zhubenfu / license-plate-detect-recognition-via-deep-neural-networks-accuracy-up-to-99.9 Goto Github PK

PNet_.reset(new Net((proto_model_dir+"/det1.cfg"), TEST));
PNet_->CopyTrainedLayersFrom(proto_model_dir+"/det1.weights");
on the branch linux-cmake, it compiles smoothly.
However when I try to run it. I met an error of not finding det1.cfg and det1.weights?
det1.cfg和det1.weights不找到。
Please upload the files or tell me how to solve it.
thank you.

I was going to build with CPU_ONLY these solutions.
But caffe files always need gpu setting.
Please tell me how to build these with CPU_ONLY or this repository is for just CUDA?

请问有mtcnn的训练步骤方法吗，只看到Adaboost的博客链接，可以分享一下吗

how to test one picture on this project, please

After spent serval days of reading your code, I find it very dirty and the resposity is more than 700M! So it takes long time to download and update.
I notetice you have modified some code from the original MTCNN, can you add some comments on these code? And what's the modification of caffe layers?
I don't think it a good practice to package lots of dlls in the resposity. Anyway, thanks for your sharing.

百度云盘下载失效，能否提供下新的下载链接谢谢

Your project looks very great, but there's no useful guide to build it.
I have read the code, but cannot find model_platecar of mtcnn detector model. Any suggestion?

如果能改成tf更好。。

the input is
./build/bin/ocr_test -ds=/home/daming/Work/Pytorch/LPR/TaiWanData/1 -mnd=/home/daming/Work/Pytorch/LPR/LPRMTCNN/src/ocr_test/model_platecar -pmd=/home/daming/Work/Pytorch/LPR/LPRMTCNN/src/ocr_test/plateCard_test
0 /home/daming/Work/Pytorch/LPR/TaiWanData

0384159482759-87_93-367&427_654&550-656&527_369&554_379&441_666&414-0_0_13_19_33_27_24-95-81.jpg
detector point 224.399368,363.916168 671.017761,263.650604 667.478333,568.336975 229.654694,683.795410
cost: 17.7248 (ms) detect plate region (0, 0) (720 x 1160) with confidence 0.000000, predict plate 桂SE5

but the detector point is wrong.How can i get right result

群已经满了，没法加进去。
最新的第三方库是在群共享里面。能不能弄个百度云的链接。

[root@greenet build]# cmake -DBLAS=open ..
-- The C compiler identification is GNU 4.8.5
-- The CXX compiler identification is GNU 4.8.5
-- Check for working C compiler: /bin/cc
-- Check for working C compiler: /bin/cc -- works
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Detecting C compile features
-- Detecting C compile features - done
-- Check for working CXX compiler: /bin/c++
-- Check for working CXX compiler: /bin/c++ -- works
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Detecting CXX compile features
-- Detecting CXX compile features - done
CMake Warning (dev) at cmake/Misc.cmake:32 (set):
implicitly converting 'BOOLEAN' to 'STRING' type.
Call Stack (most recent call first):
CMakeLists.txt:35 (include)
This warning is for project developers. Use -Wno-dev to suppress it.

-- Boost 1.54 found.
-- Found Boost components:
system;thread;filesystem
-- Found Threads: TRUE
-- Found OpenMP_C: -fopenmp (found version "3.1")
-- Found OpenMP_CXX: -fopenmp (found version "3.1")
-- Found OpenMP: TRUE (found version "3.1")
-- Found GFlags: /usr/include
-- Found gflags (include: /usr/include, library: /usr/lib64/libgflags.so)
-- Found Glog: /usr/include
-- Found glog (include: /usr/include, library: /usr/lib64/libglog.so)
-- Found Protobuf: /usr/local/lib/libprotobuf.so;-lpthread (found suitable version "3.8.0", minimum required is "3.0.0")
-- Found PROTOBUF Compiler: /usr/local/bin/protoc
-- HDF5: Using hdf5 compiler wrapper to determine C configuration
-- HDF5: Using hdf5 compiler wrapper to determine CXX configuration
-- Found HDF5: /usr/local/anaconda3/lib/libhdf5_cpp.so;/usr/local/anaconda3/lib/libhdf5.so;/usr/lib64/librt.so;/usr/lib64/libpthread.so;/usr/local/anaconda3/lib/libz.so;/usr/lib64/libdl.so;/usr/lib64/libm.so (found version "1.10.2") found components: HL
-- Found LMDB: /usr/include
-- Found lmdb (include: /usr/include, library: /usr/lib64/liblmdb.so)
-- Found LevelDB: /usr/include
-- Found LevelDB (include: /usr/include, library: /usr/lib64/libleveldb.so)
-- Found Snappy: /usr/include
-- Found Snappy (include: /usr/include, library: /usr/lib64/libsnappy.so)
-- -- CUDA is disabled. Building without it...
-- Found CUDA: /usr/local/cuda-10.1 (found suitable exact version "10.1")
-- OpenCV found (/usr/local/share/OpenCV)
-- Found OpenBLAS libraries: /usr/lib64/libopenblas.so
-- Found OpenBLAS include: /usr/include
CMake Error at src/caffe/CMakeLists.txt:21 (target_link_libraries):
The INTERFACE, PUBLIC or PRIVATE option must appear as the second argument,
just after the target name.

PUBLIC/usr/local/boost/includePUBLIC/usr/includePUBLIC/usr/includePUBLIC/usr/local/includePUBLIC/usr/local/anaconda3/includePUBLIC/usr/includePUBLIC/usr/includePRIVATE/usr/includePUBLIC/usr/local/include/usr/local/include/opencvPUBLIC/usr/include
-- Found Git: /bin/git (found version "1.8.3.1")

-- ******************* Caffe Configuration Summary *******************
-- General:
-- Version : 1.0.0
-- Git : unknown
-- System : Linux
-- C++ compiler : /bin/c++
-- Release CXX flags : -O2 -DNDEBUG -std=c++11 -fPIC -Wall -Wno-sign-compare -Wno-uninitialized
-- Debug CXX flags : -g -std=c++11 -fPIC -Wall -Wno-sign-compare -Wno-uninitialized
-- Build type : Release

-- BUILD_SHARED_LIBS : ON
-- BUILD_python : OFF
-- BUILD_matlab : OFF
-- BUILD_docs : OFF
-- CPU_ONLY : ON
-- USE_OPENCV : ON
-- USE_LEVELDB : ON
-- USE_LMDB : ON
-- USE_NCCL : OFF
-- ALLOW_LMDB_NOLOCK : ON

-- Dependencies:
-- BLAS : Yes (open)
-- Boost : Yes (ver. 1.71)
-- glog : Yes
-- gflags : Yes
-- protobuf : Yes (ver. 3.8.0)
-- lmdb : Yes (ver. 0.9.22)
-- LevelDB : Yes (ver. 1.12)
-- Snappy : Yes (ver. 1.1.0)
-- OpenCV : Yes (ver. 3.4.7)
-- CUDA : No

-- Install:
-- Install path : /home/yyh/test/License-Plate-Detect-Recognition-via-Deep-Neural-Networks-accuracy-up-to-99.9-master/build/install

CMake Error at cmake/ConfigGen.cmake:36 (export):
export given target "caffeproto" which is not built by this project.
Call Stack (most recent call first):
CMakeLists.txt:137 (caffe_generate_export_configs)

-- Configuring incomplete, errors occurred!

我想在安卓上面试一下效果，能指导一下思路么

Hi,
This is an interesting application of mtcnn!

I have tried running the caffemodels on a simple car image , however the plate detection is getting missed after passing 48-net stage. Also faces are getting detected!
Are the following caffemodels for face detection? :-
train_mtcnn_LPR / 12train_wm_lpr /det1.caffemodel
train_mtcnn_LPR / 24train_wm_lpr /det2.caffemodel
If so could you please share the license plate caffemodels?
Also the regressed points are kind of random.
Thank you!

你好，

我想试看楼主代码的效果，但是无法在Windows 8.1 Visual Studio 2015 x64 CUDA环境下编译成功。

所以，无法达到第九步来启动ocr_test.ext进行测试，便已经在VS2015内有MSB3721错误，如下图：

我的Project路径在：
_D:\GitHub\ALPR\License-Plate-Detect-Recognition-via-Deep-Neural-Networks-accuracy-up-to-99.9-master\caffe-vsproj_

我的第三方库路径在：
_D:\GitHub\ALPR\License-Plate-Detect-Recognition-via-Deep-Neural-Networks-accuracy-up-to-99.9-master\caffe-vsproj\3rdparty20180726_

Visual Studio 2015 Project Compilation MSB3721 Error Log （在附件内）：
Visual Studio 2015 Project Compilation Error MSB3721 Log.txt

想请问这个问题该如何解决呢？

谢谢！

你好，作者，我想知道你的点回归，再最后一层是怎么完成的？刚开始的效果理想吗？训练的时候对于回归的数据是怎么处理的？

下载下来后，很多环境需要配置，大佬能给个教程吗？

感谢大神，不过不知道大神用什么License开源呢？

你好，请问，ocr_test只支持mtcnn_lstm_ctc吗？基于字符分割的方法没有代码？

作者您好，很谢谢您开源这个项目，不过有一些不明白，想请问MTCNN您是怎么训练的，ONET训练中需要的landmark点是怎么决定的呢？

不懂的加我QQ。627902932

do you accept the making of the working program?

if yes please email me at [email protected] and we negotiate

thank you
best regards,
albert dalton

Any plan to publish the training data?

作者您好，请问源代码的入口在哪个文件，我想改成linux版本，感觉文件有点多，cmake也不是很熟悉，所以直接问您了，请求帮助，谢谢！

请问有人跑通了这个项目吗，需要怎么修改和配置才能实现这个项目，求详细教程

我想请教下,车牌数据下载下来文件损失,是不是要所有的样本下载下来一起解压?还是咋回事,有大佬知道吗?

如果用cmake管理工程就好了 可以跨平台

请问有Linux下版本吗，Linux下该怎么运行