def figure1k3(info, path):
if not os.path.exists(path):
os.makedirs(path)
sessions = info.groupby(level=['subject'])
for subject, sinfo in sessions:
fig = plt.figure()
subjectpath = os.path.join(activitymovies.datafolder, subject)
stepframes = []
stinfo = sinfo[sinfo.protocol == 'stable']
for key, group in stinfo.groupby(level=['session']):
stact = activitytables.read_subjects(subjectpath, days=[key])
stcr = activitytables.read_subjects(
subjectpath, days=[key], selector=activitytables.crossings)
stepframes += activitytables.stepframes(stact,
stcr,
group,
4,
3,
subtractBackground=True)
stepframes = [
cv2.threshold(f, 5, 255, cv2.cv.CV_THRESH_BINARY)[1]
for f in stepframes
]
stavg = imgproc.average(stepframes, 1)
stepframes = []
uinfo = sinfo[sinfo.protocol != 'stable']
for key, group in uinfo.groupby(level=['session']):
uact = activitytables.read_subjects(subjectpath, days=[key])
ucr = activitytables.read_subjects(
subjectpath, days=[key], selector=activitytables.crossings)
stepframes += activitytables.stepframes(uact,
ucr,
group,
4,
3,
subtractBackground=True)
stepframes = [
cv2.threshold(f, 5, 255, cv2.cv.CV_THRESH_BINARY)[1]
for f in stepframes
]
uavg = imgproc.average(stepframes, 1)
avg = cv2.merge((uavg, stavg, np.zeros(stavg.shape,
dtype=stavg.dtype)))
avg = avg.astype(np.uint8)
avg = cv2.convertScaleAbs(avg, alpha=1.4, beta=0.0)
plt.imshow(avg)
plt.title(str.format('{0}', subject))
fname = str.format("{0}_posture_stable_unstable.png", subject)
fpath = os.path.join(path, fname)
plt.axis("off")
plt.savefig(fpath)
plt.close(fig)
def figure1k2(info,path):
if not os.path.exists(path):
os.makedirs(path)
sessions = info.groupby(level=['subject','session'])
for (subject,session),sinfo in sessions:
fig = plt.figure()
subjectpath = os.path.join(activitymovies.datafolder,subject)
sact = activitytables.read_subjects(subjectpath,days=[session])
scr = activitytables.read_subjects(subjectpath,days=[session],
selector = activitytables.crossings)
stepframes = activitytables.stepframes(sact,scr,sinfo,4,3)
side = int(np.ceil(np.sqrt(len(stepframes))))
tile = imgproc.tile(stepframes,side,side)
plt.imshow(tile[0], cmap = plt.cm.Greys_r)
plt.title(str.format('{0} (session {1})',subject,session))
fname = str.format("{0}_session_{1}_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)
plt.axis("off")
plt.savefig(fpath)
plt.close(fig)
def figure1k2(info, path):
if not os.path.exists(path):
os.makedirs(path)
sessions = info.groupby(level=['subject', 'session'])
for (subject, session), sinfo in sessions:
fig = plt.figure()
subjectpath = os.path.join(activitymovies.datafolder, subject)
sact = activitytables.read_subjects(subjectpath, days=[session])
scr = activitytables.read_subjects(subjectpath,
days=[session],
selector=activitytables.crossings)
stepframes = activitytables.stepframes(sact, scr, sinfo, 4, 3)
side = int(np.ceil(np.sqrt(len(stepframes))))
tile = imgproc.tile(stepframes, side, side)
plt.imshow(tile[0], cmap=plt.cm.Greys_r)
plt.title(str.format('{0} (session {1})', subject, session))
fname = str.format("{0}_session_{1}_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)
plt.axis("off")
plt.savefig(fpath)
plt.close(fig)
def figure1k3(info,path):
if not os.path.exists(path):
os.makedirs(path)
sessions = info.groupby(level=['subject'])
for subject,sinfo in sessions:
fig = plt.figure()
subjectpath = os.path.join(activitymovies.datafolder,subject)
stepframes = []
stinfo = sinfo[sinfo.protocol == 'stable']
for key,group in stinfo.groupby(level=['session']):
stact = activitytables.read_subjects(subjectpath,days=[key])
stcr = activitytables.read_subjects(subjectpath,days=[key],
selector = activitytables.crossings)
stepframes += activitytables.stepframes(stact,stcr,group,4,3,
subtractBackground=True)
stepframes = [cv2.threshold(f, 5, 255, cv2.cv.CV_THRESH_BINARY)[1]
for f in stepframes]
stavg = imgproc.average(stepframes,1)
stepframes = []
uinfo = sinfo[sinfo.protocol != 'stable']
for key,group in uinfo.groupby(level=['session']):
uact = activitytables.read_subjects(subjectpath,days=[key])
ucr = activitytables.read_subjects(subjectpath,days=[key],
selector = activitytables.crossings)
stepframes += activitytables.stepframes(uact,ucr,group,4,3,
subtractBackground=True)
stepframes = [cv2.threshold(f, 5, 255, cv2.cv.CV_THRESH_BINARY)[1]
for f in stepframes]
uavg = imgproc.average(stepframes,1)
avg = cv2.merge((uavg,stavg,np.zeros(stavg.shape,dtype=stavg.dtype)))
avg = avg.astype(np.uint8)
avg = cv2.convertScaleAbs(avg,alpha=1.4,beta=0.0)
plt.imshow(avg)
plt.title(str.format('{0}',subject))
fname = str.format("{0}_posture_stable_unstable.png", subject)
fpath = os.path.join(path,fname)
plt.axis("off")
plt.savefig(fpath)
plt.close(fig)
def _averageposture_(cract,info,cr,cropsize=(300,300)):
stepframes = []
for key,scr in cr.groupby(level=['subject','session']):
sinfo = info.loc[[key],:]
stepframes += activitytables.stepframes(cract,scr,sinfo,4,3,
cropsize=cropsize,
subtractBackground=True)
stepframes = [cv2.threshold(f, 5, 255, cv2.cv.CV_THRESH_BINARY)[1]
for f in stepframes]
return imgproc.average(stepframes,1)
