mirror of
				https://github.com/PaddlePaddle/FastDeploy.git
				synced 2025-10-28 18:51:58 +08:00 
			
		
		
		
	
		
			
				
	
	
		
			75 lines
		
	
	
		
			2.9 KiB
		
	
	
	
		
			Markdown
		
	
	
	
	
	
			
		
		
	
	
			75 lines
		
	
	
		
			2.9 KiB
		
	
	
	
		
			Markdown
		
	
	
	
	
	
| # UltraFace 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/environment.md)  
 | ||
| - 2. FastDeploy Python whl包安装,参考[FastDeploy Python安装](../../../../../docs/quick_start)
 | ||
| 
 | ||
| 本目录下提供`infer.py`快速完成UltraFace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/ultraface/python/
 | ||
| 
 | ||
| #下载ultraface模型文件和测试图片
 | ||
| wget https://bj.bcebos.com/paddlehub/fastdeploy/version-RFB-320.onnx
 | ||
| wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg
 | ||
| 
 | ||
| # CPU推理
 | ||
| python infer.py --model version-RFB-320.onnx --image test_lite_face_detector_3.jpg --device cpu
 | ||
| # GPU推理
 | ||
| python infer.py --model version-RFB-320.onnx --image test_lite_face_detector_3.jpg --device gpu
 | ||
| # GPU上使用TensorRT推理
 | ||
| python infer.py --model version-RFB-320.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/67993288/184301821-0788483b-a72b-42b0-a566-b6430f184f6e.jpg">
 | ||
| 
 | ||
| ## UltraFace Python接口
 | ||
| 
 | ||
| ```python
 | ||
| fastdeploy.vision.facedet.UltraFace(model_file, params_file=None, runtime_option=None, model_format=Frontend.ONNX)
 | ||
| ```
 | ||
| 
 | ||
| UltraFace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**(Frontend): 模型格式,默认为ONNX
 | ||
| 
 | ||
| ### predict函数
 | ||
| 
 | ||
| > ```python
 | ||
| > UltraFace.predict(image_data, conf_threshold=0.25, 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[320, 240]
 | ||
| 
 | ||
| ## 其它文档
 | ||
| 
 | ||
| - [UltraFace 模型介绍](..)
 | ||
| - [UltraFace C++部署](../cpp)
 | ||
| - [模型预测结果说明](../../../../../docs/api/vision_results/)
 | 
