使用 MATLAB 从图像中识别不同的硬币值答案

【问题标题】：Identifying different coin values from an image using MATLAB使用 MATLAB 从图像中识别不同的硬币值
【发布时间】：2014-11-11 00:00:17
【问题描述】：

我正在尝试使用 MATLAB 识别图片中每个值的匹配数和硬币数。这是起始图片，带有火柴和 4 个不同的硬币值。（5小银、2小金、2大银、4大金币）

输出：代码如下：

close all;
img = (imread('C:\Users\Torstein\Jottacloud\Skole\Visu\Prosjekt\sample_images\sample2.jpg'));
img_gray = rgb2gray(img);

% Filter image for easier edge detection
m = 12;
n = 12;
img_filter = imfilter(img_gray, fspecial('average', [m n]));
%figure, imshow(f), title('f')

% Edge detection
[~, threshold] = edge(img_filter, 'canny');
fudgeFactor = 1.5;
img_edge = edge(img_filter, 'canny', threshold * fudgeFactor);
figure, imshow(img_edge), title('edge detection')

% Dilate image to make the coin edges complete without holes
se_disk = strel('disk',4);
se_line1 = strel('line',3,100);
se_line2 = strel('line',3,100);
img_dilated = imdilate(img_edge, se_disk);
img_dilated = imdilate(img_dilated, [se_line1 se_line2]);
figure, imshow(img_dilated), title('dilate')

% Remove small objects (noise) and fill complete objects
img_clearborder = imclearborder(img_dilated, 4);
%figure, imshow(BWclear), title('cleared border image');
img_fill = imfill(img_clearborder, 'holes');
figure, imshow(img_fill), title('fill holes')

% Erode image to make a clear cut between objects
se_diamond = strel('diamond',2);
img_erode = imerode(img_fill,se_diamond);
for k=1:3
    img_erode = imerode(img_erode,se_diamond);
end
img_nosmall = bwareaopen(img_erode,300);
figure, imshow(img_nosmall), title('erode')

[B, L] = bwboundaries(img_nosmall);
figure, imshow(label2rgb(L, @jet, [.5 .5 .5])), title('boundaries')
hold on
for k = 1:length(B)
  boundary = B{k};
  plot(boundary(:,2), boundary(:,1), 'w', 'LineWidth', 2)
end

stats = regionprops(L,img(:,:,1),...
    'Area','Centroid','Orientation','EquivDiameter','MeanIntensity');
threshold = 0.80; % For differentiating coins from matches based on an objects circularity

coinCentroids = [];
coinIntensities = [];
matchCentroids = [];
matchAngles = [];
coinRatios = [];

for k = 1:length(B)
    boundary = B{k};
    delta_sq = diff(boundary).^2;
    perimeter = sum(sqrt(sum(delta_sq,2)));
    area = stats(k).Area;
    metric = 4*pi*area/perimeter^2;
    metric_string = sprintf('%2.2f',metric);
    angle_string = sprintf('%2.2f',stats(k).Orientation);
    centroid = stats(k).Centroid;
    if metric > threshold
        % Object is round, therefore a coin
        coinCentroids = [coinCentroids; centroid];
        coinIntensities = [coinIntensities; stats(k).MeanIntensity];
        coinRatios = [coinRatios; stats(k).EquivDiameter/area];
    else
        % Object is a match
        angle = stats(k).Orientation;
        matchCentroids = [matchCentroids; centroid];
        matchAngles = [matchAngles; angle];
    end

    plot(centroid(1),centroid(2),'ko');
%     text(boundary(1,2)-35,boundary(1,1)+13,angle_string,'Color','y',...
%       'FontSize',14,'FontWeight','bold');

end

如您所见，我已经确定了哪些对象是硬币，哪些对象是火柴。但是，我很难确定硬币的价值。

例如，硬币的面积/直径给出以下结果。我看不到任何明确的方法来仅根据这些数据来区分不同类型的硬币；数字太接近了。

我也尝试从每枚硬币的起始图片中获取平均颜色强度，但这并不能帮助我将银色硬币与金色硬币分开。

红色通道的平均强度没有给出有 6 个金色硬币和 6 个银色硬币的信息。

问题：如何识别不同的硬币价值？

（在这里询问如何分离两个连接的对象：Separate two overlapping circles in an image using MATLAB）

谢谢

【问题讨论】：

我看不到图片。错误 403。
尝试 regionprops 与属性 'Image' 应该会给你一个很好的分离
@tsom 做得很好。这是一个非常好的问题 - 表明您付出了很多努力并取得了进步。
@tos 另一个可能有帮助的量是每个物体的直径和面积之间的比率。
你很亲密，所以我不会提供答案。继续 Shai 所说的内容，请查看此帖子：mathworks.com/matlabcentral/answers/85363#answer_94853。有一个很好的公式可以计算物体的圆度。如果该值更接近 1，则它更接近圆形，而小于 1 则不太像圆形。使用regionprops 和公式中的参数计算圆形度，然后使用0.5 之类的阈值提取圆形对象。该帖子还惊呼要小心使用偏心，如果您决定使用它

标签： matlab image-processing image-segmentation

【解决方案1】：

首先，regionprops 'BoundingBox'，我使用 imcrop 和已识别硬币的 BoundingBox 从起始图片中剪下硬币的图片。

然后，使用imfindcircles 我可以检测到银色硬币上的孔。最后，我使用硬币的面积来识别硬币的价值。

最终代码：

close all;
img = (imread('C:\Users\Torstein\Jottacloud\Skole\Visu\Prosjekt\sample_images\sample1.jpg'));
%figure, imshow(img);
img_gray = rgb2gray(img);

% img_hsv = rgb2hsv(img); 
% imgv = img_hsv(:,:,3);
% [Gx, Gy] = imgradientxy(imgv);
% [Gmag, Gdir] = imgradient(Gx, Gy);
% Gmag could be useful

% Filter image for easier edge detection
m = 12;
n = 12;
img_filter = imfilter(img_gray, fspecial('average', [m n]));
%figure, imshow(f), title('f')

% Edge detection
[~, threshold] = edge(img_filter, 'canny');
fudgeFactor = 1.5;
img_edge = edge(img_filter, 'canny', threshold * fudgeFactor);
%figure, imshow(img_edge), title('edge detection')

% Dilate image to make the coin edges complete without holes
se_disk = strel('disk',4);
se_line1 = strel('line',3,100);
se_line2 = strel('line',3,100);
img_dilated = imdilate(img_edge, se_disk);
img_dilated = imdilate(img_dilated, [se_line1 se_line2]);
%figure, imshow(img_dilated), title('dilate')

% Remove stuff touching the image border and fill complete objects
img_clearborder = imclearborder(img_dilated, 4);
%figure, imshow(BWclear), title('cleared border image');
img_fill = imfill(img_clearborder, 'holes');
%figure, imshow(img_fill), title('fill holes')

% Erode image to make a clear cut between objects
se_diamond = strel('diamond',2);
img_erode = imerode(img_fill,se_diamond);
for k=1:3
    img_erode = imerode(img_erode,se_diamond);
end
img_nosmall = bwareaopen(img_erode,300); % Remove small objects (noise)
%figure, imshow(img_nosmall), title('erode')

[B, L] = bwboundaries(img_nosmall);
%figure, imshow(label2rgb(L, @jet, [.5 .5 .5])), title('boundaries')
% hold on
% for k = 1:length(B)
%   boundary = B{k};
%   plot(boundary(:,2), boundary(:,1), 'w', 'LineWidth', 2)
% end

stats = regionprops(L,img(:,:,1),...
    'Area','Centroid','Orientation','EquivDiameter','Image','BoundingBox');
threshold = 0.80; % For differentiating coins from matches based on an objects circularity

coinCentroids = [];
coinTypes = []; % 0 for Silver, 1 for Gold
coinValues = []; % 1, 5, 10 eller 20 kroning
coinAreas = [];
silverCoinAreas = [];
goldCoinAreas = [];
matchCentroids = [];
matchAngles = [];
radiusRange = [8,40];

for k = 1:length(B)
    boundary = B{k};
    delta_sq = diff(boundary).^2;
    perimeter = sum(sqrt(sum(delta_sq,2)));
    area = stats(k).Area;
    metric = 4*pi*area/perimeter^2;
    metric_string = sprintf('%2.2f',metric);
    angle_string = sprintf('%2.2f',stats(k).Orientation);
    centroid = stats(k).Centroid;
    if metric > threshold
        % Object is round, therefore a coin
        coinValues = [coinValues; 0];
        coinAreas = [coinAreas; area];
        coinCentroids = [coinCentroids; centroid];
        bbox = stats(k).BoundingBox;
        im = imcrop(img,bbox);
        %figure, imshow(im);
        [centers,radii] = imfindcircles(im,radiusRange,'ObjectPolarity','bright');
        %viscircles(centers,radii);
        if length(centers) > 0
            % Coin has a hole, therefore either 1-kroning or 5-kroning
            coinTypes = [coinTypes; 0];
            silverCoinAreas = [silverCoinAreas; area];

        else
            % Coin does not have hole, therefore either 10-kroning or
            % 20-kroning
            coinTypes = [coinTypes; 1];
            goldCoinAreas = [goldCoinAreas; area];
        end

    else
        % Object is a match
        angle = stats(k).Orientation;
        matchCentroids = [matchCentroids; centroid];
        matchAngles = [matchAngles; angle];
    end

    %plot(centroid(1),centroid(2),'ko');
%     text(boundary(1,2)-35,boundary(1,1)+13,angle_string,'Color','y',...
%       'FontSize',14,'FontWeight','bold');

end

goldThreshold = 0.1;
silverThreshold = 0.1;
maxSilver = max(silverCoinAreas);
maxGold = max(goldCoinAreas);
for k=1:length(coinTypes)
    area = coinAreas(k);
    if coinTypes(k) == 0
        if  area >= maxSilver-maxSilver*silverThreshold
            % 5-kroning
            coinValues(k) = 5;
        else
            % 1-kroning
            coinValues(k) = 1;
        end
    else
        if area >= maxGold-maxGold*goldThreshold
            % 20-kroning
            coinValues(k) = 20;
        else
            % 10-kroning
            coinValues(k) = 10;
        end
    end
end

% OUTPUT:
coinCentroids
coinValues
matchCentroids
matchAngles

谢谢

【讨论】：

干得好！这个线程是提出一个好问题和由此产生的学习过程的一个很好的例子。