#define ORI_LOCAL_SIZE (360 / ORI_SEARCH_INC)
// specialized for non-image2d_t supported platform, intel HD4000, for example
-#ifdef DISABLE_IMAGE2D
-#define IMAGE_INT32 __global uint *
-#define IMAGE_INT8 __global uchar *
-#else
-#define IMAGE_INT32 image2d_t
-#define IMAGE_INT8 image2d_t
-#endif
+#ifndef HAVE_IMAGE2D
+__inline uint read_sumTex_(__global uint* sumTex, int sum_step, int img_rows, int img_cols, int2 coord)
+{
+ int x = clamp(coord.x, 0, img_cols);
+ int y = clamp(coord.y, 0, img_rows);
+ return sumTex[sum_step * y + x];
+}
-uint read_sumTex(IMAGE_INT32 img, sampler_t sam, int2 coord, int rows, int cols, int elemPerRow)
+__inline uchar read_imgTex_(__global uchar* imgTex, int img_step, int img_rows, int img_cols, float2 coord)
{
-#ifdef DISABLE_IMAGE2D
- int x = clamp(coord.x, 0, cols);
- int y = clamp(coord.y, 0, rows);
- return img[elemPerRow * y + x];
+ int x = clamp(convert_int_rte(coord.x), 0, img_cols-1);
+ int y = clamp(convert_int_rte(coord.y), 0, img_rows-1);
+ return imgTex[img_step * y + x];
+}
+
+#define read_sumTex(coord) read_sumTex_(sumTex, sum_step, img_rows, img_cols, coord)
+#define read_imgTex(coord) read_imgTex_(imgTex, img_step, img_rows, img_cols, coord)
+
+#define __PARAM_sumTex__ __global uint* sumTex, int sum_step, int sum_offset
+#define __PARAM_imgTex__ __global uchar* imgTex, int img_step, int img_offset
+
+#define __PASS_sumTex__ sumTex, sum_step, sum_offset
+#define __PASS_imgTex__ imgTex, img_step, img_offset
+
#else
- return read_imageui(img, sam, coord).x;
-#endif
+__inline uint read_sumTex_(image2d_t sumTex, sampler_t sam, int2 coord)
+{
+ return read_imageui(sumTex, sam, coord).x;
}
-uchar read_imgTex(IMAGE_INT8 img, sampler_t sam, float2 coord, int rows, int cols, int elemPerRow)
+
+__inline uchar read_imgTex_(image2d_t imgTex, sampler_t sam, float2 coord)
{
-#ifdef DISABLE_IMAGE2D
- int x = clamp(round(coord.x), 0, cols - 1);
- int y = clamp(round(coord.y), 0, rows - 1);
- return img[elemPerRow * y + x];
-#else
- return (uchar)read_imageui(img, sam, coord).x;
-#endif
+ return (uchar)read_imageui(imgTex, sam, coord).x;
}
+#define read_sumTex(coord) read_sumTex_(sumTex, sampler, coord)
+#define read_imgTex(coord) read_imgTex_(imgTex, sampler, coord)
+
+#define __PARAM_sumTex__ image2d_t sumTex
+#define __PARAM_imgTex__ image2d_t imgTex
+
+#define __PASS_sumTex__ sumTex
+#define __PASS_imgTex__ imgTex
+
+#endif
+
// dynamically change the precision used for floating type
#if defined (DOUBLE_SUPPORT)
#endif
// Image read mode
-__constant sampler_t sampler = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST;
+__constant sampler_t sampler = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP_TO_EDGE | CLK_FILTER_NEAREST;
#ifndef FLT_EPSILON
#define FLT_EPSILON (1e-15)
#define CV_PI_F 3.14159265f
#endif
-
-// Use integral image to calculate haar wavelets.
-// N = 2
-// for simple haar paatern
-float icvCalcHaarPatternSum_2(
- IMAGE_INT32 sumTex,
- __constant float2 *src,
- int oldSize,
- int newSize,
- int y, int x,
- int rows, int cols, int elemPerRow)
-{
-
- float ratio = (float)newSize / oldSize;
-
- F d = 0;
-
- int2 dx1 = convert_int2(round(ratio * src[0]));
- int2 dy1 = convert_int2(round(ratio * src[1]));
- int2 dx2 = convert_int2(round(ratio * src[2]));
- int2 dy2 = convert_int2(round(ratio * src[3]));
-
- F t = 0;
- t += read_sumTex( sumTex, sampler, (int2)(x + dx1.x, y + dy1.x), rows, cols, elemPerRow );
- t -= read_sumTex( sumTex, sampler, (int2)(x + dx1.x, y + dy2.x), rows, cols, elemPerRow );
- t -= read_sumTex( sumTex, sampler, (int2)(x + dx2.x, y + dy1.x), rows, cols, elemPerRow );
- t += read_sumTex( sumTex, sampler, (int2)(x + dx2.x, y + dy2.x), rows, cols, elemPerRow );
- d += t * src[4].x / ((dx2.x - dx1.x) * (dy2.x - dy1.x));
-
- t = 0;
- t += read_sumTex( sumTex, sampler, (int2)(x + dx1.y, y + dy1.y), rows, cols, elemPerRow );
- t -= read_sumTex( sumTex, sampler, (int2)(x + dx1.y, y + dy2.y), rows, cols, elemPerRow );
- t -= read_sumTex( sumTex, sampler, (int2)(x + dx2.y, y + dy1.y), rows, cols, elemPerRow );
- t += read_sumTex( sumTex, sampler, (int2)(x + dx2.y, y + dy2.y), rows, cols, elemPerRow );
- d += t * src[4].y / ((dx2.y - dx1.y) * (dy2.y - dy1.y));
-
- return (float)d;
-}
-
////////////////////////////////////////////////////////////////////////
// Hessian
//calculate targeted layer per-pixel determinant and trace with an integral image
__kernel void SURF_calcLayerDetAndTrace(
- IMAGE_INT32 sumTex, // input integral image
- __global float * det, // output Determinant
+ __PARAM_sumTex__, // input integral image
+ int img_rows, int img_cols,
+ int c_nOctaveLayers, int c_octave, int c_layer_rows,
+
+ __global float * det, // output determinant
+ int det_step, int det_offset,
__global float * trace, // output trace
- int det_step, // the step of det in bytes
- int trace_step, // the step of trace in bytes
- int c_img_rows,
- int c_img_cols,
- int c_nOctaveLayers,
- int c_octave,
- int c_layer_rows,
- int sumTex_step
- )
+ int trace_step, int trace_offset)
{
det_step /= sizeof(*det);
trace_step /= sizeof(*trace);
- sumTex_step/= sizeof(uint);
+ #ifndef HAVE_IMAGE2D
+ sum_step/= sizeof(uint);
+ #endif
// Determine the indices
const int gridDim_y = get_num_groups(1) / (c_nOctaveLayers + 2);
const int blockIdx_y = get_group_id(1) % gridDim_y;
const int size = calcSize(c_octave, layer);
- const int samples_i = 1 + ((c_img_rows - size) >> c_octave);
- const int samples_j = 1 + ((c_img_cols - size) >> c_octave);
+ const int samples_i = 1 + ((img_rows - size) >> c_octave);
+ const int samples_j = 1 + ((img_cols - size) >> c_octave);
// Ignore pixels where some of the kernel is outside the image
const int margin = (size >> 1) >> c_octave;
- if (size <= c_img_rows && size <= c_img_cols && i < samples_i && j < samples_j)
+ if (size <= img_rows && size <= img_cols && i < samples_i && j < samples_j)
{
int x = j << c_octave;
int y = i << c_octave;
{
// Some of the pixels needed to compute the derivative are
// repeated, so we only don't duplicate the fetch here.
- int t02 = read_sumTex( sumTex, sampler, (int2)(x, y + r2), c_img_rows, c_img_cols, sumTex_step );
- int t07 = read_sumTex( sumTex, sampler, (int2)(x, y + r7), c_img_rows, c_img_cols, sumTex_step );
- int t32 = read_sumTex( sumTex, sampler, (int2)(x + r3, y + r2), c_img_rows, c_img_cols, sumTex_step );
- int t37 = read_sumTex( sumTex, sampler, (int2)(x + r3, y + r7), c_img_rows, c_img_cols, sumTex_step );
- int t62 = read_sumTex( sumTex, sampler, (int2)(x + r6, y + r2), c_img_rows, c_img_cols, sumTex_step );
- int t67 = read_sumTex( sumTex, sampler, (int2)(x + r6, y + r7), c_img_rows, c_img_cols, sumTex_step );
- int t92 = read_sumTex( sumTex, sampler, (int2)(x + r9, y + r2), c_img_rows, c_img_cols, sumTex_step );
- int t97 = read_sumTex( sumTex, sampler, (int2)(x + r9, y + r7), c_img_rows, c_img_cols, sumTex_step );
+ int t02 = read_sumTex( (int2)(x, y + r2));
+ int t07 = read_sumTex( (int2)(x, y + r7));
+ int t32 = read_sumTex( (int2)(x + r3, y + r2));
+ int t37 = read_sumTex( (int2)(x + r3, y + r7));
+ int t62 = read_sumTex( (int2)(x + r6, y + r2));
+ int t67 = read_sumTex( (int2)(x + r6, y + r7));
+ int t92 = read_sumTex( (int2)(x + r9, y + r2));
+ int t97 = read_sumTex( (int2)(x + r9, y + r7));
d = calcAxisAlignedDerivative(t02, t07, t32, t37, (r3) * (r7 - r2),
t62, t67, t92, t97, (r9 - r6) * (r7 - r2),
{
// Some of the pixels needed to compute the derivative are
// repeated, so we only don't duplicate the fetch here.
- int t20 = read_sumTex( sumTex, sampler, (int2)(x + r2, y), c_img_rows, c_img_cols, sumTex_step );
- int t23 = read_sumTex( sumTex, sampler, (int2)(x + r2, y + r3), c_img_rows, c_img_cols, sumTex_step );
- int t70 = read_sumTex( sumTex, sampler, (int2)(x + r7, y), c_img_rows, c_img_cols, sumTex_step );
- int t73 = read_sumTex( sumTex, sampler, (int2)(x + r7, y + r3), c_img_rows, c_img_cols, sumTex_step );
- int t26 = read_sumTex( sumTex, sampler, (int2)(x + r2, y + r6), c_img_rows, c_img_cols, sumTex_step );
- int t76 = read_sumTex( sumTex, sampler, (int2)(x + r7, y + r6), c_img_rows, c_img_cols, sumTex_step );
- int t29 = read_sumTex( sumTex, sampler, (int2)(x + r2, y + r9), c_img_rows, c_img_cols, sumTex_step );
- int t79 = read_sumTex( sumTex, sampler, (int2)(x + r7, y + r9), c_img_rows, c_img_cols, sumTex_step );
+ int t20 = read_sumTex( (int2)(x + r2, y) );
+ int t23 = read_sumTex( (int2)(x + r2, y + r3) );
+ int t70 = read_sumTex( (int2)(x + r7, y) );
+ int t73 = read_sumTex( (int2)(x + r7, y + r3) );
+ int t26 = read_sumTex( (int2)(x + r2, y + r6) );
+ int t76 = read_sumTex( (int2)(x + r7, y + r6) );
+ int t29 = read_sumTex( (int2)(x + r2, y + r9) );
+ int t79 = read_sumTex( (int2)(x + r7, y + r9) );
d = calcAxisAlignedDerivative(t20, t23, t70, t73, (r7 - r2) * (r3),
t26, t29, t76, t79, (r7 - r2) * (r9 - r6),
// There's no saving us here, we just have to get all of the pixels in
// separate fetches
F t = 0;
- t += read_sumTex( sumTex, sampler, (int2)(x + r1, y + r1), c_img_rows, c_img_cols, sumTex_step );
- t -= read_sumTex( sumTex, sampler, (int2)(x + r1, y + r4), c_img_rows, c_img_cols, sumTex_step );
- t -= read_sumTex( sumTex, sampler, (int2)(x + r4, y + r1), c_img_rows, c_img_cols, sumTex_step );
- t += read_sumTex( sumTex, sampler, (int2)(x + r4, y + r4), c_img_rows, c_img_cols, sumTex_step );
+ t += read_sumTex( (int2)(x + r1, y + r1) );
+ t -= read_sumTex( (int2)(x + r1, y + r4) );
+ t -= read_sumTex( (int2)(x + r4, y + r1) );
+ t += read_sumTex( (int2)(x + r4, y + r4) );
d += t / ((r4 - r1) * (r4 - r1));
t = 0;
- t += read_sumTex( sumTex, sampler, (int2)(x + r5, y + r1), c_img_rows, c_img_cols, sumTex_step );
- t -= read_sumTex( sumTex, sampler, (int2)(x + r5, y + r4), c_img_rows, c_img_cols, sumTex_step );
- t -= read_sumTex( sumTex, sampler, (int2)(x + r8, y + r1), c_img_rows, c_img_cols, sumTex_step );
- t += read_sumTex( sumTex, sampler, (int2)(x + r8, y + r4), c_img_rows, c_img_cols, sumTex_step );
+ t += read_sumTex( (int2)(x + r5, y + r1) );
+ t -= read_sumTex( (int2)(x + r5, y + r4) );
+ t -= read_sumTex( (int2)(x + r8, y + r1) );
+ t += read_sumTex( (int2)(x + r8, y + r4) );
d -= t / ((r8 - r5) * (r4 - r1));
t = 0;
- t += read_sumTex( sumTex, sampler, (int2)(x + r1, y + r5), c_img_rows, c_img_cols, sumTex_step );
- t -= read_sumTex( sumTex, sampler, (int2)(x + r1, y + r8), c_img_rows, c_img_cols, sumTex_step );
- t -= read_sumTex( sumTex, sampler, (int2)(x + r4, y + r5), c_img_rows, c_img_cols, sumTex_step );
- t += read_sumTex( sumTex, sampler, (int2)(x + r4, y + r8), c_img_rows, c_img_cols, sumTex_step );
+ t += read_sumTex( (int2)(x + r1, y + r5) );
+ t -= read_sumTex( (int2)(x + r1, y + r8) );
+ t -= read_sumTex( (int2)(x + r4, y + r5) );
+ t += read_sumTex( (int2)(x + r4, y + r8) );
d -= t / ((r4 - r1) * (r8 - r5));
t = 0;
- t += read_sumTex( sumTex, sampler, (int2)(x + r5, y + r5), c_img_rows, c_img_cols, sumTex_step );
- t -= read_sumTex( sumTex, sampler, (int2)(x + r5, y + r8), c_img_rows, c_img_cols, sumTex_step );
- t -= read_sumTex( sumTex, sampler, (int2)(x + r8, y + r5), c_img_rows, c_img_cols, sumTex_step );
- t += read_sumTex( sumTex, sampler, (int2)(x + r8, y + r8), c_img_rows, c_img_cols, sumTex_step );
+ t += read_sumTex( (int2)(x + r5, y + r5) );
+ t -= read_sumTex( (int2)(x + r5, y + r8) );
+ t -= read_sumTex( (int2)(x + r8, y + r5) );
+ t += read_sumTex( (int2)(x + r8, y + r8) );
d += t / ((r8 - r5) * (r8 - r5));
}
const float dxy = (float)d;
////////////////////////////////////////////////////////////////////////
// NONMAX
-__constant float c_DM[5] = {0, 0, 9, 9, 1};
-
-bool within_check(IMAGE_INT32 maskSumTex, int sum_i, int sum_j, int size, int rows, int cols, int step)
-{
- float ratio = (float)size / 9.0f;
-
- float d = 0;
-
- int dx1 = round(ratio * c_DM[0]);
- int dy1 = round(ratio * c_DM[1]);
- int dx2 = round(ratio * c_DM[2]);
- int dy2 = round(ratio * c_DM[3]);
-
- float t = 0;
-
- t += read_sumTex(maskSumTex, sampler, (int2)(sum_j + dx1, sum_i + dy1), rows, cols, step);
- t -= read_sumTex(maskSumTex, sampler, (int2)(sum_j + dx1, sum_i + dy2), rows, cols, step);
- t -= read_sumTex(maskSumTex, sampler, (int2)(sum_j + dx2, sum_i + dy1), rows, cols, step);
- t += read_sumTex(maskSumTex, sampler, (int2)(sum_j + dx2, sum_i + dy2), rows, cols, step);
-
- d += t * c_DM[4] / ((dx2 - dx1) * (dy2 - dy1));
-
- return (d >= 0.5f);
-}
-
-// Non-maximal suppression to further filtering the candidates from previous step
-__kernel
-void SURF_findMaximaInLayerWithMask(
- __global const float * det,
- __global const float * trace,
- __global int4 * maxPosBuffer,
- volatile __global int* maxCounter,
- int counter_offset,
- int det_step, // the step of det in bytes
- int trace_step, // the step of trace in bytes
- int c_img_rows,
- int c_img_cols,
- int c_nOctaveLayers,
- int c_octave,
- int c_layer_rows,
- int c_layer_cols,
- int c_max_candidates,
- float c_hessianThreshold,
- IMAGE_INT32 maskSumTex,
- int mask_step
-)
-{
- volatile __local float N9[768]; // threads.x * threads.y * 3
-
- det_step /= sizeof(*det);
- trace_step /= sizeof(*trace);
- maxCounter += counter_offset;
- mask_step /= sizeof(uint);
-
- // Determine the indices
- const int gridDim_y = get_num_groups(1) / c_nOctaveLayers;
- const int blockIdx_y = get_group_id(1) % gridDim_y;
- const int blockIdx_z = get_group_id(1) / gridDim_y;
-
- const int layer = blockIdx_z + 1;
-
- const int size = calcSize(c_octave, layer);
-
- // Ignore pixels without a 3x3x3 neighbourhood in the layer above
- const int margin = ((calcSize(c_octave, layer + 1) >> 1) >> c_octave) + 1;
-
- const int j = get_local_id(0) + get_group_id(0) * (get_local_size(0) - 2) + margin - 1;
- const int i = get_local_id(1) + blockIdx_y * (get_local_size(1) - 2) + margin - 1;
-
- // Is this thread within the hessian buffer?
- const int zoff = get_local_size(0) * get_local_size(1);
- const int localLin = get_local_id(0) + get_local_id(1) * get_local_size(0) + zoff;
- N9[localLin - zoff] =
- det[det_step *
- (c_layer_rows * (layer - 1) + min(max(i, 0), c_img_rows - 1)) // y
- + min(max(j, 0), c_img_cols - 1)]; // x
- N9[localLin ] =
- det[det_step *
- (c_layer_rows * (layer ) + min(max(i, 0), c_img_rows - 1)) // y
- + min(max(j, 0), c_img_cols - 1)]; // x
- N9[localLin + zoff] =
- det[det_step *
- (c_layer_rows * (layer + 1) + min(max(i, 0), c_img_rows - 1)) // y
- + min(max(j, 0), c_img_cols - 1)]; // x
-
- barrier(CLK_LOCAL_MEM_FENCE);
-
- if (i < c_layer_rows - margin
- && j < c_layer_cols - margin
- && get_local_id(0) > 0
- && get_local_id(0) < get_local_size(0) - 1
- && get_local_id(1) > 0
- && get_local_id(1) < get_local_size(1) - 1 // these are unnecessary conditions ported from CUDA
- )
- {
- float val0 = N9[localLin];
-
- if (val0 > c_hessianThreshold)
- {
- // Coordinates for the start of the wavelet in the sum image. There
- // is some integer division involved, so don't try to simplify this
- // (cancel out sampleStep) without checking the result is the same
- const int sum_i = (i - ((size >> 1) >> c_octave)) << c_octave;
- const int sum_j = (j - ((size >> 1) >> c_octave)) << c_octave;
-
- if (within_check(maskSumTex, sum_i, sum_j, size, c_img_rows, c_img_cols, mask_step))
- {
- // Check to see if we have a max (in its 26 neighbours)
- const bool condmax = val0 > N9[localLin - 1 - get_local_size(0) - zoff]
- && val0 > N9[localLin - get_local_size(0) - zoff]
- && val0 > N9[localLin + 1 - get_local_size(0) - zoff]
- && val0 > N9[localLin - 1 - zoff]
- && val0 > N9[localLin - zoff]
- && val0 > N9[localLin + 1 - zoff]
- && val0 > N9[localLin - 1 + get_local_size(0) - zoff]
- && val0 > N9[localLin + get_local_size(0) - zoff]
- && val0 > N9[localLin + 1 + get_local_size(0) - zoff]
-
- && val0 > N9[localLin - 1 - get_local_size(0)]
- && val0 > N9[localLin - get_local_size(0)]
- && val0 > N9[localLin + 1 - get_local_size(0)]
- && val0 > N9[localLin - 1 ]
- && val0 > N9[localLin + 1 ]
- && val0 > N9[localLin - 1 + get_local_size(0)]
- && val0 > N9[localLin + get_local_size(0)]
- && val0 > N9[localLin + 1 + get_local_size(0)]
-
- && val0 > N9[localLin - 1 - get_local_size(0) + zoff]
- && val0 > N9[localLin - get_local_size(0) + zoff]
- && val0 > N9[localLin + 1 - get_local_size(0) + zoff]
- && val0 > N9[localLin - 1 + zoff]
- && val0 > N9[localLin + zoff]
- && val0 > N9[localLin + 1 + zoff]
- && val0 > N9[localLin - 1 + get_local_size(0) + zoff]
- && val0 > N9[localLin + get_local_size(0) + zoff]
- && val0 > N9[localLin + 1 + get_local_size(0) + zoff]
- ;
-
- if(condmax)
- {
- int ind = atomic_inc(maxCounter);
-
- if (ind < c_max_candidates)
- {
- const int laplacian = (int) copysign(1.0f, trace[trace_step* (layer * c_layer_rows + i) + j]);
-
- maxPosBuffer[ind] = (int4)(j, i, layer, laplacian);
- }
- }
- }
- }
- }
-}
-
__kernel
void SURF_findMaximaInLayer(
__global float * det,
+ int det_step, int det_offset,
__global float * trace,
+ int trace_step, int trace_offset,
__global int4 * maxPosBuffer,
volatile __global int* maxCounter,
int counter_offset,
- int det_step, // the step of det in bytes
- int trace_step, // the step of trace in bytes
- int c_img_rows,
- int c_img_cols,
+ int img_rows,
+ int img_cols,
int c_nOctaveLayers,
int c_octave,
int c_layer_rows,
const int zoff = get_local_size(0) * get_local_size(1);
const int localLin = get_local_id(0) + get_local_id(1) * get_local_size(0) + zoff;
- int l_x = min(max(j, 0), c_img_cols - 1);
- int l_y = c_layer_rows * layer + min(max(i, 0), c_img_rows - 1);
+ int l_x = min(max(j, 0), img_cols - 1);
+ int l_y = c_layer_rows * layer + min(max(i, 0), img_rows - 1);
N9[localLin - zoff] =
det[det_step * (l_y - c_layer_rows) + l_x];
if (det != 0)
{
- F invdet = 1.0 / det;
+ F invdet = 1.0f / det;
x[0] = invdet *
(b[0] * (A[1].y * A[2].z - A[1].z * A[2].y) -
__kernel
void SURF_interpolateKeypoint(
__global const float * det,
+ int det_step, int det_offset,
__global const int4 * maxPosBuffer,
__global float * keypoints,
- volatile __global int * featureCounter,
- int det_step,
- int keypoints_step,
- int c_img_rows,
- int c_img_cols,
+ int keypoints_step, int keypoints_offset,
+ volatile __global int* featureCounter,
+ int img_rows,
+ int img_cols,
int c_octave,
int c_layer_rows,
int c_max_features
const int grad_wav_size = 2 * round(2.0f * s);
// check when grad_wav_size is too big
- if ((c_img_rows + 1) >= grad_wav_size && (c_img_cols + 1) >= grad_wav_size)
+ if ((img_rows + 1) >= grad_wav_size && (img_cols + 1) >= grad_wav_size)
{
// Get a new feature index.
int ind = atomic_inc(featureCounter);
__kernel
void SURF_calcOrientation(
- IMAGE_INT32 sumTex,
- __global float * keypoints,
- int keypoints_step,
- int c_img_rows,
- int c_img_cols,
- int sum_step
-)
+ __PARAM_sumTex__, int img_rows, int img_cols,
+ __global float * keypoints, int keypoints_step, int keypoints_offset )
{
keypoints_step /= sizeof(*keypoints);
+ #ifndef HAVE_IMAGE2D
sum_step /= sizeof(uint);
+ #endif
__global float* featureX = keypoints + X_ROW * keypoints_step;
__global float* featureY = keypoints + Y_ROW * keypoints_step;
__global float* featureSize = keypoints + SIZE_ROW * keypoints_step;
__global float* featureDir = keypoints + ANGLE_ROW * keypoints_step;
-
__local float s_X[ORI_SAMPLES];
__local float s_Y[ORI_SAMPLES];
__local float s_angle[ORI_SAMPLES];
and building the keypoint descriptor are defined relative to 's' */
const float s = featureSize[get_group_id(0)] * 1.2f / 9.0f;
-
/* To find the dominant orientation, the gradients in x and y are
sampled in a circle of radius 6s using wavelets of size 4s.
We ensure the gradient wavelet size is even to ensure the
const int grad_wav_size = 2 * round(2.0f * s);
// check when grad_wav_size is too big
- if ((c_img_rows + 1) < grad_wav_size || (c_img_cols + 1) < grad_wav_size)
+ if ((img_rows + 1) < grad_wav_size || (img_cols + 1) < grad_wav_size)
return;
// Calc X, Y, angle and store it to shared memory
float ratio = (float)grad_wav_size / 4;
- int r2 = round(ratio * 2.0);
- int r4 = round(ratio * 4.0);
+ int r2 = round(ratio * 2.0f);
+ int r4 = round(ratio * 4.0f);
for (int i = tid; i < ORI_SAMPLES; i += ORI_LOCAL_SIZE )
{
float X = 0.0f, Y = 0.0f, angle = 0.0f;
const int x = round(featureX[get_group_id(0)] + c_aptX[i] * s - margin);
const int y = round(featureY[get_group_id(0)] + c_aptY[i] * s - margin);
- if (y >= 0 && y < (c_img_rows + 1) - grad_wav_size &&
- x >= 0 && x < (c_img_cols + 1) - grad_wav_size)
+ if (y >= 0 && y < (img_rows + 1) - grad_wav_size &&
+ x >= 0 && x < (img_cols + 1) - grad_wav_size)
{
-
float apt = c_aptW[i];
// Compute the haar sum without fetching duplicate pixels.
- float t00 = read_sumTex( sumTex, sampler, (int2)(x, y), c_img_rows, c_img_cols, sum_step);
- float t02 = read_sumTex( sumTex, sampler, (int2)(x, y + r2), c_img_rows, c_img_cols, sum_step);
- float t04 = read_sumTex( sumTex, sampler, (int2)(x, y + r4), c_img_rows, c_img_cols, sum_step);
- float t20 = read_sumTex( sumTex, sampler, (int2)(x + r2, y), c_img_rows, c_img_cols, sum_step);
- float t24 = read_sumTex( sumTex, sampler, (int2)(x + r2, y + r4), c_img_rows, c_img_cols, sum_step);
- float t40 = read_sumTex( sumTex, sampler, (int2)(x + r4, y), c_img_rows, c_img_cols, sum_step);
- float t42 = read_sumTex( sumTex, sampler, (int2)(x + r4, y + r2), c_img_rows, c_img_cols, sum_step);
- float t44 = read_sumTex( sumTex, sampler, (int2)(x + r4, y + r4), c_img_rows, c_img_cols, sum_step);
+ float t00 = read_sumTex( (int2)(x, y));
+ float t02 = read_sumTex( (int2)(x, y + r2));
+ float t04 = read_sumTex( (int2)(x, y + r4));
+ float t20 = read_sumTex( (int2)(x + r2, y));
+ float t24 = read_sumTex( (int2)(x + r2, y + r4));
+ float t40 = read_sumTex( (int2)(x + r4, y));
+ float t42 = read_sumTex( (int2)(x + r4, y + r2));
+ float t44 = read_sumTex( (int2)(x + r4, y + r4));
F t = t00 - t04 - t20 + t24;
X -= t / ((r2) * (r4));
}
__kernel
-void SURF_setUpright(
+void SURF_setUpRight(
__global float * keypoints,
int keypoints_step, int keypoints_offset,
int rows, int cols )
};
// utility for linear filter
-inline uchar readerGet(
- IMAGE_INT8 src,
- const float centerX, const float centerY, const float win_offset, const float cos_dir, const float sin_dir,
- int i, int j, int rows, int cols, int elemPerRow
-)
-{
- float pixel_x = centerX + (win_offset + j) * cos_dir + (win_offset + i) * sin_dir;
- float pixel_y = centerY - (win_offset + j) * sin_dir + (win_offset + i) * cos_dir;
- return read_imgTex(src, sampler, (float2)(pixel_x, pixel_y), rows, cols, elemPerRow);
-}
+#define readerGet(centerX, centerY, win_offset, cos_dir, sin_dir, i, j) \
+ read_imgTex((float2)(centerX + (win_offset + j) * cos_dir + (win_offset + i) * sin_dir, \
+ centerY - (win_offset + j) * sin_dir + (win_offset + i) * cos_dir))
inline float linearFilter(
- IMAGE_INT8 src,
- const float centerX, const float centerY, const float win_offset, const float cos_dir, const float sin_dir,
- float y, float x, int rows, int cols, int elemPerRow
-)
+ __PARAM_imgTex__, int img_rows, int img_cols,
+ float centerX, float centerY, float win_offset,
+ float cos_dir, float sin_dir, float y, float x )
{
x -= 0.5f;
y -= 0.5f;
const int x2 = x1 + 1;
const int y2 = y1 + 1;
- uchar src_reg = readerGet(src, centerX, centerY, win_offset, cos_dir, sin_dir, y1, x1, rows, cols, elemPerRow);
+ uchar src_reg = readerGet(centerX, centerY, win_offset, cos_dir, sin_dir, y1, x1);
out = out + src_reg * ((x2 - x) * (y2 - y));
- src_reg = readerGet(src, centerX, centerY, win_offset, cos_dir, sin_dir, y1, x2, rows, cols, elemPerRow);
+ src_reg = readerGet(centerX, centerY, win_offset, cos_dir, sin_dir, y1, x2);
out = out + src_reg * ((x - x1) * (y2 - y));
- src_reg = readerGet(src, centerX, centerY, win_offset, cos_dir, sin_dir, y2, x1, rows, cols, elemPerRow);
+ src_reg = readerGet(centerX, centerY, win_offset, cos_dir, sin_dir, y2, x1);
out = out + src_reg * ((x2 - x) * (y - y1));
- src_reg = readerGet(src, centerX, centerY, win_offset, cos_dir, sin_dir, y2, x2, rows, cols, elemPerRow);
+ src_reg = readerGet(centerX, centerY, win_offset, cos_dir, sin_dir, y2, x2);
out = out + src_reg * ((x - x1) * (y - y1));
return out;
}
void calc_dx_dy(
- IMAGE_INT8 imgTex,
+ __PARAM_imgTex__,
+ int img_rows, int img_cols,
volatile __local float *s_dx_bin,
volatile __local float *s_dy_bin,
volatile __local float *s_PATCH,
__global const float* featureX,
__global const float* featureY,
__global const float* featureSize,
- __global const float* featureDir,
- int rows,
- int cols,
- int elemPerRow
-)
+ __global const float* featureDir )
{
const float centerX = featureX[get_group_id(0)];
const float centerY = featureY[get_group_id(0)];
const float icoo = ((float)yIndex / (PATCH_SZ + 1)) * win_size;
const float jcoo = ((float)xIndex / (PATCH_SZ + 1)) * win_size;
- s_PATCH[get_local_id(1) * 6 + get_local_id(0)] = linearFilter(imgTex, centerX, centerY, win_offset, cos_dir, sin_dir, icoo, jcoo, rows, cols, elemPerRow);
+ s_PATCH[get_local_id(1) * 6 + get_local_id(0)] =
+ linearFilter(__PASS_imgTex__, img_rows, img_cols, centerX, centerY,
+ win_offset, cos_dir, sin_dir, icoo, jcoo);
barrier(CLK_LOCAL_MEM_FENCE);
__kernel
void SURF_computeDescriptors64(
- IMAGE_INT8 imgTex,
- int img_step, int img_offset,
- int rows, int cols,
+ __PARAM_imgTex__,
+ int img_rows, int img_cols,
__global const float* keypoints,
int keypoints_step, int keypoints_offset,
__global float * descriptors,
volatile __local float sdyabs[25];
volatile __local float s_PATCH[6*6];
- calc_dx_dy(imgTex, sdx, sdy, s_PATCH, featureX, featureY, featureSize, featureDir, rows, cols, img_step);
+ calc_dx_dy(__PASS_imgTex__, img_rows, img_cols, sdx, sdy, s_PATCH, featureX, featureY, featureSize, featureDir);
barrier(CLK_LOCAL_MEM_FENCE);
const int tid = get_local_id(1) * get_local_size(0) + get_local_id(0);
__kernel
void SURF_computeDescriptors128(
- IMAGE_INT8 imgTex,
- int img_step, int img_offset,
- int rows, int cols,
+ __PARAM_imgTex__,
+ int img_rows, int img_cols,
__global const float* keypoints,
int keypoints_step, int keypoints_offset,
__global float* descriptors,
volatile __local float sdabs2[25];
volatile __local float s_PATCH[6*6];
- calc_dx_dy(imgTex, sdx, sdy, s_PATCH, featureX, featureY, featureSize, featureDir, rows, cols, img_step);
+ calc_dx_dy(__PASS_imgTex__, img_rows, img_cols, sdx, sdy, s_PATCH, featureX, featureY, featureSize, featureDir);
barrier(CLK_LOCAL_MEM_FENCE);
const int tid = get_local_id(1) * get_local_size(0) + get_local_id(0);
}
__kernel
-void SURF_normalizeDescriptors128(__global float * descriptors, int descriptors_step)
+void SURF_normalizeDescriptors128(__global float * descriptors, int descriptors_step, int descriptors_offset)
{
descriptors_step /= sizeof(*descriptors);
// no need for thread ID
}
__kernel
-void SURF_normalizeDescriptors64(__global float * descriptors, int descriptors_step)
+void SURF_normalizeDescriptors64(__global float * descriptors, int descriptors_step, int descriptors_offset)
{
descriptors_step /= sizeof(*descriptors);
// no need for thread ID
enum { ORI_SEARCH_INC=5, ORI_LOCAL_SIZE=(360 / ORI_SEARCH_INC) };
-/*static void openCLExecuteKernelSURF(Context2 *clCxt, const ProgramEntry* source, String kernelName, size_t globalThreads[3],
- size_t localThreads[3], std::vector< std::pair<size_t, const void *> > &args, int channels, int depth)
-{
- std::stringstream optsStr;
- optsStr << "-D ORI_LOCAL_SIZE=" << ORI_LOCAL_SIZE << " ";
- optsStr << "-D ORI_SEARCH_INC=" << ORI_SEARCH_INC << " ";
- cl_kernel kernel;
- kernel = openCLGetKernelFromSource(clCxt, source, kernelName, optsStr.str().c_str());
- size_t wave_size = queryWaveFrontSize(kernel);
- CV_Assert(clReleaseKernel(kernel) == CL_SUCCESS);
- optsStr << "-D WAVE_SIZE=" << wave_size;
- openCLExecuteKernel(clCxt, source, kernelName, globalThreads, localThreads, args, channels, depth, optsStr.str().c_str());
-}*/
-
static inline int calcSize(int octave, int layer)
{
/* Wavelet size at first layer of first octave. */
if(ocl::haveOpenCL())
{
const ocl::Device& dev = ocl::Device::getDefault();
- if( dev.type() == ocl::Device::TYPE_CPU )
+ if( dev.type() == ocl::Device::TYPE_CPU || dev.doubleFPConfig() == 0 )
return false;
- haveImageSupport = dev.imageSupport();
- String opts = haveImageSupport ? "-D DISABLE_IMAGE2D" : "";
-
- if( kerCalcDetTrace.create("SURF_calcLayerDetAndTrace", ocl::nonfree::surf_oclsrc, opts) &&
- kerFindMaxima.create("SURF_findMaximaInLayer", ocl::nonfree::surf_oclsrc, opts) &&
- kerFindMaximaMask.create("SURF_findMaximaInLayerWithMask", ocl::nonfree::surf_oclsrc, opts) &&
- kerInterp.create("SURF_interpolateKeypoint", ocl::nonfree::surf_oclsrc, opts) &&
- kerUpRight.create("SURF_setUpRight", ocl::nonfree::surf_oclsrc, opts) &&
- kerOri.create("SURF_calcOrientation", ocl::nonfree::surf_oclsrc, opts) &&
- kerCalcDesc64.create("SURF_computeDescriptors64", ocl::nonfree::surf_oclsrc, opts) &&
- kerCalcDesc128.create("SURF_computeDescriptors128", ocl::nonfree::surf_oclsrc, opts) &&
- kerNormDesc64.create("SURF_normalizeDescriptors64", ocl::nonfree::surf_oclsrc, opts) &&
- kerNormDesc128.create("SURF_normalizeDescriptors128", ocl::nonfree::surf_oclsrc, opts))
- status = 1;
+ haveImageSupport = false;//dev.imageSupport();
+ kerOpts = haveImageSupport ? "-D HAVE_IMAGE2D -D DOUBLE_SUPPORT" : "";
+ status = 1;
}
}
return status > 0;
{
if( status <= 0 )
return false;
- CV_Assert(!_img.empty() && _img.type() == CV_8UC1);
- CV_Assert(_mask.empty() || (_mask.size() == _img.size() && _mask.type() == CV_8UC1));
+ if( !_mask.empty())
+ return false;
+ int imgtype = _img.type();
+ CV_Assert(!_img.empty());
CV_Assert(params && params->nOctaves > 0 && params->nOctaveLayers > 0);
int min_size = calcSize(params->nOctaves - 1, 0);
counters.setTo(Scalar::all(0));
img.release();
- if(_img.isUMat())
+ if(_img.isUMat() && imgtype == CV_8UC1)
img = _img.getUMat();
- else
+ else if( imgtype == CV_8UC1 )
_img.copyTo(img);
+ else
+ cvtColor(_img, img, COLOR_BGR2GRAY);
integral(img, sum);
sumTex = ocl::Image2D(sum);
}
- maskSumTex = ocl::Image2D();
-
- if(!_mask.empty())
- {
- CV_Error(Error::StsBadFunc, "Masked SURF detector is not implemented yet");
- }
return true;
}
const int layer_rows = img_rows >> octave;
const int layer_cols = img_cols >> octave;
- if(!calcLayerDetAndTrace(det, trace, octave, layer_rows))
+ if(!calcLayerDetAndTrace(octave, layer_rows))
return false;
- if(!findMaximaInLayer(det, trace, maxPosBuffer, counters, 1 + octave, octave,
- layer_rows, layer_cols))
+ if(!findMaximaInLayer(1 + octave, octave, layer_rows, layer_cols))
return false;
cpuCounters = counters.getMat(ACCESS_READ);
if (maxCounter > 0)
{
- if(!interpolateKeypoint(det, maxPosBuffer, maxCounter, keypoints,
- counters, octave, layer_rows, maxFeatures))
+ if(!interpolateKeypoint(maxCounter, keypoints, octave, layer_rows, maxFeatures))
return false;
}
}
featureCounter = std::min(featureCounter, maxFeatures);
cpuCounters.release();
- keypoints = UMat(keypoints, Rect(0, 0, featureCounter, 1));
+ keypoints = UMat(keypoints, Rect(0, 0, featureCounter, keypoints.rows));
if (params->upright)
return setUpRight(keypoints);
return true;
size_t globalThreads[3] = {nFeatures, 1};
- return kerUpRight.args(ocl::KernelArg::ReadWrite(keypoints)).run(2, globalThreads, 0, false);
+ ocl::Kernel kerUpRight("SURF_setUpRight", ocl::nonfree::surf_oclsrc, kerOpts);
+ return kerUpRight.args(ocl::KernelArg::ReadWrite(keypoints)).run(2, globalThreads, 0, true);
}
bool SURF_OCL::computeDescriptors(const UMat &keypoints, OutputArray _descriptors)
if( descriptorSize == 64 )
{
- kerCalcDesc = kerCalcDesc64;
- kerNormDesc = kerNormDesc64;
+ kerCalcDesc.create("SURF_computeDescriptors64", ocl::nonfree::surf_oclsrc, kerOpts);
+ kerNormDesc.create("SURF_normalizeDescriptors64", ocl::nonfree::surf_oclsrc, kerOpts);
}
else
{
CV_Assert(descriptorSize == 128);
- kerCalcDesc = kerCalcDesc128;
- kerNormDesc = kerNormDesc128;
+ kerCalcDesc.create("SURF_computeDescriptors128", ocl::nonfree::surf_oclsrc, kerOpts);
+ kerNormDesc.create("SURF_normalizeDescriptors128", ocl::nonfree::surf_oclsrc, kerOpts);
}
size_t localThreads[] = {6, 6};
if(haveImageSupport)
{
kerCalcDesc.args(imgTex,
+ img_rows, img_cols,
ocl::KernelArg::ReadOnlyNoSize(keypoints),
ocl::KernelArg::WriteOnlyNoSize(descriptors));
}
else
{
- kerCalcDesc.args(ocl::KernelArg::ReadOnly(img),
+ kerCalcDesc.args(ocl::KernelArg::ReadOnlyNoSize(img),
+ img_rows, img_cols,
ocl::KernelArg::ReadOnlyNoSize(keypoints),
ocl::KernelArg::WriteOnlyNoSize(descriptors));
}
- if(!kerCalcDesc.run(2, globalThreads, localThreads, false))
+ if(!kerCalcDesc.run(2, globalThreads, localThreads, true))
return false;
size_t localThreads_n[] = {descriptorSize, 1};
globalThreads[0] = nFeatures * localThreads[0];
globalThreads[1] = localThreads[1];
bool ok = kerNormDesc.args(ocl::KernelArg::ReadWriteNoSize(descriptors)).
- run(2, globalThreads_n, localThreads_n, false);
+ run(2, globalThreads_n, localThreads_n, true);
if(ok && !_descriptors.isUMat())
descriptors.copyTo(_descriptors);
return ok;
}
}
-bool SURF_OCL::detect(InputArray img, InputArray mask, UMat& keypoints)
+bool SURF_OCL::detect(InputArray _img, InputArray _mask, UMat& keypoints)
{
- if( !setImage(img, mask) )
+ if( !setImage(_img, _mask) )
return false;
return detectKeypoints(keypoints);
}
-bool SURF_OCL::detectAndCompute(InputArray img, InputArray mask, UMat& keypoints,
+bool SURF_OCL::detectAndCompute(InputArray _img, InputArray _mask, UMat& keypoints,
OutputArray _descriptors, bool useProvidedKeypoints )
{
- if( !setImage(img, mask) )
+ if( !setImage(_img, _mask) )
return false;
if( !useProvidedKeypoints && !detectKeypoints(keypoints) )
////////////////////////////
// kernel caller definitions
-bool SURF_OCL::calcLayerDetAndTrace(UMat &det, UMat &trace, int octave, int c_layer_rows)
+bool SURF_OCL::calcLayerDetAndTrace(int octave, int c_layer_rows)
{
int nOctaveLayers = params->nOctaveLayers;
const int min_size = calcSize(octave, 0);
const int max_samples_i = 1 + ((img_rows - min_size) >> octave);
const int max_samples_j = 1 + ((img_cols - min_size) >> octave);
- String kernelName = "SURF_calcLayerDetAndTrace";
- std::vector< std::pair<size_t, const void *> > args;
-
- size_t localThreads[3] = {16, 16};
- size_t globalThreads[3] =
+ size_t localThreads[] = {16, 16};
+ size_t globalThreads[] =
{
divUp(max_samples_j, localThreads[0]) *localThreads[0],
divUp(max_samples_i, localThreads[1]) *localThreads[1] *(nOctaveLayers + 2)
};
+ ocl::Kernel kerCalcDetTrace("SURF_calcLayerDetAndTrace", ocl::nonfree::surf_oclsrc, kerOpts);
if(haveImageSupport)
{
kerCalcDetTrace.args(sumTex,
ocl::KernelArg::WriteOnlyNoSize(det),
ocl::KernelArg::WriteOnlyNoSize(trace));
}
- return kerCalcDetTrace.run(2, globalThreads, localThreads, false);
+ return kerCalcDetTrace.run(2, globalThreads, localThreads, true);
}
-bool SURF_OCL::findMaximaInLayer(const UMat &det, const UMat &trace,
- UMat &maxPosBuffer, UMat &maxCounter,
- int counterOffset, int octave,
+bool SURF_OCL::findMaximaInLayer(int counterOffset, int octave,
int layer_rows, int layer_cols)
{
const int min_margin = ((calcSize(octave, 2) >> 1) >> octave) + 1;
- bool haveMask = !maskSum.empty() || (maskSumTex.ptr() != 0);
int nOctaveLayers = params->nOctaveLayers;
- ocl::Kernel ker;
- if( haveMask )
- {
- if( haveImageSupport )
- ker = kerFindMaximaMask.args(maskSumTex,
- ocl::KernelArg::ReadOnlyNoSize(det),
- ocl::KernelArg::ReadOnlyNoSize(trace),
- ocl::KernelArg::PtrReadWrite(maxPosBuffer),
- ocl::KernelArg::PtrReadWrite(maxCounter),
- counterOffset, img_rows, img_cols,
- octave, nOctaveLayers,
- layer_rows, layer_cols,
- maxCandidates,
- (float)params->hessianThreshold);
- else
- ker = kerFindMaximaMask.args(ocl::KernelArg::ReadOnlyNoSize(maskSum),
- ocl::KernelArg::ReadOnlyNoSize(det),
- ocl::KernelArg::ReadOnlyNoSize(trace),
- ocl::KernelArg::PtrReadWrite(maxPosBuffer),
- ocl::KernelArg::PtrReadWrite(maxCounter),
- counterOffset, img_rows, img_cols,
- octave, nOctaveLayers,
- layer_rows, layer_cols,
- maxCandidates,
- (float)params->hessianThreshold);
- }
- else
- {
- ker = kerFindMaxima.args(ocl::KernelArg::ReadOnlyNoSize(det),
- ocl::KernelArg::ReadOnlyNoSize(trace),
- ocl::KernelArg::PtrReadWrite(maxPosBuffer),
- ocl::KernelArg::PtrReadWrite(maxCounter),
- counterOffset, img_rows, img_cols,
- octave, nOctaveLayers,
- layer_rows, layer_cols,
- maxCandidates,
- (float)params->hessianThreshold);
- }
size_t localThreads[3] = {16, 16};
size_t globalThreads[3] =
{
divUp(layer_rows - 2 * min_margin, localThreads[1] - 2) *nOctaveLayers *localThreads[1]
};
- return ker.run(2, globalThreads, localThreads, false);
+ ocl::Kernel kerFindMaxima("SURF_findMaximaInLayer", ocl::nonfree::surf_oclsrc, kerOpts);
+ return kerFindMaxima.args(ocl::KernelArg::ReadOnlyNoSize(det),
+ ocl::KernelArg::ReadOnlyNoSize(trace),
+ ocl::KernelArg::PtrReadWrite(maxPosBuffer),
+ ocl::KernelArg::PtrReadWrite(counters),
+ counterOffset, img_rows, img_cols,
+ octave, nOctaveLayers,
+ layer_rows, layer_cols,
+ maxCandidates,
+ (float)params->hessianThreshold).run(2, globalThreads, localThreads, true);
}
-bool SURF_OCL::interpolateKeypoint(const UMat &det, const UMat &maxPosBuffer, int maxCounter,
- UMat &keypoints, UMat &counters_, int octave, int layer_rows, int max_features)
+bool SURF_OCL::interpolateKeypoint(int maxCounter, UMat &keypoints, int octave, int layer_rows, int max_features)
{
size_t localThreads[3] = {3, 3, 3};
size_t globalThreads[3] = {maxCounter*localThreads[0], localThreads[1], 3};
+ ocl::Kernel kerInterp("SURF_interpolateKeypoint", ocl::nonfree::surf_oclsrc, kerOpts);
+
return kerInterp.args(ocl::KernelArg::ReadOnlyNoSize(det),
ocl::KernelArg::PtrReadOnly(maxPosBuffer),
ocl::KernelArg::ReadWriteNoSize(keypoints),
- ocl::KernelArg::PtrReadWrite(counters_),
+ ocl::KernelArg::PtrReadWrite(counters),
img_rows, img_cols, octave, layer_rows, max_features).
- run(3, globalThreads, localThreads, false);
+ run(3, globalThreads, localThreads, true);
}
bool SURF_OCL::calcOrientation(UMat &keypoints)
int nFeatures = keypoints.cols;
if( nFeatures == 0 )
return true;
+ ocl::Kernel kerOri("SURF_calcOrientation", ocl::nonfree::surf_oclsrc, kerOpts);
+
if( haveImageSupport )
- kerOri.args(sumTex,
- ocl::KernelArg::ReadWriteNoSize(keypoints),
- img_rows, img_cols);
+ kerOri.args(sumTex, img_rows, img_cols,
+ ocl::KernelArg::ReadWriteNoSize(keypoints));
else
kerOri.args(ocl::KernelArg::ReadOnlyNoSize(sum),
- ocl::KernelArg::ReadWriteNoSize(keypoints),
- img_rows, img_cols);
+ img_rows, img_cols,
+ ocl::KernelArg::ReadWriteNoSize(keypoints));
size_t localThreads[3] = {ORI_LOCAL_SIZE, 1};
size_t globalThreads[3] = {nFeatures * localThreads[0], 1};
- return kerOri.run(2, globalThreads, localThreads, false);
+ return kerOri.run(2, globalThreads, localThreads, true);
}
}
projects / platform / upstream / opencv.git / commitdiff