From 8de92619e223225aabdca873c02f231d8e941fd1 Mon Sep 17 00:00:00 2001 From: John Richardson Date: Thu, 22 Feb 2018 14:09:31 +0000 Subject: COMPMID-585: Port OpticalFlow to new validation Change-Id: Ia36bd11ca27420d3059eea15df81b237900149ec Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/125175 Tested-by: Jenkins Reviewed-by: John Richardson Reviewed-by: Anthony Barbier --- tests/validation/reference/OpticalFlow.cpp | 404 +++++++++++++++++++++++++++++ 1 file changed, 404 insertions(+) create mode 100644 tests/validation/reference/OpticalFlow.cpp (limited to 'tests/validation/reference/OpticalFlow.cpp') diff --git a/tests/validation/reference/OpticalFlow.cpp b/tests/validation/reference/OpticalFlow.cpp new file mode 100644 index 0000000000..da0b9f9f94 --- /dev/null +++ b/tests/validation/reference/OpticalFlow.cpp @@ -0,0 +1,404 @@ +/* + * Copyright (c) 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 "OpticalFlow.h" + +#include "GaussianPyramidHalf.h" +#include "Scharr.h" +#include "Utils.h" + +namespace arm_compute +{ +namespace test +{ +namespace validation +{ +namespace reference +{ +namespace +{ +using KeyPointArray = std::vector; +using InternalKeyPointArray = std::vector; + +// Constants used for Lucas-Kanade Algorithm +constexpr int W_BITS = 14; +constexpr float D0 = 1 << W_BITS; +constexpr float DETERMINANT_THRESHOLD = 1.0e-07f; +constexpr float EIGENVALUE_THRESHOLD = 1.0e-04f; +constexpr float FLT_SCALE = 1.0f / (1 << 20); + +// Creates an InternalKeyPointArray for tracking non-integral pixel coordinates +InternalKeyPointArray create_internal_keypoints(const KeyPointArray &keypoints) +{ + InternalKeyPointArray internal_keypoints; + + for(auto keypoint : keypoints) + { + InternalKeyPoint internal_keypoint; + + internal_keypoint.x = static_cast(keypoint.x); + internal_keypoint.y = static_cast(keypoint.y); + internal_keypoint.tracking_status = static_cast(keypoint.tracking_status); + + internal_keypoints.push_back(internal_keypoint); + } + + return internal_keypoints; +} + +// Scale tracked points based on Pyramid level +void scale_tracked_points(size_t level, size_t num_levels, bool use_initial_estimate, + InternalKeyPointArray &old_points_internal, InternalKeyPointArray &new_points_internal, + const KeyPointArray &old_points, const KeyPointArray &new_points_estimates) +{ + if(level == num_levels - 1) // lowest resolution + { + const float scale = std::pow(SCALE_PYRAMID_HALF, level); + + for(size_t i = 0; i < old_points.size(); ++i) + { + old_points_internal.at(i).x = old_points.at(i).x * scale; + old_points_internal.at(i).y = old_points.at(i).y * scale; + old_points_internal.at(i).tracking_status = true; + + InternalKeyPoint keypoint_to_track; + + if(use_initial_estimate) + { + keypoint_to_track.x = new_points_estimates.at(i).x * scale; + keypoint_to_track.y = new_points_estimates.at(i).y * scale; + keypoint_to_track.tracking_status = (new_points_estimates.at(i).tracking_status == 1); + } + else + { + keypoint_to_track.x = old_points_internal.at(i).x; + keypoint_to_track.y = old_points_internal.at(i).y; + keypoint_to_track.tracking_status = true; + } + + new_points_internal.at(i) = keypoint_to_track; + } + } + else + { + for(size_t i = 0; i < old_points.size(); ++i) + { + old_points_internal.at(i).x /= SCALE_PYRAMID_HALF; + old_points_internal.at(i).y /= SCALE_PYRAMID_HALF; + new_points_internal.at(i).x /= SCALE_PYRAMID_HALF; + new_points_internal.at(i).y /= SCALE_PYRAMID_HALF; + } + } +} + +bool is_invalid_keypoint(const InternalKeyPoint &keypoint, const ValidRegion &valid_region, size_t window_dimension) +{ + const int half_window = window_dimension / 2; + const int x = std::floor(keypoint.x); + const int y = std::floor(keypoint.y); + + return (x - half_window < valid_region.start(0)) || (x + half_window >= valid_region.end(0) - 1) || (y - half_window < valid_region.start(1)) || (y + half_window >= valid_region.end(1) - 1); +} + +template +constexpr int INT_ROUND(T x, int n) +{ + return (x + (1 << (n - 1))) >> n; +} + +// Return the bilinear value at a specified coordinate with different border modes +template +int bilinear_interpolate(const SimpleTensor &in, Coordinates id, float wx, float wy, BorderMode border_mode, T constant_border_value, int scale) +{ + const int level = id.x(); + const int idy = id.y(); + + const float dx = wx; + const float dy = wy; + const float dx_1 = 1.0f - dx; + const float dy_1 = 1.0f - dy; + + const T border_value = constant_border_value; + + id.set(0, level); + id.set(1, idy); + const T tl = tensor_elem_at(in, id, border_mode, border_value); + id.set(0, level + 1); + id.set(1, idy); + const T tr = tensor_elem_at(in, id, border_mode, border_value); + id.set(0, level); + id.set(1, idy + 1); + const T bl = tensor_elem_at(in, id, border_mode, border_value); + id.set(0, level + 1); + id.set(1, idy + 1); + const T br = tensor_elem_at(in, id, border_mode, border_value); + + // weights + const int w00 = roundf(dx_1 * dy_1 * D0); + const int w01 = roundf(dx * dy_1 * D0); + const int w10 = roundf(dx_1 * dy * D0); + const int w11 = D0 - w00 - w01 - w10; + + return static_cast(INT_ROUND(tl * w00 + tr * w01 + bl * w10 + br * w11, scale)); +} + +template +std::vector compute_derivative(const SimpleTensor &input, const InternalKeyPoint &keypoint, + BorderMode border_mode, uint8_t constant_border_value, size_t window_dimension, int scale) +{ + std::vector bilinear_values; + + const int half_window = window_dimension / 2; + + float keypoint_int_x = 0; + float keypoint_int_y = 0; + + const float wx = std::modf(keypoint.x, &keypoint_int_x); + const float wy = std::modf(keypoint.y, &keypoint_int_y); + + Coordinates tl_window(static_cast(keypoint_int_x) - half_window, static_cast(keypoint_int_y) - half_window); + Coordinates br_window(static_cast(keypoint_int_x) + half_window, static_cast(keypoint_int_y) + half_window); + + for(int y = tl_window.y(); y <= br_window.y(); ++y) + { + for(int x = tl_window.x(); x <= br_window.x(); ++x) + { + bilinear_values.push_back(bilinear_interpolate(input, Coordinates(x, y), wx, wy, border_mode, static_cast(constant_border_value), scale)); + } + } + + return bilinear_values; +} + +std::tuple compute_spatial_gradient_matrix(const std::vector &bilinear_ix, const std::vector &bilinear_iy) +{ + ARM_COMPUTE_ERROR_ON(bilinear_ix.size() != bilinear_iy.size()); + + int iA11 = 0; + int iA12 = 0; + int iA22 = 0; + + for(size_t i = 0; i < bilinear_ix.size(); ++i) + { + int ixval = bilinear_ix[i]; + int iyval = bilinear_iy[i]; + + iA11 += ixval * ixval; + iA12 += ixval * iyval; + iA22 += iyval * iyval; + } + + return std::make_tuple(iA11 * FLT_SCALE, iA12 * FLT_SCALE, iA22 * FLT_SCALE); +} + +std::tuple compute_temporal_gradient_vector(const std::vector &bilinear_it_old, + const std::vector &bilinear_it_new, + const std::vector &bilinear_ix, + const std::vector &bilinear_iy) +{ + ARM_COMPUTE_ERROR_ON(bilinear_ix.size() != bilinear_iy.size()); + ARM_COMPUTE_ERROR_ON(bilinear_it_old.size() != bilinear_it_new.size()); + + int ib1 = 0; + int ib2 = 0; + + for(size_t i = 0; i < bilinear_ix.size(); ++i) + { + int ixval = bilinear_ix[i]; + int iyval = bilinear_iy[i]; + int ival = bilinear_it_old[i]; + int jval = bilinear_it_new[i]; + + const int diff = jval - ival; + + ib1 += diff * ixval; + ib2 += diff * iyval; + } + + const double b1 = ib1 * FLT_SCALE; + const double b2 = ib2 * FLT_SCALE; + + return std::make_tuple(b1, b2); +} +} // namespace + +template +std::vector optical_flow(const SimpleTensor &old_input, const SimpleTensor &new_input, + const OpticalFlowParameters ¶ms, size_t num_levels, + const std::vector &old_points, const std::vector &new_points_estimates, + BorderMode border_mode, uint8_t constant_border_value) +{ + const int filter_size = 3; // scharr filter size + const size_t max_iterations = 1000; // fixed by kernel + const size_t window_dimension = params.window_dimension; + const size_t num_iterations = (params.termination == Termination::TERM_CRITERIA_EPSILON) ? max_iterations : params.num_iterations; + + KeyPointArray new_points(old_points.size()); + + InternalKeyPointArray old_points_internal = create_internal_keypoints(old_points); + InternalKeyPointArray new_points_internal = create_internal_keypoints(new_points_estimates); + + SimpleTensor scharr_gx; + SimpleTensor scharr_gy; + + // Create pyramids + std::vector> old_pyramid = gaussian_pyramid_half(old_input, border_mode, constant_border_value, num_levels); + std::vector> new_pyramid = gaussian_pyramid_half(new_input, border_mode, constant_border_value, num_levels); + + // Iterate over each level of the pyramid + for(size_t idx = num_levels; idx > 0; --idx) + { + const size_t level = idx - 1; + + // Calculate scharr gradients + std::tie(scharr_gx, scharr_gy) = scharr(old_pyramid[level], filter_size, border_mode, constant_border_value, GradientDimension::GRAD_XY); + + scale_tracked_points(level, num_levels, params.use_initial_estimate, old_points_internal, new_points_internal, old_points, new_points_estimates); + + // Calculate valid region based on image dimensions of current pyramid level + const ValidRegion valid_region = shape_to_valid_region(old_pyramid[level].shape(), (border_mode == BorderMode::UNDEFINED), BorderSize(filter_size / 2)); + + for(size_t i = 0; i < old_points.size(); ++i) + { + InternalKeyPoint &old_keypoint = old_points_internal.at(i); + InternalKeyPoint &new_keypoint = new_points_internal.at(i); + + // Helper function for untracking keypoints when on the lowest pyramid level (high resolution) + const auto untrack_keypoint = [&](bool predicate) + { + if(predicate && (level == 0)) + { + new_keypoint.tracking_status = false; + return true; + } + return predicate; + }; + + if(!old_keypoint.tracking_status) + { + continue; + } + + // Check if tracked coordinate is outside image coordinate + if(untrack_keypoint(is_invalid_keypoint(old_keypoint, valid_region, window_dimension))) + { + continue; + } + + // Compute spatial derivative + std::vector bilinear_ix = compute_derivative(scharr_gx, old_keypoint, border_mode, constant_border_value, window_dimension, W_BITS); + std::vector bilinear_iy = compute_derivative(scharr_gy, old_keypoint, border_mode, constant_border_value, window_dimension, W_BITS); + + float A11 = 0.f; + float A12 = 0.f; + float A22 = 0.f; + std::tie(A11, A12, A22) = compute_spatial_gradient_matrix(bilinear_ix, bilinear_iy); + + // Calculate criteria for lost tracking : Matrix A is invertible + // 1. The determinant of the matrix is less than DETERMINANT_THRESHOLD + // 2. The minimum eigenvalue of the matrix is less than EIGENVALUE_THRESHOLD + const float trace_A = A11 + A22; + const float determinant = A11 * A22 - A12 * A12; + const float discriminant = (trace_A * trace_A) - 4.0f * (determinant); + const float eigenvalue_A = (trace_A - std::sqrt(discriminant)) / 2.0f; + + // Divide by window_dimension squared to reduce the floating point accummulation error + const float eigenvalue = eigenvalue_A / (window_dimension * window_dimension); + + // Check if it is a good point to track + if(untrack_keypoint(eigenvalue < EIGENVALUE_THRESHOLD || determinant < DETERMINANT_THRESHOLD)) + { + continue; + } + + float prev_delta_x = 0.f; + float prev_delta_y = 0.f; + + for(size_t j = 0; j < num_iterations; ++j) + { + // Check if tracked coordinate is outside image coordinate + if(untrack_keypoint(is_invalid_keypoint(new_keypoint, valid_region, window_dimension))) + { + break; + } + + // Compute temporal derivative + std::vector bilinear_it_old = compute_derivative(old_pyramid[level], old_keypoint, border_mode, constant_border_value, window_dimension, W_BITS - 5); + std::vector bilinear_it_new = compute_derivative(new_pyramid[level], new_keypoint, border_mode, constant_border_value, window_dimension, W_BITS - 5); + + double b1 = 0.f; + double b2 = 0.f; + std::tie(b1, b2) = compute_temporal_gradient_vector(bilinear_it_old, bilinear_it_new, bilinear_ix, bilinear_iy); + + // Compute motion vector -> A^-1 * -b + const float delta_x = (A12 * b2 - A22 * b1) / determinant; + const float delta_y = (A12 * b1 - A11 * b2) / determinant; + + // Update the new position + new_keypoint.x += delta_x; + new_keypoint.y += delta_y; + + const float magnitude_squared = delta_x * delta_x + delta_y * delta_y; + + // Check if termination criteria is EPSILON and if it is satisfied + if(magnitude_squared <= params.epsilon && (params.termination == Termination::TERM_CRITERIA_EPSILON || params.termination == Termination::TERM_CRITERIA_BOTH)) + { + break; + } + + // Check convergence analyzing the previous delta + if(j > 0 && (std::fabs(delta_x + prev_delta_x) < 0.01f && std::fabs(delta_y + prev_delta_y) < 0.01f)) + { + new_keypoint.x -= delta_x * SCALE_PYRAMID_HALF; + new_keypoint.y -= delta_y * SCALE_PYRAMID_HALF; + + break; + } + + prev_delta_x = delta_x; + prev_delta_y = delta_y; + } + } + } + + // Copy optical flow coordinates to output vector + for(size_t i = 0; i < old_points.size(); ++i) + { + const InternalKeyPoint &new_keypoint = new_points_internal.at(i); + + new_points.at(i).x = roundf(new_keypoint.x); + new_points.at(i).y = roundf(new_keypoint.y); + new_points.at(i).tracking_status = new_keypoint.tracking_status ? 1 : 0; + } + + return new_points; +} + +template std::vector optical_flow(const SimpleTensor &old_input, const SimpleTensor &new_input, + const OpticalFlowParameters ¶ms, size_t num_levels, + const std::vector &old_points, const std::vector &new_points_estimates, + BorderMode border_mode, uint8_t constant_border_value); +} // namespace reference +} // namespace validation +} // namespace test +} // namespace arm_compute -- cgit v1.2.1