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#!/usr/bin/env python
""" """
__author__ = "Théo de la Hogue"
__credits__ = []
__copyright__ = "Copyright 2023, Ecole Nationale de l'Aviation Civile (ENAC)"
__license__ = "BSD"
import argparse
import contextlib
import os
import time
from argaze import ArFeatures, GazeFeatures
import cv2
import numpy
def main():
"""
Load AR environment from .json file, detect ArUco markers into camera device images and project it.
"""
current_directory = os.path.dirname(os.path.abspath(__file__))
# Manage arguments
parser = argparse.ArgumentParser(description=main.__doc__.split('-')[0])
parser.add_argument('environment', metavar='ENVIRONMENT', type=str, help='ar environment filepath')
parser.add_argument('-s', '--source', metavar='SOURCE', type=str, default='0', help='video capture source (a number to select camera device or a filepath to load a movie)')
args = parser.parse_args()
# Load AR enviroment
ar_environment = ArFeatures.ArEnvironment.from_json(args.environment)
# Create a window to display AR environment
cv2.namedWindow(ar_environment.name, cv2.WINDOW_AUTOSIZE)
# Init timestamp
start_time = time.time()
# Fake gaze position with mouse pointer
def on_mouse_event(event, x, y, flags, param):
# Edit millisecond timestamp
timestamp = int((time.time() - start_time) * 1e3)
# Project gaze position into environment
for frame, look_data in ar_environment.look(timestamp, GazeFeatures.GazePosition((x, y))):
# Unpack look data
fixation, scan_step_analysis, aoi_scan_step_analysis, times, exception = look_data
# Do something with look data
# ...
# Attach mouse callback to window
cv2.setMouseCallback(ar_environment.name, on_mouse_event)
# Enable camera video capture into separate thread
video_capture = cv2.VideoCapture(int(args.source) if args.source.isdecimal() else args.source)
# Waiting for 'ctrl+C' interruption
with contextlib.suppress(KeyboardInterrupt):
# Capture images
while video_capture.isOpened():
# Read video image
success, video_image = video_capture.read()
if success:
# Detect and project environment
detection_time, exceptions = ar_environment.detect_and_project(video_image)
# Create environment image
environment_image = ar_environment.image
# Write detection fps
cv2.rectangle(environment_image, (0, 0), (420, 50), (63, 63, 63), -1)
cv2.putText(environment_image, f'Detection fps: {1e3/detection_time:.1f}', (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv2.LINE_AA)
# Handle exceptions
for i, (scene_name, e) in enumerate(exceptions.items()):
# Write errors
cv2.rectangle(environment_image, (0, (i+1)*50), (720, (i+2)*50), (127, 127, 127), -1)
cv2.putText(environment_image, f'{scene_name} error: {e}', (20, (i+1)*90), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA)
# Draw environment info
ar_environment.draw(environment_image)
# Display environment
cv2.imshow(ar_environment.name, environment_image)
# Draw and display each aoi frames
for aoi_frame in ar_environment.aoi_frames:
# Create frame image
aoi_frame_image = aoi_frame.image
# Draw frame info
aoi_frame.draw(aoi_frame_image)
# Display frame
cv2.imshow(f'{aoi_frame.parent.name}:{aoi_frame.name}', aoi_frame_image)
# Stop by pressing 'Esc' key
if cv2.waitKey(10) == 27:
# Close camera video capture
video_capture.release()
# Stop image display
cv2.destroyAllWindows()
if __name__ == '__main__':
main()
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