ARM Guide to OpenCL Optimizing Pyramid: Initial Implementation
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This chapter describes an initial, unoptimized implementation of pyramid.
The initial, unoptimized code uses the basic pyramid process where an unaltered Gaussian convolution is applied, then the result is subsampled. This process is then repeated on the result to create the pyramid image for further levels.
The initial Gaussian 5x5 filter implementation
The code for the initial unoptimized Gaussian pyramid uses the same convolution code as the ARM® Guide to OpenCL Optimizing Convolution example.
The following code shows the Gaussian 5x5 filter code from the ARM® Guide to OpenCL Optimizing Convolution.
// Variables declaration
#define PARTIAL_CONVOLUTION( i, m0, m1, m2, m3, m4 )\
temp = vload16(0, src + addr + i * strideByte);\
temp2 = vload4(0, src + addr + i * strideByte + 16);\
l2Row = temp.s01234567;\
l1Row = temp.s3456789A;\
mRow = temp.s6789ABCD;\
r1Row = (uchar8)(temp.s9ABC, temp.sDEF, temp2.s0);\
r2Row = (uchar8)(temp.sCDEF, temp2.s0123);\
l2Data = convert_ushort8(l2Row);\
l1Data = convert_ushort8(l1Row);\
mData = convert_ushort8(mRow);\
r1Data = convert_ushort8(r1Row);\
r2Data = convert_ushort8(r2Row);\
pixels += l2Data * (ushort8)m0;\
pixels += l1Data * (ushort8)m1;\
pixels += mData * (ushort8)m2;\
pixels += r1Data * (ushort8)m3;\
pixels += r2Data * (ushort8)m4;\
PARTIAL_CONVOLUTION( -2, MAT0, MAT1, MAT2, MAT3, MAT4);
PARTIAL_CONVOLUTION( -1, MAT5, MAT6, MAT7, MAT8, MAT9);
PARTIAL_CONVOLUTION( 0, MAT10, MAT11,...