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https://github.com/PaddlePaddle/FastDeploy.git
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91a1c72f98
* move manager initialized_ flag to ppcls * update dbdetector preprocess api * declare processor op * ppocr detector preprocessor support cvcuda * move cvcuda op to class member * ppcls use manager register api * refactor det preprocessor init api * add set preprocessor api * add create processor macro * new processor call api * ppcls preprocessor init resize on cpu * ppocr detector preprocessor set normalize api * revert ppcls pybind * remove dbdetector set preprocessor * refine dbdetector preprocessor includes * remove mean std in py constructor * add comments * update comment * Update __init__.py
102 lines
3.4 KiB
C++
102 lines
3.4 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/ocr/ppocr/det_preprocessor.h"
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#include "fastdeploy/vision/ocr/ppocr/utils/ocr_utils.h"
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namespace fastdeploy {
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namespace vision {
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namespace ocr {
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std::array<int, 4> OcrDetectorGetInfo(FDMat* img, int max_size_len) {
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int w = img->Width();
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int h = img->Height();
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float ratio = 1.f;
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int max_wh = w >= h ? w : h;
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if (max_wh > max_size_len) {
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if (h > w) {
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ratio = float(max_size_len) / float(h);
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} else {
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ratio = float(max_size_len) / float(w);
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}
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}
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int resize_h = int(float(h) * ratio);
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int resize_w = int(float(w) * ratio);
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resize_h = std::max(int(std::round(float(resize_h) / 32) * 32), 32);
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resize_w = std::max(int(std::round(float(resize_w) / 32) * 32), 32);
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return {w, h, resize_w, resize_h};
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/*
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*ratio_h = float(resize_h) / float(h);
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*ratio_w = float(resize_w) / float(w);
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*/
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}
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DBDetectorPreprocessor::DBDetectorPreprocessor() {
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resize_op_ = std::make_shared<Resize>(-1, -1);
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std::vector<float> value = {0, 0, 0};
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pad_op_ = std::make_shared<Pad>(0, 0, 0, 0, value);
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std::vector<float> mean = {0.485f, 0.456f, 0.406f};
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std::vector<float> std = {0.229f, 0.224f, 0.225f};
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bool is_scale = true;
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normalize_permute_op_ =
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std::make_shared<NormalizeAndPermute>(mean, std, is_scale);
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}
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bool DBDetectorPreprocessor::ResizeImage(FDMat* img, int resize_w, int resize_h,
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int max_resize_w, int max_resize_h) {
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resize_op_->SetWidthAndHeight(resize_w, resize_h);
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(*resize_op_)(img);
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pad_op_->SetPaddingSize(0, max_resize_h - resize_h, 0,
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max_resize_w - resize_w);
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(*pad_op_)(img);
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return true;
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}
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bool DBDetectorPreprocessor::Apply(FDMatBatch* image_batch,
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std::vector<FDTensor>* outputs) {
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int max_resize_w = 0;
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int max_resize_h = 0;
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batch_det_img_info_.clear();
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batch_det_img_info_.resize(image_batch->mats->size());
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for (size_t i = 0; i < image_batch->mats->size(); ++i) {
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FDMat* mat = &(image_batch->mats->at(i));
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batch_det_img_info_[i] = OcrDetectorGetInfo(mat, max_side_len_);
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max_resize_w = std::max(max_resize_w, batch_det_img_info_[i][2]);
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max_resize_h = std::max(max_resize_h, batch_det_img_info_[i][3]);
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}
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for (size_t i = 0; i < image_batch->mats->size(); ++i) {
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FDMat* mat = &(image_batch->mats->at(i));
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ResizeImage(mat, batch_det_img_info_[i][2], batch_det_img_info_[i][3],
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max_resize_w, max_resize_h);
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}
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(*normalize_permute_op_)(image_batch);
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outputs->resize(1);
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FDTensor* tensor = image_batch->Tensor();
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(*outputs)[0].SetExternalData(tensor->Shape(), tensor->Dtype(),
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tensor->Data(), tensor->device,
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tensor->device_id);
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return true;
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}
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} // namespace ocr
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} // namespace vision
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} // namespace fastdeploy
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