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/*
* 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.
*/
// Dense Inverse Search flow algorithm
#include <assert.h>
#include <math.h>
#include "aom_dsp/aom_dsp_common.h"
#include "aom_dsp/flow_estimation/corner_detect.h"
#include "aom_dsp/flow_estimation/disflow.h"
#include "aom_dsp/flow_estimation/ransac.h"
#include "aom_dsp/pyramid.h"
#include "aom_mem/aom_mem.h"
#include "config/aom_dsp_rtcd.h"
// Amount to downsample the flow field by.
// e.g., DOWNSAMPLE_SHIFT = 2 (DOWNSAMPLE_FACTOR == 4) means we calculate
// one flow point for each 4x4 pixel region of the frame
// Must be a power of 2
#define DOWNSAMPLE_SHIFT 3
#define DOWNSAMPLE_FACTOR (1 << DOWNSAMPLE_SHIFT)
// Filters used when upscaling the flow field from one pyramid level
// to another. See upscale_flow_component for details on kernel selection
#define FLOW_UPSCALE_TAPS 4
// Number of outermost flow field entries (on each edge) which can't be
// computed, because the patch they correspond to extends outside of the
// frame
// The border is (DISFLOW_PATCH_SIZE >> 1) pixels, which is
// (DISFLOW_PATCH_SIZE >> 1) >> DOWNSAMPLE_SHIFT many flow field entries
#define FLOW_BORDER_INNER ((DISFLOW_PATCH_SIZE >> 1) >> DOWNSAMPLE_SHIFT)
// Number of extra padding entries on each side of the flow field.
// These samples are added so that we do not need to apply clamping when
// interpolating or upsampling the flow field
#define FLOW_BORDER_OUTER (FLOW_UPSCALE_TAPS / 2)
// When downsampling the flow field, each flow field entry covers a square
// region of pixels in the image pyramid. This value is equal to the position
// of the center of that region, as an offset from the top/left edge.
//
// Note: Using ((DOWNSAMPLE_FACTOR - 1) / 2) is equivalent to the more
// natural expression ((DOWNSAMPLE_FACTOR / 2) - 1),
// unless DOWNSAMPLE_FACTOR == 1 (ie, no downsampling), in which case
// this gives the correct offset of 0 instead of -1.
#define UPSAMPLE_CENTER_OFFSET ((DOWNSAMPLE_FACTOR - 1) / 2)
static double flow_upscale_filter[2][FLOW_UPSCALE_TAPS] = {
// Cubic interpolation kernels for phase=0.75 and phase=0.25, respectively
{ -3 / 128., 29 / 128., 111 / 128., -9 / 128. },
{ -9 / 128., 111 / 128., 29 / 128., -3 / 128. }
};
static inline void get_cubic_kernel_dbl(double x, double kernel[4]) {
// Check that the fractional position is in range.
//
// Note: x is calculated from, e.g., `u_frac = u - floor(u)`.
// Mathematically, this implies that 0 <= x < 1. However, in practice it is
// possible to have x == 1 due to floating point rounding. This is fine,
// and we still interpolate correctly if we allow x = 1.
assert(0 <= x && x <= 1);
double x2 = x * x;
double x3 = x2 * x;
kernel[0] = -0.5 * x + x2 - 0.5 * x3;
kernel[1] = 1.0 - 2.5 * x2 + 1.5 * x3;
kernel[2] = 0.5 * x + 2.0 * x2 - 1.5 * x3;
kernel[3] = -0.5 * x2 + 0.5 * x3;
}
static inline void get_cubic_kernel_int(double x, int kernel[4]) {
double kernel_dbl[4];
get_cubic_kernel_dbl(x, kernel_dbl);
kernel[0] = (int)rint(kernel_dbl[0] * (1 << DISFLOW_INTERP_BITS));
kernel[1] = (int)rint(kernel_dbl[1] * (1 << DISFLOW_INTERP_BITS));
kernel[2] = (int)rint(kernel_dbl[2] * (1 << DISFLOW_INTERP_BITS));
kernel[3] = (int)rint(kernel_dbl[3] * (1 << DISFLOW_INTERP_BITS));
}
static inline double get_cubic_value_dbl(const double *p,
const double kernel[4]) {
return kernel[0] * p[0] + kernel[1] * p[1] + kernel[2] * p[2] +
kernel[3] * p[3];
}
static inline int get_cubic_value_int(const int *p, const int kernel[4]) {
return kernel[0] * p[0] + kernel[1] * p[1] + kernel[2] * p[2] +
kernel[3] * p[3];
}
static inline double bicubic_interp_one(const double *arr, int stride,
const double h_kernel[4],
const double v_kernel[4]) {
double tmp[1 * 4];
// Horizontal convolution
for (int i = -1; i < 3; ++i) {
tmp[i + 1] = get_cubic_value_dbl(&arr[i * stride - 1], h_kernel);
}
// Vertical convolution
return get_cubic_value_dbl(tmp, v_kernel);
}
static int determine_disflow_correspondence(const ImagePyramid *src_pyr,
const ImagePyramid *ref_pyr,
CornerList *corners,
const FlowField *flow,
Correspondence *correspondences) {
const int width = flow->width;
const int height = flow->height;
const int stride = flow->stride;
int num_correspondences = 0;
for (int i = 0; i < corners->num_corners; ++i) {
const int x0 = corners->corners[2 * i];
const int y0 = corners->corners[2 * i + 1];
// Offset points, to compensate for the fact that (say) a flow field entry
// at horizontal index i, is nominally associated with the pixel at
// horizontal coordinate (i << DOWNSAMPLE_FACTOR) + UPSAMPLE_CENTER_OFFSET
// This offset must be applied before we split the coordinate into integer
// and fractional parts, in order for the interpolation to be correct.
const int x = x0 - UPSAMPLE_CENTER_OFFSET;
const int y = y0 - UPSAMPLE_CENTER_OFFSET;
// Split the pixel coordinates into integer flow field coordinates and
// an offset for interpolation
const int flow_x = x >> DOWNSAMPLE_SHIFT;
const double flow_sub_x =
(x & (DOWNSAMPLE_FACTOR - 1)) / (double)DOWNSAMPLE_FACTOR;
const int flow_y = y >> DOWNSAMPLE_SHIFT;
const double flow_sub_y =
(y & (DOWNSAMPLE_FACTOR - 1)) / (double)DOWNSAMPLE_FACTOR;
// Exclude points which would sample from the outer border of the flow
// field, as this would give lower-quality results.
//
// Note: As we never read from the border region at pyramid level 0, we
// can skip filling it in. If the conditions here are removed, or any
// other logic is added which reads from this border region, then
// compute_flow_field() will need to be modified to call
// fill_flow_field_borders() at pyramid level 0 to set up the correct
// border data.
if (flow_x < 1 || (flow_x + 2) >= width) continue;
if (flow_y < 1 || (flow_y + 2) >= height) continue;
double h_kernel[4];
double v_kernel[4];
get_cubic_kernel_dbl(flow_sub_x, h_kernel);
get_cubic_kernel_dbl(flow_sub_y, v_kernel);
double flow_u = bicubic_interp_one(&flow->u[flow_y * stride + flow_x],
stride, h_kernel, v_kernel);
double flow_v = bicubic_interp_one(&flow->v[flow_y * stride + flow_x],
stride, h_kernel, v_kernel);
// Refine the interpolated flow vector one last time
const int patch_tl_x = x0 - DISFLOW_PATCH_CENTER;
const int patch_tl_y = y0 - DISFLOW_PATCH_CENTER;
aom_compute_flow_at_point(
src_pyr->layers[0].buffer, ref_pyr->layers[0].buffer, patch_tl_x,
patch_tl_y, src_pyr->layers[0].width, src_pyr->layers[0].height,
src_pyr->layers[0].stride, &flow_u, &flow_v);
// Use original points (without offsets) when filling in correspondence
// array
correspondences[num_correspondences].x = x0;
correspondences[num_correspondences].y = y0;
correspondences[num_correspondences].rx = x0 + flow_u;
correspondences[num_correspondences].ry = y0 + flow_v;
num_correspondences++;
}
return num_correspondences;
}
// Compare two regions of width x height pixels, one rooted at position
// (x, y) in src and the other at (x + u, y + v) in ref.
// This function returns the sum of squared pixel differences between
// the two regions.
static inline void compute_flow_vector(const uint8_t *src, const uint8_t *ref,
int width, int height, int stride, int x,
int y, double u, double v,
const int16_t *dx, const int16_t *dy,
int *b) {
memset(b, 0, 2 * sizeof(*b));
// Split offset into integer and fractional parts, and compute cubic
// interpolation kernels
const int u_int = (int)floor(u);
const int v_int = (int)floor(v);
const double u_frac = u - floor(u);
const double v_frac = v - floor(v);
int h_kernel[4];
int v_kernel[4];
get_cubic_kernel_int(u_frac, h_kernel);
get_cubic_kernel_int(v_frac, v_kernel);
// Storage for intermediate values between the two convolution directions
int tmp_[DISFLOW_PATCH_SIZE * (DISFLOW_PATCH_SIZE + 3)];
int *tmp = tmp_ + DISFLOW_PATCH_SIZE; // Offset by one row
// Clamp coordinates so that all pixels we fetch will remain within the
// allocated border region, but allow them to go far enough out that
// the border pixels' values do not change.
// Since we are calculating an 8x8 block, the bottom-right pixel
// in the block has coordinates (x0 + 7, y0 + 7). Then, the cubic
// interpolation has 4 taps, meaning that the output of pixel
// (x_w, y_w) depends on the pixels in the range
// ([x_w - 1, x_w + 2], [y_w - 1, y_w + 2]).
//
// Thus the most extreme coordinates which will be fetched are
// (x0 - 1, y0 - 1) and (x0 + 9, y0 + 9).
const int x0 = clamp(x + u_int, -9, width);
const int y0 = clamp(y + v_int, -9, height);
// Horizontal convolution
for (int i = -1; i < DISFLOW_PATCH_SIZE + 2; ++i) {
const int y_w = y0 + i;
for (int j = 0; j < DISFLOW_PATCH_SIZE; ++j) {
const int x_w = x0 + j;
int arr[4];
arr[0] = (int)ref[y_w * stride + (x_w - 1)];
arr[1] = (int)ref[y_w * stride + (x_w + 0)];
arr[2] = (int)ref[y_w * stride + (x_w + 1)];
arr[3] = (int)ref[y_w * stride + (x_w + 2)];
// Apply kernel and round, keeping 6 extra bits of precision.
//
// 6 is the maximum allowable number of extra bits which will avoid
// the intermediate values overflowing an int16_t. The most extreme
// intermediate value occurs when:
// * The input pixels are [0, 255, 255, 0]
// * u_frac = 0.5
// In this case, the un-scaled output is 255 * 1.125 = 286.875.
// As an integer with 6 fractional bits, that is 18360, which fits
// in an int16_t. But with 7 fractional bits it would be 36720,
// which is too large.
tmp[i * DISFLOW_PATCH_SIZE + j] = ROUND_POWER_OF_TWO(
get_cubic_value_int(arr, h_kernel), DISFLOW_INTERP_BITS - 6);
}
}
// Vertical convolution
for (int i = 0; i < DISFLOW_PATCH_SIZE; ++i) {
for (int j = 0; j < DISFLOW_PATCH_SIZE; ++j) {
const int *p = &tmp[i * DISFLOW_PATCH_SIZE + j];
const int arr[4] = { p[-DISFLOW_PATCH_SIZE], p[0], p[DISFLOW_PATCH_SIZE],
p[2 * DISFLOW_PATCH_SIZE] };
const int result = get_cubic_value_int(arr, v_kernel);
// Apply kernel and round.
// This time, we have to round off the 6 extra bits which were kept
// earlier, but we also want to keep DISFLOW_DERIV_SCALE_LOG2 extra bits
// of precision to match the scale of the dx and dy arrays.
const int round_bits = DISFLOW_INTERP_BITS + 6 - DISFLOW_DERIV_SCALE_LOG2;
const int warped = ROUND_POWER_OF_TWO(result, round_bits);
const int src_px = src[(x + j) + (y + i) * stride] << 3;
const int dt = warped - src_px;
b[0] += dx[i * DISFLOW_PATCH_SIZE + j] * dt;
b[1] += dy[i * DISFLOW_PATCH_SIZE + j] * dt;
}
}
}
static inline void sobel_filter(const uint8_t *src, int src_stride,
int16_t *dst, int dst_stride, int dir) {
int16_t tmp_[DISFLOW_PATCH_SIZE * (DISFLOW_PATCH_SIZE + 2)];
int16_t *tmp = tmp_ + DISFLOW_PATCH_SIZE;
// Sobel filter kernel
// This must have an overall scale factor equal to DISFLOW_DERIV_SCALE,
// in order to produce correctly scaled outputs.
// To work out the scale factor, we multiply two factors:
//
// * For the derivative filter (sobel_a), comparing our filter
// image[x - 1] - image[x + 1]
// to the standard form
// d/dx image[x] = image[x+1] - image[x]
// tells us that we're actually calculating -2 * d/dx image[2]
//
// * For the smoothing filter (sobel_b), all coefficients are positive
// so the scale factor is just the sum of the coefficients
//
// Thus we need to make sure that DISFLOW_DERIV_SCALE = 2 * sum(sobel_b)
// (and take care of the - sign from sobel_a elsewhere)
static const int16_t sobel_a[3] = { 1, 0, -1 };
static const int16_t sobel_b[3] = { 1, 2, 1 };
const int taps = 3;
// horizontal filter
const int16_t *h_kernel = dir ? sobel_a : sobel_b;
for (int y = -1; y < DISFLOW_PATCH_SIZE + 1; ++y) {
for (int x = 0; x < DISFLOW_PATCH_SIZE; ++x) {
int sum = 0;
for (int k = 0; k < taps; ++k) {
sum += h_kernel[k] * src[y * src_stride + (x + k - 1)];
}
tmp[y * DISFLOW_PATCH_SIZE + x] = sum;
}
}
// vertical filter
const int16_t *v_kernel = dir ? sobel_b : sobel_a;
for (int y = 0; y < DISFLOW_PATCH_SIZE; ++y) {
for (int x = 0; x < DISFLOW_PATCH_SIZE; ++x) {
int sum = 0;
for (int k = 0; k < taps; ++k) {
sum += v_kernel[k] * tmp[(y + k - 1) * DISFLOW_PATCH_SIZE + x];
}
dst[y * dst_stride + x] = sum;
}
}
}
// Computes the components of the system of equations used to solve for
// a flow vector.
//
// The flow equations are a least-squares system, derived as follows:
//
// For each pixel in the patch, we calculate the current error `dt`,
// and the x and y gradients `dx` and `dy` of the source patch.
// This means that, to first order, the squared error for this pixel is
//
// (dt + u * dx + v * dy)^2
//
// where (u, v) are the incremental changes to the flow vector.
//
// We then want to find the values of u and v which minimize the sum
// of the squared error across all pixels. Conveniently, this fits exactly
// into the form of a least squares problem, with one equation
//
// u * dx + v * dy = -dt
//
// for each pixel.
//
// Summing across all pixels in a square window of size DISFLOW_PATCH_SIZE,
// and absorbing the - sign elsewhere, this results in the least squares system
//
// M = |sum(dx * dx) sum(dx * dy)|
// |sum(dx * dy) sum(dy * dy)|
//
// b = |sum(dx * dt)|
// |sum(dy * dt)|
static inline void compute_flow_matrix(const int16_t *dx, int dx_stride,
const int16_t *dy, int dy_stride,
double *M) {
int tmp[4] = { 0 };
for (int i = 0; i < DISFLOW_PATCH_SIZE; i++) {
for (int j = 0; j < DISFLOW_PATCH_SIZE; j++) {
tmp[0] += dx[i * dx_stride + j] * dx[i * dx_stride + j];
tmp[1] += dx[i * dx_stride + j] * dy[i * dy_stride + j];
// Don't compute tmp[2], as it should be equal to tmp[1]
tmp[3] += dy[i * dy_stride + j] * dy[i * dy_stride + j];
}
}
// Apply regularization
// We follow the standard regularization method of adding `k * I` before
// inverting. This ensures that the matrix will be invertible.
//
// Setting the regularization strength k to 1 seems to work well here, as
// typical values coming from the other equations are very large (1e5 to
// 1e6, with an upper limit of around 6e7, at the time of writing).
// It also preserves the property that all matrix values are whole numbers,
// which is convenient for integerized SIMD implementation.
tmp[0] += 1;
tmp[3] += 1;
tmp[2] = tmp[1];
M[0] = (double)tmp[0];
M[1] = (double)tmp[1];
M[2] = (double)tmp[2];
M[3] = (double)tmp[3];
}
// Try to invert the matrix M
// Note: Due to the nature of how a least-squares matrix is constructed, all of
// the eigenvalues will be >= 0, and therefore det M >= 0 as well.
// The regularization term `+ k * I` further ensures that det M >= k^2.
// As mentioned in compute_flow_matrix(), here we use k = 1, so det M >= 1.
// So we don't have to worry about non-invertible matrices here.
static inline void invert_2x2(const double *M, double *M_inv) {
double det = (M[0] * M[3]) - (M[1] * M[2]);
assert(det >= 1);
const double det_inv = 1 / det;
M_inv[0] = M[3] * det_inv;
M_inv[1] = -M[1] * det_inv;
M_inv[2] = -M[2] * det_inv;
M_inv[3] = M[0] * det_inv;
}
void aom_compute_flow_at_point_c(const uint8_t *src, const uint8_t *ref, int x,
int y, int width, int height, int stride,
double *u, double *v) {
double M[4];
double M_inv[4];
int b[2];
int16_t dx[DISFLOW_PATCH_SIZE * DISFLOW_PATCH_SIZE];
int16_t dy[DISFLOW_PATCH_SIZE * DISFLOW_PATCH_SIZE];
// Compute gradients within this patch
const uint8_t *src_patch = &src[y * stride + x];
sobel_filter(src_patch, stride, dx, DISFLOW_PATCH_SIZE, 1);
sobel_filter(src_patch, stride, dy, DISFLOW_PATCH_SIZE, 0);
compute_flow_matrix(dx, DISFLOW_PATCH_SIZE, dy, DISFLOW_PATCH_SIZE, M);
invert_2x2(M, M_inv);
for (int itr = 0; itr < DISFLOW_MAX_ITR; itr++) {
compute_flow_vector(src, ref, width, height, stride, x, y, *u, *v, dx, dy,
b);
// Solve flow equations to find a better estimate for the flow vector
// at this point
const double step_u = M_inv[0] * b[0] + M_inv[1] * b[1];
const double step_v = M_inv[2] * b[0] + M_inv[3] * b[1];
*u += fclamp(step_u * DISFLOW_STEP_SIZE, -2, 2);
*v += fclamp(step_v * DISFLOW_STEP_SIZE, -2, 2);
if (fabs(step_u) + fabs(step_v) < DISFLOW_STEP_SIZE_THRESOLD) {
// Stop iteration when we're close to convergence
break;
}
}
}
static void fill_flow_field_borders(double *flow, int width, int height,
int stride) {
// Calculate the bounds of the rectangle which was filled in by
// compute_flow_field() before calling this function.
// These indices are inclusive on both ends.
const int left_index = FLOW_BORDER_INNER;
const int right_index = (width - FLOW_BORDER_INNER - 1);
const int top_index = FLOW_BORDER_INNER;
const int bottom_index = (height - FLOW_BORDER_INNER - 1);
// Left area
for (int i = top_index; i <= bottom_index; i += 1) {
double *row = flow + i * stride;
const double left = row[left_index];
for (int j = -FLOW_BORDER_OUTER; j < left_index; j++) {
row[j] = left;
}
}
// Right area
for (int i = top_index; i <= bottom_index; i += 1) {
double *row = flow + i * stride;
const double right = row[right_index];
for (int j = right_index + 1; j < width + FLOW_BORDER_OUTER; j++) {
row[j] = right;
}
}
// Top area
const double *top_row = flow + top_index * stride - FLOW_BORDER_OUTER;
for (int i = -FLOW_BORDER_OUTER; i < top_index; i++) {
double *row = flow + i * stride - FLOW_BORDER_OUTER;
size_t length = width + 2 * FLOW_BORDER_OUTER;
memcpy(row, top_row, length * sizeof(*row));
}
// Bottom area
const double *bottom_row = flow + bottom_index * stride - FLOW_BORDER_OUTER;
for (int i = bottom_index + 1; i < height + FLOW_BORDER_OUTER; i++) {
double *row = flow + i * stride - FLOW_BORDER_OUTER;
size_t length = width + 2 * FLOW_BORDER_OUTER;
memcpy(row, bottom_row, length * sizeof(*row));
}
}
// Upscale one component of the flow field, from a size of
// cur_width x cur_height to a size of (2*cur_width) x (2*cur_height), storing
// the result back into the same buffer. This function also scales the flow
// vector by 2, so that when we move to the next pyramid level down, the implied
// motion vector is the same.
//
// The temporary buffer tmpbuf must be large enough to hold an intermediate
// array of size stride * cur_height, *plus* FLOW_BORDER_OUTER rows above and
// below. In other words, indices from -FLOW_BORDER_OUTER * stride to
// (cur_height + FLOW_BORDER_OUTER) * stride - 1 must be valid.
//
// Note that the same stride is used for u before and after upscaling
// and for the temporary buffer, for simplicity.
//
// A note on phasing:
//
// The flow fields at two adjacent pyramid levels are offset from each other,
// and we need to account for this in the construction of the interpolation
// kernels.
//
// Consider an 8x8 pixel patch at pyramid level n. This is split into four
// patches at pyramid level n-1. Bringing these patches back up to pyramid level
// n, each sub-patch covers 4x4 pixels, and between them they cover the same
// 8x8 region.
//
// Therefore, at pyramid level n, two adjacent patches look like this:
//
// + - - - - - - - + - - - - - - - +
// | | |
// | x x | x x |
// | | |
// | # | # |
// | | |
// | x x | x x |
// | | |
// + - - - - - - - + - - - - - - - +
//
// where # marks the center of a patch at pyramid level n (the input to this
// function), and x marks the center of a patch at pyramid level n-1 (the output
// of this function).
//
// By counting pixels (marked by +, -, and |), we can see that the flow vectors
// at pyramid level n-1 are offset relative to the flow vectors at pyramid
// level n, by 1/4 of the larger (input) patch size. Therefore, our
// interpolation kernels need to have phases of 0.25 and 0.75.
//
// In addition, in order to handle the frame edges correctly, we need to
// generate one output vector to the left and one to the right of each input
// vector, even though these must be interpolated using different source points.
static void upscale_flow_component(double *flow, int cur_width, int cur_height,
int stride, double *tmpbuf) {
const int half_len = FLOW_UPSCALE_TAPS / 2;
// Check that the outer border is large enough to avoid needing to clamp
// the source locations
assert(half_len <= FLOW_BORDER_OUTER);
// Horizontal upscale and multiply by 2
for (int i = 0; i < cur_height; i++) {
for (int j = 0; j < cur_width; j++) {
double left = 0;
for (int k = -half_len; k < half_len; k++) {
left +=
flow[i * stride + (j + k)] * flow_upscale_filter[0][k + half_len];
}
tmpbuf[i * stride + (2 * j + 0)] = 2.0 * left;
// Right output pixel is 0.25 units to the right of the input pixel
double right = 0;
for (int k = -(half_len - 1); k < (half_len + 1); k++) {
right += flow[i * stride + (j + k)] *
flow_upscale_filter[1][k + (half_len - 1)];
}
tmpbuf[i * stride + (2 * j + 1)] = 2.0 * right;
}
}
// Fill in top and bottom borders of tmpbuf
const double *top_row = &tmpbuf[0];
for (int i = -FLOW_BORDER_OUTER; i < 0; i++) {
double *row = &tmpbuf[i * stride];
memcpy(row, top_row, 2 * cur_width * sizeof(*row));
}
const double *bottom_row = &tmpbuf[(cur_height - 1) * stride];
for (int i = cur_height; i < cur_height + FLOW_BORDER_OUTER; i++) {
double *row = &tmpbuf[i * stride];
memcpy(row, bottom_row, 2 * cur_width * sizeof(*row));
}
// Vertical upscale
int upscaled_width = cur_width * 2;
for (int i = 0; i < cur_height; i++) {
for (int j = 0; j < upscaled_width; j++) {
double top = 0;
for (int k = -half_len; k < half_len; k++) {
top +=
tmpbuf[(i + k) * stride + j] * flow_upscale_filter[0][k + half_len];
}
flow[(2 * i) * stride + j] = top;
double bottom = 0;
for (int k = -(half_len - 1); k < (half_len + 1); k++) {
bottom += tmpbuf[(i + k) * stride + j] *
flow_upscale_filter[1][k + (half_len - 1)];
}
flow[(2 * i + 1) * stride + j] = bottom;
}
}
}
// make sure flow_u and flow_v start at 0
static bool compute_flow_field(const ImagePyramid *src_pyr,
const ImagePyramid *ref_pyr, int n_levels,
FlowField *flow) {
bool mem_status = true;
double *flow_u = flow->u;
double *flow_v = flow->v;
double *tmpbuf0;
double *tmpbuf;
if (n_levels < 2) {
// tmpbuf not needed
tmpbuf0 = NULL;
tmpbuf = NULL;
} else {
// This line must match the calculation of cur_flow_height below
const int layer1_height = src_pyr->layers[1].height >> DOWNSAMPLE_SHIFT;
const size_t tmpbuf_size =
(layer1_height + 2 * FLOW_BORDER_OUTER) * flow->stride;
tmpbuf0 = aom_malloc(tmpbuf_size * sizeof(*tmpbuf0));
if (!tmpbuf0) {
mem_status = false;
goto free_tmpbuf;
}
tmpbuf = tmpbuf0 + FLOW_BORDER_OUTER * flow->stride;
}
// Compute flow field from coarsest to finest level of the pyramid
//
// Note: We stop after refining pyramid level 1 and interpolating it to
// generate an initial flow field at level 0. We do *not* refine the dense
// flow field at level 0. Instead, we wait until we have generated
// correspondences by interpolating this flow field, and then refine the
// correspondences themselves. This is both faster and gives better output
// compared to refining the flow field at level 0 and then interpolating.
for (int level = n_levels - 1; level >= 1; --level) {
const PyramidLayer *cur_layer = &src_pyr->layers[level];
const int cur_width = cur_layer->width;
const int cur_height = cur_layer->height;
const int cur_stride = cur_layer->stride;
const uint8_t *src_buffer = cur_layer->buffer;
const uint8_t *ref_buffer = ref_pyr->layers[level].buffer;
const int cur_flow_width = cur_width >> DOWNSAMPLE_SHIFT;
const int cur_flow_height = cur_height >> DOWNSAMPLE_SHIFT;
const int cur_flow_stride = flow->stride;
for (int i = FLOW_BORDER_INNER; i < cur_flow_height - FLOW_BORDER_INNER;
i += 1) {
for (int j = FLOW_BORDER_INNER; j < cur_flow_width - FLOW_BORDER_INNER;
j += 1) {
const int flow_field_idx = i * cur_flow_stride + j;
// Calculate the position of a patch of size DISFLOW_PATCH_SIZE pixels,
// which is centered on the region covered by this flow field entry
const int patch_center_x =
(j << DOWNSAMPLE_SHIFT) + UPSAMPLE_CENTER_OFFSET; // In pixels
const int patch_center_y =
(i << DOWNSAMPLE_SHIFT) + UPSAMPLE_CENTER_OFFSET; // In pixels
const int patch_tl_x = patch_center_x - DISFLOW_PATCH_CENTER;
const int patch_tl_y = patch_center_y - DISFLOW_PATCH_CENTER;
assert(patch_tl_x >= 0);
assert(patch_tl_y >= 0);
aom_compute_flow_at_point(src_buffer, ref_buffer, patch_tl_x,
patch_tl_y, cur_width, cur_height, cur_stride,
&flow_u[flow_field_idx],
&flow_v[flow_field_idx]);
}
}
// Fill in the areas which we haven't explicitly computed, with copies
// of the outermost values which we did compute
fill_flow_field_borders(flow_u, cur_flow_width, cur_flow_height,
cur_flow_stride);
fill_flow_field_borders(flow_v, cur_flow_width, cur_flow_height,
cur_flow_stride);
if (level > 0) {
const int upscale_flow_width = cur_flow_width << 1;
const int upscale_flow_height = cur_flow_height << 1;
const int upscale_stride = flow->stride;
upscale_flow_component(flow_u, cur_flow_width, cur_flow_height,
cur_flow_stride, tmpbuf);
upscale_flow_component(flow_v, cur_flow_width, cur_flow_height,
cur_flow_stride, tmpbuf);
// If we didn't fill in the rightmost column or bottommost row during
// upsampling (in order to keep the ratio to exactly 2), fill them
// in here by copying the next closest column/row
const PyramidLayer *next_layer = &src_pyr->layers[level - 1];
const int next_flow_width = next_layer->width >> DOWNSAMPLE_SHIFT;
const int next_flow_height = next_layer->height >> DOWNSAMPLE_SHIFT;
// Rightmost column
if (next_flow_width > upscale_flow_width) {
assert(next_flow_width == upscale_flow_width + 1);
for (int i = 0; i < upscale_flow_height; i++) {
const int index = i * upscale_stride + upscale_flow_width;
flow_u[index] = flow_u[index - 1];
flow_v[index] = flow_v[index - 1];
}
}
// Bottommost row
if (next_flow_height > upscale_flow_height) {
assert(next_flow_height == upscale_flow_height + 1);
for (int j = 0; j < next_flow_width; j++) {
const int index = upscale_flow_height * upscale_stride + j;
flow_u[index] = flow_u[index - upscale_stride];
flow_v[index] = flow_v[index - upscale_stride];
}
}
}
}
free_tmpbuf:
aom_free(tmpbuf0);
return mem_status;
}
static FlowField *alloc_flow_field(int frame_width, int frame_height) {
FlowField *flow = (FlowField *)aom_malloc(sizeof(FlowField));
if (flow == NULL) return NULL;
// Calculate the size of the bottom (largest) layer of the flow pyramid
flow->width = frame_width >> DOWNSAMPLE_SHIFT;
flow->height = frame_height >> DOWNSAMPLE_SHIFT;
flow->stride = flow->width + 2 * FLOW_BORDER_OUTER;
const size_t flow_size =
flow->stride * (size_t)(flow->height + 2 * FLOW_BORDER_OUTER);
flow->buf0 = aom_calloc(2 * flow_size, sizeof(*flow->buf0));
if (!flow->buf0) {
aom_free(flow);
return NULL;
}
flow->u = flow->buf0 + FLOW_BORDER_OUTER * flow->stride + FLOW_BORDER_OUTER;
flow->v = flow->u + flow_size;
return flow;
}
static void free_flow_field(FlowField *flow) {
aom_free(flow->buf0);
aom_free(flow);
}
// Compute flow field between `src` and `ref`, and then use that flow to
// compute a global motion model relating the two frames.
//
// Following the convention in flow_estimation.h, the flow vectors are computed
// at fixed points in `src` and point to the corresponding locations in `ref`,
// regardless of the temporal ordering of the frames.
bool av1_compute_global_motion_disflow(
TransformationType type, YV12_BUFFER_CONFIG *src, YV12_BUFFER_CONFIG *ref,
int bit_depth, int downsample_level, MotionModel *motion_models,
int num_motion_models, bool *mem_alloc_failed) {
// Precompute information we will need about each frame
ImagePyramid *src_pyramid = src->y_pyramid;
CornerList *src_corners = src->corners;
ImagePyramid *ref_pyramid = ref->y_pyramid;
const int src_layers =
aom_compute_pyramid(src, bit_depth, DISFLOW_PYRAMID_LEVELS, src_pyramid);
const int ref_layers =
aom_compute_pyramid(ref, bit_depth, DISFLOW_PYRAMID_LEVELS, ref_pyramid);
if (src_layers < 0 || ref_layers < 0) {
*mem_alloc_failed = true;
return false;
}
if (!av1_compute_corner_list(src, bit_depth, downsample_level, src_corners)) {
*mem_alloc_failed = true;
return false;
}
assert(src_layers == ref_layers);
const int src_width = src_pyramid->layers[0].width;
const int src_height = src_pyramid->layers[0].height;
assert(ref_pyramid->layers[0].width == src_width);
assert(ref_pyramid->layers[0].height == src_height);
FlowField *flow = alloc_flow_field(src_width, src_height);
if (!flow) {
*mem_alloc_failed = true;
return false;
}
if (!compute_flow_field(src_pyramid, ref_pyramid, src_layers, flow)) {
*mem_alloc_failed = true;
free_flow_field(flow);
return false;
}
// find correspondences between the two images using the flow field
Correspondence *correspondences =
aom_malloc(src_corners->num_corners * sizeof(*correspondences));
if (!correspondences) {
*mem_alloc_failed = true;
free_flow_field(flow);
return false;
}
const int num_correspondences = determine_disflow_correspondence(
src_pyramid, ref_pyramid, src_corners, flow, correspondences);
bool result = ransac(correspondences, num_correspondences, type,
motion_models, num_motion_models, mem_alloc_failed);
aom_free(correspondences);
free_flow_field(flow);
return result;
}