diff options
| -rw-r--r-- | src/argaze/utils/tobii_segment_gaze_movements_export.py | 459 |
1 files changed, 296 insertions, 163 deletions
diff --git a/src/argaze/utils/tobii_segment_gaze_movements_export.py b/src/argaze/utils/tobii_segment_gaze_movements_export.py index 1ffc836..9b6ee5c 100644 --- a/src/argaze/utils/tobii_segment_gaze_movements_export.py +++ b/src/argaze/utils/tobii_segment_gaze_movements_export.py @@ -2,14 +2,17 @@ import argparse import os +import math -from argaze import GazeFeatures +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(): """ @@ -18,245 +21,375 @@ def main(): # 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') - parser.add_argument('-r', '--time_range', metavar=('START_TIME', 'END_TIME'), nargs=2, type=float, default=(0., None), help='start and end time (in second)') - parser.add_argument('-d', '--dispersion_threshold', metavar='DISPERSION_THRESHOLD', type=int, default=50, help='dispersion threshold in pixel') - parser.add_argument('-t', '--duration_threshold', metavar='DURATION_THRESHOLD', type=int, default=100, help='duration threshold in millisecond') + 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('-di', '--dispersion_threshold', metavar='DISPERSION_THRESHOLD', type=int, default=50, help='dispersion threshold in pixel') + parser.add_argument('-du', '--duration_threshold', metavar='DURATION_THRESHOLD', type=int, default=100, help='duration threshold in millisecond') parser.add_argument('-o', '--output', metavar='OUT', type=str, default=None, help='destination folder path (segment folder by default)') parser.add_argument('-w', '--window', metavar='DISPLAY', type=bool, default=True, help='enable window display', action=argparse.BooleanOptionalAction) args = parser.parse_args() - if args.segment_path != None: + # Manage destination path + destination_path = '.' + if args.output != None: - # Manage destination path - destination_path = '.' - if args.output != None: + if not os.path.exists(os.path.dirname(args.output)): - 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') - os.makedirs(os.path.dirname(args.output)) - print(f'{os.path.dirname(args.output)} folder created') + destination_path = args.output - destination_path = args.output + else: - 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' - destination_path = args.segment_path + fixations_filepath = f'{destination_path}/gaze_fixations.csv' + saccades_filepath = f'{destination_path}/gaze_saccades.csv' - # 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]' + gaze_status_filepath = f'{destination_path}/gaze_status.csv' + gaze_status_video_filepath = f'{destination_path}/gaze_status.mp4' + gaze_status_image_filepath = f'{destination_path}/gaze_status.png' - destination_path = f'{destination_path}/{timerange_path}' + gaze_metrics_filepath = f'{destination_path}/gaze_metrics.csv' - if not os.path.exists(destination_path): + # Load aoi scene projection + ts_aois_projections = DataStructures.TimeStampedBuffer.from_json(aoi_filepath) - os.makedirs(destination_path) - print(f'{destination_path} folder created') + print(f'\nAOI frames: ', len(ts_aois_projections)) + aoi_names = ts_aois_projections.as_dataframe().drop(['offset','comment'], axis=1).columns + for aoi_name in aoi_names: + print(f'\t{aoi_name}') - fixations_filepath = f'{destination_path}/gaze_fixations.csv' - saccades_filepath = f'{destination_path}/gaze_saccades.csv' + # 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) - gaze_status_filepath = f'{destination_path}/gaze_status.csv' - gaze_status_video_filepath = f'{destination_path}/gaze_status.mp4' + # Get participant name + participant_name = TobiiEntities.TobiiParticipant(f'{args.segment_path}/../../').name - # Load a 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) + print(f'\nParticipant: {participant_name}') - # Load a tobii segment video - tobii_segment_video = tobii_segment.load_video() - print(f'Video properties:\n\tduration: {tobii_segment_video.duration/1e6} s\n\twidth: {tobii_segment_video.width} px\n\theight: {tobii_segment_video.height} px') + # 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') - # Load a tobii segment data - tobii_segment_data = tobii_segment.load_data() - - print(f'Loaded data count:') - for name in tobii_segment_data.keys(): - print(f'\t{name}: {len(tobii_segment_data[name])} data') + # 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 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'] + # Access to timestamped gaze 3D positions data buffer + tobii_ts_gaze_positions_3d = tobii_segment_data['GazePosition3D'] + + # Format tobii gaze position and accuracy in pixel and project it in aoi scene + ts_gaze_positions = GazeFeatures.TimeStampedGazePositions() + + # Gaze projection metrics + ts_projection_metrics = DataStructures.TimeStampedBuffer() + + # 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) + + try: - # Format tobii gaze position and accuracy in pixel - ts_gaze_positions = GazeFeatures.TimeStampedGazePositions() + # Get the last aoi projection until the current gaze position timestamp + ts_current_aois, current_aois = ts_aois_projections.pop_last_until(ts) - for ts, tobii_gaze_position in tobii_ts_gaze_positions.items(): + assert(ts_current_aois <= ts) - # Test gaze position validity - if tobii_gaze_position.validity == 0: + # 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 just provide a metric to assess its not too big + ts_projection_metrics[ts] = {'frame': ts_current_aois, 'age': ts - ts_current_aois} - gaze_position_px = (int(tobii_gaze_position.value[0] * tobii_segment_video.width), int(tobii_gaze_position.value[1] * tobii_segment_video.height)) + current_aoi_offset = current_aois.pop('offset') + current_aoi_comment = current_aois.pop('comment') + + selected_aoi = AOIFeatures.AreaOfInterest(current_aois.pop(args.aoi)) + + # Wait for aois projection + except KeyError: + + continue + + # 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_accuracy_mm = numpy.sin(numpy.deg2rad(TobiiSpecifications.ACCURACY)) * tobii_gaze_position_3d.value[2] + tobii_camera_hfov_mm = numpy.sin(numpy.deg2rad(TobiiSpecifications.CAMERA_HFOV)) * tobii_gaze_position_3d.value[2] - # Get gaze position 3D at same gaze position timestamp - tobii_gaze_position_3d = tobii_ts_gaze_positions_3d.pop(ts) + gaze_accuracy_px = round(tobii_segment_video.width * float(gaze_accuracy_mm) / float(tobii_camera_hfov_mm)) - # Test gaze position 3d validity - if tobii_gaze_position_3d.validity == 0: - - gaze_accuracy_mm = numpy.sin(numpy.deg2rad(TobiiSpecifications.ACCURACY)) * tobii_gaze_position_3d.value[2] - tobii_camera_hfov_mm = numpy.sin(numpy.deg2rad(TobiiSpecifications.CAMERA_HFOV)) * tobii_gaze_position_3d.value[2] - - gaze_accuracy_px = round(tobii_segment_video.width * float(gaze_accuracy_mm) / float(tobii_camera_hfov_mm)) + # Edit gaze position + gaze_position = GazeFeatures.GazePosition(gaze_position_px, accuracy=gaze_accuracy_px) - # Store gaze position - ts_gaze_positions[ts] = GazeFeatures.GazePosition(gaze_position_px, accuracy=gaze_accuracy_px) + # Project gaze position into selected aois + if selected_aoi.contains_point(gaze_position.value): + + inner_x, inner_y = selected_aoi.inner_axis(gaze_position.value) + + # Store inner gaze position for further movement processing + ts_gaze_positions[ts] = GazeFeatures.GazePosition((round(inner_x*1920), round(inner_y*1080))) # TEMP: This is Screen_Plan dimension continue - # Store unvalid gaze position for further movement processing - ts_gaze_positions[ts] = GazeFeatures.UnvalidGazePosition() + # Store unvalid gaze position for further movement processing + ts_gaze_positions[ts] = GazeFeatures.UnvalidGazePosition() - print(f'GazeMovement identifier parameters:') - print(f'\tDispersion threshold = {args.dispersion_threshold}') - print(f'\tDuration threshold = {args.duration_threshold}') + print(f'\nGazePositions projection metrics:') + projection_metrics_dataframe = ts_projection_metrics.as_dataframe() + print(f'\t AOI age mean (ms) = {projection_metrics_dataframe.age.mean() * 1e-3}') + print(f'\t AOI age max (ms) = {projection_metrics_dataframe.age.max() * 1e-3}') - # Start movement identification - movement_identifier = DispersionBasedGazeMovementIdentifier.GazeMovementIdentifier(args.dispersion_threshold, args.duration_threshold*1e3) - ts_fixations = GazeFeatures.TimeStampedGazeMovements() - ts_saccades = GazeFeatures.TimeStampedGazeMovements() - ts_status = GazeFeatures.TimeStampedGazeStatus() + print(f'\nGazeMovement identifier parameters:') + print(f'\tDispersion threshold = {args.dispersion_threshold}') + print(f'\tDuration threshold = {args.duration_threshold}') - # Initialise progress bar - MiscFeatures.printProgressBar(0, tobii_segment_video.duration, prefix = 'GazeMovements identification:', suffix = 'Complete', length = 100) + # Start movement identification + movement_identifier = DispersionBasedGazeMovementIdentifier.GazeMovementIdentifier(args.dispersion_threshold, args.duration_threshold*1e3) + ts_fixations = GazeFeatures.TimeStampedGazeMovements() + ts_saccades = GazeFeatures.TimeStampedGazeMovements() + ts_status = GazeFeatures.TimeStampedGazeStatus() - for gaze_movement in movement_identifier(ts_gaze_positions): + # Initialise progress bar + MiscFeatures.printProgressBar(0, tobii_segment_video.duration, prefix = '\nGazeMovements identification:', suffix = 'Complete', length = 100) - if isinstance(gaze_movement, DispersionBasedGazeMovementIdentifier.Fixation): + for gaze_movement in movement_identifier(ts_gaze_positions): - start_ts, start_position = gaze_movement.positions.first + if isinstance(gaze_movement, DispersionBasedGazeMovementIdentifier.Fixation): - ts_fixations[start_ts] = gaze_movement + start_ts, start_position = gaze_movement.positions.first - for ts, position in gaze_movement.positions.items(): + ts_fixations[start_ts] = gaze_movement - ts_status[ts] = GazeFeatures.GazeStatus.from_position(position, 'Fixation', len(ts_fixations)) + for ts, position in gaze_movement.positions.items(): - elif isinstance(gaze_movement, DispersionBasedGazeMovementIdentifier.Saccade): + ts_status[ts] = GazeFeatures.GazeStatus.from_position(position, 'Fixation', len(ts_fixations)) - start_ts, start_position = gaze_movement.positions.first - end_ts, end_position = gaze_movement.positions.last - - ts_saccades[start_ts] = gaze_movement + elif isinstance(gaze_movement, DispersionBasedGazeMovementIdentifier.Saccade): - ts_status[start_ts] = GazeFeatures.GazeStatus.from_position(start_position, 'Saccade', len(ts_saccades)) - ts_status[end_ts] = GazeFeatures.GazeStatus.from_position(end_position, 'Saccade', len(ts_saccades)) + start_ts, start_position = gaze_movement.positions.first + end_ts, end_position = gaze_movement.positions.last + + ts_saccades[start_ts] = gaze_movement - else: - continue + ts_status[start_ts] = GazeFeatures.GazeStatus.from_position(start_position, 'Saccade', len(ts_saccades)) + ts_status[end_ts] = GazeFeatures.GazeStatus.from_position(end_position, 'Saccade', len(ts_saccades)) - # Update Progress Bar - progress = ts - int(args.time_range[0] * 1e6) - MiscFeatures.printProgressBar(progress, tobii_segment_video.duration, prefix = 'GazeMovements identification:', suffix = 'Complete', length = 100) + else: + continue - print(f'\n{len(ts_fixations)} fixations and {len(ts_saccades)} saccades found') + # Update Progress Bar + progress = start_ts - int(args.time_range[0] * 1e6) + MiscFeatures.printProgressBar(progress, tobii_segment_video.duration, prefix = 'GazeMovements identification:', suffix = 'Complete', length = 100) - # Export fixations analysis - ts_fixations.as_dataframe().to_csv(fixations_filepath, index=True) - print(f'Fixations saved into {fixations_filepath}') + print(f'\nGazeMovements identification metrics:') + print(f'\t{len(ts_fixations)} fixations found') + print(f'\t{len(ts_saccades)} saccades found') - # Export saccades analysis - ts_saccades.as_dataframe().to_csv(saccades_filepath, index=True) - print(f'Saccades saved into {saccades_filepath}') + # Export fixations analysis + fixations_dataframe = ts_fixations.as_dataframe() + fixations_dataframe.to_csv(fixations_filepath, index=True) + print(f'\nFixations saved into {fixations_filepath}') - # Export gaze status analysis - ts_status.as_dataframe().to_csv(gaze_status_filepath, index=True) - print(f'Gaze status saved into {gaze_status_filepath}') + # Export saccades analysis + saccades_dataframe = ts_saccades.as_dataframe() + saccades_dataframe.to_csv(saccades_filepath, index=True) + print(f'Saccades saved into {saccades_filepath}') - # Prepare video exportation at the same format than segment video - output_video = TobiiVideo.TobiiVideoOutput(gaze_status_video_filepath, tobii_segment_video.stream) + # Export gaze status analysis + ts_status.as_dataframe().to_csv(gaze_status_filepath, index=True) + print(f'Gaze status saved into {gaze_status_filepath}') - # Video and data loop - try: + # Export gaze metrics - # Initialise progress bar - MiscFeatures.printProgressBar(0, tobii_segment_video.duration, prefix = 'Video with movements processing:', suffix = 'Complete', length = 100) + # Consider only fixations > duration threshold and saccades < duration threshold + # This is mostly usefull to filter first and last fixation/saccade as the time range can start anywhere + filtered_fixations = fixations_dataframe[fixations_dataframe.duration > args.duration_threshold*1e3] + filtered_saccades = saccades_dataframe[saccades_dataframe.duration < args.duration_threshold*1e3] - current_fixation_ts, current_fixation = ts_fixations.pop_first() - current_fixation_time_counter = 0 + segment_duration = tobii_segment_video.duration * 1e-3 + exploitation_time = filtered_fixations.duration.sum() * 1e-3 + exploration_time = filtered_saccades.duration.sum() * 1e-3 - current_saccade_ts, current_saccade = ts_saccades.pop_first() + metrics = { + 'segment_duration (ms)': segment_duration, + 'fixations_number': filtered_fixations.shape[0], + 'fixations_duration_mean (ms)': filtered_fixations.duration.mean() * 1e-3, + 'saccades_number': filtered_saccades.shape[0], + 'saccades_duration_mean (ms)': filtered_saccades.duration.mean() * 1e-3, + 'exploitation_ratio (%)': exploitation_time / segment_duration * 100, + 'exploration_ratio (%)': exploration_time / segment_duration * 100, + 'exploit_explore_ratio:': exploitation_time / exploration_time + } - # Iterate on video frames - for video_ts, video_frame in tobii_segment_video.frames(): + metrics_dataframe = pandas.DataFrame(metrics, index=[participant_name]) - # While current time belongs to the current fixation - if video_ts >= current_fixation_ts and video_ts < current_fixation_ts + current_fixation.duration: + metrics_dataframe.to_csv(gaze_metrics_filepath, index=True) + print(f'Gaze metrics saved into {gaze_metrics_filepath}') - current_fixation_time_counter += 1 + # Prepare video exportation at the same format than segment video + output_video = TobiiVideo.TobiiVideoOutput(gaze_status_video_filepath, tobii_segment_video.stream) - # Draw current fixation - cv.circle(video_frame.matrix, (int(current_fixation.centroid[0]), int(current_fixation.centroid[1])), int(current_fixation.dispersion), (0, 255, 0), current_fixation_time_counter) + # Reload aoi scene projection + ts_aois_projections = DataStructures.TimeStampedBuffer.from_json(aoi_filepath) - # Check next fixation - elif video_ts >= current_fixation_ts + current_fixation.duration and len(ts_fixations) > 0: + # + heatmap_matrix = numpy.full((1080, 1920, 3), 255, numpy.uint8) - current_fixation_ts, current_fixation = ts_fixations.pop_first() - current_fixation_time_counter = 0 + # Video and data loop + try: - # While current time belongs to the current saccade - if video_ts >= current_saccade_ts and current_fixation_time_counter == 0: + # Initialise progress bar + MiscFeatures.printProgressBar(0, tobii_segment_video.duration, prefix = '\nGazeMovements visualisation:', suffix = 'Complete', length = 100) - start_ts, start_position = current_saccade.positions.first - end_ts, end_position = current_saccade.positions.last + current_fixation_ts, current_fixation = ts_fixations.pop_first() + current_fixation_time_counter = 0 - # Draw saccade - cv.line(video_frame.matrix, start_position, end_position, (0, 0, 255), 2) + current_saccade_ts, current_saccade = ts_saccades.pop_first() - # Check next saccade - elif video_ts >= current_saccade_ts + current_saccade.duration and len(ts_saccades) > 0: + # Iterate on video frames + for video_ts, video_frame in tobii_segment_video.frames(): - current_saccade_ts, current_saccade = ts_saccades.pop_first() + visu_matrix= numpy.zeros((1080, 1920, 3), numpy.uint8) - # Check next gaze - try: + try: - # Get closest gaze position before video timestamp and remove all gaze positions before - _, nearest_gaze_position = ts_gaze_positions.pop_first_until(video_ts) + # Get next aoi projection at video frame time + ts_current_aois, current_aois = ts_aois_projections.pop_first() - # Draw gaze - nearest_gaze_position.draw(video_frame.matrix) + assert(ts_current_aois == video_ts) - # Wait for gaze position - except KeyError: - pass + selected_aoi = AOIFeatures.AreaOfInterest(current_aois.pop(args.aoi)) - # Write segment timing - cv.rectangle(video_frame.matrix, (0, 0), (550, 50), (63, 63, 63), -1) - cv.putText(video_frame.matrix, f'Segment time: {int(video_ts/1e3)} ms', (20, 40), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv.LINE_AA) - - # Write movement identification parameters - cv.rectangle(video_frame.matrix, (0, 90), (550, 150), (63, 63, 63), -1) - cv.putText(video_frame.matrix, f'Dispersion threshold: {args.dispersion_threshold} px', (20, 100), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv.LINE_AA) - cv.putText(video_frame.matrix, f'Duration threshold: {args.duration_threshold} ms', (20, 140), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv.LINE_AA) + # Wait for aois projection + except KeyError: + + continue + + # Apply Perspective Transform Algorithm + destination = numpy.float32([[0, 0],[1920, 0],[1920, 1080],[0, 1080]]) + aoi_matrix = cv.getPerspectiveTransform(selected_aoi.astype(numpy.float32), destination) + visu_matrix = cv.warpPerspective(video_frame.matrix, aoi_matrix, (1920, 1080)) + + # 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.dispersion), (0, 255, 0), current_fixation_time_counter) + + # + cv.circle(heatmap_matrix, (int(current_fixation.centroid[0]), int(current_fixation.centroid[1])), int(current_fixation.dispersion), (0, 255, 0)) + + # Check next fixation + elif 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 - if args.window: + # While current time belongs to the current saccade + if video_ts >= current_saccade_ts and current_fixation_time_counter == 0: - # Close window using 'Esc' key - if cv.waitKey(1) == 27: - break + start_ts, start_position = current_saccade.positions.first + end_ts, end_position = current_saccade.positions.last - # Display video - cv.imshow(f'Segment {tobii_segment.id} movements', video_frame.matrix) + # Draw saccade + int_start_position = (int(start_position[0]), int(start_position[1])) + int_end_position = (int(end_position[0]), int(end_position[1])) - # Write video - output_video.write(video_frame.matrix) + cv.line(visu_matrix, int_start_position, int_end_position, (0, 0, 255), 2) + cv.line(heatmap_matrix, int_start_position, int_end_position, (0, 0, 255), 2) + + # Check next saccade + elif video_ts >= current_saccade_ts + current_saccade.duration and len(ts_saccades) > 0: + + current_saccade_ts, current_saccade = ts_saccades.pop_first() + + # Check next gaze + try: + + # Get closest gaze position before video timestamp and remove all gaze positions before + _, nearest_gaze_position = ts_gaze_positions.pop_last_before(video_ts) + + # Draw gaze + nearest_gaze_position.draw(visu_matrix) + + # Wait for gaze position + except KeyError: + pass + + # Write segment timing + cv.rectangle(visu_matrix, (0, 0), (550, 50), (63, 63, 63), -1) + cv.putText(visu_matrix, f'Segment time: {int(video_ts/1e3)} ms', (20, 40), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv.LINE_AA) + + # Write movement identification parameters + cv.rectangle(visu_matrix, (0, 90), (550, 150), (63, 63, 63), -1) + cv.putText(visu_matrix, f'Dispersion threshold: {args.dispersion_threshold} px', (20, 100), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv.LINE_AA) + cv.putText(visu_matrix, f'Duration threshold: {args.duration_threshold} ms', (20, 140), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv.LINE_AA) + + if args.window: + + # 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 = 'Video with movements processing:', suffix = 'Complete', length = 100) - # Update Progress Bar - progress = video_ts - int(args.time_range[0] * 1e6) - MiscFeatures.printProgressBar(progress, tobii_segment_video.duration, prefix = 'Video with movements processing:', suffix = 'Complete', length = 100) + # Exit on 'ctrl+C' interruption + except KeyboardInterrupt: + pass - # Exit on 'ctrl+C' interruption - except KeyboardInterrupt: - pass + # + cv.imwrite(gaze_status_image_filepath, heatmap_matrix) - # End output video file - output_video.close() - print(f'\nVideo with movements saved into {gaze_status_video_filepath}') + # End output video file + output_video.close() + print(f'\nVideo with movements saved into {gaze_status_video_filepath}\n') if __name__ == '__main__': |