From 1636e8cb4e3a282767734733248db46f4416d836 Mon Sep 17 00:00:00 2001 From: liuruoze Date: Sun, 30 Apr 2017 16:33:18 +0800 Subject: [PATCH] * improve the groundMSER segmention * change readme and changelog --- ChangeLog.md | 93 +- README.md | 42 +- include/easypr/config.h | 8 +- include/easypr/core/chars_segment.h | 4 +- include/easypr/core/feature.h | 4 + include/easypr/train/svm_train.h | 1 + resources/model/annCh_bak.xml | 16766 -------------------------- result/Result.xml | 834 +- result/accuracy.txt | 120 + result/plateGroundTruth.txt | 5298 ++++++++ src/core/chars_recognise.cpp | 2 +- src/core/chars_segment.cpp | 160 +- src/core/feature.cpp | 56 + src/core/plate_recognize.cpp | 8 - src/train/annCh_train.cpp | 12 +- src/train/ann_train.cpp | 14 +- src/train/svm_train.cpp | 9 +- test/accuracy.hpp | 7 +- test/plate.hpp | 2 +- 19 files changed, 6131 insertions(+), 17309 deletions(-) delete mode 100644 resources/model/annCh_bak.xml diff --git a/ChangeLog.md b/ChangeLog.md index 3ed5d38..8ed4239 100644 --- a/ChangeLog.md +++ b/ChangeLog.md @@ -1,7 +1,91 @@ EasyPR版本更新 +====== + +本次更新版本是1.6alpha版本，主要改进如下： + +1. 采用灰度字符训练以及新的特征，使中文字符正确率上升到了86%，比上个版本提升了近14个百分点。 + +2. 借助于字符分割与识别算法的优化，在general_test上的完整识别率(0-error)从原先的59%首次上升到现在的70%，1-error则提升到了82%，提升巨大。 + +3. 在车牌判断模块中，使用了新的SVM特征（颜色+投影），从而在保持鲁棒性的同时，提升了正确率。定位指标中FScore从76%提升到82%. + +4. 新增一种新的字符分割方法，groundMSER字符分割方法。 + +5. 提供了近万张中文字符灰度图数据供训练，并且在主界面中提供了一个方法从free大神的车牌集里提取中文与英文字符。 + +6. 提供了两万两千张的字符灰度图数据，供训练灰度字符模型使用。 + +7. 代码优化与升级，许多bug修复。 + +8. Opencv3.2版本的支持，编译前仅需要将config.h中将#define CV_VERSION_THREE_ZERO改为#define CV_VERSION_THREE_TWO即可。 + +9. 更加友好的linux与mac版本支持，使用CMake即可顺利编译，单独的utf-8与gbk的文件供分别的系统使用。 + +====== + +本次更新是EasyPR 1.5正式版本，相比beta版本有以下几点更新： + +1.修正了SVM训练异常的问题！现在1.5版本也可以自由的使用SVM训练了。这个问题确实是opencv的bug，详见[讨论](https://github.com/opencv/opencv/issues/5054)，在此感谢 @tka 同学的告知。 +注意，3.2的opencv也修正了这个问题，如果你用3.2版本的话，也可以。但是不清楚3.2版本是否会引入其他的问题，在目前的EasyPR版本里，即便用3.0或者3.1版本也可以规避训练异常的问题。 + +2.支持linux和mac编译，如果碰到问题请在issue里提问。 + +3.增加一个无需配置opencv的[懒人版](http://git.oschina.net/easypr/EasyPR/attach_files)。仅仅支持vs2013，也只能在debug和x86下运行，其他情况的话还是得配置opencv。感谢范文捷同学的帮助。页面里的两个文件都要下载，下载后用[7zip](http://www.7-zip.org/)解压。 + +其他的主要改进如下： + +1.增加了一种新的基于文字定位的定位方法 (MSER), 在面对低对比度，低光照以及大图像上有较强的鲁棒性。 + +* 夜间的车牌图像 + +![夜间的车牌图像](resources/doc/res/night_1.jpg) + +* 对比度非常低的图像 + +![对比度非常低的图像](resources/doc/res/contrast_1.jpg) + +* 近距离的图像 + +![近距离的图像](resources/doc/res/near_1.jpg) + +* 高分辨率的图像 -### 更新 +![高分辨率的图像](resources/doc/res/big_1.jpg) + +2.更加合理的评价协议。结合新增的GroundTruth文件与ICDAR2003的协议，使得整体评价指标更为合理。通用数据集里同时增加了近50张新图片。文字定位方法在面对这些复杂图片时比先前的SOBEL+COLOR的方法定位率提升了27个百分点。 + +实际运行时，使用了文字定位与颜色定位的结合，最终对256张的测试图片的测试结果如下： + +![v1.5版运行结果](resources/doc/res/v1.5_result.jpg) + +3.使用了非极大值抑制算法去除相邻的车牌，使得最终输出变的合理。即便使用多个定位方法，最终也只会输出一个车牌，而且是可能性最大的车牌。 + +4.基于局部空间的大津阈值算法与自适应阈值算法，提升了文字分割与分子识别的准确率。 + +* 车牌图像 + +![车牌图像](resources/doc/res/not_avg_contrast.jpg) + +* 普通大津阈值结果 + +![普通大津阈值结果](resources/doc/res/normal_ostu.jpg) + +* 空间大津阈值结果 + +![空间大津阈值结果](resources/doc/res/spatial_ostu.jpg) + +5.新的SVM模型与特征（LBP），提升了车牌判断的鲁棒性，新的中文ANN识别模型，提升了中文识别的整体准确率近15个百分点。 + +6.增加了Grid Search方法，可以进行自动调参。 + +7.首次增加了多线程支持，基于OpenMP的文字定位方法，在最终的识别速度上，比原先的单线程方法提高了接近2倍。 + +8.替换了一部分中文注释，使得windows下的visual studio在面对全部以LF结尾的文件时，也能成功通过编译。目前的程序只要opencv配置正确，gitosc上通过zip下载下来的程序可以直接通过编译并运行。 + +关于本次改动的具体内容可以看博客中的[介绍](http://www.cnblogs.com/subconscious/p/5637735.html)。 + +====== 本次更新是1.4 正式版，主要改进在于几个方面： @@ -25,7 +109,6 @@ EasyPR版本更新 ====== - 本次更新是1.3beta版，主要改进在于提升了字符识别模块的准确性： 平均字符差距从0.7降低到0.4，完整匹配度从68%左右上升到目前的81%，平均执行时间从2秒降低到1.5秒。见下图： @@ -40,10 +123,8 @@ EasyPR版本更新 目前版本的问题是处理时间还是偏高，1.3正式版本中会对这个问题进行fix。 - ====== - 本次更新是1.3alpha版，主要改进在于提升了字符识别模块的准确性： 平均字符差距从2.0降低到0.7，完整匹配度从25%左右上升到目前的68%。 @@ -60,10 +141,8 @@ EasyPR版本更新 目前版本的问题是处理时间大幅度上升，1.3正式版本中会对这个问题进行fix。 - ====== - 本次更新是1.2版，主要改进在于提升了车牌定位模块的准确性，从70%左右到目前的94%，见下图： ![1.2版综合效果](resources/doc/res/testresult.png) @@ -86,10 +165,8 @@ EasyPR版本更新 * 与Linux版本做了整合，可以实现跨平台编译。 - ====== - 目前EasyPR的版本是1.1，相比上一个版本1.0，有以下更新（这次的更新内容较多，为了跟你现有的项目和代码不冲突，请 谨慎选择全部更新，最好新起一个目录试试新增的功能和内容）： diff --git a/README.md b/README.md index 8bc6ab4..e904d58 100644 --- a/README.md +++ b/README.md @@ -10,38 +10,28 @@ EasyPR是一个开源的中文车牌识别系统，其目标是成为一个简 ### 更新 -当前master版本修订了两个小问题： +本次更新版本是1.6alpha版本，主要有以下几点更新： -1.train_auto异常的问题。 +1. 代码优化与bug修复 -2.opencv3.2的编译问题。现在opencv3.2的接口与3.1和3.0略有变动，如果想要用3.2版本需要参照这个[issue](https://github.com/liuruoze/EasyPR/issues/152)里讨论的修改。 +2. 增加了一些数据集，例如灰度字符供训练 -3.增加灰度character数据 - -4.增加灰度中文数据，以及训练代码 - -5.新的分割方法，groundER分割 - -6.大量bug修正与代码改进 - -下个版本会尝试继续优化整体系统与算法。 - - -一个无需配置opencv的[懒人版](http://git.oschina.net/easypr/EasyPR/attach_files)。仅仅支持vs2013，也只能在debug和x86下运行，其他情况的话还是得配置opencv。感谢范文捷同学的帮助。页面里的两个文件都要下载，下载后用[7zip](http://www.7-zip.org/)解压。 - -关于1.5版本改动的具体内容可以看博客中的[介绍](http://www.cnblogs.com/subconscious/p/5637735.html)。 +3. 其他改动可见changlog. ### 跨平台 目前除了windows平台以外，还有以下其他平台的EasyPR版本。一些平台的版本可能会暂时落后于主平台。 +现在有一个无需配置opencv的1.5版本的[懒人版](http://git.oschina.net/easypr/EasyPR/attach_files)。仅仅支持vs2013，也只能在debug和x86下运行，其他情况的话还是得配置opencv。感谢范文捷同学的帮助。页面里的两个文件都要下载，下载后用[7zip](http://www.7-zip.org/)解压。 + |版本 | 开发者 | 版本 | 地址 |------|-------|-------|------- | android | goldriver | 1.4 | [linuxxx/EasyPR_Android](https://github.com/linuxxx/EasyPR_Android) -| linux | Micooz | 1.5 | 已跟EasyPR整合 +| linux | Micooz | 1.6 | 已跟EasyPR整合 | ios | zhoushiwei | 1.3 | [zhoushiwei/EasyPR-iOS](https://github.com/zhoushiwei/EasyPR-iOS) -| mac | zhoushiwei,Micooz | 1.5 | 已跟EasyPR整合 +| mac | zhoushiwei,Micooz | 1.6 | 已跟EasyPR整合 | java | fan-wenjie | 1.2 | [fan-wenjie/EasyPR-Java](https://github.com/fan-wenjie/EasyPR-Java) +| 懒人版 | fan-wenjie | 1.5 | [git/oschina](http://git.oschina.net/easypr/EasyPR/attach_files) ### 兼容性 @@ -163,6 +153,7 @@ EasyPR的resources/image/general_test文件夹下的图片数据遵循[GDSL协 | resources/train | 训练数据与说明 | resources/image | 测试用的图片 | resources/doc | 相关文档 +| tmp | 需要自建，训练数据读取目录 以下表格是resources/image目录中子目录的解释: @@ -205,6 +196,15 @@ EasyPR的resources/image/general_test文件夹下的图片数据遵循[GDSL协 | chars.hpp | 字符识别相关 | plate.hpp | 车牌识别相关 +以下表格是train目录下文件的解释: + +|文件 | 解释 +|------|---------- +| ann_train.cpp | 训练二值化字符 +| annCh_train.hpp | 训练中文灰度字符 +| svm_train.hpp | 训练车牌判断 +| create_data.hpp | 生成合成数据 + ### 使用 请参考[这里](Usage.md) @@ -235,6 +235,8 @@ EasyPR讨论QQ群号是：一群：366392603(已满)，二群：583022188， 加 * fan-wenjie：1.5版opencv整合版提供者 +* Free：1.6版数据提供者 + ### 鸣谢 -taotao1233，邱锦山，唐大侠，jsxyhelu，如果有一天(zhoushiwei)，学习奋斗，袁承志，圣城小石匠，goldriver，Micooz，梦里时光，Rain Wang，任薛纪，ahccom，星夜落尘，海豚嘎嘎(车主之家)，刘超，以及所有对EasyPR贡献数据的热心同学。 \ No newline at end of file +taotao1233，邱锦山，唐大侠，jsxyhelu，如果有一天(zhoushiwei)，学习奋斗，袁承志，圣城小石匠，goldriver，Micooz，梦里时光，Rain Wang，任薛纪，ahccom，星夜落尘，海豚嘎嘎(车主之家)，刘超，Free大神，以及所有对EasyPR贡献数据的热心同学。 \ No newline at end of file diff --git a/include/easypr/config.h b/include/easypr/config.h index 4ecd08f..e6c7cac 100644 --- a/include/easypr/config.h +++ b/include/easypr/config.h @@ -95,9 +95,9 @@ static bool kDebug = false; static const int kGrayCharWidth = 20; static const int kGrayCharHeight = 32; - static const int kCharLBPGridX = 5; - static const int kCharLBPGridY = 8; - static const int kCharLBPPatterns = 32; + static const int kCharLBPGridX = 4; + static const int kCharLBPGridY = 4; + static const int kCharLBPPatterns = 16; static const int kCharHiddenNeurans = 64; @@ -120,6 +120,8 @@ private:\ // Display the image. #define SHOW_IMAGE(imgName, debug) \ if (debug) { \ + namedWindow("imgName", WINDOW_AUTOSIZE); \ + moveWindow("imgName", 500, 500); \ imshow("imgName", imgName); \ waitKey(0); \ destroyWindow("imgName"); \ diff --git a/include/easypr/core/chars_segment.h b/include/easypr/core/chars_segment.h index 8379484..636a3e8 100644 --- a/include/easypr/core/chars_segment.h +++ b/include/easypr/core/chars_segment.h @@ -15,8 +15,8 @@ class CCharsSegment { //! using ostu algotithm the segment chars in plate int charsSegment(Mat input, std::vector& resultVec, Color color = BLUE); - //! using project the segment chars in plate - int charsSegmentUsingProject(Mat input, std::vector& resultVec, std::vector& grayChars, Color color = BLUE); + //! using methods to segment chars in plate + int charsSegmentUsingOSTU(Mat input, std::vector& resultVec, std::vector& grayChars, Color color = BLUE); int charsSegmentUsingMSER(Mat input, vector& resultVec, vector& grayChars, Color color = BLUE); //! using project diff --git a/include/easypr/core/feature.h b/include/easypr/core/feature.h index 961a8f5..d11a6b7 100644 --- a/include/easypr/core/feature.h +++ b/include/easypr/core/feature.h @@ -3,6 +3,8 @@ #include "opencv2/opencv.hpp" +using namespace cv; + namespace easypr { //! 获得车牌的特征数 @@ -50,6 +52,8 @@ void getLBPplusHistFeatures(const cv::Mat& image, cv::Mat& features); //! grayChar feauter void getGrayCharFeatures(const cv::Mat& grayChar, cv::Mat& features); + +void getGrayPlusLBP(const Mat& grayChar, Mat& features); } /*! \namespace easypr*/ #endif // EASYPR_CORE_FEATURE_H_ \ No newline at end of file diff --git a/include/easypr/train/svm_train.h b/include/easypr/train/svm_train.h index 836d206..005f7ac 100644 --- a/include/easypr/train/svm_train.h +++ b/include/easypr/train/svm_train.h @@ -35,6 +35,7 @@ class SvmTrain : public ITrain { std::vector test_file_list_; svmCallback extractFeature; + bool isPrepared = true; }; } diff --git a/resources/model/annCh_bak.xml b/resources/model/annCh_bak.xml deleted file mode 100644 index bbb8a06..0000000 --- a/resources/model/annCh_bak.xml +++ /dev/null @@ -1,16766 +0,0 @@ - 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³KK5555 + BB867A - :³KK5555 + :BB867A - BB867A + ³B995EQ - :BB867A + :³B995EQ - B3587ѧ + A82349 - :L3587 + :QA8234 - ³LD9016 + E24494 - :³LD9016 + :E24494 + :JZ3999 + :A88888 - BDB720 + ³BQG527 - :BDB720 + :³BQG527 - ³Y44748 + BDB720 - :³Y44748 + :BDB720 - A84266 + ³JRW350 - :A84266 + :³JRW350 - A16341 + B3587ѧ - :AY1634 + :L3587 @@ -1186,9 +1164,9 @@ - A1R272 + ³KK5555 - :A1R272 + :³KK5555 @@ -1198,9 +1176,9 @@ - B777C9 + A84266 - :A88888 + :A84266 @@ -1210,17 +1188,21 @@ - E24494 + ³LD9016 - :E24494 - :JZ3999 - :A88888 + :³LD9016 - A89311 + B777C9 - :A8931L + :A88888 + + + + ³Y44748 + + :³Y44748 @@ -1230,15 +1212,22 @@ - AB4444 + A16341 - :AB4444 + :AY1634 - BE7773 + E28437 - :BEJ713 + :E28437 + :G1L9Z1 + + + + A1R272 + + :A1R272 @@ -1247,6 +1236,12 @@ :AC6A99 + + A89311 + + :A8931L + + BK33E3 @@ -1254,9 +1249,9 @@ - A94372 + BE7773 - :A9A372 + :BEJ713 @@ -1266,22 +1261,28 @@ - AT1Y68 + AB4444 - :AT1Y68 + :AB4444 - E28437 + A94372 - :E28437 - :G1L9Z1 + :A9A372 - A05H22 + AT1Y68 - :A05H22 + :AT1Y68 + + + + G68991 + + :G68991 + :MRW36N @@ -1291,10 +1292,17 @@ - G68991 + A05H22 - :G68991 - :MRW36N + :A05H22 + + + + H65817 + + :H65817 + :CT0956 + :A88888 @@ -1317,11 +1325,11 @@ - H65817 + JZ3999 - :H65817 - :CT0956 - :A88888 + :E24494 + :JZ3999 + :A88888 @@ -1337,17 +1345,17 @@ - JZ3999 + AP0966 - :E24494 - :JZ3999 - :A88888 + :AP0966 - AP0966 + KNG518 - :AP0966 + :KNG518 + :A88888 + :A88888 @@ -1357,14 +1365,6 @@ :AJW532 - - KNG518 - - :KNG518 - :A88888 - :A88888 - - ATL269 @@ -1378,16 +1378,16 @@ - A0PQ76 + LS2999 - :A88888 - :A88888 + :LS2999 - LS2999 + A0PQ76 - :LS2999 + :A88888 + :A88888 @@ -1397,6 +1397,14 @@ :A88888 + + MJY929 + + :A88888 + :MJY929 + :A88888 + + AYN355 @@ -1405,17 +1413,15 @@ - MJY929 + B551QV - :A88888 - :MJY929 - :A88888 + :B551QV - B551QV + MN0888 - :B551QV + :MN0888 @@ -1426,22 +1432,22 @@ - F8ZS83 + B577CU - :PV13Q3 - :A88888 + :B577CU - B577CU + F8ZS83 - :B577CU + :PV13Q3 + :A88888 - MN0888 + NRL118 - :MN0888 + :NRL118 @@ -1457,15 +1463,15 @@ - NRL118 + RB7992 - :NRL118 + :RB7992 - RB7992 + A03168 - :RB7992 + :A03168 @@ -1474,6 +1480,12 @@ :A88888 + + A12210 + + :QA1221 + + A09T87 @@ -1487,9 +1499,9 @@ - A03168 + A12903 - :A03168 + :A12903 @@ -1499,9 +1511,9 @@ - A12210 + A13840 - :QA1221 + :WA1384 @@ -1517,15 +1529,9 @@ - A12903 - - :A12903 - - - - A13840 + A21027 - :WA1384 + :A21097 @@ -1534,12 +1540,6 @@ :A45277 - - A21027 - - :A21097 - - A80003 diff --git a/result/accuracy.txt b/result/accuracy.txt index 0818ace..0e24715 100644 --- a/result/accuracy.txt +++ b/result/accuracy.txt @@ -3128,3 +3128,123 @@ Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. 0-error:70.6122%, 1-error:82.8571%, Chinese-precise:86.5306% ܹʱ: 177, ƽִʱ:0.691406 +2017-04-24 22:40:39 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:68.5714%, 1-error:81.2245%, Chinese-precise:85.3061% +ܹʱ: 168, ƽִʱ:0.65625 +2017-04-24 22:48:53 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:70.6122%, 1-error:82.8571%, Chinese-precise:86.5306% +ܹʱ: 179, ƽִʱ:0.699219 +2017-04-25 07:25:59 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:69.3878%, 1-error:82.0408%, Chinese-precise:84.898% +ܹʱ: 156, ƽִʱ:0.609375 +2017-04-25 07:45:15 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:69.3878%, 1-error:82.0408%, Chinese-precise:84.898% +ܹʱ: 158, ƽִʱ:0.617188 +2017-04-25 07:50:08 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:70.6122%, 1-error:82.8571%, Chinese-precise:86.5306% +ܹʱ: 152, ƽִʱ:0.59375 +2017-04-30 08:47:34 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:70.6122%, 1-error:82.8571%, Chinese-precise:86.5306% +ܹʱ: 154, ƽִʱ:0.601563 +2017-04-30 10:12:47 +ͼƬ:1, Plates count:1, δλ:0, :100% +Recall:97.4057%, Precise:97.4057%, Fscore:97.4057%. +0-error:100%, 1-error:100%, Chinese-precise:100% +ܹʱ: 20, ƽִʱ:20 +2017-04-30 10:13:46 +ͼƬ:2, Plates count:2, δλ:0, :100% +Recall:98.662%, Precise:98.662%, Fscore:98.662%. +0-error:50%, 1-error:50%, Chinese-precise:50% +ܹʱ: 12, ƽִʱ:6 +2017-04-30 10:17:45 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:67.6923%, 1-error:73.4615%, Chinese-precise:76.1538% +ܹʱ: 158, ƽִʱ:0.617188 +2017-04-30 10:32:20 +ͼƬ:2, Plates count:2, δλ:0, :100% +Recall:98.662%, Precise:98.662%, Fscore:98.662%. +0-error:50%, 1-error:50%, Chinese-precise:50% +ܹʱ: 2, ƽִʱ:1 +2017-04-30 10:36:16 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:45.977%, 1-error:52.8736%, Chinese-precise:75.8621% +ܹʱ: 158, ƽִʱ:0.617188 +2017-04-30 10:41:13 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:67.3077%, 1-error:73.0769%, Chinese-precise:76.1538% +ܹʱ: 169, ƽִʱ:0.660156 +2017-04-30 10:47:55 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:54.9618%, 1-error:63.3588%, Chinese-precise:66.0305% +ܹʱ: 169, ƽִʱ:0.660156 +2017-04-30 10:54:51 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:56.9231%, 1-error:71.1538%, Chinese-precise:74.6154% +ܹʱ: 193, ƽִʱ:0.753906 +2017-04-30 11:12:08 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:67.3077%, 1-error:73.0769%, Chinese-precise:76.1538% +ܹʱ: 187, ƽִʱ:0.730469 +2017-04-30 12:00:16 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:67.3077%, 1-error:73.0769%, Chinese-precise:76.1538% +ܹʱ: 162, ƽִʱ:0.632813 +2017-04-30 12:04:03 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:70.6122%, 1-error:82.8571%, Chinese-precise:86.5306% +ܹʱ: 153, ƽִʱ:0.597656 +2017-04-30 12:21:43 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:63.1387%, 1-error:74.0876%, Chinese-precise:87.9562% +ܹʱ: 150, ƽִʱ:0.585938 +2017-04-30 12:28:44 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:63.1387%, 1-error:74.0876%, Chinese-precise:87.9562% +ܹʱ: 151, ƽִʱ:0.589844 +2017-04-30 12:32:22 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:63.8686%, 1-error:69.3431%, Chinese-precise:77.3723% +ܹʱ: 160, ƽִʱ:0.625 +2017-04-30 12:37:26 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:70.6122%, 1-error:82.8571%, Chinese-precise:86.5306% +ܹʱ: 211, ƽִʱ:0.824219 +2017-04-30 13:53:08 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:68.2353%, 1-error:74.1176%, Chinese-precise:77.2549% +ܹʱ: 159, ƽִʱ:0.621094 +2017-04-30 13:56:46 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:67.3077%, 1-error:73.0769%, Chinese-precise:76.1538% +ܹʱ: 160, ƽִʱ:0.625 +2017-04-30 14:02:45 +ͼƬ:256, Plates count:297, δλ:23, :92.2559% +Recall:85.1281%, Precise:80.5653%, Fscore:82.7839%. +0-error:70.6122%, 1-error:82.8571%, Chinese-precise:86.5306% +ܹʱ: 150, ƽִʱ:0.585938 diff --git a/result/plateGroundTruth.txt b/result/plateGroundTruth.txt index 1cf58da..4186755 100644 --- a/result/plateGroundTruth.txt +++ b/result/plateGroundTruth.txt @@ -3763,3 +3763,5301 @@ plate_3_241.jpg: plate_3_242.jpg:A85890 plate_3_243.jpg:A87271 plate_3_244.jpg:AA3610 +plate_3_0.jpg:BR75Y3 +plate_3_1.jpg:A88731 +plate_3_2.jpg:C28888 +plate_3_3.jpg:BTT255 +plate_3_4.jpg:A961F3 +plate_3_5.jpg:A26M71 +plate_3_6.jpg:BGG522 +plate_3_7.jpg:CX8888 +plate_3_8.jpg:AH8331 +plate_3_9.jpg:BW44R6 +plate_3_10.jpg:BZ756T +plate_3_11.jpg:A50819 +plate_3_12.jpg:AA0825 +plate_3_13.jpg:BZ89M9 +plate_3_14.jpg:BU5838 +plate_3_15.jpg:A54614 +plate_3_16.jpg:AK169H +plate_3_17.jpg:A11R55 +plate_3_18.jpg:A31Y83 +plate_3_19.jpg:AAA379 +plate_3_20.jpg:H99999 +plate_3_21.jpg:AL025S +plate_3_22.jpg:A51V39 +plate_3_23.jpg:AS3165 +plate_3_24.jpg:AAB457 +plate_3_25.jpg:A72220 +plate_3_26.jpg:CXE592 +plate_3_27.jpg:CTF181 +plate_3_28.jpg:A88888 +plate_3_29.jpg:AT789S +plate_3_30.jpg:AAC044 +plate_3_31.jpg:AGH092 +plate_3_32.jpg:A95044 +plate_3_33.jpg:E9R439 +plate_3_34.jpg:DLA031 +plate_3_35.jpg:CA9622 +plate_3_36.jpg:A3685C +plate_3_37.jpg:A7GB86 +plate_3_38.jpg:ATH859 +plate_3_39.jpg:AA2270 +plate_3_40.jpg:AAD348 +plate_3_41.jpg:FA3215 +plate_3_42.jpg:AT1203 +plate_3_43.jpg:ALB022 +plate_3_44.jpg:0A2160 +plate_3_45.jpg:E2Y256 +plate_3_46.jpg:AA4586 +plate_3_47.jpg:A019W2 +plate_3_48.jpg:AUB816 +plate_3_49.jpg:AP0910 +plate_3_50.jpg:E75614 +plate_3_51.jpg:0A9112 +plate_3_52.jpg:AA5547 +plate_3_53.jpg:AAF230 +plate_3_54.jpg:A095Q5 +plate_3_55.jpg:AX688A +plate_3_56.jpg:EB68W9 +plate_3_57.jpg:AC1847 +plate_3_58.jpg:A09X20 +plate_3_59.jpg:A3MA06 +plate_3_60.jpg:AY4936 +plate_3_61.jpg:BBC666 +plate_3_62.jpg:0T9048 +plate_3_63.jpg:A105LR +plate_3_64.jpg:EZM618 +plate_3_65.jpg:A883J9 +plate_3_66.jpg:AB288Y +plate_3_67.jpg:ԥK91239 +plate_3_68.jpg:D71603 +plate_3_69.jpg:A113YP +plate_3_70.jpg:F71646 +plate_3_71.jpg:SD050L +plate_3_72.jpg:AM690M +plate_3_73.jpg:A561WP +plate_3_74.jpg:ԥS33909 +plate_3_75.jpg:J32500 +plate_3_76.jpg:M69311 +plate_3_77.jpg:A53U19 +plate_3_78.jpg:A572F4 +plate_3_79.jpg:B7C289 +plate_3_80.jpg:A762ZS +plate_3_81.jpg:P77222 +plate_3_82.jpg:ԥU00000 +plate_3_83.jpg:AF9C00 +plate_3_84.jpg:C01701 +plate_3_85.jpg:A82M83 +plate_3_86.jpg:TD1291 +plate_3_87.jpg:JS6999 +plate_3_88.jpg:A88888 +plate_3_89.jpg:Q0686ѧ +plate_3_90.jpg:A5RM87 +plate_3_91.jpg:AA662F +plate_3_92.jpg:K62933 +plate_3_93.jpg:Q15538 +plate_3_94.jpg:VS1866 +plate_3_95.jpg:A82F36 +plate_3_96.jpg:AE8H60 +plate_3_97.jpg:AB148G +plate_3_98.jpg:AL6212 +plate_3_99.jpg:E6686V +plate_3_100.jpg:A88888 +plate_3_101.jpg:Q19917 +plate_3_102.jpg:Q80197 +plate_3_103.jpg:QA2825 +plate_3_104.jpg:AGU052 +plate_3_105.jpg:A97971 +plate_3_106.jpg:F397C0 +plate_3_107.jpg:AV0U41 +plate_3_108.jpg:KT5583 +plate_3_109.jpg:F99999 +plate_3_110.jpg:B99999 +plate_3_111.jpg:B0K999 +plate_3_112.jpg:A0CP56 +plate_3_113.jpg:AGU801 +plate_3_114.jpg:B2757B +plate_3_115.jpg:BG0D80 +plate_3_116.jpg:GZB388 +plate_3_117.jpg:B2LR57 +plate_3_118.jpg:AKM065 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CCharsRecognise::charsRecognise(CPlate& plate, std::string& plateLicense) { color = getPlateType(tmpMat, true); } - int result = m_charsSegment->charsSegmentUsingProject(plateMat, matChars, grayChars, color); + int result = m_charsSegment->charsSegmentUsingOSTU(plateMat, matChars, grayChars, color); if (result == 0) { int num = matChars.size(); diff --git a/src/core/chars_segment.cpp b/src/core/chars_segment.cpp index 4515d72..3edc85a 100644 --- a/src/core/chars_segment.cpp +++ b/src/core/chars_segment.cpp @@ -517,7 +517,7 @@ Mat preprocessCharMat(Mat in, int char_size) { } Mat clearLiuDingAndBorder(const Mat& grayImage, Color color) { - SHOW_IMAGE(grayImage, 1); + SHOW_IMAGE(grayImage, 0); Mat img_threshold; img_threshold = grayImage.clone(); spatial_ostu(img_threshold, 1, 1, color); @@ -526,7 +526,7 @@ Mat clearLiuDingAndBorder(const Mat& grayImage, Color color) { clearBorder(img_threshold, cropRect); Mat cropedGrayImage; resize(grayImage(cropRect), cropedGrayImage, Size(kPlateResizeWidth, kPlateResizeHeight)); - SHOW_IMAGE(cropedGrayImage, 1); + SHOW_IMAGE(cropedGrayImage, 0); return cropedGrayImage; } @@ -543,16 +543,22 @@ void NMStoCharacterByRatio(std::vector &inVec, double overlap, const float iou = computeIOU(rect, groundRect); - float w_ratio = (float)w / (float)gw; - float h_ratio = (float)h / (float)gh; + int w_diff = abs(w - gw); + int h_diff = abs(h - gh); + + //float w_ratio = (float)w / (float)gw; + //float h_ratio = (float)h / (float)gh; + + float w_ratio = 1 - (float)w_diff / (float)gw; + float h_ratio = 1 - (float)h_diff / (float)gh; float a = 0.5f; float b = 0.5f; //cout << "str:" << character.getCharacterStr() << endl; // if the charater is '1', its probalilty is redcued by its iou if ("1" == character.getCharacterStr()) { - a = 0.2f; - b = 0.8f; + a = 0.3f; //0.2f; + b = 0.7f; //0.8f; } float c = 0.1f; //float weighted_score = a * (float)score + b * w_ratio + c * h_ratio; @@ -608,7 +614,9 @@ int CCharsSegment::charsSegmentUsingMSER(Mat input, vector& resultVec, vect cvtColor(input, grayImage, CV_BGR2GRAY); std::vector bgrSplit; split(input, bgrSplit); - Mat grayChannel = grayImage; //clearLiuDingAndBorder(grayImage, color); + + //Mat grayChannel = clearLiuDingAndBorder(grayImage, color); //clearLiuDingAndBorder(grayImage, color); + Mat grayChannel = grayImage; // Mat cropedGrayImage = grayImage; // generate all channgel images; @@ -709,20 +717,28 @@ int CCharsSegment::charsSegmentUsingMSER(Mat input, vector& resultVec, vect // find character if (verifyCharRectSizes(rect)) { Mat mserMat = adaptive_image_from_points(contour, rect, Size(char_size, char_size)); - Mat charInput = preprocessCharMat(mserMat, char_size); - Rect charRect = rect; + Mat mserInput = preprocessCharMat(mserMat, char_size); + Rect charRect = rect; Point center(charRect.tl().x + charRect.width / 2, charRect.tl().y + charRect.height / 2); Mat tmpMat; double ostu_level = cv::threshold(cimage(charRect), tmpMat, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU); - + Mat grayCharMat = cimage(charRect); + Mat ostuMat; + switch (color) { + case BLUE: threshold(grayCharMat, ostuMat, 0, 255, CV_THRESH_BINARY + CV_THRESH_OTSU); break; + case YELLOW: threshold(grayCharMat, ostuMat, 0, 255, CV_THRESH_BINARY_INV + CV_THRESH_OTSU); break; + case WHITE: threshold(grayCharMat, ostuMat, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY_INV); break; + default: threshold(grayCharMat, ostuMat, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY); break; + } + Mat ostuInput = preprocessChar(ostuMat); // use judegMDOratio2 function to // remove the small lines in character like "zh-cuan" if (judegMDOratio2(cimage, rect, contour, mdoImage, 1.2f, true)) { CCharacter charCandidate; //cout << contour.size() << endl; charCandidate.setCharacterPos(charRect); - charCandidate.setCharacterMat(charInput); + charCandidate.setCharacterMat(ostuInput); //charInput or ostuInput charCandidate.setOstuLevel(ostu_level); charCandidate.setCenterPoint(center); int pos = getNearestIndex(center, groundCenters); @@ -736,62 +752,68 @@ int CCharsSegment::charsSegmentUsingMSER(Mat input, vector& resultVec, vect SHOW_IMAGE(showMSERImage(rect), 0); } } - SHOW_IMAGE(showMSERImage, 1); - SHOW_IMAGE(mdoImage, 1); + SHOW_IMAGE(showMSERImage, 0); + SHOW_IMAGE(mdoImage, 0); // classify all the images; CharsIdentify::instance()->classify(charVec); - for (auto charCandidate : charVec) { - int pos = charCandidate.getIndex(); - charsVecVec.at(pos).push_back(charCandidate); - } + Rect maxrect = groundRects.at(0); // NMS to the seven groud truth rect - charVec.clear(); - Rect maxrect = groundRects.at(0); - for (size_t c = 0; c < charsVecVec.size(); c++) { - Mat testImage_2 = cimage.clone(); - cvtColor(testImage_2, testImage_2, CV_GRAY2BGR); - vector& charPosVec = charsVecVec.at(c); - for (auto character : charPosVec) { - rectangle(testImage_2, character.getCharacterPos(), Scalar(0, 255, 0)); + bool useGround = true; + if (useGround) { + for (auto charCandidate : charVec) { + int pos = charCandidate.getIndex(); + charsVecVec.at(pos).push_back(charCandidate); } - SHOW_IMAGE(testImage_2, 0); + charVec.clear(); + for (size_t c = 0; c < charsVecVec.size(); c++) { + Mat testImage_2 = cimage.clone(); + cvtColor(testImage_2, testImage_2, CV_GRAY2BGR); + vector& charPosVec = charsVecVec.at(c); + for (auto character : charPosVec) { + rectangle(testImage_2, character.getCharacterPos(), Scalar(0, 255, 0)); + } + SHOW_IMAGE(testImage_2, 0); - double overlapThresh = 0.; - NMStoCharacterByRatio(charPosVec, overlapThresh, groundRects.at(c)); - charPosVec.shrink_to_fit(); + double overlapThresh = 0.; + NMStoCharacterByRatio(charPosVec, overlapThresh, groundRects.at(c)); + charPosVec.shrink_to_fit(); - Mat testImage_3 = cimage.clone(); - cvtColor(testImage_3, testImage_3, CV_GRAY2BGR); - for (auto character : charPosVec) { - rectangle(testImage_3, character.getCharacterPos(), Scalar(0, 255, 0)); - } + Mat testImage_3 = cimage.clone(); + cvtColor(testImage_3, testImage_3, CV_GRAY2BGR); + for (auto character : charPosVec) { + rectangle(testImage_3, character.getCharacterPos(), Scalar(0, 255, 0)); + } - // only the last group will contain more than one candidate character - if (charsVecVec.size() - 1 == c) { - for (auto charPos : charPosVec) - charVec.push_back(charPos); - } - else { - if (charPosVec.size() != 0) - charVec.push_back(charPosVec.at(0)); - } - for (auto charPos : charPosVec) { - Rect r = charPos.getCharacterPos(); - if (r.area() > maxrect.area()) - maxrect = r; + // only the last group will contain more than one candidate character + if (charsVecVec.size() - 1 == c) { + for (auto charPos : charPosVec) + charVec.push_back(charPos); + } + else { + if (charPosVec.size() != 0) { + CCharacter& inputChar = charPosVec.at(0); + charVec.push_back(inputChar); + Mat charMat = inputChar.getCharacterMat(); + SHOW_IMAGE(charMat, 0); + } + } + for (auto charPos : charPosVec) { + Rect r = charPos.getCharacterPos(); + if (r.area() > maxrect.area()) + maxrect = r; + } + SHOW_IMAGE(testImage_3, 0); } - - SHOW_IMAGE(testImage_3, 0); + } + else { + NMStoCharacterByRatio(charVec, 0.2f, maxrect); } - if (charVec.size() < kCharsCountInOnePlate) - return -1; + if (charVec.size() < kCharsCountInOnePlate) return 0x03; + std::sort(charVec.begin(), charVec.end(),[](const CCharacter& r1, const CCharacter& r2) { return r1.getCharacterPos().x < r2.getCharacterPos().x; }); - NMStoCharacterByRatio(charVec, 0.2f, maxrect); - std::sort(charVec.begin(), charVec.end(), - [](const CCharacter& r1, const CCharacter& r2) { return r1.getCharacterPos().x < r2.getCharacterPos().x; }); string predictLicense = ""; vector sortedRect; for (auto charCandidate : charVec) { @@ -805,16 +827,18 @@ int CCharsSegment::charsSegmentUsingMSER(Mat input, vector& resultVec, vect // find chinese rect size_t specIndex = 0; specIndex = GetSpecificRect(sortedRect); - //if (specIndex == 0) - // specIndex = 1; - SHOW_IMAGE(showImage(sortedRect[specIndex]), 1); + SHOW_IMAGE(showImage(sortedRect[specIndex]), 0); Rect chineseRect; if (specIndex < sortedRect.size()) chineseRect = GetChineseRect(sortedRect[specIndex]); + else + return 0x04; vector newSortedRect; newSortedRect.push_back(chineseRect); + if (newSortedRect.size() == 0) return 0x05; + SHOW_IMAGE(showImage(chineseRect), 0); RebuildRect(sortedRect, newSortedRect, specIndex); @@ -825,18 +849,8 @@ int CCharsSegment::charsSegmentUsingMSER(Mat input, vector& resultVec, vect Mat auxRoi(theImage, mr); Mat newRoi; if (i == 0) { - //mr = rectEnlarge(newSortedRect[i], cimage.cols, cimage.rows); - /* - Mat chineseRoi; - float slideLengthRatio = 0.1f; - if(!slideChineseGrayWindow(cimage, mr, chineseRoi, color, slideLengthRatio)) - chineseRoi = cimage(mr); - - Mat resizedChinese; - resize(chineseRoi, resizedChinese, Size(kGrayCharWidth, kGrayCharHeight)); - grayChars.push_back(resizedChinese); - */ - Rect large_mr = rectEnlarge(mr, theImage.cols, theImage.rows); + //Rect large_mr = rectEnlarge(mr, theImage.cols, theImage.rows); + Rect large_mr = mr; Mat grayChar(theImage, large_mr); Mat grayChinese; grayChinese.create(kGrayCharHeight, kGrayCharWidth, CV_8UC1); @@ -866,20 +880,16 @@ int CCharsSegment::charsSegmentUsingMSER(Mat input, vector& resultVec, vect rectangle(showImage, mr, Scalar(0, 0, 255), 1); resultVec.push_back(newRoi); } - SHOW_IMAGE(showImage, 1); + SHOW_IMAGE(showImage, 0); } return 0; } -int CCharsSegment::charsSegmentUsingProject(Mat input, vector& resultVec, vector& grayChars, Color color) { +int CCharsSegment::charsSegmentUsingOSTU(Mat input, vector& resultVec, vector& grayChars, Color color) { if (!input.data) return 0x01; - //vector out_indexs; - //projectSegment(input, color, out_indexs); - //charsSegmentUsingMSER(input, resultVec, grayChars, color); - Color plateType = color; Mat input_grey; cvtColor(input, input_grey, CV_BGR2GRAY); diff --git a/src/core/feature.cpp b/src/core/feature.cpp index 1eba7b1..c6476b5 100644 --- a/src/core/feature.cpp +++ b/src/core/feature.cpp @@ -384,6 +384,62 @@ void getGrayPlusProject(const Mat& grayChar, Mat& features) hconcat(feautreImg.reshape(1, 1), projectFeature.reshape(1, 1), features); } + +void getGrayPlusLBP(const Mat& grayChar, Mat& features) +{ + // TODO: check channnels == 1 + SHOW_IMAGE(grayChar, 0); + SHOW_IMAGE(255 - grayChar, 0); + + // resize to uniform size, like 20x32 + bool useResize = false; + bool useConvert = true; + bool useMean = true; + bool useLBP = true; + + Mat char_mat; + if (useResize) { + char_mat.create(kGrayCharHeight, kGrayCharWidth, CV_8UC1); + resize(grayChar, char_mat, char_mat.size(), 0, 0, INTER_LINEAR); + } + else { + char_mat = grayChar; + } + SHOW_IMAGE(char_mat, 0); + + // convert to float + Mat float_img; + if (useConvert) { + float scale = 1.f / 255; + char_mat.convertTo(float_img, CV_32FC1, scale, 0); + } + else { + float_img = char_mat; + } + SHOW_IMAGE(float_img, 0); + + // cut from mean, it can be optional + + Mat mean_img; + if (useMean) { + float_img -= mean(float_img); + mean_img = float_img; + } + else { + mean_img = float_img; + } + SHOW_IMAGE(mean_img, 0); + + // use lbp to get features, it can be changed to other + Mat originImage = mean_img.clone(); + Mat lbpimage = libfacerec::olbp(mean_img); + SHOW_IMAGE(lbpimage, 0); + lbpimage = libfacerec::spatial_histogram(lbpimage, kCharLBPPatterns, kCharLBPGridX, kCharLBPGridY); + + // 32x20 + 16x16 + hconcat(mean_img.reshape(1, 1), lbpimage.reshape(1, 1), features); +} + void getLBPplusHistFeatures(const Mat& image, Mat& features) { // TODO Mat grayImage; diff --git a/src/core/plate_recognize.cpp b/src/core/plate_recognize.cpp index bf20266..dab6d9d 100644 --- a/src/core/plate_recognize.cpp +++ b/src/core/plate_recognize.cpp @@ -14,16 +14,8 @@ CPlateRecognize::CPlateRecognize() { // 2. chars recognize int CPlateRecognize::plateRecognize(const Mat& src, std::vector &plateVecOut, int img_index) { // resize to uniform sizes - // TODO: groungTruth affect float scale = 1.f; Mat img = uniformResize(src, scale); - //Mat img = src; - if (0) { - std::stringstream ss(std::stringstream::in | std::stringstream::out); - ss << "resources/image/tmp/phone/phone_img_" << img_index << ".jpg"; - imwrite(ss.str(), img); - return -1; - } // 1. plate detect std::vector plateVec; diff --git a/src/train/annCh_train.cpp b/src/train/annCh_train.cpp index b124020..bd7b1af 100644 --- a/src/train/annCh_train.cpp +++ b/src/train/annCh_train.cpp @@ -18,7 +18,7 @@ namespace easypr { type = 1; kv_ = std::shared_ptr(new Kv); kv_->load("etc/province_mapping"); - extractFeature = getGrayCharFeatures; + extractFeature = getGrayPlusProject; } void AnnChTrain::train() @@ -34,7 +34,7 @@ namespace easypr { else input_number = kGrayCharHeight * kGrayCharWidth; - //input_number += 64; + input_number += 64; classNumber = kChineseNumber; hidden_number = kCharHiddenNeurans; @@ -75,6 +75,14 @@ namespace easypr { ann_->setBackpropWeightScale(0.1); ann_->setBackpropMomentumScale(0.1); + auto files = Utils::getFiles(chars_folder_); + if (files.size() == 0) { + fprintf(stdout, "No file found in the train folder!\n"); + fprintf(stdout, "You should create a folder named \"tmp\" in EasyPR main folder.\n"); + fprintf(stdout, "Copy train data folder(like \"annCh\") under \"tmp\". \n"); + return; + } + // using raw data or raw + synthic data. trainVal(m_number_for_count); } diff --git a/src/train/ann_train.cpp b/src/train/ann_train.cpp index e2f5bdd..5be93d8 100644 --- a/src/train/ann_train.cpp +++ b/src/train/ann_train.cpp @@ -14,7 +14,9 @@ namespace easypr { AnnTrain::AnnTrain(const char* chars_folder, const char* xml) : chars_folder_(chars_folder), ann_xml_(xml) { ann_ = cv::ml::ANN_MLP::create(); - type = 1; + // type=0, all characters + // type=1, only chinese + type = 0; kv_ = std::shared_ptr(new Kv); kv_->load("etc/province_mapping"); } @@ -56,6 +58,7 @@ void AnnTrain::train() { layers.at(2) = output_number; } else { + // Two-layers neural networks is hard to train, So do not try it fprintf(stdout, ">> Use two-layers neural networks,\n"); fprintf(stdout, ">> First_hidden_neurons: %d \n", first_hidden_neurons); fprintf(stdout, ">> Second_hidden_neurons: %d \n", second_hidden_neurons); @@ -74,8 +77,15 @@ void AnnTrain::train() { ann_->setBackpropWeightScale(0.1); ann_->setBackpropMomentumScale(0.1); + auto files = Utils::getFiles(chars_folder_); + if (files.size() == 0) { + fprintf(stdout, "No file found in the train folder!\n"); + fprintf(stdout, "You should create a folder named \"tmp\" in EasyPR main folder.\n"); + fprintf(stdout, "Copy train data folder(like \"ann\") under \"tmp\". \n"); + return; + } + //using raw data or raw + synthic data. - //auto traindata = tdata(); auto traindata = sdata(350); std::cout << "Training ANN model, please wait..." << std::endl; diff --git a/src/train/svm_train.cpp b/src/train/svm_train.cpp index d9410ca..02a3f54 100644 --- a/src/train/svm_train.cpp +++ b/src/train/svm_train.cpp @@ -32,6 +32,12 @@ void SvmTrain::train() { svm_->setP(0.1); svm_->setTermCriteria(cvTermCriteria(CV_TERMCRIT_ITER, 20000, 0.0001)); + if (train_file_list_.size() == 0) { + fprintf(stdout, "No file found in the train folder!\n"); + fprintf(stdout, "You should create a folder named \"tmp\" in EasyPR main folder.\n"); + fprintf(stdout, "Copy train data folder(like \"SVM\") under \"tmp\". \n"); + return; + } auto train_data = tdata(); fprintf(stdout, ">> Training SVM model, please wait...\n"); @@ -184,8 +190,7 @@ cv::Ptr SvmTrain::tdata() { samples.convertTo(samples_, CV_32FC1); cv::Mat(responses).copyTo(responses_); - return cv::ml::TrainData::create(samples_, cv::ml::SampleTypes::ROW_SAMPLE, - responses_); + return cv::ml::TrainData::create(samples_, cv::ml::SampleTypes::ROW_SAMPLE, responses_); } } // namespace easypr diff --git a/test/accuracy.hpp b/test/accuracy.hpp index f231640..bf9e3aa 100644 --- a/test/accuracy.hpp +++ b/test/accuracy.hpp @@ -154,6 +154,7 @@ namespace easypr { // chinese character is wrong float chinese_error_count = 0; float chinese_error_rate = 0; + float chinese_precise_rate = 0; // calucate the detect precise and recall // use icdar 2003 evalution protoocal @@ -432,6 +433,7 @@ namespace easypr { non_error_rate = non_error_count / count_recogin; one_error_rate = one_error_count / count_recogin; chinese_error_rate = chinese_error_count / count_recogin; + chinese_precise_rate = (count_recogin - chinese_error_count) / count_recogin; } double recall_2003_result = 0; @@ -459,9 +461,10 @@ namespace easypr { cout << "Fscore" << "," << fscore_2003_result * 100 << "%" << ";" << endl; cout << kv->get("char_recongize") << ": "; + //cout << "Recongtion rate:" << "," << (count_recogin / count_detect) * 100 << "%; " << endl; cout << "0-error" << "," << non_error_rate * 100 << "%; "; cout << "1-error" << "," << one_error_rate * 100 << "%; "; - cout << "Chinese-precise" << "," << (1 - chinese_error_rate) * 100 << "% " << endl; + cout << "Chinese-precise" << "," << chinese_precise_rate * 100 << "% " << endl; double seconds = difftime(end, begin); double avgsec = seconds / double(count_all); @@ -510,7 +513,7 @@ namespace easypr { myfile << "0-error" << ":" << non_error_rate * 100 << "%, "; myfile << "1-error" << ":" << one_error_rate * 100 << "%, "; - myfile << "Chinese-precise" << ":" << (1 - chinese_error_rate) * 100 << "% " << endl; + myfile << "Chinese-precise" << ":" << chinese_precise_rate * 100 << "% " << endl; myfile << kv->get("seconds") << ": " << seconds << kv->get("sec") << ", "; myfile << kv->get("seconds_average") << ":" << avgsec << kv->get("sec") << endl; diff --git a/test/plate.hpp b/test/plate.hpp index c8c54df..9ac6526 100644 --- a/test/plate.hpp +++ b/test/plate.hpp @@ -107,7 +107,7 @@ int test_plate_recognize() { CPlateRecognize pr; pr.setLifemode(true); pr.setDebug(false); - pr.setMaxPlates(4); + pr.setMaxPlates(1); //pr.setDetectType(PR_DETECT_COLOR | PR_DETECT_SOBEL); pr.setDetectType(easypr::PR_DETECT_CMSER);