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Manycore Parallel Algorithm Parallel For Image Processing

Outline Image Processing Revisited  Random Jitter  Implementation of Parallel Random Jitter  Result 

TE091585 – Komputasi Grid

Outline Image Processing Revisited  Random Jitter  Implementation of Parallel Random Jitter  Result 

TE091585 – Komputasi Grid

Image Processing • Image defined by pixel with some uniform sampling

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Image Processing • Other Properties: – Can be multiple channel (RGB, HSV, etc.). – First coordinate (0,0) lie at left-top. – A several image processing technique are essentially embarrassingly parallel. – More easier to implement then other problem.

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Image Processing • Several Method: – Image Filtering – Image Transformation – Image Warping

• At this course we will focus on image filtering

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Outline Image Processing Revisited  Random Jitter  Implementation of Parallel Random Jitter  Result 

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Random Jitter • Random Jitter: – A filter that can create oil painting effect.

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Random Jitter • Random Jitter Algorithm – Given image I – Copy image I to output image O – For each pixel • Compute random movement for current pixel in image I • If the movement not outside of the image then move current pixel in image I to new coordinate in image O

– End for

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Random Jitter • Must do: – Decide maximum movement of the pixel – The movement must remain in the image (0,0) to (width,height)

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Outline Image Processing Revisited  Random Jitter  Implementation of Parallel Random Jitter  Result 

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Implementation • Use OpenCV to reduce complexity • We still use parallel for to implement parallel random jitter • As usual, we test system with different image resolution and plot the speed-up over serial code.

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Serial Implementation int tx = img->width/2, ty = img->height/2; float PI = 3.141527f, DRAD = 180.0f / PI; RgbImage rgbimg(img); RgbImage rgbimgout(imgout); for(int y = 0; y < img->height; y++) { for(int x = 0; x < img->width; x++) { if (x == 0 || y == 0 || x == img->width-1 || y == img->height-1) rgbimgout[y][x] = rgbimg[y][x]; int xx = x + (rand() % 7) - 3; int yy = y + (rand() % 7) - 3; if (xx < 0) xx = 0; if (xx >= img->width) xx = img->width-1; if (yy < 0) yy = 0; if (yy >= img->height) yy = img->height-1; rgbimgout[yy][xx] = rgbimg[y][x]; } } TE091585 – Komputasi Grid

Parallel Implementation int tx = img->width/2, ty = img->height/2; float PI = 3.141527f, DRAD = 180.0f / PI; RgbImage rgbimg(img); RgbImage rgbimgout(imgout); #pragma omp parallel for shared(rgbimg, rgbimgout) for(int y = 0; y < img->height; y++) { for(int x = 0; x < img->width; x++) { if (x == 0 || y == 0 || x == img->width-1 || y == img->height-1) rgbimgout[y][x] = rgbimg[y][x]; int xx = x + (rand() % 7) - 3; int yy = y + (rand() % 7) - 3; if (xx < 0) xx = 0; if (xx >= img->width) xx = img->width-1; if (yy < 0) yy = 0; if (yy >= img->height) yy = img->height-1; rgbimgout[yy][xx] = rgbimg[y][x]; } } TE091585 – Komputasi Grid

Outline Image Processing Revisited  Random Jitter  Implementation of Parallel Random Jitter  Result 

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Testing Environments • Use 2 PC with 2 processor and 4 processor for comparison. • Use 4 different image resolution (256x256, 512x512, 1024x1024, 2048x2048). • Use 2 different number of thread (default and 10 thread).

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Result Speed-Up Random Jitter 3.5

3

Speed-Up

2.5

2 2P+Def.Thread 2P+10Thread

1.5

4P+Def.Thread

4P+10Thread

1

0.5

0 256

512

1024

Resolusi Image

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2048

Result • 2P+Def.Thread = 2 Processor with default thread. • 2P+10Thread = 2 Processor with 10 thread. • 4P+Def.Thread = 4 Processor with default thread. • 4P+10Thread = 4 Processor with 10 thread.

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