/* * 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 // Paper: https://arxiv.org/abs/1603.03590 #include #include #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; }