COMPMID-589: Port HOGDescriptor to new validation
Change-Id: I2021612e61de1b82aaeb49249d06929c7fceb15f
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/115216
Reviewed-by: Pablo Tello <pablo.tello@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
diff --git a/tests/validation/reference/HOGDescriptor.cpp b/tests/validation/reference/HOGDescriptor.cpp
new file mode 100644
index 0000000..369ac74
--- /dev/null
+++ b/tests/validation/reference/HOGDescriptor.cpp
@@ -0,0 +1,273 @@
+/*
+ * Copyright (c) 2017, 2018 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 size_t num_bins = hog_info.num_bins();
+ const size_t cell_height = hog_info.cell_size().height;
+ const size_t cell_width = hog_info.cell_size().width;
+
+ 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_height; ++yc)
+ {
+ for(size_t xc = 0; xc < cell_height; 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_width;
+ }
+}
+
+template <typename T>
+void hog_block_normalization_compute(SimpleTensor<T> &block, SimpleTensor<T> &desc, const HOGInfo &hog_info, size_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 size_t cell_width = hog_info.cell_size().width;
+ const size_t cell_height = hog_info.cell_size().height;
+ 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;
+
+ SimpleTensor<V> mag_cell(TensorShape(cell_width, cell_height), DataType::F32);
+ SimpleTensor<V> phase_cell(TensorShape(cell_width, cell_height), DataType::F32);
+
+ int cell_idx = 0;
+ int y_offset = 0;
+ int x_offset = 0;
+
+ // Traverse shape
+ for(auto sy = cell_height - 1; sy < shape_height; sy += cell_height)
+ {
+ x_offset = 0;
+ for(auto sx = cell_width - 1; sx < shape_width; sx += cell_width)
+ {
+ int row_idx = 0;
+ int elem_idx = 0;
+
+ // Traverse cell
+ for(auto y = 0u; y < cell_height; ++y)
+ {
+ for(auto x = 0u; x < cell_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(hog_info.num_bins());
+ hog_orientation_compute(mag_cell, phase_cell, bins, hog_info);
+
+ for(size_t i = 0; i < hog_info.num_bins(); ++i)
+ {
+ hog_space[cell_idx * hog_info.num_bins() + i] = bins[i];
+ }
+
+ x_offset += cell_width;
+ cell_idx++;
+ }
+
+ y_offset += (cell_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 size_t block_width = hog_info.block_size().width;
+ const size_t block_height = hog_info.block_size().height;
+ const size_t block_stride_width = hog_info.block_stride().width;
+ const size_t block_stride_height = hog_info.block_stride().height;
+ 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 = hog_info.num_bins();
+ const size_t num_channels = cells_per_block.area() * num_bins;
+
+ SimpleTensor<T> block(TensorShape{ 1u, 1u }, DataType::F32, num_channels);
+
+ int block_idx = 0;
+ int block_y_offset = 0;
+
+ // Traverse shape
+ for(auto sy = block_width - 1; sy < shape_height; sy += block_stride_height)
+ {
+ int block_x_offset = 0;
+ for(auto sx = block_height - 1; 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)
+ {
+ int cell_x_offset = 0;
+ for(auto x = 0u; x < cells_per_block.width; ++x)
+ {
+ for(auto bin = 0u; bin < num_bins; ++bin)
+ {
+ int idx = bin + cell_x_offset + cell_y_offset + block_x_offset + block_y_offset;
+ block[elem_idx] = hog_space[idx];
+ elem_idx++;
+ }
+
+ cell_x_offset += num_bins;
+ }
+
+ 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> _mag;
+ SimpleTensor<uint8_t> _phase;
+
+ 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);
+
+ // Calculate magnitude and phase
+ _mag = magnitude(grad_x, grad_y, MagnitudeType::L2NORM);
+ _phase = phase(grad_x, grad_y, hog_info.phase_type());
+
+ // For each cell create histogram based on magnitude and phase
+ hog_orientation_binning(_mag, _phase, hog_space, hog_info);
+
+ // Normalize histograms based on block size
+ hog_block_normalization(desc, hog_space, hog_info);
+
+ return desc;
+}
+
+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