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42
examples/vision/segmentation/ppmatting/README.md
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examples/vision/segmentation/ppmatting/README.md
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English | [简体中文](README_CN.md)
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# PP-Matting Model Deployment
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## Model Description
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- [PP-Matting Release/2.6](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting)
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## List of Supported Models
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Now FastDeploy supports the deployment of the following models
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- [PP-Matting models](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting)
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- [PP-HumanMatting models](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting)
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- [ModNet models](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting)
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## Export Deployment Model
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Before deployment, PP-Matting needs to be exported into the deployment model. Refer to [Export Model](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting) for more information. (Tips: You need to set the `--input_shape` parameter of the export script when exporting PP-Matting and PP-HumanMatting models)
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## Download Pre-trained Models
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For developers' testing, models exported by PP-Matting are provided below. Developers can download and use them directly.
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The accuracy metric is sourced from the model description in PP-Matting. (Accuracy data are not provided) Refer to the introduction in PP-Matting for more details.
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| Model | Parameter Size | Accuracy | Note |
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|:---------------------------------------------------------------- |:----- |:----- | :------ |
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| [PP-Matting-512](https://bj.bcebos.com/paddlehub/fastdeploy/PP-Matting-512.tgz) | 106MB | - |
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| [PP-Matting-1024](https://bj.bcebos.com/paddlehub/fastdeploy/PP-Matting-1024.tgz) | 106MB | - |
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| [PP-HumanMatting](https://bj.bcebos.com/paddlehub/fastdeploy/PPHumanMatting.tgz) | 247MB | - |
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| [Modnet-ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/PPModnet_ResNet50_vd.tgz) | 355MB | - |
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| [Modnet-MobileNetV2](https://bj.bcebos.com/paddlehub/fastdeploy/PPModnet_MobileNetV2.tgz) | 28MB | - |
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| [Modnet-HRNet_w18](https://bj.bcebos.com/paddlehub/fastdeploy/PPModnet_HRNet_w18.tgz) | 51MB | - |
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## Detailed Deployment Tutorials
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- [Python Deployment](python)
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- [C++ Deployment](cpp)
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43
examples/vision/segmentation/ppmatting/README_CN.md
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43
examples/vision/segmentation/ppmatting/README_CN.md
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[English](README.md) | 简体中文
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# PP-Matting模型部署
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## 模型版本说明
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- [PP-Matting Release/2.6](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting)
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## 支持模型列表
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目前FastDeploy支持如下模型的部署
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- [PP-Matting系列模型](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting)
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- [PP-HumanMatting系列模型](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting)
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- [ModNet系列模型](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting)
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## 导出部署模型
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在部署前，需要先将PP-Matting导出成部署模型，导出步骤参考文档[导出模型](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting)(Tips:导出PP-Matting系列模型和PP-HumanMatting系列模型需要设置导出脚本的`--input_shape`参数)
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## 下载预训练模型
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为了方便开发者的测试，下面提供了PP-Matting导出的各系列模型，开发者可直接下载使用。
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其中精度指标来源于PP-Matting中对各模型的介绍(未提供精度数据)，详情各参考PP-Matting中的说明。
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| 模型 | 参数大小 | 精度 | 备注 |
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|:---------------------------------------------------------------- |:----- |:----- | :------ |
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| [PP-Matting-512](https://bj.bcebos.com/paddlehub/fastdeploy/PP-Matting-512.tgz) | 106MB | - |
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| [PP-Matting-1024](https://bj.bcebos.com/paddlehub/fastdeploy/PP-Matting-1024.tgz) | 106MB | - |
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| [PP-HumanMatting](https://bj.bcebos.com/paddlehub/fastdeploy/PPHumanMatting.tgz) | 247MB | - |
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| [Modnet-ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/PPModnet_ResNet50_vd.tgz) | 355MB | - |
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| [Modnet-MobileNetV2](https://bj.bcebos.com/paddlehub/fastdeploy/PPModnet_MobileNetV2.tgz) | 28MB | - |
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| [Modnet-HRNet_w18](https://bj.bcebos.com/paddlehub/fastdeploy/PPModnet_HRNet_w18.tgz) | 51MB | - |
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## 详细部署文档
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- [Python部署](python)
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- [C++部署](cpp)
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14
examples/vision/segmentation/ppmatting/cpp/CMakeLists.txt
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examples/vision/segmentation/ppmatting/cpp/CMakeLists.txt
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PROJECT(infer_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
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# 指定下载解压后的fastdeploy库路径
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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# 添加FastDeploy依赖头文件
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include_directories(${FASTDEPLOY_INCS})
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add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
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# 添加FastDeploy库依赖
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target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
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93
examples/vision/segmentation/ppmatting/cpp/README.md
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examples/vision/segmentation/ppmatting/cpp/README.md
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English | [简体中文](README_CN.md)
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# PP-Matting C++ Deployment Example
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This directory provides examples that `infer.cc` fast finishes the deployment of PP-Matting on CPU/GPU and GPU accelerated by TensorRT.
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Before deployment, two steps require confirmation
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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Taking the PP-Matting inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.
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```bash
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mkdir build
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cd build
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# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
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tar xvf fastdeploy-linux-x64-x.x.x.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
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make -j
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# Download PP-Matting model files and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/PP-Matting-512.tgz
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tar -xvf PP-Matting-512.tgz
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wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg
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wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg
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# CPU inference
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./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 0
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# GPU inference
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./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 1
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# TensorRT inference on GPU
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./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 2
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# kunlunxin XPU inference
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./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 3
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```
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The visualized result after running is as follows
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<div width="840">
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852040-759da522-fca4-4786-9205-88c622cd4a39.jpg">
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852587-48895efc-d24a-43c9-aeec-d7b0362ab2b9.jpg">
|
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852116-cf91445b-3a67-45d9-a675-c69fe77c383a.jpg">
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852554-6960659f-4fd7-4506-b33b-54e1a9dd89bf.jpg">
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</div>
|
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|
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The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:
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- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/en/faq/use_sdk_on_windows.md)
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|
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## PP-Matting C++ Interface
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### PPMatting Class
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```c++
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fastdeploy::vision::matting::PPMatting(
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const string& model_file,
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const string& params_file = "",
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const string& config_file,
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::PADDLE)
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```
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PP-Matting model loading and initialization, among which model_file is the exported Paddle model format.
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**Parameter**
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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path
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> * **config_file**(str): Inference deployment configuration file
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> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
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> * **model_format**(ModelFormat): Model format. Paddle format by default
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#### Predict Function
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> ```c++
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> PPMatting::Predict(cv::Mat* im, MattingResult* result)
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> ```
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>
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> Model prediction interface. Input images and output detection results.
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>
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> **Parameter**
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>
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> > * **im**: Input images in HWC or BGR format
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> > * **result**: The segmentation result, including the predicted label of the segmentation and the corresponding probability of the label. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of SegmentationResult
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|
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### Class Member Variable
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#### Pre-processing Parameter
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Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
|
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|
||||
|
||||
- [Model Description](../../)
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- [Python Deployment](../python)
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- [Vision Model Prediction Results](../../../../../docs/api/vision_results/)
|
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- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)
|
||||
94
examples/vision/segmentation/ppmatting/cpp/README_CN.md
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94
examples/vision/segmentation/ppmatting/cpp/README_CN.md
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[English](README.md) | 简体中文
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# PP-Matting C++部署示例
|
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|
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本目录下提供`infer.cc`快速完成PP-Matting在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
|
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|
||||
在部署前，需确认以下两个步骤
|
||||
|
||||
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
以Linux上 PP-Matting 推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
|
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|
||||
```bash
|
||||
mkdir build
|
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cd build
|
||||
# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
|
||||
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
|
||||
tar xvf fastdeploy-linux-x64-x.x.x.tgz
|
||||
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
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make -j
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|
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# 下载PP-Matting模型文件和测试图片
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP-Matting-512.tgz
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||||
tar -xvf PP-Matting-512.tgz
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||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg
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wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg
|
||||
|
||||
|
||||
# CPU推理
|
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./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 0
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# GPU推理
|
||||
./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 1
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# GPU上TensorRT推理
|
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./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 2
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||||
# 昆仑芯XPU推理
|
||||
./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 3
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```
|
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|
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运行完成可视化结果如下图所示
|
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<div width="840">
|
||||
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852040-759da522-fca4-4786-9205-88c622cd4a39.jpg">
|
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<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852587-48895efc-d24a-43c9-aeec-d7b0362ab2b9.jpg">
|
||||
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852116-cf91445b-3a67-45d9-a675-c69fe77c383a.jpg">
|
||||
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852554-6960659f-4fd7-4506-b33b-54e1a9dd89bf.jpg">
|
||||
</div>
|
||||
|
||||
以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
|
||||
- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
|
||||
|
||||
## PP-Matting C++接口
|
||||
|
||||
### PPMatting类
|
||||
|
||||
```c++
|
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fastdeploy::vision::matting::PPMatting(
|
||||
const string& model_file,
|
||||
const string& params_file = "",
|
||||
const string& config_file,
|
||||
const RuntimeOption& runtime_option = RuntimeOption(),
|
||||
const ModelFormat& model_format = ModelFormat::PADDLE)
|
||||
```
|
||||
|
||||
PP-Matting模型加载和初始化，其中model_file为导出的Paddle模型格式。
|
||||
|
||||
**参数**
|
||||
|
||||
> * **model_file**(str): 模型文件路径
|
||||
> * **params_file**(str): 参数文件路径
|
||||
> * **config_file**(str): 推理部署配置文件
|
||||
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
|
||||
> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
|
||||
|
||||
#### Predict函数
|
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|
||||
> ```c++
|
||||
> PPMatting::Predict(cv::Mat* im, MattingResult* result)
|
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> ```
|
||||
>
|
||||
> 模型预测接口，输入图像直接输出检测结果。
|
||||
>
|
||||
> **参数**
|
||||
>
|
||||
> > * **im**: 输入图像，注意需为HWC，BGR格式
|
||||
> > * **result**: 分割结果，包括分割预测的标签以及标签对应的概率值, MattingResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||||
|
||||
### 类成员属性
|
||||
#### 预处理参数
|
||||
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
|
||||
|
||||
|
||||
- [模型介绍](../../)
|
||||
- [Python部署](../python)
|
||||
- [视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||||
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
||||
170
examples/vision/segmentation/ppmatting/cpp/infer.cc
Normal file
170
examples/vision/segmentation/ppmatting/cpp/infer.cc
Normal file
@@ -0,0 +1,170 @@
|
||||
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
||||
//
|
||||
// Licensed under the Apache License, Version 2.0 (the "License");
|
||||
// you may not use this file except in compliance with the License.
|
||||
// You may obtain a copy of the License at
|
||||
//
|
||||
// http://www.apache.org/licenses/LICENSE-2.0
|
||||
//
|
||||
// Unless required by applicable law or agreed to in writing, software
|
||||
// distributed under the License is distributed on an "AS IS" BASIS,
|
||||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
// See the License for the specific language governing permissions and
|
||||
// limitations under the License.
|
||||
|
||||
#include "fastdeploy/vision.h"
|
||||
|
||||
#ifdef WIN32
|
||||
const char sep = '\\';
|
||||
#else
|
||||
const char sep = '/';
|
||||
#endif
|
||||
|
||||
void CpuInfer(const std::string& model_dir, const std::string& image_file,
|
||||
const std::string& background_file) {
|
||||
auto model_file = model_dir + sep + "model.pdmodel";
|
||||
auto params_file = model_dir + sep + "model.pdiparams";
|
||||
auto config_file = model_dir + sep + "deploy.yaml";
|
||||
auto option = fastdeploy::RuntimeOption();
|
||||
option.UseCpu();
|
||||
auto model = fastdeploy::vision::matting::PPMatting(model_file, params_file,
|
||||
config_file, option);
|
||||
if (!model.Initialized()) {
|
||||
std::cerr << "Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
cv::Mat bg = cv::imread(background_file);
|
||||
fastdeploy::vision::MattingResult res;
|
||||
if (!model.Predict(&im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
auto vis_im = fastdeploy::vision::VisMatting(im, res);
|
||||
auto vis_im_with_bg = fastdeploy::vision::SwapBackground(im, bg, res);
|
||||
cv::imwrite("visualized_result.jpg", vis_im_with_bg);
|
||||
cv::imwrite("visualized_result_fg.jpg", vis_im);
|
||||
std::cout << "Visualized result save in ./visualized_result_replaced_bg.jpg "
|
||||
"and ./visualized_result_fg.jpg"
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
void KunlunXinInfer(const std::string& model_dir, const std::string& image_file,
|
||||
const std::string& background_file) {
|
||||
auto model_file = model_dir + sep + "model.pdmodel";
|
||||
auto params_file = model_dir + sep + "model.pdiparams";
|
||||
auto config_file = model_dir + sep + "deploy.yaml";
|
||||
auto option = fastdeploy::RuntimeOption();
|
||||
option.UseKunlunXin();
|
||||
auto model = fastdeploy::vision::matting::PPMatting(model_file, params_file,
|
||||
config_file, option);
|
||||
if (!model.Initialized()) {
|
||||
std::cerr << "Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
cv::Mat bg = cv::imread(background_file);
|
||||
fastdeploy::vision::MattingResult res;
|
||||
if (!model.Predict(&im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
auto vis_im = fastdeploy::vision::VisMatting(im, res);
|
||||
auto vis_im_with_bg = fastdeploy::vision::SwapBackground(im, bg, res);
|
||||
cv::imwrite("visualized_result.jpg", vis_im_with_bg);
|
||||
cv::imwrite("visualized_result_fg.jpg", vis_im);
|
||||
std::cout << "Visualized result save in ./visualized_result_replaced_bg.jpg "
|
||||
"and ./visualized_result_fg.jpg"
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
void GpuInfer(const std::string& model_dir, const std::string& image_file,
|
||||
const std::string& background_file) {
|
||||
auto model_file = model_dir + sep + "model.pdmodel";
|
||||
auto params_file = model_dir + sep + "model.pdiparams";
|
||||
auto config_file = model_dir + sep + "deploy.yaml";
|
||||
|
||||
auto option = fastdeploy::RuntimeOption();
|
||||
option.UseGpu();
|
||||
option.UsePaddleInferBackend();
|
||||
auto model = fastdeploy::vision::matting::PPMatting(model_file, params_file,
|
||||
config_file, option);
|
||||
if (!model.Initialized()) {
|
||||
std::cerr << "Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
cv::Mat bg = cv::imread(background_file);
|
||||
fastdeploy::vision::MattingResult res;
|
||||
if (!model.Predict(&im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
auto vis_im = fastdeploy::vision::VisMatting(im, res);
|
||||
auto vis_im_with_bg = fastdeploy::vision::SwapBackground(im, bg, res);
|
||||
cv::imwrite("visualized_result.jpg", vis_im_with_bg);
|
||||
cv::imwrite("visualized_result_fg.jpg", vis_im);
|
||||
std::cout << "Visualized result save in ./visualized_result_replaced_bg.jpg "
|
||||
"and ./visualized_result_fg.jpg"
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
void TrtInfer(const std::string& model_dir, const std::string& image_file,
|
||||
const std::string& background_file) {
|
||||
auto model_file = model_dir + sep + "model.pdmodel";
|
||||
auto params_file = model_dir + sep + "model.pdiparams";
|
||||
auto config_file = model_dir + sep + "deploy.yaml";
|
||||
|
||||
auto option = fastdeploy::RuntimeOption();
|
||||
option.UseGpu();
|
||||
option.UseTrtBackend();
|
||||
option.SetTrtInputShape("img", {1, 3, 512, 512});
|
||||
auto model = fastdeploy::vision::matting::PPMatting(model_file, params_file,
|
||||
config_file, option);
|
||||
if (!model.Initialized()) {
|
||||
std::cerr << "Failed to initialize." << std::endl;
|
||||
return;
|
||||
}
|
||||
|
||||
auto im = cv::imread(image_file);
|
||||
cv::Mat bg = cv::imread(background_file);
|
||||
fastdeploy::vision::MattingResult res;
|
||||
if (!model.Predict(&im, &res)) {
|
||||
std::cerr << "Failed to predict." << std::endl;
|
||||
return;
|
||||
}
|
||||
auto vis_im = fastdeploy::vision::VisMatting(im, res);
|
||||
auto vis_im_with_bg = fastdeploy::vision::SwapBackground(im, bg, res);
|
||||
cv::imwrite("visualized_result.jpg", vis_im_with_bg);
|
||||
cv::imwrite("visualized_result_fg.jpg", vis_im);
|
||||
std::cout << "Visualized result save in ./visualized_result_replaced_bg.jpg "
|
||||
"and ./visualized_result_fg.jpg"
|
||||
<< std::endl;
|
||||
}
|
||||
|
||||
int main(int argc, char* argv[]) {
|
||||
if (argc < 5) {
|
||||
std::cout
|
||||
<< "Usage: infer_demo path/to/model_dir path/to/image run_option, "
|
||||
"e.g ./infer_model ./PP-Matting-512 ./test.jpg ./test_bg.jpg 0"
|
||||
<< std::endl;
|
||||
std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
|
||||
"with gpu; 2: run with gpu and use tensorrt backend, 3: run "
|
||||
"with kunlunxin."
|
||||
<< std::endl;
|
||||
return -1;
|
||||
}
|
||||
if (std::atoi(argv[4]) == 0) {
|
||||
CpuInfer(argv[1], argv[2], argv[3]);
|
||||
} else if (std::atoi(argv[4]) == 1) {
|
||||
GpuInfer(argv[1], argv[2], argv[3]);
|
||||
} else if (std::atoi(argv[4]) == 2) {
|
||||
TrtInfer(argv[1], argv[2], argv[3]);
|
||||
} else if (std::atoi(argv[4]) == 3) {
|
||||
KunlunXinInfer(argv[1], argv[2], argv[3]);
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
81
examples/vision/segmentation/ppmatting/python/README.md
Executable file
81
examples/vision/segmentation/ppmatting/python/README.md
Executable file
@@ -0,0 +1,81 @@
|
||||
English | [简体中文](README_CN.md)
|
||||
# PP-Matting Python Deployment Example
|
||||
|
||||
Before deployment, two steps require confirmation
|
||||
|
||||
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
|
||||
- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
This directory provides examples that `infer.py` fast finishes the deployment of PP-Matting on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
|
||||
```bash
|
||||
# Download the deployment example code
|
||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||
cd FastDeploy/examples/vision/matting/ppmatting/python
|
||||
|
||||
# Download PP-Matting model files and test images
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP-Matting-512.tgz
|
||||
tar -xvf PP-Matting-512.tgz
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg
|
||||
# CPU inference
|
||||
python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device cpu
|
||||
# GPU inference
|
||||
python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device gpu
|
||||
# TensorRT inference on GPU（Attention: It is somewhat time-consuming for the operation of model serialization when running TensorRT inference for the first time. Please be patient.）
|
||||
python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device gpu --use_trt True
|
||||
# kunlunxin XPU inference
|
||||
python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device kunlunxin
|
||||
```
|
||||
|
||||
The visualized result after running is as follows
|
||||
<div width="840">
|
||||
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852040-759da522-fca4-4786-9205-88c622cd4a39.jpg">
|
||||
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852587-48895efc-d24a-43c9-aeec-d7b0362ab2b9.jpg">
|
||||
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852116-cf91445b-3a67-45d9-a675-c69fe77c383a.jpg">
|
||||
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852554-6960659f-4fd7-4506-b33b-54e1a9dd89bf.jpg">
|
||||
</div>
|
||||
## PP-Matting Python Interface
|
||||
|
||||
```python
|
||||
fd.vision.matting.PPMatting(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
|
||||
```
|
||||
|
||||
PP-Matting model loading and initialization, among which model_file, params_file, and config_file are the Paddle inference files exported from the training model. Refer to [Model Export](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting) for more information
|
||||
|
||||
**Parameter**
|
||||
|
||||
> * **model_file**(str): Model file path
|
||||
> * **params_file**(str): Parameter file path
|
||||
> * **config_file**(str): Inference deployment configuration file
|
||||
> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
|
||||
> * **model_format**(ModelFormat): Model format. Paddle format by default
|
||||
|
||||
### predict function
|
||||
|
||||
> ```python
|
||||
> PPMatting.predict(input_image)
|
||||
> ```
|
||||
>
|
||||
> Model prediction interface. Input images and output detection results.
|
||||
>
|
||||
> **Parameter**
|
||||
>
|
||||
> > * **input_image**(np.ndarray): Input data in HWC or BGR format
|
||||
|
||||
> **Return**
|
||||
>
|
||||
> > Return `fastdeploy.vision.MattingResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure.
|
||||
|
||||
### Class Member Variable
|
||||
|
||||
#### Pre-processing Parameter
|
||||
Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
|
||||
|
||||
|
||||
|
||||
## Other Documents
|
||||
|
||||
- [PP-Matting Model Description](..)
|
||||
- [PP-Matting C++ Deployment](../cpp)
|
||||
- [Model Prediction Results](../../../../../docs/api/vision_results/)
|
||||
- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)
|
||||
80
examples/vision/segmentation/ppmatting/python/README_CN.md
Normal file
80
examples/vision/segmentation/ppmatting/python/README_CN.md
Normal file
@@ -0,0 +1,80 @@
|
||||
[English](README.md) | 简体中文
|
||||
# PP-Matting Python部署示例
|
||||
|
||||
在部署前，需确认以下两个步骤
|
||||
|
||||
- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
|
||||
|
||||
本目录下提供`infer.py`快速完成PP-Matting在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
|
||||
|
||||
```bash
|
||||
#下载部署示例代码
|
||||
git clone https://github.com/PaddlePaddle/FastDeploy.git
|
||||
cd FastDeploy/examples/vision/matting/ppmatting/python
|
||||
|
||||
# 下载PP-Matting模型文件和测试图片
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/PP-Matting-512.tgz
|
||||
tar -xvf PP-Matting-512.tgz
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_input.jpg
|
||||
wget https://bj.bcebos.com/paddlehub/fastdeploy/matting_bgr.jpg
|
||||
# CPU推理
|
||||
python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device cpu
|
||||
# GPU推理
|
||||
python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device gpu
|
||||
# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
|
||||
python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device gpu --use_trt True
|
||||
# 昆仑芯XPU推理
|
||||
python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device kunlunxin
|
||||
```
|
||||
|
||||
运行完成可视化结果如下图所示
|
||||
<div width="840">
|
||||
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852040-759da522-fca4-4786-9205-88c622cd4a39.jpg">
|
||||
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852587-48895efc-d24a-43c9-aeec-d7b0362ab2b9.jpg">
|
||||
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852116-cf91445b-3a67-45d9-a675-c69fe77c383a.jpg">
|
||||
<img width="200" height="200" float="left" src="https://user-images.githubusercontent.com/67993288/186852554-6960659f-4fd7-4506-b33b-54e1a9dd89bf.jpg">
|
||||
</div>
|
||||
## PP-Matting Python接口
|
||||
|
||||
```python
|
||||
fd.vision.matting.PPMatting(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
|
||||
```
|
||||
|
||||
PP-Matting模型加载和初始化，其中model_file, params_file以及config_file为训练模型导出的Paddle inference文件，具体请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting)
|
||||
|
||||
**参数**
|
||||
|
||||
> * **model_file**(str): 模型文件路径
|
||||
> * **params_file**(str): 参数文件路径
|
||||
> * **config_file**(str): 推理部署配置文件
|
||||
> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
|
||||
> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
|
||||
|
||||
### predict函数
|
||||
|
||||
> ```python
|
||||
> PPMatting.predict(input_image)
|
||||
> ```
|
||||
>
|
||||
> 模型预测结口，输入图像直接输出检测结果。
|
||||
>
|
||||
> **参数**
|
||||
>
|
||||
> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
|
||||
|
||||
> **返回**
|
||||
>
|
||||
> > 返回`fastdeploy.vision.MattingResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
|
||||
|
||||
### 类成员属性
|
||||
#### 预处理参数
|
||||
用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果
|
||||
|
||||
|
||||
## 其它文档
|
||||
|
||||
- [PP-Matting 模型介绍](..)
|
||||
- [PP-Matting C++部署](../cpp)
|
||||
- [模型预测结果说明](../../../../../docs/api/vision_results/)
|
||||
- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
|
||||
70
examples/vision/segmentation/ppmatting/python/infer.py
Executable file
70
examples/vision/segmentation/ppmatting/python/infer.py
Executable file
@@ -0,0 +1,70 @@
|
||||
import fastdeploy as fd
|
||||
import cv2
|
||||
import os
|
||||
|
||||
|
||||
def parse_arguments():
|
||||
import argparse
|
||||
import ast
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--model", required=True, help="Path of PaddleSeg model.")
|
||||
parser.add_argument(
|
||||
"--image", type=str, required=True, help="Path of test image file.")
|
||||
parser.add_argument(
|
||||
"--bg",
|
||||
type=str,
|
||||
required=True,
|
||||
default=None,
|
||||
help="Path of test background image file.")
|
||||
parser.add_argument(
|
||||
"--device",
|
||||
type=str,
|
||||
default='cpu',
|
||||
help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
|
||||
parser.add_argument(
|
||||
"--use_trt",
|
||||
type=ast.literal_eval,
|
||||
default=False,
|
||||
help="Wether to use tensorrt.")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def build_option(args):
|
||||
option = fd.RuntimeOption()
|
||||
if args.device.lower() == "gpu":
|
||||
option.use_gpu()
|
||||
option.use_paddle_infer_backend()
|
||||
|
||||
if args.use_trt:
|
||||
option.use_trt_backend()
|
||||
option.set_trt_input_shape("img", [1, 3, 512, 512])
|
||||
|
||||
if args.device.lower() == "kunlunxin":
|
||||
option.use_kunlunxin()
|
||||
return option
|
||||
|
||||
|
||||
args = parse_arguments()
|
||||
|
||||
# 配置runtime，加载模型
|
||||
runtime_option = build_option(args)
|
||||
model_file = os.path.join(args.model, "model.pdmodel")
|
||||
params_file = os.path.join(args.model, "model.pdiparams")
|
||||
config_file = os.path.join(args.model, "deploy.yaml")
|
||||
model = fd.vision.matting.PPMatting(
|
||||
model_file, params_file, config_file, runtime_option=runtime_option)
|
||||
|
||||
# 预测图片抠图结果
|
||||
im = cv2.imread(args.image)
|
||||
bg = cv2.imread(args.bg)
|
||||
result = model.predict(im)
|
||||
print(result)
|
||||
# 可视化结果
|
||||
vis_im = fd.vision.vis_matting(im, result)
|
||||
vis_im_with_bg = fd.vision.swap_background(im, bg, result)
|
||||
cv2.imwrite("visualized_result_fg.jpg", vis_im)
|
||||
cv2.imwrite("visualized_result_replaced_bg.jpg", vis_im_with_bg)
|
||||
print(
|
||||
"Visualized result save in ./visualized_result_replaced_bg.jpg and ./visualized_result_fg.jpg"
|
||||
)
|
||||
Reference in New Issue
Block a user