From 4ea2eceb7043df5f49aa2a1aec1153c0699116da Mon Sep 17 00:00:00 2001 From: tt Date: Wed, 21 Jan 2015 04:49:31 +0100 Subject: [PATCH] Image Segmentation .cpp tutorial Distance Transform tutorial fixes Distance Transform fixes v.2 Distance Transform fixes v.3 Distance Transform fixes v.4 --- .../distance_transform.markdown | 57 ++++++ .../distance_transformation/images/bin.jpeg | Bin 0 -> 33265 bytes .../images/black_bg.jpeg | Bin 0 -> 49373 bytes .../images/dist_transf.jpeg | Bin 0 -> 20578 bytes .../distance_transformation/images/final.jpeg | Bin 0 -> 41745 bytes .../images/laplace.jpeg | Bin 0 -> 48125 bytes .../images/markers.jpeg | Bin 0 -> 22522 bytes .../distance_transformation/images/peaks.jpeg | Bin 0 -> 23086 bytes .../distance_transformation/images/sharp.jpeg | Bin 0 -> 50913 bytes .../images/source.jpeg | Bin 0 -> 46641 bytes .../imgproc/table_of_content_imgproc.markdown | 8 + .../ImgTrans/imageSegmentation.cpp | 168 ++++++++++++++++++ samples/data/cards.png | Bin 0 -> 77914 bytes 13 files changed, 233 insertions(+) create mode 100644 doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown create mode 100644 doc/tutorials/imgproc/imgtrans/distance_transformation/images/bin.jpeg create mode 100644 doc/tutorials/imgproc/imgtrans/distance_transformation/images/black_bg.jpeg create mode 100644 doc/tutorials/imgproc/imgtrans/distance_transformation/images/dist_transf.jpeg create mode 100644 doc/tutorials/imgproc/imgtrans/distance_transformation/images/final.jpeg create mode 100644 doc/tutorials/imgproc/imgtrans/distance_transformation/images/laplace.jpeg create mode 100644 doc/tutorials/imgproc/imgtrans/distance_transformation/images/markers.jpeg create mode 100644 doc/tutorials/imgproc/imgtrans/distance_transformation/images/peaks.jpeg create mode 100644 doc/tutorials/imgproc/imgtrans/distance_transformation/images/sharp.jpeg create mode 100644 doc/tutorials/imgproc/imgtrans/distance_transformation/images/source.jpeg create mode 100644 samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp create mode 100644 samples/data/cards.png diff --git a/doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown b/doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown new file mode 100644 index 0000000000..1674c404b8 --- /dev/null +++ b/doc/tutorials/imgproc/imgtrans/distance_transformation/distance_transform.markdown @@ -0,0 +1,57 @@ +Image Segmentation with Distance Transform and Watershed Algorithm {#tutorial_distance_transform} +============= + +Goal +---- + +In this tutorial you will learn how to: + +- Use the OpenCV function @ref cv::filter2D in order to perform some laplacian filtering for image sharpening +- Use the OpenCV function @ref cv::distanceTransform in order to obtain the derived representation of a binary image, where the value of each pixel is replaced by its distance to the nearest background pixel +- Use the OpenCV function @ref cv::watershed in order to isolate objects in the image from the background + +Theory +------ + +Code +---- + +This tutorial code's is shown lines below. You can also download it from + [here](https://github.com/Itseez/opencv/tree/master/samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp). +@includelineno samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp + +Explanation / Result +-------------------- + +-# Load the source image and check if it is loaded without any problem, then show it: + @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp load_image + ![](images/source.jpeg) + +-# Then if we have an image with white background, it is good to tranform it black. This will help us to desciminate the foreground objects easier when we will apply the Distance Transform: + @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp black_bg + ![](images/black_bg.jpeg) + +-# Afterwards we will sharp our image in order to acute the edges of the foreground objects. We will apply a laplacian filter with a quite strong filter (an approximation of second derivative): + @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp sharp + ![](images/laplace.jpeg) + ![](images/sharp.jpeg) + +-# Now we tranfrom our new sharped source image to a grayscale and a binary one, respectively: + @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp bin + ![](images/bin.jpeg) + +-# We are ready now to apply the Distance Tranform on the binary image. Moreover, we normalize the output image in order to be able visualize and threshold the result: + @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp dist + ![](images/dist_transf.jpeg) + +-# We threshold the *dist* image and then perform some morphology operation (i.e. dilation) in order to extract the peaks from the above image: + @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp peaks + ![](images/peaks.jpeg) + +-# From each blob then we create a seed/marker for the watershed algorithm with the help of the @ref cv::findContours function: + @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp seeds + ![](images/markers.jpeg) + +-# Finally, we can apply the watershed algorithm, and visualize the result: + @snippet samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp watershed + ![](images/final.jpeg) \ No newline at end of file diff --git a/doc/tutorials/imgproc/imgtrans/distance_transformation/images/bin.jpeg b/doc/tutorials/imgproc/imgtrans/distance_transformation/images/bin.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..c8515ec1f9f60f0857131dfbc88950a542713cf7 GIT binary patch literal 33265 zcmd431ytN>_b)uSyHkow@#4kZXK;6Sm*Vd3?(Xi|LUDJ8;w@U-THvGYIqx~I-Fw%% z|8K1uG9f#^?b&&Dl851W>3I`?DkUZ<1^@#C1L(c{0G?L?!au41tL3MX|0@MQg`ax? zXt01wFgge@Gypgn7z7&Fb02^Z00saDz<>e%T3}$oz#*Za;ou=&aQv4*AYP>Zf&pNV z&`{tI&no~#h!XdKyLZNFzS1OzLU<-QVMG?|w@+h%#Jn2uJU0haKj9cdH77Hx0X}6%%LH^G*&j17gl(s{y%tOcSLc~=f;uu1ZgkWi0pOPh^-hC- zlSPK{2CFgil2sWrBPL=AA(#ViAV^#u#T7O7q~B5xyW9VmNAZz|p1>6V=Jt{sUi`~h8 z*yS_)l07AmgFx1O+41SndXR_bgfKWeXXq(dy>DHj%UhT0_jr+P~*n?!oabKBGQL#*KYd$yX5(# z%cgBmE=-k!7Da&`MSzq9U=!vo>mVX~05xqKT_s(D^yW7!Bm2x{Xe_dI^H%pv%(WSw!OkMLjveUZ>AkkQt7|$$kXjM1T^D z*XRkyH3rNt`3^3L5O2LKIeDGP0$ z4J>ZF)RZ7q%0~cG(E98i_2Qqkoo+Rd!=LN#jVNTE@sRJx~4N?v?@WGz@8uGDh%v8sH^Z zKTg!->Lr`F#w!!`Z7-PXD$+U!j&vxF9!y2 z96?DXp1jz zu%mo^_S3;X7ieM0iwC{GDSQEb_LH}$000yO01guTA6@08*T8^*L%^bI zu(D%fp}&=5BVm5&GO#br2Mh{)zSim$bkG0M<_e9>KBIJ+SHsS4s~08&mXLth^?!7i z$QGB%M4CUSH*1BpNR~FOZ$o4yC@@b84uL&YaB1#ruMD za|2vysUeCOu!kD@ucgKy$|OCVsTh%eDokwWic1k!K;JwR(n98NxZL3XZT&!T$Y&G# zT=0XPVu$b-scfNV0H7rt>AQc{3K5sm1x``@v;$9cI*nek&tY%2=Xlw4qHmS^X%xCY zJB13uFb@B9^10+cgs%(-6WAr}i&NmlXCfho6Y3=GKJ_wiNsGgxhjlUZkQFVxaq55! z;}(Tp>2`XAjnY+GM@UT-vWa+mXa));Be%CPEVJbwbxO0*{y|^c>u!IfCD&2K65?$PV#EVUZ$JYJ6O_)42~_C^BAzFPCx{W+}lK zB?MB@E%Utbc9swjCGQ1EM`;@_D5kB%@sT?#9GRyVL-+X~Teiq&QmYk}ZVU?+4_eqs z+bTY?9$blZY`Cei4yt{M8$G1mPVn9=c^@*7_#>d2%dkwsW*|YEDf{z0_bOwlq2OQ$ zDRxtnR8p)`e1h`XA$yI&{xiTXmyWgKdqfK6K?3P{B=%u^0sCCxXCvkr!~M0J4oiRe zYpiqmKT@Y7IB%R{^g--Rg`O`(p10M9gDeR^->*JQYX zx(A0aEC=}6S0F+ed!K>1ldQ0YT@Lsp3{m56DmNh=SX%+(XCKnU&J2c5SiK7MI;wgzkQVeza!*ZbqKBunjwgdJS}6EBpZHd%-Gw z^C+nC)tAgu%i~_(fI>u>%{;6=XYQi_q-Cc#`{_0e_mR8O&#lz!d6z{*#1_7+(WWi& zShLePRVEptodcp3QR0LK@MYzxSEb1foY<&7TP0kDYYihk0ek4`qq5?2_|kVqD&!l2 zz!AjckB$&fq5dDW7A~4w8a27NMj0Z~9vItK_Xw!P(vIQyC9v*A>yD_m8~!*Ae9wUD zH(6PAe#uT_8%YX7r8N6aYT8f(>N$|C2J^cdpMYh2WWd$FvX2iDZoqH@KWB<{ZGW#j z$9A^X4?FsrQg3a92606FnjF=KMxDrw1?(S2Tq}P^Q}_({@W5&sNoO!PIt^|jQO;?E z6`eMskO5-4%l#}R|87}sqBN;Yq}sql8n4%`ZFW!bY^v~_ui}pVKrFzIS@AmYT>tmUmwX0zFG7glN+>YUz1JTs zJ$^r<=+;XAqqkYzgxi@Li{M@%tU|B#95cf1OX{8~qJdq~_d$ZN*DXc^_vqxaaH-+= zl;n|x$|LB#QKYEmLc=J9V)(Od_3O-MVFkAXcs=n;F*)A(*>rzgE!^K$t~4S~jhVV& zzkM2I#-f#)rIcpU+z5?5IW~VA?~kuhX-Ut>L*%k-A`L9~1}x2I)DdV34^bo=Ylur2 z9;T6^ZqGj3vHo%9+zQXSDkIv!Ao@+7AN$oU6n7(^Q`YT?oRgbp9P$2WWSZH6x{Z{J zh5at4n+PlG!l4cP${O>%mHgp!n7TC6B}kR`2)ivI^DwN{?xeWs7zS>k4-tXw$~^hu z(ZhB@w!ipyAyYoyszf)?$}miSWFRmrp0K-<|>&>s3 z%NER`Sld`SaOJPZRj!e%;-Jj}&%{~Rx_D-&&e-(=I7<`Dwe*r97uGjL zV&Q$`By?F_AYGA`s;D*uc2Ci>!I@cRP;vP}B1+pnj~-5XW6UrWx%)rGL$+M?4G&#* zmRYo4dZ0p<&}WooWjnQdhcWu#ia0N#Dilj6n(w%A+lAfQs{|2GWp>R-t^XM? z@~G6^`0D$>)A@%^+i(BZJN=H@x)(k#w~!)e001NyBr+@%I3&#P`^}dZ77$Qqm`c#7 z7}(6Latey}PH~A_By56?bq$R(=uGnZ34YV$EP45)WWol9{yo2LD_?Fv`N4)xwTi(( zQu5>x`=%GE1l?O<#Vy_i|j|^lsEf{Rld1sU-6u@uWY3{roRL zb0yWk03SDMv$pSEEnsx&Lb&(3L~Pos$((zlvVMILg+$_d(b)qb^HV3sY{82t(h=MS zqhFUkcmEkjC6#M8_I04`E@M!a{;PY0Eo(KgC!ssd1+)*be*k{nSw8{We*^v@A%2+8 zc<6|IiB#ljKhLZp&ObFQ=HoUrRt&4!rxGcBnc-Tre0G&H6;W(YHSFLd`MRS>z_;|} zeT0$L>6>ih!m(}qm~9sRmQN2sZ%vY@Sycv^#(8^*WN*STxGHoAaT?{)=JC(Yy7B`j zN*6`#}CW-1Pgf@~^`nnyL?w{7Z7_ z%9`G=KDBwQ<;_1(p8nSPzUEzZ?N=MHQVCV-qU^-rD>3BP)k+@yRY|!k* z*Pf?Bq0rfaJC$|*GvUV|q#D_?st%v2kflVT*~bCh>+b<3Q;G4ERqYaAo&lspA_K-5 z%hUX_;(fYILgvcWcO*}}fivZ+XTmShNLASOpxpd+pYad(zl%cUSc-k{CwhtUhSl

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*Author:* Theodore Tsesmelis + + Where we learn to segment objects using Laplacian filtering, the Distance Transformation and the Watershed algorithm. \ No newline at end of file diff --git a/samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp b/samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp new file mode 100644 index 0000000000..424a4901ec --- /dev/null +++ b/samples/cpp/tutorial_code/ImgTrans/imageSegmentation.cpp @@ -0,0 +1,168 @@ +/** + * @function Watershed_and_Distance_Transform.cpp + * @brief Sample code showing how to segment overlapping objects using Laplacian filtering, in addition to Watershed and Distance Transformation + * @author OpenCV Team + */ + +#include +#include + +using namespace std; +using namespace cv; + +int main(int, char** argv) +{ +//! [load_image] + // Load the image + Mat src = imread(argv[1]); + + // Check if everything was fine + if (!src.data) + return -1; + + // Show source image + imshow("Source Image", src); +//! [load_image] + +//! [black_bg] + // Change the background from white to black, since that will help later to extract + // better results during the use of Distance Transform + for( int x = 0; x < src.rows; x++ ) { + for( int y = 0; y < src.cols; y++ ) { + if ( src.at(x, y) == Vec3b(255,255,255) ) { + src.at(x, y)[0] = 0; + src.at(x, y)[1] = 0; + src.at(x, y)[2] = 0; + } + } + } + + // Show output image + imshow("Black Background Image", src); +//! [black_bg] + +//! [sharp] + // Create a kernel that we will use for accuting/sharpening our image + Mat kernel = (Mat_(3,3) << + 1, 1, 1, + 1, -8, 1, + 1, 1, 1); // an approximation of second derivative, a quite strong kernel + + // do the laplacian filtering as it is + // well, we need to convert everything in something more deeper then CV_8U + // because the kernel has some negative values, + // and we can expect in general to have a Laplacian image with negative values + // BUT a 8bits unsigned int (the one we are working with) can contain values from 0 to 255 + // so the possible negative number will be truncated + Mat imgLaplacian; + Mat sharp = src; // copy source image to another temporary one + filter2D(sharp, imgLaplacian, CV_32F, kernel); + src.convertTo(sharp, CV_32F); + Mat imgResult = sharp - imgLaplacian; + + // convert back to 8bits gray scale + imgResult.convertTo(imgResult, CV_8UC3); + imgLaplacian.convertTo(imgLaplacian, CV_8UC3); + + // imshow( "Laplace Filtered Image", imgLaplacian ); + imshow( "New Sharped Image", imgResult ); +//! [sharp] + + src = imgResult; // copy back + +//! [bin] + // Create binary image from source image + Mat bw; + cvtColor(src, bw, CV_BGR2GRAY); + threshold(bw, bw, 40, 255, CV_THRESH_BINARY | CV_THRESH_OTSU); + imshow("Binary Image", bw); +//! [bin] + +//! [dist] + // Perform the distance transform algorithm + Mat dist; + distanceTransform(bw, dist, CV_DIST_L2, 3); + + // Normalize the distance image for range = {0.0, 1.0} + // so we can visualize and threshold it + normalize(dist, dist, 0, 1., NORM_MINMAX); + imshow("Distance Transform Image", dist); +//! [dist] + +//! [peaks] + // Threshold to obtain the peaks + // This will be the markers for the foreground objects + threshold(dist, dist, .4, 1., CV_THRESH_BINARY); + + // Dilate a bit the dist image + Mat kernel1 = Mat::ones(3, 3, CV_8UC1); + dilate(dist, dist, kernel1); + imshow("Peaks", dist); +//! [peaks] + +//! [seeds] + // Create the CV_8U version of the distance image + // It is needed for findContours() + Mat dist_8u; + dist.convertTo(dist_8u, CV_8U); + + // Find total markers + vector > contours; + findContours(dist_8u, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE); + + // Create the marker image for the watershed algorithm + Mat markers = Mat::zeros(dist.size(), CV_32SC1); + + // Draw the foreground markers + for (size_t i = 0; i < contours.size(); i++) + drawContours(markers, contours, static_cast(i), Scalar::all(static_cast(i)+1), -1); + + // Draw the background marker + circle(markers, Point(5,5), 3, CV_RGB(255,255,255), -1); + imshow("Markers", markers*10000); +//! [seeds] + +//! [watershed] + // Perform the watershed algorithm + watershed(src, markers); + + Mat mark = Mat::zeros(markers.size(), CV_8UC1); + markers.convertTo(mark, CV_8UC1); + bitwise_not(mark, mark); +// imshow("Markers_v2", mark); // uncomment this if you want to see how the mark + // image looks like at that point + + // Generate random colors + vector colors; + for (size_t i = 0; i < contours.size(); i++) + { + int b = theRNG().uniform(0, 255); + int g = theRNG().uniform(0, 255); + int r = theRNG().uniform(0, 255); + + colors.push_back(Vec3b((uchar)b, (uchar)g, (uchar)r)); + } + + // Create the result image + Mat dst = Mat::zeros(markers.size(), CV_8UC3); + + // Fill labeled objects with random colors + for (int i = 0; i < markers.rows; i++) + { + for (int j = 0; j < markers.cols; j++) + { + int index = markers.at(i,j); + if (index > 0 && index <= static_cast(contours.size())) + dst.at(i,j) = colors[index-1]; + else + dst.at(i,j) = Vec3b(0,0,0); + } + } + + // Visualize the final image + imshow("Final Result", dst); +//! [watershed] + + waitKey(0); + return 0; +} \ No newline at end of file diff --git a/samples/data/cards.png b/samples/data/cards.png new file mode 100644 index 0000000000000000000000000000000000000000..1e61d874f6b874e6d87420e0d203e6a21b0a97d4 GIT binary patch literal 77914 zcmcG#byU@17cPiMOC3Ux?(UY95{W}2prn9G9J=cuC>_!b(xo6EDV-wSCEeW}=e~U3 z-1%eHnpt;d)?FTP`CEryz3<-p+0XOr5Oq}r94s;{BqSsp#aD8gNJz+jNJx(sG0?yf z!Qp?a;GaiMnhG*Vr2`aO;6G^ZlojNV5D$O8H5SBzBbfHD^qr89o)A9#eT0;fMhXt1 zJ1auv(H9;OV6)*-A*axTL(iP$^_*qxU@!|?XCzri3sYwca~d~G=Z`cBicodkPq-vV zNHj=_a?;xFGrQ>?&PHw?8&Ym?UB!eSoyScF4rwA`lv)@JpJMP6&{%TSWoi3p_kQKQ zSR8U%?Ca}!HlqG!KmZ$-&p?FCfSpVG^;6(iA=K_x)ijaF6=-#XQpJz8xqaV*TZaW* zqhA5;PV+XfK8bO!~fxftp0EWd>*m< z_dqZb(SHxfAz}UJ8yIv*sQ>u}3dHZf2mCQ!{P%z~%D4abr}^J?-v5=``~Pyk|10PH zzkhrG+h6DZ!|nb5TBiTYc}=y0vtK=x#%H3b=lDc}(bhqSElbgzRaS;4ukq)7Wv(Wx zwvN1xjsP@Pp5VW~+O{SKhf9eW!PDpGjVh9{UCz$T`JSnab_hT#raAI0vUWs^i?er* z3-SMt@7LxR6rj&m*IjRyXy_3C)}+Ni_Vn_^Xv2q&zA6}f+`QrVTxC>lT)^2-VN?KI 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