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# YOLOv7Face Python部署示例
English | [简体中文](README_CN.md)
# YOLOv7Face Python Deployment Example
在部署前，需确认以下两个步骤
Before deployment, two steps require confirmation
- 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)
- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
本目录下提供`infer.py`快速完成YOLOv7Face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
This directory provides examples that `infer.py` fast finishes the deployment of YOLOv7Face on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
```bash
#下载部署示例代码
# Download the example code for deployment
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/vision/facedet/yolov7face/python/
#下载YOLOv7Face模型文件和测试图片
# Download YOLOv7Face model files and test images
wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-lite-e.onnx
#使用yolov7-tiny-face.onnx模型
# CPU推理
# Use yolov7-tiny-face.onnx model
# CPU inference
python infer.py --model yolov7-tiny-face.onnx --image test_lite_face_detector_3.jpg --device cpu
# GPU推理
# GPU inference
python infer.py --model yolov7-tiny-face.onnx --image test_lite_face_detector_3.jpg --device gpu
# GPU上使用TensorRT推理
# TensorRT inference on GPU
python infer.py --model yolov7-tiny-face.onnx --image test_lite_face_detector_3.jpg --device gpu --use_trt True
#使用yolov7-lite-e.onnx模型
# CPU推理
# Use yolov7-lite-e.onnx model
# CPU inference
python infer.py --model yolov7-lite-e.onnx --image test_lite_face_detector_3.jpg --device cpu
# GPU推理
# GPU inference
python infer.py --model yolov7-lite-e.onnx --image test_lite_face_detector_3.jpg --device gpu
# GPU上使用TensorRT推理
# TensorRT inference on GPU
python infer.py --model yolov7-lite-e.onnx --image test_lite_face_detector_3.jpg --device gpu --use_trt True
```
运行完成可视化结果如下图所示
The visualized result after running is as follows
<img width="640" src="https://user-images.githubusercontent.com/67993288/184301839-a29aefae-16c9-4196-bf9d-9c6cf694f02d.jpg">
## YOLOv7Face Python接口
## YOLOv7Face Python Interface
```python
fastdeploy.vision.facedet.YOLOv7Face(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
```
YOLOv7Face模型加载和初始化，其中model_file为导出的ONNX模型格式
YOLOv7Face model loading and initialization, among which model_file is the exported ONNX model format
**参数**
**Parameter**
> * **model_file**(str): 模型文件路径
> * **params_file**(str): 参数文件路径，当模型格式为ONNX格式时，此参数无需设定
> * **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ONNX
> * **model_file**(str): Model file path
> * **params_file**(str): Parameter file path. No need to set when the model is in ONNX format
> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
> * **model_format**(ModelFormat): Model format. ONNX format by default
### predict函数
### predict function
> ```python
> YOLOv7Face.predict(image_data, conf_threshold=0.3, nms_iou_threshold=0.5)
> ```
>
> 模型预测结口，输入图像直接输出检测结果。
> Model prediction interface. Input images and output detection results.
>
> **参数**
> **Parameter**
>
> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
> > * **conf_threshold**(float): 检测框置信度过滤阈值
> > * **nms_iou_threshold**(float): NMS处理过程中iou阈值
> > * **image_data**(np.ndarray): Input data in HWC or BGR format
> > * **conf_threshold**(float): Filtering threshold of detection box confidence
> > * **nms_iou_threshold**(float): iou threshold during NMS processing
> **返回**
> **Return**
>
> > 返回`fastdeploy.vision.FaceDetectionResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
> > Return `fastdeploy.vision.FaceDetectionResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for its description.
### 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
### Class Member Property
#### Pre-processing Parameter
Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results
> > * **size**(list[int]): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[640, 640]
> > * **padding_value**(list[float]): 通过此参数可以修改图片在resize时候做填充(padding)的值, 包含三个浮点型元素, 分别表示三个通道的值, 默认值为[114, 114, 114]
> > * **is_no_pad**(bool): 通过此参数让图片是否通过填充的方式进行resize, `is_no_pad=True` 表示不使用填充的方式，默认值为`is_no_pad=False`
> > * **is_mini_pad**(bool): 通过此参数可以将resize之后图像的宽高这是为最接近`size`成员变量的值, 并且满足填充的像素大小是可以被`stride`成员变量整除的。默认值为`is_mini_pad=False`
> > * **stride**(int): 配合`is_mini_pad`成员变量使用, 默认值为`stride=32`
> > * **size**(list[int]): This parameter changes the size of the resize used during preprocessing, containing two integer elements for [width, height] with default value [640, 640]
> > * **padding_value**(list[float]): This parameter is used to change the padding value of images during resize, containing three floating-point elements that represent the value of three channels. Default value [114, 114, 114]
> > * **is_no_pad**(bool): Specify whether to resize the image through padding or not. `is_no_pad=True` represents no paddling. Default `is_no_pad=False`
> > * **is_mini_pad**(bool): This parameter sets the width and height of the image after resize to the value nearest to the `size` member variable and to the point where the padded pixel size is divisible by the `stride` member variable. Default `is_mini_pad=False`
> > * **stride**(int): Used with the `is_mini_pad` member variable. Default `stride=32`
## 其它文档
## Other Documents
- [YOLOv7Face 模型介绍](..)
- [YOLOv7Face C++部署](../cpp)
- [模型预测结果说明](../../../../../docs/api/vision_results/)
- [YOLOv7Face Model Description](..)
- [YOLOv7Face C++ Deployment](../cpp)
- [Model Prediction Results](../../../../../docs/api/vision_results/)

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[English](README.md) | 简体中文
# YOLOv7Face 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YOLOv7Face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 examples/vision/facedet/yolov7face/python/
#下载YOLOv7Face模型文件和测试图片
wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-lite-e.onnx
#使用yolov7-tiny-face.onnx模型
# CPU推理
python infer.py --model yolov7-tiny-face.onnx --image test_lite_face_detector_3.jpg --device cpu
# GPU推理
python infer.py --model yolov7-tiny-face.onnx --image test_lite_face_detector_3.jpg --device gpu
# GPU上使用TensorRT推理
python infer.py --model yolov7-tiny-face.onnx --image test_lite_face_detector_3.jpg --device gpu --use_trt True
#使用yolov7-lite-e.onnx模型
# CPU推理
python infer.py --model yolov7-lite-e.onnx --image test_lite_face_detector_3.jpg --device cpu
# GPU推理
python infer.py --model yolov7-lite-e.onnx --image test_lite_face_detector_3.jpg --device gpu
# GPU上使用TensorRT推理
python infer.py --model yolov7-lite-e.onnx --image test_lite_face_detector_3.jpg --device gpu --use_trt True
```
运行完成可视化结果如下图所示
<img width="640" src="https://user-images.githubusercontent.com/67993288/184301839-a29aefae-16c9-4196-bf9d-9c6cf694f02d.jpg">
## YOLOv7Face Python接口
```python
fastdeploy.vision.facedet.YOLOv7Face(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
```
YOLOv7Face模型加载和初始化，其中model_file为导出的ONNX模型格式
**参数**
> * **model_file**(str): 模型文件路径
> * **params_file**(str): 参数文件路径，当模型格式为ONNX格式时，此参数无需设定
> * **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ONNX
### predict函数
> ```python
> YOLOv7Face.predict(image_data, conf_threshold=0.3, nms_iou_threshold=0.5)
> ```
>
> 模型预测结口，输入图像直接输出检测结果。
>
> **参数**
>
> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
> > * **conf_threshold**(float): 检测框置信度过滤阈值
> > * **nms_iou_threshold**(float): NMS处理过程中iou阈值
> **返回**
>
> > 返回`fastdeploy.vision.FaceDetectionResult`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
> > * **size**(list[int]): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[640, 640]
> > * **padding_value**(list[float]): 通过此参数可以修改图片在resize时候做填充(padding)的值, 包含三个浮点型元素, 分别表示三个通道的值, 默认值为[114, 114, 114]
> > * **is_no_pad**(bool): 通过此参数让图片是否通过填充的方式进行resize, `is_no_pad=True` 表示不使用填充的方式，默认值为`is_no_pad=False`
> > * **is_mini_pad**(bool): 通过此参数可以将resize之后图像的宽高这是为最接近`size`成员变量的值, 并且满足填充的像素大小是可以被`stride`成员变量整除的。默认值为`is_mini_pad=False`
> > * **stride**(int): 配合`is_mini_pad`成员变量使用, 默认值为`stride=32`
## 其它文档
- [YOLOv7Face 模型介绍](..)
- [YOLOv7Face C++部署](../cpp)
- [模型预测结果说明](../../../../../docs/api/vision_results/)