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Canny edge dection
function output = my_edge(input_image)
mean_value = mean(mean(input_image));
[height, width] = size(input_image);
fft_mid_img = fftshift(fft2(input_image));
fft_ratio = 12;
a = round(height/fft_ratio):round((fft_ratio-1)*height/fft_ratio);
b = round(width/fft_ratio):round((fft_ratio-1)*width/fft_ratio);
% disp(a);
% disp(b);
fft_mid_img(round(height / 2), round(width / 2)) = 0;
input_image = real(ifft2(ifftshift(fft_mid_img)));
blurred = gauss_blur(gauss_blur(input_image));
% blurred = imbilatfilt(input_image);
% blurred = gauss_blur(medfilt2(input_image));
subplot(2, 2, 2); imshow(blurred);
[g, theta] = sobel(blurred);
edge = zeros(height, width);
for i = 2 : height - 1
for j = 2 : width - 1
if (...
(abs(theta(i, j)) == 90 &&...
g(i, j) >= g(i+1, j) &&...
g(i, j) >= g(i-1, j)) ||...
(theta(i, j) == 0 &&...
g(i, j) >= g(i, j + 1) &&...
g(i, j) >= g(i, j - 1)) ||...
(theta(i, j) == 45 &&...
g(i, j) >= g(i + 1, j + 1) &&...
g(i, j) >= g(i - 1, j - 1)) ||...
(theta(i, j) == -45 &&...
g(i, j) >= g(i + 1, j - 1) &&...
g(i, j) >= g(i - 1, j + 1))...
)
edge(i, j) = g(i, j);
else
edge(i, j) = 0;
end
end
end
% upper_thresh = otsu_threshold(input_image);
upper_thresh = 1.33 * mean_value;
lower_thresh = 0.66 * mean_value;
% lower_thresh = upper_thresh / 2;
disp(upper_thresh);
disp(lower_thresh);
% edge(edge > lower_thresh) = 1.0;
edge((lower_thresh < edge) & (edge < upper_thresh)) = 0.5;
edge(edge < lower_thresh) = 0;
edge(edge > upper_thresh) = 1.0;
edge = filter_low_thresh(edge);
subplot(2, 2, 3); imshow(edge);
output = edge;
function filter_ret = filter_low_thresh(filter_img)
[filter_height, filter_width] = size(filter_img);
filter_ret = zeros(filter_height, filter_width);
% filter_ret = filter_img;
fb = max(filter_height, filter_width) / 20;
fr = round(max(filter_height, filter_width) / 16);
for fi = 2:filter_height-1
for fj = 2:filter_width-1
if (filter_img(fi, fj) < 0.6 && filter_img(fi, fj) > 0.4)
filter_hr = max(1, fi-fr): min(filter_height, fi+fr);
filter_wr = max(1, fj-fr): min(filter_width, fj+fr);
% disp(filter_wr);
filter_area = filter_img(filter_hr, filter_wr);
filter_x = sum(sum(filter_area>0.4));
% filter_x(ceil(fb/2):filter_height-ceil(fb/2), ...
% ceil(fb/2):filter_width-ceil(fb/2)) = 0;
if (filter_x > 1)
% if (filter_x > 0.6)
filter_ret(fi, fj) =1;
end
elseif (filter_img(fi, fj) > 0.6)
filter_ret(fi, fj) = 1;
end
end
end
function threshold = otsu_threshold(otsu_img)
otsu_img = im2uint8(otsu_img) + 1;
hist = zeros(256, 1);
[otsu_height, otsu_width] = size(otsu_img);
for oi = 1:otsu_height
for oj = 1:otsu_width
hist(otsu_img(oi, oj)) = hist(otsu_img(oi, oj)) + 1;
end
end
weights = 0;
for oi = 1:256
weights = weights + hist(oi);
end
sum = 0.0;
tmpMax = 0.0;
threshold = 0;
background = 0;
foreground = 0;
for oi = 1:256
background = background + hist(oi);
if (background == 0)
continue
end
foreground = numel(otsu_img) - background;
if (foreground == 0)
continue
end
sum = sum + oi * hist(oi);
mean_back = sum / background;
mean_fore = (weights - sum) / foreground;
between = background * foreground * (mean_back - mean_fore) ^ 2;
if (between > tmpMax)
tmpMax = between;
threshold = oi;
end
end
threshold = double(threshold - 1) / 255;
function [sobel_g, sobel_theta] = sobel(sobel_img)
sobel_x = [-1 0 1; -2 0 2; -1 0 1];
sobel_y = [1 2 1; 0 0 0; -1 -2 -1];
x_ret = imfilter(sobel_img, sobel_x);
y_ret = imfilter(sobel_img, sobel_y);
sobel_g = sqrt(x_ret.^2 + y_ret.^2);
sobel_theta = atan(y_ret./x_ret) * 180 / pi;
sobel_theta = round(sobel_theta / 45) * 45;
sobel_theta(isnan(sobel_theta)) = 2;
function gauss_ret = gauss_blur(gauss_img)
filter_matrix = 1/159 * [
2 4 5 4 2;
4 9 12 9 4;
5 12 15 12 5;
4 9 12 9 4;
2 4 5 4 2];
gauss_ret = imfilter(gauss_img, filter_matrix);
%in this function, you should finish the edge detection utility.
%the input parameter is a matrix of a gray image
%the output parameter is a matrix contains the edge index using 0 and 1
%the entries with 1 in the matrix shows that point is on the edge of the
%image
%you can use different methods to complete the edge detection function
%the better the final result and the more methods you have used, you will get higher scores
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