mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-31 20:02:53 +08:00
147 lines
4.1 KiB
C++
147 lines
4.1 KiB
C++
// 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/classifier.h"
|
|
#include "fastdeploy/utils/perf.h"
|
|
#include "fastdeploy/vision/ocr/ppocr/utils/ocr_utils.h"
|
|
|
|
namespace fastdeploy {
|
|
namespace vision {
|
|
namespace ocr {
|
|
|
|
Classifier::Classifier() {}
|
|
Classifier::Classifier(const std::string& model_file,
|
|
const std::string& params_file,
|
|
const RuntimeOption& custom_option,
|
|
const ModelFormat& model_format) {
|
|
if (model_format == ModelFormat::ONNX) {
|
|
valid_cpu_backends = {Backend::ORT,
|
|
Backend::OPENVINO};
|
|
valid_gpu_backends = {Backend::ORT, Backend::TRT};
|
|
} else {
|
|
valid_cpu_backends = {Backend::PDINFER, Backend::ORT, Backend::OPENVINO};
|
|
valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
|
|
}
|
|
runtime_option = custom_option;
|
|
runtime_option.model_format = model_format;
|
|
runtime_option.model_file = model_file;
|
|
runtime_option.params_file = params_file;
|
|
|
|
initialized = Initialize();
|
|
}
|
|
|
|
// Init
|
|
bool Classifier::Initialize() {
|
|
// pre&post process parameters
|
|
cls_thresh = 0.9;
|
|
cls_image_shape = {3, 48, 192};
|
|
cls_batch_num = 1;
|
|
mean = {0.5f, 0.5f, 0.5f};
|
|
scale = {0.5f, 0.5f, 0.5f};
|
|
is_scale = true;
|
|
|
|
if (!InitRuntime()) {
|
|
FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
void OcrClassifierResizeImage(Mat* mat,
|
|
const std::vector<int>& rec_image_shape) {
|
|
int imgC = rec_image_shape[0];
|
|
int imgH = rec_image_shape[1];
|
|
int imgW = rec_image_shape[2];
|
|
|
|
float ratio = float(mat->Width()) / float(mat->Height());
|
|
|
|
int resize_w;
|
|
if (ceilf(imgH * ratio) > imgW)
|
|
resize_w = imgW;
|
|
else
|
|
resize_w = int(ceilf(imgH * ratio));
|
|
|
|
Resize::Run(mat, resize_w, imgH);
|
|
|
|
std::vector<float> value = {0, 0, 0};
|
|
if (resize_w < imgW) {
|
|
Pad::Run(mat, 0, 0, 0, imgW - resize_w, value);
|
|
}
|
|
}
|
|
|
|
bool Classifier::Preprocess(Mat* mat, FDTensor* output) {
|
|
// 1. cls resizes
|
|
// 2. normalize
|
|
// 3. batch_permute
|
|
OcrClassifierResizeImage(mat, cls_image_shape);
|
|
|
|
Normalize::Run(mat, mean, scale, true);
|
|
|
|
HWC2CHW::Run(mat);
|
|
Cast::Run(mat, "float");
|
|
|
|
mat->ShareWithTensor(output);
|
|
output->shape.insert(output->shape.begin(), 1);
|
|
|
|
return true;
|
|
}
|
|
|
|
bool Classifier::Postprocess(FDTensor& infer_result,
|
|
std::tuple<int, float>* cls_result) {
|
|
std::vector<int64_t> output_shape = infer_result.shape;
|
|
FDASSERT(output_shape[0] == 1, "Only support batch =1 now.");
|
|
|
|
float* out_data = static_cast<float*>(infer_result.Data());
|
|
|
|
int label = std::distance(
|
|
&out_data[0], std::max_element(&out_data[0], &out_data[output_shape[1]]));
|
|
|
|
float score =
|
|
float(*std::max_element(&out_data[0], &out_data[output_shape[1]]));
|
|
|
|
std::get<0>(*cls_result) = label;
|
|
std::get<1>(*cls_result) = score;
|
|
|
|
return true;
|
|
}
|
|
|
|
bool Classifier::Predict(cv::Mat* img, std::tuple<int, float>* cls_result) {
|
|
Mat mat(*img);
|
|
std::vector<FDTensor> input_tensors(1);
|
|
|
|
if (!Preprocess(&mat, &input_tensors[0])) {
|
|
FDERROR << "Failed to preprocess input image." << std::endl;
|
|
return false;
|
|
}
|
|
|
|
input_tensors[0].name = InputInfoOfRuntime(0).name;
|
|
std::vector<FDTensor> output_tensors;
|
|
if (!Infer(input_tensors, &output_tensors)) {
|
|
FDERROR << "Failed to inference." << std::endl;
|
|
return false;
|
|
}
|
|
|
|
if (!Postprocess(output_tensors[0], cls_result)) {
|
|
FDERROR << "Failed to post process." << std::endl;
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
} // namesapce ocr
|
|
} // namespace vision
|
|
} // namespace fastdeploy
|