def main():
args = parser.parse_args()
print("50")
if args.input_image_glob:
if args.input_video_path:
raise ValueError(
"--input_image_glob and --input_video_path are mutually exclusive")
if args.frame_rate is None:
raise ValueError(
"When --input_image_glob is provided, --frame_rate must be provided")
frame_generator = object_detection.read_images(
args.input_image_glob, frame_rate=args.frame_rate)
timestep_s = 1.0 / args.frame_rate
else:
if not args.input_video_path:
raise ValueError(
"One of --input_image_glob and --input_video_path must be provided")
frame_generator = object_detection.read_video_file(args.input_video_path)
timestep_s = 1.0 / object_detection.get_video_fps(args.input_video_path)
# TODO(cais): Support variable frame rate in video file.
events = object_detection.detect_objects(frame_generator)
tsv_rows = events_lib.convert_events_to_tsv_rows(
events,
tsv_data.VISUAL_OBJECTS_EVENTS_TIER,
timestep_s=timestep_s)
with open(args.output_tsv_path, mode="w") as f:
tsv_writer = csv.writer(f, delimiter="\t")
tsv_writer.writerow(tsv_data.COLUMN_HEADS)
for row in tsv_rows:
tsv_writer.writerow(row)
def main():
args = parser.parse_args()
wav_paths = sorted(args.input_wav_paths.split(","))
events = []
for wav_path in wav_paths:
fs, xs = wavfile.read(wav_path)
if len(xs.shape) != 1:
raise ValueError("Only mono audio is supported")
# TODO(#35): Resapmle waveform if fs doesn't meet YAMNet requirement.
def waveform_generator():
step_length = 16000
i = 0
while i < len(xs):
yield xs[i:i + step_length]
i += step_length
events.extend(
audio_events.extract_audio_events(waveform_generator,
fs=fs,
threshold_score=0.5))
tsv_rows = events_lib.convert_events_to_tsv_rows(
events,
tsv_data.AUDIO_EVENTS_TIER,
ignore_class_names=audio_events.YAMNET_IGNORE_CLASS_NAMES)
with open(args.output_tsv_path, mode="w") as f:
tsv_writer = csv.writer(f, delimiter="\t")
tsv_writer.writerow(tsv_data.COLUMN_HEADS)
for row in tsv_rows:
tsv_writer.writerow(row)
def testConvertEventsToTsvRows_withFinalEmptyClasses_ignoresSilence(self):
events = [[("Speech", 0.9)], [("Speech", 0.95)], [("Silence", 0.99)],
[("Hands", 0.55)], [("Hands", 0.6)], []]
rows = events_lib.convert_events_to_tsv_rows(
events, "AudioEvents1", ignore_class_names=("Silence", ))
self.assertEqual(rows, [
(0.0, 2.0, "AudioEvents1", "Speech"),
(3.0, 5.0, "AudioEvents1", "Hands"),
])
def testConvertEventsToTsvRows_withOverlapping(self):
events = [
[("Speech", 0.6)],
[("Speech", 0.6), ("Music", 0.3)],
[("Speech", 0.5), ("Music", 0.4)],
[("Music", 0.7)],
[("Music", 0.8)],
]
rows = events_lib.convert_events_to_tsv_rows(events, "AudioEvents1")
self.assertEqual(rows, [
(0.0, 3.0, "AudioEvents1", "Speech"),
(1.0, 5.0, "AudioEvents1", "Music"),
])
def testConvertEventsToTsvRows_singleEventTypeAtATime(self):
events = [
[("Speech", 0.9)],
[("Speech", 0.95)],
[],
[("Hands", 0.55)],
[("Hands", 0.6)],
]
rows = events_lib.convert_events_to_tsv_rows(events, "AudioEvents1")
self.assertEqual(rows, [
(0.0, 2.0, "AudioEvents1", "Speech"),
(3.0, 5.0, "AudioEvents1", "Hands"),
])
def testCustomTimestep(self):
events = [
[("Speech", 0.6)],
[("Speech", 0.6), ("Music", 0.3)],
[("Speech", 0.5), ("Music", 0.4)],
[("Music", 0.7)],
[("Music", 0.8)],
]
rows = events_lib.convert_events_to_tsv_rows(events,
"AudioEvents1",
timestep_s=2.5)
self.assertEqual(rows, [
(0.0, 7.5, "AudioEvents1", "Speech"),
(2.5, 12.5, "AudioEvents1", "Music"),
])