From 1fbb6e9523217ba4088b6af04643625476a0af4f Mon Sep 17 00:00:00 2001 From: yuxue Date: Tue, 12 May 2020 16:58:33 +0800 Subject: [PATCH] no commit message --- res/image/test_image/debug_resize_2.jpg | Bin 0 -> 4104 bytes res/train/svm准确率提高历史.txt | 23 ----------- res/train/训练说明.txt | 2 +- .../com/yuxue/easypr/core/CharsIdentify.java | 5 +-- .../com/yuxue/easypr/core/CharsRecognise.java | 2 +- .../com/yuxue/easypr/core/CharsSegment.java | 14 +++---- src/test/java/com/yuxue/test/EasyPrTest.java | 36 +++++++++++++----- 7 files changed, 37 insertions(+), 45 deletions(-) create mode 100644 res/image/test_image/debug_resize_2.jpg delete mode 100644 res/train/svm准确率提高历史.txt diff --git a/res/image/test_image/debug_resize_2.jpg b/res/image/test_image/debug_resize_2.jpg new file mode 100644 index 0000000000000000000000000000000000000000..20ea30f86d5dd7034be54a14043fca9439bebd5d GIT binary patch literal 4104 zcmbW2c{H2p+s8xfOYIazQTtY7X-8E?6|JQiwAG|TMp4C}l-eR0s$y%6rBszrTh$V~ zR#i%ZQAIT>q>D%tMMzpp$o7kwbKdu#_mB5|ujky?d7krJ_kF&f`?}8g@@9EtfUKL# zc^80y001Dse*nCX04IQ;z(E{j!GjP29%Nx5A)t_mu!zV%OH^D;L{wZf{r2Z#K-RZtiR6qXPb5&fs+|CD&I0diu1V}MMcfDS-VP5>w;z{3Ny_&SCE zk;fPNZwLqig@i>!`7$K=1zoay`9L6Fv@l;WzdD8gA0Q+rEU#_pD5BsMAgU9s_)}_r zm6-0CwgDyYDf%(1YcXl!63QxvRn_$L4GfKpt!->ioc!6&>8!Jh>$&r8J{K=t_Pqju z1_lLR54jNr&(w+3X8K}&x?1I9g2gM~1OCLR{uBol7f7}3{lCcm3+%tSNB}9I0Ka)aIRFT-U&0leW9h-wm}4w$NJ1Hm-u!f$v^R70 z@1`dO;SE;1pBFD5rJi>14__+>?i}Sj*dA-Fp)VE_d+dU~c+PdTcjl7|d#%L!y-j{l zx#AoK_#y#IE~(JT!>Q3|cPSplledCzGTdI){tD4^`p&3GzQ~oIMo6v{Fw1aQkqCH1 zO<09i@$l}I52!56gUA+*OEse*AN460Vh)Al8gp)O=~28&k}+n`MhJF+70qMLHbR*@Z=6V%5%L+4uE)dcO*Dh??SJp#itF(?mxdzW)ib@)vaT`Noh`P~b|mpFu9-5Dzfmzy`K} zP>7>?P9H_&!>QbuinVm?cDbcE_R@qobt495HGyr3`TH@Mq;c#NXSJ4M1%qH%$)j!43fx4dy?~qqBYg6Q-LR2k|9h-9zbZ4Ue{)D*Zw?X zd9b^rt$(y>)8?vhY}6Tvo8e!1091uA0U^yNa6=fu6*#3^WgtmN7*hI5DCtsehDlIw z+|hHdmqT}z=D3sdbkDp$5t5uoEuHhEGBO4gH?MBf^?9bPW!&nw&L$_@P3!zLyQP;1 ztQ!>$DRH}9e>?AK;f?^Os7ph~EDBtp8oQ_cBd|9$LQPe7IDS)(s<}OpV6_v^N$opN zf!G_6F}eGmU+U!x^~-A4ls%7*u#8JF6B_E&Jp4-;<}pq^PTFF4zr4E?$?)uSDW0IE zH`J-NEoRxJ>)tmZA<30mZ6)htEL-j{DSmivy9-zZDt(mPz z+Gy5m>3(!n+eYk=>jRzQ;nQozT}I>e$yRFcOLrT63w_q-<>7Li?0yYq648sS7EUkkX;c9ay zU_gty_iz;?*~li-{ew9(<34$Vsu(^sB4JK%KO=_S}M|k@CnO%pB2^8IVGGrQqJf&@2Di7_@p#vMiAr zq-%KTCRPNnp4Rbugo-C0XoK^$#hXgg)1}hycYKh;#7;0+Svgz*z~C3QlEB9H)*Q zdL-}^`%-it*V>mr=9J-_pKm0ebn9B1^0`M!4x1!^XD#s2<1FEXyn({dq;jf%wLyOU z*#f_o!tk4Cr&+EJDwpkgqGeyN`LXp3AN5U1U+UOR?X~|3VPH-nVidUXJ}<_(UQ+i#YWi9 zgQqXkW^iZaetWwbbIWj9Xd81MKjGc7r-M{r$Yfc^_!|uenA8=)ULGs&-F!8MRdYB3 zH)qmVNT!J+vx`i9`c0gycbyOwWc=;8gr%X=F3SFpNfqajc`SCeV}~$}4FTUF7M@6D z`&5>DpVoJg+7F&JFl|aCMelk>qYpbsu!9)jVY5?I@Ze^)$pR<*LOaXa3VFTsc)hBr zjL%Rkw!>n5lFCt_crN3$)Zk}dFf4Mn(OYpdHiq^^hRCmv!|XH>57H|ib(X>MC!O%V zSx=Tzn^r%By(s{fV#houTmI-bWxl~(dG)@F*_7t<(x}Cs~IJH%HzPLz*1p$zN@W{l;ky3<(BB+Q zwzw1Rf4Z)VV)!_R79T*mcE(}#NTALOFbXSTA6gaeZ^A5PxQ-aW7^s}sInzyZa`*9% zI0bW|F!X1oF}4o9o;F7}cs<4bnHf%v64THjEi87b^ag}pw32spc>Lj3IK$%kzM8)Q zofG0C13pqTGhTl^#gBrX3)w*sk=GSD51Py(t;f|qD>b#4&E`Ce^eK8SJ>8vX!$8wU z$Y4^tBvJ(NR?~*l8p8N|AM@;&?g}+`E&Yq#E~*6!gp$J=`^5eP3rZACID*byKpbni z6Hoc;>Uy=u-KuVTcvCVb{4P_A9zR4f$m_DW#{(ROJ1TcjJsmSUiv+I`bZAE)Ya>aD z^`Z%Dw+3(t82fthZ2UD#%ZByO1z^gSE0~(|5iE)v@Nkg$_9*$Jr^0~#hoTdIzw~mE z)kM4X@O@u{@yRZa7R&;z+81{dKi{{zk8O_8>XgBu@A;cCyB09_(IPe}$C-_|fvO(D zHr_(|jjh$2@6t}mte+92c~yko4Uil%locg?qc=Y7(-(+yZLw)kl=nzI=&oDM(x=*6 zE8i8ALcGVe!)SmC0fuO11cPFdJf>GM zxN?+**P;mag>%LE)RMpv9-xbepCq|b=JR~;@1f9gMP}}>xB4IgB6K6kP?hZK#PZf6 ziBkGeDueSG``dM;Pd-2Qi~7yIZP`_hHC3ru{k)}Y0i0%ULkA70#i!cKoS0jWP>GG` zaR^F>uk!$3KBa_x(TUzw9(hBt4^B9e5B~Ur*hD+r{IhQ&$b48Pryqcr;`>`1>Rw+2 zO9w%m(UhY{unSgw_g%AMKa@xKWn3AYJ!LgvQpz_3@Hd13!9f6ijyXpyhlY<~RaYmb z{KgA*4V&LKuRZ^6NwATj0W0D0oTqHp5c=TPC-1l#&`;tv9Y@IH&ssq@cO_~5=-8bK zQ93Z;1WwjDy!HyT#@4n?_8H)4tj72y^Q+yrDSZLO3i{Qlae*)eZ#OpPT;2X2Ivd-W z9*tvpG5hD2W=ZP>5%x*(*1oL?%L_g0%^2iJEWqm1| z<)Lvd#ar1=W+PH+WK0fMq)$8>SE0ycFl2DuyJ{Emp1KP6kPudn*g7t+;#5VcJjhvmyYG2Mb)kY%PA*ZP7o%&?l>zgy2;+^aMkoG3bwZV zwZUCR><#OwGw7A>+t_&$nu<)18|`zhNtjutmHd=g2)1rnf16ZTywtk}V;ULI0u`S& zQlC$Vh^)bR0I1=!SVJDbn3X?;-*fTFq_{=4Xv)K11*q&*$5=3pRA_;sV9n}ibcG;t zC+t-{a=;Z7R#G^DR6FAyH{PSQucYWJ+Fr|U+;BL-64h~QDpoG%o4M9^Ycjvrx!mbn zzh%OPx}R?=KLqV7`J=cJMyy{)Kt=YDu-h$;;T~v?M7BxAc_WCXzaMT+_%~-Idp-S% z-Fi`^pR}1ISHg+q0|{47dh2h}VdWZA6WLBC@2WS}aaytDTK?dJ@G_b=_Dz^M;PT=^^oTLnYVv#2GmNS*-1UtFw!I?FM9#)G!tPR(qq}CZB1+b>%<|a zpcr9AU(aJNE_m3w77zxETX8<#{i||d#LkPoXvP|Bx(~@{#6y>`hCjW+7ucP#Z9IOh g`ZV~LQ&Y}LyQT2J?USMJxFqsciOf6qb`WpwKWg>*XaE2J literal 0 HcmV?d00001 diff --git a/res/train/svm准确率提高历史.txt b/res/train/svm准确率提高历史.txt deleted file mode 100644 index 1c463ad5..00000000 --- a/res/train/svm准确率提高历史.txt +++ /dev/null @@ -1,23 +0,0 @@ -EasyPR 1.0: -svm liner -precise: 0.786207 -recall: 0.844444 -Fsocre: 0.814286 - -EasyPR 1.1: -svm liner -precise: 0.96798 -recall: 0.933492 -Fsocre: 0.950423 - -EasyPR 1.1: -svm liner: -precise: 0.968974 -recall: 0.964371 -Fsocre: 0.966667 - -EasyPR 1.1: -svm rbf(histom feature): -precise: 0.997596 -recall: 0.985748 -Fsocre: 0.991637 \ No newline at end of file diff --git a/res/train/训练说明.txt b/res/train/训练说明.txt index c6380e4e..f448bbe3 100644 --- a/res/train/训练说明.txt +++ b/res/train/训练说明.txt @@ -1,4 +1,4 @@ 要训练时，注意看src/train下的两个cpp文件 -数据在data文件夹下，svm对应Plate_detect，ann对应chars_recognise。要先解压，然后放置到正确的路径下，或者 +数据在data文件夹下，svm对应plate_detect_svm，ann对应chars_recognise。要先解压，然后放置到正确的路径下，或者 你可以修改cpp文件中的文件夹路径。 \ No newline at end of file diff --git a/src/main/java/com/yuxue/easypr/core/CharsIdentify.java b/src/main/java/com/yuxue/easypr/core/CharsIdentify.java index ab62eb95..c2c3c5c9 100644 --- a/src/main/java/com/yuxue/easypr/core/CharsIdentify.java +++ b/src/main/java/com/yuxue/easypr/core/CharsIdentify.java @@ -89,7 +89,7 @@ public class CharsIdentify { public void loadModel(String s) { this.ann.clear(); - ann=ANN_MLP.loadANN_MLP(s, "ann"); // 加载ann配置文件 + ann=ANN_MLP.loadANN_MLP(s, "ann"); // 加载ann配置文件 图像转文字的训练库文件 } @@ -118,7 +118,7 @@ public class CharsIdentify { int result = -1; Mat output = new Mat(1, numAll, CV_32FC1); - ann.predict(f, output, 0); + ann.predict(f, output, 0); // 预测结果 int ann_min = (!isChinses) ? ((isSpeci) ? 10 : 0) : numCharacter; int ann_max = (!isChinses) ? numCharacter : numAll; @@ -133,7 +133,6 @@ public class CharsIdentify { result = j; } } - return result; } diff --git a/src/main/java/com/yuxue/easypr/core/CharsRecognise.java b/src/main/java/com/yuxue/easypr/core/CharsRecognise.java index d8d0a8aa..ebed2e0c 100644 --- a/src/main/java/com/yuxue/easypr/core/CharsRecognise.java +++ b/src/main/java/com/yuxue/easypr/core/CharsRecognise.java @@ -40,7 +40,7 @@ public class CharsRecognise { if (0 == result) { for (int j = 0; j < matVec.size(); j++) { Mat charMat = matVec.get(j); - // 默认首个字符块是中文字符 + // 默认首个字符块是中文字符 第二个字符块是字母 String charcater = charsIdentify.charsIdentify(charMat, (0 == j), (1 == j)); plateIdentify = plateIdentify + charcater; } diff --git a/src/main/java/com/yuxue/easypr/core/CharsSegment.java b/src/main/java/com/yuxue/easypr/core/CharsSegment.java index 71bf2e41..ff4d809d 100644 --- a/src/main/java/com/yuxue/easypr/core/CharsSegment.java +++ b/src/main/java/com/yuxue/easypr/core/CharsSegment.java @@ -124,18 +124,19 @@ public class CharsSegment { CV_RETR_EXTERNAL, // retrieve the external contours CV_CHAIN_APPROX_NONE); // all pixels of each contours - // Start to iterate to each contour founded - // Remove patch that are no inside limits of aspect ratio and area. // 将不符合特定尺寸的图块排除出去 Vector vecRect = new Vector(); for (int i = 0; i < contours.size(); ++i) { Rect mr = boundingRect(contours.get(i)); - /*Mat temp = new Mat(img_threshold, mr); - String str = tempPath + "temp_"+i+".jpg"; - opencv_imgcodecs.imwrite(str, temp);*/ + Mat contour = new Mat(img_threshold, mr); + + if (this.isDebug) { + String str = tempPath + "debug_char_contour"+i+".jpg"; + opencv_imgcodecs.imwrite(str, contour); + } - if (verifySizes(new Mat(img_threshold, mr))) { + if (verifySizes(contour)) { // 将不符合特定尺寸的图块排除出去 vecRect.add(mr); } } @@ -149,7 +150,6 @@ public class CharsSegment { // 获得指示城市的特定Rect,如苏A的"A" int specIndex = GetSpecificRect(sortedRect, color); - System.err.println(specIndex); if (this.isDebug) { if (specIndex < sortedRect.size()) { diff --git a/src/test/java/com/yuxue/test/EasyPrTest.java b/src/test/java/com/yuxue/test/EasyPrTest.java index a9f0123b..e59972b9 100644 --- a/src/test/java/com/yuxue/test/EasyPrTest.java +++ b/src/test/java/com/yuxue/test/EasyPrTest.java @@ -1,22 +1,17 @@ package com.yuxue.test; -import static org.bytedeco.javacpp.opencv_imgproc.CV_CHAIN_APPROX_NONE; -import static org.bytedeco.javacpp.opencv_imgproc.CV_RETR_EXTERNAL; -import static org.bytedeco.javacpp.opencv_imgproc.boundingRect; -import static org.bytedeco.javacpp.opencv_imgproc.findContours; +import org.bytedeco.javacpp.opencv_imgproc; import java.io.File; import java.util.Vector; import org.bytedeco.javacpp.opencv_core.Mat; -import org.bytedeco.javacpp.opencv_core.MatVector; -import org.bytedeco.javacpp.opencv_core.Rect; +import org.bytedeco.javacpp.opencv_highgui; import org.bytedeco.javacpp.opencv_imgcodecs; import org.junit.Test; import com.yuxue.easypr.core.CharsIdentify; import com.yuxue.easypr.core.CharsRecognise; -import com.yuxue.easypr.core.CharsSegment; import com.yuxue.easypr.core.CoreFunc; import com.yuxue.easypr.core.PlateDetect; import com.yuxue.easypr.core.PlateLocate; @@ -185,8 +180,8 @@ public class EasyPrTest { @Test public void testGreenPlate() { - /*String imgPath = "res/image/test_image/result_0.png"; - Mat src = opencv_imgcodecs.imread(imgPath);*/ + String imgPath = "res/image/test_image/debug_resize_2.jpg"; + Mat src = opencv_imgcodecs.imread(imgPath); // 获取绿牌的H值范围 /*MatVector hsvSplit = new MatVector(); @@ -227,12 +222,33 @@ public class EasyPrTest { // 判断绿色车牌 /*Mat src_hsv = new Mat(); - cvtColor(src, src_hsv, CV_BGR2HSV); + opencv_imgproc.cvtColor(src, src_hsv, opencv_imgproc.CV_BGR2HSV); src_hsv = CoreFunc.colorMatch(src, PlateColor.GREEN, true); System.err.println(CoreFunc.plateColorJudge(src, PlateColor.GREEN, true)); String str = "d:/PlateDetect/src_hsv.png"; opencv_imgcodecs.imwrite(str, src_hsv);*/ + // 车牌检测对象 + PlateDetect plateDetect = new PlateDetect(); + plateDetect.setPDLifemode(true); + plateDetect.setDebug(false, ""); // 将过程的图块保存到盘符 + + Vector matVector = new Vector(); + + System.err.println(plateDetect.plateDetect(src, matVector)); + System.err.println(matVector.size()); + + for (int i = 0; i < matVector.size(); ++i) { // 遍历车牌图块Mat，进行识别 + Mat img = matVector.get(i); + // 识别的车牌，保存图片文件 + // 此方法生成的文件，中文名称都是乱码，试了各种编解码均无效，OpenCV自身的编解码问题。 + opencv_highgui.imshow("123", img); + + String str = "d:/PlateDetect/temp/result_.png"; + opencv_imgcodecs.imwrite(str, img); + + } + }