preprocess_op.cpp

// 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.

#include "opencv2/core.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/imgproc.hpp"
#include "paddle_api.h"
#include "paddle_inference_api.h"
#include <chrono>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <vector>

#include <cstring>
#include <fstream>
#include <numeric>

#include <include/preprocess_op.h>

namespace PaddleOCR {

void Permute::Run(const cv::Mat *im, float *data) {
  int rh = im->rows;
  int rw = im->cols;
  int rc = im->channels();
  for (int i = 0; i < rc; ++i) {
    cv::extractChannel(*im, cv::Mat(rh, rw, CV_32FC1, data + i * rh * rw), i);
  }
}

void PermuteBatch::Run(const std::vector<cv::Mat> imgs, float *data) {
  for (int j = 0; j < imgs.size(); j++) {
    int rh = imgs[j].rows;
    int rw = imgs[j].cols;
    int rc = imgs[j].channels();
    for (int i = 0; i < rc; ++i) {
      cv::extractChannel(
          imgs[j], cv::Mat(rh, rw, CV_32FC1, data + (j * rc + i) * rh * rw), i);
    }
  }
}

void Normalize::Run(cv::Mat *im, const std::vector<float> &mean,
                    const std::vector<float> &scale, const bool is_scale) {
  double e = 1.0;
  if (is_scale) {
    e /= 255.0;
  }
  (*im).convertTo(*im, CV_32FC3, e);
  std::vector<cv::Mat> bgr_channels(3);
  cv::split(*im, bgr_channels);
  for (auto i = 0; i < bgr_channels.size(); i++) {
    bgr_channels[i].convertTo(bgr_channels[i], CV_32FC1, 1.0 * scale[i],
                              (0.0 - mean[i]) * scale[i]);
  }
  cv::merge(bgr_channels, *im);
}

void ResizeImgType0::Run(const cv::Mat &img, cv::Mat &resize_img,
                         string limit_type, int limit_side_len, float &ratio_h,
                         float &ratio_w, bool use_tensorrt) {
  int w = img.cols;
  int h = img.rows;
  float ratio = 1.f;
  if (limit_type == "min") {
    int min_wh = min(h, w);
    if (min_wh < limit_side_len) {
      if (h < w) {
        ratio = float(limit_side_len) / float(h);
      } else {
        ratio = float(limit_side_len) / float(w);
      }
    }
  } else {
    int max_wh = max(h, w);
    if (max_wh > limit_side_len) {
      if (h > w) {
        ratio = float(limit_side_len) / float(h);
      } else {
        ratio = float(limit_side_len) / float(w);
      }
    }
  }

  int resize_h = int(float(h) * ratio);
  int resize_w = int(float(w) * ratio);

  resize_h = max(int(round(float(resize_h) / 32) * 32), 32);
  resize_w = max(int(round(float(resize_w) / 32) * 32), 32);

  cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
  ratio_h = float(resize_h) / float(h);
  ratio_w = float(resize_w) / float(w);
}

void CrnnResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img, float wh_ratio,
                        bool use_tensorrt,
                        const std::vector<int> &rec_image_shape) {
  int imgC, imgH, imgW;
  imgC = rec_image_shape[0];
  imgH = rec_image_shape[1];
  imgW = rec_image_shape[2];

  imgW = int(imgH * wh_ratio);

  float ratio = float(img.cols) / float(img.rows);
  int resize_w, resize_h;

  if (ceilf(imgH * ratio) > imgW)
    resize_w = imgW;
  else
    resize_w = int(ceilf(imgH * ratio));

  cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
             cv::INTER_LINEAR);
  cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0,
                     int(imgW - resize_img.cols), cv::BORDER_CONSTANT,
                     {127, 127, 127});
}

void ClsResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
                       bool use_tensorrt,
                       const std::vector<int> &rec_image_shape) {
  int imgC, imgH, imgW;
  imgC = rec_image_shape[0];
  imgH = rec_image_shape[1];
  imgW = rec_image_shape[2];

  float ratio = float(img.cols) / float(img.rows);
  int resize_w, resize_h;
  if (ceilf(imgH * ratio) > imgW)
    resize_w = imgW;
  else
    resize_w = int(ceilf(imgH * ratio));

  cv::resize(img, resize_img, cv::Size(resize_w, imgH), 0.f, 0.f,
             cv::INTER_LINEAR);
  if (resize_w < imgW) {
    cv::copyMakeBorder(resize_img, resize_img, 0, 0, 0, imgW - resize_w,
                       cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));
  }
}

void TableResizeImg::Run(const cv::Mat &img, cv::Mat &resize_img,
                         const int max_len) {
  int w = img.cols;
  int h = img.rows;

  int max_wh = w >= h ? w : h;
  float ratio = w >= h ? float(max_len) / float(w) : float(max_len) / float(h);

  int resize_h = int(float(h) * ratio);
  int resize_w = int(float(w) * ratio);

  cv::resize(img, resize_img, cv::Size(resize_w, resize_h));
}

void TablePadImg::Run(const cv::Mat &img, cv::Mat &resize_img,
                      const int max_len) {
  int w = img.cols;
  int h = img.rows;
  cv::copyMakeBorder(img, resize_img, 0, max_len - h, 0, max_len - w,
                     cv::BORDER_CONSTANT, cv::Scalar(0, 0, 0));
}

void Resize::Run(const cv::Mat &img, cv::Mat &resize_img, const int h,
                 const int w) {
  cv::resize(img, resize_img, cv::Size(w, h));
}

} // namespace PaddleOCR