/**********************************************************************

 * File:        otsuthr.cpp

 * Description: Simple Otsu thresholding for binarizing images.

 * Author:      Ray Smith

 *

 * (C) Copyright 2008, Google Inc.

 ** 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 "otsuthr.h"

#include <cstring>

#include "helpers.h"

#include "image.h"

namespace tesseract {

// Computes the Otsu threshold(s) for the given image rectangle, making one

// for each channel. Each channel is always one byte per pixel.

// Returns an array of threshold values and an array of hi_values, such

// that a pixel value >threshold[channel] is considered foreground if

// hi_values[channel] is 0 or background if 1. A hi_value of -1 indicates

// that there is no apparent foreground. At least one hi_value will not be -1.

// The return value is the number of channels in the input image, being

// the size of the output thresholds and hi_values arrays.

int OtsuThreshold(Image src_pix, int left, int top, int width, int height, std::vector<int> &thresholds,

                  std::vector<int> &hi_values) {

  int num_channels = pixGetDepth(src_pix) / 8;

  // Of all channels with no good hi_value, keep the best so we can always

  // produce at least one answer.

  int best_hi_value = 1;

  int best_hi_index = 0;

  bool any_good_hivalue = false;

  double best_hi_dist = 0.0;

  thresholds.resize(num_channels);

  hi_values.resize(num_channels);

  for (int ch = 0; ch < num_channels; ++ch) {

    thresholds[ch] = -1;

    hi_values[ch] = -1;

    // Compute the histogram of the image rectangle.

    int histogram[kHistogramSize];

    HistogramRect(src_pix, ch, left, top, width, height, histogram);

    int H;

    int best_omega_0;

    int best_t = OtsuStats(histogram, &H, &best_omega_0);

    if (best_omega_0 == 0 || best_omega_0 == H) {

      // This channel is empty.

      continue;

    }

    // To be a convincing foreground we must have a small fraction of H

    // or to be a convincing background we must have a large fraction of H.

    // In between we assume this channel contains no thresholding information.

    int hi_value = best_omega_0 < H * 0.5;

    thresholds[ch] = best_t;

    if (best_omega_0 > H * 0.75) {

      any_good_hivalue = true;

      hi_values[ch] = 0;

    } else if (best_omega_0 < H * 0.25) {

      any_good_hivalue = true;

      hi_values[ch] = 1;

    } else {

      // In case all channels are like this, keep the best of the bad lot.

      double hi_dist = hi_value ? (H - best_omega_0) : best_omega_0;

      if (hi_dist > best_hi_dist) {

        best_hi_dist = hi_dist;

        best_hi_value = hi_value;

        best_hi_index = ch;

      }

    }

  }

  if (!any_good_hivalue) {

    // Use the best of the ones that were not good enough.

    hi_values[best_hi_index] = best_hi_value;

  }

  return num_channels;

}

// Computes the histogram for the given image rectangle, and the given

// single channel. Each channel is always one byte per pixel.

// Histogram is always a kHistogramSize(256) element array to count

// occurrences of each pixel value.

void HistogramRect(Image src_pix, int channel, int left, int top, int width, int height,

                   int *histogram) {

  int num_channels = pixGetDepth(src_pix) / 8;

  channel = ClipToRange(channel, 0, num_channels - 1);

  int bottom = top + height;

  memset(histogram, 0, sizeof(*histogram) * kHistogramSize);

  int src_wpl = pixGetWpl(src_pix);

  l_uint32 *srcdata = pixGetData(src_pix);

  for (int y = top; y < bottom; ++y) {

    const l_uint32 *linedata = srcdata + y * src_wpl;

    for (int x = 0; x < width; ++x) {

      int pixel = Image::getDataByte(linedata, (x + left) * num_channels + channel);

      ++histogram[pixel];

    }

  }

}

// Computes the Otsu threshold(s) for the given histogram.

// Also returns H = total count in histogram, and

// omega0 = count of histogram below threshold.

int OtsuStats(const int *histogram, int *H_out, int *omega0_out) {

  int H = 0;

  double mu_T = 0.0;

  for (int i = 0; i < kHistogramSize; ++i) {

    H += histogram[i];

    mu_T += static_cast<double>(i) * histogram[i];

  }

  // Now maximize sig_sq_B over t.

  // http://www.ctie.monash.edu.au/hargreave/Cornall_Terry_328.pdf

  int best_t = -1;

  int omega_0, omega_1;

  int best_omega_0 = 0;

  double best_sig_sq_B = 0.0;

  double mu_0, mu_1, mu_t;

  omega_0 = 0;

  mu_t = 0.0;

  for (int t = 0; t < kHistogramSize - 1; ++t) {

    omega_0 += histogram[t];

    mu_t += t * static_cast<double>(histogram[t]);

    if (omega_0 == 0) {

      continue;

    }

    omega_1 = H - omega_0;

    if (omega_1 == 0) {

      break;

    }

    mu_0 = mu_t / omega_0;

    mu_1 = (mu_T - mu_t) / omega_1;

    double sig_sq_B = mu_1 - mu_0;

    sig_sq_B *= sig_sq_B * omega_0 * omega_1;

    if (best_t < 0 || sig_sq_B > best_sig_sq_B) {

      best_sig_sq_B = sig_sq_B;

      best_t = t;

      best_omega_0 = omega_0;

    }

  }

  if (H_out != nullptr) {

    *H_out = H;

  }

  if (omega0_out != nullptr) {

    *omega0_out = best_omega_0;

  }

  return best_t;

}

} // namespace tesseract.