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README.md

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PFLD Python Deployment Example

Before deployment, two steps require confirmation

This directory provides examples that infer.py fast finishes the deployment of PFLD on CPU/GPU and GPU accelerated by TensorRT. FastDeploy version 0.6.0 or above is required to support this model. The script is as follows

# Download deployment example code 
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/facealign/pfld/python

# Download the PFLD model files, test images, and videos 
## Original ONNX Model
wget https://bj.bcebos.com/paddlehub/fastdeploy/pfld-106-lite.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/facealign_input.png
# CPU inference
python infer.py --model pfld-106-lite.onnx --image facealign_input.png --device cpu
# GPU inference
python infer.py --model pfld-106-lite.onnx --image facealign_input.png --device gpu
# TRT inference
python infer.py --model pfld-106-lite.onnx --image facealign_input.png --device gpu --backend trt

The visualized result after running is as follows

PFLD Python Interface

fd.vision.facealign.PFLD(model_file, params_file=None, runtime_option=None, model_format=ModelFormat.ONNX)

PFLD model loading and initialization, among which model_file is the exported ONNX model format

Parameters

  • 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 Parameter

PFLD.predict(input_image)

Model prediction interface. Input images and output landmarks results directly

Parameter

  • input_image(np.ndarray): Input data in HWC or BGR format

Return

Return fastdeploy.vision.FaceAlignmentResult structure. Refer to Vision Model Prediction Results for the description of the structure.

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