30273446Hough_Grd.zip_霍夫变换

function [accum, axis_rho, axis_theta, varargout] = ... Hough_Grd(img, varargin) %Detect lines in a grayscale image % % [accum, axis_rho, axis_theta, lineprm, lineseg, dbg_label] = % Hough_Grd(img, grdthres, detsens) % Hough transform for line detection based on image's gradient field. % NOTE: Operates on grayscale images, NOT B/W bitmaps. % NO loops involved in the implementation of Hough transform, % which makes the operation fast. % Able to detect the two ends of line segments. % % INPUT: (img, grdthres, detsens) % img: A 2-D grayscale image (NO B/W bitmap) % grdthres: (Optional, default is 8, must be non-negative) % The algorithm is based on the gradient field of the % input image. A thresholding on the gradient magnitude % is performed before the voting process of the Hough % transform to remove the 'uniform intensity' (sort-of) % image background from the voting process. In other words, % pixels with gradient magnitudes smaller than 'grdthres' % are considered not belong to any line. % detsens: (Optional, default is 0.08) % A value that controls the sensitivity of line detection. % The range of the value is from 0 to 1, open ends. % Although the physical meaning of this parameter is not % obvious, it controls the algorithm in a fuzzy manner. % The SMALLER the value is, the MORE features in the image % will be considered as lines. % % OUTPUT: [accum, axis_rho, axis_theta, lineprm, lineseg, dbg_label] % accum: The result accumulation array from the Hough transform. % The horizontal dimension is 'theta' and the vertical % dimension is 'rho'. % axis_rho: Vector that contains the 'rho' values for the rows in % the accumulation array. % axis_theta: Vector that contains the 'theta' values for the columns % in the accumulation array. % lineprm: (Optional) % Parameters (rho, theta) of the lines detected. Is a N-by-2 % matrix with each row contains the parameters (rho, theta) % for a line. The definitions of 'rho' and 'theta' are as % the following: % 'rho' is the perpendicular distance from the line to the % origin of the image. 'rho' can be negative since 'theta' % is constrained to [0, pi]. The unit of 'rho' is in pixels. % 'theta' is the sweep angle from axis X (i.e. horizontal % axis of the image) to the direction that is perpendicular % to the line. The range of 'theta' is [0, pi]. % lineseg: (Optional) % Parameters (x1, x2, y1, y2) of line segments detected. % Lines given by (rho, theta) are infinite lines. This % function tries to detect line segments in the raw image % and output them as 'lineseg', which is a Ns-by-4 matrix % with each row contains the parameters (x1, x2, y1, y2) % that defines the two ends of a line segment. % dbg_label: (Optional, for debug purpose) % Labelled segmentation map from the search of peaks in % the accumulation array % % EXAMPLE #1: % rawimg = imread('TestHT_Chkbd1.bmp'); % fltr4img = [1 2 3 2 1; 2 3 4 3 2; 3 4 6 4 3; 2 3 4 3 2; 1 2 3 2 1]; % fltr4img = fltr4img / sum(fltr4img(:)); % imgfltrd = filter2( fltr4img , rawimg ); % tic; % [accum, axis_rho, axis_theta, lineprm, lineseg] = ... % Hough_Grd(imgfltrd, 6, 0.02); % toc; % figure(1); imagesc(axis_theta*(180/pi), axis_rho, accum); axis xy; % xlabel('Theta (degree)'); ylabel('Pho (pixels)'); % title('Accumulation Array from Hough Transform'); % figure(2); imagesc(rawimg); colormap('gray'); axis image; % DrawLines_2Ends(lineseg); % title('Raw Image with Line Segments Detected'); % % EXAMPLE #2: % rawimg = imread('TestHT_Pentagon.png'); % fltr4img = [1 1 1 1 1; 1 2 2 2 1; 1 2 4 2 1; 1 2 2 2 1; 1 1 1 1 1]; % fltr4img = fltr4img / sum(fltr4img(:)); % imgfltrd = filter2( fltr4img , rawimg ); % tic; % [accum, axis_rho, axis_theta, lineprm, lineseg] = ... % Hough_Grd(imgfltrd, 8, 0.05); % toc; % figure(1); imagesc(axis_theta*(180/pi), axis_rho, accum); axis xy; % xlabel('Theta (degree)'); ylabel('Pho (pixels)'); % title('Accumulation Array from Hough Transform'); % figure(2); imagesc(imgfltrd); colormap('gray'); axis image; % DrawLines_Polar(size(imgfltrd), lineprm); % title('Raw Image (Blurred) with Lines Detected'); % figure(3); imagesc(rawimg); colormap('gray'); axis image; % DrawLines_2Ends(lineseg); % title('Raw Image with Line Segments Detected'); % % BUG REPORT: % This is an alpha version. Please send your bug reports, comments and % suggestions to [email protected] . Thanks. % Author: Tao Peng % Department of Mechanical Engineering % University of Maryland, College Park, Maryland 20742, USA % [email protected] % Version: alpha Revision: Dec. 05, 2005 % Reserved for extension % accumres: (Optional, default is [4, 1], minimum is [2, 0.5]) % The desired resolutions in 'rho' and 'theta'. Is a % 2-element vector in following format: % [resolution in 'rho' (pixels), % resolution in 'theta' (degrees)] %%%%%%%% Arguments and parameters %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Validation of arguments if ndims(img) ~= 2 || ~isnumeric(img), error('Hough_Grd: ''img'' has to be 2 dimensional'); end if ~all(size(img) >= 16), error('Hough_Grd: ''img'' has to be larger than 16-by-16'); end % Parameters (default values) prm_grdthres = 8; prm_accumres = [4, 1]; prm_detsens = 0.08; func_compu_lineprm = true; prm_fltraccum = true; % Validation of arguments vap_grdthres = 1; if nargin > vap_grdthres, if isnumeric(varargin{vap_grdthres}) && ... varargin{vap_grdthres}(1) >= 0, prm_grdthres = varargin{vap_grdthres}(1); else error(['Hough_Grd: ''grdthres'' has to be ', ... 'a non-negative number']); end end %{ vap_accumres = 3; if nargin > vap_accumres, if numel(varargin{vap_accumres}) == 2 && ... isnumeric(varargin{vap_accumres}) && ... ( varargin{vap_accumres}(1) >= 2 && ... varargin{vap_accumres}(2) >= 0.5 ), prm_accumres = varargin{vap_accumres}; else error(['Hough_Grd: ''accumres'' has to be a two-element ', ... 'vector and no smaller than [2, 0.5]']); end end %} vap_detsens = 2; if nargin > vap_detsens, if isnumeric(varargin{vap_detsens}) && ... varargin{vap_detsens}(1) > 0 && varargin{vap_detsens}(1) < 1, prm_detsens = varargin{vap_detsens}; else error('Hough_Grd: ''detsens'' has to be in the range (0, 1)'); end end func_compu_lineprm = ( nargout > 3 ); % Size of the accumulation array imgsize = size(img); coef_rhorng = [ -imgsize(2), sqrt(sum(imgsize.^2)) ]; coef_thetarng = [-pi/18, pi+pi/18]; prm_accumsize = [ ... round( (coef_rhorng(2)-coef_rhorng(1)) * (2/prm_accumres(1)) ) , ... round( (coef_thetarng(2)-coef_thetarng(1)) * (180/pi) * ... (2/prm_accumres(2)) ) ]; % Default filter for the accumulation array prm_acmfltr_R = 4; prm_acmfltr_w = [1 2 4 8]; fltr4accum = ones(2 * prm_acmfltr_R - 1) * prm_acmfltr_w(1); for k = 2 : prm_acmfltr_R, fltr4accum(k:(2*prm_acmfltr_R-k), k:(2*prm_acmfltr_R-k)) = ... prm_acmfltr_w(k); end fltr4accum = fltr4accum / sum(fltr4accum(:)); % Parameters for the algorithm using repeated % thresholding and segmentation prm_lp_dthres = mi