matlab.rar_matlab_matlab车牌识别_车牌识别 matlab

function [seg] = character_segmentation(bw); % character_segmentation: Returns the digit segments in the supplied binary image. % The function uses the "segment" function, keeping only the seven % segments in the result with largest area, and in case less than seven % segments were found, it attempts to recall the function, making the % separation between the already found segments clearer (by cleaning the % bits which are there. DIGIT_WIDTH = 18; MIN_AREA = 250; load global_var.mat; plot_vector(sum(bw), 4, Character Segmentation - Columns Sum Graph:, debug2); seg = segment(bw, DIGIT_WIDTH, MIN_AREA); [x y] = size(seg); % If we got less than 7 digits, we try to make the sepration between them % clearer by cleaning the bits between them, and we call the "segment" % function again: if x < 7 for i = 1 : x bw(:,seg(i,2))=0; end; seg = segment(bw, DIGIT_WIDTH, MIN_AREA); end; % Keeping in the results the seven segments with the largest area: area = []; for i = 1 : x pic = bw(:, seg(i,1) : seg(i,2), :); area(i) = bwarea(pic); end; area1 = sort(area); seg = seg; for j = 1:(length(area1)-7) i = find(area == area1(j)); len = length(area); if i == 1 area = [area(2:len)]; seg = [seg(:,2:len)]; elseif i == len area = [area(1:i-1)]; seg = [seg(:,1:i-1)]; else area = [area(1:i-1) area(i+1:len)]; seg = [seg(:,1:i-1) seg(:,i+1:len)]; end; end; seg = seg; return; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [segmentation] = segment(im, digit_width, min_area); % segment: Segment the pictures in digit images according to the variable % "digit_width" and returns a matrix containing the two bounds of the each % digit segment. The function keeps in the result only segment whose % "rectangular" areas is more than "min_area". segmentation = []; % Summing the colums of the pic: t = sum(im); % Getting the segments in the pic: seg = clean(find_valleys(t, 2, 1, digit_width), 3); % Keeping in the result only the segments whose rectangular areas is more than min_area: j = 1; for i = 1 : (length(seg) - 1) band_width = seg(i+1) - seg(i); maxi = max(t(1, seg(i):seg(i+1))); if(maxi * band_width > min_area) segmentation(j, 1) = seg(i); segmentation(j, 2) = seg(i+1); j = j + 1; end; end; return; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [s] = find_valleys(t, val, offset, digit_width); % find_valleys: Uses the method named peak-to-valleyin order to segment the % pictures in digit images getting the two bounds of the each digit segment % according to the statistical parameter digit_width = 18. % The function is recursive; it uses the vector of the sums of the columns % in the LP binary image supplied in the parameter "t". % The function passes over the graph corresponding to this vector from left % to right, bottom-up, incrementing at each recursive step the height that % is examined on the graph (val). It checks the bandwidth of the first part % of the signal: if it is greater than DIGIT_WIDTH, the function is % recursively called after incrementing the height which is examined on % the graph, (val). Otherwise, if the bandwidth is good, the two bounds of % the signal with this bandwidth are taken as a digit segment, and the % function is recursively called for the part of the image which is at % the right side of the digit segment just found. This is done until the % whole width of the picture has been passed over. % Determining the points which are inferior to the examined hieght: s = find(t < val); % If no more than one point is found, incrementing val and recursively calling the function again. if(length(s) < 2) s = find_valleys(t, val + 1, offset, digit_width); return; end; % If no point is found terminating: if length(s) == 0 return; end; % Arranging the boundaries, so that if we have a big value at the beginning % or the end of the picture the algorithm still works: in this case, the % algorithm includes also those points. if((t(1,1) >= val) & s(1) ~= 1) s = [1 s]; end; if((t(1, length(t)) >= val) & s(length(s)) ~= length(t)) s = [s length(t)]; end; % Updating the real coordinates according to offset: s = add(s, offset - 1); % Cleaning points which are very close each other keeping only one of them. s = clean(s, 3); % While there is a bad segment in "s", (starting from the left side): while bad_segm(s, digit_width) == 1 for i = 1: (length(s) - 1) if (s(i + 1) - s(i)) > digit_width % The subvector which does not correspond to a valid digit % segemnt: sub_vec = t(1, s(i) - offset + 1 : s(i+1) - offset + 1); % Recursively, separating this bad segment in two or more valid % digit segments: s = [s(1 : i) find_valleys(sub_vec, val + 1, s(i), digit_width) s(i+1 : length(s))]; end; end; end; return; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [bool] = bad_segm(s, digit_width); % bad_segm: Returns true (1) iff there is a bad digit segment in s, namely, % two points that ar distant one from the other by more than "digit_width". if length(s) == 0 bool = 0; return; end; tmp = s(1); bool = 0; for i = 2 : length(s) if(s(i) - tmp) > digit_width bool = 1; return; end; tmp = s(i); end; return; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [t] = clean(s, val); % clean: Cleans form the vector s all the poins which are distant the one % from the other by less than "val" keeping only one of them. t = []; len = length(s); i = 2; j = 1; while i <= len while(s(i) - s(i-1) <= val) i = i + 1; if(i > len) return; end; end; if j == 1 | (s(i-1) - t(j-1)) > val t(j) = s(i-1); j = j + 1; end; t(j) = s(i); j = j + 1; i = i + 1; end; return; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [t] = add(s, val); % add: Adds "val" to each one of the entries in the vector s and returns the new vector. len = length(s); t = []; for i = 1:len t(i) = s(i) + val; end; return;