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[Model] Support BlazeFace Model (#1172)
* fit yolov7face file path * TODO：添加yolov7facePython接口Predict * resolve yolov7face.py * resolve yolov7face.py * resolve yolov7face.py * add yolov7face example readme file * [Doc] fix yolov7face example readme file * [Doc]fix yolov7face example readme file * support BlazeFace * add blazeface readme file * fix review problem * fix code style error * fix review problem * fix review problem * fix head file problem * fix review problem * fix review problem * fix readme file problem * add English readme file * fix English readme file
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examples/vision/facedet/blazeface/python/README.md
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examples/vision/facedet/blazeface/python/README.md
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English | [简体中文](README_CN.md)
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# BlazeFace 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 BlazeFace on CPU/GPU.
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```bash
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# Download the example code for deployment
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/vision/facedet/blazeface/python/
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# Download BlazeFace model files and test images
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wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg
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wget https://bj.bcebos.com/paddlehub/fastdeploy/blazeface-1000e.tgz
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# Use blazeface-1000e model
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# CPU Inference
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python infer.py --model blazeface-1000e/ --image test_lite_face_detector_3.jpg --device cpu
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# GPU Inference
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python infer.py --model blazeface-1000e/ --image test_lite_face_detector_3.jpg --device gpu
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```
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The visualized result after running is as follows
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<img width="640" src="https://user-images.githubusercontent.com/67993288/184301839-a29aefae-16c9-4196-bf9d-9c6cf694f02d.jpg">
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## BlazeFace Python Interface
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```python
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fastdeploy.vision.facedet.BlzaeFace(model_file, params_file=None, runtime_option=None, config_file=None, model_format=ModelFormat.PADDLE)
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```
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BlazeFace 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. No need to set when the model is in PADDLE format
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> * **config_file**(str): config file path. No need to set when the model is in PADDLE format
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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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> BlazeFace.predict(input_image)
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> ```
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> Through let BlazeFace.postprocessor.conf_threshold = 0.2，to modify conf_threshold
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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 image in HWC or BGR format
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> **Return**
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>
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> > Return`fastdeploy.vision.FaceDetectionResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for its description.
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## Other Documents
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- [BlazeFace Model Description](..)
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- [BlazeFace C++ Deployment](../cpp)
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- [Model Prediction Results](../../../../../docs/api/vision_results/)
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examples/vision/facedet/blazeface/python/README_CN.md
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examples/vision/facedet/blazeface/python/README_CN.md
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[English](README.md) | 简体中文
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# BlazeFace 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BlazeFace在CPU/GPU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 examples/vision/facedet/blazeface/python/
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#下载BlazeFace模型文件和测试图片
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wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg
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wget https://bj.bcebos.com/paddlehub/fastdeploy/blazeface-1000e.tgz
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#使用blazeface-1000e模型
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# CPU推理
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python infer.py --model blazeface-1000e/ --image test_lite_face_detector_3.jpg --device cpu
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# GPU推理
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python infer.py --model blazeface-1000e/ --image test_lite_face_detector_3.jpg --device gpu
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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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<img width="640" src="https://user-images.githubusercontent.com/67993288/184301839-a29aefae-16c9-4196-bf9d-9c6cf694f02d.jpg">
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## BlazeFace Python接口
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```python
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fastdeploy.vision.facedet.BlzaeFace(model_file, params_file=None, runtime_option=None, config_file=None, model_format=ModelFormat.PADDLE)
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```
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BlazeFace模型加载和初始化
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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，当模型格式为ONNX格式时，此参数无需设定
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> * **config_file**(str): config文件路径，当模型格式为ONNX格式时，此参数无需设定
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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
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### predict函数
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> ```python
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> BlazeFace.predict(input_image)
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> ```
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> 通过BlazeFace.postprocessor.conf_threshold = 0.2，来修改conf_threshold
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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.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/)
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## 其它文档
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- [BlazeFace 模型介绍](..)
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- [BlazeFace C++部署](../cpp)
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- [模型预测结果说明](../../../../../docs/api/vision_results/)
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58
examples/vision/facedet/blazeface/python/infer.py
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examples/vision/facedet/blazeface/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 blazeface model dir.")
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parser.add_argument(
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"--image", required=True, help="Path of test 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' 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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if args.use_trt:
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option.use_trt_backend()
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option.set_trt_input_shape("images", [1, 3, 640, 640])
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return option
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args = parse_arguments()
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model_dir = args.model
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model_file = os.path.join(model_dir, "model.pdmodel")
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params_file = os.path.join(model_dir, "model.pdiparams")
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config_file = os.path.join(model_dir, "infer_cfg.yml")
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# Configure runtime and load the model
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runtime_option = build_option(args)
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model = fd.vision.facedet.BlazeFace(model_file, params_file, config_file, runtime_option=runtime_option)
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# Predict image detection results
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im = cv2.imread(args.image)
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result = model.predict(im)
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print(result)
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# Visualization of prediction Results
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vis_im = fd.vision.vis_face_detection(im, result)
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cv2.imwrite("visualized_result.jpg", vis_im)
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print("Visualized result save in ./visualized_result.jpg")
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