// Copyright (C) 2014 Davis E. King (davis@dlib.net) // License: Boost Software License See LICENSE.txt for the full license. #undef DLIB_HOUGH_tRANSFORM_ABSTRACT_Hh_ #ifdef DLIB_HOUGH_tRANSFORM_ABSTRACT_Hh_ #include "../geometry.h" #include "../image_processing/generic_image.h" namespace dlib{// ---------------------------------------------------------------------------------------- classhough_transform{/*! WHAT THIS OBJECT REPRESENTS This object is a tool for computing the line finding version of the Hough transform given some kind of edge detection image as input. It also allows the edge pixels to be weighted such that higher weighted edge pixels contribute correspondingly more to the output of the Hough transform, allowing stronger edges to create correspondingly stronger line detections in the final Hough transform. THREAD SAFETY It is safe for multiple threads to make concurrent accesses to this object without synchronization. !*/ public: explicithough_transform(unsignedlongsize_ ); /*! requires - size_ > 0 ensures - This object will compute Hough transforms that are size_ by size_ pixels. This is in terms of both the Hough accumulator array size as well as the input image size. - #size() == size_ !*/unsignedlongsize( ) const; /*! ensures - returns the size of the Hough transforms generated by this object. In particular, this object creates Hough transform images that are size() by size() pixels in size. !*/longnr( ) const; /*! ensures - returns size() !*/longnc( ) const; /*! ensures - returns size() !*/ std::pair<point, point>get_line( const point& p ) const; /*! requires - rectangle(0,0,size()-1,size()-1).contains(p) == true (i.e. p must be a point inside the Hough accumulator array) ensures - returns the line segment in the original image space corresponding to Hough transform point p. - The returned points are inside rectangle(0,0,size()-1,size()-1). !*/doubleget_line_angle_in_degrees( const point& p ) const; /*! requires - rectangle(0,0,size()-1,size()-1).contains(p) == true (i.e. p must be a point inside the Hough accumulator array) ensures - returns the angle, in degrees, of the line corresponding to the Hough transform point p. !*/voidget_line_properties( const point& p,double& angle_in_degrees,double& radius ) const; /*! requires - rectangle(0,0,size()-1,size()-1).contains(p) == true (i.e. p must be a point inside the Hough accumulator array) ensures - Converts a point in the Hough transform space into an angle, in degrees, and a radius, measured in pixels from the center of the input image. - #angle_in_degrees == the angle of the line corresponding to the Hough transform point p. Moreover: -90 <= #angle_in_degrees < 90. - #radius == the distance from the center of the input image, measured in pixels, and the line corresponding to the Hough transform point p. Moreover: -sqrt(size()*size()/2) <= #radius <= sqrt(size()*size()/2) !*/ template < typename image_type > pointget_best_hough_point( const point& p, const image_type& himg ); /*! requires - image_type == an image object that implements the interface defined in dlib/image_processing/generic_image.h and it must contain grayscale pixels. - himg.nr() == size() - himg.nc() == size() - rectangle(0,0,size()-1,size()-1).contains(p) == true ensures - This function interprets himg as a Hough image and p as a point in the original image space. Given this, it finds the maximum scoring line that passes though p. That is, it checks all the Hough accumulator bins in himg corresponding to lines though p and returns the location with the largest score. - returns a point X such that get_rect(himg).contains(X) == true !*/ template < typename in_image_type, typename out_image_type >voidoperator() ( const in_image_type& img, const rectangle& box, out_image_type& himg ) const; /*! requires - in_image_type == an image object that implements the interface defined in dlib/image_processing/generic_image.h and it must contain grayscale pixels. - out_image_type == an image object that implements the interface defined in dlib/image_processing/generic_image.h and it must contain grayscale pixels. - box.width() == size() - box.height() == size() ensures - Computes the Hough transform of the part of img contained within box. In particular, we do a grayscale version of the Hough transform where any non-zero pixel in img is treated as a potential component of a line and accumulated into the Hough accumulator #himg. However, rather than adding 1 to each relevant accumulator bin we add the value of the pixel in img to each Hough accumulator bin. This means that, if all the pixels in img are 0 or 1 then this routine performs a normal Hough transform. However, if some pixels have larger values then they will be weighted correspondingly more in the resulting Hough transform. - #himg.nr() == size() - #himg.nc() == size() - #himg is the Hough transform of the part of img contained in box. Each point in #himg corresponds to a line in the input box. In particular, the line for #himg[y][x] is given by get_line(point(x,y)). Also, when viewing the #himg image, the x-axis gives the angle of the line and the y-axis the distance of the line from the center of the box. The conversion between Hough coordinates and angle and pixel distance can be obtained by calling get_line_properties(). !*/ template < typename in_image_type, typename out_image_type >voidoperator() ( const in_image_type& img, out_image_type& himg ) const; /*! requires - in_image_type == an image object that implements the interface defined in dlib/image_processing/generic_image.h and it must contain grayscale pixels. - out_image_type == an image object that implements the interface defined in dlib/image_processing/generic_image.h and it must contain grayscale pixels. - num_rows(img) == size() - num_columns(img) == size() ensures - performs: (*this)(img, get_rect(img), himg); That is, just runs the hough transform on the whole input image. !*/ template < typename in_image_type > std::vector<std::vector<point>>find_pixels_voting_for_lines( const in_image_type& img, const rectangle& box, const std::vector<point>& hough_points, constunsignedlongangle_window_size = 1, constunsignedlongradius_window_size = 1 ) const; /*! requires - in_image_type == an image object that implements the interface defined in dlib/image_processing/generic_image.h and it must contain grayscale pixels. - box.width() == size() - box.height() == size() - for all valid i: - get_rect(*this).contains(hough_points[i]) == true (i.e. hough_points must contain points in the output Hough transform space generated by this object.) - angle_window_size >= 1 - radius_window_size >= 1 ensures - This function computes the Hough transform of the part of img contained within box. It does the same computation as operator() defined above, except instead of accumulating into an image we create an explicit list of all the points in img that contributed to each line (i.e each point in the Hough image). To do this we take a list of Hough points as input and only record hits on these specifically identified Hough points. A typical use of find_pixels_voting_for_lines() is to first run the normal Hough transform using operator(), then find the lines you are interested in, and then call find_pixels_voting_for_lines() to determine which pixels in the input image belong to those lines. - This routine returns a vector, CONSTITUENT_POINTS, with the following properties: - #CONSTITUENT_POINTS.size() == hough_points.size() - for all valid i: - Let HP[i] = centered_rect(hough_points[i], angle_window_size, radius_window_size) - Any point in img with a non-zero value that lies on a line corresponding to one of the Hough points in HP[i] is added to CONSTITUENT_POINTS[i]. Therefore, when this routine finishes, #CONSTITUENT_POINTS[i] will contain all the points in img that voted for the lines associated with the Hough accumulator bins in HP[i]. - #CONSTITUENT_POINTS[i].size() == the number of points in img that voted for any of the lines HP[i] in Hough space. Note, however, that if angle_window_size or radius_window_size are made so large that HP[i] overlaps HP[j] for i!=j then the overlapping regions of Hough space are assign to HP[i] or HP[j] arbitrarily. Therefore, all points in CONSTITUENT_POINTS are unique, that is, there is no overlap in points between any two elements of CONSTITUENT_POINTS. !*/ template < typename in_image_type > std::vector<std::vector<point>>find_pixels_voting_for_lines( const in_image_type& img, const std::vector<point>& hough_points, constunsignedlongangle_window_size = 1, constunsignedlongradius_window_size = 1 ) const; /*! requires - in_image_type == an image object that implements the interface defined in dlib/image_processing/generic_image.h and it must contain grayscale pixels. - num_rows(img) == size() - num_columns(img) == size() - for all valid i: - get_rect(*this).contains(hough_points[i]) == true (i.e. hough_points must contain points in the output Hough transform space generated by this object.) - angle_window_size >= 1 - radius_window_size >= 1 ensures - performs: return find_pixels_voting_for_lines(img, get_rect(img), hough_points, angle_window_size, radius_window_size); That is, just runs the routine on the whole input image. !*/ template < typename image_type, typename thresh_type > std::vector<point>find_strong_hough_points( const image_type& himg, const thresh_type hough_count_threshold, constdoubleangle_nms_thresh, constdoubleradius_nms_thresh ); /*! requires - image_type == an image object that implements the interface defined in dlib/image_processing/generic_image.h and it must contain grayscale pixels. - himg.nr() == size() - himg.nc() == size() - angle_nms_thresh >= 0 - radius_nms_thresh >= 0 ensures - This routine finds strong lines in a Hough transform and performs non-maximum suppression on the detected lines. Recall that each point in Hough space is associated with a line. Therefore, this routine finds all the pixels in himg (a Hough transform image) with values >= hough_count_threshold and performs non-maximum suppression on the identified list of pixels. It does this by discarding lines that are within angle_nms_thresh degrees of a stronger line or within radius_nms_thresh distance (in terms of radius as defined by get_line_properties()) to a stronger Hough point. - The identified lines are returned as a list of coordinates in himg. !*/ template < typename in_image_type, typename record_hit_function_type >voidperform_generic_hough_transform( const in_image_type& img, const rectangle& box, record_hit_function_type record_hit ) const; /*! requires - in_image_type == an image object that implements the interface defined in dlib/image_processing/generic_image.h and it must contain grayscale pixels. - box.width() == size() - box.height() == size() - record_hit is a function object with the signature: void record_hit(const point& hough_point, const point& img_point, in_image_pixel_type value) ensures - Computes the Hough transform of the part of img contained within box. This routine is very general and allows you to implement a wide variety of Hough transforms, in fact, the operator() and find_pixels_voting_for_lines() routines defined above are implemented in terms of perform_generic_hough_transform(). The behavior is described by the following pseudo-code: for (image_coordinate : all_coordinates_in_img) for (hough_point : all_Hough_space_coordinates_for_lines_passing_through_image_coordinate) record_hit(hough_point, image_coordinate, img[image_coordinate.y][image_coordinate.x()]); That is, we perform the Hough transform, but rather than accumulating into a Hough accumulator image, we call record_hit() and record_hit() does whatever it wants. For example, in the operator() method defined above record_hit() simply accumulates into an image, and therefor performs the classic Hough transform. But there are many other options. !*/ template < typename in_image_type, typename record_hit_function_type >voidperform_generic_hough_transform( const in_image_type& img, record_hit_function_type record_hit ) const; /*! requires - in_image_type == an image object that implements the interface defined in dlib/image_processing/generic_image.h and it must contain grayscale pixels. - record_hit is a function object with the signature: void record_hit(const point& hough_point, const point& img_point, in_image_pixel_type value) - num_rows(img) == size() - num_columns(img) == size() ensures - performs: perform_generic_hough_transform(img, get_rect(img), record_hit); That is, just runs the routine on the whole input image. !*/};}#endif // DLIB_HOUGH_tRANSFORM_ABSTRACT_Hh_