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Diffstat (limited to 'src/argaze/utils/demo_gaze_analysis_run.py')
| -rw-r--r-- | src/argaze/utils/demo_gaze_analysis_run.py | 274 |
1 files changed, 0 insertions, 274 deletions
diff --git a/src/argaze/utils/demo_gaze_analysis_run.py b/src/argaze/utils/demo_gaze_analysis_run.py deleted file mode 100644 index 16644ce..0000000 --- a/src/argaze/utils/demo_gaze_analysis_run.py +++ /dev/null @@ -1,274 +0,0 @@ -#!/usr/bin/env python - -"""Gaze analysis pipeline demo script. - -This program is free software: you can redistribute it and/or modify it under -the terms of the GNU General Public License as published by the Free Software -Foundation, either version 3 of the License, or (at your option) any later -version. -This program is distributed in the hope that it will be useful, but WITHOUT -ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS -FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. -You should have received a copy of the GNU General Public License along with -this program. If not, see <http://www.gnu.org/licenses/>. -""" - -__author__ = "Théo de la Hogue" -__credits__ = [] -__copyright__ = "Copyright 2023, Ecole Nationale de l'Aviation Civile (ENAC)" -__license__ = "GPLv3" - -import argparse -import logging -import contextlib -import os -import time - -from argaze import ArFeatures, GazeFeatures -from argaze.GazeAnalysis import * -from argaze.utils import UtilsFeatures - -import cv2 - -current_directory = os.path.dirname(os.path.abspath(__file__)) - -# Manage arguments -parser = argparse.ArgumentParser(description=__doc__.split('-')[0]) -parser.add_argument('configuration', metavar='CONFIGURATION', type=str, help='configuration filepath') -parser.add_argument('-v', '--verbose', action='store_true', default=False, help='enable verbose mode to print information in console') -args = parser.parse_args() - -# Manage logging -logging.basicConfig(format = '%(levelname)s: %(message)s', level = logging.DEBUG if args.verbose else logging.INFO) - -def main(): - - # Load ArFrame - with ArFeatures.ArFrame.from_json(args.configuration) as ar_frame: - - if args.verbose: - - print(ar_frame) - - # Create a window to display ArCamera - cv2.namedWindow(ar_frame.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: - - # Project gaze position into frame with millisecond timestamp - ar_frame.look(GazeFeatures.GazePosition((x, y), timestamp=int((time.time() - start_time) * 1e3))) - - # Catch pipeline exception - #except Exception as e: - - # print('Gaze projection error:', e) - - # Attach mouse callback to window - cv2.setMouseCallback(ar_frame.name, on_mouse_event) - - # Waiting for 'ctrl+C' interruption - with contextlib.suppress(KeyboardInterrupt): - - # Draw frame and mouse position analysis - while True: - - # Get frame image - frame_image = ar_frame.image() - - # 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(frame_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) - - # Write last 5 steps of aoi scan path - path = '' - for step in ar_frame.layers["demo_layer"].aoi_scan_path[-5:]: - - path += f'> {step.aoi} ' - - path += f'> {ar_frame.layers["demo_layer"].aoi_scan_path.current_aoi}' - - cv2.putText(frame_image, path, (20, ar_frame.size[1]-40), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 1, cv2.LINE_AA) - - # Display frame analysis - analysis = ar_frame.analysis() - - # Display scan path K Coefficient analysis if loaded - try: - - kc_analysis = analysis[KCoefficient.ScanPathAnalyzer] - - # Write raw Kc analysis - if kc_analysis.K < 0.: - - cv2.putText(frame_image, f'K coefficient: Ambient attention', (20, ar_frame.size[1]-120), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA) - - elif kc_analysis.K > 0.: - - cv2.putText(frame_image, f'K coefficient: Focal attention', (20, ar_frame.size[1]-120), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 1, cv2.LINE_AA) - - except KeyError: - pass - - # Display Nearest Neighbor index analysis if loaded - try: - - nni_analysis = analysis[NearestNeighborIndex.ScanPathAnalyzer] - - cv2.putText(frame_image, f'Nearest neighbor index: {nni_analysis.nearest_neighbor_index:.3f}', (20, ar_frame.size[1]-320), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA) - - except KeyError: - pass - - # Display Explore/Exploit ratio analysis if loaded - try: - - xxr_analyser = analysis[ExploreExploitRatio.ScanPathAnalyzer] - - cv2.putText(frame_image, f'Explore/Exploit ratio: {xxr_analyser.explore_exploit_ratio:.3f}', (20, ar_frame.size[1]-360), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA) - - except KeyError: - pass - - # Display demo_layer analysis - analysis = ar_frame.layers["demo_layer"].analysis() - - # Display Transition matrix analysis if loaded - try: - - transition_matrix_analysis = analysis[TransitionMatrix.AOIScanPathAnalyzer] - - cv2.putText(frame_image, f'Transition matrix density: {transition_matrix_analysis.transition_matrix_density:.2f}', (20, ar_frame.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_analysis.transition_matrix_probabilities.iterrows(): - - # Iterate over columns (destinations) - for to_aoi, probability in row.items(): - - if from_aoi != GazeFeatures.OutsideAOI and to_aoi != GazeFeatures.OutsideAOI: - - if from_aoi != to_aoi and probability > 0.0: - - from_center = ar_frame.layers["demo_layer"].aoi_scene[from_aoi].center.astype(int) - to_center = ar_frame.layers["demo_layer"].aoi_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(frame_image, start_line, to_center, color, int(probability*10) + 2) - cv2.line(frame_image, from_center, to_center, [55, 55, 55], 2) - - except KeyError: - pass - - # Display aoi scan path basic metrics analysis if loaded - try: - - basic_analysis = analysis[Basic.AOIScanPathAnalyzer] - - # Write basic analysis - cv2.putText(frame_image, f'Step number: {basic_analysis.steps_number}', (20, ar_frame.size[1]-440), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA) - cv2.putText(frame_image, f'Step fixation duration average: {int(basic_analysis.step_fixation_durations_average)} ms', (20, ar_frame.size[1]-400), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA) - - except KeyError: - pass - - # Display aoi scan path K-modified coefficient analysis if loaded - try: - - aoi_kc_analysis = analysis[KCoefficient.AOIScanPathAnalyzer] - - # Write aoi Kc analysis - if aoi_kc_analysis.K < 0.: - - cv2.putText(frame_image, f'K-modified coefficient: Ambient attention', (20, ar_frame.size[1]-80), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA) - - elif aoi_kc_analysis.K > 0.: - - cv2.putText(frame_image, f'K-modified coefficient: Focal attention', (20, ar_frame.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_analysis = analysis[LempelZivComplexity.AOIScanPathAnalyzer] - - cv2.putText(frame_image, f'Lempel-Ziv complexity: {lzc_analysis.lempel_ziv_complexity}', (20, ar_frame.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_analysis = analysis[NGram.AOIScanPathAnalyzer] - - # Display only 3-gram analysis - start = ar_frame.size[1] - ((len(ngram_analysis.ngrams_count[3]) + 1) * 40) - cv2.putText(frame_image, f'{ngram_analysis.n_max}-Gram:', (ar_frame.size[0]-700, start-40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA) - - for i, (ngram, count) in enumerate(ngram_analysis.ngrams_count[3].items()): - - ngram_string = f'{ngram[0]}' - for g in range(1, 3): - ngram_string += f'>{ngram[g]}' - - cv2.putText(frame_image, f'{ngram_string}: {count}', (ar_frame.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_analysis = analysis[Entropy.AOIScanPathAnalyzer] - - cv2.putText(frame_image, f'Stationary entropy: {entropy_analysis.stationary_entropy:.3f},', (20, ar_frame.size[1]-280), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA) - cv2.putText(frame_image, f'Transition entropy: {entropy_analysis.transition_entropy:.3f},', (20, ar_frame.size[1]-240), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 255), 1, cv2.LINE_AA) - - except KeyError: - pass - - # Display frame image - cv2.imshow(ar_frame.name, frame_image) - - key_pressed = cv2.waitKey(10) - - #if key_pressed != -1: - # print(key_pressed) - - # Reload environment with 'h' key - if key_pressed == 114: - - ar_frame = ArFeatures.ArFrame.from_json(args.frame) - - # Enable heatmap buffer with 'b' key - if key_pressed == 98: - - enable_heatmap_buffer = not enable_heatmap_buffer - - ar_frame.heatmap.buffer = 10 if enable_heatmap_buffer else 0 - ar_frame.heatmap.clear() - - # Stop by pressing 'Esc' key - if key_pressed == 27: - break - - # Stop frame image display - cv2.destroyAllWindows() - -if __name__ == '__main__': - - main() |