On Wednesday, May 17, 2017 at 12:13:04 PM UTC-4, Stephen Woodbridge wrote:
Hi, first I want to say the LSMS Segmentation is very cool and works nicely.
I recently got access to a sever with 56 cores and 128GB of memory but I can't
seem to get it to use more than 10-15 cores. I'm running the smoothing on an
image approx 20000x20000 in size. The
image is a gdal VRT file that combines 8 DOQQ images into a mosaic. It has 4
bands R, G, B, IR with each having Mask Flags: PER_DATASET (see below). I'm
running this from a Python script like:
def smoothing(fin, fout, foutpos, spatialr, ranger, rangeramp, thres,
maxiter, ram):
app = otbApplication.Registry.CreateApplication('MeanShiftSmoothing')
app.SetParameterString('in', fin)
app.SetParameterString('fout', fout)
app.SetParameterString('foutpos', foutpos)
app.SetParameterInt('spatialr', spatialr)
app.SetParameterFloat('ranger', ranger)
app.SetParameterFloat('rangeramp', rangeramp)
app.SetParameterFloat('thres', thres)
app.SetParameterInt('maxiter', maxiter)
app.SetParameterInt('ram', ram)
app.SetParameterInt('modesearch', 0)
app.ExecuteAndWriteOutput()
Where:
spatialr: 24
ranger: 36
rangeramp: 0
thres: 0.1
maxiter: 100
ram: 102400
Any thoughts on how I can get this to utilize more of the processing power of
this machine?
-Steve
woodbri@optane28:/u/ror/buildings/tmp$ otbcli_ReadImageInfo -in
tmp-23081-areaofinterest.vrt
2017 May 17 15:36:04 : Application.logger (INFO)
Image general information:
Number of bands : 4
No data flags : Not found
Start index : [0,0]
Size : [19933,19763]
Origin : [-118.442,34.0035]
Spacing : [9.83578e-06,-9.83578e-06]
Estimated ground spacing (in meters): [0.90856,1.09369]
Image acquisition information:
Sensor :
Image identification number:
Image projection : GEOGCS["WGS 84",
DATUM["WGS_1984",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
AUTHORITY["EPSG","6326"]],
PRIMEM["Greenwich",0],
UNIT["degree",0.0174532925199433],
AUTHORITY["EPSG","4326"]]
Image default RGB composition:
[R, G, B] = [0,1,2]
Ground control points information:
Number of GCPs = 0
GCPs projection =
Output parameters value:
indexx: 0
indexy: 0
sizex: 19933
sizey: 19763
spacingx: 9.835776837e-06
spacingy: -9.835776837e-06
originx: -118.4418488
originy: 34.00345612
estimatedgroundspacingx: 0.9085595012
estimatedgroundspacingy: 1.093693733
numberbands: 4
sensor:
id:
time:
ullat: 0
ullon: 0
urlat: 0
urlon: 0
lrlat: 0
lrlon: 0
lllat: 0
lllon: 0
town:
country:
rgb.r: 0
rgb.g: 1
rgb.b: 2
projectionref: GEOGCS["WGS 84",
DATUM["WGS_1984",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
AUTHORITY["EPSG","6326"]],
PRIMEM["Greenwich",0],
UNIT["degree",0.0174532925199433],
AUTHORITY["EPSG","4326"]]
keyword:
gcp.count: 0
gcp.proj:
gcp.ids:
gcp.info:
gcp.imcoord:
gcp.geocoord:
woodbri@optane28:/u/ror/buildings/tmp$ gdalinfo tmp-23081-areaofinterest.vrt
Driver: VRT/Virtual Raster
Files: tmp-23081-areaofinterest.vrt
/u/ror/buildings/tmp/tmp-23081-areaofinterest.vrt.vrt
Size is 19933, 19763
Coordinate System is:
GEOGCS["WGS 84",
DATUM["WGS_1984",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
AUTHORITY["EPSG","6326"]],
PRIMEM["Greenwich",0],
UNIT["degree",0.0174532925199433],
AUTHORITY["EPSG","4326"]]
Origin = (-118.441851318576212,34.003461706049677)
Pixel Size = (0.000009835776490,-0.000009835776490)
Corner Coordinates:
Upper Left (-118.4418513, 34.0034617) (118d26'30.66"W, 34d 0'12.46"N)
Lower Left (-118.4418513, 33.8090773) (118d26'30.66"W, 33d48'32.68"N)
Upper Right (-118.2457948, 34.0034617) (118d14'44.86"W, 34d 0'12.46"N)
Lower Right (-118.2457948, 33.8090773) (118d14'44.86"W, 33d48'32.68"N)
Center (-118.3438231, 33.9062695) (118d20'37.76"W, 33d54'22.57"N)
Band 1 Block=128x128 Type=Byte, ColorInterp=Red
Mask Flags: PER_DATASET
Band 2 Block=128x128 Type=Byte, ColorInterp=Green
Mask Flags: PER_DATASET
Band 3 Block=128x128 Type=Byte, ColorInterp=Blue
Mask Flags: PER_DATASET
Band 4 Block=128x128 Type=Byte, ColorInterp=Gray
Mask Flags: PER_DATASET
--
