! [Initialize network]
VideoCapture cap(parser.get<String>("video"));
if(!cap.isOpened()) // check if we succeeded
{
cap = VideoCapture(0);
if(!cap.isOpened())
{
cout << "Couldn't find camera" << endl;
return -1;
}
}
Size inVideoSize = Size((int) cap.get(CV_CAP_PROP_FRAME_WIDTH), //Acquire input size
(int) cap.get(CV_CAP_PROP_FRAME_HEIGHT));
Size cropSize;
if (inVideoSize.width / (float)inVideoSize.height > WHRatio)
{
cropSize = Size(static_cast<int>(inVideoSize.height * WHRatio),
inVideoSize.height);
}
else
{
cropSize = Size(inVideoSize.width,
static_cast<int>(inVideoSize.width / WHRatio));
}
Rect crop(Point((inVideoSize.width - cropSize.width) / 2,
(inVideoSize.height - cropSize.height) / 2),
cropSize);
VideoWriter outputVideo;
outputVideo.open(parser.get<String>("out") ,
static_cast<int>(cap.get(CV_CAP_PROP_FOURCC)),
cap.get(CV_CAP_PROP_FPS), cropSize, true);
for(;;)
{
Mat frame;
cap >> frame; // get a new frame from camera
//! [Prepare blob]
Mat inputBlob = blobFromImage(frame, inScaleFactor,
Size(inWidth, inHeight), meanVal); //Convert Mat to batch of images
//! [Prepare blob]
//! [Set input blob]
net.setInput(inputBlob, "data"); //set the network input
//! [Set input blob]
TickMeter tm;
tm.start();
//! [Make forward pass]
Mat detection = net.forward("detection_out"); //compute output
tm.stop();
cout << "Inference time, ms: " << tm.getTimeMilli() << endl;
//! [Make forward pass]
Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>());
frame = frame(crop);
float confidenceThreshold = parser.get<float>("min_confidence");
for(int i = 0; i < detectionMat.rows; i++)
{
float confidence = detectionMat.at<float>(i, 2);
if(confidence > confidenceThreshold)
{
size_t objectClass = (size_t)(detectionMat.at<float>(i, 1));
int xLeftBottom = static_cast<int>(detectionMat.at<float>(i, 3) * frame.cols);
int yLeftBottom = static_cast<int>(detectionMat.at<float>(i, 4) * frame.rows);
int xRightTop = static_cast<int>(detectionMat.at<float>(i, 5) * frame.cols);
int yRightTop = static_cast<int>(detectionMat.at<float>(i, 6) * frame.rows);
ostringstream ss;
ss << confidence;
String conf(ss.str());
Rect object((int)xLeftBottom, (int)yLeftBottom,
(int)(xRightTop - xLeftBottom),
(int)(yRightTop - yLeftBottom));
rectangle(frame, object, Scalar(0, 255, 0));
String label = String(classNames[objectClass]) + ": " + conf;
int baseLine = 0;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
rectangle(frame, Rect(Point(xLeftBottom, yLeftBottom - labelSize.height),
Size(labelSize.width, labelSize.height + baseLine)),
Scalar(255, 255, 255), CV_FILLED);
putText(frame, label, Point(xLeftBottom, yLeftBottom),
FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0,0,0));
}
}
if (outputVideo.isOpened())
outputVideo << frame;
imshow("detections", frame);
if (waitKey(1) >= 0) break;
}
return 0;
} // main