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examples/vision/segmentation/ppmatting/python/README.md
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examples/vision/segmentation/ppmatting/python/README.md
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English | [简体中文](README_CN.md)
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# PP-Matting Python Deployment Example
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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. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md)
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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
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```bash
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# Download the deployment example code
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/matting/ppmatting/python
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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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python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device cpu
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# GPU inference
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python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device gpu
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# 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.）
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python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device gpu --use_trt True
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# kunlunxin XPU inference
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python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device kunlunxin
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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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## PP-Matting Python Interface
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```python
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fd.vision.matting.PPMatting(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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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
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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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> ```python
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> PPMatting.predict(input_image)
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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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> > * **input_image**(np.ndarray): Input data in HWC or BGR format
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> **Return**
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>
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> > Return `fastdeploy.vision.MattingResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure.
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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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## Other Documents
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- [PP-Matting Model Description](..)
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- [PP-Matting C++ Deployment](../cpp)
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- [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)
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examples/vision/segmentation/ppmatting/python/README_CN.md
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[English](README.md) | 简体中文
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# PP-Matting Python部署示例
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在部署前，需确认以下两个步骤
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- 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)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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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
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```bash
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd FastDeploy/examples/vision/matting/ppmatting/python
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# 下载PP-Matting模型文件和测试图片
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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推理
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python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device cpu
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# GPU推理
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python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device gpu
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# 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）
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python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device gpu --use_trt True
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# 昆仑芯XPU推理
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python infer.py --model PP-Matting-512 --image matting_input.jpg --bg matting_bgr.jpg --device kunlunxin
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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">
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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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## PP-Matting Python接口
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```python
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fd.vision.matting.PPMatting(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
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```
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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)
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **params_file**(str): 参数文件路径
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> * **config_file**(str): 推理部署配置文件
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
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### predict函数
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> ```python
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> PPMatting.predict(input_image)
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> ```
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>
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> 模型预测结口，输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
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> **返回**
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>
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> > 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/)
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### 类成员属性
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#### 预处理参数
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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
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## 其它文档
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- [PP-Matting 模型介绍](..)
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- [PP-Matting C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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examples/vision/segmentation/ppmatting/python/infer.py
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examples/vision/segmentation/ppmatting/python/infer.py
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import fastdeploy as fd
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import cv2
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import os
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def parse_arguments():
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import argparse
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import ast
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--model", required=True, help="Path of PaddleSeg model.")
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parser.add_argument(
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"--image", type=str, required=True, help="Path of test image file.")
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parser.add_argument(
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"--bg",
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type=str,
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required=True,
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default=None,
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help="Path of test background image file.")
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parser.add_argument(
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"--device",
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type=str,
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default='cpu',
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help="Type of inference device, support 'cpu', 'kunlunxin' or 'gpu'.")
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parser.add_argument(
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"--use_trt",
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type=ast.literal_eval,
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default=False,
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help="Wether to use tensorrt.")
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return parser.parse_args()
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def build_option(args):
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option = fd.RuntimeOption()
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if args.device.lower() == "gpu":
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option.use_gpu()
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option.use_paddle_infer_backend()
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if args.use_trt:
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option.use_trt_backend()
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option.set_trt_input_shape("img", [1, 3, 512, 512])
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if args.device.lower() == "kunlunxin":
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option.use_kunlunxin()
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return option
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args = parse_arguments()
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# 配置runtime，加载模型
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runtime_option = build_option(args)
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model_file = os.path.join(args.model, "model.pdmodel")
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params_file = os.path.join(args.model, "model.pdiparams")
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config_file = os.path.join(args.model, "deploy.yaml")
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model = fd.vision.matting.PPMatting(
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model_file, params_file, config_file, runtime_option=runtime_option)
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# 预测图片抠图结果
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im = cv2.imread(args.image)
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bg = cv2.imread(args.bg)
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result = model.predict(im)
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print(result)
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# 可视化结果
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vis_im = fd.vision.vis_matting(im, result)
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vis_im_with_bg = fd.vision.swap_background(im, bg, result)
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cv2.imwrite("visualized_result_fg.jpg", vis_im)
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cv2.imwrite("visualized_result_replaced_bg.jpg", vis_im_with_bg)
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print(
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"Visualized result save in ./visualized_result_replaced_bg.jpg and ./visualized_result_fg.jpg"
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)
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