Implement global motion parameter computation
This computes global motion parameters between 2 frames by
matching corresponding points using FAST feature and then
fitting a model using RANSAC.
Change-Id: Ib6664df44090e8cfa4db9f2f9e0556931ccfe5c8
diff --git a/vp10/encoder/corner_match.c b/vp10/encoder/corner_match.c
new file mode 100644
index 0000000..6b19d5b
--- /dev/null
+++ b/vp10/encoder/corner_match.c
@@ -0,0 +1,210 @@
+/*
+ * Copyright (c) 2010 The WebM project authors. All Rights Reserved.
+ *
+ * Use of this source code is governed by a BSD-style license
+ * that can be found in the LICENSE file in the root of the source
+ * tree. An additional intellectual property rights grant can be found
+ * in the file PATENTS. All contributing project authors may
+ * be found in the AUTHORS file in the root of the source tree.
+ */
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <memory.h>
+#include <math.h>
+
+#include "vp10/encoder/corner_match.h"
+
+#define MATCH_SZ 15
+#define MATCH_SZ_BY2 ((MATCH_SZ - 1) / 2)
+#define MATCH_SZ_SQ (MATCH_SZ * MATCH_SZ)
+#define SEARCH_SZ 9
+#define SEARCH_SZ_BY2 ((SEARCH_SZ - 1) / 2)
+
+#define THRESHOLD_NCC 0.80
+
+static double compute_variance(unsigned char *im, int stride, int x, int y,
+ double *mean) {
+ double sum = 0.0;
+ double sumsq = 0.0;
+ double 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) / (MATCH_SZ_SQ * MATCH_SZ_SQ);
+ if (mean) *mean = sum / MATCH_SZ_SQ;
+ return var;
+}
+
+static double compute_cross_correlation(unsigned char *im1, int stride1, int x1,
+ int y1, unsigned char *im2, int stride2,
+ int x2, int y2) {
+ double sum1 = 0;
+ double sum2 = 0;
+ double cross = 0;
+ double corr;
+ int i, j;
+ for (i = 0; i < MATCH_SZ; ++i)
+ for (j = 0; j < MATCH_SZ; ++j) {
+ sum1 += im1[(i + y1 - MATCH_SZ_BY2) * stride1 + (j + x1 - MATCH_SZ_BY2)];
+ sum2 += im2[(i + y2 - MATCH_SZ_BY2) * stride2 + (j + x2 - MATCH_SZ_BY2)];
+ cross +=
+ im1[(i + y1 - MATCH_SZ_BY2) * stride1 + (j + x1 - MATCH_SZ_BY2)] *
+ im2[(i + y2 - MATCH_SZ_BY2) * stride2 + (j + x2 - MATCH_SZ_BY2)];
+ }
+ corr = (cross * MATCH_SZ_SQ - sum1 * sum2) / (MATCH_SZ_SQ * MATCH_SZ_SQ);
+ return corr;
+}
+
+static int is_eligible_point(double pointx, double 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(double point1x, double point1y, double point2x,
+ double 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) {
+ double template_norm =
+ compute_variance(frm, frm_stride, (int)correspondences[i].x,
+ (int)correspondences[i].y, NULL);
+ 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;
+ double subimage_norm;
+ if (!is_eligible_point((int)correspondences[i].rx + x,
+ (int)correspondences[i].ry + y, width, height))
+ continue;
+ if (!is_eligible_distance(
+ (int)correspondences[i].x, (int)correspondences[i].y,
+ (int)correspondences[i].rx + x, (int)correspondences[i].ry + y,
+ width, height))
+ continue;
+ subimage_norm =
+ compute_variance(ref, ref_stride, (int)correspondences[i].rx + x,
+ (int)correspondences[i].ry + y, NULL);
+ match_ncc = compute_cross_correlation(
+ frm, frm_stride, (int)correspondences[i].x,
+ (int)correspondences[i].y, ref, ref_stride,
+ (int)correspondences[i].rx + x,
+ (int)correspondences[i].ry + y) /
+ sqrt(template_norm * subimage_norm);
+ if (match_ncc > best_match_ncc) {
+ best_match_ncc = match_ncc;
+ best_y = y;
+ best_x = x;
+ }
+ }
+ }
+ correspondences[i].rx += (double)best_x;
+ correspondences[i].ry += (double)best_y;
+ }
+ for (i = 0; i < num_correspondences; ++i) {
+ double template_norm =
+ compute_variance(ref, ref_stride, (int)correspondences[i].rx,
+ (int)correspondences[i].ry, NULL);
+ 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;
+ double subimage_norm;
+ if (!is_eligible_point((int)correspondences[i].x + x,
+ (int)correspondences[i].y + y, width, height))
+ continue;
+ if (!is_eligible_distance((int)correspondences[i].x + x,
+ (int)correspondences[i].y + y,
+ (int)correspondences[i].rx,
+ (int)correspondences[i].ry, width, height))
+ continue;
+ subimage_norm =
+ compute_variance(frm, frm_stride, (int)correspondences[i].x + x,
+ (int)correspondences[i].y + y, NULL);
+ match_ncc =
+ compute_cross_correlation(
+ frm, frm_stride, (int)correspondences[i].x + x,
+ (int)correspondences[i].y + y, ref, ref_stride,
+ (int)correspondences[i].rx, (int)correspondences[i].ry) /
+ sqrt(template_norm * subimage_norm);
+ 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,
+ double *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;
+ template_norm = compute_variance(frm, frm_stride, frm_corners[2 * i],
+ frm_corners[2 * i + 1], NULL);
+ for (j = 0; j < num_ref_corners; ++j) {
+ double match_ncc;
+ double subimage_norm;
+ 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;
+ subimage_norm = compute_variance(ref, ref_stride, ref_corners[2 * j],
+ ref_corners[2 * j + 1], NULL);
+ 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]) /
+ sqrt(template_norm * subimage_norm);
+ if (match_ncc > best_match_ncc) {
+ best_match_ncc = match_ncc;
+ best_match_j = j;
+ }
+ }
+ if (best_match_ncc > THRESHOLD_NCC) {
+ correspondences[num_correspondences].x = (double)frm_corners[2 * i];
+ correspondences[num_correspondences].y = (double)frm_corners[2 * i + 1];
+ correspondences[num_correspondences].rx =
+ (double)ref_corners[2 * best_match_j];
+ correspondences[num_correspondences].ry =
+ (double)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;
+}
