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cpp : more robust code and added more documentation

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saundersp
2024-04-27 21:08:33 +02:00
parent 45f0f6ab8e
commit c7d21e1014
10 changed files with 355 additions and 319 deletions
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22
cpp/ViolaJones.cpp
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@ -2,7 +2,6 @@
#include "data.hpp"
#include "config.hpp"
#include "ViolaJonesGPU.hpp"
#include "ViolaJonesCPU.hpp"
static inline void add_empty_feature(const np::Array<uint8_t>& feats, size_t& n) noexcept {
memset(&feats[n], 0, 4 * sizeof(uint8_t));
@ -110,11 +109,11 @@ np::Array<uint8_t> build_features(const uint16_t& width, const uint16_t& height)
return feats;
}
//np::Array<int> select_percentile(const np::Array<uint8_t> X_feat, const np::Array<uint8_t> y) noexcept {
//np::Array<int32_t> select_percentile(const np::Array<uint8_t> X_feat, const np::Array<uint8_t> y) noexcept {
// std::vector<float64_t> class_0, class_1;
//
// const int im_size = X_feat.shape[0] / y.shape[0];
// int idy = 0, n_samples_per_class_0 = 0, n_samples_per_class_1 = 0;
// const int32_t im_size = X_feat.shape[0] / y.shape[0];
// int32_t idy = 0, n_samples_per_class_0 = 0, n_samples_per_class_1 = 0;
// for (size_t i = 0; i < X_feat.shape[0]; i += im_size) {
// if (y[idy] == 0) {
// ++n_samples_per_class_0;
@ -126,24 +125,24 @@ np::Array<uint8_t> build_features(const uint16_t& width, const uint16_t& height)
// }
// ++idy;
// }
// const int n_samples = n_samples_per_class_0 + n_samples_per_class_1;
// const int32_t n_samples = n_samples_per_class_0 + n_samples_per_class_1;
//
// float64_t ss_alldata_0 = 0;
// for (int i = 0;i < n_samples_per_class_0;++i)
// for (int32_t i = 0;i < n_samples_per_class_0;++i)
// ss_alldata_0 += (class_0[i] * class_0[i]);
//
// float64_t ss_alldata_1 = 0;
// for (int i = 0;i < n_samples_per_class_1;++i)
// for (int32_t i = 0;i < n_samples_per_class_1;++i)
// ss_alldata_1 += (class_1[i] * class_1[i]);
//
// const float64_t ss_alldata = ss_alldata_0 + ss_alldata_1;
//
// float64_t sums_classes_0 = 0;
// for (int i = 0;i < n_samples_per_class_0;++i)
// for (int32_t i = 0;i < n_samples_per_class_0;++i)
// sums_classes_0 += class_0[i];
//
// float64_t sums_classes_1 = 0;
// for (int i = 0;i < n_samples_per_class_1;++i)
// for (int32_t i = 0;i < n_samples_per_class_1;++i)
// sums_classes_1 += class_1[i];
//
// float64_t sq_of_sums_alldata = sums_classes_0 + sums_classes_1;
@ -154,11 +153,11 @@ np::Array<uint8_t> build_features(const uint16_t& width, const uint16_t& height)
// const float64_t ss_tot = ss_alldata - sq_of_sums_alldata / n_samples;
// const float64_t sqd_sum_bw_n = sq_of_sums_args_0 / n_samples_per_class_0 + sq_of_sums_args_1 / n_samples_per_class_1 - sq_of_sums_alldata / n_samples;
// const float64_t ss_wn = ss_tot - sqd_sum_bw_n;
// const int df_wn = n_samples - 2;
// const int32_t df_wn = n_samples - 2;
// const float64_t msw = ss_wn / df_wn;
// const float64_t f_values = sqd_sum_bw_n / msw;
//
// const np::Array<int> res = np::empty<int>({ static_cast<size_t>(std::ceil(static_cast<float64_t>(im_size) / 10.0)) });
// const np::Array<int32_t> res = np::empty<int32_t>({ static_cast<size_t>(std::ceil(static_cast<float64_t>(im_size) / 10.0)) });
// // TODO Complete code
// return res;
//}
@ -293,4 +292,3 @@ std::tuple<uint16_t, uint16_t, uint16_t, uint16_t> confusion_matrix(const np::Ar
++false_positive;
return std::make_tuple(true_negative, false_positive, false_negative, true_positive);
}