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minmax_flt.py
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minmax_flt.py
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import numpy as np
import scipy
from scipy import signal
import scipy.ndimage as ndimage
import scipy.ndimage.filters as filters
import matplotlib.pyplot as plt
import sys
from osgeo import gdal
from osgeo.gdalconst import *
#import numpy as np
import numpy.ma as ma
import os.path
gdal.AllRegister()
gdal.UseExceptions()
#--------------------------------------------
def fspecial_gauss(size, sigma):
"""Function to mimic the 'fspecial' gaussian MATLAB function
"""
x, y = np.mgrid[-size//2 + 1:size//2 + 1, -size//2 + 1:size//2 + 1]
g = np.exp(-((x**2 + y**2)/(2.0*sigma**2)))
return g/g.sum()
#--------------------------------------------
def fill(sdata, invalid=None):
"""
Replace the value of invalid 'data' cells (indicated by 'invalid')
by the value of the nearest valid data cell
Input:
data: numpy array of any dimension
invalid: a binary array of same shape as 'data'.
data value are replaced where invalid is True
If None (default), use: invalid = np.isnan(data)
Output:
Return a filled array.
"""
if invalid is None: invalid = np.isnan(sdata)
ind = ndimage.distance_transform_edt(invalid,
return_distances=False,
return_indices=True)
return sdata[tuple(ind)]
#--------------------------------------------
def local_diff(sdata,size):
localmin = filters.minimum_filter(sdata, size)
localmax = filters.maximum_filter(sdata, size)
return (localmax-localmin)
#--------------------------------------------
def lerp(sdata,condition):
xx,yy = np.meshgrid(np.arange(sdata.shape[1]),np.arange(sdata.shape[0]))
xym = np.vstack( (np.ravel(xx[condition]), np.ravel(yy[condition])) ).T
values=np.ravel(sdata[:,:][condition])
interp=scipy.interpolate.LinearNDInterpolator(xym,values)
return interp(np.ravel(xx), np.ravel(yy)).reshape( xx.shape )
#--------------------------------------------
def localmin(sdata,size,threshold):
localmin = filters.minimum_filter(sdata, size)
localdif = sdata-localmin
with np.errstate(invalid='ignore'):
return (localdif < threshold)
# return np.where(localdif < threshold)
#--------------------------------------------
def lerpNANdwn(sdata,step):
data_back=np.zeros(sdata.shape)
data_back[:,:]=np.NAN
data_ds=data[0::step,0::step]
data_back[0::step,0::step]=data_ds
coords_val = np.array(np.nonzero(~np.isnan(data_back))).T
coords_out = np.array(np.nonzero(data_back)).T
value = data_back[coords_val[:,0],coords_val[:,1]]
return scipy.interpolate.griddata(coords_val,value,coords_out,method='cubic').reshape(sdata.shape)
#--------------------------------------------
def lerpgrd(sdata,condition):
coords_val = np.array(np.nonzero(condition)).T
coords_out = np.array(np.nonzero(sdata)).T
value = sdata[coords_val[:,0],coords_val[:,1]]
return scipy.interpolate.griddata(coords_val,value,coords_out,method='cubic').reshape(sdata.shape)
#--------------------------------------------
def mean_convoluted(image, m, n):
kernel=np.full((m,n),1.0/(m*n))
return scipy.signal.convolve2d(image, kernel, mode="same", boundary="symm")
#--------------------------------------------
def packet_mean(data):
return (mean_convoluted(data,3,3)+mean_convoluted(data,5,5)+mean_convoluted(data,9,9)+mean_convoluted(data,21,21)+mean_convoluted(data,43,43)+mean_convoluted(data,86,86))/6.0
#--------------------------------------------
def dtm_rank(data,size):
rmin = int(size[0]*size[1]*0.05)
rmax = int(size[0]*size[1]*0.95)
print "flter param:", rmin,rmax,size[0],size[1]
rX = filters.rank_filter(data,rmin,(size[0],size[1]))
rY = filters.rank_filter(data,rmin,(size[1],size[0]))
rX = filters.rank_filter(rX,rmax,(size[0],size[1]))
rY = filters.rank_filter(rY,rmax,(size[1],size[0]))
return (rX+rY)/2.0
#--------------------------------------------
#--------------------------------------------
def dtm_rank2(data,size,tr):
rmin = int(size[0]*size[1]*0.05)
rmax = int(size[0]*size[1]*0.95)
print "flter param:", rmin,rmax,size[0],size[1]
rX = filters.rank_filter(data,rmin,(size[0],size[1]))
rY = filters.rank_filter(data,rmin,(size[1],size[0]))
rX = filters.rank_filter(rX,rmax,(size[0],size[1]))
rY = filters.rank_filter(rY,rmax,(size[1],size[0]))
diff = data-((rX+rY)/2.0)
out = data[:,:]
mask = diff>tr
out[mask]=data[mask]-diff[mask]
return out
#--------------------------------------------
def dtm_rank2i(data,size,tr):
rmin = int(size[0]*size[1]*0.95)
rmax = int(size[0]*size[1]*0.05)
print "invert flter param:", rmin,rmax,size[0],size[1]
rX = filters.rank_filter(data,rmin,(size[0],size[1]))
rY = filters.rank_filter(data,rmin,(size[1],size[0]))
rX = filters.rank_filter(rX,rmax,(size[0],size[1]))
rY = filters.rank_filter(rY,rmax,(size[1],size[0]))
diff = data-((rX+rY)/2.0)
out = np.copy(data)#data[:,:]
mask = diff>tr
out[mask]=data[mask]+diff[mask]
return out
#--------------------------------------------
if len(sys.argv) < 5:
print "Usage: minmax_flt.py [h] [w] [it] [input] <invert> <out>"
sys.exit(0)
h = int(sys.argv[1])
w = int(sys.argv[2])
it = int(sys.argv[3])
fname = sys.argv[4]
invert = int(sys.argv[5])
try:
oname = sys.argv[6]
except:
#if (!oname):
oname = os.path.splitext(fname)[0] + '_dtm_flt.tif'#'DSM_8x_RAW_flt_rank90_it5_t1.tif'
#oname = 'lenag_out.tif'
print "fiter parameters:"
print "width=%d"%w
print "height=%d"%h
neighborhood_size = np.ones((5,5))
threshold = 15
nbands = 1
ds = gdal.Open(fname,gdal.GA_ReadOnly)
#data = scipy.misc.imread(fname)
nd=ds.GetRasterBand(1).GetNoDataValue()
data=ds.GetRasterBand(1).ReadAsArray().astype(np.float_)
back_mask=(data==-9999)
data[back_mask]=np.NAN
sdata=np.copy(data)
mean_data = np.nanmean(data);
data[back_mask]=mean_data
print "DTM rank filter"
#out=dtm_rank(data,(8,90))
#out=dtm_rank(data,(3,400))
if invert==0:
for i in range(0,it):
print "mean data", np.nanmean(data)
print "normal mode"
out=dtm_rank2(data,(h,w),0.5)
data=out
else:
for i in range(0,it):
print "mean data", np.nanmean(data)
print "invert mode"
out=dtm_rank2i(data,(h,w),0.5)
data=out
#out[out<0]=np.NAN
#out=local_diff(data,9)
#out[out>300]=np.NAN
#out=filters.median_filter(data,(3,3))
diff = sdata-out
mask = diff>3
sdata[mask]=sdata[mask]-diff[mask]
out=filters.median_filter(sdata,(3,3))
nd=-9999
print "Save"
#save
OutDataType=gdal.GDT_Float32
driver=gdal.GetDriverByName("Gtiff")
ods=driver.Create(oname,data.shape[1],data.shape[0],nbands,OutDataType)
ods.SetGeoTransform(ds.GetGeoTransform())
ods.SetProjection(ds.GetProjection())
ob=ods.GetRasterBand(1)
ob.SetNoDataValue(nd)
ob.WriteArray(out,0,0)
"""
ob=ods.GetRasterBand(2)
ob.WriteArray(out1,0,0)
ob=ods.GetRasterBand(3)
ob.WriteArray(out2,0,0)
ob=ods.GetRasterBand(4)
ob.WriteArray(out3,0,0)
"""
print "done"
ob=None
ods=None