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detector complete #96

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80ca765
detector complete
pavelkumbrasev Oct 1, 2018
9fba5c3
some fix in detector
pavelkumbrasev Oct 2, 2018
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15 changes: 15 additions & 0 deletions include/detection.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,8 @@
#include <string>

#include "opencv2/core/core.hpp"
#include "opencv2/opencv.hpp"


class Detector {
public:
Expand All @@ -12,3 +14,16 @@ class Detector {
virtual void Detect(const cv::Mat& frame, std::vector<cv::Rect>& objects,
std::vector<double>& scores) = 0;
};


class CascadeDetector : public Detector {
protected:
cv::CascadeClassifier detector;

public:
bool Init(const std::string& model_file_path) override;
void Detect(const cv::Mat& frame, std::vector<cv::Rect>& objects,
std::vector<double>& scores) override;


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@TheG1uy, надо удалить неоправданные пустые строки кода.

};
180 changes: 180 additions & 0 deletions logo/cascade.xml
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@@ -0,0 +1,180 @@
<?xml version="1.0"?>
<opencv_storage>
<cascade>
<stageType>BOOST</stageType>
<featureType>HAAR</featureType>
<height>32</height>
<width>32</width>
<stageParams>
<boostType>GAB</boostType>
<minHitRate>9.9500000476837158e-01</minHitRate>
<maxFalseAlarm>1.0000000149011612e-01</maxFalseAlarm>
<weightTrimRate>9.4999999999999996e-01</weightTrimRate>
<maxDepth>1</maxDepth>
<maxWeakCount>100</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount>
<featSize>1</featSize>
<mode>BASIC</mode></featureParams>
<stageNum>4</stageNum>
<stages>
<!-- stage 0 -->
<_>
<maxWeakCount>1</maxWeakCount>
<stageThreshold>9.9600797891616821e-01</stageThreshold>
<weakClassifiers>
<_>
<internalNodes>
0 -1 7 -1.7688340740278363e-03</internalNodes>
<leafValues>
9.9600797891616821e-01 -1.</leafValues></_></weakClassifiers></_>
<!-- stage 1 -->
<_>
<maxWeakCount>2</maxWeakCount>
<stageThreshold>-2.6523093692958355e-03</stageThreshold>
<weakClassifiers>
<_>
<internalNodes>
0 -1 1 1.8934525549411774e-02</internalNodes>
<leafValues>
-9.5247524976730347e-01 9.7171717882156372e-01</leafValues></_>
<_>
<internalNodes>
0 -1 10 2.6852805167436600e-02</internalNodes>
<leafValues>
-9.7436946630477905e-01 9.5748162269592285e-01</leafValues></_></weakClassifiers></_>
<!-- stage 2 -->
<_>
<maxWeakCount>4</maxWeakCount>
<stageThreshold>-3.0785626173019409e-01</stageThreshold>
<weakClassifiers>
<_>
<internalNodes>
0 -1 9 4.7774590551853180e-02</internalNodes>
<leafValues>
-9.5717346668243408e-01 8.3864915370941162e-01</leafValues></_>
<_>
<internalNodes>
0 -1 0 1.0246830061078072e-02</internalNodes>
<leafValues>
-9.2844742536544800e-01 8.5343450307846069e-01</leafValues></_>
<_>
<internalNodes>
0 -1 2 -2.2323392331600189e-02</internalNodes>
<leafValues>
7.4373298883438110e-01 -8.7865394353866577e-01</leafValues></_>
<_>
<internalNodes>
0 -1 6 2.2783260792493820e-03</internalNodes>
<leafValues>
-9.9281847476959229e-01 6.6059595346450806e-01</leafValues></_></weakClassifiers></_>
<!-- stage 3 -->
<_>
<maxWeakCount>4</maxWeakCount>
<stageThreshold>-3.2509520649909973e-01</stageThreshold>
<weakClassifiers>
<_>
<internalNodes>
0 -1 3 2.2154441103339195e-03</internalNodes>
<leafValues>
-8.5714286565780640e-01 8.2352942228317261e-01</leafValues></_>
<_>
<internalNodes>
0 -1 8 1.8704606220126152e-02</internalNodes>
<leafValues>
-9.6440863609313965e-01 7.2768908739089966e-01</leafValues></_>
<_>
<internalNodes>
0 -1 5 -8.6188763380050659e-03</internalNodes>
<leafValues>
6.9022244215011597e-01 -9.2877584695816040e-01</leafValues></_>
<_>
<internalNodes>
0 -1 4 -3.9481376297771931e-03</internalNodes>
<leafValues>
7.3313444852828979e-01 -8.5507124662399292e-01</leafValues></_></weakClassifiers></_></stages>
<features>
<_>
<rects>
<_>
4 19 9 2 -1.</_>
<_>
7 19 3 2 3.</_></rects>
<tilted>0</tilted></_>
<_>
<rects>
<_>
5 16 8 8 -1.</_>
<_>
5 16 4 4 2.</_>
<_>
9 20 4 4 2.</_></rects>
<tilted>0</tilted></_>
<_>
<rects>
<_>
6 12 6 9 -1.</_>
<_>
9 12 3 9 2.</_></rects>
<tilted>0</tilted></_>
<_>
<rects>
<_>
6 18 3 1 -1.</_>
<_>
7 18 1 1 3.</_></rects>
<tilted>0</tilted></_>
<_>
<rects>
<_>
6 22 6 4 -1.</_>
<_>
6 22 3 2 2.</_>
<_>
9 24 3 2 2.</_></rects>
<tilted>0</tilted></_>
<_>
<rects>
<_>
13 16 2 9 -1.</_>
<_>
13 19 2 3 3.</_></rects>
<tilted>0</tilted></_>
<_>
<rects>
<_>
13 17 3 1 -1.</_>
<_>
14 17 1 1 3.</_></rects>
<tilted>0</tilted></_>
<_>
<rects>
<_>
14 15 2 1 -1.</_>
<_>
15 15 1 1 2.</_></rects>
<tilted>0</tilted></_>
<_>
<rects>
<_>
17 11 4 14 -1.</_>
<_>
17 18 4 7 2.</_></rects>
<tilted>0</tilted></_>
<_>
<rects>
<_>
19 12 9 9 -1.</_>
<_>
22 12 3 9 3.</_></rects>
<tilted>0</tilted></_>
<_>
<rects>
<_>
22 14 8 14 -1.</_>
<_>
22 14 4 7 2.</_>
<_>
26 21 4 7 2.</_></rects>
<tilted>0</tilted></_></features></cascade>
</opencv_storage>
19 changes: 19 additions & 0 deletions logo/params.xml
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@@ -0,0 +1,19 @@
<?xml version="1.0"?>
<opencv_storage>
<params>
<stageType>BOOST</stageType>
<featureType>HAAR</featureType>
<height>32</height>
<width>32</width>
<stageParams>
<boostType>GAB</boostType>
<minHitRate>9.9500000476837158e-01</minHitRate>
<maxFalseAlarm>1.0000000149011612e-01</maxFalseAlarm>
<weightTrimRate>9.4999999999999996e-01</weightTrimRate>
<maxDepth>1</maxDepth>
<maxWeakCount>100</maxWeakCount></stageParams>
<featureParams>
<maxCatCount>0</maxCatCount>
<featSize>1</featSize>
<mode>BASIC</mode></featureParams></params>
</opencv_storage>
12 changes: 12 additions & 0 deletions logo/stage0.xml
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@@ -0,0 +1,12 @@
<?xml version="1.0"?>
<opencv_storage>
<stage0>
<maxWeakCount>1</maxWeakCount>
<stageThreshold>9.9600797891616821e-01</stageThreshold>
<weakClassifiers>
<_>
<internalNodes>
0 -1 360442 -1.7688340740278363e-03</internalNodes>
<leafValues>
9.9600797891616821e-01 -1.</leafValues></_></weakClassifiers></stage0>
</opencv_storage>
17 changes: 17 additions & 0 deletions logo/stage1.xml
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@@ -0,0 +1,17 @@
<?xml version="1.0"?>
<opencv_storage>
<stage1>
<maxWeakCount>2</maxWeakCount>
<stageThreshold>-2.6523093692958355e-03</stageThreshold>
<weakClassifiers>
<_>
<internalNodes>
0 -1 166367 1.8934525549411774e-02</internalNodes>
<leafValues>
-9.5247524976730347e-01 9.7171717882156372e-01</leafValues></_>
<_>
<internalNodes>
0 -1 466264 2.6852805167436600e-02</internalNodes>
<leafValues>
-9.7436946630477905e-01 9.5748162269592285e-01</leafValues></_></weakClassifiers></stage1>
</opencv_storage>
27 changes: 27 additions & 0 deletions logo/stage2.xml
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@@ -0,0 +1,27 @@
<?xml version="1.0"?>
<opencv_storage>
<stage2>
<maxWeakCount>4</maxWeakCount>
<stageThreshold>-3.0785626173019409e-01</stageThreshold>
<weakClassifiers>
<_>
<internalNodes>
0 -1 432787 4.7774590551853180e-02</internalNodes>
<leafValues>
-9.5717346668243408e-01 8.3864915370941162e-01</leafValues></_>
<_>
<internalNodes>
0 -1 142016 1.0246830061078072e-02</internalNodes>
<leafValues>
-9.2844742536544800e-01 8.5343450307846069e-01</leafValues></_>
<_>
<internalNodes>
0 -1 188405 -2.2323392331600189e-02</internalNodes>
<leafValues>
7.4373298883438110e-01 -8.7865394353866577e-01</leafValues></_>
<_>
<internalNodes>
0 -1 343658 2.2783260792493820e-03</internalNodes>
<leafValues>
-9.9281847476959229e-01 6.6059595346450806e-01</leafValues></_></weakClassifiers></stage2>
</opencv_storage>
27 changes: 27 additions & 0 deletions logo/stage3.xml
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@@ -0,0 +1,27 @@
<?xml version="1.0"?>
<opencv_storage>
<stage3>
<maxWeakCount>4</maxWeakCount>
<stageThreshold>-3.2509520649909973e-01</stageThreshold>
<weakClassifiers>
<_>
<internalNodes>
0 -1 193289 2.2154441103339195e-03</internalNodes>
<leafValues>
-8.5714286565780640e-01 8.2352942228317261e-01</leafValues></_>
<_>
<internalNodes>
0 -1 405647 1.8704606220126152e-02</internalNodes>
<leafValues>
-9.6440863609313965e-01 7.2768908739089966e-01</leafValues></_>
<_>
<internalNodes>
0 -1 343163 -8.6188763380050659e-03</internalNodes>
<leafValues>
6.9022244215011597e-01 -9.2877584695816040e-01</leafValues></_>
<_>
<internalNodes>
0 -1 195738 -3.9481376297771931e-03</internalNodes>
<leafValues>
7.3313444852828979e-01 -8.5507124662399292e-01</leafValues></_></weakClassifiers></stage3>
</opencv_storage>
77 changes: 77 additions & 0 deletions samples/detection_demo.cpp
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#include <iostream>
#include "detection.hpp"

using namespace std;
using namespace cv;

const char* kOptions =
"{ i image | <none> | image to process }"
"{ v video | <none> | video to process }"
"{ c camera | <none> | camera to get video from }"
"{ m model | <none> | path to detector file }"
"{ h ? help usage | | print help message }";

void DetectOnVideo(VideoCapture vid, CascadeDetector &detector) {
std::vector<cv::Rect> objects;
std::vector<double> scores;
while (true) {
Mat frame;
bool successRead = vid.read(frame);
if (!successRead) break;
detector.Detect(frame, objects, scores);
for (auto obj : objects)
rectangle(frame, obj, Scalar(250, 150, 10));
imshow("Result", frame);
if (waitKey(10) == 27) break;
}

}



int main(int argc, const char** argv) {
// Parse command line arguments.
CommandLineParser parser(argc, argv, kOptions);

// If help option is given, print help message and exit.
if (parser.get<bool>("h")) {
parser.printMessage();
return 0;
}

CascadeDetector detector;
std::vector<cv::Rect> objects;
std::vector<double> scores;
if (parser.has("m")) detector.Init(parser.get<std::string>("m"));




if (parser.has("i")) {
Mat img = imread(parser.get<std::string>("i"));
detector.Detect(img, objects, scores);
if (scores.size() > 0) {
namedWindow("Result");
rectangle(img, objects[0], Scalar(250, 150, 10));
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@TheG1uy, на изображении может быть много объектов одного класса, поэтому здесь должен быть цикл.

imshow("Result", img);
}
else return 0;
}
else if (parser.has("v")) {
VideoCapture video(parser.get<std::string>("v"));
DetectOnVideo(video, detector);
video.release();
}
else if (parser.has("c")) {

VideoCapture cap(0);
if (!cap.isOpened())
return -1;
DetectOnVideo(cap, detector);
cap.release();
}

waitKey(0);

return 0;
}
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