// Copyright (c) 2020 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 "../include/paddleocr.h"
|
#include "../auto_log/autolog.h"
|
|
#include <numeric>
|
namespace PaddleOCR {
|
|
PPOCR::PPOCR(std::string det_model_dir,
|
std::string cls_model_dir,
|
std::string rec_model_dir,
|
std::string rec_char_dict_path) {
|
if (!det_model_dir.empty()) {
|
this->detector_ = new DBDetector(det_model_dir);
|
}
|
if (!cls_model_dir.empty()) {
|
this->classifier_ = new Classifier(cls_model_dir);
|
}
|
if (!rec_model_dir.empty() && !rec_char_dict_path.empty()) {
|
this->recognizer_ = new CRNNRecognizer(rec_model_dir, rec_char_dict_path);
|
}
|
};
|
|
void PPOCR::det(cv::Mat img, std::vector<OCRPredictResult> &ocr_results,
|
std::vector<double> ×) {
|
std::vector<std::vector<std::vector<int>>> boxes;
|
std::vector<double> det_times;
|
|
this->detector_->Run(img, boxes, det_times);
|
|
for (int i = 0; i < boxes.size(); i++) {
|
OCRPredictResult res;
|
res.box = boxes[i];
|
ocr_results.push_back(res);
|
}
|
// sort boex from top to bottom, from left to right
|
Utility::sorted_boxes(ocr_results);
|
times[0] += det_times[0];
|
times[1] += det_times[1];
|
times[2] += det_times[2];
|
}
|
|
void PPOCR::rec(std::vector<cv::Mat> img_list,
|
std::vector<OCRPredictResult> &ocr_results,
|
std::vector<double> ×) {
|
std::vector<std::string> rec_texts(img_list.size(), "");
|
std::vector<float> rec_text_scores(img_list.size(), 0);
|
std::vector<double> rec_times;
|
this->recognizer_->Run(img_list, rec_texts, rec_text_scores, rec_times);
|
// output rec results
|
for (int i = 0; i < rec_texts.size(); i++) {
|
ocr_results[i].text = rec_texts[i];
|
ocr_results[i].score = rec_text_scores[i];
|
}
|
times[0] += rec_times[0];
|
times[1] += rec_times[1];
|
times[2] += rec_times[2];
|
}
|
|
void PPOCR::cls(std::vector<cv::Mat> img_list,
|
std::vector<OCRPredictResult> &ocr_results,
|
std::vector<double> ×) {
|
std::vector<int> cls_labels(img_list.size(), 0);
|
std::vector<float> cls_scores(img_list.size(), 0);
|
std::vector<double> cls_times;
|
this->classifier_->Run(img_list, cls_labels, cls_scores, cls_times);
|
// output cls results
|
for (int i = 0; i < cls_labels.size(); i++) {
|
ocr_results[i].cls_label = cls_labels[i];
|
ocr_results[i].cls_score = cls_scores[i];
|
}
|
times[0] += cls_times[0];
|
times[1] += cls_times[1];
|
times[2] += cls_times[2];
|
}
|
|
std::vector<std::vector<OCRPredictResult>>
|
PPOCR::ocr(std::vector<cv::Mat>& cv_all_imgs, bool det, bool rec,
|
bool cls) {
|
std::vector<double> time_info_det = {0, 0, 0};
|
std::vector<double> time_info_rec = {0, 0, 0};
|
std::vector<double> time_info_cls = {0, 0, 0};
|
std::vector<std::vector<OCRPredictResult>> ocr_results;
|
|
if (!det) {
|
std::vector<OCRPredictResult> ocr_result;
|
// read image
|
auto& img_list = cv_all_imgs;
|
for (int i = 0; i < cv_all_imgs.size(); ++i) {
|
OCRPredictResult res;
|
ocr_result.push_back(res);
|
}
|
if (cls && this->classifier_ != nullptr) {
|
this->cls(img_list, ocr_result, time_info_cls);
|
for (int i = 0; i < img_list.size(); i++) {
|
if (ocr_result[i].cls_label % 2 == 1 &&
|
ocr_result[i].cls_score > this->classifier_->cls_thresh) {
|
cv::rotate(img_list[i], img_list[i], 1);
|
}
|
}
|
}
|
if (rec) {
|
this->rec(img_list, ocr_result, time_info_rec);
|
}
|
for (int i = 0; i < cv_all_imgs.size(); ++i) {
|
std::vector<OCRPredictResult> ocr_result_tmp;
|
ocr_result_tmp.push_back(ocr_result[i]);
|
ocr_results.push_back(ocr_result_tmp);
|
}
|
} else {
|
for (int i = 0; i < cv_all_imgs.size(); ++i) {
|
std::vector<OCRPredictResult> ocr_result;
|
cv::Mat& srcimg = cv_all_imgs[i];
|
|
// det
|
this->det(srcimg, ocr_result, time_info_det);
|
// crop image
|
std::vector<cv::Mat> img_list;
|
for (int j = 0; j < ocr_result.size(); j++) {
|
cv::Mat crop_img;
|
crop_img = Utility::GetRotateCropImage(srcimg, ocr_result[j].box);
|
img_list.push_back(crop_img);
|
}
|
|
// cls
|
if (cls && this->classifier_ != nullptr) {
|
this->cls(img_list, ocr_result, time_info_cls);
|
for (int i = 0; i < img_list.size(); i++) {
|
if (ocr_result[i].cls_label % 2 == 1 &&
|
ocr_result[i].cls_score > this->classifier_->cls_thresh) {
|
cv::rotate(img_list[i], img_list[i], 1);
|
}
|
}
|
}
|
// rec
|
if (rec) {
|
this->rec(img_list, ocr_result, time_info_rec);
|
}
|
ocr_results.push_back(ocr_result);
|
}
|
}
|
return ocr_results;
|
} // namespace PaddleOCR
|
|
PPOCR::~PPOCR() {
|
if (this->detector_ != nullptr) {
|
delete this->detector_;
|
}
|
if (this->classifier_ != nullptr) {
|
delete this->classifier_;
|
}
|
if (this->recognizer_ != nullptr) {
|
delete this->recognizer_;
|
}
|
};
|
|
} // namespace PaddleOCR
|
