diff options
Diffstat (limited to 'docs')
9 files changed, 23 insertions, 23 deletions
diff --git a/docs/use_cases/simone_a320_cockpit_simulator.md b/docs/use_cases/simone_a320_cockpit_simulator.md index 7b11c01..4a4bc4f 100644 --- a/docs/use_cases/simone_a320_cockpit_simulator.md +++ b/docs/use_cases/simone_a320_cockpit_simulator.md @@ -19,7 +19,7 @@ The 3D scan have been loaded in a 3D editor to help in ArUco markers and AOI pos   -Finally, a python script connect Tobii eyetracker glasses to ArGaze toolkit. The 3D AR environment is loaded then, ArUco markers are detected from Tobii eyetracker field camera stream allowing to estimate pilote head pose. The AOI are projected into camera frame then, gaze positions are analyzed to identify fixations and saccades to finally check if fixations matched any projected AOI. +Finally, a python script connect Tobii eyetracker glasses to ArGaze toolkit. The 3D AR environment is loaded then, ArUco markers are detected from Tobii eyetracker field camera stream allowing to estimate pilote head pose. The AOI are projected into camera image then, gaze positions are analyzed to identify fixations and saccades to finally check if fixations matched any projected AOI.  diff --git a/docs/user_guide/ar_environment/environment_exploitation.md b/docs/user_guide/ar_environment/environment_exploitation.md index f07d150..a4013ea 100644 --- a/docs/user_guide/ar_environment/environment_exploitation.md +++ b/docs/user_guide/ar_environment/environment_exploitation.md @@ -4,8 +4,8 @@ Environment exploitation Once loaded, [ArEnvironment](../../../argaze/#argaze.ArFeatures.ArEnvironment) assets can be exploited as illustrated below: ```python -# Access to AR environment ArUco detector passing it a frame where to detect ArUco markers -ar_environment.aruco_detector.detect_markers(frame) +# Access to AR environment ArUco detector passing it a image where to detect ArUco markers +ar_environment.aruco_detector.detect_markers(image) # Access to an AR environment scene my_first_scene = ar_environment.scenes['my first AR scene'] @@ -15,15 +15,15 @@ try: # Try to estimate AR scene pose from detected markers tvec, rmat, consistent_markers = my_first_scene.estimate_pose(ar_environment.aruco_detector.detected_markers) - # Project AR scene into camera frame according estimated pose + # Project AR scene into camera image according estimated pose # Optional visual_hfov argument is set to 160° to clip AOI scene according a cone vision aoi2D_scene = my_first_scene.project(tvec, rmat, visual_hfov=160) # Draw estimated AR scene axis - my_first_scene.draw_axis(frame) + my_first_scene.draw_axis(image) # Draw AOI2D scene projection - aoi2D_scene.draw(frame) + aoi2D_scene.draw(image) # Do something with AOI2D scene projection ... diff --git a/docs/user_guide/areas_of_interest/aoi_matching.md b/docs/user_guide/areas_of_interest/aoi_matching.md index 1e18238..ff658a2 100644 --- a/docs/user_guide/areas_of_interest/aoi_matching.md +++ b/docs/user_guide/areas_of_interest/aoi_matching.md @@ -5,7 +5,7 @@ title: AOI matching AOI matching ============ -Once [AOI3DScene](../../../argaze/#argaze.AreaOfInterest.AOI3DScene) is projected into a frame as [AOI2DScene](../../../argaze/#argaze.AreaOfInterest.AOI2DScene), it could be needed to know which AOI is looked. +Once [AOI3DScene](../../../argaze/#argaze.AreaOfInterest.AOI3DScene) is projected as [AOI2DScene](../../../argaze/#argaze.AreaOfInterest.AOI2DScene), it could be needed to know which AOI is looked. The [AreaOfInterest](../../../argaze/#argaze.AreaOfInterest.AOIFeatures.AreaOfInterest) class in [AOIFeatures](../../../argaze/#argaze.AreaOfInterest.AOIFeatures) provides two ways to accomplish such task. diff --git a/docs/user_guide/areas_of_interest/aoi_scene_projection.md b/docs/user_guide/areas_of_interest/aoi_scene_projection.md index ad50f6f..bdb3fe0 100644 --- a/docs/user_guide/areas_of_interest/aoi_scene_projection.md +++ b/docs/user_guide/areas_of_interest/aoi_scene_projection.md @@ -5,7 +5,7 @@ title: AOI scene projection AOI scene projection ==================== -An [AOI3DScene](../../../argaze/#argaze.AreaOfInterest.AOI3DScene) can be rotated and translated according to a pose estimation before to project it onto camera frame as an [AOI2DScene](../../../argaze/#argaze.AreaOfInterest.AOI2DScene). +An [AOI3DScene](../../../argaze/#argaze.AreaOfInterest.AOI3DScene) can be rotated and translated according to a pose estimation before to project it onto camera image as an [AOI2DScene](../../../argaze/#argaze.AreaOfInterest.AOI2DScene).  @@ -18,5 +18,5 @@ An [AOI3DScene](../../../argaze/#argaze.AreaOfInterest.AOI3DScene) can be rotate aoi2D_scene = aoi3D_scene.project(tvec, rmat, optic_parameters.K) # Draw AOI 2D scene -aoi2D_scene.draw(frame) +aoi2D_scene.draw(image) ``` diff --git a/docs/user_guide/aruco_markers/camera_calibration.md b/docs/user_guide/aruco_markers/camera_calibration.md index 7bff480..1019fc1 100644 --- a/docs/user_guide/aruco_markers/camera_calibration.md +++ b/docs/user_guide/aruco_markers/camera_calibration.md @@ -24,7 +24,7 @@ Then, the calibration process needs to make many different captures of an [ArUco  -The sample of code below shows how to detect board corners into camera frames, store detected corners then process them to build calibration data and, finally, save it into a JSON file: +The sample of code below shows how to detect board corners into camera images, store detected corners then process them to build calibration data and, finally, save it into a JSON file: ``` python from argaze.ArUcoMarkers import ArUcoMarkersDictionary, ArUcoOpticCalibrator, ArUcoBoard, ArUcoDetector @@ -42,19 +42,19 @@ expected_aruco_board = ArUcoBoard.ArUcoBoard(7, 5, 5, 3, aruco_dictionary) # Create ArUco detector aruco_detector = ArUcoDetector.ArUcoDetector(dictionary=aruco_dictionary, marker_size=3) -# Capture frames from a live Full HD video stream (1920x1080) +# Capture images from a live Full HD video stream (1920x1080) while video_stream.is_alive(): - frame = video_stream.read() + image = video_stream.read() - # Detect all board corners in frame - aruco_detector.detect_board(frame, expected_aruco_board, expected_aruco_board.markers_number) + # Detect all board corners in image + aruco_detector.detect_board(image, expected_aruco_board, expected_aruco_board.markers_number) # If board corners are detected if aruco_detector.board_corners_number > 0: # Draw board corners to show that board tracking succeeded - aruco_detector.draw_board(frame) + aruco_detector.draw_board(image) # Append tracked board data for further calibration processing aruco_optic_calibrator.store_calibration_data(aruco_detector.board_corners, aruco_detector.board_corners_identifier) diff --git a/docs/user_guide/aruco_markers/markers_detection.md b/docs/user_guide/aruco_markers/markers_detection.md index 3851cb4..9a3bc9f 100644 --- a/docs/user_guide/aruco_markers/markers_detection.md +++ b/docs/user_guide/aruco_markers/markers_detection.md @@ -29,14 +29,14 @@ Here is [DetectorParameters](../../../argaze/#argaze.ArUcoMarkers.ArUcoDetector. } ``` -The [ArUcoDetector](../../../argaze/#argaze.ArUcoMarkers.ArUcoDetector.ArUcoDetector) processes frame to detect markers and allows to draw detection results onto it: +The [ArUcoDetector](../../../argaze/#argaze.ArUcoMarkers.ArUcoDetector.ArUcoDetector) processes image to detect markers and allows to draw detection results onto it: ``` python -# Detect markers into a frame and draw them -aruco_detector.detect_markers(frame) -aruco_detector.draw_detected_markers(frame) +# Detect markers into image and draw them +aruco_detector.detect_markers(image) +aruco_detector.draw_detected_markers(image) -# Get corners position into frame related to each detected markers +# Get corners position into image related to each detected markers for marker_id, marker in aruco_detector.detected_markers.items(): print(f'marker {marker_id} corners: ', marker.corners) diff --git a/docs/user_guide/timestamped_data/introduction.md b/docs/user_guide/timestamped_data/introduction.md index ed13d85..a36daca 100644 --- a/docs/user_guide/timestamped_data/introduction.md +++ b/docs/user_guide/timestamped_data/introduction.md @@ -1,6 +1,6 @@ Timestamped data ================ -Working with wearable eye tracker devices implies to handle various timestamped data like frames, gaze positions, pupils diameter, fixations, saccades, ... +Working with wearable eye tracker devices implies to handle various timestamped data like gaze positions, pupils diameter, fixations, saccades, ... This section mainly refers to [DataStructures.TimeStampedBuffer](../../../argaze/#argaze.DataStructures.TimeStampedBuffer) class. diff --git a/docs/user_guide/utils/demonstrations_scripts.md b/docs/user_guide/utils/demonstrations_scripts.md index adcc8b3..5de2927 100644 --- a/docs/user_guide/utils/demonstrations_scripts.md +++ b/docs/user_guide/utils/demonstrations_scripts.md @@ -11,7 +11,7 @@ Collection of command-line scripts for demonstration purpose. ## AR environment demonstration -Load AR environment from **setup.json** file, detect ArUco markers into camera device (-d DEVICE) frames and estimate envirnoment pose. +Load AR environment from **setup.json** file, detect ArUco markers into camera device (-d DEVICE) images and estimate envirnoment pose. ```shell python ./src/argaze/utils/demo_ar_features_run.py -d DEVICE diff --git a/docs/user_guide/utils/ready-made_scripts.md b/docs/user_guide/utils/ready-made_scripts.md index 035d697..afc5749 100644 --- a/docs/user_guide/utils/ready-made_scripts.md +++ b/docs/user_guide/utils/ready-made_scripts.md @@ -36,7 +36,7 @@ python ./src/argaze/utils/camera_calibrate.py 7 5 5 3 DICT_APRILTAG_16h5 -d DEVI ## ArUco scene exporter -Load a MOVIE with ArUco markers inside and select a frame into it, detect ArUco markers belonging to DICT_APRILTAG_16h5 dictionary with 5cm size into the selected frame thanks to given OPTIC_PARAMETERS and DETECTOR_PARAMETERS then, export detected ArUco markers scene as .obj file into an *./src/argaze/utils/_export/scenes* folder. +Load a MOVIE with ArUco markers inside and select image into it, detect ArUco markers belonging to DICT_APRILTAG_16h5 dictionary with 5cm size into the selected image thanks to given OPTIC_PARAMETERS and DETECTOR_PARAMETERS then, export detected ArUco markers scene as .obj file into an *./src/argaze/utils/_export/scenes* folder. ```shell python ./src/argaze/utils/aruco_markers_scene_export.py MOVIE DICT_APRILTAG_16h5 5 OPTIC_PARAMETERS DETECTOR_PARAMETERS -o ./src/argaze/utils/_export/scenes |