Server-side C++ Inference

This chapter introduces the C++ deployment steps of the PaddleOCR model. C++ is better than Python in terms of performance. Therefore, in CPU and GPU deployment scenarios, C++ deployment is mostly used. This section will introduce how to configure the C++ environment and deploy PaddleOCR in Linux (CPU\GPU) environment. For Windows deployment please refer to Windows compilation guidelines.

1. Prepare the Environment

1.1 Environment

• Linux, docker is recommended.
• Windows.

1.2 Compile OpenCV

• First of all, you need to download the source code compiled package in the Linux environment from the OpenCV official website. Taking OpenCV 3.4.7 as an example, the download command is as follows.

cd deploy/cpp_infer
wget https://paddleocr.bj.bcebos.com/libs/opencv/opencv-3.4.7.tar.gz
tar -xf opencv-3.4.7.tar.gz

Finally, you will see the folder of opencv-3.4.7/ in the current directory.

• Compile OpenCV, the OpenCV source path (root_path) and installation path (install_path) should be set by yourself. Enter the OpenCV source code path and compile it in the following way.

root_path=your_opencv_root_path
install_path=${root_path}/opencv3

rm -rf build
mkdir build
cd build

cmake .. \
    -DCMAKE_INSTALL_PREFIX=${install_path} \
    -DCMAKE_BUILD_TYPE=Release \
    -DBUILD_SHARED_LIBS=OFF \
    -DWITH_IPP=OFF \
    -DBUILD_IPP_IW=OFF \
    -DWITH_LAPACK=OFF \
    -DWITH_EIGEN=OFF \
    -DCMAKE_INSTALL_LIBDIR=lib64 \
    -DWITH_ZLIB=ON \
    -DBUILD_ZLIB=ON \
    -DWITH_JPEG=ON \
    -DBUILD_JPEG=ON \
    -DWITH_PNG=ON \
    -DBUILD_PNG=ON \
    -DWITH_TIFF=ON \
    -DBUILD_TIFF=ON

make -j
make install

In the above commands, root_path is the downloaded OpenCV source code path, and install_path is the installation path of OpenCV. After make install is completed, the OpenCV header file and library file will be generated in this folder for later OCR source code compilation.

The final file structure under the OpenCV installation path is as follows.

opencv3/
|-- bin
|-- include
|-- lib
|-- lib64
|-- share

1.3 Compile or Download or the Paddle Inference Library

• There are 2 ways to obtain the Paddle inference library, described in detail below.

1.3.1 Direct download and installation

Paddle inference library official website. You can review and select the appropriate version of the inference library on the official website.

• After downloading, use the following command to extract files.

tar -xf paddle_inference.tgz

Finally you will see the folder of paddle_inference/ in the current path.

1.3.2 Compile the inference source code

• If you want to get the latest Paddle inference library features, you can download the latest code from Paddle GitHub repository and compile the inference library from the source code. It is recommended to download the inference library with paddle version greater than or equal to 2.0.1.

• You can refer to Paddle inference library to get the Paddle source code from GitHub, and then compile To generate the latest inference library. The method of using git to access the code is as follows.

git clone https://github.com/PaddlePaddle/Paddle.git
git checkout develop

• Enter the Paddle directory and run the following commands to compile the paddle inference library.

rm -rf build
mkdir build
cd build

cmake  .. \
    -DWITH_CONTRIB=OFF \
    -DWITH_MKL=ON \
    -DWITH_MKLDNN=ON  \
    -DWITH_TESTING=OFF \
    -DCMAKE_BUILD_TYPE=Release \
    -DWITH_INFERENCE_API_TEST=OFF \
    -DON_INFER=ON \
    -DWITH_PYTHON=ON
make -j
make inference_lib_dist

For more compilation parameter options, please refer to the document.

• After the compilation process, you can see the following files in the folder of build/paddle_inference_install_dir/.

build/paddle_inference_install_dir/
|-- CMakeCache.txt
|-- paddle
|-- third_party
|-- version.txt

paddle is the Paddle library required for C++ prediction later, and version.txt contains the version information of the current inference library.

2. Compile and Run the Demo

2.1 Export the inference model

• You can refer to Model inference and export the inference model. After the model is exported, assuming it is placed in the inference directory, the directory structure is as follows.

inference/
|-- det_db
|   |--inference.pdiparams
|   |--inference.pdmodel
|-- rec_rcnn
|   |--inference.pdiparams
|   |--inference.pdmodel
|-- cls
|   |--inference.pdiparams
|   |--inference.pdmodel
|-- table
|   |--inference.pdiparams
|   |--inference.pdmodel
|-- layout
|   |--inference.pdiparams
|   |--inference.pdmodel

2.2 Compile PaddleOCR C++ inference demo

• The compilation commands are as follows. The addresses of Paddle C++ inference library, opencv and other Dependencies need to be replaced with the actual addresses on your own machines.

sh tools/build.sh

Specifically, you should modify the paths in tools/build.sh. The related content is as follows.

OPENCV_DIR=your_opencv_dir
LIB_DIR=your_paddle_inference_dir
CUDA_LIB_DIR=your_cuda_lib_dir
CUDNN_LIB_DIR=your_cudnn_lib_dir

OPENCV_DIR is the OpenCV installation path; LIB_DIR is the download (paddle_inference folder) or the generated Paddle inference library path (build/paddle_inference_install_dir folder); CUDA_LIB_DIR is the CUDA library file path, in docker; it is /usr/local/cuda/lib64; CUDNN_LIB_DIR is the cuDNN library file path, in docker it is /usr/lib/x86_64-linux-gnu/.

• After the compilation is completed, an executable file named ppocr will be generated in the build folder.

2.3 Run the demo

Execute the built executable file:

./build/ppocr [--param1] [--param2] [...]

Note:ppocr uses the PP-OCRv3 model by default, and the input shape used by the recognition model is 3, 48, 320, if you want to use the old version model, you should add the parameter --rec_img_h=32.

Specifically,

1. det+cls+rec

./build/ppocr --det_model_dir=inference/det_db \
    --rec_model_dir=inference/rec_rcnn \
    --cls_model_dir=inference/cls \
    --image_dir=../../doc/imgs/12.jpg \
    --use_angle_cls=true \
    --det=true \
    --rec=true \
    --cls=true \

2. det+rec

./build/ppocr --det_model_dir=inference/det_db \
    --rec_model_dir=inference/rec_rcnn \
    --image_dir=../../doc/imgs/12.jpg \
    --use_angle_cls=false \
    --det=true \
    --rec=true \
    --cls=false \

3. det

./build/ppocr --det_model_dir=inference/det_db \
    --image_dir=../../doc/imgs/12.jpg \
    --det=true \
    --rec=false

4. cls+rec

./build/ppocr --rec_model_dir=inference/rec_rcnn \
    --cls_model_dir=inference/cls \
    --image_dir=../../doc/imgs_words/ch/word_1.jpg \
    --use_angle_cls=true \
    --det=false \
    --rec=true \
    --cls=true \

5. rec

./build/ppocr --rec_model_dir=inference/rec_rcnn \
    --image_dir=../../doc/imgs_words/ch/word_1.jpg \
    --use_angle_cls=false \
    --det=false \
    --rec=true \
    --cls=false \

6. cls

./build/ppocr --cls_model_dir=inference/cls \
    --cls_model_dir=inference/cls \
    --image_dir=../../doc/imgs_words/ch/word_1.jpg \
    --use_angle_cls=true \
    --det=false \
    --rec=false \
    --cls=true \

7. layout+table

./build/ppocr --det_model_dir=inference/det_db \
    --rec_model_dir=inference/rec_rcnn \
    --table_model_dir=inference/table \
    --image_dir=../../ppstructure/docs/table/table.jpg \
    --layout_model_dir=inference/layout \
    --type=structure \
    --table=true \
    --layout=true

8. layout

./build/ppocr --layout_model_dir=inference/layout \
    --image_dir=../../ppstructure/docs/table/1.png \
    --type=structure \
    --table=false \
    --layout=true \
    --det=false \
    --rec=false

9. table

./build/ppocr --det_model_dir=inference/det_db \
    --rec_model_dir=inference/rec_rcnn \
    --table_model_dir=inference/table \
    --image_dir=../../ppstructure/docs/table/table.jpg \
    --type=structure \
    --table=true

More parameters are as follows,

Common parameters

parameter	data type	default	meaning
use_gpu	bool	false	Whether to use GPU
gpu_id	int	0	GPU id when use_gpu is true
gpu_mem	int	4000	GPU memory requested
cpu_math_library_num_threads	int	10	Number of threads when using CPU inference. When machine cores is enough, the large the value, the faster the inference speed
enable_mkldnn	bool	true	Whether to use mkdlnn library
output	str	./output	Path where visualization results are saved

forward

parameter	data type	default	meaning
det	bool	true	Whether to perform text detection in the forward direction
rec	bool	true	Whether to perform text recognition in the forward direction
cls	bool	false	Whether to perform text direction classification in the forward direction

Detection related parameters

parameter	data type	default	meaning
det_model_dir	string	-	Address of detection inference model
max_side_len	int	960	Limit the maximum image height and width to 960
det_db_thresh	float	0.3	Used to filter the binarized image of DB prediction, setting 0.-0.3 has no obvious effect on the result
det_db_box_thresh	float	0.5	DB post-processing filter box threshold, if there is a missing box detected, it can be reduced as appropriate
det_db_unclip_ratio	float	1.6	Indicates the compactness of the text box, the smaller the value, the closer the text box to the text
det_db_score_mode	string	slow	slow: use polygon box to calculate bbox score, fast: use rectangle box to calculate. Use rectangular box to calculate faster, and polygonal box more accurate for curved text area.
visualize	bool	true	Whether to visualize the results，when it is set as true, the prediction results will be saved in the folder specified by the output field on an image with the same name as the input image.

Classifier related parameters

parameter	data type	default	meaning
use_angle_cls	bool	false	Whether to use the direction classifier
cls_model_dir	string	-	Address of direction classifier inference model
cls_thresh	float	0.9	Score threshold of the direction classifier
cls_batch_num	int	1	batch size of classifier

Recognition related parameters

parameter	data type	default	meaning
rec_model_dir	string	-	Address of recognition inference model
rec_char_dict_path	string	../../ppocr/utils/ppocr_keys_v1.txt	dictionary file
rec_batch_num	int	6	batch size of recognition
rec_img_h	int	48	image height of recognition
rec_img_w	int	320	image width of recognition

Layout related parameters

parameter	data type	default	meaning
layout_model_dir	string	-	Address of layout inference model
layout_dict_path	string	../../ppocr/utils/dict/layout_dict/layout_publaynet_dict.txt	dictionary file
layout_score_threshold	float	0.5	Threshold of score.
layout_nms_threshold	float	0.5	Threshold of nms.

Table recognition related parameters

to

parameter	data type	default	meaning
table_model_dir	string	-	Address of table recognition inference model
table_char_dict_path	string	../../ppocr/utils/dict/table_structure_dict.txt	dictionary file
table_max_len	int	488	The size of the long side of the input image of the table recognition model, the final input image size of the network is（table_max_len，table_max_len）
merge_no_span_structure	bool	true	Whether to merge	and	</td

Multi-language inference is also supported in PaddleOCR, you can refer to recognition tutorial for more supported languages and models in PaddleOCR. Specifically, if you want to infer using multi-language models, you just need to modify values of rec_char_dict_path and rec_model_dir.

The detection results will be shown on the screen, which is as follows.

predict img: ../../doc/imgs/12.jpg
../../doc/imgs/12.jpg
0       det boxes: [[74,553],[427,542],[428,571],[75,582]] rec text: 打浦路252935号 rec score: 0.947724
1       det boxes: [[23,507],[513,488],[515,529],[24,548]] rec text: 绿洲仕格维花园公寓 rec score: 0.993728
2       det boxes: [[187,456],[399,448],[400,480],[188,488]] rec text: 打浦路15号 rec score: 0.964994
3       det boxes: [[42,413],[483,391],[484,428],[43,450]] rec text: 上海斯格威铂尔大酒店 rec score: 0.980086
The detection visualized image saved in ./output//12.jpg

• layout+table

predict img: ../../ppstructure/docs/table/1.png
0       type: text, region: [12,729,410,848], score: 0.781044, res: count of ocr result is : 7
********** print ocr result **********
0       det boxes: [[4,1],[79,1],[79,12],[4,12]] rec text: CTW1500. rec score: 0.769472
...
6       det boxes: [[4,99],[391,99],[391,112],[4,112]] rec text: sate-of-the-artmethods[12.34.36l.ourapproachachieves rec score: 0.90414
********** end print ocr result **********
1       type: text, region: [69,342,342,359], score: 0.703666, res: count of ocr result is : 1
********** print ocr result **********
0       det boxes: [[8,2],[269,2],[269,13],[8,13]] rec text: Table6.Experimentalresults on CTW-1500 rec score: 0.890454
********** end print ocr result **********
2       type: text, region: [70,316,706,332], score: 0.659738, res: count of ocr result is : 2
********** print ocr result **********
0       det boxes: [[373,2],[630,2],[630,11],[373,11]] rec text: oroposals.andthegreencontoursarefinal rec score: 0.919729
1       det boxes: [[8,3],[357,3],[357,11],[8,11]] rec text: Visualexperimentalresultshebluecontoursareboundar rec score: 0.915963
********** end print ocr result **********
3       type: text, region: [489,342,789,359], score: 0.630538, res: count of ocr result is : 1
********** print ocr result **********
0       det boxes: [[8,2],[294,2],[294,14],[8,14]] rec text: Table7.Experimentalresults onMSRA-TD500 rec score: 0.942251
********** end print ocr result **********
4       type: text, region: [444,751,841,848], score: 0.607345, res: count of ocr result is : 5
********** print ocr result **********
0       det boxes: [[19,3],[389,3],[389,17],[19,17]] rec text: Inthispaper,weproposeanovel adaptivebound rec score: 0.941031
1       det boxes: [[4,22],[390,22],[390,36],[4,36]] rec text: aryproposalnetworkforarbitraryshapetextdetection rec score: 0.960172
2       det boxes: [[4,42],[392,42],[392,56],[4,56]] rec text: whichadoptanboundaryproposalmodeltogeneratecoarse rec score: 0.934647
3       det boxes: [[4,61],[389,61],[389,75],[4,75]] rec text: ooundaryproposals,andthenadoptanadaptiveboundary rec score: 0.946296
4       det boxes: [[5,80],[387,80],[387,93],[5,93]] rec text: leformationmodelcombinedwithGCNandRNNtoper rec score: 0.952401
********** end print ocr result **********
5       type: title, region: [444,705,564,724], score: 0.785429, res: count of ocr result is : 1
********** print ocr result **********
0       det boxes: [[6,2],[113,2],[113,14],[6,14]] rec text: 5.Conclusion rec score: 0.856903
********** end print ocr result **********
6       type: table, region: [14,360,402,711], score: 0.963643, res: <html><body><table><thead><tr><td>Methods</td><td>Ext</td><td>R</td><td>P</td><td>F</td><td>FPS</td></tr></thead><tbody><tr><td>TextSnake [18]</td><td>Syn</td><td>85.3</td><td>67.9</td><td>75.6</td><td></td></tr><tr><td>CSE [17]</td><td>MiLT</td><td>76.1</td><td>78.7</td><td>77.4</td><td>0.38</td></tr><tr><td>LOMO[40]</td><td>Syn</td><td>76.5</td><td>85.7</td><td>80.8</td><td>4.4</td></tr><tr><td>ATRR[35]</td><td>Sy-</td><td>80.2</td><td>80.1</td><td>80.1</td><td>-</td></tr><tr><td>SegLink++ [28]</td><td>Syn</td><td>79.8</td><td>82.8</td><td>81.3</td><td>-</td></tr><tr><td>TextField [37]</td><td>Syn</td><td>79.8</td><td>83.0</td><td>81.4</td><td>6.0</td></tr><tr><td>MSR[38]</td><td>Syn</td><td>79.0</td><td>84.1</td><td>81.5</td><td>4.3</td></tr><tr><td>PSENet-1s [33]</td><td>MLT</td><td>79.7</td><td>84.8</td><td>82.2</td><td>3.9</td></tr><tr><td>DB [12]</td><td>Syn</td><td>80.2</td><td>86.9</td><td>83.4</td><td>22.0</td></tr><tr><td>CRAFT [2]</td><td>Syn</td><td>81.1</td><td>86.0</td><td>83.5</td><td>-</td></tr><tr><td>TextDragon [5]</td><td>MLT+</td><td>82.8</td><td>84.5</td><td>83.6</td><td></td></tr><tr><td>PAN [34]</td><td>Syn</td><td>81.2</td><td>86.4</td><td>83.7</td><td>39.8</td></tr><tr><td>ContourNet [36]</td><td></td><td>84.1</td><td>83.7</td><td>83.9</td><td>4.5</td></tr><tr><td>DRRG [41]</td><td>MLT</td><td>83.02</td><td>85.93</td><td>84.45</td><td>-</td></tr><tr><td>TextPerception[23]</td><td>Syn</td><td>81.9</td><td>87.5</td><td>84.6</td><td></td></tr><tr><td>Ours</td><td> Syn</td><td>80.57</td><td>87.66</td><td>83.97</td><td>12.08</td></tr><tr><td>Ours</td><td></td><td>81.45</td><td>87.81</td><td>84.51</td><td>12.15</td></tr><tr><td>Ours</td><td>MLT</td><td>83.60</td><td>86.45</td><td>85.00</td><td>12.21</td></tr></tbody></table></body></html>
The table visualized image saved in ./output//6_1.png
7       type: table, region: [462,359,820,657], score: 0.953917, res: <html><body><table><thead><tr><td>Methods</td><td>R</td><td>P</td><td>F</td><td>FPS</td></tr></thead><tbody><tr><td>SegLink [26]</td><td>70.0</td><td>86.0</td><td>77.0</td><td>8.9</td></tr><tr><td>PixelLink [4]</td><td>73.2</td><td>83.0</td><td>77.8</td><td>-</td></tr><tr><td>TextSnake [18]</td><td>73.9</td><td>83.2</td><td>78.3</td><td>1.1</td></tr><tr><td>TextField [37]</td><td>75.9</td><td>87.4</td><td>81.3</td><td>5.2 </td></tr><tr><td>MSR[38]</td><td>76.7</td><td>87.4</td><td>81.7</td><td>-</td></tr><tr><td>FTSN[3]</td><td>77.1</td><td>87.6</td><td>82.0</td><td>:</td></tr><tr><td>LSE[30]</td><td>81.7</td><td>84.2</td><td>82.9</td><td></td></tr><tr><td>CRAFT [2]</td><td>78.2</td><td>88.2</td><td>82.9</td><td>8.6</td></tr><tr><td>MCN [16]</td><td>79</td><td>88</td><td>83</td><td>-</td></tr><tr><td>ATRR[35]</td><td>82.1</td><td>85.2</td><td>83.6</td><td>-</td></tr><tr><td>PAN [34]</td><td>83.8</td><td>84.4</td><td>84.1</td><td>30.2</td></tr><tr><td>DB[12]</td><td>79.2</td><td>91.5</td><td>84.9</td><td>32.0</td></tr><tr><td>DRRG [41]</td><td>82.30</td><td>88.05</td><td>85.08</td><td>-</td></tr><tr><td>Ours (SynText)</td><td>80.68</td><td>85.40</td><td>82.97</td><td>12.68</td></tr><tr><td>Ours (MLT-17)</td><td>84.54</td><td>86.62</td><td>85.57</td><td>12.31</td></tr></tbody></table></body></html>
The table visualized image saved in ./output//7_1.png
8       type: figure, region: [14,3,836,310], score: 0.969443, res: count of ocr result is : 26
********** print ocr result **********
0       det boxes: [[506,14],[539,15],[539,22],[506,21]] rec text: E rec score: 0.318073
...
25      det boxes: [[680,290],[759,288],[759,303],[680,305]] rec text: (d) CTW1500 rec score: 0.95911
********** end print ocr result **********

3. FAQ

1. Encountered the error unable to access 'https://github.com/LDOUBLEV/AutoLog.git/': gnutls_handshake() failed: The TLS connection was non-properly terminated., change the github address in deploy/cpp_infer/external-cmake/auto-log.cmake to the https://gitee.com/Double_V/AutoLog address.

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