FastDeploy/examples/vision/facedet/yolov7face/cpp

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YOLOv7Face C++ Deployment Example

This directory provides examples that infer.cc fast finishes the deployment of YOLOv7Face on CPU/GPU and GPU accelerated by TensorRT.

Before deployment, two steps require confirmation

• 1. Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
• 2. Download the precompiled deployment library and samples code according to your development environment. Refer to FastDeploy Precompiled Library

Taking the CPU inference on Linux as an example, the compilation test can be completed by executing the following command in this directory.

mkdir build
cd build
# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above 
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz # x.x.x > 1.0.2
tar xvf fastdeploy-linux-x64-x.x.x.tgz # x.x.x > 1.0.2
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x # x.x.x > 1.0.2
make -j

# Download the official converted YOLOv7Face model files and test images 
wget https://raw.githubusercontent.com/DefTruth/lite.ai.toolkit/main/examples/lite/resources/test_lite_face_detector_3.jpg
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-lite-e.onnx
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7-tiny-face.onnx

# Use yolov7-tiny-face.onnx model
# CPU inference
./infer_demo yolov7-tiny-face.onnx test_lite_face_detector_3.jpg 0
# GPU inference
./infer_demo yolov7-tiny-face.onnx test_lite_face_detector_3.jpg 1
# TensorRT inference on GPU
./infer_demo yolov7-tiny-face.onnx test_lite_face_detector_3.jpg 2

# Use yolov7-lite-e.onnx model
# CPU inference
./infer_demo yolov7-lite-e.onnx test_lite_face_detector_3.jpg 0
# GPU inference
./infer_demo yolov7-lite-e.onnx test_lite_face_detector_3.jpg 1
# TensorRT inference on GPU
./infer_demo yolov7-lite-e.onnx test_lite_face_detector_3.jpg 2

The visualized result after running is as follows

The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:

• How to use FastDeploy C++ SDK in Windows

YOLOv7Face C++ Interface

YOLOv7Face Class

fastdeploy::vision::facedet::YOLOv7Face(
        const string& model_file,
        const string& params_file = "",
        const RuntimeOption& runtime_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::ONNX)

YOLOv7Face model loading and initialization, among which model_file is the exported ONNX model format

Parameter

• model_file(str): Model file path
• params_file(str): Parameter file path. Only passing an empty string 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 Function

YOLOv7Face::Predict(cv::Mat* im, FaceDetectionResult* result,
                float conf_threshold = 0.3,
                float nms_iou_threshold = 0.5)

Model prediction interface. Input images and output detection results.

Parameter

• im: Input images in HWC or BGR format
• result: Detection results, including detection box and confidence of each box. Refer to Vision Model Prediction Result for FaceDetectionResult
• conf_threshold: Filtering threshold of detection box confidence
• nms_iou_threshold: iou threshold during NMS processing

• Model Description
• Python Deployment
• Vision Model Prediction Results