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Code clean-up, remove trailing white spaces

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Pascal Obry 2020-05-13 14:04:44 +02:00
parent 8bdb4879a4
commit ae18a6997b
2 changed files with 56 additions and 53 deletions
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89
src/ShapeRecognition/ShapeRec_FeatureDetector.cxx
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@ -45,7 +45,7 @@
Constructor Constructor
\param theFilename - image to process \param theFilename - image to process
*/ */
ShapeRec_FeatureDetector::ShapeRec_FeatureDetector(): ShapeRec_FeatureDetector::ShapeRec_FeatureDetector():
corners() corners()
{ {
cornerCount = 2000; cornerCount = 2000;
@ -58,11 +58,11 @@ ShapeRec_FeatureDetector::ShapeRec_FeatureDetector():
/*! /*!
Sets the path of the image file to be processed Sets the path of the image file to be processed
\param thePath - Location of the image file \param thePath - Location of the image file
*/ */
void ShapeRec_FeatureDetector::SetPath( const std::string& thePath ) void ShapeRec_FeatureDetector::SetPath( const std::string& thePath )
{ {
imagePath = thePath; imagePath = thePath;
if (imagePath != "") if (imagePath != "")
{ {
IplImage* src = cvLoadImage(imagePath.c_str(),CV_LOAD_IMAGE_COLOR); IplImage* src = cvLoadImage(imagePath.c_str(),CV_LOAD_IMAGE_COLOR);
@ -82,25 +82,25 @@ void ShapeRec_FeatureDetector::ComputeCorners( bool useROI, ShapeRec_Parameters*
// Images to be used for detection // Images to be used for detection
IplImage *eig_img, *temp_img, *src_img_gray; IplImage *eig_img, *temp_img, *src_img_gray;
// Load image // Load image
src_img_gray = cvLoadImage (imagePath.c_str(), CV_LOAD_IMAGE_GRAYSCALE); src_img_gray = cvLoadImage (imagePath.c_str(), CV_LOAD_IMAGE_GRAYSCALE);
if ( useROI ) if ( useROI )
{ {
// If a ROI as been set use it for detection // If a ROI as been set use it for detection
cvSetImageROI( src_img_gray, rect ); cvSetImageROI( src_img_gray, rect );
} }
eig_img = cvCreateImage (cvGetSize (src_img_gray), IPL_DEPTH_32F, 1); eig_img = cvCreateImage (cvGetSize (src_img_gray), IPL_DEPTH_32F, 1);
temp_img = cvCreateImage (cvGetSize (src_img_gray), IPL_DEPTH_32F, 1); temp_img = cvCreateImage (cvGetSize (src_img_gray), IPL_DEPTH_32F, 1);
corners = (CvPoint2D32f *) cvAlloc (cornerCount * sizeof (CvPoint2D32f)); corners = (CvPoint2D32f *) cvAlloc (cornerCount * sizeof (CvPoint2D32f));
// image height and width // image height and width
imgHeight = src_img_gray->height; imgHeight = src_img_gray->height;
imgWidth = src_img_gray->width; imgWidth = src_img_gray->width;
// Corner detection using cvCornerMinEigenVal // Corner detection using cvCornerMinEigenVal
// (one of the methods available inOpenCV, there is also a cvConerHarris method that can be used by setting a flag in cvGoodFeaturesToTrack) // (one of the methods available inOpenCV, there is also a cvConerHarris method that can be used by setting a flag in cvGoodFeaturesToTrack)
cvGoodFeaturesToTrack (src_img_gray, eig_img, temp_img, corners, &cornerCount, aCornersParameters->qualityLevel, aCornersParameters->minDistance); cvGoodFeaturesToTrack (src_img_gray, eig_img, temp_img, corners, &cornerCount, aCornersParameters->qualityLevel, aCornersParameters->minDistance);
cvFindCornerSubPix (src_img_gray, corners, cornerCount, cvSize (aCornersParameters->kernelSize, aCornersParameters->kernelSize), cvSize (-1, -1), cvFindCornerSubPix (src_img_gray, corners, cornerCount, cvSize (aCornersParameters->kernelSize, aCornersParameters->kernelSize), cvSize (-1, -1),
@ -116,18 +116,18 @@ void ShapeRec_FeatureDetector::ComputeCorners( bool useROI, ShapeRec_Parameters*
Computes the contours of the image located at imagePath Computes the contours of the image located at imagePath
*/ */
bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters* parameters ) bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters* parameters )
{ {
// Initialising images // Initialising images
cv::Mat src, src_gray; cv::Mat src, src_gray;
cv::Mat detected_edges; cv::Mat detected_edges;
// Read image // Read image
src = cv::imread( imagePath.c_str() ); src = cv::imread( imagePath.c_str() );
if( !src.data ) if( !src.data )
return false; return false;
if ( !useROI ) // CANNY: The problem is that with that filter the detector detects double contours if ( !useROI ) // CANNY: The problem is that with that filter the detector detects double contours
{ {
// Convert the image to grayscale // Convert the image to grayscale
if (src.channels() == 3) if (src.channels() == 3)
cv::cvtColor( src, src_gray, CV_BGR2GRAY ); cv::cvtColor( src, src_gray, CV_BGR2GRAY );
@ -137,7 +137,7 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters
ShapeRec_CannyParameters* aCannyParameters = dynamic_cast<ShapeRec_CannyParameters*>( parameters ); ShapeRec_CannyParameters* aCannyParameters = dynamic_cast<ShapeRec_CannyParameters*>( parameters );
if ( !aCannyParameters ) aCannyParameters = new ShapeRec_CannyParameters(); if ( !aCannyParameters ) aCannyParameters = new ShapeRec_CannyParameters();
// Reduce noise // Reduce noise
blur( src_gray, detected_edges, cv::Size( aCannyParameters->kernelSize, aCannyParameters->kernelSize ) ); blur( src_gray, detected_edges, cv::Size( aCannyParameters->kernelSize, aCannyParameters->kernelSize ) );
// Canny detector // Canny detector
Canny( detected_edges, detected_edges, aCannyParameters->lowThreshold, aCannyParameters->lowThreshold * aCannyParameters->ratio, Canny( detected_edges, detected_edges, aCannyParameters->lowThreshold, aCannyParameters->lowThreshold * aCannyParameters->ratio,
@ -153,7 +153,7 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters
// Reduce noise // Reduce noise
cvSmooth( input_image, input_image, CV_GAUSSIAN, aColorFilterParameters->smoothSize, aColorFilterParameters->smoothSize ); cvSmooth( input_image, input_image, CV_GAUSSIAN, aColorFilterParameters->smoothSize, aColorFilterParameters->smoothSize );
// Crop the image to the selected part only (sample_image) // Crop the image to the selected part only (sample_image)
cvSetImageROI(input_image, rect); cvSetImageROI(input_image, rect);
IplImage* sample_image = cvCreateImage(cvGetSize(input_image), IplImage* sample_image = cvCreateImage(cvGetSize(input_image),
@ -161,7 +161,7 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters
input_image->nChannels); input_image->nChannels);
cvCopy(input_image, sample_image, NULL); cvCopy(input_image, sample_image, NULL);
cvResetImageROI(input_image); cvResetImageROI(input_image);
IplImage* sample_hsv = cvCreateImage( cvGetSize(sample_image),8,3 ); IplImage* sample_hsv = cvCreateImage( cvGetSize(sample_image),8,3 );
IplImage* sample_h_plane = cvCreateImage( cvGetSize(sample_image), 8, 1 ); IplImage* sample_h_plane = cvCreateImage( cvGetSize(sample_image), 8, 1 );
IplImage* sample_s_plane = cvCreateImage( cvGetSize(sample_image), 8, 1 ); IplImage* sample_s_plane = cvCreateImage( cvGetSize(sample_image), 8, 1 );
@ -171,22 +171,23 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters
cvCvtPixToPlane(sample_hsv, sample_h_plane, sample_s_plane, 0, 0); cvCvtPixToPlane(sample_hsv, sample_h_plane, sample_s_plane, 0, 0);
IplImage* sample_planes[] = { sample_h_plane, sample_s_plane }; IplImage* sample_planes[] = { sample_h_plane, sample_s_plane };
// Create the hue / saturation histogram of the SAMPLE image. // Create the hue / saturation histogram of the SAMPLE image.
// This histogramm will be representative of what is the zone // This histogramm will be representative of what is the zone
// we want to find the frontier of. Indeed, the sample image is meant to // we want to find the frontier of. Indeed, the sample image is meant to
// be representative of this zone // be representative of this zone
float hranges[] = { 0, 180 }; float hranges[] = { 0, 180 };
float sranges[] = { 0, 256 }; float sranges[] = { 0, 256 };
float* ranges[] = { hranges, sranges }; float* ranges[] = { hranges, sranges };
sample_hist = cvCreateHist( 2, aColorFilterParameters->histSize, aColorFilterParameters->histType, ranges ); sample_hist = cvCreateHist( 2, aColorFilterParameters->histSize, aColorFilterParameters->histType, ranges );
//calculate hue /saturation histogram //calculate hue /saturation histogram
cvCalcHist(sample_planes, sample_hist, 0 ,0); cvCalcHist(sample_planes, sample_hist, 0 ,0);
// // TEST print of the histogram for debugging // // TEST print of the histogram for debugging
// IplImage* hist_image = cvCreateImage(cvSize(320,300),8,3); // IplImage* hist_image = cvCreateImage(cvSize(320,300),8,3);
// //
// //draw hist on hist_test image. // //draw hist on hist_test image.
// cvZero(hist_image); // cvZero(hist_image);
// float max_value = 0; // float max_value = 0;
@ -203,32 +204,32 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters
// cvPoint((i+1)*bin_w,hist_image->height - val), // cvPoint((i+1)*bin_w,hist_image->height - val),
// color, -1, 8, 0 ); // color, -1, 8, 0 );
// } // }
// //
// //
// cvNamedWindow("hist", 1); cvShowImage("hist",hist_image); // cvNamedWindow("hist", 1); cvShowImage("hist",hist_image);
// Calculate the back projection of hue and saturation planes of the INPUT image // Calculate the back projection of hue and saturation planes of the INPUT image
// by mean of the histogram of the SAMPLE image. // by mean of the histogram of the SAMPLE image.
// //
// The pixels which (h,s) coordinates correspond to high values in the histogram // The pixels which (h,s) coordinates correspond to high values in the histogram
// will have high values in the grey image result. It means that a pixel of the INPUT image // will have high values in the grey image result. It means that a pixel of the INPUT image
// which is more probably in the zone represented by the SAMPLE image, will be whiter // which is more probably in the zone represented by the SAMPLE image, will be whiter
// in the back projection. // in the back projection.
IplImage* backproject = cvCreateImage(cvGetSize(input_image), 8, 1); IplImage* backproject = cvCreateImage(cvGetSize(input_image), 8, 1);
IplImage* binary_backproject = cvCreateImage(cvGetSize(input_image), 8, 1); IplImage* binary_backproject = cvCreateImage(cvGetSize(input_image), 8, 1);
IplImage* input_hsv = cvCreateImage(cvGetSize(input_image),8,3); IplImage* input_hsv = cvCreateImage(cvGetSize(input_image),8,3);
IplImage* input_hplane = cvCreateImage(cvGetSize(input_image),8,1); IplImage* input_hplane = cvCreateImage(cvGetSize(input_image),8,1);
IplImage* input_splane = cvCreateImage(cvGetSize(input_image),8,1); IplImage* input_splane = cvCreateImage(cvGetSize(input_image),8,1);
// Get hue and saturation planes of the INPUT image // Get hue and saturation planes of the INPUT image
cvCvtColor(input_image, input_hsv, CV_BGR2HSV); cvCvtColor(input_image, input_hsv, CV_BGR2HSV);
cvCvtPixToPlane(input_hsv, input_hplane, input_splane, 0, 0); cvCvtPixToPlane(input_hsv, input_hplane, input_splane, 0, 0);
IplImage* input_planes[] = { input_hplane, input_splane }; IplImage* input_planes[] = { input_hplane, input_splane };
// Compute the back projection // Compute the back projection
cvCalcBackProject(input_planes, backproject, sample_hist); cvCalcBackProject(input_planes, backproject, sample_hist);
// Threshold in order to obtain a binary image // Threshold in order to obtain a binary image
cvThreshold(backproject, binary_backproject, aColorFilterParameters->threshold, aColorFilterParameters->maxThreshold, CV_THRESH_BINARY); cvThreshold(backproject, binary_backproject, aColorFilterParameters->threshold, aColorFilterParameters->maxThreshold, CV_THRESH_BINARY);
cvReleaseImage(&sample_image); cvReleaseImage(&sample_image);
@ -258,7 +259,7 @@ bool ShapeRec_FeatureDetector::ComputeContours( bool useROI, ShapeRec_Parameters
findContours( detected_edges, contours, hierarchy, CV_RETR_CCOMP, parameters->findContoursMethod); findContours( detected_edges, contours, hierarchy, CV_RETR_CCOMP, parameters->findContoursMethod);
return true; return true;
} }
/*! /*!
@ -268,18 +269,18 @@ bool ShapeRec_FeatureDetector::ComputeLines(){
MESSAGE("ShapeRec_FeatureDetector::ComputeLines()") MESSAGE("ShapeRec_FeatureDetector::ComputeLines()")
// Initialising images // Initialising images
cv::Mat src, src_gray, detected_edges, dst; cv::Mat src, src_gray, detected_edges, dst;
src=cv::imread(imagePath.c_str(), 0); src=cv::imread(imagePath.c_str(), 0);
Canny( src, dst, 50, 200, 3 ); Canny( src, dst, 50, 200, 3 );
HoughLinesP( dst, lines, 1, CV_PI/180, 80, 30, 10 ); HoughLinesP( dst, lines, 1, CV_PI/180, 80, 30, 10 );
return true; return true;
} }
/*! /*!
Stores a region of interest given by user in rect Stores a region of interest given by user in rect
\param theRect - Region Of Interest of the image located at imagePath \param theRect - Region Of Interest of the image located at imagePath
*/ */
void ShapeRec_FeatureDetector::SetROI( const QRect& theRect ) void ShapeRec_FeatureDetector::SetROI( const QRect& theRect )
{ {
@ -291,30 +292,32 @@ void ShapeRec_FeatureDetector::SetROI( const QRect& theRect )
/*! /*!
Crops the image located at imagePath to the region of interest given by the user via SetROI Crops the image located at imagePath to the region of interest given by the user via SetROI
and stores the result in /tmp and stores the result in /tmp
\param theRect - Region Of Interest of the image located at imagePath \param theRect - Region Of Interest of the image located at imagePath
*/ */
std::string ShapeRec_FeatureDetector::CroppImage() std::string ShapeRec_FeatureDetector::CroppImage()
{ {
#if 0
IplImage* src = cvLoadImage(imagePath.c_str(),CV_LOAD_IMAGE_COLOR); IplImage* src = cvLoadImage(imagePath.c_str(),CV_LOAD_IMAGE_COLOR);
cvSetImageROI(src, rect); cvSetImageROI(src, rect);
IplImage* cropped_image = cvCreateImage(cvGetSize(src), IplImage* cropped_image = cvCreateImage(cvGetSize(src),
src->depth, src->depth,
src->nChannels); src->nChannels);
cvCopy(src, cropped_image, NULL); cvCopy(src, cropped_image, NULL);
cvResetImageROI(src); cvResetImageROI(src);
cvSaveImage ("/tmp/cropped_image.bmp", cropped_image); cvSaveImage ("/tmp/cropped_image.bmp", cropped_image);
cvReleaseImage(&src); cvReleaseImage(&src);
cvReleaseImage(&cropped_image); cvReleaseImage(&cropped_image);
return "/tmp/cropped_image.bmp"; return "/tmp/cropped_image.bmp";
#endif
} }
/*! /*!
\class ShapeRec_CornersParameters \class ShapeRec_CornersParameters
\brief Parameters for the corners detection \brief Parameters for the corners detection
*/ */
ShapeRec_CornersParameters::ShapeRec_CornersParameters() ShapeRec_CornersParameters::ShapeRec_CornersParameters()
{ {
@ -330,7 +333,7 @@ ShapeRec_CornersParameters::~ShapeRec_CornersParameters()
/*! /*!
\class ShapeRec_Parameters \class ShapeRec_Parameters
\brief Parameters for the contour/corners detection \brief Parameters for the contour/corners detection
*/ */
ShapeRec_Parameters::ShapeRec_Parameters() ShapeRec_Parameters::ShapeRec_Parameters()
{ {
@ -343,7 +346,7 @@ ShapeRec_Parameters::~ShapeRec_Parameters()
/*! /*!
\class ShapeRec_CannyParameters \class ShapeRec_CannyParameters
\brief Parameters for the contour detection \brief Parameters for the contour detection
*/ */
ShapeRec_CannyParameters::ShapeRec_CannyParameters() ShapeRec_CannyParameters::ShapeRec_CannyParameters()
{ {
@ -358,7 +361,7 @@ ShapeRec_CannyParameters::~ShapeRec_CannyParameters()
/*! /*!
\class ShapeRec_ColorFilterParameters \class ShapeRec_ColorFilterParameters
\brief Parameters for the contour detection \brief Parameters for the contour detection
*/ */
ShapeRec_ColorFilterParameters::ShapeRec_ColorFilterParameters() ShapeRec_ColorFilterParameters::ShapeRec_ColorFilterParameters()
{ {

20
src/ShapeRecognition/ShapeRec_FeatureDetector.hxx
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@ -82,7 +82,7 @@ class GEOM_SHAPEREC_EXPORT ShapeRec_CannyParameters : public ShapeRec_Parameters
public: public:
ShapeRec_CannyParameters(); ShapeRec_CannyParameters();
virtual ~ShapeRec_CannyParameters(); virtual ~ShapeRec_CannyParameters();
int lowThreshold; int lowThreshold;
int ratio; int ratio;
bool L2gradient; bool L2gradient;
@ -104,12 +104,12 @@ public:
class GEOM_SHAPEREC_EXPORT ShapeRec_FeatureDetector class GEOM_SHAPEREC_EXPORT ShapeRec_FeatureDetector
{ {
public: public:
typedef std::vector<cv::Point> CvContour; typedef std::vector<cv::Point> CvContour;
typedef std::vector<std::vector<cv::Point> > CvContoursArray; typedef std::vector<std::vector<cv::Point> > CvContoursArray;
ShapeRec_FeatureDetector(); // Constructor ShapeRec_FeatureDetector(); // Constructor
void SetPath( const std::string& ); // Sets the image path void SetPath( const std::string& ); // Sets the image path
void SetROI( const QRect& ); // Sets a Region Of Interest in the image void SetROI( const QRect& ); // Sets a Region Of Interest in the image
CvPoint2D32f* GetCorners() { return corners; }; CvPoint2D32f* GetCorners() { return corners; };
@ -119,23 +119,23 @@ public:
int GetCornerCount() { return cornerCount; }; int GetCornerCount() { return cornerCount; };
int GetImgHeight() { return imgHeight; }; int GetImgHeight() { return imgHeight; };
int GetImgWidth() { return imgWidth; }; int GetImgWidth() { return imgWidth; };
std::string CroppImage(); std::string CroppImage();
void ComputeCorners( bool useROI = false, ShapeRec_Parameters* parameters = 0 ); // Detects the corners from the image located at imagePath void ComputeCorners( bool useROI = false, ShapeRec_Parameters* parameters = 0 ); // Detects the corners from the image located at imagePath
bool ComputeLines(); // Detects the lines from the image located at imagePath bool ComputeLines(); // Detects the lines from the image located at imagePath
bool ComputeContours( bool useROI = false, ShapeRec_Parameters* parameters = 0 ); // Detects the contours from the image located at imagePath bool ComputeContours( bool useROI = false, ShapeRec_Parameters* parameters = 0 ); // Detects the contours from the image located at imagePath
private: private:
std::string imagePath; std::string imagePath;
CvPoint2D32f* corners; CvPoint2D32f* corners;
int cornerCount; int cornerCount;
CvContoursArray contours; CvContoursArray contours;
std::vector<cv::Vec4i> hierarchy; std::vector<cv::Vec4i> hierarchy;
std::vector<cv::Vec4i> lines; std::vector<cv::Vec4i> lines;
int imgHeight; int imgHeight;
int imgWidth; int imgWidth;
CvRect rect; CvRect rect;
}; };