forked from leewalsh/square-tracking
/
orientation.py
405 lines (366 loc) · 14.8 KB
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orientation.py
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# coding: utf-8
from __future__ import division
from itertools import izip
from math import sqrt
import numpy as np
from PIL import Image as Im
import matplotlib.pyplot as pl
import matplotlib.cm as cm
if __name__=='__main__':
from socket import gethostname
hostname = gethostname()
if 'foppl' in hostname:
computer = 'foppl'
locdir = '/home/lawalsh/Granular/Squares/orientation/'
elif 'rock' in hostname:
computer = 'rock'
locdir = '/Users/leewalsh/Physics/Squares/orientation/'
else:
print "computer not defined"
print "where are you working?"
pi = np.pi
twopi = 2*pi
def field_rename(a, old, new):
a.dtype.names = [ fn if fn != old else new for fn in a.dtype.names ]
def get_fft(ifile=None,location=None):
""" get the fft of an image
FFT information from:
http://stackoverflow.com/questions/2652415/fft-and-array-to-image-image-to-array-conversion
"""
if ifile is None:
ifile= "n20_bw_dots/n20_b_w_dots_0010.tif"
if location is None:
x = 424.66; y = 155.56; area = 125
#x = 302.95; y = 221.87; area = 145
#x = 386.27; y = 263.62; area = 141
#x = 35.39; y = 305.92; area = 154
location = x,y
wdth = int(24 * sqrt(2))
hght = wdth
cropbox = map(int,(x - wdth/2., y - hght/2.,\
x + wdth/2., y + hght/2.))
i = Im.open(ifile)
i = i.crop(cropbox)
i.show()
a = np.asarray(i)
aa = np.gradient(a)
b = np.fft.fft2(a)
j = Im.fromarray(abs(b))
ii = Im.fromarray(aa)
if do_plots:
ii.show()
return b
def get_orientation(b):
""" get orientation from `b`, the fft of an image """
p = []
for (mi,m) in enumerate(b):
for (ni, n) in enumerate(m):
ang = np.arctan2(mi - hght/2, ni - wdth/2)
p.append([ang,abs(n)])
p = np.asarray(p)
p[:,0] = p[:,0] + pi
slices = 45
slicewidth = 2*pi/slices
s = []
for sl in range(slices):
si = np.nonzero(abs(p[:,0] - sl*slicewidth) < slicewidth)
sm = np.average(p[si,1])
s.append([sl*slicewidth,sm])
s = np.asarray(s)
if do_plots and computer is 'rock':
pl.figure()
#pl.plot(p[:,0],p[:,1],'.',label='p')
#pl.plot(s[:,0],s[:,1],'o',label='s')
pl.plot(p[:,0]%(pi/2),p[:,1],'.',label='p')
pl.plot(s[:,0]%(pi/2),s[:,1],'o',label='s')
pl.legend()
pl.show()
elif do_plots and computer is 'foppl':
print "can't plot on foppl"
return s, p
def find_corner(particle, corners, tree=None,
nc=1, rc=11, drc=0, slr=False, do_average=True):
""" find_corner(particle, corners, **kwargs)
looks in the given frame for the corner-marking dot closest to (and in
appropriate range of) the particle
arguments:
particle - is particle position as [x,y] array of shape (2,)
corners - is shape (N,2) array of positions of corner dots
as [x,y] pairs
tree - a KDTree of the `corners`, if available
nc - number of corner dots
rc - is the expected distance to corner from particle position
drc - delta r_c is the tolerance on rc
slr - whether to use slr resolution
do_average - whether to average the nc corners to one value for return
returns:
pcorner - (mean) position(s) (x,y) of corner that belongs to particle
porient - (mean) particle orientation(s) (% 2pi)
cdisp - (mean) vector(s) (x,y) from particle center to corner(s)
"""
if drc <= 0:
drc = sqrt(rc)
if slr:
rc = 43 # 56 ?
drc = 10
# displacements from center to corners
if tree:
# if kdtree is available, only consider nearby corners
icnear = tree.query_ball_point(particle, rc + drc)
corners = corners[icnear]
cdisps = corners - particle
cdists = np.hypot(*cdisps.T)
cdiffs = np.abs(cdists - rc)
legal = cdiffs < drc
nfound = np.count_nonzero(legal)
if nfound == nc:
# good.
pass
elif nfound < nc:
# too few, skip.
return (None,)*3
elif nfound > nc:
# too many, keep only the nc closest to rc away
#legal[np.argsort(cdiffs)[nc:]] = False
# the following is marginally faster than the above:
legal[legal.nonzero()[0][np.argsort(cdiffs[legal])[nc:]]] = False
pcorner = corners[legal]
cdisp = cdisps[legal]
cdist = cdists[legal]
if do_average and nc > 1:
# average the angle by finding angle of mean vector displacement
# keep the corner displacements (as amplitude) and positions
meandisp = (cdisp/cdist[...,None]).mean(0)
porient = np.arctan2(*meandisp[::-1]) % twopi
else:
porient = np.arctan2(cdisp[...,1], cdisp[...,0]) % twopi
return pcorner, porient, cdist
#TODO: use p.map() to find corners in parallel
# try splitting by frame first, use views for each frame
# or just pass a tuple of (datum, cdata[f==f]) to get_angle()
def get_angle((datum, cdata)):
corner = find_corner(
np.asarray((datum['x'],datum['y'])),
np.column_stack((cdata['x'][cdata['f']==datum['f']],
cdata['y'][cdata['f']==datum['f']])))
dt = np.dtype([('corner',float,(2,)),('orient',float),('cdisp',float,(2,))])
return np.array([corner], dtype=dt)
def get_angles_map(data, cdata, nthreads=None):
""" get_angles(data, cdata, nthreads=None)
arguments:
data - data array with 'x' and 'y' fields for particle centers
cdata - data array wity 'x' and 'y' fields for corners
(these arrays need not have the same length,
but both must have 'f' field for the image frame)
nthreads - number of processing threads (cpu cores) to use
None uses all cores of machine (8 for foppl, 2 for rock)
returns:
odata - array with fields:
'orient' for orientation of particles
'corner' for particle corner (with 'x' and 'y' sub-fields)
(odata has the same shape as data)
"""
field_rename(data,'s','f')
field_rename(cdata,'s','f')
from multiprocessing import Pool
if nthreads is None or nthreads > 8:
nthreads = 8 if computer is 'foppl' else 2
elif nthreads > 2 and computer is 'rock':
nthreads = 2
print "on {}, using {} threads".format(computer,nthreads)
pool = Pool(nthreads)
datums = [ (datum,cdata[cdata['f']==datum['f']])
for datum in data ]
odatalist = pool.map(get_angle, datums)
odata = np.vstack(odatalist)
return odata
def get_angles_loop(pdata, cdata, pfsets, cfsets, cftrees, nc=3, rc=11, drc=0, do_average=True, verbose=False):
""" get_angles(pdata, cdata, pfsets, cfsets, cftrees, nc=3, rc=11, drc=0, do_average=True)
arguments:
pdata - data array with 'x' and 'y' fields for particle centers
cdata - data array wity 'x' and 'y' fields for corners
(these arrays need not have the same length,
but both must have 'f' field for the image frame)
pfsets - slices into pdata, by frame
cfsets - and for cdata
cftrees - dict of KDTrees for corners, by frame
nc - number of corner dots
rc - distance between center and corner dot
drc - tolerance for rc
do_average - whether to average the nc corners to one value for return
returns:
odata - array with fields:
'orient' for orientation of particles
'corner' for particle corner (with 'x' and 'y' sub-fields)
(odata has the same shape as data)
"""
import helpy
if do_average or nc == 1:
dt = [('corner',float,(nc,2)),
('orient',float),
('cdisp',float,(nc,))]
elif nc > 1:
dt = [('corner',float,(nc,2,)),
('orient',float,(nc,)),
('cdisp',float,(nc,))]
odata = np.full(len(pdata), np.nan, dtype=dt)
odata_corner = odata['corner']
odata_orient = odata['orient']
odata_cdisp = odata['cdisp']
full_ids = pdata['id']
id_ok = full_ids[0]==0 and np.all(np.diff(full_ids)==1)
print_freq = len(pfsets)//(100 if verbose>1 else 5) + 1
if verbose:
print 'seeking orientations'
for f in pfsets:
if verbose and not f % print_freq:
print f,
fpdata = pfsets[f]
fcdata = cfsets[f]
tree = cftrees[f]
positions = helpy.consecutive_fields_view(fpdata, 'xy')
cpositions = helpy.consecutive_fields_view(fcdata, 'xy')
frame_ids = helpy.quick_field_view(fpdata, 'id')
for frame_id, posi in izip(frame_ids, positions):
#TODO could probably be sped up by looping through the output of
# ptree.query_ball_tree(ctree)
corner, orient, disp = \
find_corner(posi, cpositions, tree=tree,
nc=nc, rc=rc, drc=drc, do_average=do_average)
if orient is None: continue
full_id = frame_id if id_ok else np.searchsorted(full_ids, frame_id)
odata_corner[full_id] = corner
odata_orient[full_id] = orient
odata_cdisp[full_id] = disp
if do_average or nc == 1:
mask = np.isfinite(odata['orient'])
elif nc > 1:
mask = np.all(np.isfinite(odata['orient']), axis=1)
return odata, mask
def plot_orient_hist(odata, figtitle=''):
if computer is not 'rock':
print 'computer must be on rock'
return False
pl.figure()
pl.hist(odata['orient'][np.isfinite(odata['orient'])], bins=90)
pl.title('orientation histogram' if figtitle is '' else figtitle)
return True
def plot_orient_quiver(data, odata, mask=None, imfile='', fps=1, savename='', figsize=None):
""" plot_orient_quiver(data, odata, mask=None, imfile='')
"""
import matplotlib.colors as mcolors
import matplotlib.colorbar as mcolorbar
pl.figure(tight_layout=False, figsize=figsize)
if imfile is not None:
bgimage = Im.open(extdir+prefix+'_0001.tif' if imfile is '' else imfile)
pl.imshow(bgimage, cmap=cm.gray, origin='upper')
#pl.quiver(X, Y, U, V, **kw)
if mask is None:
try:
mask = np.all(np.isfinite(odata['orient']), axis=1)
except ValueError:
mask = np.isfinite(odata['orient'])
n = odata.shape[-1] if odata.ndim > 1 else 1
ndex = np.repeat(np.arange(mask.sum()), n)
nz = mcolors.Normalize()
nz.autoscale(data['f'][mask]/fps)
qq = pl.quiver(
data['y'][mask][ndex], data['x'][mask][ndex],
odata['cdisp'][mask][...,1].flatten(), -odata['cdisp'][mask][...,0].flatten(),
color=cm.jet(nz(data['f'][mask]/fps)),
scale=1, scale_units='xy')
#pl.title(', '.join(imfile.split('/')[-1].split('_')[:-1]) if imfile else '')
cax,_ = mcolorbar.make_axes(pl.gca())
cb = mcolorbar.ColorbarBase(cax, cmap=cm.jet, norm=nz)
cb.set_label('time '+('(s)'if fps > 1 else '(frame)'))
if savename:
print "saving to", savename
pl.savefig(savename)
pl.show()
return qq, cb
def track_orient(orients, omask=None, cutoff=pi, inplace=True):
""" tracks branch cut crossings for orientation data
assumes that dtheta << cutoff for each frame
"""
if omask is None:
omask = np.isfinite(orients)
if not omask.any():
# all nan, return as is
return orients
if not inplace:
orients = orients.copy()
tracked = orients[omask]
deltas = np.diff(tracked)
crossings = (np.abs(deltas) > cutoff)*np.sign(deltas)
tracked[1:] -= twopi*crossings.cumsum()
orients[omask] = tracked
return orients
def plot_orient_time(data, odata, tracks, omask=None, delta=False, fps=1, save='', singletracks=False):
if omask is None:
omask = np.isfinite(odata['orient'])
goodtracks = np.unique(tracks[omask])
if goodtracks[0] == -1: goodtracks = goodtracks[1:]
if singletracks:
if singletracks is True:
goodtracks = list(goodtracks)[:4]
elif isinstance(singletracks, list):
goodtracks = singletracks
print 'tracks used are', goodtracks
#tmask = np.in1d(tracks, goodtracks)
pl.figure(figsize=(6,5))
colors = ['red','green','blue','cyan','black','magenta','yellow']
for goodtrack in goodtracks:
tmask = tracks == goodtrack
fullmask = omask & tmask
if np.count_nonzero(fullmask) < 1:
continue
plotrange = slice(None, 600 if singletracks is True else None)
if delta:
c = colors[goodtrack%7]
pl.plot(data['f'][fullmask][plotrange]/fps,
odata['orient'][fullmask][plotrange],
c=c,label="Track {}".format(goodtrack))
pl.plot(data['f'][fullmask][plotrange][0 if singletracks else 1:]/fps,
np.diff(odata['orient'][fullmask])[plotrange],
'o', c=c,label='delta {}'.format(goodtrack))
else:
pl.plot(data['f'][fullmask][plotrange]/fps,
track_orient(odata['orient'][fullmask])[plotrange],
'--', label='tracked {}'.format(goodtrack))
if delta:
for n in np.arange(-2 if delta else 0,2.5,0.5):
pl.plot(np.ones_like(odata['orient'][fullmask])[plotrange]*n*pi,'k--')
if len(goodtracks) < 10:
pl.legend()
#pl.title('Orientation over time')#\ninitial orientation = 0')
pl.xlabel('Time ({})'.format('s' if fps > 1 else 'frame'), fontsize='x-large')
pl.ylabel('orientation', fontsize='x-large')
pl.xlim(0, data['f'].max()/fps)
if save:
pl.savefig(save)
pl.show()
def plot_orient_location(data,odata,tracks):
import correlation as corr
omask = np.isfinite(odata['orient'])
goodtracks = np.array([78,95,191,203,322])
ss = 22.
pl.figure()
for goodtrack in goodtracks:
tmask = tracks == goodtrack
fullmask = np.all(np.asarray(zip(omask,tmask)),axis=1)
loc_start = (data['x'][fullmask][0],data['y'][fullmask][0])
orient_start = odata['orient'][fullmask][0]
sc = pl.scatter(
(odata['orient'][fullmask] - orient_start + pi) % twopi,
np.asarray(map(corr.get_norm,
zip([loc_start]*fullmask.sum(),
zip(data['x'][fullmask],data['y'][fullmask]))
))/ss,
#marker='*',
label = 'track {}'.format(goodtrack),
color = cm.jet(1.*goodtrack/max(tracks)))
#color = cm.jet(1.*data['f'][fullmask]/1260.))
print "track",goodtrack
pl.legend()
pl.show()
return True