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This commit is contained in:
kennyboy55
2017-12-20 16:41:32 +01:00
parent 6c3eeac6fb
commit f1b60e4fcb
12 changed files with 323 additions and 140 deletions
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NeuralDetector/Camera.cpp
+54 -9
View File
@@ -8,8 +8,11 @@ Camera::Camera(int port)
Camera::port = port;
capture = VideoCapture(port);
cameraAvailable = true;
if (!capture.isOpened())
{
cameraAvailable = false;
cout << "Failed to open camera on port " << port << endl;
}
@@ -31,6 +34,9 @@ Camera::~Camera()
bool Camera::Calibrate()
{
cout << "Hold a 9x7 chessboard below the camera" << endl;
cout << "Press space bar to take a picture" << endl;
// The number of boards you want to capture, the number of internal corners horizontally
// and the number of internal corners vertically (That's just how the algorithm works).
int numBoards = 10;
@@ -156,23 +162,43 @@ bool Camera::Calibrate()
fs.release();
destroyAllWindows();
cout << "Calibration finished, press enter to exit" << endl;
cin.ignore();
return true;
}
Mat Camera::getImage()
{
Mat imageUndistorted;
Mat image;
Mat RGB_img;
capture >> image;
if (!cameraAvailable)
{
vector<string> files;
string dir = "training/";
read_directory(dir, files);
undistort(image, imageUndistorted, intrinsic, distCoeffs);
Rect region_of_interest = Rect(10, 10, image.cols - 20, image.rows - 20);
Mat image_roi = imageUndistorted(region_of_interest);
string file = files[rand() % files.size()];
return image_roi;
cout << file << endl;
image = imread("training/" + file, CV_LOAD_IMAGE_COLOR);
return image;
}
else
{
Mat imageUndistorted;
Mat RGB_img;
capture >> image;
undistort(image, imageUndistorted, intrinsic, distCoeffs);
Rect region_of_interest = Rect(10, 10, image.cols - 20, image.rows - 20);
Mat image_roi = imageUndistorted(region_of_interest);
return image_roi;
}
}
Mat Camera::takeImage()
@@ -181,9 +207,13 @@ Mat Camera::takeImage()
bool finished = false;
if (!cameraAvailable)
image = getImage();
while (!finished)
{
image = getImage();
if(cameraAvailable)
image = getImage();
imshow("Live feed", image);
@@ -196,3 +226,18 @@ Mat Camera::takeImage()
return image;
}
void Camera::read_directory(const string& name, vector<string> &v)
{
string pattern = name;
pattern.append("\\*");
WIN32_FIND_DATA data;
HANDLE hFind;
if ((hFind = FindFirstFile(pattern.c_str(), &data)) != INVALID_HANDLE_VALUE) {
do {
v.push_back(data.cFileName);
} while (FindNextFile(hFind, &data) != 0);
FindClose(hFind);
}
}
NeuralDetector/Camera.h
+3
View File
@@ -5,6 +5,7 @@
#include <opencv2/calib3d/calib3d.hpp>
#include <iostream>
#include <string>
#include <Windows.h>
#include "avansvisionlib20.h"
@@ -21,5 +22,7 @@ private:
int port = 0;
VideoCapture capture;
Mat intrinsic, distCoeffs;
bool cameraAvailable;
void read_directory(const string& name, vector<string> &v);
};
NeuralDetector/FeatureExtractor.cpp
+12 -1
View File
@@ -16,6 +16,7 @@ FeatureExtractor::~FeatureExtractor()
void FeatureExtractor::Extract(Mat &ref)
{
//Bepaal verschillende features
double ar = AspectRatio();
double cr = Circularity();
double bendingEnergy = getBendingEnergy();
@@ -24,10 +25,10 @@ void FeatureExtractor::Extract(Mat &ref)
double perimeter = getPerimeter();
double numDefects = convexDefects();
//ref = (Mat_<double>(1, 7) << 1.0, convexHullBendingEnergy / 100.0, ar, cr, bendingEnergy / 10000.0, , perimeter / 10000.0);
ref = (Mat_<double>(1, 5) << 1.0, ar, cr / 10.0, convexHullBendingEnergy / 100.0, numDefects / 100.0);
}
//Rotatie onafhankelijke aspect ratio
double FeatureExtractor::AspectRatio()
{
double ar = 0;
@@ -40,6 +41,8 @@ double FeatureExtractor::AspectRatio()
return ar;
}
//Oppervlakte van de minimale circle gedeeld door het oppervlakte van de bounding box
double FeatureExtractor::Circularity()
{
double radius = getMinEnclosingCircleRadius();
@@ -50,11 +53,13 @@ double FeatureExtractor::Circularity()
return cir;
}
//Rotated bounding box om het object
RotatedRect FeatureExtractor::Rectangle()
{
return rect;
}
//De radius van de minimale circle
double FeatureExtractor::getMinEnclosingCircleRadius()
{
float radius;
@@ -66,6 +71,7 @@ double FeatureExtractor::getMinEnclosingCircleRadius()
return rad;
}
//De lengte van het contour
double FeatureExtractor::getPerimeter()
{
double per = arcLength(contour, true);
@@ -73,6 +79,7 @@ double FeatureExtractor::getPerimeter()
return per;
}
//De bending energy van de contour
double FeatureExtractor::getBendingEnergy()
{
double energy = 0;
@@ -92,6 +99,7 @@ double FeatureExtractor::getBendingEnergy()
return energy;
}
//De bending energy van de convex hull
double FeatureExtractor::getConvexHullBendingEnergy() {
vector<Point> convex;
@@ -113,6 +121,7 @@ double FeatureExtractor::getConvexHullBendingEnergy() {
return energy;
}
//Bending energy support methode, zoekt naar de hoek tussen twee pixels volgens de freeman code
int FeatureExtractor::discoverNextRelativeDirection(const cv::Point &pos, const cv::Point &target)
{
for (int i = 0; i < 8; i++) {
@@ -128,6 +137,7 @@ int FeatureExtractor::discoverNextRelativeDirection(const cv::Point &pos, const
return -1;
}
//Het aantal defecten die in de convexhull zitten
double FeatureExtractor::convexDefects()
{
vector<int> hullsI(contour.size()); // Indices to contour points
@@ -138,6 +148,7 @@ double FeatureExtractor::convexDefects()
return (double)defects.size();
}
//Statische help functie om het contour te bepalen in een plaatje
void FeatureExtractor::findContour(Mat &image, vector<Point> &contour)
{
Mat canny_output;
NeuralDetector/Main.cpp
+46 -53
View File
@@ -17,30 +17,41 @@ int main(int argc, char** argv)
Training tr;
cout << "Welcome to Jarvis" << endl;
cout << "Would you like to calibrate the camera (c), take pictures (p), train the network (t) or run the neural network (r)?" << endl;
char c;
cin >> c;
bool running = true;
switch (c) {
case 'c':
cam.Calibrate();
break;
case 'p':
tr.CreateTrainingSet();
break;
case 't':
tr.LoadTrainingSet();
break;
case 'r':
run();
break;
default:
return 0;
while (running)
{
cout << endl << "What would you like to do?" << endl;
cout << "Calibrate the camera (c), take pictures (p), train the network (t), run the neural network (r) or exit (e)?" << endl;
char c;
cin >> c;
cout << endl;
switch (c) {
case 'c':
cam.Calibrate();
break;
case 'p':
tr.CreateTrainingSet();
break;
case 't':
tr.LoadTrainingSet();
break;
case 'r':
run();
break;
case 'e':
running = false;
break;
default:
return 0;
}
}
cout << "The program has finished, press enter to exit" << endl;
cin.ignore();
cout << "The program has finished" << endl;
return 0;
}
@@ -51,7 +62,13 @@ int run()
NeuralNetwork bpn;
bpn.Read();
while (true)
cout << "The neural network is ready to be used" << endl;
cout << "Please place an item below the camera and press the spacebar" << endl << endl;
bool running = true;
while (running)
{
//Take picture and pre-process
Mat image, gray_image, binaryImage;
@@ -92,41 +109,17 @@ int run()
vector<vector<Point>> contours;
contours.push_back(contour);
drawContours(image, contours, -1, CV_RGB(0, 255, 0), 4);
putText(image, cls, cvPoint(15, 30),
FONT_HERSHEY_COMPLEX, 1.0, cvScalar(0, 0, 0), 1, CV_AA);
putText(image, cls, cvPoint(15, 30), FONT_HERSHEY_COMPLEX, 1.0, cvScalar(0, 0, 0), 1, CV_AA);
imshow("Neural detection", image);
waitKey(0);
auto c = waitKey(0);
if (c == 27)
{
running = false;
}
destroyAllWindows();
}
}
/*
// Creeer een witte image
IplImage* iplimage = cvCreateImage(cvSize(binaryImage.cols, binaryImage.rows), IPL_DEPTH_8U, 3);
Mat contourImage = cvarrToMat(iplimage);
contourImage = Scalar(255, 255, 255);
// teken de contouren op de witte image
vector<vector<Point>> contours;
contours.push_back(contour);
Point2f vertices2f[4];
ext.Rectangle().points(vertices2f);
// Convert them so we can use them in a fillConvexPoly
Point vertices[4];
for (int i = 0; i < 4; ++i) {
vertices[i] = vertices2f[i];
}
// Now we can fill the rotated rectangle with our specified color
fillConvexPoly(contourImage, vertices, 4, Scalar(0, 255, 0));
drawContours(contourImage, contours, -1, CV_RGB(255, 0, 0));
imshow("Features", contourImage);
waitKey(0);
*/
}
NeuralDetector/NeuralNetwork.cpp
+46 -8
View File
@@ -4,6 +4,8 @@
#include "NeuralNetwork.h"
using namespace chrono;
NeuralNetwork::NeuralNetwork()
{
V0 = Mat();
@@ -40,11 +42,18 @@ Mat NeuralNetwork::Train(Mat& ITset, Mat& OTset)
// outputError1: error on output for the current input and new calculated
// weighfactors, i.e. V1, W1
double outputError0, outputError1, sumSqrDiffError = MAX_OUTPUT_ERROR + 1;
double lowestError = 10;
Mat V0l, W0l;
Mat V1, W1;
cout << endl << "Starting neural training..." << endl;
cout << endl << "Starting neural training, this might take a while..." << endl;
int runs = 0;
milliseconds lasttime = timestamp();
milliseconds starttime = timestamp();
bool shown = false;
while ((sumSqrDiffError > MAX_OUTPUT_ERROR) && (runs < MAXRUNS)) {
sumSqrDiffError = 0;
@@ -67,16 +76,40 @@ Mat NeuralNetwork::Train(Mat& ITset, Mat& OTset)
sumSqrDiffError += (outputError1 - outputError0) * (outputError1 - outputError0);
if (sumSqrDiffError < lowestError)
{
lowestError = sumSqrDiffError;
V0l = V0;
W0l = W0;
}
V0 = V1;
W0 = W1;
}
runs++;
if (runs % 1000 == 0)
cout << "Completed " << runs << " runs, still working on it.. (" << sumSqrDiffError << ")" << endl;
if (timestamp() > lasttime + milliseconds(15000) && !shown)
{
cout << "We are still working on it, hold on..." << endl;
shown = true;
}
if (runs % 500 == 0)
{
shown = false;
auto runtime = (timestamp() - lasttime) / 1000;
cout << "Completed " << runs << " runs, still working on it.. (" << sumSqrDiffError << ")(" << runtime.count() << "s)" << endl;
lasttime = timestamp();
}
}
cout << "Training complete in " << runs << " runs" << endl;
V0 = V0l;
W0 = W0l;
auto totalduration = (timestamp() - starttime) / 1000;
cout << "Training finished in " << runs << " runs, it took " << totalduration.count() << "s" << endl;
cout << "The lowest error rate was " << lowestError << ", we are savind the matching factors" << endl;
Mat inputVectorTrainingSet, outputVectorTrainingSet, outputVectorBPN;
@@ -116,9 +149,6 @@ void NeuralNetwork::Read()
void NeuralNetwork::write()
{
for (pair<int, string> p : classes)
cout << p.first << " - " << p.second << endl;
FileStorage fs("factors.yml", FileStorage::WRITE);
fs << "W0" << W0 << "V0" << V0;
fs.release();
@@ -218,5 +248,13 @@ void NeuralNetwork::mat_class(Mat& ref, string& name)
}
}
name = classes[index] + " " + to_string(maxvalue*100) + "%";
name = classes[index] + " - " + to_string(maxvalue*100) + "%";
}
milliseconds NeuralNetwork::timestamp()
{
milliseconds ms = duration_cast<milliseconds>(
system_clock::now().time_since_epoch());
return ms;
}
NeuralDetector/NeuralNetwork.h
+3
View File
@@ -6,6 +6,7 @@
#include <iomanip>
#include <string>
#include <map>
#include <chrono>
#include "avansvisionlib20.h" // versie 2.0 (!)
@@ -31,4 +32,6 @@ private:
void save_class(const string& name, Mat& ref);
void class_mat(int index, Mat& ref);
void mat_class(Mat& ref, string& name);
chrono::milliseconds timestamp();
};
NeuralDetector/Training.cpp
+5 -4
View File
@@ -1,7 +1,7 @@
#include "Training.h"
using namespace cv;
using namespace std;
using namespace cv;
Training::Training()
@@ -32,6 +32,8 @@ void Training::CreateTrainingSet()
break;
}
cout << "Press space to take a picture, fill in a new category (n) or stop (esc)" << endl;
bool takingPhotos = true;
int i = 0;
@@ -78,6 +80,7 @@ void Training::LoadTrainingSet()
read_directory(dir, files);
cout << "Found " << files.size()-2 << " files in " << dir << endl;
cout << "Please wait while they are being processed..." << endl;
random_shuffle(files.begin(), files.end());
@@ -91,7 +94,7 @@ void Training::LoadTrainingSet()
if (!image.data)
continue;
cout << "Loaded " << file << endl;
//cout << "Loaded " << file << endl;
string classname;
class_name(file, classname);
@@ -120,8 +123,6 @@ void Training::LoadTrainingSet()
bpn.Train(ITset, OTset);
cout << "Training complete" << endl;
cin.ignore();
}
NeuralDetector/calibration.yml
+5 -5
View File
@@ -4,12 +4,12 @@ intrinsic: !!opencv-matrix
rows: 3
cols: 3
dt: d
data: [ 7.7102786562030786e+04, 0., 3.2013752810898518e+02, 0.,
1.1313075108780187e+05, 2.4041984682542122e+02, 0., 0., 1. ]
data: [ 1.9751044074930709e+04, 0., 3.1998804311606534e+02, 0.,
1.8502329761413108e+04, 2.3996853716247819e+02, 0., 0., 1. ]
distCoeffs: !!opencv-matrix
rows: 1
cols: 5
dt: d
data: [ -1.6033489811253014e+02, -3.6422783919263818e-02,
-2.9072693275266626e-02, -3.5312598551078556e-01,
-6.9187392701624002e-07 ]
data: [ -8.1460191784170419e+00, -3.9067690989523931e+03,
1.4768943058283386e-02, 1.2753528858688477e-03,
-2.8023335779025366e-01 ]
NeuralDetector/factors.yml
+60 -60
View File
@@ -4,67 +4,67 @@ W0: !!opencv-matrix
rows: 8
cols: 10
dt: d
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V0: !!opencv-matrix
rows: 5
cols: 8
dt: d
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calibration.yml
+15
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@@ -0,0 +1,15 @@
%YAML:1.0
---
intrinsic: !!opencv-matrix
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classes.yml
+4
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@@ -0,0 +1,4 @@
%YAML:1.0
---
classes: { e0:weerstand, e1:irtrans, e2:switch, e3:reflsens, e4:irsens,
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factors.yml
+70
View File
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