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044ab993d2
* ppocr cls preprocessor use manager * hwc2chw cvcuda * ppocr rec preproc use manager * ocr rec preproc cvcuda * fix rec preproc bug * ppocr cls&rec preproc set normalize * fix pybind * address comment
103 lines
3.4 KiB
C++
103 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/cls_preprocessor.h"
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#include "fastdeploy/function/concat.h"
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#include "fastdeploy/utils/perf.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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ClassifierPreprocessor::ClassifierPreprocessor() {
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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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normalize_op_ =
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std::make_shared<Normalize>(std::vector<float>({0.5f, 0.5f, 0.5f}),
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std::vector<float>({0.5f, 0.5f, 0.5f}), true);
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hwc2chw_op_ = std::make_shared<HWC2CHW>();
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}
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void ClassifierPreprocessor::OcrClassifierResizeImage(
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FDMat* mat, const std::vector<int>& cls_image_shape) {
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int img_c = cls_image_shape[0];
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int img_h = cls_image_shape[1];
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int img_w = cls_image_shape[2];
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float ratio = float(mat->Width()) / float(mat->Height());
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int resize_w;
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if (ceilf(img_h * ratio) > img_w)
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resize_w = img_w;
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else
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resize_w = int(ceilf(img_h * ratio));
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resize_op_->SetWidthAndHeight(resize_w, img_h);
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(*resize_op_)(mat);
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}
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bool ClassifierPreprocessor::Run(std::vector<FDMat>* images,
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std::vector<FDTensor>* outputs,
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size_t start_index, size_t end_index) {
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if (images->size() == 0 || start_index < 0 || end_index <= start_index ||
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end_index > images->size()) {
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FDERROR << "images->size() or index error. Correct is: 0 <= start_index < "
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"end_index <= images->size()"
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<< std::endl;
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return false;
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}
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std::vector<FDMat> mats(end_index - start_index);
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for (size_t i = start_index; i < end_index; ++i) {
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mats[i - start_index] = images->at(i);
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}
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return Run(&mats, outputs);
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}
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bool ClassifierPreprocessor::Apply(FDMatBatch* image_batch,
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std::vector<FDTensor>* outputs) {
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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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OcrClassifierResizeImage(mat, cls_image_shape_);
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if (!disable_normalize_) {
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(*normalize_op_)(mat);
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}
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std::vector<float> value = {0, 0, 0};
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if (mat->Width() < cls_image_shape_[2]) {
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pad_op_->SetPaddingSize(0, 0, 0, cls_image_shape_[2] - mat->Width());
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(*pad_op_)(mat);
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}
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if (!disable_permute_) {
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(*hwc2chw_op_)(mat);
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}
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}
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// Only have 1 output tensor.
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outputs->resize(1);
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// Get the NCHW tensor
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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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