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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

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

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 \

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 前向是否执行文字检测
rec bool true 前向是否执行文字识别
cls bool false 前向是否执行文字方向分类
  • 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
  • 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

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.