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
2025-10-18 14:40:44 +08:00 · 2022-08-27 15:09:30 +08:00
parent 820a5c5647
commit d96e98cd4d
45 changed files with 8323 additions and 2 deletions
--- a/csrc/fastdeploy/vision/ocr/ppocr/classifier.cc
+++ b/csrc/fastdeploy/vision/ocr/ppocr/classifier.cc
@@ -0,0 +1,148 @@
+// 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 Frontend& model_format) {
+  if (model_format == Frontend::ONNX) {
+    valid_cpu_backends = {Backend::ORT};  // 指定可用的CPU后端
+    valid_gpu_backends = {Backend::ORT, Backend::TRT};  // 指定可用的GPU后端
+  } else {
+    valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
+    valid_gpu_backends = {Backend::PDINFER, Backend::TRT, Backend::ORT};
+  }
+  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 = {0.485f, 0.456f, 0.406f};
+  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);
+  }
+}
+
+//预处理
+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;
+}
+
+//后处理
+bool Classifier::Postprocess(FDTensor& infer_result, int& cls_labels,
+                             float& cls_scores) {
+  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]]));
+
+  cls_labels = label;
+  cls_scores = score;
+
+  return true;
+}
+
+//预测
+bool Classifier::Predict(cv::Mat* img, int& cls_labels, float& cls_socres) {
+  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_labels, cls_socres)) {
+    FDERROR << "Failed to post process." << std::endl;
+    return false;
+  }
+
+  return true;
+}
+
+}  // namesapce ocr
+}  // namespace vision
+}  // namespace fastdeploy