remove debug print

clic/src/tier4/threshold_otsu.cpp

@@ -11,102 +11,37 @@
 namespace cle::tier4
 {
 
-//
-// auto
-// threshold_otsu_func(const Device::Pointer & device, const Array::Pointer & src, Array::Pointer dst) -> Array::Pointer
-// {
-// constexpr int bin = 256;
-// const float min_intensity = tier2::minimum_of_all_pixels_func(device, src);
-// const float max_intensity = tier2::maximum_of_all_pixels_func(device, src);
-// auto hist_array = Array::create(bin, 1, 1, 1, dType::FLOAT, mType::BUFFER, src->device());
-// tier3::histogram_func(device, src, hist_array, bin, min_intensity, max_intensity);
-// std::vector<float> histogram_array(hist_array->size());
-// hist_array->readTo(histogram_array.data());
-// double threshold = -1;
-// double max_variance = -1;
-// double variance = 0;
-// double sum_1 = 0;
-// double sum_2 = 0;
-// double weight_1 = 0;
-// double weight_2 = 0;
-// double mean_1 = 0;
-// double mean_2 = 0;
-// const double nb_pixels = src->size();
-// const double intensity_factor = static_cast<double>(max_intensity - min_intensity);
-// // const double intensity_factor = static_cast<double>(max_intensity - min_intensity) / static_cast<double>(bin -
-// 1);
-// // old implementation - to be removed
-// // std::vector<float> range(histogram_array.size());
-// // std::iota(range.begin(), range.end(), 0.0);
-// // std::transform(range.begin(), range.end(), range.begin(), [intensity_factor, min_intensity](float intensity) {
-// // return intensity * intensity_factor + static_cast<float>(min_intensity);
-// // });
-// //
-// std::vector<double> range(bin);
-// std::iota(range.begin(), range.end(), 0.0f);
-// std::transform(range.begin(), range.end(), range.begin(), [intensity_factor, min_intensity](double value) {
-// return (value * intensity_factor) / (bin - 1) + static_cast<double>(min_intensity);
-// });
-// sum_1 = std::transform_reduce(
-// range.begin(), range.end(), histogram_array.begin(), 0.0f, std::plus<>(), [](double intensity, float hist_value)
-// {
-// return intensity * static_cast<double>(hist_value);
-// });
-// for (size_t index = 0; index < range.size(); ++index)
-// {
-// if (histogram_array[index] == 0)
-// {
-// continue;
-// }
-// weight_1 += histogram_array[index];
-// weight_2 = nb_pixels - weight_1;
-// sum_2 += range[index] * static_cast<double>(histogram_array[index]);
-// mean_1 = sum_2 / weight_1;
-// mean_2 = (sum_1 - sum_2) / weight_2;
-// variance = weight_1 * weight_2 * ((mean_1 - mean_2) * (mean_1 - mean_2));
-// if (variance > max_variance)
-// {
-// threshold = range[index];
-// max_variance = variance;
-// }
-// }
-// std::cout << "Threshold: " << threshold << std::endl;
-// tier0::create_like(src, dst, dType::BINARY);
-// return tier1::greater_constant_func(device, src, dst, static_cast<float>(threshold));
-// }
-
-
 auto
 threshold_otsu_func(const Device::Pointer & device, const Array::Pointer & src, Array::Pointer dst) -> Array::Pointer
 {
+ // Initialize histogram
  constexpr int bin = 256;
  const float min_intensity = tier2::minimum_of_all_pixels_func(device, src);
  const float max_intensity = tier2::maximum_of_all_pixels_func(device, src);
-
+ double range = max_intensity - min_intensity;
+
+ // Compute histogram
  auto hist_array = Array::create(bin, 1, 1, 1, dType::FLOAT, mType::BUFFER, src->device());
  tier3::histogram_func(device, src, hist_array, bin, min_intensity, max_intensity);
  std::vector<float> counts(hist_array->size());
  hist_array->readTo(counts.data());
 
-
- double range = max_intensity - min_intensity;
-
+ // Compute bin centers
  std::vector<double> bin_centers(bin);
  std::iota(bin_centers.begin(), bin_centers.end(), 0.0);
  std::transform(
  bin_centers.begin(), bin_centers.end(), bin_centers.begin(), [range, min_intensity, bin](double value) {
  return (value * range) / (bin - 1) + static_cast<double>(min_intensity);
  });
 
- std::vector<double> weight1(bin), weight2(bin), mean1(bin), mean2(bin), variance12(bin - 1);
-
  // Compute weight1
+ std::vector<double> weight1(bin), weight2(bin), mean1(bin), mean2(bin), variance12(bin - 1);
  std::partial_sum(counts.begin(), counts.end(), weight1.begin());
 
  // Compute weight2
  std::vector<double> reversed_counts(counts.rbegin(), counts.rend());
  std::partial_sum(reversed_counts.begin(), reversed_counts.end(), weight2.rbegin());
-
+ 
  // Compute mean1
  std::vector<double> counts_bin_centers(bin);
  std::transform(counts.begin(), counts.end(), bin_centers.begin(), counts_bin_centers.begin(), std::multiplies<>());
@@ -123,13 +58,12 @@ threshold_otsu_func(const Device::Pointer & device, const Array::Pointer & src,
  variance12[i] = weight1[i] * weight2[i + 1] * (mean1[i] - mean2[i + 1]) * (mean1[i] - mean2[i + 1]);
  }
 
+ // Find the maximum variance and threshold value associated with it
  auto max_it = std::max_element(variance12.begin(), variance12.end());
  size_t idx = std::distance(variance12.begin(), max_it);
-
  double threshold = bin_centers[idx];
-
- std::cout << "Threshold: " << threshold << std::endl;
-
+
+ // Create binary image with threshold
  tier0::create_like(src, dst, dType::BINARY);
  return tier1::greater_constant_func(device, src, dst, static_cast<float>(threshold));
 }