145 lines
6.3 KiB
Markdown
145 lines
6.3 KiB
Markdown
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---
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comments: true
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---
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# Visual Studio 2019 Community CMake Compilation Guide
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PaddleOCR is tested on Windows based on `Visual Studio 2019 Community`. Microsoft has supported direct management of `CMake` cross-platform compilation projects since `Visual Studio 2017`, but it was not until `2019` that stable and complete support was provided, so if you want to use CMake to manage project compilation and build, we recommend that you use the `Visual Studio 2019` environment to build.
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**All the examples below are demonstrated with the working directory as `D:\projects\cpp`**.
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## 1. Environment Preparation
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### 1.1 Install the required environment
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- Visual Studio 2019
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- CUDA 10.2, cudnn 7+ (only required when using the GPU version of the prediction library)
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- CMake 3.22+
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Please make sure the system has the above basic software installed. We use the community version of `VS2019`.
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### 1.2 Download PaddlePaddle C++ prediction library and Opencv
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#### 1.2.1 Download PaddlePaddle C++ prediction library
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PaddlePaddle C++ prediction library provides different precompiled versions for different `CPU` and `CUDA` versions. Please download according to the actual situation: [C++ prediction library download list](https://www.paddlepaddle.org.cn/inference/master/guides/install/download_lib.html#windows)
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After decompression, the `D:\projects\paddle_inference` directory contains the following contents:
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```
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paddle_inference
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├── paddle # paddle core library and header files
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├── third_party # third-party dependent libraries and header files
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└── version.txt # version and compilation information
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```
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#### 1.2.2 Install and configure OpenCV
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1. Download Opencv for Windows platform from the OpenCV official website, [Download address](https://github.com/opencv/opencv/releases)
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2. Run the downloaded executable file and unzip OpenCV to the specified directory, such as `D:\projects\cpp\opencv`
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#### 1.2.3 Download PaddleOCR code
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```bash linenums="1"
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git clone -b dygraph https://github.com/PaddlePaddle/PaddleOCR
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```
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## 2. Start running
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### Step1: Build Visual Studio project
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After cmake is installed, there will be a cmake-gui program in the system. Open cmake-gui, fill in the source code path in the first input box, and fill in the compilation output path in the second input box
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### Step2: Execute cmake configuration
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Click the `Configure` button at the bottom of the interface. The first click will pop up a prompt box for Visual Studio configuration, as shown below. Select your Visual Studio version is fine, and the target platform is x64. Then click the `finish` button to start the automatic configuration.
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The first execution will report an error, which is normal. Next, configure Opencv and the prediction library
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- For cpu version, only the three parameters OPENCV_DIR, OpenCV_DIR, and PADDLE_LIB need to be considered
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- OPENCV_DIR: Fill in the location of the opencv lib folder
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- OpenCV_DIR: Fill in the location of the opencv lib folder
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- PADDLE_LIB: The location of the paddle_inference folder
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- For GPU version, on the basis of the cpu version, the following variables need to be filled in
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CUDA_LIB, CUDNN_LIB, TENSORRT_DIR, WITH_GPU, WITH_TENSORRT
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- CUDA_LIB: CUDA address, such as `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\lib\x64`
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- CUDNN_LIB: The same as CUDA_LIB
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- TENSORRT_DIR: The location where TRT is unzipped after downloading, such as `D:\TensorRT-8.0.1.6`
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- WITH_GPU: Check
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- WITH_TENSORRT: Check
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The configured screenshot is as follows
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After the configuration is completed, click the `Configure` button again.
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**Note:**
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1. If you are using the `openblas` version, please uncheck `WITH_MKL`
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2. If you encounter 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 <https://gitee.com/Double_V/AutoLog> address.
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### Step3: Generate Visual Studio Project
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Click the `Generate` button to generate the sln file of the Visual Studio project.
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Click the `Open Project` button to open the project in Visual Studio. The screenshot after opening is as follows
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Before starting to generate the solution, perform the following steps:
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1. Change `Debug` to `Release`
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2. Download [dirent.h](https://paddleocr.bj.bcebos.com/deploy/cpp_infer/cpp_files/dirent.h) and copy it to the include folder of Visual Studio, such as `C:\Program Files (x86)\Microsoft Visual Studio\2019\Community\VC\Auxiliary\VS\include`.
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Click `Build->Generate Solution`, and you can see the `ppocr.exe` file in the `build/Release/` folder.
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Before running, copy the following files to the `build/Release/` folder
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1. `paddle_inference/paddle/lib/paddle_inference.dll`
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2. `paddle_inference/third_party/install/onnxruntime/lib/onnxruntime.dll`
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3. `paddle_inference/third_party/install/paddle2onnx/lib/paddle2onnx.dll`
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4. `opencv/build/x64/vc15/bin/opencv_world455.dll`
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5. If you use the prediction library of the openblas version, you also need to copy `paddle_inference/third_party/install/openblas/lib/openblas.dll`
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### Step4: Prediction
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The above `Visual Studio The executable file compiled by 2019 is in the directory of build/Release/. Open cmd and switch to D:\projects\cpp\PaddleOCR\deploy\cpp_infer\:
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cd /d D:\projects\cpp\PaddleOCR\deploy\cpp_infer
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The executable file ppocr.exe is the sample prediction program. Its main usage is as follows. For more usage, please refer to the [Instructions](./cpp_infer.en.md) section of running demo.
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```bash linenums="1"
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# Switch terminal encoding to utf8
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CHCP 65001
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# Execute prediction
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.\build\Release\ppocr.exe system --det_model_dir=D:\projects\cpp\ch_PP-OCRv2_det_slim_quant_infer --rec_model_dir=D:\projects\cpp\ch_PP-OCRv2_rec_slim_quant_infer --image_dir=D:\projects\cpp\PaddleOCR\doc\imgs\11.jpg
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```
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The recognition result is as follows
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## FAQ
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- When running, a pop-up window prompts `The application cannot be started normally (0xc0000142)`, and the `cmd` window prompts `You are using Paddle compiled with TensorRT, but TensorRT dynamic library is not found.`, copy all the dll files in the lib in the tensor directory to the release directory, and run it again.
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