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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 os
import time
import queue
from argaze import ArFeatures, GazeFeatures
from argaze.AreaOfInterest import AOIFeatures
from argaze.GazeAnalysis import *
import cv2
import numpy
import pandas
def main():
"""
Load AR environment from .json file to project AOI scene on screen and use mouse pointer to simulate gaze positions.
"""
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')
args = parser.parse_args()
# Load AR environment
ar_environment = ArFeatures.ArEnvironment.from_json(args.environment)
# Select AR screen
ar_screen = ar_environment.scenes["AR Scene Demo"].screens["GrayRectangle"]
# Create a window to display AR environment
cv2.namedWindow(ar_screen.name, cv2.WINDOW_AUTOSIZE)
# Heatmap buffer display option
enable_heatmap_buffer = False
# Init timestamp
start_time = time.time()
# Update pointer position
def on_mouse_event(event, x, y, flags, param):
try:
# Edit millisecond timestamp
timestamp = int((time.time() - start_time) * 1e3)
# Project gaze position into screen
ar_screen.look(timestamp, GazeFeatures.GazePosition((x, y)))
except GazeFeatures.AOIScanStepError as e:
print(f'Error on {e.aoi} step:', e)
# Attach mouse callback to window
cv2.setMouseCallback(ar_screen.name, on_mouse_event)
# Waiting for 'ctrl+C' interruption
try:
# Analyse mouse positions
while True:
# Draw screen
image = ar_screen.background.copy()
# Draw heatmap
if ar_screen.heatmap:
image = cv2.addWeighted(ar_screen.heatmap.image, 0.5, image, 1., 0)
# Write heatmap buffer manual
buffer_on_off = 'on' if enable_heatmap_buffer else 'off'
buffer_display_disable = 'disable' if enable_heatmap_buffer else 'enable'
cv2.putText(image, f'Heatmap buffer: {buffer_on_off} (Press \'b\' key to {buffer_display_disable})', (20, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255) if enable_heatmap_buffer else (255, 255, 255), 1, cv2.LINE_AA)
# Draw AOI
ar_screen.aoi_2d_scene.draw(image, color=(0, 0, 0))
# Draw gaze position
ar_screen.current_gaze_position.draw(image, color=(255, 255, 255))
# Draw gaze movements
current_gaze_movement = ar_screen.current_gaze_movement
current_gaze_movement.draw(image, color=(0, 255, 255))
current_gaze_movement.draw_positions(image)
# Check screen fixation
if GazeFeatures.is_fixation(current_gaze_movement):
# Draw looked AOI
ar_screen.aoi_2d_scene.draw_circlecast(image, current_gaze_movement.focus, current_gaze_movement.deviation_max, base_color=(0, 0, 0), matching_color=(255, 255, 255))
# Write last 5 steps of aoi scan path
path = ''
for step in ar_screen.aoi_scan_path[-5:]:
path += f'> {step.aoi} '
path += f'> {ar_screen.aoi_scan_path.current_aoi}'
cv2.putText(image, path, (20, ar_screen.size[1]-40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv2.LINE_AA)
# Display Transition matrix analysis if loaded
try:
transition_matrix_analyzer = ar_screen.aoi_scan_path_analyzers["TransitionMatrix"]
cv2.putText(image, f'Transition matrix density: {transition_matrix_analyzer.transition_matrix_density:.2f}', (20, ar_screen.size[1]-160), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA)
# Iterate over indexes (departures)
for from_aoi, row in transition_matrix_analyzer.transition_matrix_probabilities.iterrows():
# Iterate over columns (destinations)
for to_aoi, probability in row.items():
if from_aoi != to_aoi and probability > 0.0:
from_center = ar_screen.aoi_2d_scene[from_aoi].center.astype(int)
to_center = ar_screen.aoi_2d_scene[to_aoi].center.astype(int)
start_line = (0.5 * from_center + 0.5 * to_center).astype(int)
color = [int(probability*200) + 55, int(probability*200) + 55, int(probability*200) + 55]
cv2.line(image, start_line, to_center, color, int(probability*10) + 2)
cv2.line(image, from_center, to_center, [55, 55, 55], 2)
except KeyError:
pass
# Display scan path K Coefficient analysis if loaded
try:
kc_analyzer = ar_screen.scan_path_analyzers["KCoefficient"]
# Write raw Kc analysis
if kc_analyzer.K < 0.:
cv2.putText(image, f'K coefficient: Ambient attention', (20, ar_screen.size[1]-120), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA)
elif kc_analyzer.K > 0.:
cv2.putText(image, f'K coefficient: Focal attention', (20, ar_screen.size[1]-120), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 1, cv2.LINE_AA)
except KeyError:
pass
# Display aoi scan path K-modified coefficient analysis if loaded
try:
aoi_kc_analyzer = ar_screen.aoi_scan_path_analyzers["KCoefficient"]
# Write aoi Kc analysis
if aoi_kc_analyzer.K < 0.:
cv2.putText(image, f'K-modified coefficient: Ambient attention', (20, ar_screen.size[1]-80), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA)
elif aoi_kc_analyzer.K > 0.:
cv2.putText(image, f'K-modified coefficient: Focal attention', (20, ar_screen.size[1]-80), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 1, cv2.LINE_AA)
except KeyError:
pass
# Display Lempel-Ziv complexity analysis if loaded
try:
lzc_analyzer = ar_screen.aoi_scan_path_analyzers["LempelZivComplexity"]
cv2.putText(image, f'Lempel-Ziv complexity: {lzc_analyzer.lempel_ziv_complexity}', (20, ar_screen.size[1]-200), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA)
except KeyError:
pass
# Display N-Gram analysis if loaded
try:
ngram_analyzer = ar_screen.aoi_scan_path_analyzers["NGram"]
# Display only 3-gram analysis
start = ar_screen.size[1] - ((len(ngram_analyzer.ngrams_count[3]) + 1) * 40)
cv2.putText(image, f'{ngram_analyzer.n_max}-Gram:', (ar_screen.size[0]-700, start-40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA)
for i, (ngram, count) in enumerate(ngram_analyzer.ngrams_count[3].items()):
ngram_string = f'{ngram[0]}'
for g in range(1, 3):
ngram_string += f'>{ngram[g]}'
cv2.putText(image, f'{ngram_string}: {count}', (ar_screen.size[0]-700, start+(i*40)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA)
except KeyError:
pass
# Display Entropy analysis if loaded
try:
entropy_analyzer = ar_screen.aoi_scan_path_analyzers["Entropy"]
cv2.putText(image, f'Stationary entropy: {entropy_analyzer.stationary_entropy:.3f},', (20, ar_screen.size[1]-280), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA)
cv2.putText(image, f'Transition entropy: {entropy_analyzer.transition_entropy:.3f},', (20, ar_screen.size[1]-240), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA)
except KeyError:
pass
# Display Nearest Neighbor index analysis if loaded
try:
nni_analyzer = ar_screen.scan_path_analyzers["NearestNeighborIndex"]
cv2.putText(image, f'Nearest neighbor index: {nni_analyzer.nearest_neighbor_index:.3f}', (20, ar_screen.size[1]-320), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA)
except KeyError:
pass
# Display Exploit/Explore ratio analysis if loaded
try:
xxr_analyser = ar_screen.scan_path_analyzers["ExploitExploreRatio"]
cv2.putText(image, f'Exploit explore ratio: {xxr_analyser.exploit_explore_ratio:.3f}', (20, ar_screen.size[1]-360), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA)
except KeyError:
pass
# Draw image
cv2.imshow(ar_screen.name, image)
key_pressed = cv2.waitKey(10)
#if key_pressed != -1:
# print(key_pressed)
# Reload environment with 'h' key
if key_pressed == 114:
ar_environment = ArFeatures.ArEnvironment.from_json(args.environment)
ar_screen = ar_environment.scenes["AR Scene Demo"].screens["GrayRectangle"]
# Enable heatmap buffer with 'b' key
if key_pressed == 98:
enable_heatmap_buffer = not enable_heatmap_buffer
ar_screen.heatmap.init(10 if enable_heatmap_buffer else 0)
# Stop calibration by pressing 'Esc' key
if key_pressed == 27:
break
# Stop calibration on 'ctrl+C' interruption
except KeyboardInterrupt:
pass
# Stop image display
cv2.destroyAllWindows()
if __name__ == '__main__':
main()
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