Hough-Circle-Detect3.rar_LOAN_TestImg_CHT_a2.b_hough circle dete

function [accum, varargout] = CircularHough_Grd(img, radrange, varargin) %Detect circular shapes in a grayscale image. Resolve their center %positions and radii. % % [accum, circen, cirrad, dbg_LMmask] = CircularHough_Grd( % img, radrange, grdthres, fltr4LM_R, multirad, fltr4accum) % Circular Hough transform based on the gradient field of an image. % NOTE: Operates on grayscale images, NOT B/W bitmaps. % NO loops in the implementation of Circular Hough transform, % which means faster operation but at the same time larger % memory consumption. % %%%%%%%% INPUT: (img, radrange, grdthres, fltr4LM_R, multirad, fltr4accum) % % img: A 2-D grayscale image (NO B/W bitmap) % % radrange: The possible minimum and maximum radii of the circles % to be searched, in the format of % [minimum_radius , maximum_radius] (unit: pixels) % **NOTE**: A smaller range saves computational time and % memory. % % grdthres: (Optional, default is 10, 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 Circular % 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 NOT considered in the computation. % **NOTE**: The default parameter value is chosen for % images with a maximum intensity close to 255. For cases % with dramatically different maximum intensities, e.g. % 10-bit bitmaps in stead of the assumed 8-bit, the default % value can NOT be used. A value of 4% to 10% of the maximum % intensity may work for general cases. % % fltr4LM_R: (Optional, default is 8, minimum is 3) % The radius of the filter used in the search of local % maxima in the accumulation array. To detect circles whose % shapes are less perfect, the radius of the filter needs % to be set larger. % % multirad: (Optional, default is 0.5) % In case of concentric circles, multiple radii may be % detected corresponding to a single center position. This % argument sets the tolerance of picking up the likely % radii values. It ranges from 0.1 to 1, where 0.1 % corresponds to the largest tolerance, meaning more radii % values will be detected, and 1 corresponds to the smallest % tolerance, in which case only the "principal" radius will % be picked up. % % fltr4accum: (Optional. A default filter will be used if not given) % Filter used to smooth the accumulation array. Depending % on the image and the parameter settings, the accumulation % array built has different noise level and noise pattern % (e.g. noise frequencies). The filter should be set to an % appropriately size such that it's able to suppress the % dominant noise frequency. % %%%%%%%% OUTPUT: [accum, circen, cirrad, dbg_LMmask] % % accum: The result accumulation array from the Circular Hough % transform. The accumulation array has the same dimension % as the input image. % % circen: (Optional) % Center positions of the circles detected. Is a N-by-2 % matrix with each row contains the (x, y) positions % of a circle. For concentric circles (with the same center % position), say k of them, the same center position will % appear k times in the matrix. % % cirrad: (Optional) % Estimated radii of the circles detected. Is a N-by-1 % column vector with a one-to-one correspondance to the % output 'circen'. A value 0 for the radius indicates a % failed detection of the circle's radius. % % dbg_LMmask: (Optional, for debugging purpose) % Mask from the search of local maxima in the accumulation % array. % %%%%%%%%% EXAMPLE #0: % rawimg = imread('TestImg_CHT_a2.bmp'); % tic; % [accum, circen, cirrad] = CircularHough_Grd(rawimg, [15 60]); % toc; % figure(1); imagesc(accum); axis image; % title('Accumulation Array from Circular Hough Transform'); % figure(2); imagesc(rawimg); colormap('gray'); axis image; % hold on; % plot(circen(:,1), circen(:,2), 'r+'); % for k = 1 : size(circen, 1), % DrawCircle(circen(k,1), circen(k,2), cirrad(k), 32, 'b-'); % end % hold off; % title(['Raw Image with Circles Detected ', ... % '(center positions and radii marked)']); % figure(3); surf(accum, 'EdgeColor', 'none'); axis ij; % title('3-D View of the Accumulation Array'); % % COMMENTS ON EXAMPLE #0: % Kind of an easy case to handle. To detect circles in the image whose % radii range from 15 to 60. Default values for arguments 'grdthres', % 'fltr4LM_R', 'multirad' and 'fltr4accum' are used. % %%%%%%%%% EXAMPLE #1: % rawimg = imread('TestImg_CHT_a3.bmp'); % tic; % [accum, circen, cirrad] = CircularHough_Grd(rawimg, [15 60], 10, 20); % toc; % figure(1); imagesc(accum); axis image; % title('Accumulation Array from Circular Hough Transform'); % figure(2); imagesc(rawimg); colormap('gray'); axis image; % hold on; % plot(circen(:,1), circen(:,2), 'r+'); % for k = 1 : size(circen, 1), % DrawCircle(circen(k,1), circen(k,2), cirrad(k), 32, 'b-'); % end % hold off; % title(['Raw Image with Circles Detected ', ... % '(center positions and radii marked)']); % figure(3); surf(accum, 'EdgeColor', 'none'); axis ij; % title('3-D View of the Accumulation Array'); % % COMMENTS ON EXAMPLE #1: % The shapes in the raw image are not very good circles. As a result, % the profile of the peaks in the accumulation array are kind of % 'stumpy', which can be seen clearly from the 3-D view of the % accumulation array. (As a comparison, please see the sharp peaks in % the accumulation array in example #0) To extract the peak positions % nicely, a value of 20 (default is 8) is used for argument 'fltr4LM_R', % which is the radius of the filter used in the search of peaks. % %%%%%%%%% EXAMPLE #2: % rawimg = imread('TestImg_CHT_b3.bmp'); % 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, circen, cirrad] = CircularHough_Grd(imgfltrd, [15 80], 8, 10); % toc; % figure(1); imagesc(accum); axis image; % title('Accumulation Array from Circular Hough Transform'); % figure(2); imagesc(rawimg); colormap('gray'); axis image; % hold on; % plot(circen(:,1), circen(:,2), 'r+'); % for k = 1 : size(circen, 1), % DrawCircle(circen(k,1), circen(k,2), cirrad(k), 32, 'b-'); % end % hold off; % title(['Raw Image with Circles Detected ', ... % '(center positions and radii marked)']); % % COMMENTS ON EXAMPLE #2: % The circles in the raw image have small scale irregularities along % the edges, which could lead to an accumulation array that is bad for % local maxima detection. A 5-by-5 filter is used to smooth out the % small scale irregularities. A blurred image is actually good for the % algorithm implemented here which is based on the image's gradient % field. % %%%%%%%%% EXAMPLE #3: % rawimg = imread('TestImg_CHT_c3.bmp'); % 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(