Files
FastDeploy/fastdeploy/vision/ocr/ppocr/ppocr_v2.cc
yunyaoXYY d0fa2fcf5a [Bug Fix] Fix PPOCR bug when cls model is not used (#695)
* Imporve OCR Readme

* Improve OCR Readme

* Improve OCR Readme

* Improve OCR Readme

* Improve OCR Readme

* Add Initialize function to PP-OCR

* Add Initialize function to PP-OCR

* Add Initialize function to PP-OCR

* Make all the model links come from PaddleOCR

* Improve OCR readme

* Improve OCR readme

* Improve OCR readme

* Improve OCR readme

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add Readme for vision results

* Add check for label file in postprocess of Rec model

* Add check for label file in postprocess of Rec model

* Add check for label file in postprocess of Rec model

* Add check for label file in postprocess of Rec model

* Add check for label file in postprocess of Rec model

* Add check for label file in postprocess of Rec model

* Add comments to create API docs

* Improve OCR comments

* Rename OCR and add comments

* Make sure previous python example works

* Make sure previous python example works

* Fix Rec model bug

* Fix Rec model bug

* Fix rec model bug

* Add SetTrtMaxBatchSize function for TensorRT

* Add SetTrtMaxBatchSize Pybind

* Add set_trt_max_batch_size python function

* Set TRT dynamic shape in PPOCR examples

* Set TRT dynamic shape in PPOCR examples

* Set TRT dynamic shape in PPOCR examples

* Fix PPOCRv2 python example

* Fix PPOCR dynamic input shape bug

* Remove useless code

* Fix PPOCR bug

Co-authored-by: Jason <jiangjiajun@baidu.com>
2022-11-25 09:01:08 +08:00

119 lines
4.2 KiB
C++
Executable File

// 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/ocr/ppocr/ppocr_v2.h"
#include "fastdeploy/utils/perf.h"
#include "fastdeploy/vision/ocr/ppocr/utils/ocr_utils.h"
namespace fastdeploy {
namespace pipeline {
PPOCRv2::PPOCRv2(fastdeploy::vision::ocr::DBDetector* det_model,
fastdeploy::vision::ocr::Classifier* cls_model,
fastdeploy::vision::ocr::Recognizer* rec_model)
: detector_(det_model), classifier_(cls_model), recognizer_(rec_model) {
Initialized();
recognizer_->preprocessor_.rec_image_shape_[1] = 32;
}
PPOCRv2::PPOCRv2(fastdeploy::vision::ocr::DBDetector* det_model,
fastdeploy::vision::ocr::Recognizer* rec_model)
: detector_(det_model), recognizer_(rec_model) {
Initialized();
recognizer_->preprocessor_.rec_image_shape_[1] = 32;
}
bool PPOCRv2::Initialized() const {
if (detector_ != nullptr && !detector_->Initialized()) {
return false;
}
if (classifier_ != nullptr && !classifier_->Initialized()) {
return false;
}
if (recognizer_ != nullptr && !recognizer_->Initialized()) {
return false;
}
return true;
}
bool PPOCRv2::Predict(cv::Mat* img,
fastdeploy::vision::OCRResult* result) {
std::vector<fastdeploy::vision::OCRResult> batch_result(1);
BatchPredict({*img},&batch_result);
*result = std::move(batch_result[0]);
return true;
};
bool PPOCRv2::BatchPredict(const std::vector<cv::Mat>& images,
std::vector<fastdeploy::vision::OCRResult>* batch_result) {
batch_result->clear();
batch_result->resize(images.size());
std::vector<std::vector<std::array<int, 8>>> batch_boxes(images.size());
if (!detector_->BatchPredict(images, &batch_boxes)) {
FDERROR << "There's error while detecting image in PPOCR." << std::endl;
return false;
}
for(int i_batch = 0; i_batch < batch_boxes.size(); ++i_batch) {
vision::ocr::SortBoxes(&(batch_boxes[i_batch]));
(*batch_result)[i_batch].boxes = batch_boxes[i_batch];
}
for(int i_batch = 0; i_batch < images.size(); ++i_batch) {
fastdeploy::vision::OCRResult& ocr_result = (*batch_result)[i_batch];
// Get croped images by detection result
const std::vector<std::array<int, 8>>& boxes = ocr_result.boxes;
const cv::Mat& img = images[i_batch];
std::vector<cv::Mat> image_list;
if (boxes.size() == 0) {
image_list.emplace_back(img);
}else{
image_list.resize(boxes.size());
for (size_t i_box = 0; i_box < boxes.size(); ++i_box) {
image_list[i_box] = vision::ocr::GetRotateCropImage(img, boxes[i_box]);
}
}
std::vector<int32_t>* cls_labels_ptr = &ocr_result.cls_labels;
std::vector<float>* cls_scores_ptr = &ocr_result.cls_scores;
std::vector<std::string>* text_ptr = &ocr_result.text;
std::vector<float>* rec_scores_ptr = &ocr_result.rec_scores;
if (nullptr != classifier_){
if (!classifier_->BatchPredict(image_list, cls_labels_ptr, cls_scores_ptr)) {
FDERROR << "There's error while recognizing image in PPOCR." << std::endl;
return false;
}else{
for (size_t i_img = 0; i_img < image_list.size(); ++i_img) {
if(cls_labels_ptr->at(i_img) % 2 == 1 && cls_scores_ptr->at(i_img) > classifier_->postprocessor_.cls_thresh_) {
cv::rotate(image_list[i_img], image_list[i_img], 1);
}
}
}
}
if (!recognizer_->BatchPredict(image_list, text_ptr, rec_scores_ptr)) {
FDERROR << "There's error while recognizing image in PPOCR." << std::endl;
return false;
}
}
return true;
}
} // namesapce pipeline
} // namespace fastdeploy