def start_eye_process(self, eye_id):
potential_locs = [
os.path.join(self.g_pool.rec_dir, "eye{}{}".format(eye_id, ext))
for ext in (".mjpeg", ".mp4", ".mkv")
]
existing_locs = [loc for loc in potential_locs if os.path.exists(loc)]
if not existing_locs:
logger.error("no eye video for eye '{}' found.".format(eye_id))
self.detection_status[eye_id] = "No eye video found."
return
rec, file_ = os.path.split(existing_locs[0])
set_name = os.path.splitext(file_)[0]
self.videoset = VideoSet(rec, set_name, fill_gaps=False)
self.videoset.load_or_build_lookup()
timestamp_len = (self.videoset.lookup.container_idx > -1).sum()
if not timestamp_len:
logger.error(
"no timestamps for eye video for eye '{}' found.".format(
eye_id))
self.detection_status[eye_id] = "No eye video found."
return
video_loc = existing_locs[0]
self.eye_frame_num[eye_id] = timestamp_len
capure_settings = "File_Source", {
"source_path": video_loc,
"timing": None
}
self.notify_all({
"subject": "eye_process.should_start",
"eye_id": eye_id,
"overwrite_cap_settings": capure_settings,
})
self.eye_video_loc[eye_id] = video_loc
self.detection_status[eye_id] = "Detecting..."
def generate_frame_indices_with_deserialized_gaze(g_pool):
recording = PupilRecording(g_pool.rec_dir)
video_name = recording.files().world().videos()[0].stem
videoset = VideoSet(rec=g_pool.rec_dir, name=video_name, fill_gaps=True)
videoset.load_or_build_lookup()
frame_indices = np.flatnonzero(videoset.lookup.container_idx > -1)
frame_count = len(frame_indices)
for frame_index in frame_indices:
progress = (frame_index + 1) / frame_count
frame_ts_window = pm.enclosing_window(g_pool.timestamps, frame_index)
gaze_data = g_pool.gaze_positions.by_ts_window(frame_ts_window)
gaze_data = [(frame_index, g["timestamp"], g["norm_pos"][0],
g["norm_pos"][1]) for g in gaze_data
if g["confidence"] >= g_pool.min_data_confidence]
gaze_data = scan_path_numpy_array_from(gaze_data)
yield progress, gaze_data