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42d14e7119
* 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
93 lines
3.1 KiB
C++
93 lines
3.1 KiB
C++
// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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#include "fastdeploy/vision/facedet/ppdet/blazeface/blazeface.h"
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#include "fastdeploy/utils/perf.h"
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#include "fastdeploy/vision/utils/utils.h"
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namespace fastdeploy{
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namespace vision{
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namespace facedet{
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BlazeFace::BlazeFace(const std::string& model_file,
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const std::string& params_file,
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const std::string& config_file,
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const RuntimeOption& custom_option,
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const ModelFormat& model_format)
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: preprocessor_(config_file){
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valid_cpu_backends = {Backend::OPENVINO, Backend::PDINFER, Backend::LITE};
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valid_gpu_backends = {Backend::OPENVINO, Backend::LITE, Backend::PDINFER};
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runtime_option = custom_option;
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runtime_option.model_format = model_format;
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runtime_option.model_file = model_file;
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runtime_option.params_file = params_file;
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initialized = Initialize();
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}
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bool BlazeFace::Initialize(){
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if (!InitRuntime()){
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FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
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return false;
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}
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return true;
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}
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bool BlazeFace::Predict(const cv::Mat& im, FaceDetectionResult* result){
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std::vector<FaceDetectionResult> results;
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if (!this->BatchPredict({im}, &results)) {
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return false;
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}
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*result = std::move(results[0]);
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return true;
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}
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bool BlazeFace::BatchPredict(const std::vector<cv::Mat>& images,
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std::vector<FaceDetectionResult>* results){
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std::vector<FDMat> fd_images = WrapMat(images);
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FDASSERT(images.size() == 1, "Only support batch = 1 now.");
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std::vector<std::map<std::string, std::array<float, 2>>> ims_info;
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if (!preprocessor_.Run(&fd_images, &reused_input_tensors_, &ims_info)) {
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FDERROR << "Failed to preprocess the input image." << std::endl;
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return false;
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}
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reused_input_tensors_[0].name = "image";
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reused_input_tensors_[1].name = "scale_factor";
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reused_input_tensors_[2].name = "im_shape";
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// Some models don't need scale_factor and im_shape as input
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while (reused_input_tensors_.size() != NumInputsOfRuntime()) {
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reused_input_tensors_.pop_back();
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}
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if (!Infer(reused_input_tensors_, &reused_output_tensors_)) {
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FDERROR << "Failed to inference by runtime." << std::endl;
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return false;
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}
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if (!postprocessor_.Run(reused_output_tensors_, results, ims_info)){
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FDERROR << "Failed to postprocess the inference results by runtime." << std::endl;
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return false;
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}
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return true;
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}
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} // namespace facedet
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} // namespace vision
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} // namespace fastdeploy
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