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https://github.com/FFmpeg/FFmpeg.git
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648 lines
21 KiB
Common Lisp
648 lines
21 KiB
Common Lisp
/*
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* This file is part of FFmpeg.
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*
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* FFmpeg is free software; you can redistribute it and/or
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* modify it under the terms of the GNU Lesser General Public
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* License as published by the Free Software Foundation; either
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* version 2.1 of the License, or (at your option) any later version.
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*
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* FFmpeg is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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* Lesser General Public License for more details.
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*
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* You should have received a copy of the GNU Lesser General Public
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* License along with FFmpeg; if not, write to the Free Software
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* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
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*
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* Copyright (C) 2000, Intel Corporation, all rights reserved.
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* Copyright (C) 2013, OpenCV Foundation, all rights reserved.
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* Third party copyrights are property of their respective owners.
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*
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* Redistribution and use in source and binary forms, with or without modification,
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* are permitted provided that the following conditions are met:
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*
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* * Redistribution's of source code must retain the above copyright notice,
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* this list of conditions and the following disclaimer.
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*
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* * Redistribution's in binary form must reproduce the above copyright notice,
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* this list of conditions and the following disclaimer in the documentation
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* and/or other materials provided with the distribution.
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*
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* * The name of the copyright holders may not be used to endorse or promote products
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* derived from this software without specific prior written permission.
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*
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* This software is provided by the copyright holders and contributors "as is" and
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* any express or implied warranties, including, but not limited to, the implied
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* warranties of merchantability and fitness for a particular purpose are disclaimed.
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* In no event shall the Intel Corporation or contributors be liable for any direct,
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* indirect, incidental, special, exemplary, or consequential damages
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* (including, but not limited to, procurement of substitute goods or services;
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* loss of use, data, or profits; or business interruption) however caused
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* and on any theory of liability, whether in contract, strict liability,
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* or tort (including negligence or otherwise) arising in any way out of
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* the use of this software, even if advised of the possibility of such damage.
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*/
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#define HARRIS_THRESHOLD 3.0f
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// Block size over which to compute harris response
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//
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// Note that changing this will require fiddling with the local array sizes in
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// harris_response
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#define HARRIS_RADIUS 2
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#define DISTANCE_THRESHOLD 80
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// Sub-pixel refinement window for feature points
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#define REFINE_WIN_HALF_W 5
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#define REFINE_WIN_HALF_H 5
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#define REFINE_WIN_W 11 // REFINE_WIN_HALF_W * 2 + 1
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#define REFINE_WIN_H 11
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// Non-maximum suppression window size
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#define NONMAX_WIN 30
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#define NONMAX_WIN_HALF 15 // NONMAX_WIN / 2
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typedef struct PointPair {
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// Previous frame
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float2 p1;
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// Current frame
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float2 p2;
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} PointPair;
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typedef struct SmoothedPointPair {
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// Non-smoothed point in current frame
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int2 p1;
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// Smoothed point in current frame
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float2 p2;
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} SmoothedPointPair;
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typedef struct MotionVector {
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PointPair p;
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// Used to mark vectors as potential outliers
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int should_consider;
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} MotionVector;
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const sampler_t sampler = CLK_NORMALIZED_COORDS_FALSE |
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CLK_ADDRESS_CLAMP_TO_EDGE |
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CLK_FILTER_NEAREST;
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const sampler_t sampler_linear = CLK_NORMALIZED_COORDS_FALSE |
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CLK_ADDRESS_CLAMP_TO_EDGE |
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CLK_FILTER_LINEAR;
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const sampler_t sampler_linear_mirror = CLK_NORMALIZED_COORDS_TRUE |
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CLK_ADDRESS_MIRRORED_REPEAT |
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CLK_FILTER_LINEAR;
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// Writes to a 1D array at loc, treating it as a 2D array with the same
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// dimensions as the global work size.
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static void write_to_1d_arrf(__global float *buf, int2 loc, float val) {
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buf[loc.x + loc.y * get_global_size(0)] = val;
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}
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static void write_to_1d_arrul8(__global ulong8 *buf, int2 loc, ulong8 val) {
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buf[loc.x + loc.y * get_global_size(0)] = val;
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}
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static void write_to_1d_arrvec(__global MotionVector *buf, int2 loc, MotionVector val) {
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buf[loc.x + loc.y * get_global_size(0)] = val;
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}
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static void write_to_1d_arrf2(__global float2 *buf, int2 loc, float2 val) {
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buf[loc.x + loc.y * get_global_size(0)] = val;
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}
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static ulong8 read_from_1d_arrul8(__global const ulong8 *buf, int2 loc) {
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return buf[loc.x + loc.y * get_global_size(0)];
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}
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static float2 read_from_1d_arrf2(__global const float2 *buf, int2 loc) {
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return buf[loc.x + loc.y * get_global_size(0)];
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}
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// Returns the grayscale value at the given point.
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static float pixel_grayscale(__read_only image2d_t src, int2 loc) {
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float4 pixel = read_imagef(src, sampler, loc);
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return (pixel.x + pixel.y + pixel.z) / 3.0f;
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}
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static float convolve(
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__local const float *grayscale,
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int local_idx_x,
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int local_idx_y,
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float mask[3][3]
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) {
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float ret = 0;
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// These loops touch each pixel surrounding loc as well as loc itself
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for (int i = 1, i2 = 0; i >= -1; --i, ++i2) {
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for (int j = -1, j2 = 0; j <= 1; ++j, ++j2) {
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ret += mask[i2][j2] * grayscale[(local_idx_x + 3 + j) + (local_idx_y + 3 + i) * 14];
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}
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}
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return ret;
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}
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// Sums dx * dy for all pixels within radius of loc
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static float sum_deriv_prod(
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__local const float *grayscale,
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float mask_x[3][3],
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float mask_y[3][3]
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) {
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float ret = 0;
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for (int i = HARRIS_RADIUS; i >= -HARRIS_RADIUS; --i) {
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for (int j = -HARRIS_RADIUS; j <= HARRIS_RADIUS; ++j) {
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ret += convolve(grayscale, get_local_id(0) + j, get_local_id(1) + i, mask_x) *
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convolve(grayscale, get_local_id(0) + j, get_local_id(1) + i, mask_y);
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}
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}
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return ret;
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}
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// Sums d<>^2 (determined by mask) for all pixels within radius of loc
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static float sum_deriv_pow(__local const float *grayscale, float mask[3][3])
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{
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float ret = 0;
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for (int i = HARRIS_RADIUS; i >= -HARRIS_RADIUS; --i) {
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for (int j = -HARRIS_RADIUS; j <= HARRIS_RADIUS; ++j) {
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float deriv = convolve(grayscale, get_local_id(0) + j, get_local_id(1) + i, mask);
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ret += deriv * deriv;
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}
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}
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return ret;
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}
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// Fills a box with the given radius and pixel around loc
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static void draw_box(__write_only image2d_t dst, int2 loc, float4 pixel, int radius)
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{
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for (int i = -radius; i <= radius; ++i) {
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for (int j = -radius; j <= radius; ++j) {
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write_imagef(
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dst,
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(int2)(
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// Clamp to avoid writing outside image bounds
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clamp(loc.x + i, 0, get_image_dim(dst).x - 1),
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clamp(loc.y + j, 0, get_image_dim(dst).y - 1)
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),
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pixel
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);
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}
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}
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}
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// Converts the src image to grayscale
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__kernel void grayscale(
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__read_only image2d_t src,
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__write_only image2d_t grayscale
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) {
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int2 loc = (int2)(get_global_id(0), get_global_id(1));
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write_imagef(grayscale, loc, (float4)(pixel_grayscale(src, loc), 0.0f, 0.0f, 1.0f));
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}
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// This kernel computes the harris response for the given grayscale src image
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// within the given radius and writes it to harris_buf
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__kernel void harris_response(
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__read_only image2d_t grayscale,
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__global float *harris_buf
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) {
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int2 loc = (int2)(get_global_id(0), get_global_id(1));
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if (loc.x > get_image_width(grayscale) - 1 || loc.y > get_image_height(grayscale) - 1) {
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write_to_1d_arrf(harris_buf, loc, 0);
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return;
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}
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float scale = 1.0f / ((1 << 2) * HARRIS_RADIUS * 255.0f);
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float sobel_mask_x[3][3] = {
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{-1, 0, 1},
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{-2, 0, 2},
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{-1, 0, 1}
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};
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float sobel_mask_y[3][3] = {
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{ 1, 2, 1},
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{ 0, 0, 0},
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{-1, -2, -1}
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};
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// 8 x 8 local work + 3 pixels around each side (needed to accomodate for the
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// block size radius of 2)
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__local float grayscale_data[196];
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int idx = get_group_id(0) * get_local_size(0);
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int idy = get_group_id(1) * get_local_size(1);
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for (int i = idy - 3, it = 0; i < idy + (int)get_local_size(1) + 3; i++, it++) {
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for (int j = idx - 3, jt = 0; j < idx + (int)get_local_size(0) + 3; j++, jt++) {
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grayscale_data[jt + it * 14] = read_imagef(grayscale, sampler, (int2)(j, i)).x;
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}
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}
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barrier(CLK_LOCAL_MEM_FENCE);
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float sumdxdy = sum_deriv_prod(grayscale_data, sobel_mask_x, sobel_mask_y);
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float sumdx2 = sum_deriv_pow(grayscale_data, sobel_mask_x);
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float sumdy2 = sum_deriv_pow(grayscale_data, sobel_mask_y);
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float trace = sumdx2 + sumdy2;
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// r = det(M) - k(trace(M))^2
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// k usually between 0.04 to 0.06
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float r = (sumdx2 * sumdy2 - sumdxdy * sumdxdy) - 0.04f * (trace * trace) * pown(scale, 4);
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// Threshold the r value
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harris_buf[loc.x + loc.y * get_image_width(grayscale)] = r * step(HARRIS_THRESHOLD, r);
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}
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// Gets a patch centered around a float coordinate from a grayscale image using
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// bilinear interpolation
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static void get_rect_sub_pix(
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__read_only image2d_t grayscale,
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float *buffer,
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int size_x,
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int size_y,
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float2 center
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) {
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float2 offset = ((float2)(size_x, size_y) - 1.0f) * 0.5f;
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for (int i = 0; i < size_y; i++) {
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for (int j = 0; j < size_x; j++) {
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buffer[i * size_x + j] = read_imagef(
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grayscale,
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sampler_linear,
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(float2)(j, i) + center - offset
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).x * 255.0f;
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}
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}
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}
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// Refines detected features at a sub-pixel level
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//
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// This function is ported from OpenCV
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static float2 corner_sub_pix(
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__read_only image2d_t grayscale,
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float2 feature,
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float *mask
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) {
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float2 init = feature;
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int src_width = get_global_size(0);
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int src_height = get_global_size(1);
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const int max_iters = 40;
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const float eps = 0.001f * 0.001f;
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int i, j, k;
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int iter = 0;
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float err = 0;
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float subpix[(REFINE_WIN_W + 2) * (REFINE_WIN_H + 2)];
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const float flt_epsilon = 0x1.0p-23f;
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do {
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float2 feature_tmp;
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float a = 0, b = 0, c = 0, bb1 = 0, bb2 = 0;
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get_rect_sub_pix(grayscale, subpix, REFINE_WIN_W + 2, REFINE_WIN_H + 2, feature);
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float *subpix_ptr = subpix;
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subpix_ptr += REFINE_WIN_W + 2 + 1;
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// process gradient
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for (i = 0, k = 0; i < REFINE_WIN_H; i++, subpix_ptr += REFINE_WIN_W + 2) {
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float py = i - REFINE_WIN_HALF_H;
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for (j = 0; j < REFINE_WIN_W; j++, k++) {
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float m = mask[k];
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float tgx = subpix_ptr[j + 1] - subpix_ptr[j - 1];
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float tgy = subpix_ptr[j + REFINE_WIN_W + 2] - subpix_ptr[j - REFINE_WIN_W - 2];
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float gxx = tgx * tgx * m;
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float gxy = tgx * tgy * m;
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float gyy = tgy * tgy * m;
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float px = j - REFINE_WIN_HALF_W;
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a += gxx;
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b += gxy;
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c += gyy;
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bb1 += gxx * px + gxy * py;
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bb2 += gxy * px + gyy * py;
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}
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}
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float det = a * c - b * b;
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if (fabs(det) <= flt_epsilon * flt_epsilon) {
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break;
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}
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// 2x2 matrix inversion
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float scale = 1.0f / det;
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feature_tmp.x = (float)(feature.x + (c * scale * bb1) - (b * scale * bb2));
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feature_tmp.y = (float)(feature.y - (b * scale * bb1) + (a * scale * bb2));
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err = dot(feature_tmp - feature, feature_tmp - feature);
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feature = feature_tmp;
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if (feature.x < 0 || feature.x >= src_width || feature.y < 0 || feature.y >= src_height) {
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break;
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}
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} while (++iter < max_iters && err > eps);
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// Make sure new point isn't too far from the initial point (indicates poor convergence)
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if (fabs(feature.x - init.x) > REFINE_WIN_HALF_W || fabs(feature.y - init.y) > REFINE_WIN_HALF_H) {
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feature = init;
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}
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return feature;
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}
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// Performs non-maximum suppression on the harris response and writes the resulting
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// feature locations to refined_features.
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//
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// Assumes that refined_features and the global work sizes are set up such that the image
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// is split up into a grid of 32x32 blocks where each block has a single slot in the
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// refined_features buffer. This kernel finds the best corner in each block (if the
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// block has any) and writes it to the corresponding slot in the buffer.
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//
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// If subpixel_refine is true, the features are additionally refined at a sub-pixel
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// level for increased precision.
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__kernel void refine_features(
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__read_only image2d_t grayscale,
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__global const float *harris_buf,
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__global float2 *refined_features,
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int subpixel_refine
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) {
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int2 loc = (int2)(get_global_id(0), get_global_id(1));
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// The location in the grayscale buffer rather than the compacted grid
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int2 loc_i = (int2)(loc.x * 32, loc.y * 32);
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float new_val;
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float max_val = 0;
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float2 loc_max = (float2)(-1, -1);
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int end_x = min(loc_i.x + 32, (int)get_image_dim(grayscale).x - 1);
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int end_y = min(loc_i.y + 32, (int)get_image_dim(grayscale).y - 1);
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for (int i = loc_i.x; i < end_x; ++i) {
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for (int j = loc_i.y; j < end_y; ++j) {
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new_val = harris_buf[i + j * get_image_dim(grayscale).x];
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if (new_val > max_val) {
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max_val = new_val;
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loc_max = (float2)(i, j);
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}
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}
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}
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if (max_val == 0) {
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// There are no features in this part of the frame
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write_to_1d_arrf2(refined_features, loc, loc_max);
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return;
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}
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if (subpixel_refine) {
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float mask[REFINE_WIN_H * REFINE_WIN_W];
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for (int i = 0; i < REFINE_WIN_H; i++) {
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float y = (float)(i - REFINE_WIN_HALF_H) / REFINE_WIN_HALF_H;
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float vy = exp(-y * y);
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for (int j = 0; j < REFINE_WIN_W; j++) {
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float x = (float)(j - REFINE_WIN_HALF_W) / REFINE_WIN_HALF_W;
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mask[i * REFINE_WIN_W + j] = (float)(vy * exp(-x * x));
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}
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}
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loc_max = corner_sub_pix(grayscale, loc_max, mask);
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}
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write_to_1d_arrf2(refined_features, loc, loc_max);
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}
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// Extracts BRIEF descriptors from the grayscale src image for the given features
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// using the provided sampler.
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__kernel void brief_descriptors(
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__read_only image2d_t grayscale,
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__global const float2 *refined_features,
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// for 512 bit descriptors
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__global ulong8 *desc_buf,
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__global const PointPair *brief_pattern
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) {
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int2 loc = (int2)(get_global_id(0), get_global_id(1));
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float2 feature = read_from_1d_arrf2(refined_features, loc);
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// There was no feature in this part of the frame
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if (feature.x == -1) {
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write_to_1d_arrul8(desc_buf, loc, (ulong8)(0));
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return;
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}
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ulong8 desc = 0;
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ulong *p = &desc;
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for (int i = 0; i < 8; ++i) {
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for (int j = 0; j < 64; ++j) {
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PointPair pair = brief_pattern[j * (i + 1)];
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float l1 = read_imagef(grayscale, sampler_linear, feature + pair.p1).x;
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float l2 = read_imagef(grayscale, sampler_linear, feature + pair.p2).x;
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if (l1 < l2) {
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p[i] |= 1UL << j;
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}
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}
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}
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write_to_1d_arrul8(desc_buf, loc, desc);
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}
|
|
|
|
// Given buffers with descriptors for the current and previous frame, determines
|
|
// which ones match, writing correspondences to matches_buf.
|
|
//
|
|
// Feature and descriptor buffers are assumed to be compacted (each element sourced
|
|
// from a 32x32 block in the frame being processed).
|
|
__kernel void match_descriptors(
|
|
__global const float2 *prev_refined_features,
|
|
__global const float2 *refined_features,
|
|
__global const ulong8 *desc_buf,
|
|
__global const ulong8 *prev_desc_buf,
|
|
__global MotionVector *matches_buf
|
|
) {
|
|
int2 loc = (int2)(get_global_id(0), get_global_id(1));
|
|
ulong8 desc = read_from_1d_arrul8(desc_buf, loc);
|
|
const int search_radius = 3;
|
|
|
|
MotionVector invalid_vector = (MotionVector) {
|
|
(PointPair) {
|
|
(float2)(-1, -1),
|
|
(float2)(-1, -1)
|
|
},
|
|
0
|
|
};
|
|
|
|
if (desc.s0 == 0 && desc.s1 == 0) {
|
|
// There was no feature in this part of the frame
|
|
write_to_1d_arrvec(
|
|
matches_buf,
|
|
loc,
|
|
invalid_vector
|
|
);
|
|
return;
|
|
}
|
|
|
|
int2 start = max(loc - search_radius, 0);
|
|
int2 end = min(loc + search_radius, (int2)(get_global_size(0) - 1, get_global_size(1) - 1));
|
|
|
|
for (int i = start.x; i < end.x; ++i) {
|
|
for (int j = start.y; j < end.y; ++j) {
|
|
int2 prev_point = (int2)(i, j);
|
|
int total_dist = 0;
|
|
|
|
ulong8 prev_desc = read_from_1d_arrul8(prev_desc_buf, prev_point);
|
|
|
|
if (prev_desc.s0 == 0 && prev_desc.s1 == 0) {
|
|
continue;
|
|
}
|
|
|
|
ulong *prev_desc_p = &prev_desc;
|
|
ulong *desc_p = &desc;
|
|
|
|
for (int i = 0; i < 8; i++) {
|
|
total_dist += popcount(desc_p[i] ^ prev_desc_p[i]);
|
|
}
|
|
|
|
if (total_dist < DISTANCE_THRESHOLD) {
|
|
write_to_1d_arrvec(
|
|
matches_buf,
|
|
loc,
|
|
(MotionVector) {
|
|
(PointPair) {
|
|
read_from_1d_arrf2(prev_refined_features, prev_point),
|
|
read_from_1d_arrf2(refined_features, loc)
|
|
},
|
|
1
|
|
}
|
|
);
|
|
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
|
|
// There is no found match for this point
|
|
write_to_1d_arrvec(
|
|
matches_buf,
|
|
loc,
|
|
invalid_vector
|
|
);
|
|
}
|
|
|
|
// Returns the position of the given point after the transform is applied
|
|
static float2 transformed_point(float2 p, __global const float *transform) {
|
|
float2 ret;
|
|
|
|
ret.x = p.x * transform[0] + p.y * transform[1] + transform[2];
|
|
ret.y = p.x * transform[3] + p.y * transform[4] + transform[5];
|
|
|
|
return ret;
|
|
}
|
|
|
|
|
|
// Performs the given transform on the src image
|
|
__kernel void transform(
|
|
__read_only image2d_t src,
|
|
__write_only image2d_t dst,
|
|
__global const float *transform
|
|
) {
|
|
int2 loc = (int2)(get_global_id(0), get_global_id(1));
|
|
float2 norm = convert_float2(get_image_dim(src));
|
|
|
|
write_imagef(
|
|
dst,
|
|
loc,
|
|
read_imagef(
|
|
src,
|
|
sampler_linear_mirror,
|
|
transformed_point((float2)(loc.x, loc.y), transform) / norm
|
|
)
|
|
);
|
|
}
|
|
|
|
// Returns the new location of the given point using the given crop bounding box
|
|
// and the width and height of the original frame.
|
|
static float2 cropped_point(
|
|
float2 p,
|
|
float2 top_left,
|
|
float2 bottom_right,
|
|
int2 orig_dim
|
|
) {
|
|
float2 ret;
|
|
|
|
float crop_width = bottom_right.x - top_left.x;
|
|
float crop_height = bottom_right.y - top_left.y;
|
|
|
|
float width_norm = p.x / (float)orig_dim.x;
|
|
float height_norm = p.y / (float)orig_dim.y;
|
|
|
|
ret.x = (width_norm * crop_width) + top_left.x;
|
|
ret.y = (height_norm * crop_height) + ((float)orig_dim.y - bottom_right.y);
|
|
|
|
return ret;
|
|
}
|
|
|
|
// Upscales the given cropped region to the size of the original frame
|
|
__kernel void crop_upscale(
|
|
__read_only image2d_t src,
|
|
__write_only image2d_t dst,
|
|
float2 top_left,
|
|
float2 bottom_right
|
|
) {
|
|
int2 loc = (int2)(get_global_id(0), get_global_id(1));
|
|
|
|
write_imagef(
|
|
dst,
|
|
loc,
|
|
read_imagef(
|
|
src,
|
|
sampler_linear,
|
|
cropped_point((float2)(loc.x, loc.y), top_left, bottom_right, get_image_dim(dst))
|
|
)
|
|
);
|
|
}
|
|
|
|
// Draws boxes to represent the given point matches and uses the given transform
|
|
// and crop info to make sure their positions are accurate on the transformed frame.
|
|
//
|
|
// model_matches is an array of three points that were used by the RANSAC process
|
|
// to generate the given transform
|
|
__kernel void draw_debug_info(
|
|
__write_only image2d_t dst,
|
|
__global const MotionVector *matches,
|
|
__global const MotionVector *model_matches,
|
|
int num_model_matches,
|
|
__global const float *transform
|
|
) {
|
|
int loc = get_global_id(0);
|
|
MotionVector vec = matches[loc];
|
|
// Black box: matched point that RANSAC considered an outlier
|
|
float4 big_rect_color = (float4)(0.1f, 0.1f, 0.1f, 1.0f);
|
|
|
|
if (vec.should_consider) {
|
|
// Green box: matched point that RANSAC considered an inlier
|
|
big_rect_color = (float4)(0.0f, 1.0f, 0.0f, 1.0f);
|
|
}
|
|
|
|
for (int i = 0; i < num_model_matches; i++) {
|
|
if (vec.p.p2.x == model_matches[i].p.p2.x && vec.p.p2.y == model_matches[i].p.p2.y) {
|
|
// Orange box: point used to calculate model
|
|
big_rect_color = (float4)(1.0f, 0.5f, 0.0f, 1.0f);
|
|
}
|
|
}
|
|
|
|
float2 transformed_p1 = transformed_point(vec.p.p1, transform);
|
|
float2 transformed_p2 = transformed_point(vec.p.p2, transform);
|
|
|
|
draw_box(dst, (int2)(transformed_p2.x, transformed_p2.y), big_rect_color, 5);
|
|
// Small light blue box: the point in the previous frame
|
|
draw_box(dst, (int2)(transformed_p1.x, transformed_p1.y), (float4)(0.0f, 0.3f, 0.7f, 1.0f), 3);
|
|
}
|