def FFT3D(array):
"""Returns the FFT of a three dimensional array
This method can be used to obtain the FFT of a three dimensional array.
"""
import FFT
N1, N2, N3 = array.shape
return FFT.fft(FFT.fft2d(array, (N1, N2), axes=(0, 1)), N3, axis=2)
def filter2d_freq(img, the_filter):
height = len(img)
width = len(img[0])
adjust_height = calculate_pow2(height)
adjust_width = calculate_pow2(width)
FFT_filter = np.zeros((adjust_height,adjust_width), np.int32)
adjust_img = np.zeros((adjust_height,adjust_width), np.int32)
for x in xrange(height):
for y in xrange(width):
adjust_img[x, y] = img[x, y]
for x in xrange(len(the_filter)):
for y in xrange(len(the_filter[0])):
FFT_filter[x, y] = the_filter[x, y]
FFT_filter = FFT.centralize(FFT_filter)
adjust_img = FFT.centralize(adjust_img)
FFT_filter = FFT.fft2d(FFT_filter, 1)
FFT_image = FFT.fft2d(adjust_img, 1)
print "done Fourier transform"
Result = FFT_image * FFT_filter
print "done frequecy processing"
Result = FFT.fft2d(Result, -1).real
print "done Inverse Fourier transform"
Result_img = np.zeros((height, width), np.float64)
Result = diolog_transform(Result)
for x in xrange(height):
for y in xrange(width):
Result_img[x, y] = Result[x, y]
Result_img = centralize(Result_img)
return Result_img
def execute_cb( self, *args ):
layer = gview.app.sel_manager.get_active_layer()
if not layer_is_raster(layer):
gvutils.error("Please select a raster layer.");
return
ds = layer.get_parent().get_dataset()
data = gdalnumeric.DatasetReadAsArray(ds)
if self.switch_forward.get_active():
data_tr = FFT.fft2d(data)
else:
data_tr = FFT.inverse_fft2d(data)
array_name = gdalnumeric.GetArrayFilename(data_tr)
if self.switch_new_view.get_active():
gview.app.new_view()
gview.app.file_open_by_name(array_name)
def execute_cb(self, *args):
layer = gview.app.sel_manager.get_active_layer()
if not layer_is_raster(layer):
gvutils.error("Please select a raster layer.")
return
ds = layer.get_parent().get_dataset()
data = gdalnumeric.DatasetReadAsArray(ds)
if self.switch_forward.get_active():
data_tr = FFT.fft2d(data)
else:
data_tr = FFT.inverse_fft2d(data)
array_name = gdalnumeric.GetArrayFilename(data_tr)
if self.switch_new_view.get_active():
gview.app.new_view()
gview.app.file_open_by_name(array_name)
else:
Usage()
i = i + 1
if infile is None:
Usage()
if outfile is None:
Usage()
if type == None:
type = gdal.GDT_CFloat32
indataset = gdal.Open(infile, gdal.GA_ReadOnly)
out_driver = gdal.GetDriverByName(format)
outdataset = out_driver.Create(outfile, indataset.RasterXSize,
indataset.RasterYSize, indataset.RasterCount,
type)
for iBand in range(1, indataset.RasterCount + 1):
inband = indataset.GetRasterBand(iBand)
outband = outdataset.GetRasterBand(iBand)
data = inband.ReadAsArray(0, 0)
if transformation == 'forward':
data_tr = FFT.fft2d(data)
else:
data_tr = FFT.inverse_fft2d(data)
outband.WriteArray(data_tr)
def main(argv):
infile = None
outfile = None
frmt = 'GTiff'
typ = None
transformation = 'forward'
# Parse command line arguments.
i = 1
while i < len(argv):
arg = argv[i]
if arg == '-inv':
transformation = 'inverse'
if typ is None:
typ = gdal.GDT_Float32
elif arg == '-of':
i = i + 1
frmt = argv[i]
elif arg == '-ot':
i = i + 1
typ = ParseType(argv[i])
# set_type = 'yes'
elif infile is None:
infile = arg
elif outfile is None:
outfile = arg
else:
return Usage()
i = i + 1
if infile is None:
return Usage()
if outfile is None:
return Usage()
if typ is None:
typ = gdal.GDT_CFloat32
indataset = gdal.Open(infile, gdal.GA_ReadOnly)
out_driver = gdal.GetDriverByName(frmt)
outdataset = out_driver.Create(outfile, indataset.RasterXSize,
indataset.RasterYSize,
indataset.RasterCount, typ)
for iBand in range(1, indataset.RasterCount + 1):
inband = indataset.GetRasterBand(iBand)
outband = outdataset.GetRasterBand(iBand)
data = inband.ReadAsArray(0, 0)
if transformation == 'forward':
data_tr = FFT.fft2d(data)
else:
data_tr = FFT.inverse_fft2d(data)
outband.WriteArray(data_tr)
return 0
else:
Usage()
i = i + 1
if infile is None:
Usage()
if outfile is None:
Usage()
if type == None:
type = GDT_CFloat32
indataset = gdal.Open( infile, GA_ReadOnly )
out_driver = gdal.GetDriverByName(format)
outdataset = out_driver.Create(outfile, indataset.RasterXSize, indataset.RasterYSize, indataset.RasterCount, type)
for iBand in range(1, indataset.RasterCount + 1):
inband = indataset.GetRasterBand(iBand)
outband = outdataset.GetRasterBand(iBand)
data = inband.ReadAsArray(0, 0)
if transformation == 'forward':
data_tr = FFT.fft2d(data)
else:
data_tr = FFT.inverse_fft2d(data)
outband.WriteArray(data_tr)
def espectrofourier(img, libdir, ts, frings, fsectors, raios, angulos):
M,N = img.shape[0], img.shape[1]
d = min(M,N) # dimensao menor das imagens
ci, cj = M/2, N/2 # coordenadas do centro da img
rmax = d/2 # raio maximo da circ. que cabe na img
slices = []
desc = []
F = FFT.fft2d(img)
Fview = iadftview(F) # passar threshold aqui!
# threshold realizado com dinamica - valores de extinção:
#iawrite(Fview,'../tmp/fv.pgm')
#os.system('%s./extinction htop ../tmp/fv.pgm ../tmp/result.pgm' % (libdir))
#Fvd = iaread('../tmp/result.pgm')
#iashow(Fv)
#Fvd = (Fvd>1)*Fview
#iawrite(Fv,'../tmp/fv2.pgm')
#iashow(dimask)
#nz = nonzeroarea(dimask)
#print nz
#print "soma de tudo = "+str(nz.sum())
#ts = nz.sum()/nz.shape[0]
#print ts
#iashow(Fview)
# threshold simples:
#Fv = Fview
Fv = Fview * (Fview > ts)
#iashow(Fv)
#a = raw_input()
# em seguida é cortado o lado direito da imagem do espectro
if M>N: # se a imagem for mais alta que larga:
rightside = iaroi(Fv,[abs(ci-rmax),cj],[(ci+rmax-1),N])
#rightsided = iaroi(Fvd,[abs(ci-rmax),cj],[(ci+rmax-1),N])
elif N>M: # se for mais larga que alta:
rightside = iaroi(Fv,[0,cj],[M-1,(cj+rmax-1)])
#rightsided = iaroi(Fvd,[0,cj],[M-1,(cj+rmax-1)])
else: # se for quadrada:
rightside = iaroi(Fv,[0,cj],[M,N])
#rightsided = iaroi(Fvd,[0,cj],[M,N])
# carrega uma lista com templates para setores
for ang in angulos:
file = "../imgs/templates/%d.pbm" % (ang)
slices.append(iaread(file))
for r in raios:
circ = iacircle([d,d],r,[rmax,rmax])
circ = iaroi(circ,[0,rmax],[d,d]) # recorta lado direito do circulo
circ2 = iacircle([d,d],50,[rmax,rmax])
circ2 = iaroi(circ2,[0,rmax],[d,d])
ring = circ-circ2 # diminui circulo maior pelo menor para formar anel
if frings == "yes":
# quantidade de pixels do anel
qtd = ring.sum()
# aplica a mascara para o anel inteiro
rough = rightside*ring
#roughd = rightsided*ring
# calcular só p/ valores não-zero
#nz = nonzeroarea(roughd)
mean = rough.sum()/float(qtd)
std = sqrt(((rough - mean)**2).sum()/qtd)
desc += [std,mean]
#desc += [nz.sum()]
# media e desvio padrão dos setores
if fsectors == "yes":
for slice in slices:
sector = slice*ring
qtd = sector.sum()
area = rightside*sector
#aread = rightsided*sector
#nz = nonzeroarea(aread)
mean = area.sum()/float(qtd)
std = sqrt(((area - mean)**2).sum()/qtd)
#
desc += [std,mean]
#desc += [nz.sum()]
return desc