93 lines
2.8 KiB
C++
93 lines
2.8 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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#pragma once
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#include "paddle_api.h" // NOLINT
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#include "json/json.h"
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#include <arm_neon.h>
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#include <chrono>
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#include <fstream>
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#include <iostream>
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#include <math.h>
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#include <opencv2/opencv.hpp>
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#include <stdlib.h>
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#include <sys/time.h>
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#include <vector>
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#include <include/preprocess_op.h>
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using namespace paddle::lite_api; // NOLINT
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using namespace std;
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namespace PPShiTu {
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struct RESULT {
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std::string class_name;
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int class_id;
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float score;
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};
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class FeatureExtract {
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public:
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explicit FeatureExtract(const Json::Value &config_file) {
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MobileConfig config;
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if (config_file["Global"]["rec_model_path"].as<std::string>().empty()) {
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std::cout << "Please set [rec_model_path] in config file" << std::endl;
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exit(-1);
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}
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config.set_model_from_file(
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config_file["Global"]["rec_model_path"].as<std::string>());
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this->predictor = CreatePaddlePredictor<MobileConfig>(config);
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SetPreProcessParam(config_file["RecPreProcess"]["transform_ops"]);
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printf("feature extract model create!\n");
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}
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void SetPreProcessParam(const Json::Value &config_file) {
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for (const auto &item : config_file) {
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auto op_name = item["type"].as<std::string>();
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if (op_name == "ResizeImage") {
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this->size = item["size"].as<int>();
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} else if (op_name == "NormalizeImage") {
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this->mean.clear();
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this->std.clear();
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for (auto tmp : item["mean"]) {
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this->mean.emplace_back(tmp.as<float>());
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}
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for (auto tmp : item["std"]) {
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this->std.emplace_back(tmp.as<float>());
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}
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this->scale = item["scale"].as<double>();
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}
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}
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}
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void RunRecModel(const cv::Mat &img, double &cost_time, std::vector<float> &feature);
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//void PostProcess(std::vector<float> &feature);
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void FeatureNorm(std::vector<float> &featuer);
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private:
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std::shared_ptr<PaddlePredictor> predictor;
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//std::vector<std::string> label_list;
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std::vector<float> mean = {0.485f, 0.456f, 0.406f};
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std::vector<float> std = {0.229f, 0.224f, 0.225f};
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double scale = 0.00392157;
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int size = 224;
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// pre-process
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Resize resize_op_;
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NormalizeImage normalize_op_;
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Permute permute_op_;
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};
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} // namespace PPShiTu
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