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[Model] add PFLD model (#433)
* support face alignment PFLD * add PFLD demo * fixed FaceAlignmentResult * fixed bugs * fixed img size * fixed readme * deal with comments * fixed readme * add pfld testcase * update infer.py * add gflags for example * update c++ readme * add gflags in example * fixed for ci * fixed gflags.cmake * deal with comments * update infer demo Co-authored-by: Jason <jiangjiajun@baidu.com>
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WJJ1995
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examples/vision/facealign/pfld/python/README.md
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examples/vision/facealign/pfld/python/README.md
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# PFLD 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PFLD在CPU/GPU，以及GPU上通过TensorRT加速部署的示例，保证 FastDeploy 版本 >= 0.6.0 支持PFLD模型。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/facealign/pfld/python
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# 下载PFLD模型文件和测试图片以及视频
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## 原版ONNX模型
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wget https://bj.bcebos.com/paddlehub/fastdeploy/pfld-106-lite.onnx
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wget https://bj.bcebos.com/paddlehub/fastdeploy/facealign_input.png
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# CPU推理
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python infer.py --model pfld-106-lite.onnx --image facealign_input.png --device cpu
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# GPU推理
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python infer.py --model pfld-106-lite.onnx --image facealign_input.png --device gpu
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# TRT推理
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python infer.py --model pfld-106-lite.onnx --image facealign_input.png --device gpu --backend trt
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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="500">
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<img width="470" height="384" float="left" src="https://user-images.githubusercontent.com/19977378/197931737-c2d8e760-a76d-478a-a6c9-4574fb5c70eb.png">
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</div>
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## PFLD Python接口
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```python
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fd.vision.facealign.PFLD(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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PFLD模型加载和初始化，其中model_file为导出的ONNX模型格式
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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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> * **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ONNX
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### predict函数
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> ```python
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> PFLD.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landmarks坐标结果。
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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.FaceAlignmentResult`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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- [PFLD 模型介绍](..)
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- [PFLD 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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88
examples/vision/facealign/pfld/python/infer.py
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examples/vision/facealign/pfld/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("--model", required=True, help="Path of PFLD model.")
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parser.add_argument("--image", type=str, 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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"--backend",
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type=str,
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default="ort",
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help="inference backend, ort, ov, trt, paddle, paddle_trt.")
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parser.add_argument(
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"--enable_trt_fp16",
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type=bool,
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default=False,
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help="whether enable fp16 in trt/paddle_trt backend")
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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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device = args.device
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backend = args.backend
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enable_trt_fp16 = args.enable_trt_fp16
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if device == "gpu":
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option.use_gpu()
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if backend == "ort":
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option.use_ort_backend()
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elif backend == "paddle":
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option.use_paddle_backend()
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elif backend in ["trt", "paddle_trt"]:
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option.use_trt_backend()
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option.set_trt_input_shape("input", [1, 3, 112, 112])
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if backend == "paddle_trt":
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option.enable_paddle_to_trt()
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if enable_trt_fp16:
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option.enable_trt_fp16()
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elif backend == "default":
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return option
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else:
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raise Exception(
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"While inference with GPU, only support default/ort/paddle/trt/paddle_trt now, {} is not supported.".
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format(backend))
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elif device == "cpu":
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if backend == "ort":
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option.use_ort_backend()
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elif backend == "ov":
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option.use_openvino_backend()
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elif backend == "paddle":
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option.use_paddle_backend()
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elif backend == "default":
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return option
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else:
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raise Exception(
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"While inference with CPU, only support default/ort/ov/paddle now, {} is not supported.".
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format(backend))
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else:
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raise Exception(
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"Only support device CPU/GPU now, {} is not supported.".format(
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device))
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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 = fd.vision.facealign.PFLD(args.model, runtime_option=runtime_option)
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# for image
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im = cv2.imread(args.image)
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result = model.predict(im.copy())
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print(result)
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# 可视化结果
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vis_im = fd.vision.vis_face_alignment(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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