def fromPCMString(self, buffer):
from numarray import fromstring, Int16, Float32
#print("buffer", len(buffer))
b = fromstring(buffer, Int16)
b = b.astype(Float32)
b = ( b + 32768/2 ) / self.SCALE
return b
def detect(self, sample):
""" Test for DTMF in a sample. Returns either a string with the
DTMF digit, or the empty string if none is present. Accepts
a string of length 640, representing 320 16 bit signed PCM
samples (host endianness). This is _two_ samples at normal
shtoom sampling rates.
"""
import numarray
from sets import Set
a = numarray.fromstring(sample, numarray.Int16)
if len(a) != 320:
raise ValueError("samples length %d != 320 (40ms)"%len(a))
peaks = self.getpeaks(a)
matched = Set()
for p in peaks:
m = self.freqmatch.get(p)
if m is None:
if p-1 in self.freqmatch:
m = self.freqmatch.get(p-1)
elif p+1 in self.freqmatch:
m = self.freqmatch.get(p+1)
matched.add(m)
# If we got a None, that means there was a significant component
# that wasn't a DTMF frequency.
if None in matched:
return ''
else:
m = list(matched)
m.sort()
return self.dtmf.get(tuple(m), '')
def detect(self, sample):
""" Test for DTMF in a sample. Returns either a string with the
DTMF digit, or the empty string if none is present. Accepts
a string of length 640, representing 320 16 bit signed PCM
samples (host endianness). This is _two_ samples at normal
shtoom sampling rates.
"""
import numarray
from sets import Set
a = numarray.fromstring(sample, numarray.Int16)
if len(a) != 320:
raise ValueError("samples length %d != 320 (40ms)"%len(a))
peaks = self.getpeaks(a)
matched = Set()
for p in peaks:
m = self.freqmatch.get(p)
if m is None:
if p-1 in self.freqmatch:
m = self.freqmatch.get(p-1)
elif p+1 in self.freqmatch:
m = self.freqmatch.get(p+1)
matched.add(m)
# If we got a None, that means there was a significant component
# that wasn't a DTMF frequency.
if None in matched:
return ''
else:
m = list(matched)
m.sort()
return self.dtmf.get(tuple(m), '')
def fromPCMString(self, buffer):
from numarray import fromstring, Int16, Float32
#print "buffer", len(buffer)
b = fromstring(buffer, Int16)
b = b.astype(Float32)
b = ( b + 32768/2 ) / self.SCALE
return b
def __xmlEndTagFound(self, in_name):
if in_name == "adcdata":
# ADC_Result
if self.__filetype == ResultReader.ADCDATA_TYPE:
if self.try_base64:
tmp_string=base64.standard_b64decode(self.adc_result_trailing_chars)
self.adc_result_trailing_chars=""
tmp=numarray.fromstring(tmp_string, numarray.Int16,(len(tmp_string)/2))
del tmp_string
self.result.x[self.adc_result_sample_counter:]=(numarray.arange(tmp.size()/2)+self.adc_result_sample_counter)/self.result.get_sampling_rate()
self.result.y[0][self.adc_result_sample_counter:]=tmp[::2]
self.result.y[1][self.adc_result_sample_counter:]=tmp[1::2]
self.adc_result_sample_counter+=tmp.size()/2
else:
if self.adc_result_trailing_chars!="":
self.__xmlCharacterDataFound(" ")
return
elif in_name == "result":
pass
# Error_Result
elif self.__filetype == ResultReader.ERROR_TYPE:
pass
# Temp_Result
elif self.__filetype == ResultReader.TEMP_TYPE:
pass
# Config_Result
elif self.__filetype == ResultReader.CONFIG_TYPE:
pass
def write(self, in_array):
""" prepare at least 'items' samples.
"""
fragment, self.state = audioop.ratecv(in_array,
2,
CHANNELS,
self.in_hz,
self.out_hz,
self._state)
ar = numarray.fromstring(fragment, type=numarray.Int16)
ringbuffer.RingBuffer.write(self, ar)
def __xmlEndTagFound(self, in_name):
if in_name == "adcdata":
# ADC_Result
if self.__filetype == ResultReader.ADCDATA_TYPE:
if self.try_base64:
tmp_string=base64.standard_b64decode(self.adc_result_trailing_chars)
self.adc_result_trailing_chars = ""
tmp=numarray.fromstring(tmp_string, numarray.Int16)
del tmp_string
expected_samples=self.result.index[-1][1]-self.result.index[-1][0]+1
if len(tmp)/2 != expected_samples:
self.__gui.new_log_message("ResultReader Warning: size missmatch %d!=%d samples"%(len(tmp)/2, expected_samples), "DH")
size_used=min(expected_samples,len(tmp)/2)
self.result.x[self.adc_result_sample_counter:]=(numarray.arange(expected_samples)+self.adc_result_sample_counter)/self.result.get_sampling_rate()
self.result.y[0][self.adc_result_sample_counter:self.adc_result_sample_counter+size_used]=tmp[0:size_used*2:2]
self.result.y[1][self.adc_result_sample_counter:self.adc_result_sample_counter+size_used]=tmp[1:size_used*2:2]
del tmp
if size_used<expected_samples:
# zero padding...
self.result.y[0][self.adc_result_sample_counter+size_used:self.adc_result_sample_counter+samples_expected]=numarray.zeros((samples_expected-size_used,))
self.result.y[1][self.adc_result_sample_counter+size_used:self.adc_result_sample_counter+samples_expected]=numarray.zeros((samples_expected-size_used,))
self.adc_result_sample_counter+=expected_samples
else:
if self.adc_result_trailing_chars!="":
self.__xmlCharacterDataFound(" ")
return
elif in_name == "result":
self.__gui.new_log_message("Result Reader: Successfully parsed and saved %s" % os.path.join(self.path, self.filename % self.files_read), "DH")
# Error_Result
elif self.__filetype == ResultReader.ERROR_TYPE:
self.__gui.new_log_message("Result Reader: Error Result parsed! (%s)" % os.path.join(self.path, self.filename % self.files_read), "DH")
# Temp_Result
elif self.__filetype == ResultReader.TEMP_TYPE:
self.__gui.new_log_message("ResultReader: Temperature Result parsed!", "DH")
# Config_Result
elif self.__filetype == ResultReader.CONFIG_TYPE:
self.__gui.new_log_message("ResultReader: Config Result parsed!", "DH")
self.result_queue.append(self.result)
self.result=None
def calc_array(self): wave_object = self._get_wave() wave_object.setpos(0) self.frames = wave_object.readframes(self.number_of_frames) wave_object.close() w = wave_object.getsampwidth() if w == 2: #16 bits arr = numarray.fromstring(self.frames, numarray.Int16) #http://www.pythonapocrypha.com/Chapter32/Chapter32.shtml elif w == 1: #8 bits arr = numarray.array(self.frames, typecode = numarray.UnsignedInt8, savespace = 1) else: print "Not handled: samples are", w * 8, "bit" raise Error self.array = arr
def sharpenImage(self, image, alpha):
# FIXME: support RGB images
rep = image.representations()[0]
height = rep.pixelsHigh()
width = rep.pixelsWide()
samplesPerPixel = rep.samplesPerPixel()
bytesPerRow = rep.bytesPerRow()
bitmapData = rep.bitmapData()
# Convert the image to a numarray array
size = (width, height)
imgData = numarray.fromstring(datastring=bitmapData,
type=numarray.UInt8,
shape=size)
# Convert to double and do a FFT2D
G = numarray.fft.fft2d(imgData.astype(numarray.Float64))
# Compute the power spectrum
SG = (numarray.abs(G)**2.) / (width * height)
# KD = 0.14711
D1 = (numarray.abs(SG)**(alpha / 2.))
# Find the normalization constant KD by requiring ||D1|| <= 1
firstPass = numarray.add.reduce(D1**2)
norm = math.sqrt(numarray.add.reduce(firstPass))
KD = 1. / norm
D1 *= KD
# Divide the fft by its power spectrum and go back to real space.
F1 = G / D1
f2 = numarray.fft.inverse_fft2d(F1).real
# Bring f2 into the [0, 255] range
min = f2.min()
if (min != 0):
f2 -= min
max = f2.max()
if (max != 0):
f2 /= max
f2 *= 255.
return(f2.astype(numarray.UInt8))
def __call__(self,lon,lat,inverse=False):
"""
Calling a Proj class instance with the arguments lon, lat will
convert lon/lat (in degrees) to x/y native map projection
coordinates (in meters). If optional keyword 'inverse' is
True (default is False), the inverse transformation from x/y
to lon/lat is performed.
Example usage:
>>> from proj import Proj
>>> params = {}
>>> params['proj'] = 'lcc' # lambert conformal conic
>>> params['lat_1'] = 30. # standard parallel 1
>>> params['lat_2'] = 50. # standard parallel 2
>>> params['lon_0'] = -96 # central meridian
>>> map = Proj(params)
>>> x,y = map(-107,40) # find x,y coords of lon=-107,lat=40
>>> print x,y
-92272.1004461 477477.890988
lon,lat can be either scalar floats or numarray arrays.
"""
npts = 1
# if inputs are numarray arrays, get shape and total number of pts.
try:
shapein = lon.shape
npts = reduce(lambda x, y: x*y, shapein)
lontypein = lon.typecode()
lattypein = lat.typecode()
except:
shapein = False
# make sure inputs have same shape.
if shapein and lat.shape != shapein:
raise ValueError, 'lon, lat must be scalars or numarray arrays with the same shape'
# make a rank 1 array with x/y (or lon/lat) pairs.
xy = numarray.zeros(2*npts,'d')
xy[::2] = numarray.ravel(lon)
xy[1::2] = numarray.ravel(lat)
# convert degrees to radians, meters to cm.
if not inverse:
xy = _dg2rad*xy
else:
xy = 10.*xy
# write input data to binary file.
xyout = array.array('d')
xyout.fromlist(xy.tolist())
# set up cmd string, scale factor for output.
if inverse:
cmd = self.projcmd+' -I'
scale = _rad2dg
else:
cmd = self.projcmd
scale = 0.1
# use pipes for input and output if amount of
# data less than default buffer size (usually 8192).
# otherwise use pipe for input, tempfile for output
if 2*npts*8 > 8192:
fd, fn=tempfile.mkstemp(); os.close(fd); stdout=open(fn,'rb')
cmd = cmd+' > '+fn
stdin=os.popen(cmd,'w')
xyout.tofile(stdin)
stdin.close()
outxy = scale*numarray.fromstring(stdout.read(),'d')
stdout.close()
os.remove(fn)
else:
stdin,stdout=os.popen2(cmd,mode='b')
xyout.tofile(stdin)
stdin.close()
outxy = scale*numarray.fromstring(stdout.read(),'d')
stdout.close()
# return numarray arrays or scalars, depending on type of input.
if shapein:
outx = numarray.reshape(outxy[::2],shapein).astype(lontypein)
outy = numarray.reshape(outxy[1::2],shapein).astype(lattypein)
else:
outx = outxy[0]; outy = outxy[1]
return outx,outy
def __call__(self, lon, lat, inverse=False):
"""
Calling a Proj class instance with the arguments lon, lat will
convert lon/lat (in degrees) to x/y native map projection
coordinates (in meters). If optional keyword 'inverse' is
True (default is False), the inverse transformation from x/y
to lon/lat is performed.
Example usage:
>>> from proj import Proj
>>> params = {}
>>> params['proj'] = 'lcc' # lambert conformal conic
>>> params['lat_1'] = 30. # standard parallel 1
>>> params['lat_2'] = 50. # standard parallel 2
>>> params['lon_0'] = -96 # central meridian
>>> map = Proj(params)
>>> x,y = map(-107,40) # find x,y coords of lon=-107,lat=40
>>> print x,y
-92272.1004461 477477.890988
lon,lat can be either scalar floats or numarray arrays.
"""
npts = 1
# if inputs are numarray arrays, get shape and total number of pts.
try:
shapein = lon.shape
npts = reduce(lambda x, y: x * y, shapein)
lontypein = lon.typecode()
lattypein = lat.typecode()
except:
shapein = False
# make sure inputs have same shape.
if shapein and lat.shape != shapein:
raise ValueError, 'lon, lat must be scalars or numarray arrays with the same shape'
# make a rank 1 array with x/y (or lon/lat) pairs.
xy = numarray.zeros(2 * npts, 'd')
xy[::2] = numarray.ravel(lon)
xy[1::2] = numarray.ravel(lat)
# convert degrees to radians, meters to cm.
if not inverse:
xy = _dg2rad * xy
else:
xy = 10. * xy
# write input data to binary file.
xyout = array.array('d')
xyout.fromlist(xy.tolist())
# set up cmd string, scale factor for output.
if inverse:
cmd = self.projcmd + ' -I'
scale = _rad2dg
else:
cmd = self.projcmd
scale = 0.1
# use pipes for input and output if amount of
# data less than default buffer size (usually 8192).
# otherwise use pipe for input, tempfile for output
if 2 * npts * 8 > 8192:
fd, fn = tempfile.mkstemp()
os.close(fd)
stdout = open(fn, 'rb')
cmd = cmd + ' > ' + fn
stdin = os.popen(cmd, 'w')
xyout.tofile(stdin)
stdin.close()
outxy = scale * numarray.fromstring(stdout.read(), 'd')
stdout.close()
os.remove(fn)
else:
stdin, stdout = os.popen2(cmd, mode='b')
xyout.tofile(stdin)
stdin.close()
outxy = scale * numarray.fromstring(stdout.read(), 'd')
stdout.close()
# return numarray arrays or scalars, depending on type of input.
if shapein:
outx = numarray.reshape(outxy[::2], shapein).astype(lontypein)
outy = numarray.reshape(outxy[1::2], shapein).astype(lattypein)
else:
outx = outxy[0]
outy = outxy[1]
return outx, outy
def main():
if len(sys.argv) > 1:
im_name = sys.argv[1]
else:
import tkFileDialog, Tkinter
im_name = tkFileDialog.askopenfilename(defaultextension=".png",
filetypes = (
(_("Depth images"), ".gif .png .jpg"),
(_("All files"), "*")))
if not im_name: raise SystemExit
Tkinter._default_root.destroy()
Tkinter._default_root = None
im = Image.open(im_name)
size = im.size
im = im.convert("L") #grayscale
w, h = im.size
nim = numarray.fromstring(im.tostring(), 'UInt8', (h, w)).astype('Float32')
options = ui(im, nim, im_name)
step = options['pixelstep']
depth = options['depth']
if options['normalize']:
a = nim.min()
b = nim.max()
if a != b:
nim = (nim - a) / (b-a)
else:
nim = nim / 255.0
maker = tool_makers[options['tool_type']]
tool_diameter = options['tool_diameter']
pixel_size = options['pixel_size']
tool = make_tool_shape(maker, tool_diameter, pixel_size)
if options['expand']:
if options['expand'] == 1: pixel = 1
else: pixel = 0
tw, th = tool.shape
w1 = w + 2*tw
h1 = h + 2*th
nim1 = numarray.zeros((w1, h1), 'Float32') + pixel
nim1[tw:tw+w, th:th+w] = nim
nim = nim1
w, h = w1, h1
nim = nim * depth
if options['invert']:
nim = -nim
else:
nim = nim - depth
rows = options['pattern'] != 1
columns = options['pattern'] != 0
columns_first = options['pattern'] == 3
spindle_speed = options['spindle_speed']
if rows: convert_rows = convert_makers[options['converter']]()
else: convert_rows = None
if columns: convert_cols = convert_makers[options['converter']]()
else: convert_cols = None
if options['bounded'] and rows and columns:
slope = tan(options['contact_angle'] * pi / 180)
if columns_first:
convert_rows = Reduce_Scan_Lace(convert_rows, slope, step+1)
else:
convert_cols = Reduce_Scan_Lace(convert_cols, slope, step+1)
if options['bounded'] > 1:
if columns_first:
convert_cols = Reduce_Scan_Lace(convert_cols, slope, step+1)
else:
convert_rows = Reduce_Scan_Lace(convert_rows, slope, step+1)
units = unitcodes[options['units']]
convert(nim, units, tool, pixel_size, step,
options['safety_height'], options['tolerance'], options['feed_rate'],
convert_rows, convert_cols, columns_first, ArcEntryCut(options['plunge_feed_rate'], .125),
spindle_speed, options['roughing_offset'], options['roughing_depth'], options['feed_rate'])
def main():
if len(sys.argv) > 1:
im_name = sys.argv[1]
else:
import tkFileDialog, Tkinter
im_name = tkFileDialog.askopenfilename(defaultextension=".png",
filetypes=((_("Depth images"),
".gif .png .jpg"),
(_("All files"),
"*")))
if not im_name: raise SystemExit
Tkinter._default_root.destroy()
Tkinter._default_root = None
im = Image.open(im_name)
size = im.size
im = im.convert("L") #grayscale
w, h = im.size
nim = numarray.fromstring(im.tostring(), 'UInt8', (h, w)).astype('Float32')
options = ui(im, nim, im_name)
step = options['pixelstep']
depth = options['depth']
if options['normalize']:
a = nim.min()
b = nim.max()
if a != b:
nim = (nim - a) / (b - a)
else:
nim = nim / 255.0
maker = tool_makers[options['tool_type']]
tool_diameter = options['tool_diameter']
pixel_size = options['pixel_size']
tool = make_tool_shape(maker, tool_diameter, pixel_size)
if options['expand']:
if options['expand'] == 1: pixel = 1
else: pixel = 0
tw, th = tool.shape
w1 = w + 2 * tw
h1 = h + 2 * th
nim1 = numarray.zeros((w1, h1), 'Float32') + pixel
nim1[tw:tw + w, th:th + w] = nim
nim = nim1
w, h = w1, h1
nim = nim * depth
if options['invert']:
nim = -nim
else:
nim = nim - depth
rows = options['pattern'] != 1
columns = options['pattern'] != 0
columns_first = options['pattern'] == 3
spindle_speed = options['spindle_speed']
if rows: convert_rows = convert_makers[options['converter']]()
else: convert_rows = None
if columns: convert_cols = convert_makers[options['converter']]()
else: convert_cols = None
if options['bounded'] and rows and columns:
slope = tan(options['contact_angle'] * pi / 180)
if columns_first:
convert_rows = Reduce_Scan_Lace(convert_rows, slope, step + 1)
else:
convert_cols = Reduce_Scan_Lace(convert_cols, slope, step + 1)
if options['bounded'] > 1:
if columns_first:
convert_cols = Reduce_Scan_Lace(convert_cols, slope, step + 1)
else:
convert_rows = Reduce_Scan_Lace(convert_rows, slope, step + 1)
units = unitcodes[options['units']]
convert(nim, units, tool, pixel_size, step, options['safety_height'],
options['tolerance'], options['feed_rate'], convert_rows,
convert_cols, columns_first,
ArcEntryCut(options['plunge_feed_rate'],
.125), spindle_speed, options['roughing_offset'],
options['roughing_depth'], options['feed_rate'])
