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1dabfdf3f1
* 更新交叉编译 * 更新交叉编译 * 更新交叉编译 * 更新交叉编译 * 更新交叉编译 * 更新交叉编译 * 更新交叉编译 * 更新交叉编译 * 更新交叉编译 * Update issues.md * Update fastdeploy_init.sh * 更新交叉编译 * 更新insightface系列模型的rknpu2支持 * 更新insightface系列模型的rknpu2支持 * 更新说明文档 * 更新insightface * 尝试解决pybind问题 Co-authored-by: Jason <928090362@qq.com> Co-authored-by: Jason <jiangjiajun@baidu.com>
124 lines
4.3 KiB
C++
124 lines
4.3 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.h"
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void CpuInfer(const std::string& model_file,
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const std::vector<std::string>& image_file) {
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auto model = fastdeploy::vision::faceid::ArcFace(model_file, "");
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cv::Mat face0 = cv::imread(image_file[0]);
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fastdeploy::vision::FaceRecognitionResult res0;
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if (!model.Predict(face0, &res0)) {
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std::cerr << "Prediction Failed." << std::endl;
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}
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cv::Mat face1 = cv::imread(image_file[1]);
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fastdeploy::vision::FaceRecognitionResult res1;
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if (!model.Predict(face1, &res1)) {
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std::cerr << "Prediction Failed." << std::endl;
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}
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cv::Mat face2 = cv::imread(image_file[2]);
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fastdeploy::vision::FaceRecognitionResult res2;
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if (!model.Predict(face2, &res2)) {
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std::cerr << "Prediction Failed." << std::endl;
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return;
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}
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std::cout << "Prediction Done!" << std::endl;
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std::cout << "--- [Face 0]:" << res0.Str();
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std::cout << "--- [Face 1]:" << res1.Str();
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std::cout << "--- [Face 2]:" << res2.Str();
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float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
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res0.embedding, res1.embedding,
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model.GetPostprocessor().GetL2Normalize());
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float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
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res0.embedding, res2.embedding,
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model.GetPostprocessor().GetL2Normalize());
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std::cout << "Detect Done! Cosine 01: " << cosine01
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<< ", Cosine 02:" << cosine02 << std::endl;
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}
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void RKNPUInfer(const std::string& model_file,
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const std::vector<std::string>& image_file) {
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std::string params_file;
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auto option = fastdeploy::RuntimeOption();
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option.UseRKNPU2();
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auto format = fastdeploy::ModelFormat::RKNN;
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auto model = fastdeploy::vision::faceid::ArcFace(model_file, params_file,
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option, format);
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model.GetPreprocessor().DisableNormalize();
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model.GetPreprocessor().DisablePermute();
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cv::Mat face0 = cv::imread(image_file[0]);
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fastdeploy::vision::FaceRecognitionResult res0;
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if (!model.Predict(face0, &res0)) {
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std::cerr << "Prediction Failed." << std::endl;
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return;
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}
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cv::Mat face1 = cv::imread(image_file[1]);
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fastdeploy::vision::FaceRecognitionResult res1;
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if (!model.Predict(face1, &res1)) {
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std::cerr << "Prediction Failed." << std::endl;
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return;
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}
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cv::Mat face2 = cv::imread(image_file[2]);
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fastdeploy::vision::FaceRecognitionResult res2;
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if (!model.Predict(face2, &res2)) {
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std::cerr << "Prediction Failed." << std::endl;
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return;
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}
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std::cout << "Prediction Done!" << std::endl;
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std::cout << "--- [Face 0]:" << res0.Str();
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std::cout << "--- [Face 1]:" << res1.Str();
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std::cout << "--- [Face 2]:" << res2.Str();
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float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
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res0.embedding, res1.embedding,
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model.GetPostprocessor().GetL2Normalize());
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float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
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res0.embedding, res2.embedding,
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model.GetPostprocessor().GetL2Normalize());
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std::cout << "Detect Done! Cosine 01: " << cosine01
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<< ", Cosine 02:" << cosine02 << std::endl;
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}
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int main(int argc, char* argv[]) {
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if (argc < 6) {
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std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
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"e.g ./infer_arcface_demo ms1mv3_arcface_r100.onnx "
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"face_0.jpg face_1.jpg face_2.jpg 0"
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<< std::endl;
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std::cout << "The data type of run_option is int, "
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"0: run with cpu; 1: run with rknpu2."
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<< std::endl;
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return -1;
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}
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std::vector<std::string> image_files = {argv[2], argv[3], argv[4]};
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if (std::atoi(argv[5]) == 0) {
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CpuInfer(argv[1], image_files);
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} else if (std::atoi(argv[5]) == 1) {
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RKNPUInfer(argv[1], image_files);
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}
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return 0;
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}
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