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%% MSER algorithm - Linear Time MSER %clcclearclose alltic% Read input imageimg = imread('img/smp_6.jpg');height = size(img,1);width = size(img,2);total_pixels_img = width*height;usage = 'segmented'; % select between segmented or gray scale % image as input: 'segmented' / 'gray'% Parameters to setmin_area_mser = 1000;max_area_mser = 1000000;mser_p.delta = 2;mser_p.min_area = 0.00001*width*height;mser_p.max_area = 0.25*width*height;mser_p.max_variation = 0.5;mser_p.min_diversity = 0.33;color_of_interest = 'blue'; % Options are: red, green, bluecolor_threshold = 100; % range from 0-255ratio = 1.2; % ration between color interest and % others: 20%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% INIT ALGORITHM% 1) Select input image to process if it's gray or segmented and create a single% row vector with the imageswitch usage case 'segmented' % Simple segmentation by color threshold img_seg = f_seg(img, color_threshold, color_of_interest, ratio); subplot(2,2,1); imshow(img); title(['Original Image (' num2str(width) 'x' num2str(height) ')']); subplot(2,2,2); imshow(img_seg); title(['Segmented img ( Color filt:' color_of_interest ' Threshold:' ... num2str(color_threshold) ')']); i = 1; for y=1:height for x=1:width if img_seg(y,x) == 0 img_scan(i,1) = 1; else img_scan(i,1) = img_seg(y,x); end i=i+1; end end img_selected = img_seg; case 'gray' % Image converted in gray scale img_grey = rgb2gray(img); subplot(2,2,1); imshow(img); title(['Original Image (' num2str(width) 'x' num2str(height) ')']); subplot(2,2,2); imshow(img_grey); title(['Gray Image (' num2str(size(img_grey,2)) 'x' ... num2str(size(img_grey,1)) ')']); i = 1; for y=1:height for x=1:width if img_grey(y,x) == 0 img_scan(i,1) = 1; else img_scan(i,1) = img_grey(y,x); end i=i+1; end end img_selected = img_grey;end% 2) Create the bin mask with accessed pixelsbin_mask_access = zeros(total_pixels_img,1);% 3) Initialize control variablespriority = 256; % Variable that defines the smallest % 'dark' pixelcurrent_pixel = 1;current_edge = 0;current_level = img_scan(current_pixel);bin_mask_access(current_pixel) = 1;index_regions = 0;g_index_stack = 0;% Create the LIFO for the 256 gray levesfor i=1:256 boundary_pixels(i) = CStack();end% Insert into the tree the most 'bright' pixel that equivalent to 256g_index_stack = g_index_stack + 1;index_regions = index_regions + 1;region_stack(index_regions).level = 256;region_stack(index_regions).area = 0;region_stack(index_regions).mom(1) = 0;region_stack(index_regions).mom(2) = 0;region_stack(index_regions).mom(3) = 0;region_stack(index_regions).mom(4) = 0;region_stack(index_regions).mom(5) = 0;region_stack(index_regions).variation_mser = 999999;region_stack(index_regions).stable = 0;region_stack(index_regions).parent = 0;region_stack(index_regions).child = 0;region_stack(index_regions).next = 0;% This is an auxiliary vector (LIFO) to store the regions pushed and not process% ed by the function 'process_stack' yet, which defines the parent and child nod% es. In normal behavior this must inflate and deinflate during the image proces% singstack(g_index_stack).node = index_regions;% Each region_stack has a correspondent rect that represents the rectangle assoc% iated with that region, it facilitates in the later steprect(index_regions).top = Inf;rect(index_regions).bottom = 0;rect(index_regions).left = Inf;rect(index_regions).right = 0;rect(index_regions).draw = 1;% Insert into the tree the first region for the first pixel level in the imageg_index_stack = g_index_stack + 1;index_regions = index_regions + 1;region_stack(index_regions).level = current_level;region_stack(index_regions).area = 0;region_stack(index_regions).mom(1) = 0;region_stack(index_regions).mom(2) = 0;region_stack(index_regions).mom(3) = 0;region_stack(index_regions).mom(4) = 0;region_stack(index_regions).mom(5) = 0;region_stack(index_regions).variation_mser = 999999;region_stack(index_regions).stable = 0;region_stack(index_regions).parent = 0;region_stack(index_regions).child = 0;region_stack(index_regions).next = 0;stack(g_index_stack).node = index_regions;rect(index_regions).top = Inf;rect(index_regions).bottom = 0;rect(index_regions).left = Inf;rect(index_regions).right = 0;rect(index_regions).draw = 1;% 4) Run the main algorithm that will scan all pixels inside the imagegCounter = 0;done = 0;while (done == 0) gCounter = gCounter+1; % ...it'll always be the total_pixels_img % While loop to scan all edges of the pixel in analisys while current_edge < 4 % ...get the neighbor pixel according to correspondent edge in the BIG row v % ector that contains all pixels neighbor_pixel = f_neighbor_pixel(current_pixel,current_edge,width,height); if (bin_mask_access(neighbor_pixel) == 0) neighbor_level = img_scan(neighbor_pixel,1); bin_mask_access(neighbor_pixel) = 1; % If the neighbor pixel has a 'lowest (black)' level than the current one, % let push a new region and define as our new current pixel if (neighbor_level < current_level) % In this step we need to store the old current pixel and its current ed % ge and to execute this, we are joining with OR - logical operation the % two informations with this approach: % % 8 bits 4 bits % Pixel to map after -> current pixel position current edge % (remember that now, this is) (we add +1) % (just a number in a row) (because we want) % (the next edge) % % Example: % CUR_PIXEL (8) 1010 1010 << 4 % EDGE (4) + 1111 % Later processing (12) 1010 1010 1111 boundary_pixels(current_level).push(bitor(bitshift(current_pixel,4), ... (current_edge+1))); % ..always define priority as the 'darkest' pixel founded, because we'll % search for that pixel in the boundary stack after if we do not find an % y pixel lowest (black) in the edges if (current_level < priority) priority = current_level; end current_pixel = neighbor_pixel; current_edge = 0; current_level = neighbor_level; % Push a new region with the new 'darkest' pixel founded index_regions = index_regions + 1; region_stack(index_regions).level = current_level; region_stack(index_regions).area = 0; region_stack(index_regions).mom(1) = 0; region_stack(index_regions).mom(2) = 0; region_stack(index_regions).mom(3) = 0; region_stack(index_regions).mom(4) = 0; region_stack(index_regions).mom(5) = 0; region_stack(index_regions).variation_mser = 999999; region_stack(index_regions).stable = 0; region_stack(index_regions).parent = 0; region_stack(index_regions).child = 0; region_stack(index_regions).next = 0; g_index_stack = g_index_stack + 1; stack(g_index_stack).node = index_regions; % ..and its rectangle combined rect(index_regions).top = Inf; rect(index_regions).bottom = 0; rect(index_regions).left = Inf; rect(index_regions).right = 0; rect(index_regions).draw = 1; continue; end % If the current pixel is the 'lowest (black)', store the neighboor for la % ter search iteration boundary_pixels(neighbor_level).push(bitor(bitshift(neighbor_pixel,4),0)); if (neighbor_level < priority) priority = neighbor_level; end end current_edge = current_edge + 1; end % We need to discover in the MxN representation, the value of the pixel for im % age math processing, for later computing x = mod(current_pixel, width); % Give us the offset in the line of the image if x == 0 x = width; end y = floor(current_pixel/width); % As the MSER alg. we need to accumulate the latest pixel in the latest region [region_stack rect] = f_accumulate(region_stack, rect, stack, ... g_index_stack, x, y); % If our priority is 256 we don't have a lowest pixel anymore, then we finish! % ..and we need to process all stack creating the tree with the regions to be % this way: % Example: % ________________256_______________ % | | | | % _243_ _ 251_ _202_ _215_ % | | | | | | | | % 145 20 200 10 198 52 20 112 % ................................................... % % PARENT % | % NODE % / \ % NEXT CHILD % if (priority == 256) % PROCESS STACK new_pixel_grey_level = 256; % Passing 256 as the new pixel grey level matche % s to create the root tree node [region_stack ... rect ... index_regions ... g_index_stack ... stack] = f_process_stack(new_pixel_grey_level, ... region_stack, ... stack, ... g_index_stack, ... index_regions, ... rect); done = 1; break; end % Remove the pixel with lowest (black) value stored in the stack to process... % we remove top because it's a LIFO pixel_component = boundary_pixels(priority).top(); % Undo the concatenation made previous before current_pixel = bitshift(pixel_component,-4); current_edge = bitand(pixel_component,15); boundary_pixels(priority).pop(); % If we empty the stack in that black level, we need to increase the priority while (boundary_pixels(priority).isempty() && (priority < 256)) priority = double(priority + 1); end % Get the black level for our new current pixel new_pixel_grey_level = img_scan(current_pixel); if (new_pixel_grey_level ~= current_level) % PROCESS STACK [region_stack ... rect ... index_regions ... g_index_stack ... stack] = f_process_stack(new_pixel_grey_level, ... region_stack, ... stack, ... g_index_stack, ... index_regions, ... rect); current_level = new_pixel_grey_level; endend% END OF MAIN ALGORITHM%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Post-processing step% 5) Stable analisys of each region to determine the MSER regionsstable_counter = 0;for i=1:size(region_stack,2) reg = region_stack(i); parent = region_stack(i); while (parent.parent && region_stack(parent.parent).level <= reg.level + ... mser_p.delta) parent = region_stack(parent.parent); end reg.variation_mser = (parent.area - reg.area)/reg.area; reg.stable = (reg.area >= mser_p.min_area) && ... (reg.area <= mser_p.max_area) && (reg.variation_mser <= ... mser_p.max_variation); id = reg.parent; if id ~= 0 parent = region_stack(id); while (parent.parent && reg.area > mser_p.min_diversity*parent.area) if(parent.variation_mser <= reg.variation_mser) reg.stable = false; end if(reg.variation_mser < parent.variation_mser) parent.stable = false; end rect(id).stable = parent.stable; region_stack(id) = parent; id = parent.parent; parent = region_stack(id); end end if (reg.stable == 1) stable_counter = stable_counter + 1; end rect(i).stable = reg.stable; region_stack(i) = reg;end% Saving just the stable regionst = 0;for p=1:size(rect,2) if(rect(p).stable == 1) t = t+1; tmp(t) = rect(p); endendrect = tmp;subplot(2,2,3);imshow(img_selected);title(['Original Image (' num2str(width) 'x' num2str(height) ... ') with all MSER Regions']);k=0;for i=1:size(rect,2) if (rect(i).draw == 1) k=k+1; width_n = rect(i).right-rect(i).left; height_n = rect(i).bottom-rect(i).top; rectangle('Position',[rect(i).left rect(i).top width_n height_n], ... 'EdgeColor','r'); endend% 6) Filtering just the rectangles with some specific area rangefor i=1:size(rect,2) rect(i).height = rect(i).bottom - rect(i).top; rect(i).width = rect(i).right - rect(i).left; rect(i).size = rect(i).height*rect(i).width; if (rect(i).size > min_area_mser && rect(i).size < max_area_mser) rect(i).draw = 1; else rect(i).draw = 0; endendsubplot(2,2,4);imshow(img);title(['Original Image (' num2str(width) 'x' num2str(height) ... ') with filtered MSER Regions']);% 7) Draw the rectangles in the original imagek=0;for i=1:size(rect,2) if (rect(i).draw == 1) k=k+1; width_n = rect(i).right-rect(i).left; height_n = rect(i).bottom-rect(i).top; rectangle('Position',[rect(i).left rect(i).top width_n height_n], ... 'EdgeColor','r'); endendtime_p = toc;% 8) Reportsfprintf('\tConclusion Reports');fprintf('\nImage size: Width=%d x Height=%d',width,height);fprintf('\nTotal number of pixels: %d',total_pixels_img);fprintf('\nFounded regions: %d',index_regions);fprintf('\nStable regions: %d',stable_counter);fprintf('\nRectangles drawed: %d', k);fprintf('\nTime to process the image: %d seconds', time_p);fprintf('\nMSER Parameters: ');mser_p 1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.41.42.43.44.45.46.47.48.49.50.51.52.53.54.55.56.57.58.59.60.61.62.63.64.65.66.67.68.69.70.71.72.73.74.75.76.77.78.79.80.81.82.83.84.85.86.87.88.89.90.91.92.93.94.95.96.97.98.99.100.101.102.103.104.105.106.107.108.109.110.111.112.113.114.115.116.117.118.119.120.121.122.123.124.125.126.127.128.129.130.131.132.133.134.135.136.137.138.139.140.141.142.143.144.145.146.147.148.149.150.151.152.153.154.155.156.157.158.159.160.161.162.163.164.165.166.167.168.169.170.171.172.173.174.175.176.177.178.179.180.181.182.183.184.185.186.187.188.189.190.191.192.193.194.195.196.197.198.199.200.201.202.203.204.205.206.207.208.209.210.211.212.213.214.215.216.217.218.219.220.221.222.223.224.225.226.227.228.229.230.231.232.233.234.235.236.237.238.239.240.241.242.243.244.245.246.247.248.249.250.251.252.253.254.255.256.257.258.259.260.261.262.263.264.265.266.267.268.269.270.271.272.273.274.275.276.277.278.279.280.281.282.283.284.285.286.287.288.289.290.291.292.293.294.295.296.297.298.299.300.301.302.303.304.305.306.307.308.309.310.311.312.313.314.315.316.317.318.319.320.321.322.323.324.325.326.327.328.329.330.331.332.333.334.335.336.337.338.339.340.341.342.343.344.345.346.347.348.349.350.351.352.353.354.355.356.357.358.359.360.361.362.363.364.365.366.367.368.369.370.371.372.373.374.375.376.377.378.379.380.381.382.383.384.385.386.387.388.389.390.391.392.393.394.395.396.397.398.399.400.401.402.403.404.405.406.407.408.409.410.411.412.413.414.415.416.417.418.419.420.
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