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-rw-r--r--src/argaze/utils/export_tobii_segment_aruco_visual_scan.py220
1 files changed, 133 insertions, 87 deletions
diff --git a/src/argaze/utils/export_tobii_segment_aruco_visual_scan.py b/src/argaze/utils/export_tobii_segment_aruco_visual_scan.py
index 4825c9e..653a5fa 100644
--- a/src/argaze/utils/export_tobii_segment_aruco_visual_scan.py
+++ b/src/argaze/utils/export_tobii_segment_aruco_visual_scan.py
@@ -95,6 +95,9 @@ def main():
# Access to timestamped gaze 3D positions data buffer
tobii_ts_gaze_positions_3d = tobii_segment_data['GazePosition3D']
+ # Access to timestamped head rotations data buffer
+ tobii_ts_head_rotations = tobii_segment_data['Gyroscope']
+
# Prepare video exportation at the same format than segment video
output_video = TobiiVideo.TobiiVideoOutput(vs_video_filepath, tobii_segment_video.get_stream())
@@ -199,140 +202,183 @@ def main():
# Initialise progress bar
#MiscFeatures.printProgressBar(0, tobii_segment_video.get_duration()/1000, prefix = 'Progress:', suffix = 'Complete', length = 100)
+ head_moving = False
+ head_movement_last = 0.
+
# Iterate on video frames
for video_ts, video_frame in tobii_segment_video.frames():
video_ts_ms = video_ts / 1000
-
- # Track markers with pose estimation and draw them
- aruco_tracker.track(video_frame.matrix)
- aruco_tracker.draw(video_frame.matrix)
+ visu_frame = video_frame.copy()
# Write segment timing
- cv.putText(video_frame.matrix, f'Segment time: {int(video_ts_ms)} ms', (20, 40), cv.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv.LINE_AA)
-
+ cv.putText(visu_frame.matrix, f'Segment time: {int(video_ts_ms)} ms', (20, 40), cv.FONT_HERSHEY_SIMPLEX, 1, (127, 127, 127), 1, cv.LINE_AA)
+
+ # Draw focus area
+ cv.rectangle(visu_frame.matrix, (int(video_frame.width/6), 0), (int(video_frame.width*(1-1/6)), int(video_frame.height)), (255, 150, 150), 1)
+
+ # Draw center
+ cv.line(visu_frame.matrix, (int(visu_frame.width/2) - 50, int(visu_frame.height/2)), (int(visu_frame.width/2) + 50, int(visu_frame.height/2)), (255, 150, 150), 1)
+ cv.line(visu_frame.matrix, (int(visu_frame.width/2), int(visu_frame.height/2) - 50), (int(visu_frame.width/2), int(visu_frame.height/2) + 50), (255, 150, 150), 1)
+
+ # Process video and data frame
try:
+ # Get nearest head rotation before video timestamp and remove all head rotations before
+ _, nearest_head_rotation = tobii_ts_head_rotations.pop_first_until(video_ts)
+
+ # Calculate head movement considering only head yaw and pitch
+ head_movement = numpy.array(nearest_head_rotation.value)
+ head_movement_px = head_movement.astype(int)
+ head_movement_norm = numpy.linalg.norm(head_movement[0:2])
+
+ # Draw movement vector
+ cv.line(visu_frame.matrix, (int(visu_frame.width/2), int(visu_frame.height/2)), (int(visu_frame.width/2) + head_movement_px[1], int(visu_frame.height/2) - head_movement_px[0]), (150, 150, 150), 3)
+
+ # Head movement detection hysteresis
+ # TODO : pass the threshold value as argument
+ if not head_moving and head_movement_norm > 50:
+ head_moving = True
+
+ if head_moving and head_movement_norm < 10:
+ head_moving = False
+
+ # Ignore frame where head is moving
+ if head_moving:
+ raise UserWarning('Head is moving')
+
# Get nearest gaze position before video timestamp and remove all gaze positions before
_, nearest_gaze_position = tobii_ts_gaze_positions.pop_first_until(video_ts)
+ gaze_position_pixel = (int(nearest_gaze_position.value[0] * visu_frame.width), int(nearest_gaze_position.value[1] * visu_frame.height))
+
+ # Draw gaze position
+ cv.circle(visu_frame.matrix, gaze_position_pixel, 2, (0, 255, 255), -1)
+
# Get nearest gaze position 3D before video timestamp and remove all gaze positions before
_, nearest_gaze_position_3d = tobii_ts_gaze_positions_3d.pop_first_until(video_ts)
- # Consider gaze position if gaze precision can be evaluated
- if nearest_gaze_position_3d.value[2] > 0:
+ # Ignore frame when gaze precison can't be evaluated
+ if nearest_gaze_position_3d.value[2] <= 0:
+ raise UserWarning('Negative Z gaze position 3D value')
- gaze_position_pixel = (int(nearest_gaze_position.value[0] * video_frame.width), int(nearest_gaze_position.value[1] * video_frame.height))
+ gaze_accuracy_mm = numpy.tan(numpy.deg2rad(tobii_accuracy)) * nearest_gaze_position_3d.value[2]
+ tobii_camera_hfov_mm = numpy.tan(numpy.deg2rad(tobii_camera_hfov / 2)) * nearest_gaze_position_3d.value[2]
+ gaze_accuracy_pixel = round(visu_frame.width * float(gaze_accuracy_mm) / float(tobii_camera_hfov_mm))
- gaze_accuracy_mm = numpy.tan(numpy.deg2rad(tobii_accuracy)) * nearest_gaze_position_3d.value[2]
- tobii_camera_hfov_mm = numpy.tan(numpy.deg2rad(tobii_camera_hfov / 2)) * nearest_gaze_position_3d.value[2]
- gaze_accuracy_pixel = round(video_frame.width * float(gaze_accuracy_mm) / float(tobii_camera_hfov_mm))
+ # Draw gaze accuracy
+ cv.circle(visu_frame.matrix, gaze_position_pixel, gaze_accuracy_pixel, (0, 255, 255), 1)
- # Draw gaze position and accuracy
- cv.circle(video_frame.matrix, gaze_position_pixel, 2, (0, 255, 255), -1)
- cv.circle(video_frame.matrix, gaze_position_pixel, gaze_accuracy_pixel, (0, 255, 255), 1)
+ # Store gaze position and precision at this time in millisecond
+ ts_gaze_positions[round(video_ts_ms)] = gaze_position_pixel
+ ts_gaze_accuracies[round(video_ts_ms)] = gaze_accuracy_pixel
- # Store gaze position and precision at this time in millisecond
- ts_gaze_positions[round(video_ts_ms)] = gaze_position_pixel
- ts_gaze_accuracies[round(video_ts_ms)] = gaze_accuracy_pixel
+ # Hide frame left and right borders before tracking to ignore markers outside focus area
+ cv.rectangle(video_frame.matrix, (0, 0), (int(video_frame.width/6), int(video_frame.height)), (0, 0, 0), -1)
+ cv.rectangle(video_frame.matrix, (int(video_frame.width*(1 - 1/6)), 0), (int(video_frame.width), int(video_frame.height)), (0, 0, 0), -1)
- else:
+ # Track markers with pose estimation and draw them
+ aruco_tracker.track(video_frame.matrix)
+ aruco_tracker.draw(visu_frame.matrix)
- ValueError('Unable to evaluate gaze precision')
+ # Project 3D scene on each video frame and the visualisation frame
+ if aruco_tracker.get_markers_number():
- # Wait for gaze position
- except ValueError:
- continue
+ # Store aoi 2D video for further scene merging
+ aoi2D_dict = {}
- # Draw focus area
- cv.circle(video_frame.matrix, (int(video_frame.width/2), int(video_frame.height/2)), int(video_frame.width/3), (255, 150, 150), 1)
-
- # Draw focus area center
- cv.line(video_frame.matrix, (int(video_frame.width/2) - 50, int(video_frame.height/2)), (int(video_frame.width/2) + 50, int(video_frame.height/2)), (255, 150, 150), 1)
- cv.line(video_frame.matrix, (int(video_frame.width/2), int(video_frame.height/2) - 50), (int(video_frame.width/2), int(video_frame.height/2) + 50), (255, 150, 150), 1)
-
- # Project 3D scene on each video frame and the visualisation frame
- if aruco_tracker.get_markers_number():
+ for (i, marker_id) in enumerate(aruco_tracker.get_markers_ids()):
- # Store aoi 2D video for further scene merging
- aoi2D_dict = {}
+ # Process marker pose
+ try:
- for (i, marker_id) in enumerate(aruco_tracker.get_markers_ids()):
+ # Copy 3D scene related to detected marker
+ aoi3D_scene = aoi3D_scene_selector(marker_id)
+
+ if aoi3D_scene == None:
+ raise UserWarning('No AOI 3D scene')
- # Copy 3D scene related to detected marker
- aoi3D_scene = aoi3D_scene_selector(marker_id)
-
- if aoi3D_scene == None:
- continue
-
- # Ignore marker out of focus area
- marker_x, marker_y = aruco_tracker.get_marker_center(i)
- distance_to_center = ( (video_frame.width/2 - marker_x)**2 + (video_frame.height/2 - marker_y)**2 )**0.5
+ # Transform scene into camera referential
+ aoi3D_camera = aoi3D_scene.transform(aruco_tracker.get_marker_translation(i), aruco_tracker.get_marker_rotation(i))
- if distance_to_center > int(video_frame.width/3):
- continue
+ # Get aoi inside vision cone field
+ cone_vision_height_cm = nearest_gaze_position_3d.value[2]/10 # cm
+ cone_vision_radius_cm = numpy.tan(numpy.deg2rad(tobii_visual_hfov / 2)) * cone_vision_height_cm
- # Transform scene into camera referential
- aoi3D_camera = aoi3D_scene.transform(aruco_tracker.get_marker_translation(i), aruco_tracker.get_marker_rotation(i))
+ aoi3D_inside, aoi3D_outside = aoi3D_camera.vision_cone(cone_vision_radius_cm, cone_vision_height_cm)
- # Get aoi inside vision cone field
- cone_vision_height_cm = nearest_gaze_position_3d.value[2]/10 # cm
- cone_vision_radius_cm = numpy.tan(numpy.deg2rad(tobii_visual_hfov / 2)) * cone_vision_height_cm
+ # Keep only aoi inside vision cone field
+ aoi3D_scene = aoi3D_scene.copy(exclude=aoi3D_outside.keys())
- aoi3D_inside, aoi3D_outside = aoi3D_camera.vision_cone(cone_vision_radius_cm, cone_vision_height_cm)
+ # DON'T APPLY CAMERA DISTORSION : it projects points which are far from the frame into it
+ # This hack isn't realistic but as the gaze will mainly focus on centered AOI, where the distorsion is low, it is acceptable.
+ aoi2D_video_scene = aoi3D_scene.project(aruco_tracker.get_marker_translation(i), aruco_tracker.get_marker_rotation(i), aruco_camera.get_K())
- # Keep only aoi inside vision cone field
- aoi3D_scene = aoi3D_scene.copy(exclude=aoi3D_outside.keys())
+ # Store each 2D aoi for further scene merging
+ for name, aoi in aoi2D_video_scene.items():
- # DON'T APPLY CAMERA DISTORSION : it projects points which are far from the frame into it
- # This hack isn't realistic but as the gaze will mainly focus on centered AOI, where the distorsion is low, it is acceptable.
- aoi2D_video_scene = aoi3D_scene.project(aruco_tracker.get_marker_translation(i), aruco_tracker.get_marker_rotation(i), aruco_camera.get_K())
+ if name not in aoi2D_dict.keys():
+ aoi2D_dict[name] = []
- # Store each 2D aoi for further scene merging
- for name, aoi in aoi2D_video_scene.items():
+ aoi2D_dict[name].append(aoi.clockwise())
- if name not in aoi2D_dict.keys():
- aoi2D_dict[name] = []
+ # Select 2D visu scene if there is one for the detected marker
+ aoi2D_visu_scene = aoi2D_visu_scene_selector(marker_id)
+ aoi2D_visu_frame = aoi2D_visu_frame_selector(marker_id)
+
+ if aoi2D_visu_scene == None:
+ continue
+
+ look_at = aoi2D_video_scene['Visualisation_Plan'].look_at(gaze_position_pixel)
- aoi2D_dict[name].append(aoi.clockwise())
+ visu_gaze_pixel = aoi2D_visu_scene['Visualisation_Plan'].looked_pixel(look_at)
+ cv.circle(aoi2D_visu_frame, visu_gaze_pixel, 4, (0, 0, 255), -1)
- # Select 2D visu scene if there is one for the detected marker
- aoi2D_visu_scene = aoi2D_visu_scene_selector(marker_id)
- aoi2D_visu_frame = aoi2D_visu_frame_selector(marker_id)
-
- if aoi2D_visu_scene == None:
- continue
-
- look_at = aoi2D_video_scene['Visualisation_Plan'].look_at(gaze_position_pixel)
+ # Write warning related to marker pose processing
+ except UserWarning as e:
+
+ cv.putText(visu_frame.matrix, f'Marker {marker_id}: {e}', (20, int(visu_frame.height) - (marker_id+1) * 40), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv.LINE_AA)
- visu_gaze_pixel = aoi2D_visu_scene['Visualisation_Plan'].looked_pixel(look_at)
- cv.circle(aoi2D_visu_frame, visu_gaze_pixel, 4, (0, 0, 255), -1)
+ # Merge all 2D aoi into a single 2D scene
+ aoi2D_merged_scene = AOI2DScene.AOI2DScene()
+ for name, aoi_array in aoi2D_dict.items():
+ aoi2D_merged_scene[name] = numpy.sum(aoi_array, axis=0) / len(aoi_array)
- # Merge all 2D aoi into a single 2D scene
- aoi2D_merged_scene = AOI2DScene.AOI2DScene()
- for name, aoi_array in aoi2D_dict.items():
- aoi2D_merged_scene[name] = numpy.sum(aoi_array, axis=0) / len(aoi_array)
+ aoi2D_merged_scene.draw(visu_frame.matrix, gaze_position_pixel, gaze_accuracy_pixel, exclude=['Visualisation_Plan'])
+
+ # Store 2D merged scene at this time in millisecond
+ ts_aois_scenes[round(video_ts_ms)] = aoi2D_merged_scene
- aoi2D_merged_scene.draw(video_frame.matrix, gaze_position_pixel, gaze_accuracy_pixel, exclude=['Visualisation_Plan'])
-
- # Store 2D merged scene at this time in millisecond
- ts_aois_scenes[round(video_ts_ms)] = aoi2D_merged_scene
+ else:
+
+ raise UserWarning('No marker detected')
+ # Write warning related to video and data frame processing
+ except UserWarning as e:
+
+ cv.putText(visu_frame.matrix, str(e), (20, 80), cv.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv.LINE_AA)
+
+ except ValueError:
+ pass
+
if args.window:
# Close window using 'Esc' key
if cv.waitKey(1) == 27:
break
- # Display video
- cv.imshow(f'Segment {tobii_segment.get_id()} ArUco AOI', video_frame.matrix)
+ # Display video frame
+ cv.imshow(f'Segment {tobii_segment.get_id()}', video_frame.matrix)
+
+ # Display visualisation
+ cv.imshow(f'Segment {tobii_segment.get_id()} ArUco AOI', visu_frame.matrix)
# Display each visual scan frame
- for marker_id, visu_frame in aoi2D_visu_frames.items():
- cv.imshow(f'Segment {tobii_segment.get_id()} visual scan for marker {marker_id}', visu_frame)
+ for marker_id, aoi2D_visu_frame in aoi2D_visu_frames.items():
+ cv.imshow(f'Segment {tobii_segment.get_id()} visual scan for marker {marker_id}', visu_frame.matrix)
# Write video
- output_video.write(video_frame.matrix)
+ output_video.write(visu_frame.matrix)
# Update Progress Bar
progress = video_ts_ms - int(args.time_range[0] * 1000)
@@ -367,8 +413,8 @@ def main():
print(f'Visual scan data saved into {vs_data_filepath}')
# Export each visual scan picture
- for marker_id, visu_frame in aoi2D_visu_frames.items():
- cv.imwrite(vs_visu_filepath % marker_id, visu_frame)
+ for marker_id, aoi2D_visu_frame in aoi2D_visu_frames.items():
+ cv.imwrite(vs_visu_filepath % marker_id, visu_frame.matrix)
print(f'Visual scan picture for marker {marker_id} saved into {vs_visu_filepath % marker_id}')
# Notify when the visual scan video has been exported