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Hi, I am analysing set of images for subpixel image shifts. I have code which essantially loops through:

loop(){

• read binary image, send it to GpuMat/cuda

//next 2 points are based on dft, mulSpectrums, magnitude (all cuda "Streamable")

• convolve with smoothing/gradient kernels (cuda)
• cross-correlate (phase-correlate) with base image (cuda)

// next are locating maximum with subpixel precision

• find maxLoc (cuda, but value sent to Point.x/Point.y on CPU)
• copy maxLoc 3x3 neighbours into Mat (CPU)
• subpixel registration by quadratic fit (CPU)
• resulting (x,y) pixel shifts are placed in shift maps (CPU) }

All this is computed ~65000 times, it takes about 8 minutes to compute (256x256 base 16 bit B&W images). Cuda card is not even heating up (nvidia-smi shows 6% GPU-Util).

Any suggestions on how to parallelize (the faster the better) this?

Hi, I am analysing set of images for subpixel image shifts. I have code which essantially loops through:

loop(){

• read binary image, send it to GpuMat/cuda

//next 2 points are based on dft, mulSpectrums, magnitude (all cuda "Streamable")

• convolve with smoothing/gradient kernels (cuda)
• cross-correlate (phase-correlate) with base image (cuda)

// next are locating maximum with subpixel precision

• find maxLoc (cuda, but value sent to Point.x/Point.y on CPU)
• copy maxLoc 3x3 neighbours into Mat (CPU)
• subpixel registration by quadratic fit (CPU)
• resulting (x,y) pixel shifts are placed in shift maps (CPU) }

All this is computed ~65000 times, it takes about 8 minutes to compute (256x256 base 16 bit B&W images). Cuda card is not even heating up (nvidia-smi shows 6% GPU-Util).

Any suggestions on how to parallelize (the faster the better) this?this? (also thanks to L.Berger who got me this far)

Hi, I am analysing set of images for subpixel image shifts. I have code which essantially loops through:

loop(){

• read binary image, send it to GpuMat/cuda

//next 2 points are based on dft, mulSpectrums, magnitude (all cuda "Streamable")

• convolve with smoothing/gradient kernels (cuda)
• cross-correlate (phase-correlate) with base image (cuda)

// next are locating maximum with subpixel precision

• find maxLoc (cuda, but value sent to Point.x/Point.y on CPU)
• copy maxLoc 3x3 neighbours into Mat (CPU)
• subpixel registration by quadratic fit (CPU)
• resulting (x,y) pixel shifts are placed in shift maps (CPU) }

All this is computed ~65000 times, it takes about 8 minutes to compute (256x256 base 16 bit B&W images). Cuda card is not even heating up (nvidia-smi shows 6% GPU-Util).

Any suggestions on how to parallelize (the faster the better) this? this?

(also thanks to L.Berger who got me this far)

Hi, I am analysing set of images for subpixel image shifts. I have code which essantially loops through:

loop(){

• read binary image, send it to GpuMat/cuda

//next 2 points are based on dft, mulSpectrums, magnitude (all cuda "Streamable")

• convolve with smoothing/gradient kernels (cuda)
• cross-correlate (phase-correlate) with base image (cuda)

// next are locating maximum of correlation pattern with subpixel precision

• find maxLoc (cuda, but value sent to Point.x/Point.y on CPU)
• copy maxLoc 3x3 neighbours into Mat (CPU)
• subpixel registration by quadratic fit (CPU)
• resulting (x,y) pixel shifts are placed in shift maps (CPU) }

All this is computed ~65000 times, it takes about 8 minutes to compute (256x256 base 16 bit B&W images). Cuda card is not even heating up (nvidia-smi shows 6% GPU-Util).

Any suggestions on how to parallelize (the faster the better) this?

(also thanks to L.Berger who got me this far)

Hi, I am analysing set of images for subpixel image shifts. I have code which essantially loops through:

loop(){

• read binary image, send it to GpuMat/cuda

//next 2 points are based on dft, mulSpectrums, magnitude (all cuda "Streamable")

• convolve with smoothing/gradient kernels (cuda)
• cross-correlate (phase-correlate) with base image (cuda)

// next are locating maximum of correlation pattern with subpixel precision

• find maxLoc (cuda, but value sent to Point.x/Point.y on CPU)
• copy maxLoc 3x3 neighbours into Mat (CPU)
• subpixel registration by quadratic fit of 3x3 maxima neighbours (CPU)
• resulting (x,y) pixel shifts are placed in shift maps (CPU) }

All this is computed ~65000 times, it takes about 8 minutes to compute (256x256 base 16 bit B&W images). Cuda card is not even heating up (nvidia-smi shows 6% GPU-Util).

Any suggestions on how to parallelize (the faster the better) this?

(also thanks to L.Berger who got me this far)