从信号图像 (RGB) 中移除背景 'noise'
removing background 'noise' from signal images (RGB)
我有一些信号图片：
如您所见，有些包含颜色信号，有些只是 gray/black 颜色信号。
我的任务是仅提取 具有白色背景 的纯信号。这意味着我需要删除图像中除信号以外的所有内容。
我检查过虚线、虚线、实线（顶部和底部）具有接近 0;0;0 的相同 RGB 值（例如：0;0;0, 2;2;2;或 8;8;8) 在 RGB 方面。
因此，我首先想到的是访问每个像素的 RGB 值，如果所有 RGB 值都相同，则分配白色。使用这种繁重的计算，我可以提取所有颜色信号，因为 RGB 值对于红色、蓝色、绿色（或它们在某种程度上的阴影）等颜色永远不会相同。
但是，该过程会删除信号像素值相同的信号。大多数黑色信号都会发生这种情况（例如前两个样本）。
我也想过提取信号，如果它保持水平和一些垂直连续性，但老实说我不知道如何为它编写代码。
我不要求任何代码解决这个挑战。
对于如何成功提取原始信号，我想有不同的看法。
我期待收到您的想法、见解和资源。谢谢
注意：我所有的图片（约3k）都在一个文件夹中，我将应用一种通用算法来完成任务。
您可以使用Hough transform找到水平线和垂直线。
找到线条后，擦除它们很简单。
去除线条只是第一步，但它看起来是一个很好的起点...
保留彩色像素（如您所建议的）也是一项简单的任务。
您提到您不要求任何代码解决方案，但我决定使用 MATLAB 代码演示我的建议：
close all
clear
origI = imread('I.png'); %Read image
I = imbinarize(rgb2gray(origI)); %Convert to binary
I = ~I; %Invert - the line color should be white.
%Apply hough transform: Find lines with angles very close to 0 degrees and with angles close to 90 degrees.
[H,theta,rho] = hough(I, 'RhoResolution', 1, 'Theta', [-0.3:0.02:0.3, -90:0.02:-89.7, 89.7:0.02:89.98]);
P = houghpeaks(H, numel(H), 'Threshold', 0.1, 'NHoodSize', [11, 1]); %Use low thresholds
lines = houghlines(I,theta,rho,P,'FillGap',25,'MinLength',200); %Fill large gaps and keep only the long lines.
%Plot the lines for debugging, and erase them by drawing black lines over them
J = im2uint8(I);
figure, imshow(I), hold on
for k = 1:length(lines)
xy = [lines(k).point1; lines(k).point2];
plot(xy(:,1),xy(:,2),'LineWidth',2,'Color','green');
% Plot beginnings and ends of lines
plot(xy(1,1),xy(1,2),'x','LineWidth',2,'Color','yellow');
plot(xy(2,1),xy(2,2),'x','LineWidth',2,'Color','red');
% Draw black line over each line.
J = insertShape(J, 'Line', [xy(1,1), xy(1,2), xy(2,1), xy(2,2)], 'Color', 'Black');
end
%Covert J image to binary (because MATLAB function insertShape returns RGB output).
J = imbinarize(rgb2gray(J));
figure, imshow(J)
%Color mask: 1 where color is not black or white.
I = double(origI);
C = (abs(I(:,:,1) - I(:,:,2)) > 20) | (abs(I(:,:,1) - I(:,:,3)) > 20) | (abs(I(:,:,2) - I(:,:,3)) > 20);
figure, imshow(C)
%Build a mask that combines "lines" mask and "color" mask.
Mask = J | C;
Mask = cat(3, Mask, Mask, Mask);
%Put white color where mask value is 0.
K = origI;
K(~Mask) = 255;
figure, imshow(K)
检测到的行：
删除行后的结果：
最终结果：
如您所见，还有剩菜。
我对上述结果应用了第二次迭代（相同的代码）。
结果得到改善：
您可以尝试使用形态学操作去除剩余部分。
不擦除虚线图会很困难。
迭代所有 PNG 图像文件：
- 将代码放在
m 文件（MATLAB 脚本文件）中。
- 将
m 文件放在 PNG 图像文件的同一文件夹中。
代码如下：
%ExtractSignals.m
close all
clear
%List all PNG files in the working directory (where ExtractSignals.m is placed).
imagefiles = dir('*.png');
nfiles = length(imagefiles);
result_images = cell(1, nfiles); %Allocate cell array for storing output images
for ii = 1:nfiles
currentfilename = imagefiles(ii).name; %PNG file name
origI = imread(currentfilename); %Read image
%Verify origI is in RGB format (just in case...)
if (size(origI, 3) ~= 3)
error([currentfilename, ' is not RGB image format!']);
end
I = imbinarize(rgb2gray(origI)); %Convert to binary
I = ~I; %Invert - the line color should be white.
%Apply hough transform: Find lines with angles very close to 0 degrees and with angles close to 90 degrees.
[H,theta,rho] = hough(I, 'RhoResolution', 1, 'Theta', [-0.3:0.02:0.3, -90:0.02:-89.7, 89.7:0.02:89.98]);
P = houghpeaks(H, numel(H), 'Threshold', 0.1, 'NHoodSize', [11, 1]); %Use low thresholds
lines = houghlines(I,theta,rho,P,'FillGap',25,'MinLength',200); %Fill large gaps and keep only the long lines.
%Plot the lines for debugging, and erase them by drawing black lines over them
J = im2uint8(I);
%figure, imshow(I), hold on
for k = 1:length(lines)
xy = [lines(k).point1; lines(k).point2];
%plot(xy(:,1),xy(:,2),'LineWidth',2,'Color','green');
% Plot beginnings and ends of lines
%plot(xy(1,1),xy(1,2),'x','LineWidth',2,'Color','yellow');
%plot(xy(2,1),xy(2,2),'x','LineWidth',2,'Color','red');
% Draw black line over each line.
J = insertShape(J, 'Line', [xy(1,1), xy(1,2), xy(2,1), xy(2,2)], 'Color', 'Black');
end
%Covert J image to binary (because MATLAB function insertShape returns RGB output).
J = imbinarize(rgb2gray(J));
%figure, imshow(J)
%Color mask: 1 where color is not black or white.
I = double(origI);
C = (abs(I(:,:,1) - I(:,:,2)) > 20) | (abs(I(:,:,1) - I(:,:,3)) > 20) | (abs(I(:,:,2) - I(:,:,3)) > 20);
%figure, imshow(C)
%Build a mask that combines "lines" mask and "color" mask.
Mask = J | C;
Mask = cat(3, Mask, Mask, Mask);
%Put white color where mask value is 0.
K = origI;
K(~Mask) = 255;
%figure, imshow(K)
%Second iteration - applied by "copy and paste" of the above code (it is recommended to use a function instead).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
origI = K; %Set origI to the result of the first iteration
I = imbinarize(rgb2gray(origI)); %Convert to binary
I = ~I; %Invert - the line color should be white.
%Apply hough transform: Find lines with angles very close to 0 degrees and with angles close to 90 degrees.
[H,theta,rho] = hough(I, 'RhoResolution', 1, 'Theta', [-0.3:0.02:0.3, -90:0.02:-89.7, 89.7:0.02:89.98]);
P = houghpeaks(H, numel(H), 'Threshold', 0.1, 'NHoodSize', [11, 1]); %Use low thresholds
lines = houghlines(I,theta,rho,P,'FillGap',25,'MinLength',200); %Fill large gaps and keep only the long lines.
%Plot the lines for debugging, and erase them by drawing black lines over them
J = im2uint8(I);
%figure, imshow(I), hold on
for k = 1:length(lines)
xy = [lines(k).point1; lines(k).point2];
% Draw black line over each line.
J = insertShape(J, 'Line', [xy(1,1), xy(1,2), xy(2,1), xy(2,2)], 'Color', 'Black');
end
%Covert J image to binary (because MATLAB function insertShape returns RGB output).
J = imbinarize(rgb2gray(J));
%figure, imshow(J)
%Color mask: 1 where color is not black or white.
I = double(origI);
C = (abs(I(:,:,1) - I(:,:,2)) > 20) | (abs(I(:,:,1) - I(:,:,3)) > 20) | (abs(I(:,:,2) - I(:,:,3)) > 20);
%figure, imshow(C)
%Build a mask that combines "lines" mask and "color" mask.
Mask = J | C;
Mask = cat(3, Mask, Mask, Mask);
%Put white color where mask value is 0.
K = origI;
K(~Mask) = 255;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Store result image in a cell array
result_images{ii} = K;
end
%Display all result images
for ii = 1:nfiles
figure;
imshow(result_images{ii});
title(['Processed ', imagefiles(ii).name]);
end
我有一些信号图片：
如您所见，有些包含颜色信号，有些只是 gray/black 颜色信号。 我的任务是仅提取 具有白色背景 的纯信号。这意味着我需要删除图像中除信号以外的所有内容。
我检查过虚线、虚线、实线（顶部和底部）具有接近 0;0;0 的相同 RGB 值（例如：0;0;0, 2;2;2;或 8;8;8) 在 RGB 方面。
因此，我首先想到的是访问每个像素的 RGB 值，如果所有 RGB 值都相同，则分配白色。使用这种繁重的计算，我可以提取所有颜色信号，因为 RGB 值对于红色、蓝色、绿色（或它们在某种程度上的阴影）等颜色永远不会相同。
但是，该过程会删除信号像素值相同的信号。大多数黑色信号都会发生这种情况（例如前两个样本）。
我也想过提取信号，如果它保持水平和一些垂直连续性，但老实说我不知道如何为它编写代码。
我不要求任何代码解决这个挑战。 对于如何成功提取原始信号，我想有不同的看法。
我期待收到您的想法、见解和资源。谢谢
注意：我所有的图片（约3k）都在一个文件夹中，我将应用一种通用算法来完成任务。
您可以使用Hough transform找到水平线和垂直线。
找到线条后，擦除它们很简单。
去除线条只是第一步，但它看起来是一个很好的起点...
保留彩色像素（如您所建议的）也是一项简单的任务。
您提到您不要求任何代码解决方案，但我决定使用 MATLAB 代码演示我的建议：
close all
clear
origI = imread('I.png'); %Read image
I = imbinarize(rgb2gray(origI)); %Convert to binary
I = ~I; %Invert - the line color should be white.
%Apply hough transform: Find lines with angles very close to 0 degrees and with angles close to 90 degrees.
[H,theta,rho] = hough(I, 'RhoResolution', 1, 'Theta', [-0.3:0.02:0.3, -90:0.02:-89.7, 89.7:0.02:89.98]);
P = houghpeaks(H, numel(H), 'Threshold', 0.1, 'NHoodSize', [11, 1]); %Use low thresholds
lines = houghlines(I,theta,rho,P,'FillGap',25,'MinLength',200); %Fill large gaps and keep only the long lines.
%Plot the lines for debugging, and erase them by drawing black lines over them
J = im2uint8(I);
figure, imshow(I), hold on
for k = 1:length(lines)
xy = [lines(k).point1; lines(k).point2];
plot(xy(:,1),xy(:,2),'LineWidth',2,'Color','green');
% Plot beginnings and ends of lines
plot(xy(1,1),xy(1,2),'x','LineWidth',2,'Color','yellow');
plot(xy(2,1),xy(2,2),'x','LineWidth',2,'Color','red');
% Draw black line over each line.
J = insertShape(J, 'Line', [xy(1,1), xy(1,2), xy(2,1), xy(2,2)], 'Color', 'Black');
end
%Covert J image to binary (because MATLAB function insertShape returns RGB output).
J = imbinarize(rgb2gray(J));
figure, imshow(J)
%Color mask: 1 where color is not black or white.
I = double(origI);
C = (abs(I(:,:,1) - I(:,:,2)) > 20) | (abs(I(:,:,1) - I(:,:,3)) > 20) | (abs(I(:,:,2) - I(:,:,3)) > 20);
figure, imshow(C)
%Build a mask that combines "lines" mask and "color" mask.
Mask = J | C;
Mask = cat(3, Mask, Mask, Mask);
%Put white color where mask value is 0.
K = origI;
K(~Mask) = 255;
figure, imshow(K)
检测到的行：
删除行后的结果：
最终结果：
如您所见，还有剩菜。
我对上述结果应用了第二次迭代（相同的代码）。
结果得到改善：
您可以尝试使用形态学操作去除剩余部分。
不擦除虚线图会很困难。
迭代所有 PNG 图像文件：
- 将代码放在
m文件（MATLAB 脚本文件）中。 - 将
m文件放在 PNG 图像文件的同一文件夹中。
代码如下：
%ExtractSignals.m
close all
clear
%List all PNG files in the working directory (where ExtractSignals.m is placed).
imagefiles = dir('*.png');
nfiles = length(imagefiles);
result_images = cell(1, nfiles); %Allocate cell array for storing output images
for ii = 1:nfiles
currentfilename = imagefiles(ii).name; %PNG file name
origI = imread(currentfilename); %Read image
%Verify origI is in RGB format (just in case...)
if (size(origI, 3) ~= 3)
error([currentfilename, ' is not RGB image format!']);
end
I = imbinarize(rgb2gray(origI)); %Convert to binary
I = ~I; %Invert - the line color should be white.
%Apply hough transform: Find lines with angles very close to 0 degrees and with angles close to 90 degrees.
[H,theta,rho] = hough(I, 'RhoResolution', 1, 'Theta', [-0.3:0.02:0.3, -90:0.02:-89.7, 89.7:0.02:89.98]);
P = houghpeaks(H, numel(H), 'Threshold', 0.1, 'NHoodSize', [11, 1]); %Use low thresholds
lines = houghlines(I,theta,rho,P,'FillGap',25,'MinLength',200); %Fill large gaps and keep only the long lines.
%Plot the lines for debugging, and erase them by drawing black lines over them
J = im2uint8(I);
%figure, imshow(I), hold on
for k = 1:length(lines)
xy = [lines(k).point1; lines(k).point2];
%plot(xy(:,1),xy(:,2),'LineWidth',2,'Color','green');
% Plot beginnings and ends of lines
%plot(xy(1,1),xy(1,2),'x','LineWidth',2,'Color','yellow');
%plot(xy(2,1),xy(2,2),'x','LineWidth',2,'Color','red');
% Draw black line over each line.
J = insertShape(J, 'Line', [xy(1,1), xy(1,2), xy(2,1), xy(2,2)], 'Color', 'Black');
end
%Covert J image to binary (because MATLAB function insertShape returns RGB output).
J = imbinarize(rgb2gray(J));
%figure, imshow(J)
%Color mask: 1 where color is not black or white.
I = double(origI);
C = (abs(I(:,:,1) - I(:,:,2)) > 20) | (abs(I(:,:,1) - I(:,:,3)) > 20) | (abs(I(:,:,2) - I(:,:,3)) > 20);
%figure, imshow(C)
%Build a mask that combines "lines" mask and "color" mask.
Mask = J | C;
Mask = cat(3, Mask, Mask, Mask);
%Put white color where mask value is 0.
K = origI;
K(~Mask) = 255;
%figure, imshow(K)
%Second iteration - applied by "copy and paste" of the above code (it is recommended to use a function instead).
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
origI = K; %Set origI to the result of the first iteration
I = imbinarize(rgb2gray(origI)); %Convert to binary
I = ~I; %Invert - the line color should be white.
%Apply hough transform: Find lines with angles very close to 0 degrees and with angles close to 90 degrees.
[H,theta,rho] = hough(I, 'RhoResolution', 1, 'Theta', [-0.3:0.02:0.3, -90:0.02:-89.7, 89.7:0.02:89.98]);
P = houghpeaks(H, numel(H), 'Threshold', 0.1, 'NHoodSize', [11, 1]); %Use low thresholds
lines = houghlines(I,theta,rho,P,'FillGap',25,'MinLength',200); %Fill large gaps and keep only the long lines.
%Plot the lines for debugging, and erase them by drawing black lines over them
J = im2uint8(I);
%figure, imshow(I), hold on
for k = 1:length(lines)
xy = [lines(k).point1; lines(k).point2];
% Draw black line over each line.
J = insertShape(J, 'Line', [xy(1,1), xy(1,2), xy(2,1), xy(2,2)], 'Color', 'Black');
end
%Covert J image to binary (because MATLAB function insertShape returns RGB output).
J = imbinarize(rgb2gray(J));
%figure, imshow(J)
%Color mask: 1 where color is not black or white.
I = double(origI);
C = (abs(I(:,:,1) - I(:,:,2)) > 20) | (abs(I(:,:,1) - I(:,:,3)) > 20) | (abs(I(:,:,2) - I(:,:,3)) > 20);
%figure, imshow(C)
%Build a mask that combines "lines" mask and "color" mask.
Mask = J | C;
Mask = cat(3, Mask, Mask, Mask);
%Put white color where mask value is 0.
K = origI;
K(~Mask) = 255;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%Store result image in a cell array
result_images{ii} = K;
end
%Display all result images
for ii = 1:nfiles
figure;
imshow(result_images{ii});
title(['Processed ', imagefiles(ii).name]);
end