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Diffstat (limited to 'tests/validation/reference/HOGDescriptor.cpp')
| -rw-r--r-- | tests/validation/reference/HOGDescriptor.cpp | 264 |
1 files changed, 0 insertions, 264 deletions
diff --git a/tests/validation/reference/HOGDescriptor.cpp b/tests/validation/reference/HOGDescriptor.cpp deleted file mode 100644 index e00beaf5d7..0000000000 --- a/tests/validation/reference/HOGDescriptor.cpp +++ /dev/null @@ -1,264 +0,0 @@ -/* - * Copyright (c) 2017-2019 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "HOGDescriptor.h" - -#include "Derivative.h" -#include "Magnitude.h" -#include "Phase.h" - -namespace arm_compute -{ -namespace test -{ -namespace validation -{ -namespace reference -{ -namespace -{ -template <typename T> -void hog_orientation_compute(const SimpleTensor<T> &mag, const SimpleTensor<T> &phase, std::vector<T> &bins, const HOGInfo &hog_info) -{ - const Size2D &cell_size = hog_info.cell_size(); - const size_t num_bins = hog_info.num_bins(); - - float phase_scale = (PhaseType::SIGNED == hog_info.phase_type() ? num_bins / 360.0f : num_bins / 180.0f); - phase_scale *= (PhaseType::SIGNED == hog_info.phase_type() ? 360.0f / 255.0f : 1.0f); - - int row_idx = 0; - for(size_t yc = 0; yc < cell_size.height; ++yc) - { - for(size_t xc = 0; xc < cell_size.width; xc++) - { - const float mag_value = mag[(row_idx + xc)]; - const float phase_value = phase[(row_idx + xc)] * phase_scale + 0.5f; - const float w1 = phase_value - floor(phase_value); - - // The quantised phase is the histogram index [0, num_bins - 1] - // Check limit of histogram index. If hidx == num_bins, hidx = 0 - const auto hidx = static_cast<unsigned int>(phase_value) % num_bins; - - // Weighted vote between 2 bins - bins[hidx] += mag_value * (1.0f - w1); - bins[(hidx + 1) % num_bins] += mag_value * w1; - } - - row_idx += cell_size.width; - } -} - -template <typename T> -void hog_block_normalization_compute(SimpleTensor<T> &block, SimpleTensor<T> &desc, const HOGInfo &hog_info, uint32_t block_idx) -{ - const int num_bins_per_block = desc.num_channels(); - const HOGNormType norm_type = hog_info.normalization_type(); - const Coordinates id = index2coord(desc.shape(), block_idx); - - float sum = 0.0f; - - // Calculate sum - for(int i = 0; i < num_bins_per_block; ++i) - { - const float val = block[i]; - sum += (norm_type == HOGNormType::L1_NORM) ? std::fabs(val) : val * val; - } - - // Calculate normalization scale - float scale = 1.0f / (std::sqrt(sum) + num_bins_per_block * 0.1f); - - if(norm_type == HOGNormType::L2HYS_NORM) - { - // Reset sum - sum = 0.0f; - for(int i = 0; i < num_bins_per_block; ++i) - { - float val = block[i] * scale; - - // Clip scaled input_value if over l2_hyst_threshold - val = fmin(val, hog_info.l2_hyst_threshold()); - sum += val * val; - block[i] = val; - } - - // We use the same constants of OpenCV - scale = 1.0f / (std::sqrt(sum) + 1e-3f); - } - - for(int i = 0; i < num_bins_per_block; ++i) - { - block[i] *= scale; - reinterpret_cast<float *>(desc(id))[i] = block[i]; - } -} -} // namespace - -template <typename T, typename U, typename V> -void hog_orientation_binning(const SimpleTensor<T> &mag, const SimpleTensor<U> &phase, SimpleTensor<V> &hog_space, const HOGInfo &hog_info) -{ - const Size2D &cell_size = hog_info.cell_size(); - - const size_t num_bins = hog_info.num_bins(); - const size_t shape_width = hog_space.shape().x() * hog_info.cell_size().width; - const size_t shape_height = hog_space.shape().y() * hog_info.cell_size().height; - - TensorShape cell_shape(cell_size.width, cell_size.height); - - SimpleTensor<V> mag_cell(cell_shape, DataType::F32); - SimpleTensor<V> phase_cell(cell_shape, DataType::F32); - - int cell_idx = 0; - int y_offset = 0; - - // Traverse shape - for(auto sy = cell_size.height; sy <= shape_height; sy += cell_size.height) - { - int x_offset = 0; - for(auto sx = cell_size.width; sx <= shape_width; sx += cell_size.width) - { - int row_idx = 0; - int elem_idx = 0; - - // Traverse cell - for(auto y = 0u; y < cell_size.height; ++y) - { - for(auto x = 0u; x < cell_size.width; ++x) - { - int shape_idx = x + row_idx + x_offset + y_offset; - mag_cell[elem_idx] = mag[shape_idx]; - phase_cell[elem_idx] = phase[shape_idx]; - elem_idx++; - } - - row_idx += shape_width; - } - - // Partition magnitude values into bins based on phase values - std::vector<V> bins(num_bins); - hog_orientation_compute(mag_cell, phase_cell, bins, hog_info); - - for(size_t i = 0; i < num_bins; ++i) - { - hog_space[cell_idx * num_bins + i] = bins[i]; - } - - x_offset += cell_size.width; - cell_idx++; - } - - y_offset += (cell_size.height * shape_width); - } -} - -template <typename T> -void hog_block_normalization(SimpleTensor<T> &desc, const SimpleTensor<T> &hog_space, const HOGInfo &hog_info) -{ - const Size2D cells_per_block = hog_info.num_cells_per_block(); - const Size2D cells_per_block_stride = hog_info.num_cells_per_block_stride(); - const Size2D &block_size = hog_info.block_size(); - const Size2D &block_stride = hog_info.block_stride(); - const size_t num_bins = hog_info.num_bins(); - - const size_t shape_width = hog_space.shape().x() * hog_info.cell_size().width; - const size_t shape_height = hog_space.shape().y() * hog_info.cell_size().height; - const size_t num_bins_per_block_x = cells_per_block.width * num_bins; - - // Tensor representing single block - SimpleTensor<T> block(TensorShape{ 1u, 1u }, DataType::F32, cells_per_block.area() * num_bins); - - uint32_t block_idx = 0; - int block_y_offset = 0; - - // Traverse shape - for(auto sy = block_size.height; sy <= shape_height; sy += block_stride.height) - { - int block_x_offset = 0; - for(auto sx = block_size.width; sx <= shape_width; sx += block_stride.width) - { - int cell_y_offset = 0; - int elem_idx = 0; - - // Traverse block - for(auto y = 0u; y < cells_per_block.height; ++y) - { - for(auto x = 0u; x < num_bins_per_block_x; ++x) - { - int idx = x + cell_y_offset + block_x_offset + block_y_offset; - block[elem_idx] = hog_space[idx]; - elem_idx++; - } - - cell_y_offset += hog_space.shape().x() * num_bins; - } - - // Normalize block and write to descriptor - hog_block_normalization_compute(block, desc, hog_info, block_idx); - - block_x_offset += cells_per_block_stride.width * num_bins; - block_idx++; - } - - block_y_offset += cells_per_block_stride.height * num_bins * hog_space.shape().x(); - } -} - -template <typename T, typename U> -SimpleTensor<T> hog_descriptor(const SimpleTensor<U> &src, BorderMode border_mode, U constant_border_value, const HOGInfo &hog_info) -{ - SimpleTensor<int16_t> grad_x; - SimpleTensor<int16_t> grad_y; - - // Create tensor info for HOG descriptor - TensorInfo desc_info(hog_info, src.shape().x(), src.shape().y()); - SimpleTensor<T> desc(desc_info.tensor_shape(), DataType::F32, desc_info.num_channels()); - - // Create HOG space tensor (num_cells_x, num_cells_y) - TensorShape hog_space_shape(src.shape().x() / hog_info.cell_size().width, - src.shape().y() / hog_info.cell_size().height); - - // For each cell a histogram with a num_bins is created - TensorInfo info_hog_space(hog_space_shape, hog_info.num_bins(), DataType::F32); - SimpleTensor<T> hog_space(info_hog_space.tensor_shape(), DataType::F32, info_hog_space.num_channels()); - - // Calculate derivative - std::tie(grad_x, grad_y) = derivative<int16_t>(src, border_mode, constant_border_value, GradientDimension::GRAD_XY); - - // For each cell create histogram based on magnitude and phase - hog_orientation_binning(magnitude(grad_x, grad_y, MagnitudeType::L2NORM), - phase(grad_x, grad_y, hog_info.phase_type()), - hog_space, - hog_info); - - // Normalize histograms based on block size - hog_block_normalization(desc, hog_space, hog_info); - - return desc; -} - -template void hog_orientation_binning(const SimpleTensor<int16_t> &mag, const SimpleTensor<uint8_t> &phase, SimpleTensor<float> &hog_space, const HOGInfo &hog_info); -template void hog_block_normalization(SimpleTensor<float> &desc, const SimpleTensor<float> &hog_space, const HOGInfo &hog_info); -template SimpleTensor<float> hog_descriptor(const SimpleTensor<uint8_t> &src, BorderMode border_mode, uint8_t constant_border_value, const HOGInfo &hog_info); -} // namespace reference -} // namespace validation -} // namespace test -} // namespace arm_compute |