| /* |
| * 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 av1_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 = av1_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 = av1_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 av1_determine_correspondence(unsigned char *src, int *src_corners, |
| int num_src_corners, unsigned char *ref, |
| int *ref_corners, int num_ref_corners, |
| int width, int height, int src_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_src_corners; ++i) { |
| double best_match_ncc = 0.0; |
| double template_norm; |
| int best_match_j = -1; |
| if (!is_eligible_point(src_corners[2 * i], src_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(src_corners[2 * i], src_corners[2 * i + 1], |
| ref_corners[2 * j], ref_corners[2 * j + 1], |
| width, height)) |
| continue; |
| match_ncc = av1_compute_cross_correlation( |
| src, src_stride, src_corners[2 * i], src_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 |
| // av1_compute_cross_correlation. |
| template_norm = compute_variance(src, src_stride, src_corners[2 * i], |
| src_corners[2 * i + 1]); |
| if (best_match_ncc > THRESHOLD_NCC * sqrt(template_norm)) { |
| correspondences[num_correspondences].x = src_corners[2 * i]; |
| correspondences[num_correspondences].y = src_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(src, ref, width, height, src_stride, ref_stride, |
| correspondences, num_correspondences); |
| return num_correspondences; |
| } |
