更换文档检测模型

paddle_detection/deploy/cpp/include/preprocess_op.h

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+//   Copyright (c) 2020 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.
+
+#pragma once
+
+#include <glog/logging.h>
+#include <yaml-cpp/yaml.h>
+
+#include <iostream>
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+namespace PaddleDetection {
+
+// Object for storing all preprocessed data
+class ImageBlob {
+ public:
+  // image width and height
+  std::vector<float> im_shape_;
+  // Buffer for image data after preprocessing
+  std::vector<float> im_data_;
+  // in net data shape(after pad)
+  std::vector<float> in_net_shape_;
+  // Evaluation image width and height
+  // std::vector<float>  eval_im_size_f_;
+  // Scale factor for image size to origin image size
+  std::vector<float> scale_factor_;
+  // in net image after preprocessing
+  cv::Mat in_net_im_;
+};
+
+// Abstraction of preprocessing opration class
+class PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) = 0;
+  virtual void Run(cv::Mat* im, ImageBlob* data) = 0;
+};
+
+class InitInfo : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {}
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+};
+
+class NormalizeImage : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    mean_ = item["mean"].as<std::vector<float>>();
+    scale_ = item["std"].as<std::vector<float>>();
+    if (item["is_scale"]) is_scale_ = item["is_scale"].as<bool>();
+    if (item["norm_type"]) norm_type_ = item["norm_type"].as<std::string>();
+  }
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  // CHW or HWC
+  std::vector<float> mean_;
+  std::vector<float> scale_;
+  bool is_scale_ = true;
+  std::string norm_type_ = "mean_std";
+};
+
+class Permute : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {}
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+};
+
+class Resize : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    interp_ = item["interp"].as<int>();
+    keep_ratio_ = item["keep_ratio"].as<bool>();
+    target_size_ = item["target_size"].as<std::vector<int>>();
+  }
+
+  // Compute best resize scale for x-dimension, y-dimension
+  std::pair<float, float> GenerateScale(const cv::Mat& im);
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int interp_;
+  bool keep_ratio_;
+  std::vector<int> target_size_;
+  std::vector<int> in_net_shape_;
+};
+
+class LetterBoxResize : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    target_size_ = item["target_size"].as<std::vector<int>>();
+  }
+
+  float GenerateScale(const cv::Mat& im);
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  std::vector<int> target_size_;
+  std::vector<int> in_net_shape_;
+};
+// Models with FPN need input shape % stride == 0
+class PadStride : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    stride_ = item["stride"].as<int>();
+  }
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int stride_;
+};
+
+class TopDownEvalAffine : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    trainsize_ = item["trainsize"].as<std::vector<int>>();
+  }
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int interp_ = 1;
+  std::vector<int> trainsize_;
+};
+
+class WarpAffine : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    input_h_ = item["input_h"].as<int>();
+    input_w_ = item["input_w"].as<int>();
+    keep_res_ = item["keep_res"].as<bool>();
+  }
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  int input_h_;
+  int input_w_;
+  int interp_ = 1;
+  bool keep_res_ = true;
+  int pad_ = 31;
+};
+
+class Pad : public PreprocessOp {
+ public:
+  virtual void Init(const YAML::Node& item) {
+    size_ = item["size"].as<std::vector<int>>();
+    fill_value_ = item["fill_value"].as<std::vector<float>>();
+  }
+
+  virtual void Run(cv::Mat* im, ImageBlob* data);
+
+ private:
+  std::vector<int> size_;
+  std::vector<float> fill_value_;
+};
+
+void CropImg(cv::Mat& img,
+             cv::Mat& crop_img,
+             std::vector<int>& area,
+             std::vector<float>& center,
+             std::vector<float>& scale,
+             float expandratio = 0.15);
+
+// check whether the input size is dynamic
+bool CheckDynamicInput(const std::vector<cv::Mat>& imgs);
+
+// Pad images in batch
+std::vector<cv::Mat> PadBatch(const std::vector<cv::Mat>& imgs);
+
+class Preprocessor {
+ public:
+  void Init(const YAML::Node& config_node) {
+    // initialize image info at first
+    ops_["InitInfo"] = std::make_shared<InitInfo>();
+    for (const auto& item : config_node) {
+      auto op_name = item["type"].as<std::string>();
+
+      ops_[op_name] = CreateOp(op_name);
+      ops_[op_name]->Init(item);
+    }
+  }
+
+  std::shared_ptr<PreprocessOp> CreateOp(const std::string& name) {
+    if (name == "Resize") {
+      return std::make_shared<Resize>();
+    } else if (name == "LetterBoxResize") {
+      return std::make_shared<LetterBoxResize>();
+    } else if (name == "Permute") {
+      return std::make_shared<Permute>();
+    } else if (name == "NormalizeImage") {
+      return std::make_shared<NormalizeImage>();
+    } else if (name == "PadStride") {
+      // use PadStride instead of PadBatch
+      return std::make_shared<PadStride>();
+    } else if (name == "TopDownEvalAffine") {
+      return std::make_shared<TopDownEvalAffine>();
+    } else if (name == "WarpAffine") {
+      return std::make_shared<WarpAffine>();
+    }else if (name == "Pad") {
+      return std::make_shared<Pad>();
+    }
+    std::cerr << "can not find function of OP: " << name
+              << " and return: nullptr" << std::endl;
+    return nullptr;
+  }
+
+  void Run(cv::Mat* im, ImageBlob* data);
+
+ public:
+  static const std::vector<std::string> RUN_ORDER;
+
+ private:
+  std::unordered_map<std::string, std::shared_ptr<PreprocessOp>> ops_;
+};
+
+}  // namespace PaddleDetection