d = [ [0] * x for i in range(y) ]
for eyetrack in eyetrackList:
ex = eyetrack["AbsoluteX"]
ey = eyetrack["AbsoluteY"]
print("X:",ex,"Y:",ey)
d[ey][ex] += 1
print("New total:", d[ey][ex])
return d
def refineTimeframe(eyetrackList, timeframe=(0, 60000)):
rtn = []
for et in eyetrackList:
time = et["Time"]
if time >= timeframe[0] and time <= timeframe[1]:
rtn.append(et)
return rtn
#print(type(eyetrackList[1]))
#print "Total Eyetrack Events:", len(eyetrackList)
#refinedList = refineTimeframe(eyetrackList, (3000,4000))
#print "Events w/in seconds 3 and 4:", len(refinedList)
#makeDataMap(refinedList)
#print(makeDataMap(eyetrackList))
#pprint(makeDataMap(eyetrackList))
#Task2.run(sessions,eventList,eyetrackList)
Task4.run(sessions,eventList,eyetrackList)
# Central points of gaze mean the "clusters" of gaze. If this doesn't make sense, please # ask for clarification or skip it. ######################################## # ... ######################################################################################## # 3b) Create a separate movie showing this. ######################################## ######################################################################################## # 4) Figure out how to overlay the pictures on top of the actual video. The video can # be found here: http://g3.eyetrackshop.com/content/CT13C39DD417BLYWHILLT-SJJ ######################################## Task4.run(sessions, eventList, eyetrackList) ######################################################################################## # 5) Output a quality report in a test file, listing the % of sessions which are: # Complete - attribute session.last_state = "7.COMPLETE" # Usable - attribute eyetrack.quality = "GOOD" # Group eyetrack.quality failure reasons and output % of each ######################################## ######################################################################################## # 6) For each frame, add 4 'area of interest' rectangles which are the 4 quadrants of # the image. On each eye tracking frame, output a % noting in the middle of the frame in text # which says what percent of people who have data during that time period were looking # within the area of interest. ########################################
