def track(self):
fileformat = self.cmb_fmt.currentText()
self.exp.camera.kill_event.set()
reader = imageio.get_reader(str(self.input_path))
data = []
self.exp.window_main.stream_plot.toggle_freeze()
output_name = str(self.output_path) + "." + fileformat
self.diag_track.show()
l = reader.get_length()
if not (0 < l < 100000):
l = 1
self.diag_track.prog_track.setMaximum(l)
self.diag_track.lbl_status.setText("Tracking to " + output_name)
for i, frame in enumerate(reader):
data.append(self.exp.pipeline.run(frame[:, :, 0]).data)
self.diag_track.prog_track.setValue(i)
if i % 100 == 0:
self.app.processEvents()
self.diag_track.lbl_status.setText("Saving " + output_name)
df = pd.DataFrame.from_records(data, columns=data[0]._fields)
save_df(df, self.output_path, fileformat)
self.diag_track.lbl_status.setText("Completed " + output_name)
self.exp.wrap_up()
def complete(self):
save_df(
pd.DataFrame(self.times, columns="t"),
self.filename_base + "video_times",
self.log_format,
)
self.recording = False
def _complete(self, filename: str) -> None:
""" "
Saves a dataframe containing the timestamps of all the frames.
Can be extended by subclasses for other logic that should be executed upon finishing the recording.
Parameters
----------
filename
part of the filename that the data will be saved to
(other parts consist of 'video_times' and the log format).
"""
save_df(
pd.DataFrame(self._times, columns=["t"]),
Path(str(filename) + "video_times"),
self._log_format,
)
def save(self, path, format="csv"):
""" Saves the content of the accumulator in a tabular format.
Choose CSV for widest compatibility, HDF if using Python only,
or feather for efficient storage compatible with Python and Julia
data frames
Parameters
----------
path : str
output path, without extension name
format : str
output format, csv, feather, hdf5, json
"""
df = self.get_dataframe()
if df is None:
return
saved_filename = save_df(df, path, format)
return basename(saved_filename)