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Add PaddleOCRv3 & PaddleOCRv2 Support (#139)
* Add PaddleOCR Support * Add PaddleOCR Support * Add PaddleOCRv3 Support * Add PaddleOCRv3 Support * Update README.md * Update README.md * Update README.md * Update README.md * Add PaddleOCRv3 Support * Add PaddleOCRv3 Supports * Add PaddleOCRv3 Suport * Fix Rec diff * Remove useless functions * Remove useless comments * Add PaddleOCRv2 Support
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yunyaoXYY
207
csrc/fastdeploy/vision/ocr/ppocr/recognizer.cc
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207
csrc/fastdeploy/vision/ocr/ppocr/recognizer.cc
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// 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/recognizer.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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std::vector<std::string> ReadDict(const std::string& path) {
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std::ifstream in(path);
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std::string line;
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std::vector<std::string> m_vec;
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if (in) {
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while (getline(in, line)) {
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m_vec.push_back(line);
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}
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} else {
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std::cout << "no such label file: " << path << ", exit the program..."
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<< std::endl;
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exit(1);
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}
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return m_vec;
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}
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Recognizer::Recognizer() {}
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Recognizer::Recognizer(const std::string& model_file,
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const std::string& params_file,
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const std::string& label_path,
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const RuntimeOption& custom_option,
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const Frontend& model_format) {
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if (model_format == Frontend::ONNX) {
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valid_cpu_backends = {Backend::ORT}; // 指定可用的CPU后端
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valid_gpu_backends = {Backend::ORT, Backend::TRT}; // 指定可用的GPU后端
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} else {
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// NOTE:此模型暂不支持paddle-inference-Gpu推理
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valid_cpu_backends = {Backend::ORT, Backend::PDINFER};
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valid_gpu_backends = {Backend::ORT, Backend::TRT};
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}
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runtime_option = custom_option;
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runtime_option.model_format = model_format;
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runtime_option.model_file = model_file;
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runtime_option.params_file = params_file;
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// Recognizer在使用CPU推理，并把PaddleInference作为推理后端时,需要删除以下2个pass//
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runtime_option.DeletePaddleBackendPass("matmul_transpose_reshape_fuse_pass");
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runtime_option.DeletePaddleBackendPass(
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"matmul_transpose_reshape_mkldnn_fuse_pass");
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initialized = Initialize();
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// init label_lsit
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label_list = ReadDict(label_path);
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label_list.insert(label_list.begin(), "#"); // blank char for ctc
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label_list.push_back(" ");
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}
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// Init
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bool Recognizer::Initialize() {
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// pre&post process parameters
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rec_batch_num = 1;
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rec_img_h = 48;
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rec_img_w = 320;
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rec_image_shape = {3, rec_img_h, rec_img_w};
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mean = {0.5f, 0.5f, 0.5f};
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scale = {0.5f, 0.5f, 0.5f};
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is_scale = true;
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if (!InitRuntime()) {
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FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
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return false;
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}
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return true;
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}
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void OcrRecognizerResizeImage(Mat* mat, float wh_ratio,
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const std::vector<int>& rec_image_shape) {
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int imgC, imgH, imgW;
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imgC = rec_image_shape[0];
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imgH = rec_image_shape[1];
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imgW = rec_image_shape[2];
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imgW = int(imgH * wh_ratio);
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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(imgH * ratio) > imgW)
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resize_w = imgW;
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else
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resize_w = int(ceilf(imgH * ratio));
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Resize::Run(mat, resize_w, imgH);
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std::vector<float> value = {127, 127, 127};
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Pad::Run(mat, 0, 0, 0, int(imgW - mat->Width()), value);
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}
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//预处理
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bool Recognizer::Preprocess(Mat* mat, FDTensor* output,
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const std::vector<int>& rec_image_shape) {
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int imgH = rec_image_shape[1];
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int imgW = rec_image_shape[2];
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float wh_ratio = imgW * 1.0 / imgH;
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float ori_wh_ratio = mat->Width() * 1.0 / mat->Height();
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wh_ratio = std::max(wh_ratio, ori_wh_ratio);
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OcrRecognizerResizeImage(mat, wh_ratio, rec_image_shape);
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Normalize::Run(mat, mean, scale, true);
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HWC2CHW::Run(mat);
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Cast::Run(mat, "float");
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mat->ShareWithTensor(output);
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output->shape.insert(output->shape.begin(), 1);
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return true;
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}
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//后处理
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bool Recognizer::Postprocess(FDTensor& infer_result, std::string& rec_texts,
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float& rec_text_scores) {
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std::vector<int64_t> output_shape = infer_result.shape;
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FDASSERT(output_shape[0] == 1, "Only support batch =1 now.");
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float* out_data = static_cast<float*>(infer_result.Data());
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std::string str_res;
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int argmax_idx;
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int last_index = 0;
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float score = 0.f;
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int count = 0;
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float max_value = 0.0f;
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for (int n = 0; n < output_shape[1]; n++) {
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argmax_idx = int(
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std::distance(&out_data[n * output_shape[2]],
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std::max_element(&out_data[n * output_shape[2]],
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&out_data[(n + 1) * output_shape[2]])));
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max_value = float(*std::max_element(&out_data[n * output_shape[2]],
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&out_data[(n + 1) * output_shape[2]]));
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if (argmax_idx > 0 && (!(n > 0 && argmax_idx == last_index))) {
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score += max_value;
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count += 1;
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str_res += label_list[argmax_idx];
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}
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last_index = argmax_idx;
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}
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score /= count;
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rec_texts = str_res;
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rec_text_scores = score;
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return true;
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}
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//预测
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bool Recognizer::Predict(cv::Mat* img, std::string& rec_texts,
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float& rec_text_scores) {
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Mat mat(*img);
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std::vector<FDTensor> input_tensors(1);
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if (!Preprocess(&mat, &input_tensors[0], rec_image_shape)) {
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FDERROR << "Failed to preprocess input image." << std::endl;
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return false;
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}
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input_tensors[0].name = InputInfoOfRuntime(0).name;
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std::vector<FDTensor> output_tensors;
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if (!Infer(input_tensors, &output_tensors)) {
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FDERROR << "Failed to inference." << std::endl;
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return false;
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}
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if (!Postprocess(output_tensors[0], rec_texts, rec_text_scores)) {
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FDERROR << "Failed to post process." << std::endl;
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return false;
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}
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return true;
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}
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} // namesapce ocr
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} // namespace vision
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} // namespace fastdeploy
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