def get_fixations(df):
df = df[df["Type"] == "SMP"]
df["Time"] = df["Time"] // 1000
fixations = detectors.fixation_detection(
df["L POR X [px]"].to_numpy(),
df["L POR Y [px]"].to_numpy(),
df["Time"].to_numpy(),
maxdist=45,
mindur=50,
)
entries = [
create_dataframe_entry(*fixation, df) for fixation in fixations[1]
]
return pd.DataFrame(entries)
def read_eyetribe(filename, start, stop=None, missing=0.0, debug=False):
"""Returns a list with dicts for every trial. A trial dict contains the
following keys:
x - numpy array of x positions
y - numpy array of y positions
size - numpy array of pupil size
time - numpy array of timestamps, t=0 at trialstart
trackertime- numpy array of timestamps, according to the tracker
events - dict with the following keys:
Sfix - list of lists, each containing [starttime]
Ssac - EMPTY! list of lists, each containing [starttime]
Sblk - list of lists, each containing [starttime]
Efix - list of lists, each containing [starttime, endtime, duration, endx, endy]
Esac - EMPTY! list of lists, each containing [starttime, endtime, duration, startx, starty, endx, endy]
Eblk - list of lists, each containing [starttime, endtime, duration]
msg - list of lists, each containing [time, message]
NOTE: timing is in EyeTribe time!
arguments
filename - path to the file that has to be read
start - trial start string
keyword arguments
stop - trial ending string (default = None)
missing - value to be used for missing data (default = 0.0)
debug - Boolean indicating if DEBUG mode should be on or off;
if DEBUG mode is on, information on what the script
currently is doing will be printed to the console
(default = False)
returns
data - a list with a dict for every trial (see above)
"""
# # # # #
# debug mode
if debug:
def message(msg):
print(msg)
else:
def message(msg):
pass
# # # # #
# file handling
# check if the file exists
if os.path.isfile(filename):
# open file
message("opening file '%s'" % filename)
f = open(filename, 'r')
# raise exception if the file does not exist
else:
raise Exception("Error in read_eyetribe: file '%s' does not exist" %
filename)
# read file contents
message("reading file '%s'" % filename)
raw = f.readlines()
# close file
message("closing file '%s'" % filename)
f.close()
# # # # #
# parse lines
# variables
data = []
x = []
y = []
size = []
time = []
trackertime = []
events = {
'Sfix': [],
'Ssac': [],
'Sblk': [],
'Efix': [],
'Esac': [],
'Eblk': [],
'msg': []
}
starttime = 0
started = False
trialend = False
# loop through all lines
for i in range(len(raw)):
# string to list
line = raw[i].replace('\n', '').replace('\r', '').split('\t')
# check if trial has already started
if started:
# only check for stop if there is one
if stop != None:
if (line[0] == 'MSG' and stop in line[3]) or i == len(raw) - 1:
started = False
trialend = True
# check for new start otherwise
else:
if start in line or i == len(raw) - 1:
started = True
trialend = True
# # # # #
# trial ending
if trialend:
message("trialend %d; %d samples found" % (len(data), len(x)))
# trial dict
trial = {}
trial['x'] = numpy.array(x)
trial['y'] = numpy.array(y)
trial['size'] = numpy.array(size)
trial['time'] = numpy.array(time)
trial['trackertime'] = numpy.array(trackertime)
trial['events'] = copy.deepcopy(events)
# events
trial['events']['Sblk'], trial['events'][
'Eblk'] = blink_detection(trial['x'],
trial['y'],
trial['trackertime'],
missing=missing)
trial['events']['Sfix'], trial['events'][
'Efix'] = fixation_detection(trial['x'],
trial['y'],
trial['trackertime'],
missing=missing)
trial['events']['Ssac'], trial['events'][
'Esac'] = saccade_detection(trial['x'],
trial['y'],
trial['trackertime'],
missing=missing)
# add trial to data
data.append(trial)
# reset stuff
x = []
y = []
size = []
time = []
trackertime = []
events = {
'Sfix': [],
'Ssac': [],
'Sblk': [],
'Efix': [],
'Esac': [],
'Eblk': [],
'msg': []
}
trialend = False
# check if the current line contains start message
else:
if line[0] == "MSG":
if start in line[3]:
message("trialstart %d" % len(data))
# set started to True
started = True
# find starting time
starttime = int(line[2])
# # # # #
# parse line
if started:
# message lines will start with MSG, followed by a tab, then a
# timestamp, a tab, the time, a tab and the message, e.g.:
# "MSG\t2014-07-01 17:02:33.770\t853589802\tsomething of importance here"
if line[0] == "MSG":
t = int(line[2]) # time
m = line[3] # message
events['msg'].append([t, m])
# regular lines will contain tab separated values, beginning with
# a timestamp, follwed by the values that were asked to be stored
# in the data file. Usually, this comes down to
# timestamp, time, fix, state, rawx, rawy, avgx, avgy, psize,
# Lrawx, Lrawy, Lavgx, Lavgy, Lpsize, Lpupilx, Lpupily,
# Rrawx, Rrawy, Ravgx, Ravgy, Rpsize, Rpupilx, Rpupily
# e.g.:
# '2014-07-01 17:02:33.770, 853589802, False, 7, 512.5897, 510.8104, 614.6975, 614.3327, 16.8657,
# 523.3592, 475.2756, 511.1529, 492.7412, 16.9398, 0.4037, 0.5209,
# 501.8202, 546.3453, 609.3405, 623.2287, 16.7916, 0.5539, 0.5209'
else:
# see if current line contains relevant data
try:
# extract data
x.append(float(line[6]))
y.append(float(line[7]))
size.append(float(line[8]))
time.append(int(line[1]) - starttime)
trackertime.append(int(line[1]))
except:
message("line '%s' could not be parsed" % line)
continue # skip this line
# # # # #
# return
return data
# # # # # # IMAGES if DOIMAGES: # READ DATA imagedata = read_data[trackertype](DATAFILE, "image_on", stop="image_off", missing=0.0, debug=False) # CUSTOM FIXATION DETECTION # calculate maximal inter-sampel distance maxdist = ang2cm(FIXTHRESH, SCREENDIST) * PIXPERCM # overwrite fixations for i in range(len(imagedata)): imagedata[i]['events']['Sfix'], imagedata[i]['events']['Efix'] = \ fixation_detection(imagedata[i]['x'], imagedata[i]['y'], imagedata[i]['trackertime'], missing=0.0, maxdist=maxdist, mindur=FIXMINDUR) # PLOT DATA for i in range(len(imagedata)): # image name imgname = os.path.splitext(os.path.basename(IMAGES[i]))[0] # pickle fixation data fixfile = open(os.path.join(outputdir, "fixations_%s.dat" % imgname), 'w') pickle.dump(imagedata[i]['events']['Efix'], fixfile) fixfile.close() # plot raw data fig = gazeplotter.draw_raw(imagedata[i]['x'], imagedata[i]['y'], DISPSIZE, imagefile=IMAGES[i], savefilename=os.path.join(outputdir, "image_%s_raw.png" % imgname)) pyplot.close(fig) # plot fixations fig = gazeplotter.draw_fixations(imagedata[i]['events']['Efix'], DISPSIZE, imagefile=IMAGES[i], durationsize=True, durationcolour=False, alpha=0.5, savefilename=os.path.join(outputdir, "image_%s_fixations.png" % imgname)) pyplot.close(fig)
def read_tobii(filename, reduced, missing=0.0, debug=False):
"""Returns a list with dicts for every trial. A trial dict contains the
following keys:
x - numpy array of x positions
y - numpy array of y positions
time - numpy array of timestamps
events - dict with the following keys:
Sfix - list of lists, each containing [starttime]
Ssac - list of lists, each containing [starttime]
Sblk - EMPTY! list of lists, each containing [starttime]
Efix - list of lists, each containing \
[starttime, endtime, duration, endx, endy]
Esac - list of lists, each containing \
[starttime, endtime, dur, startx, starty, endx, endy]
Eblk - EMPTY! list of lists, each containing [starttime, endtime, dur]
msg - list of lists, each containing [time, message]
Args:
filename - path to the file that has to be read
Kwargs:
missing - value to be used for missing data (default = 0.0)
debug - Boolean indicating if DEBUG mode should be on or off;
if DEBUG mode is on, information on what the script
currently is doing will be printed to the console
(default = False)
Returns
data - a list with a dict for every trial (see above)
"""
# debug mode
if debug:
def message(msg):
print(msg)
else:
def message(msg):
pass
# check if the file exists
if os.path.isfile(filename):
# open file
message("opening file '%s'" % filename)
f = open(filename, 'r')
# raise exception if the file does not exist
else:
raise Exception("Error in read_eyetribe: file '%s' does not exist" %
filename)
# read file contents
message("reading file '%s'" % filename)
raw = f.readlines()
# close file
message("closing file '%s'" % filename)
f.close()
# variables
data = []
time = []
x = []
y = []
events = {
'Sfix': [],
'Ssac': [],
'Sblk': [],
'Efix': [],
'Esac': [],
'Eblk': [],
'msg': []
}
if reduced:
limit = 5000
else:
limit = len(raw)
# loop through all lines
for i in range(limit):
# string to list
line = [float(s) for s in raw[i].replace('\n', '').split(',')]
line[0] = line[0] * 1000
# line[1] = line[1] - 1920 # Correcting for display to the left
# see if current line contains relevant data
try:
# extract data
time.append(line[0])
x.append(line[1])
y.append(line[2])
except IndexError:
message("line '%s' could not be parsed" % line)
continue # skip this line
message("%d samples found" % (len(raw)))
message("plotting %d samples" % (limit))
# trial dict
trial = {}
trial['x'] = numpy.array(x)
trial['y'] = numpy.array(y)
trial['time'] = numpy.array(time)
trial['events'] = copy.deepcopy(events)
# events
trial['events']['Sblk'], trial['events']['Eblk'] = blink_detection(
trial['x'], trial['y'], trial['time'], missing=missing)
trial['events']['Sfix'], trial['events']['Efix'] = fixation_detection(
trial['x'], trial['y'], trial['time'], missing=missing)
trial['events']['Ssac'], trial['events']['Esac'] = saccade_detection(
trial['x'], trial['y'], trial['time'], missing=missing)
# add trial to data
data.append(trial)
return data
def heatMap(startTime, endTime, totalTime):
#(in seconds)
#start time of video segment you want to extract
#end time of video segment you want to extract
#total time of video
#saveLocation: location where output heatmap is to be saved
#gazeFilePath: location of gaze csv
#videoLocation: location of video
#imageWidth and IimageHeight: dimension of a frame in a video
saveLocation = '/Users/jeffhe/Desktop/commitments/urap/resources/heatmap_processing/0308_1630_text_02/heatmap.png'
gazeFilePath = "/Users/jeffhe/Desktop/commitments/urap/resources/heatmap_processing/0308_1630_text_02/gaze_positions.csv"
videoLocation = "/Users/jeffhe/Desktop/commitments/urap/resources/heatmap_processing/0308_1630_text_02/world.mp4"
imageWidth = 1280
imageHeight = 720
def StartTimeSeconds():
with open(gazeFilePath) as f:
reader = csv.reader(f)
reader = list(reader)
startTimeSeconds = float(reader[1][0])
return startTimeSeconds
startTimeSeconds = StartTimeSeconds() + startTime
endTimeSeconds = StartTimeSeconds() + endTime
vidcap = cv2.VideoCapture(videoLocation)
frames = []
success, image = vidcap.read()
frames += [image]
while success:
success, image = vidcap.read()
frames += [image]
startFrame = (startTime / totalTime) * len(frames)
endFrame = (endTime / totalTime) * len(frames)
image = frames[int((startFrame + endFrame) / 2)]
with open(gazeFilePath) as f:
reader = csv.reader(f)
line_num = 0
norm_pos_x = []
norm_pos_y = []
timeStamps = []
for row in reader:
if (line_num != 0):
if not (float(row[2]) < 0.5 or float(row[3]) > 1
or float(row[3]) < 0 or float(row[4]) > 1
or float(row[4]) < 0):
if (float(row[0]) > startTimeSeconds
and float(row[0]) < endTimeSeconds):
norm_pos_x += [int(float(row[3]) * 1280)]
norm_pos_y += [imageHeight - int(float(row[4]) * 720)]
timeStamps += [float(row[0])]
line_num += 1
count = 0
while (count < len(timeStamps)):
timeStamps[count] *= 1000
count += 1
Sfix, Efix = fixation_detection(norm_pos_x, norm_pos_y, timeStamps)
draw_heatmap(Efix, (imageWidth, imageHeight),
image,
savefilename=saveLocation)
#the above function call isn't necessary in Shaantam's case. The fixation data is stored in Efix
#in the form of [[start time, end time, duration, x-coordinates, y-coordinates][][]...]
#here, the y-coordinate is calculated from top (meaning that top pixel is 0, bottom pixel is 720)
return Efix
def read_idf(filename, start, stop=None, missing=0.0, debug=False):
"""Returns a list with dicts for every trial. A trial dict contains the
following keys:
x - numpy array of x positions
y - numpy array of y positions
size - numpy array of pupil size
time - numpy array of timestamps, t=0 at trialstart
trackertime- numpy array of timestamps, according to the tracker
events - dict with the following keys:
Sfix - list of lists, each containing [starttime]
Ssac - EMPTY! list of lists, each containing [starttime]
Sblk - list of lists, each containing [starttime]
Efix - list of lists, each containing [starttime, endtime, duration, endx, endy]
Esac - EMPTY! list of lists, each containing [starttime, endtime, duration, startx, starty, endx, endy]
Eblk - list of lists, each containing [starttime, endtime, duration]
msg - list of lists, each containing [time, message]
NOTE: timing is in EyeTribe time!
arguments
filename - path to the file that has to be read
start - trial start string
keyword arguments
stop - trial ending string (default = None)
missing - value to be used for missing data (default = 0.0)
debug - Boolean indicating if DEBUG mode should be on or off;
if DEBUG mode is on, information on what the script
currently is doing will be printed to the console
(default = False)
returns
data - a list with a dict for every trial (see above)
"""
# # # # #
# debug mode
if debug:
def message(msg):
print(msg)
else:
def message(msg):
pass
# # # # #
# file handling
# check if the file exists
if os.path.isfile(filename):
# open file
message("opening file '%s'" % filename)
f = open(filename, 'r')
# raise exception if the file does not exist
else:
raise Exception("Error in read_eyetribe: file '%s' does not exist" %
filename)
# read file contents
message("reading file '%s'" % filename)
raw = f.readlines()
# close file
message("closing file '%s'" % filename)
f.close()
# # # # #
# parse lines
# variables
data = []
x = []
y = []
size = []
time = []
trackertime = []
events = {
'Sfix': [],
'Ssac': [],
'Sblk': [],
'Efix': [],
'Esac': [],
'Eblk': [],
'msg': []
}
starttime = 0
started = False
trialend = False
filestarted = False
# loop through all lines
for i in range(len(raw)):
# string to list
line = raw[i].replace('\n', '').replace('\r', '').split('\t')
# check if the line starts with '##' (denoting header)
if '##' in line[0]:
# skip processing
continue
elif '##' not in line[0] and not filestarted:
# check the indexes for several key things we want to extract
# (we need to do this, because ASCII outputs of the IDF reader
# are different, based on whatever the user wanted to extract)
timei = line.index("Time")
typei = line.index("Type")
msgi = -1
xi = {'L': None, 'R': None}
yi = {'L': None, 'R': None}
sizei = {'L': None, 'R': None}
if "L POR X [px]" in line:
xi['L'] = line.index("L POR X [px]")
if "R POR X [px]" in line:
xi['R'] = line.index("R POR X [px]")
if "L POR Y [px]" in line:
yi['L'] = line.index("L POR Y [px]")
if "R POR Y [px]" in line:
yi['R'] = line.index("R POR Y [px]")
if "L Dia X [px]" in line:
sizei['L'] = line.index("L Dia X [px]")
if "R Dia X [px]" in line:
sizei['R'] = line.index("R Dia X [px]")
# set filestarted to True, so we don't attempt to extract
# this info on all consecutive lines
filestarted = True
# check if trial has already started
if started:
# only check for stop if there is one
if stop != None:
if (line[typei] == 'MSG'
and stop in line[msgi]) or i == len(raw) - 1:
started = False
trialend = True
# check for new start otherwise
else:
if start in line or i == len(raw) - 1:
started = True
trialend = True
# # # # #
# trial ending
if trialend:
message("trialend %d; %d samples found" % (len(data), len(x)))
# trial dict
trial = {}
trial['x'] = numpy.array(x)
trial['y'] = numpy.array(y)
trial['size'] = numpy.array(size)
trial['time'] = numpy.array(time)
trial['trackertime'] = numpy.array(trackertime)
trial['events'] = copy.deepcopy(events)
# events
trial['events']['Sblk'], trial['events'][
'Eblk'] = blink_detection(
trial['x'],
trial['y'],
trial['trackertime'],
missing=missing,
minlen=5) # TEJADA: decreased from 10 to 5
trial['events']['Sfix'], trial['events'][
'Efix'] = fixation_detection(
trial['x'],
trial['y'],
trial['trackertime'],
missing=missing,
maxdist=80,
mindur=80) # Parametros ajustados para BeGaze
trial['events']['Ssac'], trial['events'][
'Esac'] = saccade_detection(
trial['x'],
trial['y'],
trial['trackertime'],
missing=missing,
minlen=1,
maxvel=60,
maxacc=450
) #TEJADA: still trying to obtain the best adjust
# add trial to data
data.append(trial)
# reset stuff
x = []
y = []
size = []
time = []
trackertime = []
events = {
'Sfix': [],
'Ssac': [],
'Sblk': [],
'Efix': [],
'Esac': [],
'Eblk': [],
'msg': []
}
trialend = False
# check if the current line contains start message
else:
if line[typei] == "MSG":
if start in line[msgi]:
message("trialstart %d" % len(data))
# set started to True
started = True
# find starting time
starttime = int(line[timei])
# # # # #
# parse line
if started:
# message lines will usually start with a timestamp, followed
# by 'MSG', the trial number and the actual message, e.g.:
# "7818328012 MSG 1 # Message: 3"
if line[typei] == "MSG":
t = int(line[timei]) # time
m = line[msgi] # message
events['msg'].append([t, m])
# regular lines will contain tab separated values, beginning with
# a timestamp, follwed by the values that were chosen to be
# extracted by the IDF converter
else:
# see if current line contains relevant data
try:
# extract data on POR and pupil size
for var in ['x', 'y', 'size']:
exec("vi = %si" % var)
exec("v = %s" % var)
# nothing
if vi['L'] == None and vi['R'] == None:
val = 'not in IDF'
# only left eye
elif vi['L'] != None and vi['R'] == None:
val = float(line[vi['L']])
# only right eye
elif vi['L'] == None and vi['R'] != None:
val = float(line[vi['R']])
# average the two eyes, but only if they both
# contain valid data
elif vi['L'] != None and vi['R'] != None:
if float(line[vi['L']]) == 0:
val = float(line[vi['R']])
elif float(line[vi['R']]) == 0:
val = float(line[vi['L']])
else:
val = (float(line[vi['L']]) +
float(line[vi['R']])) / 2.0
v.append(val)
# extract time data
time.append((int(line[timei]) - starttime))
# TEJADA: tracker time was divided by 1000 to adjust timestamp of SMI
trackertime.append((int(line[timei]) - starttime) / 1000)
except:
message("line '%s' could not be parsed" % line)
continue # skip this line
# # # # #
# return
return data