def roiframes(indices,
side,
info,
leftroi,
rightroi,
croproi,
cropsize=(300, 300),
subtractBackground=False):
# Tile step frames
vidpaths = activitymovies.getmoviepath(info)
timepaths = activitymovies.gettimepath(info)
backpaths = activitymovies.getbackgroundpath(info)
videos = [
video.video(path, timepath)
for path, timepath in zip(vidpaths, timepaths)
]
frames = []
for frameindex, side in zip(indices, side):
leftwards = side == 'leftwards'
roiindex = leftroi if leftwards else rightroi
frame = videos[0].frame(frameindex)
background = None
if subtractBackground:
timestamp = videos[0].timestamps[frameindex]
background = activitymovies.getbackground(backpaths[0], timestamp)
frame = croproi(frame, roiindex, cropsize, background,
roiindex == rightroi)
frames.append(frame)
return frames
def figure1k1(info,path):
if not os.path.exists(path):
os.makedirs(path)
sessions = info.groupby(level=['subject','session'])
for (subject,session),sinfo in sessions:
subjectpath = os.path.join(activitymovies.datafolder,subject)
sact = activitytables.read_subjects(subjectpath,days=[session])
scr = activitytables.read_subjects(subjectpath,days=[session],
selector = activitytables.crossings)
features = activitytables.stepfeature(sact,scr,4,3)
leftwards = features.side == 'leftwards'
features.xhead[leftwards] = max_width_cm - features.xhead[leftwards]
median = np.sort(features.xhead)[len(features)/2+1]
median = features[features.xhead == median].iloc[-1,:]
vidpaths = activitymovies.getmoviepath(sinfo)
timepaths = activitymovies.gettimepath(info)
movie = [video.video(mpath,mtimepath)
for mpath,mtimepath in zip(vidpaths,timepaths)][0]
medianframe = movie.frame(sact.index.get_loc(median.name))
flip = median.side == 'rightwards'
stepindex = 3 if flip else 4
medianframe = activitytables.cropstep(medianframe,stepindex,flip=flip)
fname = str.format("{0}_session_{1}_median_step_posture.png",
subject, session)
fpath = os.path.join(path,subject)
if not os.path.exists(fpath):
os.makedirs(fpath)
fpath = os.path.join(fpath,fname)
cv2.imwrite(fpath,medianframe)
def slipframes(slipactivity,
info,
cropsize=(300, 200),
subtractBackground=False):
vidpaths = activitymovies.getmoviepath(info)
timepaths = activitymovies.gettimepath(info)
backpaths = activitymovies.getbackgroundpath(info)
videos = [
video.video(path, timepath)
for path, timepath in zip(vidpaths, timepaths)
]
frames = []
for index, trial in slipactivity.iterrows():
rightwards = trial.side == 'rightwards'
frame = videos[0].frame(trial.frame)
background = None
if subtractBackground:
timestamp = videos[0].timestamps[trial.frame]
background = activitymovies.getbackground(backpaths[0], timestamp)
if cropsize is not None:
frame = cropslip(frame, trial.gapindex, cropsize, background,
rightwards)
frames.append(frame)
return frames
def __getclips__(act,cr,info,rowidx=slice(None)):
features = []
moviepath = activitymovies.getmoviepath(info)
for (subject,session),group in cr.groupby(level=['subject','session']):
steps = activitytables.stepfeature(act,group,4,3).iloc[rowidx,:]
steps['subject'] = subject
steps['session'] = session
steps.reset_index(inplace=True)
steps.set_index(['subject','session'],inplace=True)
features.append(steps[['frame','side']].join(moviepath))
return pd.concat(features)
def __getclips__(act, cr, info, rowidx=slice(None)):
features = []
moviepath = activitymovies.getmoviepath(info)
for (subject, session), group in cr.groupby(level=['subject', 'session']):
steps = activitytables.stepfeature(act, group, 4, 3).iloc[rowidx, :]
steps['subject'] = subject
steps['session'] = session
steps.reset_index(inplace=True)
steps.set_index(['subject', 'session'], inplace=True)
features.append(steps[['frame', 'side']].join(moviepath))
return pd.concat(features)
def clusterstepframes(cr, info, leftstep, rightstep):
# Compute step times
stepactivity = cr.iloc[:, 16:24]
stepdiff = stepactivity.diff()
steppeaks = siphon.findpeaksMax(stepdiff, 1500)
pksloc = [[stepdiff.index.get_loc(peak) for peak in step]
for step in steppeaks]
# Tile step frames
vidpaths = activitymovies.getmoviepath(info)
timepaths = activitymovies.gettimepath(info)
backpaths = activitymovies.getbackgroundpath(info)
videos = [
video.video(path, timepath)
for path, timepath in zip(vidpaths, timepaths)
]
def getstepframes(stepindex, flip=False):
stepcenterxcm = stepcenters[stepindex][1] * preprocess.width_pixel_to_cm
framehead = [
p for p in pksloc[stepindex]
if (cr.xhead[p] < stepcenterxcm
if not flip else cr.xhead[p] > stepcenterxcm)
]
frames = [
imgproc.croprect(stepcenters[stepindex], (200, 200),
videos[0].frame(p)) for p in framehead
]
backgrounds = [
imgproc.croprect(
stepcenters[stepindex], (200, 200),
activitymovies.getbackground(backpaths[0],
videos[0].timestamps[p]))
for p in framehead
]
frames = [cv2.subtract(f, b) for f, b in zip(frames, backgrounds)]
if flip:
frames = [cv2.flip(f, 1) for f in frames]
return frames, framehead
leftframes, leftindices = getstepframes(leftstep, False)
rightframes, rightindices = getstepframes(rightstep, True)
frames = np.array(leftframes + rightframes)
frameindices = np.array(leftindices + rightindices)
sortindices = np.argsort(frameindices)
frames = frames[sortindices]
frameindices = frameindices[sortindices]
R, labels, h = imgproc.cluster(frames, videos[0], frameindices)
return frames, stepdiff, steppeaks, pksloc, R, labels
def clusterroiframes(act, roiactivity, info, leftroi, rightroi, roicenter_cm,
cropframes):
# Compute step times
roidiff = roiactivity.diff()
roipeaks = activitytables.findpeaks(roidiff, 1500)
pksloc = [[roidiff.index.get_loc(peak) for peak in roi]
for roi in roipeaks]
# Tile step frames
vidpaths = activitymovies.getmoviepath(info)
timepaths = activitymovies.gettimepath(info)
backpaths = activitymovies.getbackgroundpath(info)
videos = [
video.video(path, timepath)
for path, timepath in zip(vidpaths, timepaths)
]
def getroiframes(roiindex, flip=False):
roicenterxcm = roicenter_cm[roiindex][1]
headdistance = [act.xhead[p] - roicenterxcm for p in pksloc[roiindex]]
print headdistance
framehead = [
p for i, p in enumerate(pksloc[roiindex])
if (-25 < headdistance[i] < -5
if not flip else 5 < headdistance[i] < 25)
]
frames = [cropframes(videos[0].frame(p), roiindex) for p in framehead]
backgrounds = [
cropframes(
activitymovies.getbackground(backpaths[0],
videos[0].timestamps[p]),
roiindex) for p in framehead
]
frames = [cv2.subtract(f, b) for f, b in zip(frames, backgrounds)]
if flip:
frames = [cv2.flip(f, 1) for f in frames]
return frames, framehead
leftframes, leftindices = getroiframes(leftroi, False)
rightframes, rightindices = getroiframes(rightroi, True)
print "==========================="
frames = np.array(leftframes + rightframes)
frameindices = np.array(leftindices + rightindices)
sortindices = np.argsort(frameindices)
frames = frames[sortindices]
frameindices = frameindices[sortindices]
Z, R, labels, h = imgproc.cluster(frames, videos[0], frameindices)
return frames, roidiff, roipeaks, pksloc, Z, R, labels
def __saveselectedtiledclips__(clips,info,path,suffix='.avi'):
if not os.path.exists(path):
os.makedirs(path)
moviepath = activitymovies.getmoviepath(info)
for ((subject,session),group),indices in it.izip(info.groupby(level=['subject',
'session']),
clips):
crossings = pd.DataFrame(indices)
crossings['path'] = moviepath.loc[(subject,session)]
vidclips = __processclips__(crossings.iterrows(),ycrop=slice(320,-1))
frames = __tileclips__(it.izip(*vidclips),1,len(crossings))
fname = str.format("{0}"+suffix,subject)
vidpath = os.path.join(path,fname)
activitymovies.savemovie(frames,vidpath,frames_per_second)
def __saveselectedtiledclips__(clips, info, path, suffix='.avi'):
if not os.path.exists(path):
os.makedirs(path)
moviepath = activitymovies.getmoviepath(info)
for ((subject, session),
group), indices in it.izip(info.groupby(level=['subject', 'session']),
clips):
crossings = pd.DataFrame(indices)
crossings['path'] = moviepath.loc[(subject, session)]
vidclips = __processclips__(crossings.iterrows(), ycrop=slice(320, -1))
frames = __tileclips__(it.izip(*vidclips), 1, len(crossings))
fname = str.format("{0}" + suffix, subject)
vidpath = os.path.join(path, fname)
activitymovies.savemovie(frames, vidpath, frames_per_second)
def clusterroiframes(act, roiactivity, info, leftroi, rightroi, roicenter_cm, cropframes):
# Compute step times
roidiff = roiactivity.diff()
roipeaks = activitytables.findpeaks(roidiff, 1500)
pksloc = [[roidiff.index.get_loc(peak) for peak in roi] for roi in roipeaks]
# Tile step frames
vidpaths = activitymovies.getmoviepath(info)
timepaths = activitymovies.gettimepath(info)
backpaths = activitymovies.getbackgroundpath(info)
videos = [video.video(path, timepath) for path, timepath in zip(vidpaths, timepaths)]
def getroiframes(roiindex, flip=False):
roicenterxcm = roicenter_cm[roiindex][1]
headdistance = [act.xhead[p] - roicenterxcm for p in pksloc[roiindex]]
print headdistance
framehead = [
p
for i, p in enumerate(pksloc[roiindex])
if (-25 < headdistance[i] < -5 if not flip else 5 < headdistance[i] < 25)
]
frames = [cropframes(videos[0].frame(p), roiindex) for p in framehead]
backgrounds = [
cropframes(activitymovies.getbackground(backpaths[0], videos[0].timestamps[p]), roiindex) for p in framehead
]
frames = [cv2.subtract(f, b) for f, b in zip(frames, backgrounds)]
if flip:
frames = [cv2.flip(f, 1) for f in frames]
return frames, framehead
leftframes, leftindices = getroiframes(leftroi, False)
rightframes, rightindices = getroiframes(rightroi, True)
print "==========================="
frames = np.array(leftframes + rightframes)
frameindices = np.array(leftindices + rightindices)
sortindices = np.argsort(frameindices)
frames = frames[sortindices]
frameindices = frameindices[sortindices]
Z, R, labels, h = imgproc.cluster(frames, videos[0], frameindices)
return frames, roidiff, roipeaks, pksloc, Z, R, labels
def figure2a3(act,info,path):
if not os.path.exists(path):
os.makedirs(path)
mframes = __getmanipulationframes__(act,info)
mframes = mframes.join(info)
mframes = mframes[mframes.frame != 0]
mframes = mframes.query('subject not in ["JPAK_26","JPAK_27","JPAK_28","JPAK_29"]')
mframes = mframes.join(activitymovies.getmoviepath(mframes))
mframes = activitytables.groupbylesionvolumes(mframes[['path','frame','side']],mframes)
mframes.reset_index()
vidclips = __processclips__(mframes.iterrows(),ycrop=slice(320,-1))
vidclips = [__transposeclips__(clip) for clip in vidclips]
frames = __tileclips__(it.izip(*vidclips),len(mframes)/2,2)
frames = __transposeclips__(frames)
frames = __resizeclips__(frames,dsize=(1280,896))
fname = str.format("manipulations_big_lesion.avi")
vidpath = os.path.join(path,fname)
activitymovies.savemovie(frames,vidpath,frames_per_second)
def figure2a3(act, info, path):
if not os.path.exists(path):
os.makedirs(path)
mframes = __getmanipulationframes__(act, info)
mframes = mframes.join(info)
mframes = mframes[mframes.frame != 0]
mframes = mframes.query(
'subject not in ["JPAK_26","JPAK_27","JPAK_28","JPAK_29"]')
mframes = mframes.join(activitymovies.getmoviepath(mframes))
mframes = activitytables.groupbylesionvolumes(
mframes[['path', 'frame', 'side']], mframes)
mframes.reset_index()
vidclips = __processclips__(mframes.iterrows(), ycrop=slice(320, -1))
vidclips = [__transposeclips__(clip) for clip in vidclips]
frames = __tileclips__(it.izip(*vidclips), len(mframes) / 2, 2)
frames = __transposeclips__(frames)
frames = __resizeclips__(frames, dsize=(1280, 896))
fname = str.format("manipulations_big_lesion.avi")
vidpath = os.path.join(path, fname)
activitymovies.savemovie(frames, vidpath, frames_per_second)
def roiframes(activity,crossings,info,leftroi,rightroi,roiframeindices,croproi,
cropsize=(300,300),subtractBackground=False):
# Tile step frames
vidpaths = activitymovies.getmoviepath(info)
timepaths = activitymovies.gettimepath(info)
backpaths = activitymovies.getbackgroundpath(info)
videos = [video.video(path,timepath) for path,timepath in zip(vidpaths,timepaths)]
frames = []
indices,side = roiframeindices(activity,crossings,leftroi,rightroi)
for frameindex,side in zip(indices,side):
leftwards = side == 'leftwards'
roiindex = leftroi if leftwards else rightroi
frame = videos[0].frame(frameindex)
background = None
if subtractBackground:
timestamp = videos[0].timestamps[frameindex]
background = activitymovies.getbackground(backpaths[0],timestamp)
frame = croproi(frame,roiindex,cropsize,background,roiindex == rightroi)
frames.append(frame)
return frames
def clusterstepframes(cr,info,leftstep,rightstep):
# Compute step times
stepactivity = cr.iloc[:,16:24]
stepdiff = stepactivity.diff()
steppeaks = siphon.findpeaksMax(stepdiff,1500)
pksloc = [[stepdiff.index.get_loc(peak) for peak in step] for step in steppeaks]
# Tile step frames
vidpaths = activitymovies.getmoviepath(info)
timepaths = activitymovies.gettimepath(info)
backpaths = activitymovies.getbackgroundpath(info)
videos = [video.video(path,timepath) for path,timepath in zip(vidpaths,timepaths)]
def getstepframes(stepindex,flip=False):
stepcenterxcm = stepcenters[stepindex][1] * preprocess.width_pixel_to_cm
framehead = [p for p in pksloc[stepindex]
if (cr.xhead[p] < stepcenterxcm if not flip else cr.xhead[p] > stepcenterxcm)]
frames = [imgproc.croprect(stepcenters[stepindex],(200,200),videos[0].frame(p))
for p in framehead]
backgrounds = [imgproc.croprect(stepcenters[stepindex],(200,200),activitymovies.getbackground(backpaths[0],videos[0].timestamps[p]))
for p in framehead]
frames = [cv2.subtract(f,b) for f,b in zip(frames,backgrounds)]
if flip:
frames = [cv2.flip(f,1) for f in frames]
return frames,framehead
leftframes,leftindices = getstepframes(leftstep,False)
rightframes,rightindices = getstepframes(rightstep,True)
frames = np.array(leftframes + rightframes)
frameindices = np.array(leftindices + rightindices)
sortindices = np.argsort(frameindices)
frames = frames[sortindices]
frameindices = frameindices[sortindices]
R,labels,h = imgproc.cluster(frames,videos[0],frameindices)
return frames,stepdiff,steppeaks,pksloc,R,labels
def figure1k1(info, path):
if not os.path.exists(path):
os.makedirs(path)
sessions = info.groupby(level=['subject', 'session'])
for (subject, session), sinfo in sessions:
subjectpath = os.path.join(activitymovies.datafolder, subject)
sact = activitytables.read_subjects(subjectpath, days=[session])
scr = activitytables.read_subjects(subjectpath,
days=[session],
selector=activitytables.crossings)
features = activitytables.stepfeature(sact, scr, 4, 3)
leftwards = features.side == 'leftwards'
features.xhead[leftwards] = max_width_cm - features.xhead[leftwards]
median = np.sort(features.xhead)[len(features) / 2 + 1]
median = features[features.xhead == median].iloc[-1, :]
vidpaths = activitymovies.getmoviepath(sinfo)
timepaths = activitymovies.gettimepath(info)
movie = [
video.video(mpath, mtimepath)
for mpath, mtimepath in zip(vidpaths, timepaths)
][0]
medianframe = movie.frame(sact.index.get_loc(median.name))
flip = median.side == 'rightwards'
stepindex = 3 if flip else 4
medianframe = activitytables.cropstep(medianframe,
stepindex,
flip=flip)
fname = str.format("{0}_session_{1}_median_step_posture.png", subject,
session)
fpath = os.path.join(path, subject)
if not os.path.exists(fpath):
os.makedirs(fpath)
fpath = os.path.join(fpath, fname)
cv2.imwrite(fpath, medianframe)
