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main_timings.py
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import cv2
import time
import numpy as np
import pyopencl as cl
import matplotlib as mpl
mpl.use('agg')
import matplotlib.pyplot as plt
mpl.rcParams['savefig.dpi'] = 100
from pylab import *
import csv
import pyopencl.array
# Select the desired OpenCL platform; you shouldn't need to change this:
NAME = 'NVIDIA CUDA'
platforms = cl.get_platforms()
devs = None
for platform in platforms:
if platform.name == NAME:
devs = platform.get_devices()
print devs
# Set up a command queue:
ctx = cl.Context(devs)
queue = cl.CommandQueue(ctx)
###########################################################
######## Setting up the OpenCL environment ################
###########################################################
left = cv2.imread('left.png',0).astype(np.float32)
right = cv2.imread('right.png',0).astype(np.float32)
#print left
print left.shape, " is the image dimension."
disparityRange = 64
halfBlockSize = 3
blockSize = (2 * halfBlockSize + 1)
DbasicSubpixel = np.zeros(left.shape).astype(np.float32)
imgHeight, imgWidth = left.shape
#print imgWidth, "This is image Width"
###########################################################
####################### KERNEL 1 ##########################
###########################################################
func1 = cl.Program(ctx, """
#include <pyopencl-complex.h>
__kernel void basic1(const int halfBlockSize, const int blockSize, const int disparityRange ,__global float* left, __global float* right, const int imgHeight , const int imgWidth, __global float* output) {
int row = get_global_id(1);
int col = get_global_id(0);
const int elements = (2*halfBlockSize+1)*(2*halfBlockSize+1);
//int* template = calloc(elements, sizeof(int));
//int* block = calloc(elements, sizeof(int));
//__local int template[49];
//__local int block[49];
int template[49];
int block[49];
int l,i,j,t;
int minc,maxc,mind,maxd,numBlocks,elemin,elementnum,blockIndex;
int blockDiffs[65];
float d;
int C1,C2,C3,bestMatchIndex ;
int h,k,r,sum1,indx;
int minr = 0 > (row - halfBlockSize) ? 0 : (row - halfBlockSize);
int maxr = (imgHeight - 1) < (row + halfBlockSize) ? (imgHeight - 1) : (row + halfBlockSize) ;
minc = (0 > (col - halfBlockSize) ? 0 : (col - halfBlockSize));
maxc = (imgWidth - 1) < (col + halfBlockSize) ? (imgWidth - 1) : (col + halfBlockSize);
mind = 0;
maxd = disparityRange < (imgWidth - 1 - maxc) ? disparityRange : (imgWidth - 1 - maxc);
elementnum = (maxr+1-minr)*(maxc+1-minc);
int templateWidth = maxc+1-minc;
//////////////////template/////////////////////
h = 0;
for ( i = minr ; i <= maxr ; i++ )
{
k = 0;
for (j = minc ; j <= maxc ; j++)
{
template[k + h * templateWidth] = right[i * imgWidth + j];
k++;
}
h++;
}
///////////////////////////////////////////////
numBlocks = maxd - mind + 1;
//blockDiffs[numBlocks-1]={255};
for ( j = mind ; j <= maxd ; j++)
{
h = 0;
for ( k = minr ; k <= maxr ; k++)
{
r = 0;
for (l = minc+j ; l <= maxc+j ; l++)
{
block[r + h * templateWidth] = left[k * imgWidth + l];
r++;
}
h++;
}
blockIndex = j;
sum1 = 0;
for ( r = 0 ; r < elementnum ; r ++ )
{
sum1 = sum1 + abs(template[r]- block[r]);
}
blockDiffs[blockIndex] = sum1;
}
elemin = blockDiffs[0];
indx = 0;
for ( t = 0 ; t < numBlocks ; t++)
{
if (blockDiffs[t] < elemin )
{
indx = t ;
elemin = blockDiffs[t];
}
}
bestMatchIndex = indx;
d = bestMatchIndex ;
//output[row*imgWidth + col]= row*imgWidth + col;
if (bestMatchIndex == 0 || bestMatchIndex == numBlocks - 1)
{
output[row*imgWidth + col] = d;
}
else
{
C1 = blockDiffs[bestMatchIndex - 1];
C2 = blockDiffs[bestMatchIndex ];
C3 = blockDiffs[bestMatchIndex + 1];
output[row* imgWidth + col] = d - (0.5*(C3-C1)/(C1 - 2*C2 + C3));
}
//free(template);
//free(block);
}
""").build().basic1
""" """
func1.set_scalar_arg_dtypes([np.uint32, np.uint32, np.uint32, None, None, np.uint32, np.uint32, None])
def hist_op_1_time(BS, D, left_buf, right_buf, H, W, out_buf):
start = time.time()
func1(queue, (H,W), None, np.int32(BS/2) ,np.int32(BS) , np.int32(D) , left_buf, right_buf,np.int32(H), np.int32(W), out_buf)
return time.time()-start
def hist_op_1_data (BS, D, left, right, H, W, out):
left_buf,right_buf, out_buf = mem_alloc(left,right,out)
t=hist_op_1_time(BS, D, left_buf, right_buf, H, W, out_buf)
out=mem_transfer(out,out_buf)
return t, out
##########################################################################
func2 = cl.Program(ctx, """
#include <pyopencl-complex.h>
__kernel void basic2(const int halfBlockSize, const int blockSize, const int disparityRange ,__global float* left, __global float* right, const int imgHeight , const int imgWidth, __global float* output2) {
int row = get_global_id(1);
int col = get_global_id(0);
const int elements = (2*halfBlockSize+1)*(2*halfBlockSize+1);
//int* template = calloc(elements, sizeof(int));
//int* block = calloc(elements, sizeof(int));
__local int template[49];
__local int block[49];
int l,i,j,t;
int minc,maxc,mind,maxd,numBlocks,elemin,elementnum,blockIndex;
int blockDiffs[65];
float d;
int C1,C2,C3,bestMatchIndex ;
int h,k,r,sum1,indx;
int minr = 0 > (row - halfBlockSize) ? 0 : (row - halfBlockSize);
int maxr = (imgHeight - 1) < (row + halfBlockSize) ? (imgHeight - 1) : (row + halfBlockSize) ;
minc = (0 > (col - halfBlockSize) ? 0 : (col - halfBlockSize));
maxc = (imgWidth - 1) < (col + halfBlockSize) ? (imgWidth - 1) : (col + halfBlockSize);
mind = 0;
maxd = disparityRange < (imgWidth - 1 - maxc) ? disparityRange : (imgWidth - 1 - maxc);
elementnum = (maxr+1-minr)*(maxc+1-minc);
int templateWidth = maxc+1-minc;
//////////////////template/////////////////////
h = 0;
for ( i = minr ; i <= maxr ; i++ )
{
k = 0;
for (j = minc ; j <= maxc ; j++)
{
template[k + h * templateWidth] = right[i * imgWidth + j];
k++;
}
h++;
}
barrier(CLK_LOCAL_MEM_FENCE);
///////////////////////////////////////////////
numBlocks = maxd - mind + 1;
//blockDiffs[numBlocks-1]={255};
for ( j = mind ; j <= maxd ; j++)
{
h = 0;
for ( k = minr ; k <= maxr ; k++)
{
r = 0;
for (l = minc+j ; l <= maxc+j ; l++)
{
block[r + h * templateWidth] = left[k * imgWidth + l];
r++;
}
h++;
}
barrier(CLK_LOCAL_MEM_FENCE);
blockIndex = j;
sum1 = 0;
for ( r = 0 ; r < elementnum ; r ++ )
{
sum1 = sum1 + abs(template[r]- block[r]);
}
blockDiffs[blockIndex] = sum1;
}
elemin = blockDiffs[0];
indx = 0;
for ( t = 0 ; t < numBlocks ; t++)
{
if (blockDiffs[t] < elemin )
{
indx = t ;
elemin = blockDiffs[t];
}
}
bestMatchIndex = indx;
d = bestMatchIndex ;
//output[row*imgWidth + col]= row*imgWidth + col;
if (bestMatchIndex == 0 || bestMatchIndex == numBlocks - 1)
{
output2[row*imgWidth + col] = d;
}
else
{
C1 = blockDiffs[bestMatchIndex - 1];
C2 = blockDiffs[bestMatchIndex ];
C3 = blockDiffs[bestMatchIndex + 1];
output2[row* imgWidth + col] = d - (0.5*(C3-C1)/(C1 - 2*C2 + C3));
}
//free(template);
//free(block);
}
""").build().basic2
""" """
func2.set_scalar_arg_dtypes([np.uint32, np.uint32, np.uint32, None, None, np.uint32, np.uint32, None])
def hist_op_2_time(BS, D, left_buf, right_buf, H, W, out_buf):
start = time.time()
func2(queue, (H,W), None, np.int32(BS/2) ,np.int32(BS) , np.int32(D) , left_buf, right_buf,np.int32(H), np.int32(W), out_buf)
return time.time()-start
def hist_op_2_data(BS,D, left, right, H, W, out):
left_buf,right_buf, out_buf = mem_alloc(left,right,out)
t=hist_op_2_time(BS, D, left_buf, right_buf, H, W, out_buf)
out=mem_transfer(out,out_buf)
return t, out
###################################################################
################# KERNEL 3 #######################################
##################################################################
func3 = cl.Program(ctx, """
#include <pyopencl-complex.h>
__kernel void basic3(const int halfBlockSize, const int blockSize, const int disparityRange ,__global float* left, __global float* right, const int imgHeight , const int imgWidth, __global float* output3) {
int row = get_global_id(1);
int col = get_global_id(0);
const int elements = (2*halfBlockSize+1)*(2*halfBlockSize+1);
//int* template = calloc(elements, sizeof(int));
//int* block = calloc(elements, sizeof(int));
private int template[49];
private int block[49];
int l,i,j,t;
int minc,maxc,mind,maxd,numBlocks,elemin,elementnum,blockIndex;
int blockDiffs[65];
float d;
int C1,C2,C3,bestMatchIndex ;
int h,k,r,sum1,indx;
int minr = 0 > (row - halfBlockSize) ? 0 : (row - halfBlockSize);
int maxr = (imgHeight - 1) < (row + halfBlockSize) ? (imgHeight - 1) : (row + halfBlockSize) ;
minc = (0 > (col - halfBlockSize) ? 0 : (col - halfBlockSize));
maxc = (imgWidth - 1) < (col + halfBlockSize) ? (imgWidth - 1) : (col + halfBlockSize);
mind = 0;
maxd = disparityRange < (imgWidth - 1 - maxc) ? disparityRange : (imgWidth - 1 - maxc);
elementnum = (maxr+1-minr)*(maxc+1-minc);
int templateWidth = maxc+1-minc;
//////////////////template/////////////////////
h = 0;
for ( i = minr ; i <= maxr ; i++ )
{
k = 0;
for (j = minc ; j <= maxc ; j++)
{
template[k + h * templateWidth] = right[i * imgWidth + j];
k++;
}
h++;
}
///////////////////////////////////////////////
numBlocks = maxd - mind + 1;
//blockDiffs[numBlocks-1]={255};
for ( j = mind ; j <= maxd ; j++)
{
h = 0;
for ( k = minr ; k <= maxr ; k++)
{
r = 0;
for (l = minc+j ; l <= maxc+j ; l++)
{
block[r + h * templateWidth] = left[k * imgWidth + l];
r++;
}
h++;
}
blockIndex = j;
sum1 = 0;
for ( r = 0 ; r < elementnum ; r ++ )
{
sum1 = sum1 + abs(template[r]- block[r]);
}
blockDiffs[blockIndex] = sum1;
}
elemin = blockDiffs[0];
indx = 0;
for ( t = 0 ; t < numBlocks ; t++)
{
if (blockDiffs[t] < elemin )
{
indx = t ;
elemin = blockDiffs[t];
}
}
bestMatchIndex = indx;
d = bestMatchIndex ;
//output[row*imgWidth + col]= row*imgWidth + col;
if (bestMatchIndex == 0 || bestMatchIndex == numBlocks - 1)
{
output3[row*imgWidth + col] = d;
}
else
{
C1 = blockDiffs[bestMatchIndex - 1];
C2 = blockDiffs[bestMatchIndex ];
C3 = blockDiffs[bestMatchIndex + 1];
output3[row* imgWidth + col] = d - (0.5*(C3-C1)/(C1 - 2*C2 + C3));
}
//free(template);
//free(block);
}
""").build().basic3
""" """
func3.set_scalar_arg_dtypes([np.uint32, np.uint32, np.uint32, None, None, np.uint32, np.uint32, None])
def hist_op_3_time(BS, D, left_buf, right_buf, H, W, out_buf):
start = time.time()
func3(queue, (H,W), None, np.int32(BS/2) ,np.int32(BS) , np.int32(D) , left_buf, right_buf,np.int32(H), np.int32(W), out_buf)
return time.time()-start
def hist_op_3_data(BS,D, left, right, H, W, out):
left_buf,right_buf, out_buf = mem_alloc(left,right,out)
t=hist_op_3_time(BS, D, left_buf, right_buf, H, W, out_buf)
out=mem_transfer(out,out_buf)
return t, out
###################################################################
################# KERNEL 4 #######################################
##################################################################
func4=cl.Program(ctx, """
#include <pyopencl-complex.h>
__kernel void basic4(__local int* rightBlock, __local int* leftBlock, const int halfBlockSize, const int disparityRange ,__global float* left, __global float* right, const int imgHeight, const int imgWidth, __global float* output) {
int row = get_global_id(0);
int col = get_global_id(1);
int loc_row = get_local_id(0);
int loc_col = get_local_id(1);
int grp_id_x = get_group_id(0);
int grp_id_y = get_group_id(1);
int offset = halfBlockSize*imgWidth+halfBlockSize;
int blockSize = 2*halfBlockSize + 1;
int idx = loc_col+loc_row*9;
volatile __local int disp_min;
volatile __local int bin[63];
if (loc_row < 7 && loc_col < 7)
{
rightBlock[loc_col+loc_row*blockSize] = right[loc_col+loc_row*imgWidth + grp_id_y+grp_id_x*imgWidth];
}
barrier(CLK_LOCAL_MEM_FENCE);
int priv_rightBlock[49]; //7x7
for (int k = 0; k < blockSize*blockSize; k++)
{
priv_rightBlock[k] = rightBlock[k];
}
int loop_length = 8;
if (loc_col > 7)
{
loop_length = 5;
}
for (int i = loc_col*8; i < loc_col*8+loop_length; i++)
{
leftBlock[i+loc_row*69] = left[i+loc_row*imgWidth + grp_id_y+grp_id_x*imgWidth];
}
barrier(CLK_LOCAL_MEM_FENCE);
int priv_leftBlock[49]; //7x7
for (int n = 0; n < 7; n++)
{
for (int m = 0; m < 7; m++)
{
priv_leftBlock[m+n*7] = leftBlock[m+idx+n*69];
}
}
int sum = 0;
for (int l = 0; l < 49; l++)
{
sum = sum + abs(priv_leftBlock[l] - priv_rightBlock[l]);
}
bin[idx] = sum;
barrier(CLK_LOCAL_MEM_FENCE);
int elemin = 1024;
if(idx == 1)
{
disp_min = 0;
for (int z = 0; z<63; z++)
{
if (bin[z] < elemin )
{
disp_min = z;
elemin = bin[z];
}
}
output[grp_id_y + grp_id_x*(imgWidth-6-disparityRange)] = disp_min;
}
barrier(CLK_LOCAL_MEM_FENCE);
}
""").build().basic4
""" """
func4.set_scalar_arg_dtypes([None, None, np.uint32, np.uint32, None, None, np.uint32, np.uint32, None])
def hist_op_4_time(BS, D, left_buf, right_buf, H, W, out_buf):
start = time.time()
global_size = np.zeros(shape=(7*(H-6),9*(W-6-D))).astype(np.float32) # 7x9 group shape 63 work item
group_size = np.zeros(shape=(7,9)).astype(np.float32)
lclLeft = cl.LocalMemory(np.int32().nbytes*483)
lclRight = cl.LocalMemory(np.int32().nbytes*49)
func4(queue, global_size.shape, group_size.shape, lclRight, lclLeft, np.int32(BS/2), np.int32(D), left_buf, right_buf, np.int32(H), np.int32(W), out_buf)
return time.time()-start
def hist_op_4_data(BS,D, left, right, H, W, out):
left_buf,right_buf, out_buf = mem_alloc(left,right,out)
t=hist_op_3_time(BS, D, left_buf, right_buf, H, W, out_buf)
out=mem_transfer(out,out_buf)
return t, out
###################################################################
################# KERNEL 5 #######################################
##################################################################
func5=cl.Program(ctx, """
#include <pyopencl-complex.h>
__kernel void basic5(__local int* rightBlock, __local int* leftBlock, const int halfBlockSize, const int disparityRange ,__global float* left, __global float* right, const int imgHeight, const int imgWidth, __global float* output) {
int row = get_global_id(0);
int col = get_global_id(1);
int loc_row = get_local_id(0);
int loc_col = get_local_id(1);
int grp_id_x = get_group_id(0);
int grp_id_y = get_group_id(1);
int offset = halfBlockSize*imgWidth+halfBlockSize;
int blockSize = 2*halfBlockSize + 1;
int idx = loc_col+loc_row*9;
volatile __local int disp_min;
volatile __local int bin[64];
volatile __local int location[64];
volatile __local int factor;
if (loc_row < 7 && loc_col < 7)
{
rightBlock[loc_col+loc_row*blockSize] = right[loc_col+loc_row*imgWidth + grp_id_y+grp_id_x*imgWidth];
}
int loop_length = 8;
if (loc_col > 7)
{
loop_length = 5;
}
for (int i = loc_col*8; i < loc_col*8+loop_length; i++)
{
leftBlock[i+loc_row*69] = left[i+loc_row*imgWidth + grp_id_y+grp_id_x*imgWidth];
}
barrier(CLK_LOCAL_MEM_FENCE);
int sum = 0;
for (int n = 0; n < 7; n++)
{
for (int m = 0; m < 7; m++)
{
sum = sum + abs(leftBlock[m+idx+n*69] - rightBlock[m+n*7]);
}
}
bin[idx] = sum;
barrier(CLK_LOCAL_MEM_FENCE);
//int elemin = 1024;
//if(idx == 1)
//{
// disp_min = 0;
// for (int z = 0; z<63; z++)
// {
// if (bin[z] < elemin )
// {
// disp_min = z;
// elemin = bin[z];
// }
// }
// output[grp_id_y + grp_id_x*(imgWidth-6-disparityRange)] = disp_min;
//}
//barrier(CLK_LOCAL_MEM_FENCE);
location[idx] = idx;
if(idx == 0)
{
factor = 2;
location[63] = 63;
bin[63] = 1024;
}
int element = 64;
while (element/factor !=0)
{
if(idx/(element/factor) == 0)
{
location[idx] = (bin[idx*2]<bin[idx*2+1]) ? location[idx*2] : location[idx*2+1];
bin[idx] = (bin[idx*2]<bin[idx*2+1]) ? bin[idx*2] : bin[idx*2+1];
}
barrier(CLK_LOCAL_MEM_FENCE);
if(idx == 0)
{
factor = factor*2;
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if(idx == 0)
{
output[grp_id_y + grp_id_x*(imgWidth-6-disparityRange)] = location[idx];
}
}
""").build().basic5
""" """
func5.set_scalar_arg_dtypes([None, None, np.uint32, np.uint32, None, None, np.uint32, np.uint32, None])
def hist_op_5_time(BS, D, left_buf, right_buf, H, W, out_buf):
start = time.time()
global_size = np.zeros(shape=(7*(H-6),9*(W-6-D))).astype(np.float32) # 7x9 group shape 63 work item
group_size = np.zeros(shape=(7,9)).astype(np.float32)
lclLeft = cl.LocalMemory(np.int32().nbytes*483)
lclRight = cl.LocalMemory(np.int32().nbytes*49)
func5(queue, global_size.shape, group_size.shape, lclRight, lclLeft, np.int32(BS/2), np.int32(D), left_buf, right_buf, np.int32(H), np.int32(W), out_buf)
return time.time()-start
def hist_op_5_data(BS,D, left, right, H, W, out):
left_buf,right_buf, out_buf = mem_alloc(left,right,out)
t=hist_op_5_time(BS, D, left_buf, right_buf, H, W, out_buf)
out=mem_transfer(out,out_buf)
return t, out
###################################################################
################# KERNEL 6 #######################################
##################################################################
func6=cl.Program(ctx, """
#include <pyopencl-complex.h>
__kernel void basic6(__local int* rightBlock, __local int* leftBlock, const int halfBlockSize, const int disparityRange ,__global float* left, __global float* right, const int imgHeight, const int imgWidth, __global float* output) {
int row = get_global_id(0);
int col = get_global_id(1);
//int loc_row = get_local_id(0);
int loc_col = get_local_id(1);
int grp_id_x = get_group_id(0);
int grp_id_y = get_group_id(1);
int offset = halfBlockSize*imgWidth+halfBlockSize;
int blockSize = 2*halfBlockSize + 1;
//int idx = loc_col+loc_row*9;
int SAD[65];
int idx[65];
//volatile __local int disp_min;
//volatile __local int bin[63];
//if (loc_row < 7 && loc_col < 7)
//{
// rightBlock[loc_col+loc_row*blockSize] = right[loc_col+loc_row*imgWidth + grp_id_y+grp_id_x*imgWidth];
//}
if (loc_col < 14)
{
for (int h = loc_col*13; h < loc_col*13+13; h++)
{
rightBlock[h] = right[h%26+(h/26)*imgWidth+grp_id_y*20+grp_id_x*imgWidth];
}
}
int loop_length = 30;
if (loc_col/18==1)
{
loop_length = 45;
}
for (int i = loc_col*30 + (loc_col/19)*15; i < loc_col*30 + (loc_col/19)*15+loop_length; i++)
{
leftBlock[i] = left[i%90 + (i/90)*imgWidth + grp_id_y*20 + grp_id_x*imgWidth];
}
barrier(CLK_LOCAL_MEM_FENCE);
int priv_rightBlock[49]; //7x7
for (int k = 0; k < blockSize*blockSize; k++)
{
priv_rightBlock[k] = rightBlock[k%7 + (k/7)*26 + loc_col];
}
int priv_leftBlock[49];
int sum;
for (int d=0; d < disparityRange; d++)
{
for (int m = 0; m < 49; m++)
{
priv_leftBlock[m] = leftBlock[m%7 + (m/7)*90 + d + loc_col];
}
sum = 0;
for (int j = 0; j<49; j++)
{
sum = sum + abs(priv_rightBlock[j]-priv_leftBlock[j]);
}
SAD[d] = sum;
}
int elemin = SAD[0];
int index = 0;
for (int l = 1; l<disparityRange; l++)
{
if(SAD[l] < elemin)
{
index = l;
elemin = SAD[l];
}
}
//output[loc_col+grp_id_y*20+grp_id_x*imgWidth]=index;
output[col + row*380]=index;
}
""").build().basic6
func6.set_scalar_arg_dtypes([None, None, np.uint32, np.uint32, None, None, np.uint32, np.uint32, None])
def hist_op_6_time(BS, D, left_buf, right_buf, H, W, out_buf):
start = time.time()
global_size = np.zeros(shape=(7*(H-6),9*(W-6-D))).astype(np.float32) # 7x9 group shape 63 work item
group_size = np.zeros(shape=(7,9)).astype(np.float32)
lclLeft = cl.LocalMemory(np.int32().nbytes*630)
lclRight = cl.LocalMemory(np.int32().nbytes*182)
func6(queue, global_size.shape, group_size.shape, lclRight, lclLeft, np.int32(BS/2), np.int32(D), left_buf, right_buf, np.int32(H), np.int32(W), out_buf)
return time.time()-start
def hist_op_6_data(BS,D, left, right, H, W, out):
left_buf,right_buf, out_buf = mem_alloc(left,right,out)
t=hist_op_6_time(BS, D, left_buf, right_buf, H, W, out_buf)
out=mem_transfer(out,out_buf)
return t, out
###################################################################################################################################
#########################################################################################
#########################################################################################
#########################################################################################
def create_arrays(H,W):
#N=R*C
#img = np.random.randint(0, 255, N).astype(np.uint8).reshape(R*C)
#img = np.memmap('/opt/data/random.dat', dtype=np.uint8, mode='r').reshape(R*C,)
left = np.memmap('/opt/data/random.dat', dtype=np.uint8, mode='r',shape=(H,W))
right = np.memmap('/opt/data/random.dat', dtype=np.uint8, mode='r',shape=(H,W))
out=np.zeros(left.shape).astype(np.float32)
return left,right,out
def mem_alloc(left, right, out):
mf = cl.mem_flags
left_buf = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=left)
right_buf = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=right)
#r_buf = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=readimg)
out_buf = cl.Buffer(ctx, mf.WRITE_ONLY | mf.COPY_HOST_PTR, hostbuf=out)
init_arr=np.zeros(out.shape).astype(np.float32)
cl.enqueue_copy(queue,out_buf,init_arr) #Initializing the Memory of the Output Buffers
return left_buf, right_buf, out_buf
#prg=cl.Program(ctx,kernel).build() #PROGRAM
#prg.mat_transpose(queue,A_trans.shape,None,a_buf,atrans_buf,np.uint32(height_A),np.uint32(width_A)) #KERNEL LAUNCH
def mem_transfer(out, out_buf):
cl.enqueue_copy(queue,out,out_buf) #Copying Final Data into Python Buffers
return out
#########################################################################################
#########################################################################################
#########################################################################################
def python_basic(BS,D, left, right, H, W, out):
imgHeight=H
imgWidth=W
disparityRange=D
halfBlockSize=int(BS/2)
start_time=time.time()
for m in np.arange(0 , imgHeight):
minr = np.maximum(0, m - halfBlockSize)
maxr = np.minimum(imgHeight-1, m + halfBlockSize)
for n in range (0, imgWidth):
minc = np.maximum(0, n - halfBlockSize)
maxc = np.minimum(imgWidth-1, n + halfBlockSize)
mind = 0
maxd = np.minimum(disparityRange, imgWidth - maxc)
template = np.array(right[minr:maxr+1, minc:maxc+1])
numBlocks = maxd - mind + 1
blockDiffs = np.zeros((numBlocks-1, 1))
for i in range (mind, maxd):
block = np.array(left[minr:maxr+1, minc + i: maxc + i+1])
blockIndex = i - mind +1 -1
blockDiffs[blockIndex, 0] = np.sum(np.sum(np.absolute(template - block)))
z=blockDiffs.shape
sortedIndeces = np.argmin(blockDiffs,axis = 0);
if type(sortedIndeces)==np.ndarray:
bestMatchIndex = sortedIndeces[0]
else:
bestMatchIndex = sortedIndeces
d = (bestMatchIndex) + mind
if ((bestMatchIndex == 0 ) or (bestMatchIndex == numBlocks-2)):
DbasicSubpixel[m, n] = d
ctr1=ctr1+1;
else :
C1 = blockDiffs[bestMatchIndex -1]
C2 = blockDiffs[bestMatchIndex]
C3 = blockDiffs[bestMatchIndex + 1]
DbasicSubpixel[m, n] = d - (0.5 * (C3 - C1) / (C1 - (2*C2) + C3))
# print type(DbasicSubpixel), ": Type Dbasicsubpixel"
# print DbasicSubpixel.shape, ": its shape"
t=time.time()-start
minDBS=np.min(DbasicSubpixel)
maxDBS=np.max(DbasicSubpixel)
DbasicSubpixel=150*((DbasicSubpixel-minDBS)/(maxDBS-minDBS))
# print DbasicSubpixel
# cv2.imwrite('disparity_with_Sobel-150.png',DbasicSubpixel)
# print "ctr1=%d, ctr2=%d" %(ctr1,ctr2)
# np.savetxt('DbasicSubpixel.xls',DbasicSubpixel,delimiter='\t')
out = DbasicSubpixel
return t, out
######################################################################################################################################################################
##### Evaluating Array Timings: ########
#Only in the case of python execution do we keep the M=1
def py_hist_time(BS,D,H,W,M=1):
times = []
left,right,out =create_arrays(H,W)
left_buf, right_buf, out_buf = mem_alloc(left,right, out)
for i in xrange(M):
t,y=python_basic(BS, D, left, right, H, W, out)
times.append(t)
#print 'python time: ', np.average(times)
return np.average(times)
def cl_op1_hist_time(BS,D,H,W, M=4):
times = []
left,right, out =create_arrays(H,W)
left_buf,right_buf, out_buf = mem_alloc(left,right, out)
for i in xrange(M):
#t=hist_op_1_time(img_buf,bins_buf, N)
t=hist_op_1_time(BS, D, left_buf, right_buf, H, W, out_buf)
times.append(t)
out=mem_transfer(out,out_buf)
#print 'opencl time: ', np.average(times)
return np.average(times)
def cl_op2_hist_time(BS,D,H,W, M=4):
times = []
left,right, out =create_arrays(H,W)
left_buf,right_buf, out_buf = mem_alloc(left,right, out)
for i in xrange(M):
#t=hist_op_1_time(img_buf,bins_buf, N)
t=hist_op_2_time(BS, D, left_buf, right_buf, H, W, out_buf)
times.append(t)
out=mem_transfer(out,out_buf)
#print 'opencl time: ', np.average(times)
return np.average(times)
def cl_op3_hist_time(BS,D,H,W, M=4):
times = []
left,right, out =create_arrays(H,W)
left_buf,right_buf, out_buf = mem_alloc(left,right, out)
for i in xrange(M):
#t=hist_op_1_time(img_buf,bins_buf, N)
t=hist_op_3_time(BS, D, left_buf, right_buf, H, W, out_buf)
times.append(t)
out=mem_transfer(out,out_buf)
#print 'opencl time: ', np.average(times)
return np.average(times)
def cl_op4_hist_time(BS,D,H,W, M=4):
times = []
left,right, out =create_arrays(H,W)
left_buf,right_buf, out_buf = mem_alloc(left,right, out)
for i in xrange(M):
#t=hist_op_1_time(img_buf,bins_buf, N)
t=hist_op_4_time(BS, D, left_buf, right_buf, H, W, out_buf)
times.append(t)
out=mem_transfer(out,out_buf)
#print 'opencl time: ', np.average(times)
return np.average(times)
def cl_op5_hist_time(BS,D,H,W, M=4):
times = []
left,right, out =create_arrays(H,W)
left_buf,right_buf, out_buf = mem_alloc(left,right, out)
for i in xrange(M):
#t=hist_op_1_time(img_buf,bins_buf, N)
t=hist_op_5_time(BS, D, left_buf, right_buf, H, W, out_buf)
times.append(t)
out=mem_transfer(out,out_buf)
#print 'opencl time: ', np.average(times)
return np.average(times)
def cl_op6_hist_time(BS,D,H,W, M=4):
times = []
left,right, out =create_arrays(H,W)
left_buf,right_buf, out_buf = mem_alloc(left,right, out)
for i in xrange(M):
#t=hist_op_1_time(img_buf,bins_buf, N)
t=hist_op_6_time(BS, D, left_buf, right_buf, H, W, out_buf)
times.append(t)
out=mem_transfer(out,out_buf)
#print 'opencl time: ', np.average(times)
return np.average(times)
####### BASIC OPENCV #######
def cl_op7_hist_time(BS,D,H,W,M=4):
times = []
left,right, out =create_arrays(H,W)
for i in xrange(M):
#t=hist_op_1_time(img_buf,bins_buf, N)
start=time.time()
stereo = cv2.StereoBM(cv2.STEREO_BM_BASIC_PRESET,ndisparities=D, SADWindowSize=BS)
##http://docs.opencv.org/2.4.1/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html#stereobm-stereobm
#out = stereo.compute(left,right)
t=time.time()-start
times.append(t)
#print 'opencl time: ', np.average(times)
return np.average(times)
H=320
W=480
D=16
BS=7
##### PLot against a Variation in BS ####
python_times=[]
pyopencl_op1_times=[]
pyopencl_op2_times=[]
pyopencl_op3_times=[]
pyopencl_op4_times=[]
pyopencl_op5_times=[]
pyopencl_op6_times=[]
pyopencl_op7_times=[]
param=np.arange(3,8,2).astype(np.int32)