116 lines
4.1 KiB
C++
116 lines
4.1 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.h"
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#ifdef WIN32
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const char sep = '\\';
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#else
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const char sep = '/';
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#endif
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void KunlunXinInfer(const std::string &det_model_dir,
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const std::string &cls_model_dir,
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const std::string &rec_model_dir,
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const std::string &rec_label_file,
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const std::string &image_file) {
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auto det_model_file = det_model_dir + sep + "inference.pdmodel";
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auto det_params_file = det_model_dir + sep + "inference.pdiparams";
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auto cls_model_file = cls_model_dir + sep + "inference.pdmodel";
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auto cls_params_file = cls_model_dir + sep + "inference.pdiparams";
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auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
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auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
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auto option = fastdeploy::RuntimeOption();
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option.UseKunlunXin();
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auto det_option = option;
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auto cls_option = option;
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auto rec_option = option;
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// The cls and rec model can inference a batch of images now.
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// User could initialize the inference batch size and set them after create
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// PP-OCR model.
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int cls_batch_size = 1;
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int rec_batch_size = 6;
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auto det_model = fastdeploy::vision::ocr::DBDetector(
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det_model_file, det_params_file, det_option);
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auto cls_model = fastdeploy::vision::ocr::Classifier(
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cls_model_file, cls_params_file, cls_option);
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auto rec_model = fastdeploy::vision::ocr::Recognizer(
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rec_model_file, rec_params_file, rec_label_file, rec_option);
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assert(det_model.Initialized());
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assert(cls_model.Initialized());
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assert(rec_model.Initialized());
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// The classification model is optional, so the PP-OCR can also be connected
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// in series as follows
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// auto ppocr_v3 = fastdeploy::pipeline::PPOCRv3(&det_model, &rec_model);
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auto ppocr_v3 =
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fastdeploy::pipeline::PPOCRv3(&det_model, &cls_model, &rec_model);
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// Set inference batch size for cls model and rec model, the value could be -1
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// and 1 to positive infinity.
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// When inference batch size is set to -1, it means that the inference batch
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// size
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// of the cls and rec models will be the same as the number of boxes detected
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// by the det model.
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ppocr_v3.SetClsBatchSize(cls_batch_size);
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ppocr_v3.SetRecBatchSize(rec_batch_size);
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if (!ppocr_v3.Initialized()) {
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std::cerr << "Failed to initialize PP-OCR." << std::endl;
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return;
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}
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auto im = cv::imread(image_file);
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auto im_bak = im.clone();
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fastdeploy::vision::OCRResult result;
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if (!ppocr_v3.Predict(&im, &result)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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std::cout << result.Str() << std::endl;
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auto vis_im = fastdeploy::vision::VisOcr(im_bak, result);
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cv::imwrite("vis_result.jpg", vis_im);
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std::cout << "Visualized result saved in ./vis_result.jpg" << 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/det_model path/to/cls_model "
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"path/to/rec_model path/to/rec_label_file path/to/image "
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"e.g ./infer_demo ./ch_PP-OCRv3_det_infer "
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"./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer "
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"./ppocr_keys_v1.txt ./12.jpg"
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<< std::endl;
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return -1;
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}
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std::string det_model_dir = argv[1];
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std::string cls_model_dir = argv[2];
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std::string rec_model_dir = argv[3];
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std::string rec_label_file = argv[4];
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std::string test_image = argv[5];
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KunlunXinInfer(det_model_dir, cls_model_dir, rec_model_dir, rec_label_file,
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test_image);
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return 0;
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}
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