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windows_vs2019_build.en.md

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Visual Studio 2022 Community CMake Compilation Guide

PaddleOCR has been tested on Windows using Visual Studio 2022 Community. Microsoft started supporting direct CMake project management from Visual Studio 2017, but it wasn't fully stable and reliable until 2019. If you want to use CMake for project management and compilation, we recommend using Visual Studio 2022.

**All examples below assume the working directory is D:\projects\cpp.**

1. Environment Preparation

1.1 Install Required Dependencies

  • Visual Studio 2019 or newer
  • CUDA 10.2, cuDNN 7+ (only required for the GPU version of the prediction library). Additionally, the NVIDIA Computing Toolkit must be installed, and the NVIDIA cuDNN library must be downloaded.
  • CMake 3.22+

Ensure that the above dependencies are installed before proceeding. In this tutorial the Community Edition of VS2022 was used.

1.2 Download PaddlePaddle C++ Prediction Library and OpenCV

1.2.1 Download PaddlePaddle C++ Prediction Library

PaddlePaddle C++ prediction libraries offer different precompiled versions for various CPU and CUDA configurations. Download the appropriate version from: C++ Prediction Library Download List

After extraction, the D:\projects\paddle_inference directory should contain:

paddle_inference
├── paddle # Core Paddle library and header files
|
├── third_party # Third-party dependencies and headers
|
└── version.txt # Version and compilation information

1.2.2 Install and Configure OpenCV

  1. Download OpenCV for Windows from the official release page.
  2. Run the downloaded executable and extract OpenCV to a specified directory, e.g., D:\projects\cpp\opencv.

1.2.3 Download PaddleOCR Code

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

2. Running the Project

Step 1: Create a Visual Studio Project

Once CMake is installed, open the cmake-gui application. Specify the source code directory in the first input box and the build output directory in the second input box.

step1

Step 2: Run CMake Configuration

Click the Configure button at the bottom of the interface. The first time you run it, a prompt will appear asking for the Visual Studio configuration. Select your Visual Studio version and set the target platform to x64. Click Finish to start the configuration process.

step2

The first run will result in errors, which is expected. You now need to configure OpenCV and the prediction library.

  • For CPU version, configure the following variables:

    • OPENCV_DIR: Path to the OpenCV lib folder
    • OpenCV_DIR: Same as OPENCV_DIR
    • PADDLE_LIB: Path to the paddle_inference folder

  • For GPU version, configure additional variables:

    • CUDA_LIB: CUDA path, e.g., C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\lib\x64
    • CUDNN_LIB: Path to extracted CuDNN library, e.g., D:\CuDNN-8.9.7.29
    • TENSORRT_DIR: Path to extracted TensorRT, e.g., D:\TensorRT-8.0.1.6
    • WITH_GPU: Check this option
    • WITH_TENSORRT: Check this option

Example configuration:

step3

Once configured, click Configure again.

Note:

  1. If using openblas, uncheck WITH_MKL.
  2. If you encounter the error unable to access 'https://github.com/LDOUBLEV/AutoLog.git/': gnutls_handshake() failed, update deploy/cpp_infer/external-cmake/auto-log.cmake to use https://gitee.com/Double_V/AutoLog.

Step 3: Generate Visual Studio Project

Click Generate to create the .sln file for the Visual Studio project. step4

Click Open Project to launch the project in Visual Studio. The interface should look like this: step5

Before building the solution, perform the following steps:

  1. Change Debug to Release mode.
  2. Download dirent.h and copy it to the Visual Studio include directory, e.g., C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\include.

Click Build -> Build Solution. Once completed, the ppocr.exe file should appear in the build/Release/ folder.

Before running, copy the following files to build/Release/:

  1. paddle_inference/paddle/lib/paddle_inference.dll
  2. paddle_inference/paddle/lib/common.dll
  3. paddle_inference/third_party/install/mklml/lib/mklml.dll
  4. paddle_inference/third_party/install/mklml/lib/libiomp5md.dll
  5. paddle_inference/third_party/install/onednn/lib/mkldnn.dll
  6. opencv/build/x64/vc15/bin/opencv_world455.dll
  7. If using the openblas version, also copy paddle_inference/third_party/install/openblas/lib/openblas.dll.

Step 4: Run the Prediction

The compiled executable is located in the build/Release/ directory. Open cmd and navigate to D:\projects\cpp\PaddleOCR\deploy\cpp_infer\:

cd /d D:\projects\cpp\PaddleOCR\deploy\cpp_infer

Run the prediction using ppocr.exe. For more usage details, refer to the to the Instructions section of running the demo.

# Switch terminal encoding to UTF-8
CHCP 65001

# If using PowerShell, run this command before execution to fix character encoding issues:
$OutputEncoding = [console]::InputEncoding = [console]::OutputEncoding = New-Object System.Text.UTF8Encoding

# Execute prediction
.\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


Sample result:

result

FAQ

  • Issue: Application fails to start with error (0xc0000142) and cmd output shows You are using Paddle compiled with TensorRT, but TensorRT dynamic library is not found.
  • Solution: Copy all .dll files from the TensorRT directory's lib folder into the release directory and try running it again.