My code is working just fine in debug mode and here is the Output. Once I tried it in release mode I got this error :
Unhandled exception at 0x5E3ADF2C (msvcp120d.dll) in Project4.exe: 0xC0000005: Access violation reading location 0x00000000.
From what I read on the internet, I think it is something that has to do with uninitialized Pointer or some other variable. my bet is on : vector< vector< Point > > contours;in the findSquares function. I tried to intitialize it with everything I could think of but no luck so far.
I am using Visual Studio 2013 with OpenCV.3.0.0 the X86 version.Here is the complet code :
#include <stdio.h>
#include <iostream>
#include "opencv2/core/core.hpp"
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include <sstream>
#include "opencv2/imgproc/imgproc.hpp"
#include <math.h>
#include <string.h>
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#include <algorithm>
using namespace cv;
using namespace std;
// helper function:
// finds a cosine of angle between vectors
// from pt0->pt1 and from pt0->pt2
static double angle(Point pt1, Point pt2, Point pt0)
{
double dx1 = pt1.x - pt0.x;
double dy1 = pt1.y - pt0.y;
double dx2 = pt2.x - pt0.x;
double dy2 = pt2.y - pt0.y;
return (dx1*dx2 + dy1*dy2) / sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) +
1e-10);
}
// returns sequence of squares detected on the image.
// the sequence is stored in the specified memory storage
static void findSquares(const Mat& image, vector<vector<Point> >& squares)
{
squares.clear();
vector<vector<Point>> contours;
// find white and yellow patch
Mat grayscal, grayscal1;
cvtColor(image, grayscal, CV_BGR2GRAY);
// try several threshold levels
for (int l = 0; l < 1; l++)
{
Mat imgThresholded, imgThresholded1, imgThresholded2;
cv::adaptiveThreshold(grayscal, grayscal1, 255,
cv::ADAPTIVE_THRESH_MEAN_C, cv::THRESH_BINARY, 11, 0);
inRange(grayscal, Scalar(100, 100, 100), Scalar(255, 255, 255),
imgThresholded1);
//morphological closing (fill small holes in the foreground)
//dilate(imgThresholded1, imgThresholded1,
getStructuringElement(MORPH_RECT, Size(7, 7)));
erode(imgThresholded1, imgThresholded1,
getStructuringElement(MORPH_RECT, Size(7, 7)));
// find contours and store them all as a list
findContours(imgThresholded1, contours, RETR_LIST, CHAIN_APPROX_SIMPLE);
vector<Point> approx;
// test each contour
for (size_t i = 0; i < contours.size(); i++)
{
// approximate contour with accuracy proportional
// to the contour perimeter
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]),
true)*0.02, true);
// square contours should have 4 vertices after approximation
// relatively large area (to filter out noisy contours)
// and be convex.
// Note: absolute value of an area is used because
// area may be positive or negative - in accordance with the
// contour orientation
if (approx.size() == 4 &&
fabs(contourArea(Mat(approx))) > 4000 &&
fabs(contourArea(Mat(approx))) < 400000 &&
isContourConvex(Mat(approx)))
{
double maxCosine = 0;
for (int j = 2; j < 5; j++)
{
// find the maximum cosine of the angle between joint edges
double cosine = fabs(angle(approx[j % 4], approx[j - 2],
approx[j - 1]));
maxCosine = MAX(maxCosine, cosine);
}
// if cosines of all angles are small
// (all angles are ~90 degree) then write quandrange
// vertices to resultant sequence
if (maxCosine < 0.07)
squares.push_back(approx);
}
}
cout << "size of squares:" << squares.size() << endl;
}
}
// the function draws all the squares in the image
cv::Mat drawSquares(Mat& image, const vector<vector<Point> >& squares)
{
std::vector<cv::Mat> listOfMatrices, listOfMatrices2;
vector<Point> centers;
int m = listOfMatrices.size();
int n = listOfMatrices2.size();
int q = centers.size();
for (size_t i = 0; i < squares.size(); i++)
{
const Point* p = &squares[i][0];
int n = (int)squares[i].size();
Rect r = boundingRect(Mat(squares[i]));
cv::Size inflationSize(2, 2);
r -= inflationSize;
r.x = r.x + r.width / 4;
r.y = r.y + r.height / 4;
r.width = r.width / 2;
r.height = r.height / 2;
//dont detect the border
//Mat roi = image(r);
cv::Mat Image(image);
cv::Mat croppedImage = Image(Rect(r.x, r.y, r.width - 4, r.height - 4));
Point center(r.x + r.width / 2, r.y + r.height / 2);
centers.push_back(center);
q++;
listOfMatrices.push_back(croppedImage);
m++;
}
int maxbleu = 0;
Scalar tempVal0 = mean(listOfMatrices[0]);
double myMAtMeanB0 = tempVal0.val[0];
for (int j = 1; j < q; j++)
{
Scalar tempVal = mean(listOfMatrices[j]);
double myMAtMeanB = tempVal.val[0];
if (myMAtMeanB > myMAtMeanB0)
{
myMAtMeanB0 = myMAtMeanB;
maxbleu = j;
}
}
int maxdistance = 0, indicemax = 0, resmax = 0;
for (int i = 0; i < q; i++)
{
//listOfMatrices[i].release();
double xDiff = abs(centers[maxbleu].x - centers[i].x);
double yDiff = abs(centers[maxbleu].y - centers[i].y);
resmax = sqrt((xDiff * xDiff) + (yDiff * yDiff));
if (i == maxbleu)
{
continue;
}
else if (resmax>maxdistance)
{
maxdistance = resmax;
indicemax = i;
}
}
int mindistance = 1000, indicemin = 0, resmin = 0;
for (int i = 0; i < q; i++)
{
//listOfMatrices[i].release();
double xDiff = abs(centers[maxbleu].x - centers[i].x);
double yDiff = abs(centers[maxbleu].y - centers[i].y);
resmin = sqrt((xDiff * xDiff) + (yDiff * yDiff));
if (i == maxbleu)
{
continue;
}
else if (resmin<mindistance)
{
mindistance = resmin;
indicemin = i;
}
}
cout << "cyan" << centers[indicemax] << endl;
cout << "white" << centers[maxbleu] << endl;
cout << "gray" << centers[indicemin] << endl;
vector<Point> centersV2;
for (int j = 0; j < 4; j++)
{
for (int i = 0; i < 6; i++)
{
if (abs(centers[maxbleu].x - centers[indicemax].x) <
abs(centers[maxbleu].y - centers[indicemax].y))
{
if (centers[maxbleu].y - centers[indicemax].y > 0)
{
if (5 * abs(centers[maxbleu].x - centers[indicemin].x) > 30)
{
Point tmpV2(centers[maxbleu].x - i*(centers[maxbleu].x -
centers[indicemin].x) - j*(centers[maxbleu].x - centers[indicemax].x) / 3.3,
centers[maxbleu].y - i*(abs(centers[maxbleu].y - centers[indicemax].y)) /
5);
centersV2.push_back(tmpV2);
}
else {
Point tmpV2(centers[maxbleu].x - i*(centers[maxbleu].x -
centers[indicemin].x) - j*(centers[maxbleu].x - centers[indicemax].x) / 3,
centers[maxbleu].y - i*(abs(centers[maxbleu].y - centers[indicemax].y)) /
5);
centersV2.push_back(tmpV2);
}
}
else {
if (5 * abs(centers[maxbleu].x - centers[indicemin].x) > 30)
{
Point tmpV2(centers[maxbleu].x - i*
(abs(centers[maxbleu].x - centers[indicemin].x)) - j*(abs(centers[maxbleu].x
- centers[indicemax].x)) / 3.3, centers[maxbleu].y + i*
(abs(centers[maxbleu].y - centers[indicemax].y) / 5));
centersV2.push_back(tmpV2);
}
else {
Point tmpV2(centers[maxbleu].x - i*
(abs(centers[maxbleu].x - centers[indicemin].x)) - j*
(abs(centers[maxbleu].x - centers[indicemax].x)) / 3, centers[maxbleu].y
+ i*(abs(centers[maxbleu].y - centers[indicemax].y) / 5));
centersV2.push_back(tmpV2);
}
}
}
else {
if (centers[maxbleu].x - centers[indicemin].x > 0)
{
if (5 * abs(centers[maxbleu].y - centers[indicemin].y) > 30)
{
Point tmpV2(centers[maxbleu].x - i*
(abs(centers[maxbleu].x - centers[indicemax].x) / 5) + i,
centers[indicemin].y - i*(centers[maxbleu].y - centers[indicemin].y) - j*
(centers[maxbleu].y - centers[indicemax].y) / 3.3);
centersV2.push_back(tmpV2);
}
else {
Point tmpV2(centers[maxbleu].x - i*
(abs(centers[maxbleu].x - centers[indicemax].x) / 5) + i,
centers[indicemin].y - i*(centers[maxbleu].y - centers[indicemin].y) - j*
(centers[maxbleu].y - centers[indicemax].y) / 3);
centersV2.push_back(tmpV2);
}
}
else {
if (5 * abs(centers[maxbleu].y - centers[indicemin].y) > 30)
{
Point tmpV2(centers[maxbleu].x + i*
(abs(centers[maxbleu].x - centers[indicemax].x) / 5) + i,
centers[maxbleu].y - i*((centers[maxbleu].y - centers[indicemin].y)) - j*
(centers[maxbleu].y - centers[indicemax].y) / 3.3);
centersV2.push_back(tmpV2);
}
else
{
Point tmpV2(centers[maxbleu].x + i*
(abs(centers[maxbleu].x - centers[indicemax].x) / 5) + i,
centers[maxbleu].y - i*((centers[maxbleu].y - centers[indicemin].y)) - j*
(centers[maxbleu].y - centers[indicemax].y) / 3);
centersV2.push_back(tmpV2);
}
}
}
}
}
for (int i = 0; i < centersV2.size(); i++)
{
cv::Mat IImage;
image.copyTo(IImage);
cv::Mat roi = IImage(Rect(centersV2[i].x -
0.66*listOfMatrices[maxbleu].size().width / 2, centersV2[i].y -
0.66*listOfMatrices[maxbleu].size().height / 2,
0.66*listOfMatrices[maxbleu].size().width,
0.66*listOfMatrices[maxbleu].size().height));
listOfMatrices2.push_back(roi);
n++;
cout << "centre de patchs :" << i + 1 << " :est:" << centersV2[i] << "
colour :" << mean(listOfMatrices2[i]) << endl;
rectangle(image, Point(centersV2[i].x -
0.66*listOfMatrices[maxbleu].size().width, centersV2[i].y -
0.66*listOfMatrices[maxbleu].size().height), Point(centersV2[i].x +
0.66*listOfMatrices[maxbleu].size().width, centersV2[i].y +
0.66*listOfMatrices[maxbleu].size().height), Scalar(0, 255, 0), 4, 8,
0);
//ellipse(image, centersV2[i],
Size(0.66*listOfMatrices[maxbleu].size().width,
0.66*listOfMatrices[maxbleu].size().height), 0, 0, 360, Scalar(0, 255,
0), 2, LINE_AA);
stringstream numero;
numero << i + 1;
putText(image, numero.str(), Point(centersV2[i].x - 15, centersV2[i].y +
5), 5, 2, Scalar(0, 0, 255), 4, 8, false);
}
}
int main(int /*argc*/, char** /*argv*/)
{
static const char* filename[] = { "E:/Zouhair Jimmouh-Colorimetrie/Example
Etudes/Exemple2/AS1606001A-008-R045-HP-01.jpg", 0};
vector<vector<Point> > Squares;
for (int i = 0; filename[i] != 0; i++)
{
Mat Image = imread(filename[i], 1);
if (Image.empty())
{
cout << "Couldn't load " << endl;
//continue;
}
Mat blackTOwhite;
findSquares(Image, Squares);
(drawSquares(Image, Squares)).copyTo(blackTOwhite);
//show image with detected patches
namedWindow("RECT", CV_WINDOW_NORMAL);
imshow("RECT", Image);
int c = waitKey();
if ((char)c == 27)
break;
}
return 0;
}
Any help guys is appreciated ! i've been struggling with this for days now.
You're mixing debug and release libraries in your linking settings.
From the comments to the question:
I linked both opencv_world300d.lib and opencv_world300.lib in Additional Dependencies.
You shouldn't link to both of them. Link to:
opencv_world300d.lib in Debug
opencv_world300.lib in Release
Related
Here I got the code of extracting the orientation of the object in the image. I am new to OpenCV and C++. But I need to get this work done.
My question is, how to extract, write out the information of the angle and axis in this code?
#include "pch.h"
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
using namespace std;
using namespace cv;
// Function declarations
void drawAxis(Mat&, Point, Point, Scalar, const float);
double getOrientation(const vector<Point> &, Mat&);
void drawAxis(Mat& img, Point p, Point q, Scalar colour, const float scale = 0.2)
{
double angle = atan2((double)p.y - q.y, (double)p.x - q.x); // angle in radians
double hypotenuse = sqrt((double)(p.y - q.y) * (p.y - q.y) + (p.x - q.x) * (p.x - q.x));
// Here we lengthen the arrow by a factor of scale
q.x = (int)(p.x - scale * hypotenuse * cos(angle));
q.y = (int)(p.y - scale * hypotenuse * sin(angle));
line(img, p, q, colour, 1, LINE_AA);
// create the arrow hooks
p.x = (int)(q.x + 9 * cos(angle + CV_PI / 4));
p.y = (int)(q.y + 9 * sin(angle + CV_PI / 4));
line(img, p, q, colour, 1, LINE_AA);
p.x = (int)(q.x + 9 * cos(angle - CV_PI / 4));
p.y = (int)(q.y + 9 * sin(angle - CV_PI / 4));
line(img, p, q, colour, 1, LINE_AA);
}
double getOrientation(const vector<Point> &pts, Mat &img)
{
//Construct a buffer used by the pca analysis
int sz = static_cast<int>(pts.size());
Mat data_pts = Mat(sz, 2, CV_64F);
for (int i = 0; i < data_pts.rows; i++)
{
data_pts.at<double>(i, 0) = pts[i].x;
data_pts.at<double>(i, 1) = pts[i].y;
}
//Perform PCA analysis
PCA pca_analysis(data_pts, Mat(), PCA::DATA_AS_ROW);
//Store the center of the object
Point cntr = Point(static_cast<int>(pca_analysis.mean.at<double>(0, 0)),
static_cast<int>(pca_analysis.mean.at<double>(0, 1)));
//Store the eigenvalues and eigenvectors
vector<Point2d> eigen_vecs(2);
vector<double> eigen_val(2);
for (int i = 0; i < 2; i++)
{
eigen_vecs[i] = Point2d(pca_analysis.eigenvectors.at<double>(i, 0),
pca_analysis.eigenvectors.at<double>(i, 1));
eigen_val[i] = pca_analysis.eigenvalues.at<double>(i);
}
// Draw the principal components
circle(img, cntr, 3, Scalar(255, 0, 255), 2);
Point p1 = cntr + 0.02 * Point(static_cast<int>(eigen_vecs[0].x * eigen_val[0]), static_cast<int>(eigen_vecs[0].y * eigen_val[0]));
Point p2 = cntr - 0.02 * Point(static_cast<int>(eigen_vecs[1].x * eigen_val[1]), static_cast<int>(eigen_vecs[1].y * eigen_val[1]));
drawAxis(img, cntr, p1, Scalar(0, 255, 0), 1);
drawAxis(img, cntr, p2, Scalar(255, 255, 0), 5);
double angle = atan2(eigen_vecs[0].y, eigen_vecs[0].x); // orientation in radians
return angle;
}
int main(int argc, char** argv)
{
// Load image
CommandLineParser parser(argc, argv, "{#input | joint2.bmp | input image}");
parser.about("This program demonstrates how to use OpenCV PCA to extract the orientation of an object.\n");
parser.printMessage();
Mat src = imread(parser.get<String>("#input"));
// Check if image is loaded successfully
if (src.empty())
{
cout << "Problem loading image!!!" << endl;
return EXIT_FAILURE;
}
imshow("src", src);
// Convert image to grayscale
Mat gray;
cvtColor(src, gray, COLOR_BGR2GRAY);
// Convert image to binary
Mat bw;
threshold(gray, bw, 200, 255, THRESH_BINARY | THRESH_OTSU);
// Find all the contours in the thresholded image
vector<vector<Point> > contours;
findContours(bw, contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);
for (size_t i = 0; i < contours.size(); i++)
{
// Calculate the area of each contour
double area = contourArea(contours[i]);
// Ignore contours that are too small or too large
if (area < 1e2 || 1e5 < area) continue;
// Draw each contour only for visualisation purposes
drawContours(src, contours, static_cast<int>(i), Scalar(0, 0, 255), 2);
// Find the orientation of each shape
getOrientation(contours[i], src);
}
imshow("output", src);
waitKey();
return 0;
}
Here is the image of the object:
And here is the result:
As you see, it finds orientation correctly, but I need the information about the angle and which is which axis to be written.
Will be very grateful if someone knows how to do it!
EDIT: I have figured out how to find the information about the center, area, and the angle.
/// Get the moments
vector<Moments> mu(contours.size());
for (size_t i = 0; i < contours.size(); i++)
{
mu[i] = moments(contours[i]);
}
/// Get the mass centers
vector<Point2f> mc(contours.size());
for (size_t i = 0; i < contours.size(); i++)
{
//add 1e-5 to avoid division by zero
mc[i] = Point2f(static_cast<float>(mu[i].m10 / (mu[i].m00 + 1e-5)),
static_cast<float>(mu[i].m01 / (mu[i].m00 + 1e-5)));
}
imshow("output", src);
cout << "\t Info: Area and angle \n";
for (size_t i = 0; i < contours.size(); i++)
{
cout << " * Contour[" << i << "] - Center: "<< mc[i]
<< " - Area: " << contourArea(contours[i]) << " - Angle: " << getOrientation(contours[i],src)*180/CV_PI << endl;
}
But still don't know how to denote which arrow is which axis in the image.
So I have figured out everything I need (almost).
Here is the final code:
#include "pch.h"
#include "opencv2/core.hpp"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include <iostream>
using namespace std;
using namespace cv;
// Function declarations
void drawAxis(Mat&, Point, Point, Scalar, const float);
double getOrientation(const vector<Point> &, Mat&);
string s = "";
void drawAxis(Mat& img, Point p, Point q, Scalar colour, const float scale = 0.2)
{
double angle = atan2((double)p.y - q.y, (double)p.x - q.x); // angle in radians
double hypotenuse = sqrt((double)(p.y - q.y) * (p.y - q.y) + (p.x - q.x) * (p.x - q.x));
// Here we lengthen the arrow by a factor of scale
q.x = (int)(p.x - scale * hypotenuse * cos(angle));
q.y = (int)(p.y - scale * hypotenuse * sin(angle));
line(img, p, q, colour, 1, LINE_AA);
// create the arrow hooks
p.x = (int)(q.x + 9 * cos(angle + CV_PI / 4));
p.y = (int)(q.y + 9 * sin(angle + CV_PI / 4));
line(img, p, q, colour, 1, LINE_AA);
p.x = (int)(q.x + 9 * cos(angle - CV_PI / 4));
p.y = (int)(q.y + 9 * sin(angle - CV_PI / 4));
line(img, p, q, colour, 1, LINE_AA);
}
double getOrientation(const vector<Point> &pts, Mat &img)
{
//Construct a buffer used by the pca analysis
int sz = static_cast<int>(pts.size());
Mat data_pts = Mat(sz, 2, CV_64F);
for (int i = 0; i < data_pts.rows; i++)
{
data_pts.at<double>(i, 0) = pts[i].x;
data_pts.at<double>(i, 1) = pts[i].y;
}
//Perform PCA analysis
PCA pca_analysis(data_pts, Mat(), PCA::DATA_AS_ROW);
//Store the center of the object
Point cntr = Point(static_cast<int>(pca_analysis.mean.at<double>(0, 0)),
static_cast<int>(pca_analysis.mean.at<double>(0, 1)));
//Store the eigenvalues and eigenvectors
vector<Point2d> eigen_vecs(2);
vector<double> eigen_val(2);
for (int i = 0; i < 2; i++)
{
eigen_vecs[i] = Point2d(pca_analysis.eigenvectors.at<double>(i, 0),
pca_analysis.eigenvectors.at<double>(i, 1));
eigen_val[i] = pca_analysis.eigenvalues.at<double>(i);
}
// Draw the principal components
circle(img, cntr, 3, Scalar(255, 0, 255), 2);
Point p1 = cntr + 0.01 * Point(static_cast<int>(eigen_vecs[0].x * eigen_val[0]), static_cast<int>(eigen_vecs[0].y * eigen_val[0]));
Point p2 = cntr - 0.005 * Point(static_cast<int>(eigen_vecs[1].x * eigen_val[1]), static_cast<int>(eigen_vecs[1].y * eigen_val[1]));
drawAxis(img, cntr, p1, Scalar(0, 255, 0), 1);
putText(img, s = "Y-axis", p1, cv::FONT_HERSHEY_COMPLEX_SMALL, 1, cv::Scalar(255, 0, 100));
drawAxis(img, cntr, p2, Scalar(255, 255, 0), 5);
putText(img, s = "X-axis", p2/1.1 , cv::FONT_HERSHEY_COMPLEX_SMALL, 1, cv::Scalar(255, 0, 255));
double angle = atan2(eigen_vecs[0].y, eigen_vecs[0].x); // orientation in radians
return angle;
}
int main(int argc, char** argv)
{
// Load image
CommandLineParser parser(argc, argv, "{#input | circle3.bmp | input image}");
parser.about("This program demonstrates how to use OpenCV PCA to extract the orientation of an object.\n");
parser.printMessage();
Mat src = imread(parser.get<String>("#input"));
// Check if image is loaded successfully
if (src.empty())
{
cout << "Problem loading image!!!" << endl;
return EXIT_FAILURE;
}
imshow("src", src);
// Convert image to grayscale
Mat gray;
cvtColor(src, gray, COLOR_BGR2GRAY);
// Convert image to binary
Mat bw;
threshold(gray, bw, 70, 255, THRESH_BINARY | THRESH_OTSU);
// Find all the contours in the thresholded image
vector<vector<Point> > contours;
findContours(bw, contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);
for (size_t i = 0; i < contours.size(); i++)
{
// Calculate the area of each contour
double area = contourArea(contours[i]);
// Ignore contours that are too small or too large
if (area < 1e2 || 1e5 < area) continue;
// Draw each contour only for visualisation purposes
drawContours(src, contours, static_cast<int>(i), Scalar(0, 0, 255), 2);
// Find the orientation of each shape
getOrientation(contours[i], src);
}
/// Get the moments
vector<Moments> mu(contours.size());
for (size_t i = 0; i < contours.size(); i++)
{
mu[i] = moments(contours[i]);
}
/// Get the mass centers
vector<Point2f> mc(contours.size());
for (size_t i = 0; i < contours.size(); i++)
{
//add 1e-5 to avoid division by zero
mc[i] = Point2f(static_cast<float>(mu[i].m10 / (mu[i].m00 + 1e-5)),
static_cast<float>(mu[i].m01 / (mu[i].m00 + 1e-5)));
for (int i = 0; i < contours.size(); i++) {
std::stringstream ss; ss << i;
putText(src, ss.str(), mc[i] + Point2f(10,-10), cv::FONT_HERSHEY_COMPLEX_SMALL, 1, cv::Scalar(255, 0, 255));
}
}
imshow("output", src);
cout << "\t Info: Area and angle \n";
for (size_t i = 0; i < contours.size(); i++)
{
cout << " * Contour[" << i << "] - Center: "<< mc[i]
<< " - Area: " << contourArea(contours[i]) << " - Angle X: " << getOrientation(contours[i],src)*180/CV_PI << endl;
}
waitKey();
return 0;
}
The only thing I want to know how to draw the coordinate system in the corner of the image. Because I don't understand the angle results.
The results of the final code: https://imgur.com/l7t9bns
And relevant information: https://imgur.com/OuE79rR
I'm making a painterly rendering.
And now I'm doing that clipping line things.
But I got this error:
<<unsigned><pt.x*DataType<_Tp>::channels> <<unsigned<size.p[1]*channels<>>>
And
template<typename _Tp> inline const _Tp& Mat::at(int i0, int i1) const
{
CV_DbgAssert( dims <= 2 && data && (unsigned)i0 < (unsigned)size.p[0] &&
(unsigned)(i1*DataType<_Tp>::channels) < (unsigned)(size.p[1]*channels()) &&
CV_ELEM_SIZE1(DataType<_Tp>::depth) == elemSize1());
return ((const _Tp*)(data + step.p[0]*i0))[i1];
}
Maybe this is the error that on 'Lineclipping()'
Please, tell me another good idea that clipped line.
this is my code. And I'm just a student so my codding skill is very beginner.
#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <sstream>
#include <cmath>
#include <stdio.h>
#include <cstdlib>
#include <time.h>
#include <random>
using namespace cv;
using namespace std;
random_device rd;
mt19937_64 rng(rd());
double PI = 3.141592;
int perturbLength = (rand() % 6) + 1;
int perturbRadius = ((rand() % 5) + 0) / 10;
int perturbAngle = (rand() % 15) + (-15);
int Maxlength = 10 - perturbLength;
int radius = 2 - perturbRadius;
int angle = 45 - perturbAngle;
double theta = angle*(PI / 180);
void Lineclipping(int x, int y, double theta, int len, Point2d& pt1, Point2d& pt2, Mat& EdgeMap)
{
double length = ceil(len);
enter code here
float detectPT = len / length;
for (int i = detectPT; i <= len;)
{
Point2d Mpt1(x + length*cos(theta), y + length*sin(theta));
if (EdgeMap.at<uchar>(Mpt1.y, Mpt1.x) > 0)
{
pt1.x = Mpt1.x;
pt1.y = Mpt1.y;
}
else if (i == length)
{
pt1.x = Mpt1.x;
pt1.y = Mpt1.y;
}
i = i + detectPT;
}
for (int i = detectPT; i <= len;)
{
Point2d Mpt2(x - length*cos(theta), y - length*sin(theta));
if (EdgeMap.at<uchar>(Mpt2.y, Mpt2.x) > 0)
{
pt2.x = Mpt2.x;
pt2.y = Mpt2.y;
}
else if (i == length)
{
pt2.x = Mpt2.x;
pt2.y = Mpt2.y;
}
i = i + detectPT;
}
}
Mat EdgeDetect(Mat& referenceimg, Mat& Edge)
{
Mat image = referenceimg.clone();
//Make Edge Map
Mat IntensityImg(image.size(), CV_8U, 255);
Mat sobelx, sobely;
for (int i = 0; i < image.rows; i++)
{
for (int j = 0; j < image.cols; j++)
{
Vec3b intensity = image.at<Vec3b>(j, i);
uchar blue = intensity.val[0];
uchar green = intensity.val[1];
uchar red = intensity.val[2];
IntensityImg.at<uchar>(j, i) = (30 * red + 59 * green + 11 * blue) / 100;
}
}
GaussianBlur(IntensityImg, IntensityImg, Size(5, 5), 0.1, 0.1);
Sobel(IntensityImg, sobelx, CV_32F, 1, 0);
Sobel(IntensityImg, sobely, CV_32F, 0, 1);
Mat magnitudeXY = abs(sobelx), abs(sobely);
magnitudeXY.convertTo(Edge, CV_8U);
Mat mask(3, 3, CV_8UC1, 1);
morphologyEx(Edge, Edge, MORPH_ERODE, mask);
for (int i = 0; i < image.rows; i++)
{
for (int j = 0; j < image.cols; j++)
{
Edge.at<uchar>(j, i) = (Edge.at<uchar>(j, i) > 20 ? 255 : 0);
}
}
imshow("intensity", Edge);
return Edge;
}
void paint(Mat &image, int snum)
{
Mat Edge;
EdgeDetect(image, Edge);
for (int n = 0; n < snum; n++)
{
int x = rand() % image.cols;
int y = rand() % image.rows;
if (image.channels() == 1)
{
image.at<uchar>(x, y) = 255;
}
else if (image.channels() == 3)
{
int length = Maxlength / 2;
Point2d pt1(x + length*cos(theta), y + length*sin(theta));
Point2d pt2(x - length*cos(theta), y - length*sin(theta));
Lineclipping(x, y, theta, length, fpt1, fpt2, Edge);
//draw line
Scalar color(image.at<Vec3b>(y, x)[0], image.at<Vec3b>(y, x)[1], image.at<Vec3b>(y, x)[2]);
line(image, pt1, pt2, color, radius);
}
}
}
int main()
{
Mat Img = imread("fruit.jpg", IMREAD_COLOR);
CV_Assert(Img.data);
Mat resultImage = Img.clone();
Mat sobel = Img.clone();
int num = Img.rows*Img.cols;
paint(resultImage, num);
imshow("result", resultImage);
waitKey();
return 0;
}
And This is the error parts.
for (int i = detectPT; i <= len;)
{
Point2d Mpt1(x + length*cos(theta), y + length*sin(theta));
if (EdgeMap.at<uchar>(Mpt1.y, Mpt1.x) > 0)
{
pt1.x = Mpt1.x;
pt1.y = Mpt1.y;
}
else if (i == length)
{
pt1.x = Mpt1.x;
pt1.y = Mpt1.y;
}
i = i + detectPT;
}
for (int i = detectPT; i <= len;)
{
Point2d Mpt2(x - length*cos(theta), y - length*sin(theta));
if (EdgeMap.at<uchar>(Mpt2.y, Mpt2.x) > 0)
{
pt2.x = Mpt2.x;
pt2.y = Mpt2.y;
}
else if (i == length)
{
pt2.x = Mpt2.x;
pt2.y = Mpt2.y;
}
i = i + detectPT;
}
Thank you!
Since I can't compile this and run it, I am going to run through a possible execution and show you where you can hit this out of range error.
int perturbLength = (rand() % 6) + 1; // Range is 1 to 6, let's assume 4
int perturbAngle = (rand() % 15) + (-15); // Range is -15 to -1 let's assume -14
int Maxlength = 10 - perturbLength; // 6
int angle = 45 - perturbAngle; // 44
double theta = angle*(PI / 180); // .7679
Now we get into this code inside the paint method:
int x = rand() % image.cols; // Let's assume image.cols - 2
int y = rand() % image.rows; // Let's assume image.rows - 1
Inside of paint we will reach this code:
int length = Maxlength / 2; // Maxlength is 6 so this is 3
Lineclipping(x, y, theta, length, fpt1, fpt2, Edge);
Which leads to the Lineclipping method and here we get a problem:
Point2d Mpt1(x + length*cos(theta), y + length*sin(theta));
if (EdgeMap.at<uchar>(Mpt1.y, Mpt1.x) > 0)
This is the problem. Remember, x is image.cols - 2. Now we perform the operations x + length * cos(theta), which is (image.cols-2) + 3 * cos(.7679). 3 * cos(.7679) is 2.999 which whether you floor it or round it is going to cause a problem when you add it to image.cols - 2. If it is floored and you get 2 we have image.cols which causes out of range, if it is rounded then we have image.cols + 1, so in either case we go beyond the bounds of the array.
The following code is for partical filter for mouse and I change it for track on video with color, this works.
But I want to add scale to it now which only works with x and y. I tried to add scale to it but I failed. Please help me to add scale to the object detected to partical filter.
// Module "core"
#include <opencv2/core/core.hpp>
#include < opencv2/video/background_segm.hpp>
// Module "highgui"
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/legacy/legacy.hpp>
// Module "imgproc"
#include <opencv2/imgproc/imgproc.hpp>
#include "opencv2/videostab/videostab.hpp"
// Module "video"
#include <opencv2/video/video.hpp>
// Output
#include <iostream>
// Vector
#include <vector>
#define drawCross( center, color, d ) \
line( frame, cv::Point( center.x - d, center.y - d ), \
cv::Point( center.x + d, center.y + d ), color, 1, CV_AA, 0); \
line( frame, cv::Point( center.x + d, center.y - d ), \
cv::Point( center.x - d, center.y + d ), color, 1, CV_AA, 0 )
#define PLOT_PARTICLES 1
using namespace std;
using namespace cv;
// >>>>> Color to be tracked
#define MIN_H_BLUE 200
#define MAX_H_BLUE 300
// <<<<< Color to be tracked
vector<cv::Point> mouseV, particleV;
int main()
{
// Camera frame
cv::Mat frame;
char code = (char)-1;
cv::namedWindow("mouse particle");
cv::Mat_<float> measurement(2,1);
measurement.setTo(cv::Scalar(0));
int dim = 2;
int nParticles = 300;
float xRange = 650.0;
float yRange = 650.0;
float minRange[] = { 0, 0 };
float maxRange[] = { xRange, yRange };
CvMat LB, UB;
cvInitMatHeader(&LB, 2, 1, CV_32FC1, minRange);
cvInitMatHeader(&UB, 2, 1, CV_32FC1, maxRange);
CvConDensation* condens = cvCreateConDensation(dim, dim, nParticles);
cvConDensInitSampleSet(condens, &LB, &UB);
condens->DynamMatr[0] = 1.0;
condens->DynamMatr[1] = 0.0;
condens->DynamMatr[2] = 0.0;
condens->DynamMatr[3] = 1.0;
// Camera Index
string idx = "a.mp4";
// Camera Capture
cv::VideoCapture cap;
// >>>>> Camera Settings
if (!cap.open(idx))
{
cout << "Webcam not connected.\n" << "Please verify\n";
return EXIT_FAILURE;
}
cap.set(CV_CAP_PROP_FRAME_WIDTH, 1024);
cap.set(CV_CAP_PROP_FRAME_HEIGHT, 768);
// <<<<< Camera Settings
cout << "\nHit 'q' to exit...\n";
char ch = 0;
double ticks = 0;
bool found = false;
int notFoundCount = 0;
// >>>>> Main loop
while (ch != 'q' && ch != 'Q')
{
double precTick = ticks;
ticks = (double) cv::getTickCount();
double dT = (ticks - precTick) / cv::getTickFrequency(); //seconds
// Frame acquisition
cap >> frame;
mouseV.clear();
particleV.clear();
// >>>>> Noise smoothing
cv::Mat blur;
cv::GaussianBlur(frame, blur, cv::Size(5, 5), 3.0, 3.0);
// <<<<< Noise smoothing
// >>>>> HSV conversion
cv::Mat frmHsv;
cv::cvtColor(blur, frmHsv, CV_BGR2HSV);
// <<<<< HSV conversion
// >>>>> Color Thresholding
// Note: change parameters for different colors
cv::Mat rangeRes = cv::Mat::zeros(frame.size(), CV_8UC1);
cv::inRange(frmHsv, cv::Scalar(MIN_H_BLUE / 2, 100, 80),
cv::Scalar(MAX_H_BLUE / 2, 255, 255), rangeRes);
// <<<<< Color Thresholding
// >>>>> Improving the result
cv::erode(rangeRes, rangeRes, cv::Mat(), cv::Point(-1, -1), 2);
cv::dilate(rangeRes, rangeRes, cv::Mat(), cv::Point(-1, -1), 2);
// <<<<< Improving the result
// >>>>> Contours detection
vector<vector<cv::Point> > contours;
cv::findContours(rangeRes, contours, CV_RETR_EXTERNAL,
CV_CHAIN_APPROX_NONE);
// <<<<< Contours detection
// >>>>> Filtering
vector<vector<cv::Point> > balls;
vector<cv::Rect> ballsBox;
for (size_t i = 0; i < contours.size(); i++)
{
cv::Rect bBox;
bBox = cv::boundingRect(contours[i]);
float ratio = (float) bBox.width / (float) bBox.height;
if (ratio > 1.0f)
ratio = 1.0f / ratio;
// Searching for a bBox almost square
// if (ratio > 0.55 && bBox.area() >= 50)
// {
balls.push_back(contours[i]);
ballsBox.push_back(bBox);
measurement(0) = bBox.x;
measurement(1) = bBox.y;
measurement(2) = ballsBox.size();
//cout << "Balls found:" << bBox.x << endl;
// }
}
/*
cout << "Balls found:" << ballsBox.size() << endl;
*/
cv::Point measPt(measurement(0),measurement(1));
mouseV.push_back(measPt);
for (int i = 0; i < condens->SamplesNum; i++) {
float diffX = (measurement(0) - condens->flSamples[i][0])/xRange;
float diffY = (measurement(1) - condens->flSamples[i][1])/yRange;
condens->flConfidence[i] = 1.0 / (sqrt(diffX * diffX + diffY * diffY));
// plot particles
#ifdef PLOT_PARTICLES
cv::Point partPt(condens->flSamples[i][0], condens->flSamples[i][1]);
drawCross(partPt , cv::Scalar(255,0,255), 2);
#endif
}
cvConDensUpdateByTime(condens);
cv::Point statePt(condens->State[0], condens->State[1]);
particleV.push_back(statePt);
for (int i = 0; i < particleV.size() - 1; i++) {
line(frame, particleV[i], particleV[i+1], cv::Scalar(0,255,0), 1);
}
drawCross( statePt, cv::Scalar(255,255,255), 5 );
drawCross( measPt, cv::Scalar(0,0,255), 5 );
for (size_t i = 0; i < balls.size(); i++)
{
cv::drawContours(frame, balls, i, CV_RGB(20,150,20), 1);
cv::rectangle(frame, ballsBox[i], CV_RGB(0,255,0), 2);
cv::Point center;
center.x = ballsBox[i].x + ballsBox[i].width / 2;
center.y = ballsBox[i].y + ballsBox[i].height / 2;
cv::circle(frame, center, 2, CV_RGB(20,150,20), -1);
stringstream sstr;
sstr << "(" << center.x << "," << center.y << ")";
cv::putText(frame, sstr.str(),
cv::Point(center.x + 3, center.y - 3),
cv::FONT_HERSHEY_SIMPLEX, 0.5, CV_RGB(20,150,20), 2);
}
cv::imshow("mouse particle", frame);
cv::imshow("ssssssss", rangeRes);
ch = cv::waitKey(1);
}
// <<<<< Main loop
return EXIT_SUCCESS;
}
#robot_sherrick answered me this question, this is a follow-up question for his answer.
cv::SimpleBlobDetector in Opencv 2.4 looks very exciting but I am not sure I can make it work for more detailed data extraction.
I have the following concerns:
if this only returns center of the blob, I can't have an entire, labelled Mat, can I?
how can I access the features of the detected blobs like area, convexity, color and so on?
can I display an exact segmentation with this? (like with say, waterfall)
So the code should look something like this:
cv::Mat inputImg = imread(image_file_name, CV_LOAD_IMAGE_COLOR); // Read a file
cv::SimpleBlobDetector::Params params;
params.minDistBetweenBlobs = 10.0; // minimum 10 pixels between blobs
params.filterByArea = true; // filter my blobs by area of blob
params.minArea = 20.0; // min 20 pixels squared
params.maxArea = 500.0; // max 500 pixels squared
SimpleBlobDetector myBlobDetector(params);
std::vector<cv::KeyPoint> myBlobs;
myBlobDetector.detect(inputImg, myBlobs);
If you then want to have these keypoints highlighted on your image:
cv::Mat blobImg;
cv::drawKeypoints(inputImg, myBlobs, blobImg);
cv::imshow("Blobs", blobImg);
To access the info in the keypoints, you then just access each element like so:
for(std::vector<cv::KeyPoint>::iterator blobIterator = myBlobs.begin(); blobIterator != myBlobs.end(); blobIterator++){
std::cout << "size of blob is: " << blobIterator->size << std::endl;
std::cout << "point is at: " << blobIterator->pt.x << " " << blobIterator->pt.y << std::endl;
}
Note: this has not been compiled and may have typos.
Here is a version that will allow you to get the last contours back, via the getContours() method. They will match up by index to the keypoints.
class BetterBlobDetector : public cv::SimpleBlobDetector
{
public:
BetterBlobDetector(const cv::SimpleBlobDetector::Params ¶meters = cv::SimpleBlobDetector::Params());
const std::vector < std::vector<cv::Point> > getContours();
protected:
virtual void detectImpl( const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask=cv::Mat()) const;
virtual void findBlobs(const cv::Mat &image, const cv::Mat &binaryImage,
std::vector<Center> ¢ers, std::vector < std::vector<cv::Point> >&contours) const;
};
Then cpp
using namespace cv;
BetterBlobDetector::BetterBlobDetector(const SimpleBlobDetector::Params ¶meters)
{
}
void BetterBlobDetector::findBlobs(const cv::Mat &image, const cv::Mat &binaryImage,
vector<Center> ¢ers, std::vector < std::vector<cv::Point> >&curContours) const
{
(void)image;
centers.clear();
curContours.clear();
std::vector < std::vector<cv::Point> >contours;
Mat tmpBinaryImage = binaryImage.clone();
findContours(tmpBinaryImage, contours, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
for (size_t contourIdx = 0; contourIdx < contours.size(); contourIdx++)
{
Center center;
center.confidence = 1;
Moments moms = moments(Mat(contours[contourIdx]));
if (params.filterByArea)
{
double area = moms.m00;
if (area < params.minArea || area >= params.maxArea)
continue;
}
if (params.filterByCircularity)
{
double area = moms.m00;
double perimeter = arcLength(Mat(contours[contourIdx]), true);
double ratio = 4 * CV_PI * area / (perimeter * perimeter);
if (ratio < params.minCircularity || ratio >= params.maxCircularity)
continue;
}
if (params.filterByInertia)
{
double denominator = sqrt(pow(2 * moms.mu11, 2) + pow(moms.mu20 - moms.mu02, 2));
const double eps = 1e-2;
double ratio;
if (denominator > eps)
{
double cosmin = (moms.mu20 - moms.mu02) / denominator;
double sinmin = 2 * moms.mu11 / denominator;
double cosmax = -cosmin;
double sinmax = -sinmin;
double imin = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmin - moms.mu11 * sinmin;
double imax = 0.5 * (moms.mu20 + moms.mu02) - 0.5 * (moms.mu20 - moms.mu02) * cosmax - moms.mu11 * sinmax;
ratio = imin / imax;
}
else
{
ratio = 1;
}
if (ratio < params.minInertiaRatio || ratio >= params.maxInertiaRatio)
continue;
center.confidence = ratio * ratio;
}
if (params.filterByConvexity)
{
vector < Point > hull;
convexHull(Mat(contours[contourIdx]), hull);
double area = contourArea(Mat(contours[contourIdx]));
double hullArea = contourArea(Mat(hull));
double ratio = area / hullArea;
if (ratio < params.minConvexity || ratio >= params.maxConvexity)
continue;
}
center.location = Point2d(moms.m10 / moms.m00, moms.m01 / moms.m00);
if (params.filterByColor)
{
if (binaryImage.at<uchar> (cvRound(center.location.y), cvRound(center.location.x)) != params.blobColor)
continue;
}
//compute blob radius
{
vector<double> dists;
for (size_t pointIdx = 0; pointIdx < contours[contourIdx].size(); pointIdx++)
{
Point2d pt = contours[contourIdx][pointIdx];
dists.push_back(norm(center.location - pt));
}
std::sort(dists.begin(), dists.end());
center.radius = (dists[(dists.size() - 1) / 2] + dists[dists.size() / 2]) / 2.;
}
centers.push_back(center);
curContours.push_back(contours[contourIdx]);
}
static std::vector < std::vector<cv::Point> > _contours;
const std::vector < std::vector<cv::Point> > BetterBlobDetector::getContours() {
return _contours;
}
void BetterBlobDetector::detectImpl(const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, const cv::Mat&) const
{
//TODO: support mask
_contours.clear();
keypoints.clear();
Mat grayscaleImage;
if (image.channels() == 3)
cvtColor(image, grayscaleImage, CV_BGR2GRAY);
else
grayscaleImage = image;
vector < vector<Center> > centers;
vector < vector<cv::Point> >contours;
for (double thresh = params.minThreshold; thresh < params.maxThreshold; thresh += params.thresholdStep)
{
Mat binarizedImage;
threshold(grayscaleImage, binarizedImage, thresh, 255, THRESH_BINARY);
vector < Center > curCenters;
vector < vector<cv::Point> >curContours, newContours;
findBlobs(grayscaleImage, binarizedImage, curCenters, curContours);
vector < vector<Center> > newCenters;
for (size_t i = 0; i < curCenters.size(); i++)
{
bool isNew = true;
for (size_t j = 0; j < centers.size(); j++)
{
double dist = norm(centers[j][ centers[j].size() / 2 ].location - curCenters[i].location);
isNew = dist >= params.minDistBetweenBlobs && dist >= centers[j][ centers[j].size() / 2 ].radius && dist >= curCenters[i].radius;
if (!isNew)
{
centers[j].push_back(curCenters[i]);
size_t k = centers[j].size() - 1;
while( k > 0 && centers[j][k].radius < centers[j][k-1].radius )
{
centers[j][k] = centers[j][k-1];
k--;
}
centers[j][k] = curCenters[i];
break;
}
}
if (isNew)
{
newCenters.push_back(vector<Center> (1, curCenters[i]));
newContours.push_back(curContours[i]);
//centers.push_back(vector<Center> (1, curCenters[i]));
}
}
std::copy(newCenters.begin(), newCenters.end(), std::back_inserter(centers));
std::copy(newContours.begin(), newContours.end(), std::back_inserter(contours));
}
for (size_t i = 0; i < centers.size(); i++)
{
if (centers[i].size() < params.minRepeatability)
continue;
Point2d sumPoint(0, 0);
double normalizer = 0;
for (size_t j = 0; j < centers[i].size(); j++)
{
sumPoint += centers[i][j].confidence * centers[i][j].location;
normalizer += centers[i][j].confidence;
}
sumPoint *= (1. / normalizer);
KeyPoint kpt(sumPoint, (float)(centers[i][centers[i].size() / 2].radius));
keypoints.push_back(kpt);
_contours.push_back(contours[i]);
}
}
//Access SimpleBlobDetector datas for video
#include "opencv2/imgproc/imgproc.hpp" //
#include "opencv2/highgui/highgui.hpp"
#include <iostream>
#include <math.h>
#include <vector>
#include <fstream>
#include <string>
#include <sstream>
#include <algorithm>
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/features2d/features2d.hpp"
using namespace cv;
using namespace std;
int main(int argc, char *argv[])
{
const char* fileName ="C:/Users/DAGLI/Desktop/videos/new/m3.avi";
VideoCapture cap(fileName); //
if(!cap.isOpened()) //
{
cout << "Couldn't open Video " << fileName << "\n";
return -1;
}
for(;;) // videonun frameleri icin sonsuz dongu
{
Mat frame,labelImg;
cap >> frame;
if(frame.empty()) break;
//imshow("main",frame);
Mat frame_gray;
cvtColor(frame,frame_gray,CV_RGB2GRAY);
//////////////////////////////////////////////////////////////////////////
// convert binary_image
Mat binaryx;
threshold(frame_gray,binaryx,120,255,CV_THRESH_BINARY);
Mat src, gray, thresh, binary;
Mat out;
vector<KeyPoint> keyPoints;
SimpleBlobDetector::Params params;
params.minThreshold = 120;
params.maxThreshold = 255;
params.thresholdStep = 100;
params.minArea = 20;
params.minConvexity = 0.3;
params.minInertiaRatio = 0.01;
params.maxArea = 1000;
params.maxConvexity = 10;
params.filterByColor = false;
params.filterByCircularity = false;
src = binaryx.clone();
SimpleBlobDetector blobDetector( params );
blobDetector.create("SimpleBlob");
blobDetector.detect( src, keyPoints );
drawKeypoints( src, keyPoints, out, CV_RGB(255,0,0), DrawMatchesFlags::DEFAULT);
cv::Mat blobImg;
cv::drawKeypoints(frame, keyPoints, blobImg);
cv::imshow("Blobs", blobImg);
for(int i=0; i<keyPoints.size(); i++){
//circle(out, keyPoints[i].pt, 20, cvScalar(255,0,0), 10);
//cout<<keyPoints[i].response<<endl;
//cout<<keyPoints[i].angle<<endl;
//cout<<keyPoints[i].size()<<endl;
cout<<keyPoints[i].pt.x<<endl;
cout<<keyPoints[i].pt.y<<endl;
}
imshow( "out", out );
if ((cvWaitKey(40)&0xff)==27) break; // esc 'ye basilinca break
}
system("pause");
}
For a project I'm writing some code to compute the HoG of some images, but I'm stuck with the fact that my orientations are only between 0 ~ 90 degrees, while using the atan2 function.
I'm guessing that this problem occurs due to the filter2D function of OpenCV but I'm not sure if this is the reason or that I'm doing something else wrong:
Vector<Vector<Mat_<float>>> HoG(Mat image) {
Mat img_x;
Mat img_y;
IplImage img = image;
Mat kern_x = (Mat_<char>(1, 3) << -1, 0, 1);
Mat kern_y = (Mat_<char>(3, 1) << -1, 0, 1);
filter2D(image, img_x, image.depth(), kern_x);
filter2D(image, img_y, image.depth(), kern_y);
Vector<Vector<Mat_<float>>> histograms;
for(int y = 0; y < image.rows - size; y += size) {
Vector<Mat_<float>> temp_hist;
for(int x = 0; x < image.cols - size; x += size) {
float total_mag = 0;
Mat hist = Mat::zeros(1, 8, CV_32FC1);
for(int i = y; i < y + size; ++i) {
for(int j = x; j < x + size; ++j) {
float grad_x = (float)img_x.at<uchar>(i, j);
float grad_y = (float)img_y.at<uchar>(i, j);
double ori = myatan2(grad_x, grad_y);
float mag = sqrt(pow(grad_x, 2) + pow(grad_y, 2));
int bin = round(ori/45);
hist.at<float>(0, (bin - 1 < 0 ? 7 : bin - 1)) += - (float)(ori - ((round(ori/45) - 1) * 45.0 + 22.5)) / 45.0f;
hist.at<float>(0, bin) += -(float)(ori - ((round(ori/45) - 1) * 45.0 + 22.5)) / 45.0f;
total_mag += mag;
}
}
// Normalize the histogram
for(int i = 0; i < 8; ++i) {
hist.at<float>(0, i) = hist.at<float>(0, i) / total_mag;
}
temp_hist.push_back(hist);
}
histograms.push_back(temp_hist);
}
return histograms;
}
If you have any other tips to increase a speed-up in my code or something else that is also welcome of course.
I notice this:
float grad_x = (float)img_x.at<uchar>(i, j);
float grad_y = (float)img_y.at<uchar>(i, j);
You seem to be using uchar. Should this not be char?