Car_recgnition.zip_MATLAB识别_matlab车牌识别_动态识别_识别_车牌识别

function [bw, crop] = extract_LP(pic1); % extract_LP: Extracts the License Plate from the supplied picture. Returns % a binary image which contains the normalized LP, and the rgb picture of % the LP precisely cropped from the image given in parameter. load global_var.mat; % chosen size for the normalized LP. XSIZE = 50; YSIZE = 150; % Yellow region extraction: yellow_pic = extract_yellow_region(pic1); display_picture(yellow_pic, debug1 + debug2, 'Yellow Regions Filter:', 3); % Fixing the LP Region: [x, x2, y, y2] = detect_lp_area(yellow_pic, 50); lp_area = pic1(y:y2, x:x2, :); display_picture(lp_area, debug1 + debug2, 'License Plate Region:', 3); % Fixing the LP angle and rotating the ROI accordingly: [angle, lines] = find_angle(lp_area); if(debug1 + debug2 ~= 0) load global_var.mat; if is_killed == 1 close all; return; end; subplot(2,2,3), imshow(pic1(y:y2, x:x2, :)); draw_lines(lines); title('Determining the angle of the plate using the Radon transform:'); pause(speed); end; pic = imrotate(yellow_pic(y:y2, x:x2), angle, 'bilinear'); % Used to show the rotation on the GUI only.. display_picture(pic, debug1 + debug2, 'Yellow Region Rotated:', 3); % improving the LP region on the smaller rotated region obtained before: [small_pic, xx, xx2, yy, yy2] = improved_lp_area(pic, angle); display_picture(small_pic, debug2, 'Improved License Plate region:', 4); display_picture('internal_images/black.jpg', debug2, '', 4, 0); display_picture(small_pic, debug1 + debug2, 'Improved License Plate region:', 3, 0); display_picture('internal_images/black.jpg', debug2, '', 2, 0); % cropping the LP: [image, RECTx, RECTy] = crop_lp(small_pic, lp_area, xx, xx2, yy, yy2, angle); display_picture('internal_images/black.jpg', debug2, '', 2, 0); crop = image; % output... display_picture(image, debug2, 'LP Crop:', 4); display_picture('internal_images/black.jpg', debug2, '', 4, 0); display_picture(image, 1, 'LP Crop:', 3); % Image quantization: [grayImage, quantImage, bw] = quantizeImage(image); display_picture(grayImage, debug1 + debug2, 'Gray Scale LP:', 3); display_picture(quantImage, debug2, 'LP Quantisation and Equalization:', 4); display_picture('internal_images/black.jpg', debug2, '', 4, 0); display_picture(quantImage, debug1 + debug2, 'LP Quantisation and Equalization:', 3); display_picture(bw, debug1 + debug2, 'Binary LP:', 3); % Normalized LP: bw = normalized_lp(bw, RECTx, RECTy, XSIZE, YSIZE); display_picture(imcomplement(bw), debug2, 'Normalized LP:', 4); display_picture('internal_images/black.jpg', debug2, '', 4, 0); display_picture(imcomplement(bw), debug1 + debug2, 'Normalized LP:', 3); % Adjusting the LP horizontal contours: (the vertical contours are not % adjusted in order not to cut digits: this will be done transparently by % the segmentation machine. bw = normalized_lp_contour(bw, [XSIZE, YSIZE]); display_picture(bw, debug2, 'LP Horizontal Contours Adjusted:', 4); display_picture('internal_images/black.jpg', debug2, '', 4, 0); display_picture(bw, debug1 + debug2, 'LP Horizontal Contours Adjusted:', 3); return; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % detect_lp_area % Finds the License Plate region in a RGB picture with the supplied safety % spacing around the plate, and returns the coordinates of the found region. function [x, x2, y, y2] = detect_lp_area (yellow_pic, spacing); LP_MIN_AREA = 2670; LP_MAX_RATIO = 0.67; LP_MIN_RATIO = 0.16; % Dilating the yellow regions: dilated_pic = imdilate(yellow_pic, strel('diamond', 5)); load global_var.mat; display_picture(dilated_pic, debug1 + debug2, 'Yellow Region Dilated:', 3); % Separating the pictures into connected components: stat = imfeature(bwlabel(dilated_pic)); % Selecting the license plates from the candidates in stat. % The chosen area is the deepest region in the frame which has the % following properties: % area > LP_MIN_AREA % LP_MIN_RATIO <= height/width <= LP_MAX_RATIO % area >= max(areas of the candidates)/3.5 depth = -1; for i = 1 : length([stat.Area]) if stat(i).BoundingBox(2) >= depth && stat(i).Area > LP_MIN_AREA && ... stat(i).BoundingBox(4) <= LP_MAX_RATIO*stat(i).BoundingBox(3) && ... stat(i).BoundingBox(4) >= (LP_MIN_RATIO)*stat(i).BoundingBox(3) && stat(i).Area >= max([stat.Area])/3.5 depth = stat(i).BoundingBox(2); end; end; % finding the components which are at the depth "depth": r = []; for i = 1 : length([stat.Area]) if stat(i).BoundingBox(2) == depth && stat(i).Area > LP_MIN_AREA && ... stat(i).BoundingBox(4) <= LP_MAX_RATIO*stat(i).BoundingBox(3) && ... stat(i).BoundingBox(4) >= (LP_MIN_RATIO)*stat(i).BoundingBox(3) && stat(i).Area >= max([stat.Area])/3.5 r = [r stat(i).Area]; end; end; % if we did not find any region with the above criterion, taking the % candidate of maximum area. if(length(r) == 0) index = (find([stat.Area] == max([stat.Area]))); else % otherwise, taking the candidate with maximum area from the % filtered candidates: index = (find([stat.Area] == max(r))); end; % set the coordinates of the supposed license plate region: x = max(floor(stat(index).BoundingBox(1) - spacing), 1); y = max(floor(stat(index).BoundingBox(2) - spacing), 1); width = ceil(stat(index).BoundingBox(3) + 2*spacing); height = ceil(stat(index).BoundingBox(4) + 2*spacing); y2 = min(y + height, size(yellow_pic, 1)); x2 = min(x + width, size(yellow_pic, 2)); return; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % Returns an improved LP area running again the algorithm in the function % detect_lp_area with zero spacing and on the smaller rotated pictures % given as parameter. function [pic, x1, x2, y1, y2] = improved_lp_area(image, angle); [x1,x2,y1,y2] = detect_lp_area(image, 0); pic = image(y1:y2, x1:x2); return; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [image, RECTx, RECTy] = crop_lp(pic, lp_area, x1, x2, y1, y2, angle); rec = find_lp_location(pic); image = imrotate(lp_area, angle, 'bilinear'); image = image(y1:y2, x1:x2, :); RECTy = [rec(2), rec(2), rec(2) + rec(4), rec(2) + rec(4)]; RECTx = [rec(1), rec(1) + rec(3), rec(1) + rec(3), rec(1)]; image = imcrop(image, rec); return; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % find_lp_location: Returns the coordinates of the LP rectangle inside the % supplied small picture: uses "find_contours" for the sum of the lines and of the % columns in the picture. function [rec] = find_lp_location(im); load global_var.mat; p1 = sum(im); plot_vector(p1, 4, 'Adjusting the LP Vertical Contours - Columns Sum Graph:', debug2); [x1, x2] = find_contours(p1); p2 = sum(im'); plot_vector(p2, 2, 'Adjusting the LP Horizontal Contours - Lines Sum Graph:', debug2); [y1, y2] = find_contours(p2); rec = [x1, y1, x2-x1, y2-y1]; return; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % find_contours: Returns the x-coordinates respectively of the first point at % the left and the first point at the right of the vector which is superior % or equal to the average of the vector. This permits to delimit the plate % eliminating noises around it. function [index1, index2] = find_contours(vec); avg = mean(vec); % left side: for j = 1 : length(vec) if(vec(1,j) <= avg) continue; end; index1 = j - 1; break; end; % right side: for j = length(vec) : -1 : 1 if(vec(1,j) <= avg) continue; end; index2 = j + 1; break;