基于色彩图像分类直线检测project代码自编合成.rar_borderwk4_电线分类_电线检测_直线检测_输电线

function [s,theta,acc]=hough_lines(varargin); %HOUGH_LINES Hough transform line detection. % CMP Vision Algorithms http://visionbook.felk.cvut.cz % % Find all straight lines in an image using the Hough transform % : the input of this routine is an % edge image, obtained for example by the Canny edge detector % We use the (theta,s) line parameterization, with origin in the % center of the image . % % Usage: [s,theta,acc] = hough_lines(im,theta_step,s_step,thresh) % Inputs: % im [m x n] Input edge image: non-zero values are edges, % the value corresponds to edge strength. This may be % used for weighting the edges by the gradient magnitude. % theta_step (default /360) Discretization step for the % angle theta in radians. % s_step (default 1) Discretization step for the radius s % in pixels. % thresh (default 0.5) Values above thresh*max(acc) % in the accumulator acc are considered to correspond to valid lines. % Outputs: % s [k x 1] Vector containing the s parameters of the lines found. % theta [k x 1] Vector containing the theta parameters of % the lines found. % acc [_s x _theta] The accumulator. % % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% im=double(varargin{1}); % Handle the input variable theta_step. If unspecified assign theta_step=pi/360 if nargin>1 theta_step=varargin{2};%%%%%% else theta_step = pi/360; end % Handle the input variable s_step. If unspecified assign s_step=1 if nargin>2 s_step=varargin{3};%%%%%%%% else s_step= 1; end if (nargin>3) threshold= varargin{4};%%%%%% else % If the threshold value is not specified at the input, use 0.5* max(acc) % where max(acc) is the maximum value in the accumulator (multiplication % by max(acc) follows later): threshold= 0.5;%0.5 end % Set limits for theta and s to cover all pixels in the % image, initialize the accumulator acc and pre-calculate whatever will be needed % later. [m,n] = size(im); theta_min = 0; theta_max = pi-theta_step; s_max = sqrt( (m*m +n*n)/4 ) + s_step; theta = theta_min:theta_step:theta_max; theta_num_values = length(theta); s_num_values = round( 2*s_max/s_step ) + 1; s_num_half = ceil( s_num_values/2 ); cos_theta = cos(theta); sin_theta = sin(theta); acc = double( zeros(s_num_values,theta_num_values) ); % Find coordinates of all edge pixels with respect to the image center. [i,j,edge_value] = find(im); edge_value = edge_value/max(edge_value); center_x = n/2; center_y = m/2; x = j-center_x; y = center_y-i; % The heart of the algorithm is a loop over all edge pixels, % incrementing the appropriate % accumulator cells. Observe how the values of s and theta correspond % to the accumulator coordinates. Two tricks are used to speed up the % execution: First, s is calculated in parallel for all theta. Second, % lin_idx_offset converts - also in parallel - the 2D indices % into a linear index. This is similar to the Matlab function % sub2ind but much faster. lin_idx_offset = (0:(theta_num_values-1)) * s_num_values; % auxiliary table for l = 1:length(x) s = x(l)*cos_theta + y(l)*sin_theta; % calculate s for all theta s = round(s/s_step) + s_num_half; % accumulator coordinates lin_idx = lin_idx_offset + s; % find linear indices acc(lin_idx) = acc(lin_idx) + edge_value(l); % increment the accumulator end % The accumulator is thresholded and non-maximal suppression is applied: we % keep only those accumulator cells whose values are equal to % a maximum in a 5 x 5 neighborhood. % This helps to generate a unique response % for each line. To correctly handle maxima at accumulator boundaries % between (theta=0,s) and (theta=,-s), we create an extended % `wrapped-around' accumulator acc_rep. idx = find(acc>threshold*max(max(acc))); num_n = 24; % number of neighbors wid = 5; % width of the neighbourhood%%%%%%%%%5 w_h = (wid-1)/2; acc_rep = [acc(end:-1:1,end-w_h+1:end) acc acc(end:-1:1,1:w_h)]; acc_rep_offset = w_h*s_num_values; % the offset between acc_rep and acc % The 2D offsets of neighboring cells are combined into linear % offsets and stored in neighborhood_offsets. The indices % of all cells idx above the threshold are % augmented by the indices of their neighbors and stored in % neighborhoods_idx, one neighborhood per row. [neighborhood_offsets_n neighborhood_offsets_m] = meshgrid(-w_h:w_h); neighborhood_offsets_n = neighborhood_offsets_n( [1:num_n/2 (num_n/2)+2:end] ); neighborhood_offsets_m = neighborhood_offsets_m( [1:num_n/2 (num_n/2)+2:end] ); neighborhood_offsets = ... s_num_values*neighborhood_offsets_n + neighborhood_offsets_m; neighborhoods_idx = repmat( idx+acc_rep_offset, 1, num_n ) + ... repmat( neighborhood_offsets, length(idx), 1 ); % We need to handle the boundary cases - whenever an index falls outside the % accumulator, the cell is effectively ignored by replacing the index % with the index of the neighborhood % center cell. out_idx = find( ... (neighborhoods_idx<1) + ... (neighborhoods_idx>((2*w_h+s_num_values)*theta_num_values)) ); neighborhoods_idx(out_idx) = neighborhoods_idx( rem(out_idx-1,s_num_values)+1 ); % Finding the local maxima consists of taking only such % elements from idx at which the corresponding accumulator cell is % at least equal to the maximum of the neighborhood; this can be written % very concisely in Matlab. Finally, surviving indices are converted % to the corresponding parameter values s and theta. local_maxima_idx = idx( acc(idx)>=max(acc_rep(neighborhoods_idx),[],2) ); s = s_step * ( rem(local_maxima_idx,s_num_values) - s_num_half ); theta = theta_step*floor(local_maxima_idx/s_num_values) + theta_min;