Convert channel counts to size_t.

webrtc/modules/audio_processing/beamformer/nonlinear_beamformer.cc

 /*
  *  Copyright (c) 2014 The WebRTC project authors. All Rights Reserved.
  *
  *  Use of this source code is governed by a BSD-style license
  *  that can be found in the LICENSE file in the root of the source
  *  tree. An additional intellectual property rights grant can be found
  *  in the file PATENTS.  All contributing project authors may
  *  be found in the AUTHORS file in the root of the source tree.
  */
 
@@ skipping 61 matching lines @@
 // To compensate for the attenuation this algorithm introduces to the target
 // signal. It was estimated empirically from a low-noise low-reverberation
 // recording from broadside.
 const float kCompensationGain = 2.f;
 
 // Does conjugate(|norm_mat|) * |mat| * transpose(|norm_mat|). No extra space is
 // used; to accomplish this, we compute both multiplications in the same loop.
 // The returned norm is clamped to be non-negative.
 float Norm(const ComplexMatrix<float>& mat,
            const ComplexMatrix<float>& norm_mat) {
-  RTC_CHECK_EQ(norm_mat.num_rows(), 1);
+  RTC_CHECK_EQ(1u, norm_mat.num_rows());
   RTC_CHECK_EQ(norm_mat.num_columns(), mat.num_rows());
   RTC_CHECK_EQ(norm_mat.num_columns(), mat.num_columns());
 
   complex<float> first_product = complex<float>(0.f, 0.f);
   complex<float> second_product = complex<float>(0.f, 0.f);
 
   const complex<float>* const* mat_els = mat.elements();
   const complex<float>* const* norm_mat_els = norm_mat.elements();
 
-  for (int i = 0; i < norm_mat.num_columns(); ++i) {
-    for (int j = 0; j < norm_mat.num_columns(); ++j) {
+  for (size_t i = 0; i < norm_mat.num_columns(); ++i) {
+    for (size_t j = 0; j < norm_mat.num_columns(); ++j) {
       first_product += conj(norm_mat_els[0][j]) * mat_els[j][i];
     }
     second_product += first_product * norm_mat_els[0][i];
     first_product = 0.f;
   }
   return std::max(second_product.real(), 0.f);
 }
 
 // Does conjugate(|lhs|) * |rhs| for row vectors |lhs| and |rhs|.
 complex<float> ConjugateDotProduct(const ComplexMatrix<float>& lhs,
                                    const ComplexMatrix<float>& rhs) {
-  RTC_CHECK_EQ(lhs.num_rows(), 1);
-  RTC_CHECK_EQ(rhs.num_rows(), 1);
+  RTC_CHECK_EQ(1u, lhs.num_rows());
+  RTC_CHECK_EQ(1u, rhs.num_rows());
   RTC_CHECK_EQ(lhs.num_columns(), rhs.num_columns());
 
   const complex<float>* const* lhs_elements = lhs.elements();
   const complex<float>* const* rhs_elements = rhs.elements();
 
   complex<float> result = complex<float>(0.f, 0.f);
-  for (int i = 0; i < lhs.num_columns(); ++i) {
+  for (size_t i = 0; i < lhs.num_columns(); ++i) {
     result += conj(lhs_elements[0][i]) * rhs_elements[0][i];
   }
 
   return result;
 }
 
 // Works for positive numbers only.
 size_t Round(float x) {
   return static_cast<size_t>(std::floor(x + 0.5f));
 }
 
 // Calculates the sum of absolute values of a complex matrix.
 float SumAbs(const ComplexMatrix<float>& mat) {
   float sum_abs = 0.f;
   const complex<float>* const* mat_els = mat.elements();
-  for (int i = 0; i < mat.num_rows(); ++i) {
-    for (int j = 0; j < mat.num_columns(); ++j) {
+  for (size_t i = 0; i < mat.num_rows(); ++i) {
+    for (size_t j = 0; j < mat.num_columns(); ++j) {
       sum_abs += std::abs(mat_els[i][j]);
     }
   }
   return sum_abs;
 }
 
 // Calculates the sum of squares of a complex matrix.
 float SumSquares(const ComplexMatrix<float>& mat) {
   float sum_squares = 0.f;
   const complex<float>* const* mat_els = mat.elements();
-  for (int i = 0; i < mat.num_rows(); ++i) {
-    for (int j = 0; j < mat.num_columns(); ++j) {
+  for (size_t i = 0; i < mat.num_rows(); ++i) {
+    for (size_t j = 0; j < mat.num_columns(); ++j) {
       float abs_value = std::abs(mat_els[i][j]);
       sum_squares += abs_value * abs_value;
     }
   }
   return sum_squares;
 }
 
 // Does |out| = |in|.' * conj(|in|) for row vector |in|.
 void TransposedConjugatedProduct(const ComplexMatrix<float>& in,
                                  ComplexMatrix<float>* out) {
-  RTC_CHECK_EQ(in.num_rows(), 1);
+  RTC_CHECK_EQ(1u, in.num_rows());
   RTC_CHECK_EQ(out->num_rows(), in.num_columns());
   RTC_CHECK_EQ(out->num_columns(), in.num_columns());
   const complex<float>* in_elements = in.elements()[0];
   complex<float>* const* out_elements = out->elements();
-  for (int i = 0; i < out->num_rows(); ++i) {
-    for (int j = 0; j < out->num_columns(); ++j) {
+  for (size_t i = 0; i < out->num_rows(); ++i) {
+    for (size_t j = 0; j < out->num_columns(); ++j) {
       out_elements[i][j] = in_elements[i] * conj(in_elements[j]);
     }
   }
 }
 
 std::vector<Point> GetCenteredArray(std::vector<Point> array_geometry) {
   for (size_t dim = 0; dim < 3; ++dim) {
     float center = 0.f;
     for (size_t i = 0; i < array_geometry.size(); ++i) {
       center += array_geometry[i].c[dim];
@@ skipping 231 matching lines @@
 }
 
 bool NonlinearBeamformer::IsInBeam(const SphericalPointf& spherical_point) {
   // If more than half-beamwidth degrees away from the beam's center,
   // you are out of the beam.
   return fabs(spherical_point.azimuth() - target_angle_radians_) <
          kHalfBeamWidthRadians;
 }
 
 void NonlinearBeamformer::ProcessAudioBlock(const complex_f* const* input,
-                                            int num_input_channels,
+                                            size_t num_input_channels,
                                             size_t num_freq_bins,
-                                            int num_output_channels,
+                                            size_t num_output_channels,
                                             complex_f* const* output) {
   RTC_CHECK_EQ(kNumFreqBins, num_freq_bins);
   RTC_CHECK_EQ(num_input_channels_, num_input_channels);
-  RTC_CHECK_EQ(1, num_output_channels);
+  RTC_CHECK_EQ(1u, num_output_channels);
 
   // Calculating the post-filter masks. Note that we need two for each
   // frequency bin to account for the positive and negative interferer
   // angle.
   for (size_t i = low_mean_start_bin_; i <= high_mean_end_bin_; ++i) {
     eig_m_.CopyFromColumn(input, i, num_input_channels_);
     float eig_m_norm_factor = std::sqrt(SumSquares(eig_m_));
     if (eig_m_norm_factor != 0.f) {
       eig_m_.Scale(1.f / eig_m_norm_factor);
     }
@@ skipping 48 matching lines @@
 }
 
 void NonlinearBeamformer::ApplyMasks(const complex_f* const* input,
                                      complex_f* const* output) {
   complex_f* output_channel = output[0];
   for (size_t f_ix = 0; f_ix < kNumFreqBins; ++f_ix) {
     output_channel[f_ix] = complex_f(0.f, 0.f);
 
     const complex_f* delay_sum_mask_els =
         normalized_delay_sum_masks_[f_ix].elements()[0];
-    for (int c_ix = 0; c_ix < num_input_channels_; ++c_ix) {
+    for (size_t c_ix = 0; c_ix < num_input_channels_; ++c_ix) {
       output_channel[f_ix] += input[c_ix][f_ix] * delay_sum_mask_els[c_ix];
     }
 
     output_channel[f_ix] *= kCompensationGain * final_mask_[f_ix];
   }
 }
 
 // Smooth new_mask_ into time_smooth_mask_.
 void NonlinearBeamformer::ApplyMaskTimeSmoothing() {
   for (size_t i = low_mean_start_bin_; i <= high_mean_end_bin_; ++i) {
@@ skipping 64 matching lines @@
                    new_mask_ + high_mean_end_bin_ + 1);
   if (new_mask_[quantile] > kMaskTargetThreshold) {
     is_target_present_ = true;
     interference_blocks_count_ = 0;
   } else {
     is_target_present_ = interference_blocks_count_++ < hold_target_blocks_;
   }
 }
 
 }  // namespace webrtc