175 lines
6.5 KiB
C++
175 lines
6.5 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 InitAndInfer(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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const fastdeploy::RuntimeOption &option) {
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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 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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// If use TRT backend, the dynamic shape will be set as follow.
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// We recommend that users set the length and height of the detection model to
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// a multiple of 32.
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// We also recommend that users set the Trt input shape as follow.
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det_option.SetTrtInputShape("x", {1, 3, 64, 64}, {1, 3, 640, 640},
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{1, 3, 960, 960});
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cls_option.SetTrtInputShape("x", {1, 3, 48, 10}, {cls_batch_size, 3, 48, 320},
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{cls_batch_size, 3, 48, 1024});
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rec_option.SetTrtInputShape("x", {1, 3, 48, 10}, {rec_batch_size, 3, 48, 320},
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{rec_batch_size, 3, 48, 2304});
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// Users could save TRT cache file to disk as follow.
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// det_option.SetTrtCacheFile(det_model_dir + sep + "det_trt_cache.trt");
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// cls_option.SetTrtCacheFile(cls_model_dir + sep + "cls_trt_cache.trt");
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// rec_option.SetTrtCacheFile(rec_model_dir + sep + "rec_trt_cache.trt");
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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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// Parameters settings for pre and post processing of Det/Cls/Rec Models.
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// All parameters are set to default values.
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det_model.GetPreprocessor().SetMaxSideLen(960);
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det_model.GetPostprocessor().SetDetDBThresh(0.3);
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det_model.GetPostprocessor().SetDetDBBoxThresh(0.6);
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det_model.GetPostprocessor().SetDetDBUnclipRatio(1.5);
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det_model.GetPostprocessor().SetDetDBScoreMode("slow");
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det_model.GetPostprocessor().SetUseDilation(0);
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cls_model.GetPostprocessor().SetClsThresh(0.9);
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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 < 7) {
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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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"run_option, "
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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 0"
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<< std::endl;
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std::cout << "The data type of run_option is int, e.g. 0: run with paddle "
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"inference on cpu;"
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<< std::endl;
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return -1;
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}
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fastdeploy::RuntimeOption option;
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int flag = std::atoi(argv[6]);
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if (flag == 0) {
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option.UseCpu();
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option.UsePaddleBackend(); // Paddle Inference
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} else if (flag == 1) {
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option.UseCpu();
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option.UseOpenVINOBackend(); // OpenVINO
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} else if (flag == 2) {
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option.UseCpu();
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option.UseOrtBackend(); // ONNX Runtime
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} else if (flag == 3) {
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option.UseCpu();
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option.UseLiteBackend(); // Paddle Lite
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} else if (flag == 4) {
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option.UseGpu();
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option.UsePaddleBackend(); // Paddle Inference
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} else if (flag == 5) {
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option.UseGpu();
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option.UsePaddleInferBackend();
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option.paddle_infer_option.collect_trt_shape = true;
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option.paddle_infer_option.enable_trt = true; // Paddle-TensorRT
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} else if (flag == 6) {
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option.UseGpu();
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option.UseOrtBackend(); // ONNX Runtime
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} else if (flag == 7) {
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option.UseGpu();
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option.UseTrtBackend(); // TensorRT
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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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InitAndInfer(det_model_dir, cls_model_dir, rec_model_dir, rec_label_file,
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test_image, option);
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return 0;
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}
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