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* cpp example run success * add landmarks * fix reviewed problem * add pybind * add readme in examples * fix reviewed problem * new file: tests/models/test_centerface.py * fix reviewed problem 230202
76 lines
3.1 KiB
Markdown
76 lines
3.1 KiB
Markdown
English | [简体中文](README_CN.md)
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# CenterFace 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 CenterFace on CPU/GPU and GPU accelerated by TensorRT. The script is as follows
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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/CenterFace/python/
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# Download CenterFace 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/CenterFace.onnx
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# Use CenterFace.onnx model
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# CPU inference
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python infer.py --model CenterFace.onnx --image test_lite_face_detector_3.jpg --device cpu
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# GPU inference
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python infer.py --model CenterFace.onnx --image test_lite_face_detector_3.jpg --device gpu
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# TensorRT inference on GPU
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python infer.py --model CenterFace.onnx --image test_lite_face_detector_3.jpg --device gpu --use_trt True
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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/44280887/215670067-e14b5205-e303-4c3a-9812-be4a81173dc6.jpg">
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## CenterFace Python Interface
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```python
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fastdeploy.vision.facedet.CenterFace(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)
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```
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CenterFace model loading and initialization, among which model_file is the exported ONNX 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 ONNX 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. ONNX format by default
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### predict function
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> ```python
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> CenterFace.predict(image_data)
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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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> > * **image_data**(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.FaceDetectionResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for its description.
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### Class Member Property
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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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> > * **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]
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## Other Documents
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- [CenterFace Model Description](..)
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- [CenterFace C++ Deployment](../cpp)
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- [Model Prediction Results](../../../../../docs/api/vision_results/)
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