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https://github.com/PaddlePaddle/FastDeploy.git
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f441ffe56b
* 更新ppdet * 更新ppdet * 更新ppdet * 更新ppdet * 更新ppdet * 新增ppdet_decode * 更新多batch支持 * 更新多batch支持 * 更新多batch支持 * 更新注释内容 * 尝试解决pybind问题 * 尝试解决pybind的问题 * 尝试解决pybind的问题 * 重构代码 * 重构代码 * 重构代码 * 按照要求修改 * 更新Picodet文档 * 更新Picodet文档，更新yolov8文档 * 修改picodet 以及 yolov8 example * 更新Picodet模型转换脚本 * 更新example代码 * 更新yolov8量化代码 * 修复部分bug 加入pybind * 修复pybind * 修复pybind错误的问题 * 更新说明文档 * 更新说明文档
97 lines
3.0 KiB
C++
97 lines
3.0 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 <iostream>
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#include <string>
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#include "fastdeploy/vision.h"
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void ONNXInfer(const std::string& model_dir, const std::string& image_file) {
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std::string model_file = model_dir + "/picodet_s_416_coco_lcnet.onnx";
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std::string params_file;
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std::string config_file = model_dir + "/infer_cfg.yml";
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auto option = fastdeploy::RuntimeOption();
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option.UseCpu();
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auto format = fastdeploy::ModelFormat::ONNX;
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auto model = fastdeploy::vision::detection::PicoDet(
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model_file, params_file, config_file, option, format);
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fastdeploy::TimeCounter tc;
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tc.Start();
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auto im = cv::imread(image_file);
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fastdeploy::vision::DetectionResult res;
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if (!model.Predict(im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
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tc.End();
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tc.PrintInfo("PPDet in ONNX");
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cv::imwrite("infer_onnx.jpg", vis_im);
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std::cout << "Visualized result saved in ./infer_onnx.jpg" << std::endl;
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}
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void RKNPU2Infer(const std::string& model_dir, const std::string& image_file) {
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auto model_file = model_dir + "/picodet_s_416_coco_lcnet_rk3588.rknn";
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auto params_file = "";
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auto config_file = model_dir + "/infer_cfg.yml";
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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::detection::PicoDet(
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model_file, params_file, config_file, option, format);
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model.GetPreprocessor().DisablePermute();
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model.GetPreprocessor().DisableNormalize();
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model.GetPostprocessor().ApplyDecodeAndNMS();
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auto im = cv::imread(image_file);
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fastdeploy::vision::DetectionResult res;
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fastdeploy::TimeCounter tc;
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tc.Start();
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if (!model.Predict(&im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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tc.End();
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tc.PrintInfo("PPDet in RKNPU2");
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std::cout << res.Str() << std::endl;
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auto vis_im = fastdeploy::vision::VisDetection(im, res, 0.5);
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cv::imwrite("infer_rknpu2.jpg", vis_im);
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std::cout << "Visualized result saved in ./infer_rknpu2.jpg" << std::endl;
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}
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int main(int argc, char* argv[]) {
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if (argc < 4) {
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std::cout
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<< "Usage: infer_demo path/to/model_dir path/to/image run_option, "
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"e.g ./infer_model ./picodet_model_dir ./test.jpeg"
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<< std::endl;
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return -1;
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}
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if (std::atoi(argv[3]) == 0) {
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ONNXInfer(argv[1], argv[2]);
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} else if (std::atoi(argv[3]) == 1) {
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RKNPU2Infer(argv[1], argv[2]);
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}
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return 0;
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}
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