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ccv_daisy.c
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ccv_daisy.c
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#include "ccv.h"
#include "ccv_internal.h"
/* the method is adopted from original author's published C++ code under BSD Licence.
* Here is the copyright:
* //////////////////////////////////////////////////////////////////////////
* // Software License Agreement (BSD License) //
* // //
* // Copyright (c) 2009 //
* // Engin Tola //
* // web : http://cvlab.epfl.ch/~tola //
* // email : [email protected] //
* // //
* // All rights reserved. //
* // //
* // Redistribution and use in source and binary forms, with or without //
* // modification, are permitted provided that the following conditions //
* // are met: //
* // //
* // * Redistributions of source code must retain the above copyright //
* // notice, this list of conditions and the following disclaimer. //
* // * Redistributions in binary form must reproduce the above //
* // copyright notice, this list of conditions and the following //
* // disclaimer in the documentation and/or other materials provided //
* // with the distribution. //
* // * Neither the name of the EPFL nor the names of its //
* // contributors may be used to endorse or promote products derived //
* // from this software without specific prior written permission. //
* // //
* // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS //
* // "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT //
* // LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS //
* // FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE //
* // COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, //
* // INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, //
* // BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; //
* // LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER //
* // CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT //
* // LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN //
* // ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE //
* // POSSIBILITY OF SUCH DAMAGE. //
* // //
* // See licence.txt file for more details //
* //////////////////////////////////////////////////////////////////////////
*/
void ccv_daisy(ccv_dense_matrix_t* a, ccv_dense_matrix_t** b, int type, ccv_daisy_param_t params)
{
int grid_point_number = params.rad_q_no * params.th_q_no + 1;
int desc_size = grid_point_number * params.hist_th_q_no;
char* identifier = (char*)alloca(ccv_max(sizeof(ccv_daisy_param_t) + 9, sizeof(double) * params.rad_q_no));
memset(identifier, 0, ccv_max(sizeof(ccv_daisy_param_t) + 9, sizeof(double) * params.rad_q_no));
memcpy(identifier, "ccv_daisy", 9);
memcpy(identifier + 9, ¶ms, sizeof(ccv_daisy_param_t));
uint64_t sig = (a->sig == 0) ? 0 : ccv_cache_generate_signature(identifier, sizeof(ccv_daisy_param_t) + 9, a->sig, CCV_EOF_SIGN);
type = (type == 0) ? CCV_32F | CCV_C1 : CCV_GET_DATA_TYPE(type) | CCV_C1;
ccv_dense_matrix_t* db = *b = ccv_dense_matrix_renew(*b, a->rows, a->cols * desc_size, CCV_C1 | CCV_ALL_DATA_TYPE, type, sig);
int layer_size = a->rows * a->cols;
int cube_size = layer_size * params.hist_th_q_no;
float* workspace_memory = (float*)ccmalloc(cube_size * (params.rad_q_no + 2) * sizeof(float));
/* compute_cube_sigmas */
int i, j, k, r, t;
double* cube_sigmas = (double*)identifier;
double r_step = params.radius / (double)params.rad_q_no;
for (i = 0; i < params.rad_q_no; i++)
cube_sigmas[i] = (i + 1) * r_step * 0.5;
/* compute_grid_points */
double t_step = 2 * 3.141592654 / params.th_q_no;
double* grid_points = (double*)alloca(grid_point_number * 2 * sizeof(double));
for (i = 0; i < params.rad_q_no; i++)
for (j = 0; j < params.th_q_no; j++)
{
grid_points[(i * params.th_q_no + 1 + j) * 2] = sin(j * t_step) * (i + 1) * r_step;
grid_points[(i * params.th_q_no + 1 + j) * 2 + 1] = cos(j * t_step) * (i + 1) * r_step;
}
/* TODO: require 0.5 gaussian smooth before gradient computing */
/* NOTE: the default sobel already applied a sigma = 0.85 gaussian blur by using a
* | -1 0 1 | | 0 0 0 | | 1 2 1 |
* | -2 0 2 | = | -1 0 1 | * | 2 4 2 |
* | -1 0 1 | | 0 0 0 | | 1 2 1 | */
ccv_dense_matrix_t* dx = ccv_dense_matrix_new(a->rows, a->cols, CCV_32F | CCV_C1, 0, 0);
ccv_sobel(a, &dx, 0, 1, 0);
ccv_dense_matrix_t* dy = ccv_dense_matrix_new(a->rows, a->cols, CCV_32F | CCV_C1, 0, 0);
ccv_sobel(a, &dy, 0, 0, 1);
double sobel_sigma = sqrt(0.5 / -log(0.5));
double sigma_init = 1.6;
double sigma = sqrt(sigma_init * sigma_init - sobel_sigma * sobel_sigma);
/* layered_gradient & smooth_layers */
for (k = params.hist_th_q_no - 1; k >= 0; k--)
{
float radius = k * 2 * 3.141592654 / params.th_q_no;
float kcos = cos(radius);
float ksin = sin(radius);
float* w_ptr = workspace_memory + cube_size + (k - 1) * layer_size;
for (i = 0; i < layer_size; i++)
w_ptr[i] = ccv_max(0, kcos * dx->data.f32[i] + ksin * dy->data.f32[i]);
ccv_dense_matrix_t src = ccv_dense_matrix(a->rows, a->cols, CCV_32F | CCV_C1, w_ptr, 0);
ccv_dense_matrix_t des = ccv_dense_matrix(a->rows, a->cols, CCV_32F | CCV_C1, w_ptr + layer_size, 0);
ccv_dense_matrix_t* desp = &des;
ccv_blur(&src, &desp, 0, sigma);
}
ccv_matrix_free(dx);
ccv_matrix_free(dy);
/* compute_smoothed_gradient_layers & compute_histograms (rearrange memory) */
for (k = 0; k < params.rad_q_no; k++)
{
sigma = (k == 0) ? cube_sigmas[0] : sqrt(cube_sigmas[k] * cube_sigmas[k] - cube_sigmas[k - 1] * cube_sigmas[k - 1]);
float* src_ptr = workspace_memory + (k + 1) * cube_size;
float* des_ptr = src_ptr + cube_size;
for (i = 0; i < params.hist_th_q_no; i++)
{
ccv_dense_matrix_t src = ccv_dense_matrix(a->rows, a->cols, CCV_32F | CCV_C1, src_ptr + i * layer_size, 0);
ccv_dense_matrix_t des = ccv_dense_matrix(a->rows, a->cols, CCV_32F | CCV_C1, des_ptr + i * layer_size, 0);
ccv_dense_matrix_t* desp = &des;
ccv_blur(&src, &desp, 0, sigma);
}
float* his_ptr = src_ptr - cube_size;
for (i = 0; i < layer_size; i++)
for (j = 0; j < params.hist_th_q_no; j++)
his_ptr[i * params.hist_th_q_no + j] = src_ptr[i + j * layer_size];
}
/* petals of the flower */
memset(db->data.u8, 0, db->rows * db->step);
for (i = 0; i < a->rows; i++)
for (j = 0; j < a->cols; j++)
{
float* a_ptr = workspace_memory + i * params.hist_th_q_no * a->cols + j * params.hist_th_q_no;
float* b_ptr = db->data.f32 + i * db->cols + j * desc_size;
memcpy(b_ptr, a_ptr, params.hist_th_q_no * sizeof(float));
for (r = 0; r < params.rad_q_no; r++)
{
int rdt = r * params.th_q_no + 1;
for (t = rdt; t < rdt + params.th_q_no; t++)
{
double y = i + grid_points[t * 2];
double x = j + grid_points[t * 2 + 1];
int iy = (int)(y + 0.5);
int ix = (int)(x + 0.5);
float* bh = b_ptr + t * params.hist_th_q_no;
if (iy < 0 || iy >= a->rows || ix < 0 || ix >= a->cols)
continue;
// bilinear interpolation
int jy = (int)y;
int jx = (int)x;
float yr = y - jy, _yr = 1 - yr;
float xr = x - jx, _xr = 1 - xr;
if (jy >= 0 && jy < a->rows && jx >= 0 && jx < a->cols)
{
float* ah = workspace_memory + (r + 1) * cube_size + jy * params.hist_th_q_no * a->cols + jx * params.hist_th_q_no;
for (k = 0; k < params.hist_th_q_no; k++)
bh[k] += ah[k] * _yr * _xr;
}
if (jy + 1 >= 0 && jy + 1 < a->rows && jx >= 0 && jx < a->cols)
{
float* ah = workspace_memory + (r + 1) * cube_size + (jy + 1) * params.hist_th_q_no * a->cols + jx * params.hist_th_q_no;
for (k = 0; k < params.hist_th_q_no; k++)
bh[k] += ah[k] * yr * _xr;
}
if (jy >= 0 && jy < a->rows && jx + 1 >= 0 && jx + 1 < a->cols)
{
float* ah = workspace_memory + (r + 1) * cube_size + jy * params.hist_th_q_no * a->cols + (jx + 1) * params.hist_th_q_no;
for (k = 0; k < params.hist_th_q_no; k++)
bh[k] += ah[k] * _yr * xr;
}
if (jy + 1 >= 0 && jy + 1 < a->rows && jx + 1 >= 0 && jx + 1 < a->cols)
{
float* ah = workspace_memory + (r + 1) * cube_size + (jy + 1) * params.hist_th_q_no * a->cols + (jx + 1) * params.hist_th_q_no;
for (k = 0; k < params.hist_th_q_no; k++)
bh[k] += ah[k] * yr * xr;
}
}
}
}
ccfree(workspace_memory);
for (i = 0; i < a->rows; i++)
for (j = 0; j < a->cols; j++)
{
float* b_ptr = db->data.f32 + i * db->cols + j * desc_size;
float norm;
int iter, changed;
switch (params.normalize_method)
{
case CCV_DAISY_NORMAL_PARTIAL:
for (t = 0; t < grid_point_number; t++)
{
norm = 0;
float* bh = b_ptr + t * params.hist_th_q_no;
for (k = 0; k < params.hist_th_q_no; k++)
norm += bh[k] * bh[k];
if (norm > 1e-6)
{
norm = 1.0 / sqrt(norm);
for (k = 0; k < params.hist_th_q_no; k++)
bh[k] *= norm;
}
}
break;
case CCV_DAISY_NORMAL_FULL:
norm = 0;
for (t = 0; t < desc_size; t++)
norm += b_ptr[t] * b_ptr[t];
if (norm > 1e-6)
{
norm = 1.0 / sqrt(norm);
for (t = 0; t < desc_size; t++)
b_ptr[t] *= norm;
}
break;
case CCV_DAISY_NORMAL_SIFT:
for (iter = 0, changed = 1; changed && iter < 5; iter++)
{
norm = 0;
for (t = 0; t < desc_size; t++)
norm += b_ptr[t] * b_ptr[t];
changed = 0;
if (norm > 1e-6)
{
norm = 1.0 / sqrt(norm);
for (t = 0; t < desc_size; t++)
{
b_ptr[t] *= norm;
if (b_ptr[t] < params.normalize_threshold)
{
b_ptr[t] = params.normalize_threshold;
changed = 1;
}
}
}
}
break;
}
}
}