def main(options, args):
starttime = Timer.starttimer()
#Cache thrashing is common when working with large files, we help alleviate misses by setting a larger than normal cache. 1GB
gdal.SetCacheMax(1073741824)
#Check for input
if not args:
print "\nERROR: You must supply an input data set.\n"
sys.exit(0)
#Get stretch type
stretch = OptParse.get_stretch(options)
#Get some info about the machine for multiprocessing
cores = multiprocessing.cpu_count()
cores *= 2
print "Processing on %i cores." %cores
#Load the input dataset using the GdalIO class and get / set the output datatype.
dataset = GdalIO.GdalIO(args[0])
raster = dataset.load()
#Default is none, unless user specified
if options['dtype'] == None:
dtype = gdal.GetDataTypeName(raster.GetRasterBand(1).DataType)
else:
dtype=options['dtype']
#Create an output if the stretch is written to disk
xsize, ysize, bands, projection, geotransform = dataset.info(raster)
output = dataset.create_output("",options['output'],xsize,ysize,bands,projection, geotransform, gdal.GetDataTypeByName(dtype))
#Segment the image to handle either RAM constraints or selective processing
segments = Segment.segment_image(xsize,ysize,options['vint'], options['hint'])
for b in xrange(bands):
band = raster.GetRasterBand(b+1)
bandstats = Stats.get_band_stats(band)
for key in bandstats.iterkeys():
options[key] = bandstats[key]
#Get the size of the segments to be manipulated
piecenumber = 1
for chunk in segments:
print "Image segmented. Processing segment %i of %i" %(piecenumber, len(segments))
piecenumber += 1
(xstart, ystart, intervalx, intervaly) = chunk
array = band.ReadAsArray(xstart, ystart, intervalx, intervaly).astype(numpy.float32)
if options['ndv_band'] != None:
array = numpy.ma.masked_values(array, options['ndv_band'], copy=False)
elif options['ndv'] != None:
array = numpy.ma.masked_values(array, options['ndv'], copy=False)
if 'stretch' in stretch.__name__:
array = Stats.normalize(array, options['bandmin'], options['bandmax'], dtype)
#If the user wants to calc stats per segment:
if options['segment'] == True:
stats = Stats.get_array_stats(array, stretch)
for key in stats.iterkeys():
options[key] = stats[key]
#Otherwise use the stats per band for each segment
else:
options['mean'] = options['bandmean']
options['maximum'] = options['bandmax']
options['minimum'] = options['bandmin']
options['standard_deviation'] = options['bandstd']
y,x = array.shape
#Calculate the hist and cdf if we need it. This way we do not calc it per core.
if options['histequ_stretch'] == True:
cdf, bins = Stats.gethist_cdf(array,options['num_bins'])
options['cdf'] = cdf
options['bins'] = bins
#Fill the masked values with NaN to get to a shared array
if options['ndv'] != None:
array = array.filled(numpy.nan)
#Create an ctypes array
init(ArrayConvert.SharedMemArray(array))
step = y // cores
jobs = []
if step != 0:
for i in range(0,y,step):
p = multiprocessing.Process(target=stretch,args= (shared_arr,slice(i, i+step)),kwargs=options)
jobs.append(p)
for job in jobs:
job.start()
del job
for job in jobs:
job.join()
del job
#Return the array to the proper data range and write it out. Scale if that is what the user wants
if options['histequ_stretch'] or options['gamma_stretch']== True:
pass
elif 'filter' in stretch.__name__:
pass
else:
Stats.denorm(shared_arr.asarray(), dtype, kwargs=options)
if options['scale'] != None:
Stats.scale(shared_arr.asarray(), kwargs=options)
#If their are NaN in the array replace them with the dataset no data value
Stats.setnodata(shared_arr, options['ndv'])
#Write the output
output.GetRasterBand(b+1).WriteArray(shared_arr.asarray(), xstart,ystart)
#Manually cleanup to stop memory leaks.
del array, jobs, shared_arr.data
try:
del stats
except:
pass
del globals()['shared_arr']
gc.collect()
if options['ndv'] != None:
output.GetRasterBand(b+1).SetNoDataValue(float(options['ndv']))
elif options['ndv_band'] != None:
output.GetRasterBand(b+1).SetNoDataValue(float(options['ndv_band']))
if options['visualize'] == True:
Plot.show_hist(shared_arr.asarray())
Timer.totaltime(starttime)
#Close up
dataset = None
output = None
gc.collect()
def main(options, args):
starttime = Timer.starttimer()
#Cache thrashing is common when working with large files, we help alleviate misses by setting a larger than normal cache. 1GB
gdal.SetCacheMax(1073741824)
#Check for input
if not args:
print "\nERROR: You must supply an input data set.\n"
sys.exit(0)
#Get stretch type
stretch = OptParse.get_stretch(options)
#Get some info about the machine for multiprocessing
cores = multiprocessing.cpu_count()
cores *= 2
print "Processing on %i cores." % cores
#Load the input dataset using the GdalIO class and get / set the output datatype.
dataset = GdalIO.GdalIO(args[0])
raster = dataset.load()
#Default is none, unless user specified
if options['dtype'] == None:
dtype = gdal.GetDataTypeName(raster.GetRasterBand(1).DataType)
else:
dtype = options['dtype']
#Create an output if the stretch is written to disk
xsize, ysize, bands, projection, geotransform = dataset.info(raster)
output = dataset.create_output("", options['output'], xsize, ysize, bands,
projection, geotransform,
gdal.GetDataTypeByName(dtype))
#Segment the image to handle either RAM constraints or selective processing
segments = Segment.segment_image(xsize, ysize, options['vint'],
options['hint'])
for b in xrange(bands):
band = raster.GetRasterBand(b + 1)
bandstats = Stats.get_band_stats(band)
for key in bandstats.iterkeys():
options[key] = bandstats[key]
#Get the size of the segments to be manipulated
piecenumber = 1
for chunk in segments:
print "Image segmented. Processing segment %i of %i" % (
piecenumber, len(segments))
piecenumber += 1
(xstart, ystart, intervalx, intervaly) = chunk
array = band.ReadAsArray(xstart, ystart, intervalx,
intervaly).astype(numpy.float32)
if options['ndv_band'] != None:
array = numpy.ma.masked_values(array,
options['ndv_band'],
copy=False)
elif options['ndv'] != None:
array = numpy.ma.masked_values(array,
options['ndv'],
copy=False)
if 'stretch' in stretch.__name__:
array = Stats.normalize(array, options['bandmin'],
options['bandmax'], dtype)
#If the user wants to calc stats per segment:
if options['segment'] == True:
stats = Stats.get_array_stats(array, stretch)
for key in stats.iterkeys():
options[key] = stats[key]
#Otherwise use the stats per band for each segment
else:
options['mean'] = options['bandmean']
options['maximum'] = options['bandmax']
options['minimum'] = options['bandmin']
options['standard_deviation'] = options['bandstd']
y, x = array.shape
#Calculate the hist and cdf if we need it. This way we do not calc it per core.
if options['histequ_stretch'] == True:
cdf, bins = Stats.gethist_cdf(array, options['num_bins'])
options['cdf'] = cdf
options['bins'] = bins
#Fill the masked values with NaN to get to a shared array
if options['ndv'] != None:
array = array.filled(numpy.nan)
#Create an ctypes array
init(ArrayConvert.SharedMemArray(array))
step = y // cores
jobs = []
if step != 0:
for i in range(0, y, step):
p = multiprocessing.Process(target=stretch,
args=(shared_arr,
slice(i, i + step)),
kwargs=options)
jobs.append(p)
for job in jobs:
job.start()
del job
for job in jobs:
job.join()
del job
#Return the array to the proper data range and write it out. Scale if that is what the user wants
if options['histequ_stretch'] or options['gamma_stretch'] == True:
pass
elif 'filter' in stretch.__name__:
pass
else:
Stats.denorm(shared_arr.asarray(), dtype, kwargs=options)
if options['scale'] != None:
Stats.scale(shared_arr.asarray(), kwargs=options)
#If their are NaN in the array replace them with the dataset no data value
Stats.setnodata(shared_arr, options['ndv'])
#Write the output
output.GetRasterBand(b + 1).WriteArray(shared_arr.asarray(),
xstart, ystart)
#Manually cleanup to stop memory leaks.
del array, jobs, shared_arr.data
try:
del stats
except:
pass
del globals()['shared_arr']
gc.collect()
if options['ndv'] != None:
output.GetRasterBand(b + 1).SetNoDataValue(
float(options['ndv']))
elif options['ndv_band'] != None:
output.GetRasterBand(b + 1).SetNoDataValue(
float(options['ndv_band']))
if options['visualize'] == True:
Plot.show_hist(shared_arr.asarray())
Timer.totaltime(starttime)
#Close up
dataset = None
output = None
gc.collect()
