144 lines
6.1 KiB
Markdown
144 lines
6.1 KiB
Markdown
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English | [简体中文](windows_vs2019_build.md)
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# Visual Studio 2022 Community CMake Compilation Guide
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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`.
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**All examples below assume the working directory is **`D:\projects\cpp`**.**
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## 1. Environment Preparation
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### 1.1 Install Required Dependencies
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- Visual Studio 2019 or newer
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- 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.
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- CMake 3.22+
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Ensure that the above dependencies are installed before proceeding. In this tutorial the Community Edition of `VS2022` was used.
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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 libraries offer different precompiled versions for various `CPU` and `CUDA` configurations. Download the appropriate version from: [C++ Prediction Library Download List](https://www.paddlepaddle.org.cn/inference/master/guides/install/download_lib.html#windows)
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After extraction, the `D:\projects\paddle_inference` directory should contain:
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```
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paddle_inference
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├── paddle # Core Paddle library and header files
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├── third_party # Third-party dependencies and headers
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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 from the [official release page](https://github.com/opencv/opencv/releases).
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2. Run the downloaded executable and extract OpenCV to a specified directory, e.g., `D:\projects\cpp\opencv`.
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#### 1.2.3 Download PaddleOCR Code
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```bash
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git clone https://github.com/PaddlePaddle/Paddle.git
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git checkout develop
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```
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## 2. Running the Project
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### Step 1: Create a Visual Studio Project
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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.
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### Step 2: Run CMake Configuration
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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.
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The first run will result in errors, which is expected. You now need to configure OpenCV and the prediction library.
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- **For CPU version**, configure the following variables:
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- `OPENCV_DIR`: Path to the OpenCV `lib` folder
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- `OpenCV_DIR`: Same as `OPENCV_DIR`
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- `PADDLE_LIB`: Path to the `paddle_inference` folder
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- **For GPU version**, configure additional variables:
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- `CUDA_LIB`: CUDA path, e.g., `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\lib\x64`
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- `CUDNN_LIB`: Path to extracted CuDNN library, e.g., `D:\CuDNN-8.9.7.29`
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- `TENSORRT_DIR`: Path to extracted TensorRT, e.g., `D:\TensorRT-8.0.1.6`
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- `WITH_GPU`: Check this option
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- `WITH_TENSORRT`: Check this option
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Example configuration:
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Once configured, click `Configure` again.
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**Note:**
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1. If using `openblas`, 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`, update `deploy/cpp_infer/external-cmake/auto-log.cmake` to use `https://gitee.com/Double_V/AutoLog`.
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### Step 3: Generate Visual Studio Project
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Click `Generate` to create the `.sln` file for the Visual Studio project.
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Click `Open Project` to launch the project in Visual Studio. The interface should look like this:
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Before building the solution, perform the following steps:
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1. Change `Debug` to `Release` mode.
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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 Visual Studio include directory, e.g., `C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\include`.
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Click `Build -> Build Solution`. Once completed, the `ppocr.exe` file should appear in the `build/Release/` folder.
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Before running, copy the following files to `build/Release/`:
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1. `paddle_inference/paddle/lib/paddle_inference.dll`
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2. `paddle_inference/paddle/lib/common.dll`
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3. `paddle_inference/third_party/install/mklml/lib/mklml.dll`
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4. `paddle_inference/third_party/install/mklml/lib/libiomp5md.dll`
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5. `paddle_inference/third_party/install/onednn/lib/mkldnn.dll`
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6. `opencv/build/x64/vc15/bin/opencv_world455.dll`
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7. If using the `openblas` version, also copy `paddle_inference/third_party/install/openblas/lib/openblas.dll`.
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### Step 4: Run the Prediction
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The compiled executable is located in the `build/Release/` directory. Open `cmd` and navigate to `D:\projects\cpp\PaddleOCR\deploy\cpp_infer\`:
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```
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cd /d D:\projects\cpp\PaddleOCR\deploy\cpp_infer
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```
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Run the prediction using `ppocr.exe`. For more usage details, refer to the [documentation](../readme_ch.md).
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```shell
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# Switch terminal encoding to UTF-8
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CHCP 65001
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# If using PowerShell, run this command before execution to fix character encoding issues:
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$OutputEncoding = [console]::InputEncoding = [console]::OutputEncoding = New-Object System.Text.UTF8Encoding
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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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<br>
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## Sample result:
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## FAQ
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- **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.`
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- **Solution:** Copy all `.dll` files from the `TensorRT` directory's `lib` folder into the `release` directory and try running it again.
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