diff options
Diffstat (limited to 'third_party/aom/av1/encoder/corner_match.c')
| -rw-r--r-- | third_party/aom/av1/encoder/corner_match.c | 191 |
1 files changed, 191 insertions, 0 deletions
diff --git a/third_party/aom/av1/encoder/corner_match.c b/third_party/aom/av1/encoder/corner_match.c new file mode 100644 index 000000000..29e934deb --- /dev/null +++ b/third_party/aom/av1/encoder/corner_match.c @@ -0,0 +1,191 @@ +/* + * Copyright (c) 2016, Alliance for Open Media. All rights reserved + * + * This source code is subject to the terms of the BSD 2 Clause License and + * the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License + * was not distributed with this source code in the LICENSE file, you can + * obtain it at www.aomedia.org/license/software. If the Alliance for Open + * Media Patent License 1.0 was not distributed with this source code in the + * PATENTS file, you can obtain it at www.aomedia.org/license/patent. + */ + +#include <stdlib.h> +#include <memory.h> +#include <math.h> + +#include "config/av1_rtcd.h" + +#include "av1/encoder/corner_match.h" + +#define SEARCH_SZ 9 +#define SEARCH_SZ_BY2 ((SEARCH_SZ - 1) / 2) + +#define THRESHOLD_NCC 0.75 + +/* Compute var(im) * MATCH_SZ_SQ over a MATCH_SZ by MATCH_SZ window of im, + centered at (x, y). +*/ +static double compute_variance(unsigned char *im, int stride, int x, int y) { + int sum = 0; + int sumsq = 0; + int var; + int i, j; + for (i = 0; i < MATCH_SZ; ++i) + for (j = 0; j < MATCH_SZ; ++j) { + sum += im[(i + y - MATCH_SZ_BY2) * stride + (j + x - MATCH_SZ_BY2)]; + sumsq += im[(i + y - MATCH_SZ_BY2) * stride + (j + x - MATCH_SZ_BY2)] * + im[(i + y - MATCH_SZ_BY2) * stride + (j + x - MATCH_SZ_BY2)]; + } + var = sumsq * MATCH_SZ_SQ - sum * sum; + return (double)var; +} + +/* Compute corr(im1, im2) * MATCH_SZ * stddev(im1), where the + correlation/standard deviation are taken over MATCH_SZ by MATCH_SZ windows + of each image, centered at (x1, y1) and (x2, y2) respectively. +*/ +double compute_cross_correlation_c(unsigned char *im1, int stride1, int x1, + int y1, unsigned char *im2, int stride2, + int x2, int y2) { + int v1, v2; + int sum1 = 0; + int sum2 = 0; + int sumsq2 = 0; + int cross = 0; + int var2, cov; + int i, j; + for (i = 0; i < MATCH_SZ; ++i) + for (j = 0; j < MATCH_SZ; ++j) { + v1 = im1[(i + y1 - MATCH_SZ_BY2) * stride1 + (j + x1 - MATCH_SZ_BY2)]; + v2 = im2[(i + y2 - MATCH_SZ_BY2) * stride2 + (j + x2 - MATCH_SZ_BY2)]; + sum1 += v1; + sum2 += v2; + sumsq2 += v2 * v2; + cross += v1 * v2; + } + var2 = sumsq2 * MATCH_SZ_SQ - sum2 * sum2; + cov = cross * MATCH_SZ_SQ - sum1 * sum2; + return cov / sqrt((double)var2); +} + +static int is_eligible_point(int pointx, int pointy, int width, int height) { + return (pointx >= MATCH_SZ_BY2 && pointy >= MATCH_SZ_BY2 && + pointx + MATCH_SZ_BY2 < width && pointy + MATCH_SZ_BY2 < height); +} + +static int is_eligible_distance(int point1x, int point1y, int point2x, + int point2y, int width, int height) { + const int thresh = (width < height ? height : width) >> 4; + return ((point1x - point2x) * (point1x - point2x) + + (point1y - point2y) * (point1y - point2y)) <= thresh * thresh; +} + +static void improve_correspondence(unsigned char *frm, unsigned char *ref, + int width, int height, int frm_stride, + int ref_stride, + Correspondence *correspondences, + int num_correspondences) { + int i; + for (i = 0; i < num_correspondences; ++i) { + int x, y, best_x = 0, best_y = 0; + double best_match_ncc = 0.0; + for (y = -SEARCH_SZ_BY2; y <= SEARCH_SZ_BY2; ++y) { + for (x = -SEARCH_SZ_BY2; x <= SEARCH_SZ_BY2; ++x) { + double match_ncc; + if (!is_eligible_point(correspondences[i].rx + x, + correspondences[i].ry + y, width, height)) + continue; + if (!is_eligible_distance(correspondences[i].x, correspondences[i].y, + correspondences[i].rx + x, + correspondences[i].ry + y, width, height)) + continue; + match_ncc = compute_cross_correlation( + frm, frm_stride, correspondences[i].x, correspondences[i].y, ref, + ref_stride, correspondences[i].rx + x, correspondences[i].ry + y); + if (match_ncc > best_match_ncc) { + best_match_ncc = match_ncc; + best_y = y; + best_x = x; + } + } + } + correspondences[i].rx += best_x; + correspondences[i].ry += best_y; + } + for (i = 0; i < num_correspondences; ++i) { + int x, y, best_x = 0, best_y = 0; + double best_match_ncc = 0.0; + for (y = -SEARCH_SZ_BY2; y <= SEARCH_SZ_BY2; ++y) + for (x = -SEARCH_SZ_BY2; x <= SEARCH_SZ_BY2; ++x) { + double match_ncc; + if (!is_eligible_point(correspondences[i].x + x, + correspondences[i].y + y, width, height)) + continue; + if (!is_eligible_distance( + correspondences[i].x + x, correspondences[i].y + y, + correspondences[i].rx, correspondences[i].ry, width, height)) + continue; + match_ncc = compute_cross_correlation( + ref, ref_stride, correspondences[i].rx, correspondences[i].ry, frm, + frm_stride, correspondences[i].x + x, correspondences[i].y + y); + if (match_ncc > best_match_ncc) { + best_match_ncc = match_ncc; + best_y = y; + best_x = x; + } + } + correspondences[i].x += best_x; + correspondences[i].y += best_y; + } +} + +int determine_correspondence(unsigned char *frm, int *frm_corners, + int num_frm_corners, unsigned char *ref, + int *ref_corners, int num_ref_corners, int width, + int height, int frm_stride, int ref_stride, + int *correspondence_pts) { + // TODO(sarahparker) Improve this to include 2-way match + int i, j; + Correspondence *correspondences = (Correspondence *)correspondence_pts; + int num_correspondences = 0; + for (i = 0; i < num_frm_corners; ++i) { + double best_match_ncc = 0.0; + double template_norm; + int best_match_j = -1; + if (!is_eligible_point(frm_corners[2 * i], frm_corners[2 * i + 1], width, + height)) + continue; + for (j = 0; j < num_ref_corners; ++j) { + double match_ncc; + if (!is_eligible_point(ref_corners[2 * j], ref_corners[2 * j + 1], width, + height)) + continue; + if (!is_eligible_distance(frm_corners[2 * i], frm_corners[2 * i + 1], + ref_corners[2 * j], ref_corners[2 * j + 1], + width, height)) + continue; + match_ncc = compute_cross_correlation( + frm, frm_stride, frm_corners[2 * i], frm_corners[2 * i + 1], ref, + ref_stride, ref_corners[2 * j], ref_corners[2 * j + 1]); + if (match_ncc > best_match_ncc) { + best_match_ncc = match_ncc; + best_match_j = j; + } + } + // Note: We want to test if the best correlation is >= THRESHOLD_NCC, + // but need to account for the normalization in compute_cross_correlation. + template_norm = compute_variance(frm, frm_stride, frm_corners[2 * i], + frm_corners[2 * i + 1]); + if (best_match_ncc > THRESHOLD_NCC * sqrt(template_norm)) { + correspondences[num_correspondences].x = frm_corners[2 * i]; + correspondences[num_correspondences].y = frm_corners[2 * i + 1]; + correspondences[num_correspondences].rx = ref_corners[2 * best_match_j]; + correspondences[num_correspondences].ry = + ref_corners[2 * best_match_j + 1]; + num_correspondences++; + } + } + improve_correspondence(frm, ref, width, height, frm_stride, ref_stride, + correspondences, num_correspondences); + return num_correspondences; +} |