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#!/usr/bin/env python
import argparse
import os
import math
from argaze import DataStructures, GazeFeatures
from argaze.AreaOfInterest import AOIFeatures
from argaze.GazeAnalysis import DispersionBasedGazeMovementIdentifier
from argaze.TobiiGlassesPro2 import TobiiEntities, TobiiVideo, TobiiSpecifications
from argaze.utils import MiscFeatures
import cv2 as cv
import numpy
import pandas
def main():
"""
Project gaze positions into an AOI and identify particular gaze movements like fixations and saccades
"""
# Manage arguments
parser = argparse.ArgumentParser(description=main.__doc__.split('-')[0])
parser.add_argument('-s', '--segment_path', metavar='SEGMENT_PATH', type=str, default=None, help='path to a tobii segment folder', required=True)
parser.add_argument('-a', '--aoi', metavar='AOI_NAME', type=str, default=None, help='aoi name where to project gaze', required=True)
parser.add_argument('-t', '--time_range', metavar=('START_TIME', 'END_TIME'), nargs=2, type=float, default=(0., None), help='start and end time (in second)')
parser.add_argument('-dev', '--deviation_max_threshold', metavar='DISPERSION_THRESHOLD', type=int, default=None, help='maximal distance for fixation identification in pixel')
parser.add_argument('-dmin', '--duration_min_threshold', metavar='DURATION_MIN_THRESHOLD', type=int, default=200, help='minimal duration for fixation identification in millisecond')
parser.add_argument('-o', '--output', metavar='OUT', type=str, default=None, help='destination folder path (segment folder by default)')
parser.add_argument('-d', '--debug', metavar='DEBUG', type=bool, default=False, help='Enable visualisation and console outputs')
args = parser.parse_args()
# Manage destination path
destination_path = '.'
if args.output != None:
if not os.path.exists(os.path.dirname(args.output)):
os.makedirs(os.path.dirname(args.output))
print(f'{os.path.dirname(args.output)} folder created')
destination_path = args.output
else:
destination_path = args.segment_path
# Export into a dedicated time range folder
if args.time_range[1] != None:
timerange_path = f'[{int(args.time_range[0])}s - {int(args.time_range[1])}s]'
else:
timerange_path = f'[all]'
destination_path = f'{destination_path}/{timerange_path}/{args.aoi}'
if not os.path.exists(destination_path):
os.makedirs(destination_path)
print(f'{destination_path} folder created')
aoi_filepath = f'{destination_path}/../aoi.json'
positions_json_filepath = f'{destination_path}/gaze_positions.json'
fixations_json_filepath = f'{destination_path}/gaze_fixations.json'
saccades_json_filepath = f'{destination_path}/gaze_saccades.json'
movements_json_filepath = f'{destination_path}/gaze_movements.json'
gaze_status_json_filepath = f'{destination_path}/gaze_status.json'
gaze_status_video_filepath = f'{destination_path}/gaze_status.mp4'
gaze_status_image_filepath = f'{destination_path}/gaze_status.png'
# Load aoi scene projection
ts_aois_projections = DataStructures.TimeStampedBuffer.from_json(aoi_filepath)
print(f'\nAOI frames: ', len(ts_aois_projections))
aoi_names = ts_aois_projections.as_dataframe().drop(['offset','error'], axis=1).columns
for aoi_name in aoi_names:
print(f'\t{aoi_name}')
# Load tobii segment
tobii_segment = TobiiEntities.TobiiSegment(args.segment_path, int(args.time_range[0] * 1e6), int(args.time_range[1] * 1e6) if args.time_range[1] != None else None)
# Get participant name
participant_name = TobiiEntities.TobiiParticipant(f'{args.segment_path}/../../').name
print(f'\nParticipant: {participant_name}')
# Load a tobii segment video
tobii_segment_video = tobii_segment.load_video()
print(f'\nVideo properties:\n\tduration: {tobii_segment_video.duration/1e6} s\n\twidth: {tobii_segment_video.width} px\n\theight: {tobii_segment_video.height} px')
# Check that gaze positions have already been exported to not process them again
if os.path.exists(positions_json_filepath):
# Load gaze positions
ts_gaze_positions = GazeFeatures.TimeStampedGazePositions.from_json(positions_json_filepath)
print(f'\nLoaded gaze positions count:')
print(f'\tPositions: {len(ts_gaze_positions)}')
invalid_gaze_position_count = 0
inner_precisions_px = []
for ts, gaze_position in ts_gaze_positions.items():
if not gaze_position.valid:
invalid_gaze_position_count += 1
else:
inner_precisions_px.append(gaze_position.precision)
print(f'\tInvalid positions: {invalid_gaze_position_count}/{len(ts_gaze_positions)} ({100*invalid_gaze_position_count/len(ts_gaze_positions):.2f} %)')
inner_precision_px_mean = round(numpy.mean(inner_precisions_px))
print(f'\tMean of projected precisions: {inner_precision_px_mean} px')
# Project gaze positions into the selected AOI
else:
# Load a tobii segment data
tobii_segment_data = tobii_segment.load_data()
print(f'\nLoaded data count:')
for name in tobii_segment_data.keys():
print(f'\t{name}: {len(tobii_segment_data[name])} data')
# Access to timestamped gaze position data buffer
tobii_ts_gaze_positions = tobii_segment_data['GazePosition']
# Access to timestamped gaze 3D positions data buffer
tobii_ts_gaze_positions_3d = tobii_segment_data['GazePosition3D']
# Format tobii gaze position and precision in pixel and project it in aoi scene
ts_gaze_positions = GazeFeatures.TimeStampedGazePositions()
# Gaze projection metrics
ts_projection_metrics = DataStructures.TimeStampedBuffer()
invalid_gaze_position_count = 0
inner_precisions_px = []
# Starting with no AOI projection
ts_current_aoi = 0
current_aoi = AOIFeatures.AreaOfInterest()
# Initialise progress bar
MiscFeatures.printProgressBar(0, tobii_segment_video.duration, prefix = '\nGazePositions projection:', suffix = 'Complete', length = 100)
for ts, tobii_gaze_position in tobii_ts_gaze_positions.items():
# Update Progress Bar
progress = ts - int(args.time_range[0] * 1e6)
MiscFeatures.printProgressBar(progress, tobii_segment_video.duration, prefix = 'GazePositions projection:', suffix = 'Complete', length = 100)
# Edit default aoi error
current_aoi_error = 'No available AOI projection'
try:
# Get the last aoi projection until the current gaze position timestamp
ts_current_aois, current_aois = ts_aois_projections.pop_last_until(ts)
assert(ts_current_aois <= ts)
# Catch aoi error to not update current aoi
if 'error' in current_aois.keys():
# Remove extra error info after ':'
current_aoi_error = current_aois.pop('error').split(':')[0]
# Or update current aoi
elif args.aoi in current_aois.keys():
ts_current_aoi = ts_current_aois
current_aoi = AOIFeatures.AreaOfInterest(current_aois.pop(args.aoi))
current_aoi_error = ''
# No aoi projection at the beginning
except KeyError as e:
pass
# Wait for available aoi
if current_aoi.empty:
ts_gaze_positions[ts] = GazeFeatures.UnvalidGazePosition(current_aoi_error)
invalid_gaze_position_count += 1
continue
# QUESTION: What todo if the current aoi is too old ?
# if the aoi didn't move it is not a problem...
# For the moment, we avoid 1s old aoi and we provide a metric to assess the problem
ts_difference = ts - ts_current_aoi
# If aoi is not updated after the
if ts_difference >= args.duration_min_threshold*1e3:
current_aoi = AOIFeatures.AreaOfInterest()
ts_gaze_positions[ts] = GazeFeatures.UnvalidGazePosition('AOI projection is too old (> 1s)')
invalid_gaze_position_count += 1
continue
ts_projection_metrics[ts] = {'frame': ts_current_aois, 'age': ts_difference}
# Test gaze position validity
if tobii_gaze_position.validity == 0:
gaze_position_px = (int(tobii_gaze_position.value[0] * tobii_segment_video.width), int(tobii_gaze_position.value[1] * tobii_segment_video.height))
# Get gaze position 3D at same gaze position timestamp
tobii_gaze_position_3d = tobii_ts_gaze_positions_3d.pop(ts)
# Test gaze position 3d validity
if tobii_gaze_position_3d.validity == 0:
gaze_precision_mm = numpy.sin(numpy.deg2rad(TobiiSpecifications.PRECISION)) * tobii_gaze_position_3d.value[2]
tobii_camera_hfov_mm = numpy.sin(numpy.deg2rad(TobiiSpecifications.CAMERA_HFOV)) * tobii_gaze_position_3d.value[2]
gaze_precision_px = round(tobii_segment_video.width * float(gaze_precision_mm) / float(tobii_camera_hfov_mm))
# Edit gaze position
gaze_position = GazeFeatures.GazePosition(gaze_position_px)
# Project gaze position into selected aois
if current_aoi.contains_point(gaze_position.value):
inner_x, inner_y = current_aoi.inner_axis(gaze_position.value)
inner_precision_px = gaze_precision_px * tobii_segment_video.width * tobii_segment_video.height / current_aoi.area
# Store inner precision for metrics
inner_precisions_px.append(inner_precision_px)
# Store inner gaze position for further movement processing
# TEMP: 1920x1080 are Screen_Plan dimensions
ts_gaze_positions[ts] = GazeFeatures.GazePosition((round(inner_x*1920), round((1.0 - inner_y)*1080)), precision=inner_precision_px)
else:
ts_gaze_positions[ts] = GazeFeatures.UnvalidGazePosition(f'GazePosition not inside {args.aoi}')
invalid_gaze_position_count += 1
else:
ts_gaze_positions[ts] = GazeFeatures.UnvalidGazePosition(f'Invalid Tobii GazePosition3D')
invalid_gaze_position_count += 1
else:
ts_gaze_positions[ts] = GazeFeatures.UnvalidGazePosition(f'Invalid Tobii GazePosition')
invalid_gaze_position_count += 1
print(f'\nGazePositions projection metrics:')
print(f'\tInvalid positions: {invalid_gaze_position_count}/{len(tobii_ts_gaze_positions)} ({100*invalid_gaze_position_count/len(tobii_ts_gaze_positions):.2f} %)')
if len(ts_projection_metrics):
projection_metrics_dataframe = ts_projection_metrics.as_dataframe()
print(f'\tAOI age mean: {projection_metrics_dataframe.age.mean() * 1e-3:.3f} ms')
print(f'\tAOI age max: {projection_metrics_dataframe.age.max() * 1e-3:.3f} ms')
inner_precision_px_mean = round(numpy.mean(inner_precisions_px))
print(f'\tMean of projected precisions: {inner_precision_px_mean} px')
else:
print(print(f'\t no AOI projected'))
ts_gaze_positions.to_json(positions_json_filepath)
print(f'\nProjected gaze positions saved into {positions_json_filepath}')
print(f'\nGazeMovement identifier setup:')
if args.deviation_max_threshold == None:
selected_deviation_max_threshold = inner_precision_px_mean
print(f'\tDispersion threshold: {selected_deviation_max_threshold} px (equal to mean of projected precisions)')
else:
selected_deviation_max_threshold = args.deviation_max_threshold
print(f'\tDispersion threshold: {selected_deviation_max_threshold} px')
print(f'\tDuration threshold: {args.duration_min_threshold} ms')
movement_identifier = DispersionBasedGazeMovementIdentifier.GazeMovementIdentifier(selected_deviation_max_threshold, args.duration_min_threshold*1e3)
# Start movement identification
ts_fixations = GazeFeatures.TimeStampedGazeMovements()
ts_saccades = GazeFeatures.TimeStampedGazeMovements()
ts_status = GazeFeatures.TimeStampedGazeStatus()
# Initialise progress bar
MiscFeatures.printProgressBar(0, tobii_segment_video.duration, prefix = '\nGazeMovements identification:', suffix = 'Complete', length = 100)
for ts, gaze_position in ts_gaze_positions.items():
gaze_movement = movement_identifier.identify(ts, gaze_position)
if isinstance(gaze_movement, DispersionBasedGazeMovementIdentifier.Fixation):
start_ts, start_position = gaze_movement.positions.first
ts_fixations[start_ts] = gaze_movement
for ts, position in gaze_movement.positions.items():
ts_status[ts] = GazeFeatures.GazeStatus.from_position(position, 'Fixation', len(ts_fixations))
elif isinstance(gaze_movement, DispersionBasedGazeMovementIdentifier.Saccade):
start_ts, start_position = gaze_movement.positions.first
ts_saccades[start_ts] = gaze_movement
for ts, position in gaze_movement.positions.items():
ts_status[ts] = GazeFeatures.GazeStatus.from_position(position, 'Saccade', len(ts_saccades))
# Update Progress Bar
progress = ts - int(args.time_range[0] * 1e6)
MiscFeatures.printProgressBar(progress, tobii_segment_video.duration, prefix = 'Gaze movements identification:', suffix = 'Complete', length = 100)
print(f'\nGazeMovements identification metrics:')
print(f'\t{len(ts_fixations)} fixations found')
print(f'\t{len(ts_saccades)} saccades found')
ts_fixations.to_json(fixations_json_filepath)
print(f'\nGaze fixations saved into {fixations_json_filepath}')
ts_saccades.to_json(saccades_json_filepath)
print(f'Gaze saccades saved into {saccades_json_filepath}')
ts_status.to_json(gaze_status_json_filepath)
print(f'Gaze status saved into {gaze_status_json_filepath}')
# DEBUG
ts_status.as_dataframe().to_csv(f'{destination_path}/gaze_status.csv')
# Edit data visualisation
if args.debug:
# Prepare video exportation at the same format than segment video
output_video = TobiiVideo.TobiiVideoOutput(gaze_status_video_filepath, tobii_segment_video.stream)
# Reload aoi scene projection
ts_aois_projections = DataStructures.TimeStampedBuffer.from_json(aoi_filepath)
# Prepare gaze satus image
gaze_status_matrix = numpy.zeros((1080, 1920, 3), numpy.uint8)
# Video loop
try:
# Initialise progress bar
MiscFeatures.printProgressBar(0, tobii_segment_video.duration, prefix = '\nGaze status video processing:', suffix = 'Complete', length = 100)
fixations_exist = len(ts_fixations) > 0
saccades_exist = len(ts_saccades) > 0
status_exist = len(ts_status) > 0
if fixations_exist:
current_fixation_ts, current_fixation = ts_fixations.pop_first()
current_fixation_time_counter = 0
if saccades_exist:
current_saccade_ts, current_saccade = ts_saccades.pop_first()
# Iterate on video frames
for video_ts, video_frame in tobii_segment_video.frames():
# This video frame is the reference until the next frame
# Here next frame is at + 40ms (25 fps)
# TODO: Get video fps to adapt
next_video_ts = video_ts + 40000
visu_matrix = numpy.zeros((1080, 1920, 3), numpy.uint8)
try:
# Get current aoi projection at video frame time
ts_current_aois, current_aois = ts_aois_projections.pop_first()
assert(ts_current_aois == video_ts)
# Catch aoi error to not update current aoi
if 'error' in current_aois.keys():
# Display error (remove extra info after ':')
current_aoi_error = current_aois.pop('error').split(':')[0]
# Select color error
if current_aoi_error == 'VideoTimeStamp missing':
color_error = (0, 0, 255)
else:
color_error = (0, 255, 255)
cv.rectangle(visu_matrix, (0, 100), (550, 150), (127, 127, 127), -1)
cv.putText(visu_matrix, current_aoi_error, (20, 130), cv.FONT_HERSHEY_SIMPLEX, 1, color_error, 1, cv.LINE_AA)
# Or update current aoi
elif args.aoi in current_aois.keys():
ts_current_aoi = ts_current_aois
current_aoi = AOIFeatures.AreaOfInterest(current_aois.pop(args.aoi))
# Apply perspective transform algorithm
destination = numpy.float32([[0, 1080],[1920, 1080],[1920, 0],[0, 0]])
aoi_matrix = cv.getPerspectiveTransform(current_aoi.astype(numpy.float32), destination)
visu_matrix = cv.warpPerspective(video_frame.matrix, aoi_matrix, (1920, 1080))
# Wait for aois projection
except KeyError:
pass
if fixations_exist:
# Check next fixation
if video_ts >= current_fixation_ts + current_fixation.duration and len(ts_fixations) > 0:
current_fixation_ts, current_fixation = ts_fixations.pop_first()
current_fixation_time_counter = 0
# While current time belongs to the current fixation
if video_ts >= current_fixation_ts and video_ts < current_fixation_ts + current_fixation.duration:
current_fixation_time_counter += 1
# Draw current fixation
cv.circle(visu_matrix, (int(current_fixation.centroid[0]), int(current_fixation.centroid[1])), int(current_fixation.deviation_max), (0, 255, 0), current_fixation_time_counter)
cv.circle(gaze_status_matrix, (int(current_fixation.centroid[0]), int(current_fixation.centroid[1])), int(current_fixation.deviation_max), (0, 155, 0))
if saccades_exist:
# Check next saccade
if video_ts >= current_saccade_ts + current_saccade.duration and len(ts_saccades) > 0:
current_saccade_ts, current_saccade = ts_saccades.pop_first()
# While current time belongs to the current saccade
if video_ts >= current_saccade_ts and video_ts < current_saccade_ts + current_saccade.duration:
pass
# Draw gaze status until next frame
try:
# Get next gaze status
ts_start, start_gaze_status = ts_status.first
ts_next, next_gaze_status = ts_status.first
# Check next gaze status is not after next frame time
while ts_next < next_video_ts:
ts_start, start_gaze_status = ts_status.pop_first()
ts_next, next_gaze_status = ts_status.first
# Draw movement type
if start_gaze_status.valid and next_gaze_status.valid \
and start_gaze_status.movement_index == next_gaze_status.movement_index \
and start_gaze_status.movement_type == next_gaze_status.movement_type:
if next_gaze_status.movement_type == 'Fixation':
movement_color = (0, 255, 0)
elif next_gaze_status.movement_type == 'Saccade':
movement_color = (0, 0, 255)
else:
movement_color = (255, 0, 0)
cv.line(visu_matrix, start_gaze_status, next_gaze_status, movement_color, 3)
cv.line(gaze_status_matrix, start_gaze_status, next_gaze_status, movement_color, 3)
# Empty gaze position
except IndexError:
pass
# Draw gaze positions until next frame
try:
# Get next gaze position
ts_start, start_gaze_position = ts_gaze_positions.first
ts_next, next_gaze_position = ts_gaze_positions.first
# Gaze position count
gaze_position_count = 0
# Check next gaze position is not after next frame time
while ts_next < next_video_ts:
ts_start, start_gaze_position = ts_gaze_positions.pop_first()
ts_next, next_gaze_position = ts_gaze_positions.first
if not start_gaze_position.valid:
# Select color error
if start_gaze_position.message == 'VideoTimeStamp missing':
color_error = (0, 0, 255)
else:
color_error = (0, 255, 255)
# Write unvalid error message
cv.putText(visu_matrix, f'{ts_start*1e-3:.3f} ms: {start_gaze_position.message}', (20, 1060 - (gaze_position_count)*50), cv.FONT_HERSHEY_SIMPLEX, 1, color_error, 1, cv.LINE_AA)
# Draw start gaze
start_gaze_position.draw(visu_matrix, draw_precision=False)
start_gaze_position.draw(gaze_status_matrix, draw_precision=False)
if start_gaze_position.valid and next_gaze_position.valid:
# Draw movement from start to next
cv.line(visu_matrix, start_gaze_position, next_gaze_position, (0, 55, 55), 1)
cv.line(gaze_status_matrix, start_gaze_position, next_gaze_position, (0, 55, 55), 1)
gaze_position_count += 1
if start_gaze_position.valid:
# Write last start gaze position
cv.putText(visu_matrix, str(start_gaze_position.value), start_gaze_position.value, cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv.LINE_AA)
# Write last start gaze position timing
cv.rectangle(visu_matrix, (0, 50), (550, 100), (31, 31, 31), -1)
cv.putText(visu_matrix, f'Gaze time: {ts_start*1e-3:.3f} ms', (20, 85), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv.LINE_AA)
# Empty gaze position
except IndexError:
pass
# Write segment timing
cv.rectangle(visu_matrix, (0, 0), (550, 50), (63, 63, 63), -1)
cv.putText(visu_matrix, f'Video time: {video_ts*1e-3:.3f} ms', (20, 40), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv.LINE_AA)
# Write movement identification parameters
cv.rectangle(visu_matrix, (0, 150), (550, 310), (63, 63, 63), -1)
cv.putText(visu_matrix, f'Deviation max: {selected_deviation_max_threshold} px', (20, 210), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv.LINE_AA)
cv.putText(visu_matrix, f'Duration min: {args.duration_min_threshold} ms', (20, 270), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv.LINE_AA)
# Draw dispersion threshold circle
cv.circle(visu_matrix, (selected_deviation_max_threshold + 400, 230), 2, (0, 255, 255), -1)
cv.circle(visu_matrix, (selected_deviation_max_threshold + 400, 230), selected_deviation_max_threshold, (255, 150, 150), 1)
# Close window using 'Esc' key
if cv.waitKey(1) == 27:
break
# Display video
cv.imshow(f'Segment {tobii_segment.id} movements', visu_matrix)
# Write video
output_video.write(visu_matrix)
# Update Progress Bar
progress = video_ts - int(args.time_range[0] * 1e6)
MiscFeatures.printProgressBar(progress, tobii_segment_video.duration, prefix = 'Gaze status video processing:', suffix = 'Complete', length = 100)
# Exit on 'ctrl+C' interruption
except KeyboardInterrupt:
pass
# Saving gaze status image
cv.imwrite(gaze_status_image_filepath, gaze_status_matrix)
# End output video file
output_video.close()
print(f'\nGaze status video saved into {gaze_status_video_filepath}\n')
if __name__ == '__main__':
main()
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