diff --git a/examples/vision/segmentation/ppmatting/README.md b/examples/vision/segmentation/ppmatting/README.md new file mode 100644 index 000000000..a2cbdc346 --- /dev/null +++ b/examples/vision/segmentation/ppmatting/README.md @@ -0,0 +1,42 @@ +English | [简体中文](README_CN.md) +# PP-Matting Model Deployment + +## Model Description + +- [PP-Matting Release/2.6](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting) + +## List of Supported Models + +Now FastDeploy supports the deployment of the following models + +- [PP-Matting models](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting) +- [PP-HumanMatting models](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting) +- [ModNet models](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting) + + +## Export Deployment Model + +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) + + +## Download Pre-trained Models + +For developers' testing, models exported by PP-Matting are provided below. Developers can download and use them directly. + +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. + +| Model | Parameter Size | Accuracy | Note | +|:---------------------------------------------------------------- |:----- |:----- | :------ | +| [PP-Matting-512](https://bj.bcebos.com/paddlehub/fastdeploy/PP-Matting-512.tgz) | 106MB | - | +| [PP-Matting-1024](https://bj.bcebos.com/paddlehub/fastdeploy/PP-Matting-1024.tgz) | 106MB | - | +| [PP-HumanMatting](https://bj.bcebos.com/paddlehub/fastdeploy/PPHumanMatting.tgz) | 247MB | - | +| [Modnet-ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/PPModnet_ResNet50_vd.tgz) | 355MB | - | +| [Modnet-MobileNetV2](https://bj.bcebos.com/paddlehub/fastdeploy/PPModnet_MobileNetV2.tgz) | 28MB | - | +| [Modnet-HRNet_w18](https://bj.bcebos.com/paddlehub/fastdeploy/PPModnet_HRNet_w18.tgz) | 51MB | - | + + + +## Detailed Deployment Tutorials + +- [Python Deployment](python) +- [C++ Deployment](cpp) diff --git a/examples/vision/segmentation/ppmatting/README_CN.md b/examples/vision/segmentation/ppmatting/README_CN.md new file mode 100644 index 000000000..a1c9801aa --- /dev/null +++ b/examples/vision/segmentation/ppmatting/README_CN.md @@ -0,0 +1,43 @@ +[English](README.md) | 简体中文 +# PP-Matting模型部署 + +## 模型版本说明 + +- [PP-Matting Release/2.6](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting) + +## 支持模型列表 + +目前FastDeploy支持如下模型的部署 + +- [PP-Matting系列模型](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting) +- [PP-HumanMatting系列模型](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting) +- [ModNet系列模型](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.6/Matting) + + +## 导出部署模型 + +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) + + +## 下载预训练模型 + +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。 + +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。 + + +| 模型 | 参数大小 | 精度 | 备注 | +|:---------------------------------------------------------------- |:----- |:----- | :------ | +| [PP-Matting-512](https://bj.bcebos.com/paddlehub/fastdeploy/PP-Matting-512.tgz) | 106MB | - | +| [PP-Matting-1024](https://bj.bcebos.com/paddlehub/fastdeploy/PP-Matting-1024.tgz) | 106MB | - | +| [PP-HumanMatting](https://bj.bcebos.com/paddlehub/fastdeploy/PPHumanMatting.tgz) | 247MB | - | +| [Modnet-ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/PPModnet_ResNet50_vd.tgz) | 355MB | - | +| [Modnet-MobileNetV2](https://bj.bcebos.com/paddlehub/fastdeploy/PPModnet_MobileNetV2.tgz) | 28MB | - | +| [Modnet-HRNet_w18](https://bj.bcebos.com/paddlehub/fastdeploy/PPModnet_HRNet_w18.tgz) | 51MB | - | + + + +## 详细部署文档 + +- [Python部署](python) +- [C++部署](cpp) diff --git a/examples/vision/segmentation/ppmatting/cpp/CMakeLists.txt b/examples/vision/segmentation/ppmatting/cpp/CMakeLists.txt new file mode 100644 index 000000000..93540a7e8 --- /dev/null +++ b/examples/vision/segmentation/ppmatting/cpp/CMakeLists.txt @@ -0,0 +1,14 @@ +PROJECT(infer_demo C CXX) +CMAKE_MINIMUM_REQUIRED (VERSION 3.10) + +# 指定下载解压后的fastdeploy库路径 +option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.") + +include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake) + +# 添加FastDeploy依赖头文件 +include_directories(${FASTDEPLOY_INCS}) + +add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc) +# 添加FastDeploy库依赖 +target_link_libraries(infer_demo ${FASTDEPLOY_LIBS}) diff --git a/examples/vision/segmentation/ppmatting/cpp/README.md b/examples/vision/segmentation/ppmatting/cpp/README.md new file mode 100755 index 000000000..f678fabd4 --- /dev/null +++ b/examples/vision/segmentation/ppmatting/cpp/README.md @@ -0,0 +1,93 @@ +English | [简体中文](README_CN.md) +# PP-Matting C++ Deployment Example + +This directory provides examples that `infer.cc` fast finishes the deployment of PP-Matting on CPU/GPU and GPU accelerated by TensorRT. +Before deployment, two steps require confirmation + +- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md) +- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md) + +Taking the PP-Matting inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model. + +```bash +mkdir build +cd build +# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above +wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz +tar xvf fastdeploy-linux-x64-x.x.x.tgz +cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x +make -j + +# 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 +./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 0 +# GPU inference +./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 1 +# TensorRT inference on GPU +./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 2 +# kunlunxin XPU inference +./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 3 +``` + +The visualized result after running is as follows +
+ + + + +
+ +The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to: +- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/en/faq/use_sdk_on_windows.md) + +## PP-Matting C++ Interface + +### PPMatting Class + +```c++ +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 model loading and initialization, among which model_file is the exported Paddle model format. + +**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 + +> ```c++ +> PPMatting::Predict(cv::Mat* im, MattingResult* result) +> ``` +> +> Model prediction interface. Input images and output detection results. +> +> **Parameter** +> +> > * **im**: Input images in HWC or BGR format +> > * **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 + +### 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 + + +- [Model Description](../../) +- [Python Deployment](../python) +- [Vision Model Prediction Results](../../../../../docs/api/vision_results/) +- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md) diff --git a/examples/vision/segmentation/ppmatting/cpp/README_CN.md b/examples/vision/segmentation/ppmatting/cpp/README_CN.md new file mode 100644 index 000000000..38e2e592a --- /dev/null +++ b/examples/vision/segmentation/ppmatting/cpp/README_CN.md @@ -0,0 +1,94 @@ +[English](README.md) | 简体中文 +# PP-Matting C++部署示例 + +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。 + +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) + +```bash +mkdir build +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 +make -j + +# 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 +./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 0 +# GPU推理 +./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 1 +# GPU上TensorRT推理 +./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 2 +# 昆仑芯XPU推理 +./infer_demo PP-Matting-512 matting_input.jpg matting_bgr.jpg 3 +``` + +运行完成可视化结果如下图所示 +
+ + + + +
+ +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++ +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 + +> ```c++ +> PPMatting::Predict(cv::Mat* im, MattingResult* result) +> ``` +> +> 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) diff --git a/examples/vision/segmentation/ppmatting/cpp/infer.cc b/examples/vision/segmentation/ppmatting/cpp/infer.cc new file mode 100644 index 000000000..e7f1ffbcb --- /dev/null +++ b/examples/vision/segmentation/ppmatting/cpp/infer.cc @@ -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; +} diff --git a/examples/vision/segmentation/ppmatting/python/README.md b/examples/vision/segmentation/ppmatting/python/README.md new file mode 100755 index 000000000..fb7d42617 --- /dev/null +++ b/examples/vision/segmentation/ppmatting/python/README.md @@ -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 +
+ + + + +
+## 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) diff --git a/examples/vision/segmentation/ppmatting/python/README_CN.md b/examples/vision/segmentation/ppmatting/python/README_CN.md new file mode 100644 index 000000000..5e676a1cf --- /dev/null +++ b/examples/vision/segmentation/ppmatting/python/README_CN.md @@ -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 +
+ + + + +
+## 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) diff --git a/examples/vision/segmentation/ppmatting/python/infer.py b/examples/vision/segmentation/ppmatting/python/infer.py new file mode 100755 index 000000000..512d0ca86 --- /dev/null +++ b/examples/vision/segmentation/ppmatting/python/infer.py @@ -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" +)