def test_metrics():
""" Test StatsManager metric registration/setting/getting with a set of pre-defined
key-value pairs (metric_dict).
"""
metric_dict = {'some_metric': 1.2345, 'another_metric': 6.7890}
metric_keys = list(metric_dict.keys())
stats = StatsManager()
frame_key = 100
assert not stats.is_save_required()
stats.register_metrics(metric_keys)
assert not stats.is_save_required()
with pytest.raises(FrameMetricRegistered):
stats.register_metrics(metric_keys)
assert not stats.metrics_exist(frame_key, metric_keys)
assert stats.get_metrics(frame_key, metric_keys) == [None] * len(metric_keys)
stats.set_metrics(frame_key, metric_dict)
assert stats.is_save_required()
assert stats.metrics_exist(frame_key, metric_keys)
assert stats.metrics_exist(frame_key, metric_keys[1:])
assert stats.get_metrics(frame_key, metric_keys) == [
metric_dict[metric_key] for metric_key in metric_keys]
def test_metrics():
""" Test StatsManager metric registration/setting/getting with a set of pre-defined
key-value pairs (metric_dict).
"""
metric_dict = {'some_metric': 1.2345, 'another_metric': 6.7890}
metric_keys = list(metric_dict.keys())
stats = StatsManager()
frame_key = 100
assert not stats.is_save_required()
stats.register_metrics(metric_keys)
assert not stats.is_save_required()
with pytest.raises(FrameMetricRegistered):
stats.register_metrics(metric_keys)
assert not stats.metrics_exist(frame_key, metric_keys)
assert stats.get_metrics(frame_key, metric_keys) == [None] * len(metric_keys)
stats.set_metrics(frame_key, metric_dict)
assert stats.is_save_required()
assert stats.metrics_exist(frame_key, metric_keys)
assert stats.metrics_exist(frame_key, metric_keys[1:])
assert stats.get_metrics(frame_key, metric_keys) == [
metric_dict[metric_key] for metric_key in metric_keys]
def test_load_hardcoded_file():
""" Test loading a stats file with some hard-coded data generated by this test case. """
stats_manager = StatsManager()
with open(TEST_STATS_FILES[0], 'w') as stats_file:
stats_writer = get_csv_writer(stats_file)
some_metric_key = 'some_metric'
some_metric_value = 1.2
some_frame_key = 100
base_timecode = FrameTimecode(0, 29.97)
some_frame_timecode = base_timecode + some_frame_key
# Write out a valid file.
stats_writer.writerow(
[COLUMN_NAME_FRAME_NUMBER, COLUMN_NAME_TIMECODE, some_metric_key])
stats_writer.writerow([
some_frame_key,
some_frame_timecode.get_timecode(),
str(some_metric_value)
])
stats_file.close()
stats_file = open(TEST_STATS_FILES[0], 'r')
stats_manager.load_from_csv(csv_file=stats_file)
# Check that we decoded the correct values.
assert stats_manager.metrics_exist(some_frame_key, [some_metric_key])
assert stats_manager.get_metrics(
some_frame_key,
[some_metric_key])[0] == pytest.approx(some_metric_value)
def test_save_load_from_video(test_video_file):
""" Test generating and saving some frame metrics from TEST_VIDEO_FILE to a file on disk, and
loading the file back to ensure the loaded frame metrics agree with those that were saved.
"""
video_manager = VideoManager([test_video_file])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
base_timecode = video_manager.get_base_timecode()
scene_manager.add_detector(ContentDetector())
try:
video_fps = video_manager.get_framerate()
start_time = FrameTimecode('00:00:00', video_fps)
duration = FrameTimecode('00:00:20', video_fps)
video_manager.set_duration(start_time=start_time, end_time=duration)
video_manager.set_downscale_factor()
video_manager.start()
scene_manager.detect_scenes(frame_source=video_manager)
with open(TEST_STATS_FILES[0], 'w') as stats_file:
stats_manager.save_to_csv(stats_file, base_timecode)
stats_manager_new = StatsManager()
with open(TEST_STATS_FILES[0], 'r') as stats_file:
stats_manager_new.load_from_csv(stats_file, base_timecode)
# Choose the first available frame key and compare all metrics in both.
frame_key = min(stats_manager._frame_metrics.keys())
metric_keys = list(stats_manager._registered_metrics)
assert stats_manager.metrics_exist(frame_key, metric_keys)
orig_metrics = stats_manager.get_metrics(frame_key, metric_keys)
new_metrics = stats_manager_new.get_metrics(frame_key, metric_keys)
for i, metric_val in enumerate(orig_metrics):
assert metric_val == pytest.approx(new_metrics[i])
finally:
os.remove(TEST_STATS_FILES[0])
video_manager.release()
def test_save_load_from_video(test_video_file):
""" Test generating and saving some frame metrics from TEST_VIDEO_FILE to a file on disk, and
loading the file back to ensure the loaded frame metrics agree with those that were saved.
"""
video_manager = VideoManager([test_video_file])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
base_timecode = video_manager.get_base_timecode()
scene_manager.add_detector(ContentDetector())
try:
video_fps = video_manager.get_framerate()
start_time = FrameTimecode('00:00:00', video_fps)
duration = FrameTimecode('00:00:20', video_fps)
video_manager.set_duration(start_time=start_time, end_time=duration)
video_manager.set_downscale_factor()
video_manager.start()
scene_manager.detect_scenes(frame_source=video_manager)
with open(TEST_STATS_FILES[0], 'w') as stats_file:
stats_manager.save_to_csv(stats_file, base_timecode)
stats_manager_new = StatsManager()
with open(TEST_STATS_FILES[0], 'r') as stats_file:
stats_manager_new.load_from_csv(stats_file, base_timecode)
# Choose the first available frame key and compare all metrics in both.
frame_key = min(stats_manager._frame_metrics.keys())
metric_keys = list(stats_manager._registered_metrics)
assert stats_manager.metrics_exist(frame_key, metric_keys)
orig_metrics = stats_manager.get_metrics(frame_key, metric_keys)
new_metrics = stats_manager_new.get_metrics(frame_key, metric_keys)
for i, metric_val in enumerate(orig_metrics):
assert metric_val == pytest.approx(new_metrics[i])
finally:
os.remove(TEST_STATS_FILES[0])
video_manager.release()
def test_detector_metrics(test_video_file):
""" Test passing StatsManager to a SceneManager and using it for storing the frame metrics
from a ContentDetector.
"""
video_manager = VideoManager([test_video_file])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
#base_timecode = video_manager.get_base_timecode()
assert not stats_manager._registered_metrics
scene_manager.add_detector(ContentDetector())
# add_detector should trigger register_metrics in the StatsManager.
assert stats_manager._registered_metrics
try:
video_fps = video_manager.get_framerate()
start_time = FrameTimecode('00:00:00', video_fps)
duration = FrameTimecode('00:00:20', video_fps)
video_manager.set_duration(start_time=start_time, end_time=duration)
video_manager.set_downscale_factor()
video_manager.start()
scene_manager.detect_scenes(frame_source=video_manager)
# Check that metrics were written to the StatsManager.
assert stats_manager._frame_metrics
frame_key = min(stats_manager._frame_metrics.keys())
assert stats_manager._frame_metrics[frame_key]
assert stats_manager.metrics_exist(
frame_key, list(stats_manager._registered_metrics))
# Since we only added 1 detector, the number of metrics from get_metrics
# should equal the number of metric keys in _registered_metrics.
assert len(
stats_manager.get_metrics(
frame_key, list(stats_manager._registered_metrics))) == len(
stats_manager._registered_metrics)
finally:
video_manager.release()
def test_load_hardcoded_file(test_video_file):
""" Test loading a stats file with some hard-coded data generated by this test case. """
from scenedetect.stats_manager import COLUMN_NAME_FPS
from scenedetect.stats_manager import COLUMN_NAME_FRAME_NUMBER
from scenedetect.stats_manager import COLUMN_NAME_TIMECODE
stats_manager = StatsManager()
stats_file = open(TEST_STATS_FILES[0], 'w')
try:
stats_writer = get_csv_writer(stats_file)
some_metric_key = 'some_metric'
some_metric_value = 1.2
some_frame_key = 100
base_timecode = FrameTimecode(0, 29.97)
some_frame_timecode = base_timecode + some_frame_key
# Write out a valid file.
stats_writer.writerow([COLUMN_NAME_FPS, '%.10f' % base_timecode.get_framerate()])
stats_writer.writerow(
[COLUMN_NAME_FRAME_NUMBER, COLUMN_NAME_TIMECODE, some_metric_key])
stats_writer.writerow(
[some_frame_key, some_frame_timecode.get_timecode(), str(some_metric_value)])
stats_file.close()
stats_file = open(TEST_STATS_FILES[0], 'r')
stats_manager.load_from_csv(csv_file=stats_file, base_timecode=base_timecode)
# Check that we decoded the correct values.
assert stats_manager.metrics_exist(some_frame_key, [some_metric_key])
assert stats_manager.get_metrics(
some_frame_key, [some_metric_key])[0] == pytest.approx(some_metric_value)
finally:
stats_file.close()
os.remove(TEST_STATS_FILES[0])
def test_detector_metrics(test_video_file):
""" Test passing StatsManager to a SceneManager and using it for storing the frame metrics
from a ContentDetector.
"""
video_manager = VideoManager([test_video_file])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
#base_timecode = video_manager.get_base_timecode()
assert not stats_manager._registered_metrics
scene_manager.add_detector(ContentDetector())
# add_detector should trigger register_metrics in the StatsManager.
assert stats_manager._registered_metrics
try:
video_fps = video_manager.get_framerate()
start_time = FrameTimecode('00:00:00', video_fps)
duration = FrameTimecode('00:00:20', video_fps)
video_manager.set_duration(start_time=start_time, end_time=duration)
video_manager.set_downscale_factor()
video_manager.start()
scene_manager.detect_scenes(frame_source=video_manager)
# Check that metrics were written to the StatsManager.
assert stats_manager._frame_metrics
frame_key = min(stats_manager._frame_metrics.keys())
assert stats_manager._frame_metrics[frame_key]
assert stats_manager.metrics_exist(frame_key, list(stats_manager._registered_metrics))
# Since we only added 1 detector, the number of metrics from get_metrics
# should equal the number of metric keys in _registered_metrics.
assert len(stats_manager.get_metrics(
frame_key, list(stats_manager._registered_metrics))) == len(
stats_manager._registered_metrics)
finally:
video_manager.release()
def test_load_hardcoded_file_backwards_compat():
""" Test loading a stats file with some hard-coded data generated by this test case.
Ensures backwards compatibility with old statsfiles which included an addional header.
"""
stats_manager = StatsManager()
with open(TEST_STATS_FILES[0], 'w') as stats_file:
stats_writer = get_csv_writer(stats_file)
some_metric_key = 'some_metric'
some_metric_value = 1.2
some_frame_key = 100
base_timecode = FrameTimecode(0, 29.97)
some_frame_timecode = base_timecode + some_frame_key
# Write out a valid file as per PySceneDetect v0.5.4 and prior.
stats_writer.writerow(['Video Framerate', '23.976'])
stats_writer.writerow(
[COLUMN_NAME_FRAME_NUMBER, COLUMN_NAME_TIMECODE, some_metric_key])
stats_writer.writerow([
some_frame_key,
some_frame_timecode.get_timecode(),
str(some_metric_value)
])
stats_file.close()
stats_file = open(TEST_STATS_FILES[0], 'r')
stats_manager.load_from_csv(csv_file=stats_file)
# Check that we decoded the correct values.
assert stats_manager.metrics_exist(some_frame_key, [some_metric_key])
assert stats_manager.get_metrics(
some_frame_key,
[some_metric_key])[0] == pytest.approx(some_metric_value)
def make_dataset(video_path, video_name, timecodes, save_dir):
# type: (str) -> List[Tuple[FrameTimecode, FrameTimecode]]
video_manager = VideoManager([video_path])
stats_manager = StatsManager()
# Construct our SceneManager and pass it our StatsManager.
scene_manager = SceneManager(stats_manager)
# Add ContentDetector algorithm (each detector's constructor
# takes detector options, e.g. threshold).
scene_manager.add_detector(ContentDetector())
base_timecode = video_manager.get_base_timecode()
# We save our stats file to {VIDEO_PATH}.stats.csv.
stats_file_path = 'stats/%s.stats.csv' % video_name
scene_list = []
try:
# If stats file exists, load it.
if os.path.exists(stats_file_path):
# Read stats from CSV file opened in read mode:
with open(stats_file_path, 'r') as stats_file:
stats_manager.load_from_csv(stats_file, base_timecode)
# Set downscale factor to improve processing speed.
video_manager.set_downscale_factor()
# Start video_manager.
video_manager.start()
# Perform scene detection on video_manager.
scene_manager.detect_scenes(frame_source=video_manager)
# We only write to the stats file if a save is required:
if stats_manager.is_save_required():
with open(stats_file_path, 'w') as stats_file:
stats_manager.save_to_csv(stats_file, base_timecode)
start_timecode = ""
start_content_val = 0
end_timecode = ""
end_content_val = 0
metric_keys = sorted(
list(
stats_manager._registered_metrics.union(
stats_manager._loaded_metrics)))
frame_keys = sorted(stats_manager._frame_metrics.keys())
for frame_key in frame_keys:
frame_timecode = base_timecode + frame_key
timecode = frame_timecode.get_timecode()
if timecode > timecodes[0] and timecode < timecodes[1]:
content_val = stats_manager.get_metrics(
frame_key, metric_keys)[0]
if start_content_val < content_val:
start_content_val = content_val
start_timecode = timecode
if timecode > timecodes[2] and timecode < timecodes[3]:
content_val = stats_manager.get_metrics(
frame_key, metric_keys)[0]
if end_content_val < content_val:
end_content_val = content_val
end_timecode = timecode
threshold = min(start_content_val, end_content_val)
print(f"Start Time: {start_timecode}, End Time: {end_timecode}")
finally:
video_manager.release()
video_manager = VideoManager([video_path])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
scene_manager.add_detector(ContentDetector(threshold=threshold))
base_timecode = video_manager.get_base_timecode()
scene_list = []
try:
# If stats file exists, load it.
if os.path.exists(stats_file_path):
# Read stats from CSV file opened in read mode:
with open(stats_file_path, 'r') as stats_file:
stats_manager.load_from_csv(stats_file, base_timecode)
# Set downscale factor to improve processing speed.
video_manager.set_downscale_factor()
# Start video_manager.
video_manager.start()
# Perform scene detection on video_manager.
scene_manager.detect_scenes(frame_source=video_manager)
# Obtain list of detected scenes.
scene_list = scene_manager.get_scene_list(base_timecode)
# Each scene is a tuple of (start, end) FrameTimecodes.
start_video_num = 0
end_video_num = 0
for i, scene in enumerate(scene_list):
if scene[0].get_timecode(
) >= start_timecode and start_video_num == 0:
start_video_num = i
print(f"start video: {start_video_num}")
if scene[1].get_timecode() >= end_timecode and end_video_num == 0:
end_video_num = i
print(f"end video: {end_video_num}")
finally:
video_manager.release()
video_dir = os.path.join(save_dir, video_name)
if not os.path.exists(video_dir):
os.makedirs(video_dir)
split_video_ffmpeg([video_path], scene_list,
os.path.join(video_dir,
"${VIDEO_NAME}-${SCENE_NUMBER}.mp4"),
video_name)
return start_video_num, end_video_num, len(scene_list)
