def process_import(fname, dct=image_import_dict):
imglist = imageio.list_of_frames(fname)
fsett = dct[str(len(imglist))]
pwa, pwoa, df1, df2 = fsett.pwa, fsett.pwoa, fsett.df1, fsett.df2
# split the individual frames if kinetics mode is specified
if fsett.kinetics:
templist = []
for frame in imglist:
for i in xrange(fsett.strips_per_frame):
if fsett.startat == 'BR':
if fsett.orientation == 'V':
size = imglist[pwa].shape[0]
else:
size = imglist[pwa].shape[1]
idx = slice(size - (i+1)*fsett.lineshift,
size - i*fsett.lineshift)
else:
idx = slice(i*fsett.lineshift, (i+1)*fsett.lineshift)
if fsett.orientation == 'V':
templist.append(frame[idx, :])
else:
templist.append(frame[:, idx])
imglist = templist
rawframes = np.dstack(imglist)
if not fsett.usetext:
transimg, odimg = default_calc_transimg(imglist[pwa], imglist[pwoa],
imglist[df1], imglist[df2])
else:
funcstr = _construct_custom_func(fsett.text)
exec(funcstr) # create the function
transimg = imgfunc(imglist[pwa], imglist[pwoa],
imglist[df1], imglist[df2], imglist)
odimg = imageprocess.trans2od(transimg)
return rawframes, transimg, odimg
def process_import(fname, dct=image_import_dict):
imglist = imageio.list_of_frames(fname)
fsett = dct[str(len(imglist))]
pwa, pwoa, df1, df2 = fsett.pwa, fsett.pwoa, fsett.df1, fsett.df2
# split the individual frames if kinetics mode is specified
if fsett.kinetics:
templist = []
for frame in imglist:
for i in xrange(fsett.strips_per_frame):
if fsett.startat == 'BR':
if fsett.orientation == 'V':
size = imglist[pwa].shape[0]
else:
size = imglist[pwa].shape[1]
idx = slice(size - (i + 1) * fsett.lineshift,
size - i * fsett.lineshift)
else:
idx = slice(i * fsett.lineshift, (i + 1) * fsett.lineshift)
if fsett.orientation == 'V':
templist.append(frame[idx, :])
else:
templist.append(frame[:, idx])
imglist = templist
rawframes = np.dstack(imglist)
if not fsett.usetext:
transimg, odimg = default_calc_transimg(imglist[pwa], imglist[pwoa],
imglist[df1], imglist[df2])
else:
funcstr = _construct_custom_func(fsett.text)
exec(funcstr) # create the function
transimg = imgfunc(imglist[pwa], imglist[pwoa], imglist[df1],
imglist[df2], imglist)
odimg = imageprocess.trans2od(transimg)
return rawframes, transimg, odimg
def add_noise(img, ampl=0.05, noisetype='random', fringeargs=None):
"""Noise is added to an image.
**Inputs**
* img: 2d array, containing image data
* ampl: float, amplitude of the noise
* noisetype: string, value can be one of
* 'random', adds unbiased white noise
* 'linear_x', adds a linear gradient along x from 0 to ampl
* 'linear_y', adds a linear gradient along y from 0 to ampl
* 'fringes', adds fringes with parameters fringeargs
* fringeargs: sequence, containing four values
* angle: float, angle of fringes in radians with respect to the x-axis
* freq: float, frequency of the fringes in pixels^{-1}
* pos: tuple, central position of the fringes with respect to the CoM
* size: float, size of the Gaussian envelope of the fringes
**Outputs**
* img: 2d array, the input image with noise added to it
"""
noisetypes = ['random', 'linear_x', 'linear_y', 'fringes']
if not noisetype in noisetypes:
raise ValueError, \
"""noisetype is one of: %s"""%noisetypes
if noisetype=='random':
img = img + (np.random.random_sample(img.shape)-0.5)*ampl
elif noisetype=='linear_x':
noise = np.ones(img.shape).transpose()*np.arange(img.shape[0])\
/img.shape[0]*ampl
img = img + noise.transpose()
elif noisetype=='linear_y':
noise = np.ones(img.shape)*np.arange(img.shape[1])/img.shape[1]*ampl
img = img + noise
elif noisetype=='fringes':
if not len(fringeargs)==4:
print "fringeargs needs to contain four values: angle, freq, pos, size"
angle, freq, pos, size = fringeargs
xx, yy = np.mgrid[0:img.shape[0], 0:img.shape[1]]
# center of mass coordinates
odimg = trans2od(img)
com = center_of_mass(odimg)
xx0 = xx - com[0] - pos[0]
yy0 = yy - com[1] - pos[1]
yy0 = np.where(yy0==0, 1e-6, yy0)
rr = np.sqrt(xx0**2 + yy0**2)
# coordinate projection along fringe axis
rangle = np.arctan(xx0.astype(float)/yy0)
rangle = np.where(yy0>0, rangle, rangle + np.pi)
rfringe = rr*np.cos(angle - rangle)
noise = fitfuncs.gaussian(rr, ampl, size) * np.sin(2*np.pi*rfringe*freq)
img = img + noise
return img
def add_noise(img, ampl=0.05, noisetype='random', fringeargs=None):
"""Noise is added to an image.
**Inputs**
* img: 2d array, containing image data
* ampl: float, amplitude of the noise
* noisetype: string, value can be one of
* 'random', adds unbiased white noise
* 'linear_x', adds a linear gradient along x from 0 to ampl
* 'linear_y', adds a linear gradient along y from 0 to ampl
* 'fringes', adds fringes with parameters fringeargs
* fringeargs: sequence, containing four values
* angle: float, angle of fringes in radians with respect to the x-axis
* freq: float, frequency of the fringes in pixels^{-1}
* pos: tuple, central position of the fringes with respect to the CoM
* size: float, size of the Gaussian envelope of the fringes
**Outputs**
* img: 2d array, the input image with noise added to it
"""
noisetypes = ['random', 'linear_x', 'linear_y', 'fringes']
if not noisetype in noisetypes:
raise ValueError, \
"""noisetype is one of: %s"""%noisetypes
if noisetype == 'random':
img = img + (np.random.random_sample(img.shape) - 0.5) * ampl
elif noisetype == 'linear_x':
noise = np.ones(img.shape).transpose()*np.arange(img.shape[0])\
/img.shape[0]*ampl
img = img + noise.transpose()
elif noisetype == 'linear_y':
noise = np.ones(img.shape) * np.arange(
img.shape[1]) / img.shape[1] * ampl
img = img + noise
elif noisetype == 'fringes':
if not len(fringeargs) == 4:
print "fringeargs needs to contain four values: angle, freq, pos, size"
angle, freq, pos, size = fringeargs
xx, yy = np.mgrid[0:img.shape[0], 0:img.shape[1]]
# center of mass coordinates
odimg = trans2od(img)
com = center_of_mass(odimg)
xx0 = xx - com[0] - pos[0]
yy0 = yy - com[1] - pos[1]
yy0 = np.where(yy0 == 0, 1e-6, yy0)
rr = np.sqrt(xx0**2 + yy0**2)
# coordinate projection along fringe axis
rangle = np.arctan(xx0.astype(float) / yy0)
rangle = np.where(yy0 > 0, rangle, rangle + np.pi)
rfringe = rr * np.cos(angle - rangle)
noise = fitfuncs.gaussian(rr, ampl, size) * np.sin(
2 * np.pi * rfringe * freq)
img = img + noise
return img
