使用MATLAB检测手指峰值

时间：2014-11-25 00:34:48

标签： matlab image-processing

我必须用Matlab创建一个算法，用手的图像，可以通过凸起手指的数量和拇指的存在与否来知道手的形状。到目前为止，算法几乎已经完成，但我不知道我能做些什么才能找到代表手指的峰值。我们尝试了很多东西，但没有任何作用。我们的想法是找到突然增加的时间，但由于像素从未完全对齐，我们尝试过的任何工作都没有。有人有什么想法吗？这是迄今为止的代码。

他正在阅读的图片就是这个：

要知道手指是否相关，我们已经有了一个可能有效的想法......但我们需要先找到手指。

clear all
close all

image=imread('mao2.jpg');

YCBCR = rgb2ycbcr(image);
image=YCBCR;

cb = image(:,:,2);
cr = image(:,:,3);
imagek(:,1) = cb(:);
imagek(:,2) = cr(:);
imagek = double(imagek);

[IDX, C] = kmeans(imagek, 2, 'EmptyAction', 'singleton');

s=size(image);
IDX= uint8(IDX);
C2=round(C);
imageNew = zeros(s(1),s(2));
temp = reshape(IDX, [s(1) s(2)]);
for i = 1 : 1 : s(1)
 for j = 1 : 1 : s(2)
   imageNew(i,j,:) = C2(temp(i,j));
 end
end
imageNew=uint8(imageNew);

[m,n]=size(imageNew);

for i=1:1:m
    for j = 1:1:n
        if(imageNew(i,j)>=127)
            pretobranco(i,j)=0;
        else
            pretobranco(i,j)=1;
        end
    end
end

I2=imfill(pretobranco);

imshow(I2);
imwrite(I2, 'mao1trab.jpg');


[m,n]=size(I2);

B=edge(I2);
figure
imshow(B);
hold on;

stats=regionprops(I2,'BoundingBox');

rect=rectangle('position', [stats(1).BoundingBox(1), stats(1).BoundingBox(2), stats(1).BoundingBox(3), stats(1).BoundingBox(4)], 'EdgeColor', 'r');
stats(1).BoundingBox(1)
stats(1).BoundingBox(2)
stats(1).BoundingBox(3)
stats(1).BoundingBox(4)
figure
Bound = B( stats(1).BoundingBox(2): stats(1).BoundingBox(2)+stats(1).BoundingBox(4)-1, stats(1).BoundingBox(1):stats(1).BoundingBox(1)+stats(1).BoundingBox(3)-1);
imshow(Bound)

y1 = round(stats(1).BoundingBox(2))
y2 = round(stats(1).BoundingBox(2)+stats(1).BoundingBox(4)-1)
x1 = round(stats(1).BoundingBox(1))
x2 = round(stats(1).BoundingBox(1)+stats(1).BoundingBox(3)-1)

% Bounding box contida em imagem[M, N].

[M,N] = size(Bound)
vertical=0;
horizontal=0;
if M > N
    vertical = 1 %imagem vertical
else
    horizontal = 1 %imagem horizontal
end

%Find thumb

MaoLeft = 0;
MaoRight = 0;
nPixelsBrancos = 0;

if vertical==1
for i = x1:1:x2
    for j= y1:1:y2
        if I2(j,i) == 1
            nPixelsBrancos = nPixelsBrancos + 1; %Numero de pixels da mão
        end
    end
end

for i=x1:1:x1+30
    for j=y1:1:y2
        if I2(j,i) == 1
            MaoLeft = MaoLeft + 1; %Number of pixels of the hand between the 30 first colums
        end
    end
end

for i=x2-30:1:x2
    for j=y1:1:y2
        if I2(j,1) == 1
            MaoRight = MaoRight + 1; %Number of pixels of the hand between the 30 last colums
        end
    end
end

TaxaBrancoLeft = MaoLeft/nPixelsBrancos
TaxaBrancoRight = MaoRight/nPixelsBrancos

if TaxaBrancoLeft <= (7/100)
    if TaxaBrancoRight <= (7/100)
        Thumb = 0 %Thumb in both borders is defined as no Thumb.
    else
        ThumbEsquerdo = 1 %Thumb on left
    end
end

if TaxaBrancoRight <= (7/100) && TaxaBrancoLeft >= (7/100) 
    ThumbDireito = 1 %Thumb on right
end
end

if horizontal==1
for i = x1:1:x2
    for j= y1:y2
        if I2(i,j) == 1
            nPixelsBrancos = nPixelsBrancos + 1; %Numero de pixels da mão
        end
    end
end

for i=x1:1:x2
    for j=y1:1:y1+30
        if I2(i,j) == 1
            MaoLeft = MaoLeft + 1; %Numero de pixels da mão entre as 30 primeiras colunas
        end
    end
end

for i=x1:1:x2
    for j=y2-30:1:y2
        if I2(j,1) == 1
            MaoRight = MaoRight + 1; %Numero de pixels da mão entre as 30 ultimas colunas
        end
    end
end
TaxaBrancoLeft = MaoLeft/nPixelsBrancos
TaxaBrancoRight = MaoRight/nPixelsBrancos

if TaxaBrancoLeft <= (7/100)
    if TaxaBrancoRight <= (7/100)
        Thumb = 0 %Polegar nas duas bordas. Definimos como sem polegar.
    else
        ThumbEsquerdo = 1 %Polegar na borda esquerda
    end
end

if TaxaBrancoRight <= (7/100) && TaxaBrancoLeft >= (7/100) 
    ThumbDireito = 1 %Polegar na borda direita
end
end
figure
imshow(I2);

%detecção da centroid
Ibw = im2bw(I2);

Ilabel = bwlabel(Ibw);
stat = regionprops(Ilabel,'centroid');
figure
imshow(I2); hold on;

for x = 1: numel(stat)
    plot(stat(x).Centroid(1),stat(x).Centroid(2),'ro');
end

centroid = [stat(x).Centroid(1) stat(x).Centroid(2)] %coordenadas x e y da centroid
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

1 个答案:

答案 0 :(得分：2)

看起来像一个有趣的问题，所以我试了一下。基本上，您首先使用Sobel滤镜来查找图像中的边缘（稍微去噪后）。然后清理生成的线条，使用它们分离手的二进制掩模内的区域，使用分水岭变换找到手腕，一些距离变换以找到其他标记，然后移除手掌。你留下的是每个手指和拇指的不同区域。您可以轻松地计算这些区域，或者找到他们指向的方向，或者您喜欢的任何方式。 

imgURL = 'https://encrypted-tbn2.gstatic.com/imgs?q=tbn:ANd9GcRQsqJtlrOnSbJNTnj35Z0uG9BXsecX2AXn1vV0YDKodq-zSuqnnQ';

imgIn=imread(imgURL);

gaussfilt = fspecial('gaussian', 3, .5); % Blur starting image
blurImg = imfilter(double(img(:,:,1)), gaussfilt); 

edgeImg = edge(blurImg, 'sobel'); % Use Sobel edge filter to pick out contours of hand + fingers
% Clean up contours
edgeImg = bwmorph(edgeImg, 'close', 1); 
edgeImg = bwmorph(edgeImg, 'thin', Inf);

% Clean up rogue spots in corners
edgeImg([2 end-1], 2) = 0;
edgeImg([2 end-1], end-1) = 0;

% Extend lines to edge of image (correct for 'close' operation above
edgeImg([1 end],:) = edgeImg([2 end-1],:);
edgeImg(:, [1 end]) = edgeImg(:, [2 end-1]);

% Remove all but the longest line
regs = regionprops(edgeImg, 'Area', 'PixelIdxList');
regs(vertcat(regs.Area) ~= max(vertcat(regs.Area))) = [];

lineImg = false(size(edgeImg, 1), size(edgeImg, 2));
lineImg(regs.PixelIdxList) = 1;

fillImg = edgeImg;

% Close in wrist

if any(fillImg(1,:))
    fillImg(1,:) = 1;
end

if any(fillImg(end,:))
    fillImg(end,:) = 1;
end

if any(fillImg(:,1))
    fillImg(:,1) = 1;
end

if any(fillImg(:,end))
    fillImg(:,end) = 1;
end

fillImg = imfill(fillImg, 'holes');

fillImg([1 end], :) = 0;
fillImg(:, [1 end]) = 0;

fillImg([1 end],:) = fillImg([2 end-1],:);
fillImg(:, [1 end]) = fillImg(:, [2 end-1]);

% Start segmenting out hand + fingers
handBin = fillImg;
% Set lines in above image to 0 to separate closely-spaced fingers
handBin(lineImg) = 0;
% Erode these lines to make fingers a bit more separate
handBin = bwmorph(handBin, 'erode', 1);

% Segment out just hand (remove wrist)
distImg = bwdist(~handBin);

[cDx, cDy] = find(distImg == max(distImg(:)));

midWrist = distImg;
midWrist = max(midWrist(:)) - midWrist;
midWrist(distImg == 0) = Inf;
wristWatershed = watershed(imerode(midWrist, strel('disk', 10)));
whichRegion = wristWatershed(cDx, cDy);
handBin(wristWatershed ~= whichRegion) = 0;

regs = regionprops(handBin, 'Area', 'PixelIdxList');
regs(vertcat(regs.Area) ~= max(vertcat(regs.Area))) = [];
handOnly = zeros(size(handBin, 1), size(handBin, 2));
handOnly(regs.PixelIdxList) = 1;

% Find radius of circle around palm centroid that excludes wrist and splits
% fingers into separate regions.  
% This is estimated as D = 1/3 * [(Centroid->Fingertip) + 2*(Centroid->Wrist)]
% Find Centroid-> Wrist distance
dist2w = wristWatershed ~= whichRegion;
dist2w = bwdist(dist2w);
distToWrist = dist2w(cDx, cDy);

% Find Centroid-> Fingertip distance
dist2FE = zeros(size(handOnly, 1), size(handOnly, 2));
dist2FE(cDx, cDy) = 1;
dist2FE = bwdist(dist2FE).*handOnly;
distToFingerEnd = max(dist2FE(:));

circRad = mean([distToFingerEnd, distToWrist, distToWrist]); % Estimage circle diameter
% Draw circle
X = bsxfun(@plus,(1:size(handOnly, 1))',zeros(1,size(handOnly, 2)));
Y = bsxfun(@plus,(1:size(handOnly, 2)),zeros(size(handOnly, 1),1));
B = sqrt(sum(bsxfun(@minus,cat(3,X,Y),reshape([cDx, cDy],1,1,[])).^2,3))<=circRad;
% Cut out binary mask within circle
handOnly(B) = 0;

% Label separate regions, where each now corresponds to a separate digit
fingerCount = bwlabel(handOnly);

% Display overlay image
figure()
imshow(imgIn)
hold on
overlayImg = imshow(label2rgb(fingerCount, 'jet', 'k'));
set(overlayImg, 'AlphaData', 0.5);
hold off

结果： http://imgur.com/ySn1fPy

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