PaddleOCR/deploy/fastdeploy/cpu-gpu/cpp/infer_cls.cc

80 lines
2.4 KiB
C++

// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "fastdeploy/vision.h"
#ifdef WIN32
const char sep = '\\';
#else
const char sep = '/';
#endif
void InitAndInfer(const std::string &cls_model_dir,
const std::string &image_file,
const fastdeploy::RuntimeOption &option) {
auto cls_model_file = cls_model_dir + sep + "inference.pdmodel";
auto cls_params_file = cls_model_dir + sep + "inference.pdiparams";
auto cls_option = option;
auto cls_model = fastdeploy::vision::ocr::Classifier(
cls_model_file, cls_params_file, cls_option);
assert(cls_model.Initialized());
// Parameters settings for pre and post processing of Cls Model.
cls_model.GetPostprocessor().SetClsThresh(0.9);
auto im = cv::imread(image_file);
auto im_bak = im.clone();
fastdeploy::vision::OCRResult result;
if (!cls_model.Predict(im, &result)) {
std::cerr << "Failed to predict." << std::endl;
return;
}
// User can infer a batch of images by following code.
// if (!cls_model.BatchPredict({im}, &result)) {
// std::cerr << "Failed to predict." << std::endl;
// return;
// }
std::cout << result.Str() << std::endl;
}
int main(int argc, char *argv[]) {
if (argc < 4) {
std::cout << "Usage: infer_demo path/to/cls_model path/to/image "
"run_option, "
"e.g ./infer_demo ./ch_ppocr_mobile_v2.0_cls_infer ./12.jpg 0"
<< std::endl;
std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
"with gpu;."
<< std::endl;
return -1;
}
fastdeploy::RuntimeOption option;
int flag = std::atoi(argv[3]);
if (flag == 0) {
option.UseCpu();
} else if (flag == 1) {
option.UseGpu();
}
std::string cls_model_dir = argv[1];
std::string test_image = argv[2];
InitAndInfer(cls_model_dir, test_image, option);
return 0;
}