You can find `cmake-3.22.0/` folder in the current directory.
- To compile cmake, first set the source path of `cmake` (`root_path`) and installation path (`install_path`). In this example, the source path is `cmake-3.22.0/` in the current directory.
- First, download the package for source compilation in Linux environment from the official website of opencv. Taking version 3.4.7 as an example, follow the command below to download and unzip it:
You can find`opencv-3.4.7/`folder in the current directory.
- To compile opencv, first set the source path of opencv(`root_path`) and installation path (`install_path`). In this example, the source path is`opencv-3.4.7/`in the current directory.
```
cd ./opencv-3.4.7
export root_path=$PWD
export install_path=${root_path}/opencv3
```
- Then compile under the source path as follows:
```shell
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
```
- After `make install` is done, opencv header and library files will be generated in this folder for later compilation of PaddleClas code.
For opencv version 3.4.7, the final file structure under the installation path is shown below. **Note**: The following file structure may vary for different opencv versions.
#### 1.3.1 Compile the Source of Inference Library
- To obtain the latest features of the inference library, you can clone the latest code from Paddle github and compile the source code of the library.
- Please refer to the website of [Paddle Inference Library](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_ and_install_lib_cn.html#id16) to get Paddle code from github and then compile it to generate the latest inference library. The method to obtain the code using git is as follows.
- Adopt the following method to compile after entering Paddle directory.
```shell
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
```
See the official website of [Paddle C++ Inference Library](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/guides/05_inference_deployment/inference/build_and_install_lib_cn.html#id16) for more compilation parameters.
- The following files and folders can be found generated under `build/paddle_inference_install_dir/` after compilation.
```
build/paddle_inference_install_dir/
|-- CMakeCache.txt
|-- paddle
|-- third_party
|-- version.txt
```
`paddle` is the Paddle library needed for later C++ inference, and `version.txt` contains the version information of the current inference library.
- The Linux inference library of different cuda versions are available on the official website of [Paddle Inference Library ](https://paddle-inference.readthedocs.io/en/latest/user_guides/download_lib.html), where you can choose the appropriate version. Note that you must select the `develop` version.
For the `develop` version of `https://paddle-inference-lib.bj.bcebos.com/2.1.1-gpu-cuda10.2-cudnn8.1-mkl-gcc8.2/paddle_inference.tgz`, use the following command to download and unzip it:
Please install `openblas` before `faiss`, the installation command in `ubuntu` system is as follows:
```
apt-get install libopenblas-dev
```
Note that this tutorial installs the cpu version of faiss as an example, please install it as your need by referring to the official documents of [faiss](https://github.com/facebookresearch/faiss).
The command is as follows, where the address of Paddle C++ inference library, opencv and other dependency libraries need to be replaced with the actual address on your own machine. Also, you need to download and compile `yaml-cpp` and other C++ libraries during the compilation, so please keep the network unblocked.
```shell
sh tools/build.sh
```
Specifically, the contents of `tools/build.sh` are as follows, please modify according to the specific path.
```shell
OPENCV_DIR=${opencv_install_dir}
LIB_DIR=${paddle_inference_dir}
CUDA_LIB_DIR=/usr/local/cuda/lib64
CUDNN_LIB_DIR=/usr/lib/x86_64-linux-gnu/
FAISS_DIR=${faiss_install_dir}
FAISS_WITH_MKL=OFF
BUILD_DIR=build
rm -rf ${BUILD_DIR}
mkdir ${BUILD_DIR}
cd ${BUILD_DIR}
cmake .. \
-DPADDLE_LIB=${LIB_DIR} \
-DWITH_MKL=ON \
-DWITH_GPU=OFF \
-DWITH_STATIC_LIB=OFF \
-DUSE_TENSORRT=OFF \
-DOPENCV_DIR=${OPENCV_DIR} \
-DCUDNN_LIB=${CUDNN_LIB_DIR} \
-DCUDA_LIB=${CUDA_LIB_DIR} \
-DFAISS_DIR=${FAISS_DIR} \
-DFAISS_WITH_MKL=${FAISS_WITH_MKL}
make -j
cd ..
```
In the above commands:
-`OPENCV_DIR` is the address of the opencv compilation and installation (in this case, the path of the `opencv-3.4.7/opencv3` folder).
-`LIB_DIR` is the path of the downloaded Paddle inference library (`paddle_inference` folder), or the generated Paddle inference library after compilation (`build/paddle_inference_install_dir` folder).
-`CUDA_LIB_DIR` is path of the cuda library file, which in docker is `/usr/local/cuda/lib64`.
-`CUDNN_LIB_DIR` is the path of the cudnn library file, which in docker is `/usr/lib/x86_64-linux-gnu/` .
-`TENSORRT_DIR` is the path of the tensorrt library file, which in docker is `/usr/local/TensorRT6-cuda10.0-cudnn7/`. TensorRT needs to be used in combination with GPU.
-`FAISS_DIR` is the installation path of faiss.
-`FAISS_WITH_MKL` means whether mkldnn is used during the compilation of faiss. The compilation in this document employs openbals instead of mkldnn, so it is set to `OFF`, otherwise it is `ON`.
A `build` folder will be created in the current path after the compilation, which generates an executable file named `pp_shitu`.
- Please refer to the [Quick Start of Recognition](../../docs/en/quick_start/quick_start_recognition_en.md), download the corresponding Lightweight Generic Mainbody Detection Model, Lightweight Generic Recognition Model, and the beverage test data and unzip them.
`id_map.txt` is generated in `IndexProcess.index_dir` directory for convenience of C++ reading.
- Execute the program
```shell
./build/pp_shitu -c inference_drink.yaml
# or
./build/pp_shitu -config inference_drink.yaml
```
The following results can be obtained after searching the image set.
At the same time, it should be noticed that a slight difference may occur during the pre-processing of the image due to the version of opencv, resulting in a minor discrepancy in python and c++ results, such as a few pixels for bbox, 3 decimal places for retrieval results, etc. But it has no impact on the final search label.
You can also use your self-trained models. Please refer to [model export](../../docs/en/inference_deployment/export_model_en.md) to export ` inference model` for model inference.
