I trained my pc with opencv_traincascade all one day long to detect 2€ coins using more than 6000 positive images similar to the following:

Now, I have just tried to run a simple OpenCV program to see the results and to check the file cascade.xml. The final result is very disappointing:

There are many points on the coin but there are also many other points on the background. Could it be a problem with my positive images used for training? Or maybe, am I using the detectMultiScale() with wrong parameters?

Here's my code:

include "opencv2/opencv.hpp"

using namespace cv;

int main(int, char**) {

  Mat src = imread("2c.jpg", CV_LOAD_IMAGE_COLOR); 

  Mat src_gray;

  std::vector<cv::Rect> money;

  CascadeClassifier euro2_cascade;

  cvtColor(src, src_gray, CV_BGR2GRAY );
  //equalizeHist(src_gray, src_gray);

  if ( !euro2_cascade.load( "/Users/lory/Desktop/cascade.xml" ) ) {
     printf("--(!)Error loading\n");
     return -1;
  }

  euro2_cascade.detectMultiScale( src_gray, money, 1.1, 0, 0, cv::Size(10, 10),cv::Size(2000, 2000) );

  for( size_t i = 0; i < money.size(); i++ ) {
     cv::Point center( money[i].x + money[i].width*0.5, money[i].y + money[i].height*0.5 );
     ellipse( src, center, cv::Size( money[i].width*0.5, money[i].height*0.5), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );
  }

  //namedWindow( "Display window", WINDOW_AUTOSIZE );
  imwrite("result.jpg",src);
}

I have also tried to reduce the number of neighbours but the effect is the same, just with many less points...

I trained my pc with opencv_traincascade all one day long to detect 2€ coins using more than 6000 positive images similar to the following:

Now, I have just tried to run a simple OpenCV program to see the results and to check the file cascade.xml. The final result is very disappointing:

There are many points on the coin but there are also many other points on the background. Could it be a problem with my positive images used for training? Or maybe, am I using the detectMultiScale() with wrong parameters?

Here's my code:

include "opencv2/opencv.hpp"

using namespace cv;

int main(int, char**) {

  Mat src = imread("2c.jpg", CV_LOAD_IMAGE_COLOR); 

  Mat src_gray;

  std::vector<cv::Rect> money;

  CascadeClassifier euro2_cascade;

  cvtColor(src, src_gray, CV_BGR2GRAY );
  //equalizeHist(src_gray, src_gray);

  if ( !euro2_cascade.load( "/Users/lory/Desktop/cascade.xml" ) ) {
     printf("--(!)Error loading\n");
     return -1;
  }

  euro2_cascade.detectMultiScale( src_gray, money, 1.1, 0, 0, cv::Size(10, 10),cv::Size(2000, 2000) );

  for( size_t i = 0; i < money.size(); i++ ) {
     cv::Point center( money[i].x + money[i].width*0.5, money[i].y + money[i].height*0.5 );
     ellipse( src, center, cv::Size( money[i].width*0.5, money[i].height*0.5), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );
  }

  //namedWindow( "Display window", WINDOW_AUTOSIZE );
  imwrite("result.jpg",src);
}

I have also tried to reduce the number of neighbours but the effect is the same, just with many less points...points... Could it be a problem the fact that in positive images there are those 4 corners as background around the coin? I generated png images with Gimp from a shot video showing the coin, so I don't know why opencv_createsamples puts those 4 corners.

I trained my pc with opencv_traincascade all one day long to detect 2€ coins using more than 6000 positive images similar to the following:

Now, I have just tried to run a simple OpenCV program to see the results and to check the file cascade.xml. The final result is very disappointing:

There are many points on the coin but there are also many other points on the background. Could it be a problem with my positive images used for training? Or maybe, am I using the detectMultiScale() with wrong parameters?

Here's my code:

#include "opencv2/opencv.hpp"
using namespace cv;

int main(int, char**) {

  Mat src = imread("2c.jpg", CV_LOAD_IMAGE_COLOR); 

  Mat src_gray;

  std::vector<cv::Rect> money;

  CascadeClassifier euro2_cascade;

  cvtColor(src, src_gray, CV_BGR2GRAY );
  //equalizeHist(src_gray, src_gray);

  if ( !euro2_cascade.load( "/Users/lory/Desktop/cascade.xml" ) ) {
     printf("--(!)Error loading\n");
     return -1;
  }

  euro2_cascade.detectMultiScale( src_gray, money, 1.1, 0, 0, cv::Size(10, 10),cv::Size(2000, 2000) );

  for( size_t i = 0; i < money.size(); i++ ) {
     cv::Point center( money[i].x + money[i].width*0.5, money[i].y + money[i].height*0.5 );
     ellipse( src, center, cv::Size( money[i].width*0.5, money[i].height*0.5), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );
  }

  //namedWindow( "Display window", WINDOW_AUTOSIZE );
  imwrite("result.jpg",src);
}

I have also tried to reduce the number of neighbours but the effect is the same, just with many less points... Could it be a problem the fact that in positive images there are those 4 corners as background around the coin? I generated png images with Gimp from a shot video showing the coin, so I don't know why opencv_createsamples puts those 4 corners.

I trained my pc with opencv_traincascade all one day long to detect 2€ coins using more than 6000 positive images similar to the following:

Now, I have just tried to run a simple OpenCV program to see the results and to check the file cascade.xml. The final result is very disappointing:

There are many points on the coin but there are also many other points on the background. Could it be a problem with my positive images used for training? Or maybe, am I using the detectMultiScale() with wrong parameters?

Here's my code:

#include "opencv2/opencv.hpp"
using namespace cv;

int main(int, char**) {

  Mat src = imread("2c.jpg", CV_LOAD_IMAGE_COLOR); 

  Mat src_gray;

  std::vector<cv::Rect> money;

  CascadeClassifier euro2_cascade;

  cvtColor(src, src_gray, CV_BGR2GRAY );
  //equalizeHist(src_gray, equalizeHist(src_gray, src_gray);

  if ( !euro2_cascade.load( "/Users/lory/Desktop/cascade.xml" ) ) {
     printf("--(!)Error loading\n");
     return -1;
  }

  euro2_cascade.detectMultiScale( src_gray, money, 1.1, 0, 0, cv::Size(10, 10),cv::Size(2000, 2000) );

  for( size_t i = 0; i < money.size(); i++ ) {
     cv::Point center( money[i].x + money[i].width*0.5, money[i].y + money[i].height*0.5 );
     ellipse( src, center, cv::Size( money[i].width*0.5, money[i].height*0.5), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );
  }

  //namedWindow( "Display window", WINDOW_AUTOSIZE );
  imwrite("result.jpg",src);
}

I have also tried to reduce the number of neighbours but the effect is the same, just with many less points... Could it be a problem the fact that in positive images there are those 4 corners as background around the coin? I generated png images with Gimp from a shot video showing the coin, so I don't know why opencv_createsamples puts those 4 corners.

I trained my pc with opencv_traincascade all one day long to detect 2€ coins using more than 6000 positive images similar to the following:

Now, I have just tried to run a simple OpenCV program to see the results and to check the file cascade.xml. The final result is very disappointing:

There are many points on the coin but there are also many other points on the background. Could it be a problem with my positive images used for training? Or maybe, am I using the detectMultiScale() with wrong parameters?

Here's my code:

#include "opencv2/opencv.hpp"
using namespace cv;

int main(int, char**) {

  Mat src = imread("2c.jpg", CV_LOAD_IMAGE_COLOR); 

  Mat src_gray;

  std::vector<cv::Rect> money;

  CascadeClassifier euro2_cascade;

  cvtColor(src, src_gray, CV_BGR2GRAY );
  equalizeHist(src_gray, src_gray);

  if ( !euro2_cascade.load( "/Users/lory/Desktop/cascade.xml" ) ) {
     printf("--(!)Error loading\n");
     return -1;
  }

  euro2_cascade.detectMultiScale( src_gray, money, 1.1, 0, 0, cv::Size(10, 10),cv::Size(2000, 2000) );

  for( size_t i = 0; i < money.size(); i++ ) {
     cv::Point center( money[i].x + money[i].width*0.5, money[i].y + money[i].height*0.5 );
     ellipse( src, center, cv::Size( money[i].width*0.5, money[i].height*0.5), 0, 0, 360, Scalar( 255, 0, 255 ), 4, 8, 0 );
  }

  //namedWindow( "Display window", WINDOW_AUTOSIZE );
  imwrite("result.jpg",src);
}

I have also tried to reduce the number of neighbours but the effect is the same, just with many less points... Could it be a problem the fact that in positive images there are those 4 corners as background around the coin? I generated png images with Gimp from a shot video showing the coin, so I don't know why opencv_createsamples puts those 4 corners.

UPDATE I also tried to create a LBP cascade.xml but this is quite strange: in fact, if I use, in the abve OpenCV program, an image used as training, then the detection is good:

Instead, if I use another image (for example, taken by my smartphone) there there's nothing detected. What does it mean this? Have I made any error during training?