def __init__(self):
# Properties for main scenedetect command options (-i, -s, etc...) and CliContext logic.
self.options_processed = False # True when CLI option parsing is complete.
self.scene_manager = None # detect-content, detect-threshold, etc...
self.video_manager = None # -i/--input, -d/--downscale
self.base_timecode = None # -f/--framerate
self.start_frame = 0 # time -s/--start
self.stats_manager = StatsManager() # -s/--stats
self.stats_file_path = None # -s/--stats
self.output_directory = None # -o/--output
self.quiet_mode = False # -q/--quiet or -v/--verbosity quiet
self.frame_skip = 0 # -fs/--frame-skip
# Properties for save-images command.
self.save_images = False # save-images command
self.image_extension = 'jpg' # save-images -j/--jpeg, -w/--webp, -p/--png
self.image_directory = None # save-images -o/--output
self.image_param = None # save-images -q/--quality if -j/-w, -c/--compression if -p
self.image_name_format = '$VIDEO_NAME-Scene-$SCENE_NUMBER-$IMAGE_NUMBER' # save-images -f/--name-format
self.num_images = 2 # save-images -n/--num-images
self.imwrite_params = get_cv2_imwrite_params()
# Properties for split-video command.
self.split_video = False # split-video command
self.split_mkvmerge = False # split-video -c/--copy
self.split_args = None # split-video -a/--override-args
self.split_directory = None # split-video -o/--output
self.split_name_format = '$VIDEO_NAME-Scene-$SCENE_NUMBER' # split-video -f/--filename
self.split_quiet = False # split-video -q/--quiet
# Properties for list-scenes command.
self.list_scenes = False # list-scenes command
self.print_scene_list = False # list-scenes --quiet/-q
self.scene_list_directory = None # list-scenes -o/--output
self.scene_list_name_format = None # list-scenes -f/--filename
self.scene_list_output = False # list-scenes -n/--no-output
def test_load_corrupt_stats():
""" Test loading a corrupted stats file created by outputting data in the wrong format. """
stats_manager = StatsManager()
with open(TEST_STATS_FILES[0], 'wt') as stats_file:
stats_writer = get_csv_writer(stats_file)
some_metric_key = 'some_metric'
some_metric_value = str(1.2)
some_frame_key = 100
base_timecode = FrameTimecode(0, 29.97)
some_frame_timecode = base_timecode + some_frame_key
# Write out some invalid files.
# File #0: Wrong Header Names [StatsFileCorrupt]
# Swapped timecode & frame number.
stats_writer.writerow(
[COLUMN_NAME_TIMECODE, COLUMN_NAME_FRAME_NUMBER, some_metric_key])
stats_writer.writerow([
some_frame_key,
some_frame_timecode.get_timecode(), some_metric_value
])
stats_file.close()
with pytest.raises(StatsFileCorrupt):
stats_manager.load_from_csv(TEST_STATS_FILES[0])
def __init__(self):
# Properties for main scenedetect command options (-i, -s, etc...) and CliContext logic.
self.options_processed = False # True when CLI option parsing is complete.
self.scene_manager = None # detect-content, detect-threshold, etc...
self.video_manager = None # -i/--input, -d/--downscale
self.base_timecode = None # -f/--framerate
self.start_frame = 0 # time -s/--start [start_frame]
self.stats_manager = StatsManager() # -s/--stats
self.stats_file_path = None # -s/--stats [stats_file_path]
self.output_directory = None # -o/--output [output_directory]
self.quiet_mode = False # -q/--quiet or -v/--verbosity quiet
self.frame_skip = 0 # -fs/--frame-skip [frame_skip]
# Properties for save-images command.
self.save_images = False # save-images command
self.image_extension = 'jpg' # save-images -j/--jpeg, -w/--webp, -p/--png
self.image_directory = None # save-images -o/--output [image_directory]
self.image_param = None # save-images -q/--quality if -j/-w, -c/--compression if -p
self.num_images = 2 # save-images -n/--num-images
self.imwrite_params = get_cv2_imwrite_params()
# Properties for split-video command.
self.split_video = False # split-video command
self.split_mkvmerge = False # split-video -m/--mkvmerge (or split-video without ffmpeg)
self.split_args = None # split-video -f/--ffmpeg-args [split_args]
self.split_directory = None # split-video -o/--output [split_directory]
self.split_quiet = False # split-video -q/--quiet
# Properties for list-scenes command.
self.list_scenes = False # list-scenes command
self.print_scene_list = False # list-scenes --quiet/-q
self.scene_list_path = None # list-scenes -o [scene_list_path]
def _open_stats_file(self):
if self.stats_manager is None:
self.stats_manager = StatsManager()
if self.stats_file_path is not None:
if os.path.exists(self.stats_file_path):
self.logger.info('Loading frame metrics from stats file: %s',
os.path.basename(self.stats_file_path))
try:
with open(self.stats_file_path, 'rt') as stats_file:
self.stats_manager.load_from_csv(stats_file)
except StatsFileCorrupt:
error_info = (
'Could not load frame metrics from stats file - file is either corrupt,'
' or not a valid PySceneDetect stats file. If the file exists, ensure that'
' it is a valid stats file CSV, otherwise delete it and run PySceneDetect'
' again to re-generate the stats file.')
error_strs = [
'Could not load stats file.',
'Failed to parse stats file:', error_info
]
self.logger.error('\n'.join(error_strs))
raise click.BadParameter(
'\n Could not load given stats file, see above output for details.',
param_hint='input stats file')
def add_detector(self, detector):
# type: (SceneDetector) -> None
""" Adds/registers a SceneDetector (e.g. ContentDetector, ThresholdDetector) to
run when detect_scenes is called. The SceneManager owns the detector object,
so a temporary may be passed.
Arguments:
detector (SceneDetector): Scene detector to add to the SceneManager.
"""
if self._stats_manager is None and detector.stats_manager_required():
# Make sure the lists are empty so that the detectors don't get
# out of sync (require an explicit statsmanager instead)
assert not self._detector_list and not self._sparse_detector_list
self._stats_manager = StatsManager()
detector.stats_manager = self._stats_manager
if self._stats_manager is not None:
# Allow multiple detection algorithms of the same type to be added
# by suppressing any FrameMetricRegistered exceptions due to attempts
# to re-register the same frame metric keys.
try:
self._stats_manager.register_metrics(detector.get_metrics())
except FrameMetricRegistered:
pass
if not issubclass(type(detector), SparseSceneDetector):
self._detector_list.append(detector)
else:
self._sparse_detector_list.append(detector)
def test_load_empty_stats():
""" Test loading an empty stats file, ensuring it results in no errors. """
open(TEST_STATS_FILES[0], 'w').close()
with open(TEST_STATS_FILES[0], 'r') as stats_file:
stats_manager = StatsManager()
stats_reader = get_csv_reader(stats_file)
stats_manager.load_from_csv(stats_reader)
def find_shots(video_path, stats_file, threshold):
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(threshold=threshold))
base_timecode = video_manager.get_base_timecode()
scene_list = []
try:
# 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.
print('List of shots obtained:')
for i, scene in enumerate(scene_list):
print('Scene %2d: Start %s / Frame %d, End %s / Frame %d' % (
i + 1,
scene[0].get_timecode(),
scene[0].get_frames(),
scene[1].get_timecode(),
scene[1].get_frames(),
))
# Save a list of stats to a csv
if stats_manager.is_save_required():
with open(stats_file, 'w') as stats_file:
stats_manager.save_to_csv(stats_file, base_timecode)
except Exception as err:
print(
"Failed to find shots for: video: " + video_path + ", stats: " +
stats_file + ", threshold: " + threshold, err)
traceback.print_exc()
finally:
video_manager.release()
return scene_list
def run(self):
self.logger.info('run(): started')
self.logger.info("Running PySceneDetect API test...")
self.logger.info("PySceneDetect version being used: %s" %
str(scenedetect.__version__))
# Create a video_manager point to video file testvideo.mp4
video_manager = VideoManager([self.video_filename])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
# Add ContentDetector algorithm (constructor takes detector options like threshold).
scene_manager.add_detector(ContentDetector())
base_timecode = video_manager.get_base_timecode()
print('base_timecode', base_timecode)
try:
# If stats file exists, load it.
if os.path.exists(self.stats_filename):
# Read stats from CSV file opened in read mode:
with open(self.stats_filename, 'r') as stats_file:
stats_manager.load_from_csv(stats_file, base_timecode)
# Set video_manager duration
start_time = 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,
start_time=start_time)
# Obtain list of detected scenes.
self.scene_list = scene_manager.get_scene_list(base_timecode)
# Like FrameTimecodes, each scene in the scene_list can be sorted if the
# list of scenes becomes unsorted.
"""
# We only write to the stats file if a save is required:
if stats_manager.is_save_required():
with open(self.stats_filename, 'w') as stats_file:
stats_manager.save_to_csv(stats_file, base_timecode)
"""
finally:
video_manager.release()
self.logger.info('run(): finished')
def extract_shots_with_pyscenedetect(src_video,
threshold=0,
min_scene_length=15):
video_manager = VideoManager([src_video])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
scene_manager.add_detector(ContentDetector(threshold, min_scene_length))
base_timecode = video_manager.get_base_timecode()
scene_list = []
try:
start_time = base_timecode
video_manager.set_duration(start_time=start_time)
video_manager.set_downscale_factor(downscale_factor=1)
video_manager.start()
scene_manager.detect_scenes(frame_source=video_manager, frame_skip=0)
scene_list = scene_manager.get_scene_list(base_timecode)
# print('List of scenes obtained:')
# for i, scene in enumerate(scene_list):
# print(' Scene %2d: Start %s / Frame %d, End %s / Frame %d' % (
# i + 1,
# scene[0].get_timecode(), scene[0].get_frames(),
# scene[1].get_timecode(), scene[1].get_frames(),))
write_csv(src_video, scene_list)
except:
print("error")
finally:
video_manager.release()
return scene_list
def detect_scene(path, fps, min_sec):
video_manager = VideoManager([path], framerate=fps)
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
scene_manager.add_detector(
ContentDetector(threshold=30, min_scene_len=fps * min_sec))
base_timecode = video_manager.get_base_timecode()
video_manager.set_downscale_factor()
video_manager.start()
scene_manager.detect_scenes(frame_source=video_manager,
show_progress=False)
scene_list = scene_manager.get_scene_list(base_timecode)
# Like FrameTimecodes, each scene in the scene_list can be sorted if the
# list of scenes becomes unsorted.
# print('List of scenes obtained:')
# for i, scene in enumerate(scene_list):
# print(' Scene %2d: Start %s / Frame %d, End %s / Frame %d' % (
# i + 1,
# scene[0].get_timecode(), scene[0].get_frames(),
# scene[1].get_timecode(), scene[1].get_frames(),))
scene_list = [(round(s[0].get_seconds()), round(s[1].get_seconds()))
for s in scene_list]
video_manager.release()
return scene_list
def frameExtraction(video, method, treshold):
video_manager = VideoManager([video])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
# Add ContentDetector algorithm (constructor takes detector options like threshold).
if method == 'ContentDetector':
scene_manager.add_detector(ContentDetector(treshold))
if method == 'threshold_detector':
scene_manager.add_detector(ThresholdDetector(treshold))
base_timecode = video_manager.get_base_timecode()
# Set downscale factor to improve processing speed (no args means default).
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)
cap = cv2.VideoCapture(video)
for i, scene in enumerate(scene_list):
i = i + 1
cut_frame = scene[0].get_frames()
cap.set(1, cut_frame)
ret, frame = cap.read()
frame_name = "shot " + str(i) + ".jpg"
cv2.imwrite(frame_name, frame)
def find_scenes(video_path):
# 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()
try:
# 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)
finally:
video_manager.release()
scene_list_new = []
for cur_scene in scene_list:
start_frame = cur_scene[0].get_frames()
end_frame = cur_scene[1].get_frames()
scene_list_new.append((start_frame, end_frame))
return scene_list_new
def main():
#import csv files as pandas
movieRuntimes = pd.read_csv(
'C:\\Users\\hasna\\Desktop\\Smell of Fear\\Numerical Data\\movie_runtimes.csv'
)
movieList = list(movieRuntimes['movie'])
#detect number of scenes within a 30 second period
for movie in movieList:
aslList = list()
effectiveRuntime = movieRuntimes['effective runtime'][movieList.index(
movie)]
for j in range(0, effectiveRuntime):
moviePath = 'C:\\Users\\hasna\\Desktop\\Smell of Fear\\Movie Intervals\\' + movie + '\\' + movie + '_' + str(
j) + '.mp4'
video_manager = VideoManager([moviePath])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
scene_manager.add_detector(ContentDetector(40))
#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)
scene_list = scene_manager.get_scene_list(
video_manager.get_base_timecode())
aslList.append(len(scene_list))
video_manager.release()
#save the ASL List
savePath = 'C:\\Users\\hasna\\Desktop\\Smell of Fear\\Pickle Objects\\ASL\\' + movie + '.p'
pickle.dump(aslList, open(savePath, "wb"))
def get_scene_startFrames(filepath):
# Create a video_manager point to video file testvideo.mp4. Note that multiple
# videos can be appended by simply specifying more file paths in the list
# passed to the VideoManager constructor. Note that appending multiple videos
# requires that they all have the same frame size, and optionally, framerate.
video_manager = VideoManager([filepath])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
# Add ContentDetector algorithm (constructor takes detector options like threshold).
scene_manager.add_detector(ContentDetector())
base_timecode = video_manager.get_base_timecode()
try:
# 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)
scene_startFrames = []
for i, scene in enumerate(scene_list):
scene_startFrames.append(scene[0].get_frames())
finally:
video_manager.release()
return scene_startFrames
def scene_detect(opt):
video_manager = VideoManager(
[os.path.join(opt.avi_dir, opt.reference, 'video.avi')])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
# Add ContentDetector algorithm (constructor takes detector options like threshold).
scene_manager.add_detector(ContentDetector())
base_timecode = video_manager.get_base_timecode()
video_manager.set_downscale_factor()
video_manager.start()
scene_manager.detect_scenes(frame_source=video_manager)
scene_list = scene_manager.get_scene_list(base_timecode)
savepath = os.path.join(opt.work_dir, opt.reference, 'scene.pckl')
if scene_list == []:
scene_list = [(video_manager.get_base_timecode(),
video_manager.get_current_timecode())]
with open(savepath, 'wb') as fil:
pickle.dump(scene_list, fil)
print('%s - scenes detected %d' % (os.path.join(
opt.avi_dir, opt.reference, 'video.avi'), len(scene_list)))
return scene_list
def test_detectors_with_stats(test_video_file):
""" Test all detectors functionality with a StatsManager. """
for detector in [ContentDetector, ThresholdDetector, AdaptiveDetector]:
vm = VideoManager([test_video_file])
stats = StatsManager()
sm = SceneManager(stats_manager=stats)
sm.add_detector(create_detector(detector, vm))
try:
end_time = FrameTimecode('00:00:15', vm.get_framerate())
vm.set_duration(end_time=end_time)
vm.set_downscale_factor()
vm.start()
sm.detect_scenes(frame_source=vm)
initial_scene_len = len(sm.get_scene_list())
assert initial_scene_len > 0 # test case must have at least one scene!
# Re-analyze using existing stats manager.
sm = SceneManager(stats_manager=stats)
sm.add_detector(create_detector(detector, vm))
vm.release()
vm.reset()
vm.set_duration(end_time=end_time)
vm.set_downscale_factor()
vm.start()
sm.detect_scenes(frame_source=vm)
scene_list = sm.get_scene_list()
assert len(scene_list) == initial_scene_len
finally:
vm.release()
def videodataset_scenedetect(dirpath):
from scenedetect.video_manager import VideoManager
from scenedetect.scene_manager import SceneManager
from scenedetect.stats_manager import StatsManager
from scenedetect.detectors import ContentDetector
from scenedetect.detectors import ThresholdDetector
result_list = []
for video_name in sorted(os.listdir(dirpath)):
video_path = os.path.join(dirpath, video_name)
print(video_path)
video_info = get_video_info(video_path)
# print (video_info)
total_frames = int(video_info['duration_ts'])
print('总帧数：' + str(total_frames))
# 创建一个video_manager指向视频文件
video_manager = VideoManager([video_path])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
# ＃添加ContentDetector算法（构造函数采用阈值等检测器选项）
scene_manager.add_detector(ContentDetector(threshold=20))
base_timecode = video_manager.get_base_timecode()
try:
frames_num = total_frames
# 设置缩减系数以提高处理速度。
video_manager.set_downscale_factor()
# 启动 video_manager.
video_manager.start()
# 在video_manager上执行场景检测。
scene_manager.detect_scenes(frame_source=video_manager)
# 获取检测到的场景列表。
scene_list = scene_manager.get_scene_list(base_timecode)
# 与FrameTimecodes一样，如果是，则可以对scene_list中的每个场景进行排序
# 场景列表变为未排序。
# print('List of scenes obtained:')
# print(scene_list)
# 如果scene_list不为空，整理结果列表，否则，视频为单场景
re_scene_list = []
if scene_list:
for i, scene in enumerate(scene_list):
# print('%d , %d' % (scene[0].get_frames(), scene[1].get_frames()))
re_scene = (scene[0].get_frames(),
scene[1].get_frames() - 1)
re_scene_list.append(re_scene)
else:
re_scene = (0, frames_num - 1)
re_scene_list.append(re_scene)
# 输出切分场景的列表结果
print("\n")
print(re_scene_list)
print("\n")
# for item in re_scene_list:
# assert item[1] - item[0] + 1 >= 3
result_list.append(re_scene_list)
finally:
video_manager.release()
with open(os.path.join(dirpath, 'scene.pickle'), 'wb') as f:
pickle.dump(result_list, f)
def test_load_empty_stats(test_video_file):
""" Test loading an empty stats file, ensuring it results in no errors. """
try:
stats_file = open(TEST_STATS_FILES[0], 'w')
stats_file.close()
stats_file = open(TEST_STATS_FILES[0], 'r')
stats_manager = StatsManager()
stats_reader = get_csv_reader(stats_file)
stats_manager.load_from_csv(stats_reader)
finally:
stats_file.close()
os.remove(TEST_STATS_FILES[0])
def test_load_empty_stats(test_video_file):
""" Test loading an empty stats file, ensuring it results in no errors. """
try:
stats_file = open(TEST_STATS_FILES[0], 'w')
stats_file.close()
stats_file = open(TEST_STATS_FILES[0], 'r')
stats_manager = StatsManager()
stats_reader = get_csv_reader(stats_file)
stats_manager.load_from_csv(stats_reader)
finally:
stats_file.close()
os.remove(TEST_STATS_FILES[0])
def make_elements(video_path, video_name, 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(threshold=threshold))
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)
# Obtain list of detected scenes.
scene_list = scene_manager.get_scene_list(base_timecode)
# Each scene is a tuple of (start, end) FrameTimecodes.
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)
def find_scenes(video_path):
"""
based on changes between frames in the HSV color space
"""
# three main calsses: VideoManager, SceneManager, StatsManager
video_manager = VideoManager([video_path])
stats_manager = 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 = '%s.stats.csv' % video_path
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.
print('List of scenes obtained:')
for i, scene in enumerate(scene_list):
print('Scene %2d: Start %s / Frame %d, End %s / Frame %d' % (
i + 1,
scene[0].get_timecode(),
scene[0].get_frames(),
scene[1].get_timecode(),
scene[1].get_frames(),
))
# 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)
finally:
video_manager.release()
return scene_list
async def hello(request):
entry_time = time.time()
# file_path = request.app['file_path']
file_key, cache_conn = app['cache_params']
video_file = tempfile.NamedTemporaryFile(delete=False)
try:
file_byte = cache_conn.get(file_key)
if file_byte is None:
file_path = app['file_path']
with open(file_path, 'rb') as f:
file_byte = f.read()
cache_conn.set(file_key, file_byte)
video_file.write(file_byte)
# print(video_file.name)
video_file.close()
video_file_name = video_file.name
video_manager = VideoManager([video_file_name])
print("load time: {}".format(time.time()-entry_time))
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
scene_manager.add_detector(ContentDetector())
base_timecode = video_manager.get_base_timecode()
try:
start_time = base_timecode # 00:00:00.667
# Set video_manager duration to read frames from 00:00:00 to 00:00:20.
end_time = start_time + 60
video_manager.set_duration(start_time=start_time, end_time=end_time)
# Set downscale factor to improve processing speed (no args means default).
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)
# Like FrameTimecodes, each scene in the scene_list can be sorted if the
# list of scenes becomes unsorted.
output_string = 'List of scenes obtained:\n'
for i, scene in enumerate(scene_list):
output_string += (' Scene %2d: Start %s / Frame %d, End %s / Frame %d\n' % (
i+1,
scene[0].get_timecode(), scene[0].get_frames(),
scene[1].get_timecode(), scene[1].get_frames(),))
finally:
video_manager.release()
finally:
os.unlink(video_file.name)
video_file.close()
print("overall latency:{}".format(time.time()-entry_time))
return web.Response(text="%s" % output_string)
def detect(fnm):
# Create a video_manager point to video file testvideo.mp4. Note that multiple
# videos can be appended by simply specifying more file paths in the list
# passed to the VideoManager constructor. Note that appending multiple videos
# requires that they all have the same frame size, and optionally, framerate.
video_manager = VideoManager([fnm])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
# Add ContentDetector algorithm (constructor takes detector options like threshold).
scene_manager.add_detector(ContentDetector())
base_timecode = video_manager.get_base_timecode()
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)
start_time = base_timecode + 20 # 00:00:00.667
end_time = base_timecode + 20.0 # 00:00:20.000
# Set video_manager duration to read frames from 00:00:00 to 00:00:20.
#video_manager.set_duration(start_time=start_time, end_time=end_time)
# Set downscale factor to improve processing speed (no args means default).
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) #,
#start_time=start_time)
# Obtain list of detected scenes.
scene_list = scene_manager.get_scene_list(base_timecode)
# Like FrameTimecodes, each scene in the scene_list can be sorted if the
# list of scenes becomes unsorted.
ls = []
print('List of scenes obtained:')
for i, scene in enumerate(scene_list):
ls.append((scene[0].get_frames(), scene[1].get_frames() - 1))
#print(' Scene %2d: Start %s / Frame %d, End %s / Frame %d' % (
# i+1,
# scene[0].get_timecode(), scene[0].get_frames(),
# scene[1].get_timecode(), scene[1].get_frames(),))
# 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)
finally:
video_manager.release()
return ls
def getSceneList(name="testvideo.mp4", start_frame=0):
print("Running PySceneDetect API test...")
print("PySceneDetect version being used: %s" %
str(scenedetect.__version__))
# Create a video_manager point to video file testvideo.mp4. Note that multiple
# videos can be appended by simply specifying more file paths in the list
# passed to the VideoManager constructor. Note that appending multiple videos
# requires that they all have the same frame size, and optionally, framerate.
video_manager = VideoManager([name])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
# Add ContentDetector algorithm (constructor takes detector options like threshold).
scene_manager.add_detector(ContentDetector())
base_timecode = video_manager.get_base_timecode()
try:
start_time = base_timecode + start_frame # + 20 # 00:00:00.667
#end_time = base_timecode + 20.0 # 00:00:20.000
# Set video_manager duration to read frames from 00:00:00 to 00:00:20.
video_manager.set_duration(start_time=start_time)
# Set downscale factor to improve processing speed.
video_manager.set_downscale_factor(16)
# 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)
# Like FrameTimecodes, each scene in the scene_list can be sorted if the
# list of scenes becomes unsorted.
print('List of scenes obtained:')
for i, scene in enumerate(scene_list):
print(' Scene %2d: Start %s / Frame %d, End %s / Frame %d' % (
i + 1,
scene[0].get_timecode(),
scene[0].get_frames(),
scene[1].get_timecode(),
scene[1].get_frames(),
))
#import code
#code.interact(local=locals())
a = [(scene_start.get_frames(), scene_end.get_frames())
for scene_start, scene_end in scene_list]
a[-1] = (a[-1][0], a[-1][-1] + start_frame)
return a
finally:
video_manager.release()
def _open_stats_file(self):
if self.stats_manager is None:
self.stats_manager = StatsManager()
if self.stats_file_path is not None:
if os.path.exists(self.stats_file_path):
logging.info('Loading frame metrics from stats file: %s',
os.path.basename(self.stats_file_path))
try:
with open(self.stats_file_path, 'rt') as stats_file:
self.stats_manager.load_from_csv(
stats_file, self.base_timecode)
except StatsFileCorrupt:
error_strs = [
'Could not load stats file.',
'Failed to parse stats file:',
'Could not load frame metrics from stats file - file is corrupt or not a'
' valid PySceneDetect stats file. If the file exists, ensure that it is'
' a valid stats file CSV, otherwise delete it and run PySceneDetect again'
' to re-generate the stats file.'
]
logging.error('\n'.join(error_strs))
raise click.BadParameter(
'\n Could not load given stats file, see above output for details.',
param_hint='input stats file')
except StatsFileFramerateMismatch as ex:
error_strs = [
'could not load stats file.',
'Failed to parse stats file:',
'Framerate differs between stats file (%.2f FPS) and input'
' video%s (%.2f FPS)' %
(ex.stats_file_fps, 's'
if self.video_manager.get_num_videos() > 1 else '',
ex.base_timecode_fps),
'Ensure the correct stats file path was given, or delete and re-generate'
' the stats file.'
]
logging.error('\n'.join(error_strs))
raise click.BadParameter(
'framerate differs between given stats file and input video(s).',
param_hint='input stats file')
def get_plans(video_path, threshold):
video_manager = VideoManager([video_path])
base_timecode = video_manager.get_base_timecode()
video_manager.set_downscale_factor()
video_manager.start()
stats_manager = StatsManager()
plan_manager = SceneManager(stats_manager)
plan_manager.add_detector(ContentDetector(threshold=threshold))
plan_manager.detect_scenes(frame_source=video_manager)
plan_list = plan_manager.get_scene_list(base_timecode)
return plan_list
def __init__(self):
'''
Init the detection manager.
'''
print("Init the shot detector")
print("PySceneDetect version being used: %s" %
str(scenedetect.__version__))
self.stats_manager = StatsManager()
self.scene_manager = SceneManager(self.stats_manager)
self.scene_manager.add_detector(ContentDetector())
def chunk_videos(vid_paths):
"""Chunks videos into different scenes based on their content for later processing."""
for vp in vid_paths:
try:
# Setup the different managers for chunking the scenes.
video_manager = VideoManager([str(vp)])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
# Add ContentDetector algorithm (constructor takes detector options like threshold).
scene_manager.add_detector(ContentDetector())
base_timecode = video_manager.get_base_timecode()
# Set downscale factor to improve processing speed (no args means default).
video_manager.set_downscale_factor()
# Set the duration to be however long the video is and start the video manager.
video_manager.set_duration()
video_manager.start()
# Perform scene detection on video_manager and grab the scenes.
scene_manager.detect_scenes(frame_source=video_manager)
scene_list = scene_manager.get_scene_list(base_timecode)
# If the output dir of the chunked videos does not exist, create it.
if not (vp.parent / "chunks").exists():
(vp.parent / "chunks").mkdir()
# Split the video into chunks based on the scene list and save to the "chunks" folder.
split_video_ffmpeg(
[vp],
scene_list,
str(vp.parent / "chunks/$VIDEO_NAME-$SCENE_NUMBER.mp4"),
"chunk" #, arg_override = args
)
with open("stats.csv", 'w') as stats_file:
stats_manager.save_to_csv(stats_file, base_timecode)
finally:
# Close out the video_manager.
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_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_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_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_api():
print("Running PySceneDetect API test...")
print("PySceneDetect version being used: %s" % str(scenedetect.__version__))
# Create a video_manager point to video file testvideo.mp4. Note that multiple
# videos can be appended by simply specifying more file paths in the list
# passed to the VideoManager constructor. Note that appending multiple videos
# requires that they all have the same frame size, and optionally, framerate.
video_manager = VideoManager(['testvideo.mp4'])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
# Add ContentDetector algorithm (constructor takes detector options like threshold).
scene_manager.add_detector(ContentDetector())
base_timecode = video_manager.get_base_timecode()
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)
start_time = base_timecode + 20 # 00:00:00.667
end_time = base_timecode + 20.0 # 00:00:20.000
# Set video_manager duration to read frames from 00:00:00 to 00:00:20.
video_manager.set_duration(start_time=start_time, end_time=end_time)
# 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)
# Like FrameTimecodes, each scene in the scene_list can be sorted if the
# list of scenes becomes unsorted.
print('List of scenes obtained:')
for i, scene in enumerate(scene_list):
print(' Scene %2d: Start %s / Frame %d, End %s / Frame %d' % (
i+1,
scene[0].get_timecode(), scene[0].get_frames(),
scene[1].get_timecode(), scene[1].get_frames(),))
# 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)
finally:
video_manager.release()
def detect_scenes(self, video_id):
if video_id in self.cache:
return self.cache[video_id]
scenes_file_path = self._get_scenes_path(video_id)
if exists(scenes_file_path):
print('loading scenes from ', scenes_file_path)
with open(scenes_file_path, 'rb') as f:
scenes = pickle.load(f)
self.cache[video_id] = scenes
return scenes
print('Detecting scenes for {}'.format(video_id))
stats_file_path = self._get_stats_path(video_id)
video_manager = VideoManager([get_video_path(video_id)])
stats_manager = StatsManager()
scene_manager = SceneManager(stats_manager)
scene_manager.add_detector(self._create_detector())
base_timecode = video_manager.get_base_timecode()
try:
if exists(stats_file_path):
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 (no args means default).
video_manager.set_downscale_factor()
video_manager.start()
scene_manager.detect_scenes(frame_source=video_manager)
scenes_list = scene_manager.get_scene_list(base_timecode)
scenes = [(scene[0].get_seconds(), scene[1].get_seconds())
for scene in scenes_list]
self.cache[video_id] = scenes
if self.save_scenes:
scenes_file_path = self._get_scenes_path(video_id)
print('saving scenes to ', scenes_file_path)
with open(scenes_file_path, 'wb') as f:
pickle.dump(scenes, f)
# 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)
return self.cache[video_id]
finally:
video_manager.release()
def _open_stats_file(self):
if self.stats_manager is None:
self.stats_manager = StatsManager()
if self.stats_file_path is not None:
if os.path.exists(self.stats_file_path):
logging.info('Loading frame metrics from stats file: %s',
os.path.basename(self.stats_file_path))
try:
with open(self.stats_file_path, 'rt') as stats_file:
self.stats_manager.load_from_csv(stats_file, self.base_timecode)
except StatsFileCorrupt:
error_strs = [
'Could not load stats file.', 'Failed to parse stats file:',
'Could not load frame metrics from stats file - file is corrupt or not a'
' valid PySceneDetect stats file. If the file exists, ensure that it is'
' a valid stats file CSV, otherwise delete it and run PySceneDetect again'
' to re-generate the stats file.']
logging.error('\n'.join(error_strs))
raise click.BadParameter(
'\n Could not load given stats file, see above output for details.',
param_hint='input stats file')
except StatsFileFramerateMismatch as ex:
error_strs = [
'could not load stats file.', 'Failed to parse stats file:',
'Framerate differs between stats file (%.2f FPS) and input'
' video%s (%.2f FPS)' % (
ex.stats_file_fps,
's' if self.video_manager.get_num_videos() > 1 else '',
ex.base_timecode_fps),
'Ensure the correct stats file path was given, or delete and re-generate'
' the stats file.']
logging.error('\n'.join(error_strs))
raise click.BadParameter(
'framerate differs between given stats file and input video(s).',
param_hint='input stats file')
def test_load_corrupt_stats(test_video_file):
""" Test loading a corrupted stats file created by outputting data in the wrong format. """
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_files = [open(stats_file, 'wt') for stats_file in TEST_STATS_FILES]
try:
stats_writers = [get_csv_writer(stats_file) for stats_file in stats_files]
some_metric_key = 'some_metric'
some_metric_value = str(1.2)
some_frame_key = 100
base_timecode = FrameTimecode(0, 29.97)
some_frame_timecode = base_timecode + some_frame_key
# Write out some invalid files.
# File 0: Blank FPS [StatsFileCorrupt]
stats_writers[0].writerow([COLUMN_NAME_FPS])
stats_writers[0].writerow(
[COLUMN_NAME_FRAME_NUMBER, COLUMN_NAME_TIMECODE, some_metric_key])
stats_writers[0].writerow(
[some_frame_key, some_frame_timecode.get_timecode(), some_metric_value])
# File 1: Invalid FPS [StatsFileCorrupt]
stats_writers[1].writerow([COLUMN_NAME_FPS, '%0.10f' % 0.0000001])
stats_writers[1].writerow(
[COLUMN_NAME_FRAME_NUMBER, COLUMN_NAME_TIMECODE, some_metric_key])
stats_writers[1].writerow(
[some_frame_key, some_frame_timecode.get_timecode(), some_metric_value])
# File 2: Wrong FPS [StatsFileFramerateMismatch]
stats_writers[2].writerow(
[COLUMN_NAME_FPS, '%.10f' % (base_timecode.get_framerate() / 2.0)])
stats_writers[2].writerow(
[COLUMN_NAME_FRAME_NUMBER, COLUMN_NAME_TIMECODE, some_metric_key])
stats_writers[2].writerow(
[some_frame_key, some_frame_timecode.get_timecode(), some_metric_value])
# File 3: Wrong Header Names [StatsFileCorrupt]
stats_writers[3].writerow([COLUMN_NAME_FPS, '%.10f' % base_timecode.get_framerate()])
stats_writers[3].writerow(
[COLUMN_NAME_TIMECODE, COLUMN_NAME_FRAME_NUMBER, some_metric_key])
stats_writers[3].writerow(
[some_frame_key, some_frame_timecode.get_timecode(), some_metric_value])
for stats_file in stats_files: stats_file.close()
stats_files = [open(stats_file, 'rt') for stats_file in TEST_STATS_FILES]
with pytest.raises(StatsFileCorrupt):
stats_manager.load_from_csv(stats_files[0], base_timecode)
with pytest.raises(StatsFileCorrupt):
stats_manager.load_from_csv(stats_files[1], base_timecode)
with pytest.raises(StatsFileFramerateMismatch):
stats_manager.load_from_csv(stats_files[2], base_timecode)
with pytest.raises(StatsFileCorrupt):
stats_manager.load_from_csv(stats_files[3], base_timecode)
finally:
for stats_file in stats_files: stats_file.close()
for stats_file in TEST_STATS_FILES: os.remove(stats_file)
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]
class CliContext(object):
""" Context of the command-line interface passed between the various sub-commands.
Pools all options, processing the main program options as they come in (e.g. those
not passed to a command), followed by parsing each sub-command's options, preparing
the actions to be executed in the process_input() method, which is called after the
whole command line has been processed (successfully nor not).
This class and the cli.__init__ module make up the bulk of the PySceneDetect
application logic for the command line.
"""
def __init__(self):
# Properties for main scenedetect command options (-i, -s, etc...) and CliContext logic.
self.options_processed = False # True when CLI option parsing is complete.
self.scene_manager = None # detect-content, detect-threshold, etc...
self.video_manager = None # -i/--input, -d/--downscale
self.base_timecode = None # -f/--framerate
self.start_frame = 0 # time -s/--start
self.stats_manager = None # -s/--stats
self.stats_file_path = None # -s/--stats
self.output_directory = None # -o/--output
self.quiet_mode = False # -q/--quiet or -v/--verbosity quiet
self.frame_skip = 0 # -fs/--frame-skip
# Properties for save-images command.
self.save_images = False # save-images command
self.image_extension = 'jpg' # save-images -j/--jpeg, -w/--webp, -p/--png
self.image_directory = None # save-images -o/--output
self.image_param = None # save-images -q/--quality if -j/-w,
# -c/--compression if -p
self.image_name_format = ( # save-images -f/--name-format
'$VIDEO_NAME-Scene-$SCENE_NUMBER-$IMAGE_NUMBER')
self.num_images = 2 # save-images -n/--num-images
self.imwrite_params = get_cv2_imwrite_params()
# Properties for split-video command.
self.split_video = False # split-video command
self.split_mkvmerge = False # split-video -c/--copy
self.split_args = None # split-video -a/--override-args
self.split_directory = None # split-video -o/--output
self.split_name_format = '$VIDEO_NAME-Scene-$SCENE_NUMBER' # split-video -f/--filename
self.split_quiet = False # split-video -q/--quiet
# Properties for list-scenes command.
self.list_scenes = False # list-scenes command
self.print_scene_list = False # list-scenes --quiet/-q
self.scene_list_directory = None # list-scenes -o/--output
self.scene_list_name_format = None # list-scenes -f/--filename
self.scene_list_output = False # list-scenes -n/--no-output
self.export_html = False # export-html command
self.html_name_format = None # export-html -f/--filename
self.html_include_images = True # export-html --no-images
self.image_filenames = None # export-html used for embedding images
self.image_width = None # export-html -w/--image-width
self.image_height = None # export-html -h/--image-height
def cleanup(self):
# type: () -> None
""" Cleanup: Releases all resources acquired by the CliContext (esp. the VideoManager). """
try:
logging.debug('Cleaning up...\n\n')
finally:
if self.video_manager is not None:
self.video_manager.release()
def _generate_images(self, scene_list, video_name,
image_name_template='$VIDEO_NAME-Scene-$SCENE_NUMBER-$IMAGE_NUMBER',
output_dir=None):
# type: (List[Tuple[FrameTimecode, FrameTimecode]) -> None
if not scene_list:
return
if not self.options_processed:
return
if self.num_images <= 0:
raise ValueError()
self.check_input_open()
imwrite_param = []
if self.image_param is not None:
imwrite_param = [self.imwrite_params[self.image_extension], self.image_param]
# Reset video manager and downscale factor.
self.video_manager.release()
self.video_manager.reset()
self.video_manager.set_downscale_factor(1)
self.video_manager.start()
# Setup flags and init progress bar if available.
completed = True
logging.info('Generating output images (%d per scene)...', self.num_images)
progress_bar = None
if tqdm and not self.quiet_mode:
progress_bar = tqdm(
total=len(scene_list) * self.num_images, unit='images')
filename_template = Template(image_name_template)
scene_num_format = '%0'
scene_num_format += str(max(3, math.floor(math.log(len(scene_list), 10)) + 1)) + 'd'
image_num_format = '%0'
image_num_format += str(math.floor(math.log(self.num_images, 10)) + 2) + 'd'
timecode_list = dict()
self.image_filenames = dict()
for i in range(len(scene_list)):
timecode_list[i] = []
self.image_filenames[i] = []
if self.num_images == 1:
for i, (start_time, end_time) in enumerate(scene_list):
duration = end_time - start_time
timecode_list[i].append(start_time + int(duration.get_frames() / 2))
else:
middle_images = self.num_images - 2
for i, (start_time, end_time) in enumerate(scene_list):
timecode_list[i].append(start_time)
if middle_images > 0:
duration = (end_time.get_frames() - 1) - start_time.get_frames()
duration_increment = None
duration_increment = int(duration / (middle_images + 1))
for j in range(middle_images):
timecode_list[i].append(start_time + ((j+1) * duration_increment))
# End FrameTimecode is always the same frame as the next scene's start_time
# (one frame past the end), so we need to subtract 1 here.
timecode_list[i].append(end_time - 1)
for i in timecode_list:
for j, image_timecode in enumerate(timecode_list[i]):
self.video_manager.seek(image_timecode)
self.video_manager.grab()
ret_val, frame_im = self.video_manager.retrieve()
if ret_val:
file_path = '%s.%s' % (filename_template.safe_substitute(
VIDEO_NAME=video_name,
SCENE_NUMBER=scene_num_format % (i + 1),
IMAGE_NUMBER=image_num_format % (j + 1)),
self.image_extension)
self.image_filenames[i].append(file_path)
cv2.imwrite(
self.get_output_file_path(file_path,
output_dir=output_dir),
frame_im, imwrite_param)
else:
completed = False
break
if progress_bar:
progress_bar.update(1)
if not completed:
logging.error('Could not generate all output images.')
def get_output_file_path(self, file_path, output_dir=None):
# type: (str, Optional[str]) -> str
""" Get Output File Path: Gets full path to output file passed as argument, in
the specified global output directory (scenedetect -o/--output) if set, creating
any required directories along the way.
Arguments:
file_path (str): File name to get path for. If file_path is an absolute
path (e.g. starts at a drive/root), no modification of the path
is performed, only ensuring that all output directories are created.
output_dir (Optional[str]): An optional output directory to override the
global output directory option, if set.
Returns:
(str) Full path to output file suitable for writing.
"""
if file_path is None:
return None
output_dir = self.output_directory if output_dir is None else output_dir
# If an output directory is defined and the file path is a relative path, open
# the file handle in the output directory instead of the working directory.
if output_dir is not None and not os.path.isabs(file_path):
file_path = os.path.join(output_dir, file_path)
# Now that file_path is an absolute path, let's make sure all the directories
# exist for us to start writing files there.
try:
os.makedirs(os.path.split(os.path.abspath(file_path))[0])
except OSError:
pass
return file_path
def _open_stats_file(self):
if self.stats_manager is None:
self.stats_manager = StatsManager()
if self.stats_file_path is not None:
if os.path.exists(self.stats_file_path):
logging.info('Loading frame metrics from stats file: %s',
os.path.basename(self.stats_file_path))
try:
with open(self.stats_file_path, 'rt') as stats_file:
self.stats_manager.load_from_csv(stats_file, self.base_timecode)
except StatsFileCorrupt:
error_strs = [
'Could not load stats file.', 'Failed to parse stats file:',
'Could not load frame metrics from stats file - file is corrupt or not a'
' valid PySceneDetect stats file. If the file exists, ensure that it is'
' a valid stats file CSV, otherwise delete it and run PySceneDetect again'
' to re-generate the stats file.']
logging.error('\n'.join(error_strs))
raise click.BadParameter(
'\n Could not load given stats file, see above output for details.',
param_hint='input stats file')
except StatsFileFramerateMismatch as ex:
error_strs = [
'could not load stats file.', 'Failed to parse stats file:',
'Framerate differs between stats file (%.2f FPS) and input'
' video%s (%.2f FPS)' % (
ex.stats_file_fps,
's' if self.video_manager.get_num_videos() > 1 else '',
ex.base_timecode_fps),
'Ensure the correct stats file path was given, or delete and re-generate'
' the stats file.']
logging.error('\n'.join(error_strs))
raise click.BadParameter(
'framerate differs between given stats file and input video(s).',
param_hint='input stats file')
def process_input(self):
# type: () -> None
""" Process Input: Processes input video(s) and generates output as per CLI commands.
Run after all command line options/sub-commands have been parsed.
"""
logging.debug('Processing input...')
if not self.options_processed:
logging.debug('Skipping processing, CLI options were not parsed successfully.')
return
self.check_input_open()
if not self.scene_manager.get_num_detectors() > 0:
logging.error(
'No scene detectors specified (detect-content, detect-threshold, etc...),\n'
' or failed to process all command line arguments.')
return
# Handle scene detection commands (detect-content, detect-threshold, etc...).
self.video_manager.start()
base_timecode = self.video_manager.get_base_timecode()
start_time = time.time()
logging.info('Detecting scenes...')
num_frames = self.scene_manager.detect_scenes(
frame_source=self.video_manager, frame_skip=self.frame_skip,
show_progress=not self.quiet_mode)
duration = time.time() - start_time
logging.info('Processed %d frames in %.1f seconds (average %.2f FPS).',
num_frames, duration, float(num_frames)/duration)
# Handle -s/--statsfile option.
if self.stats_file_path is not None:
if self.stats_manager.is_save_required():
with open(self.stats_file_path, 'wt') as stats_file:
logging.info('Saving frame metrics to stats file: %s',
os.path.basename(self.stats_file_path))
self.stats_manager.save_to_csv(
stats_file, base_timecode)
else:
logging.debug('No frame metrics updated, skipping update of the stats file.')
# Get list of detected cuts and scenes from the SceneManager to generate the required output
# files with based on the given commands (list-scenes, split-video, save-images, etc...).
cut_list = self.scene_manager.get_cut_list(base_timecode)
scene_list = self.scene_manager.get_scene_list(base_timecode)
video_paths = self.video_manager.get_video_paths()
video_name = os.path.basename(video_paths[0])
if video_name.rfind('.') >= 0:
video_name = video_name[:video_name.rfind('.')]
# Ensure we don't divide by zero.
if scene_list:
logging.info('Detected %d scenes, average shot length %.1f seconds.',
len(scene_list),
sum([(end_time - start_time).get_seconds()
for start_time, end_time in scene_list]) / float(len(scene_list)))
else:
logging.info('No scenes detected.')
# Handle list-scenes command.
if self.scene_list_output:
scene_list_filename = Template(self.scene_list_name_format).safe_substitute(
VIDEO_NAME=video_name)
if not scene_list_filename.lower().endswith('.csv'):
scene_list_filename += '.csv'
scene_list_path = self.get_output_file_path(
scene_list_filename, self.scene_list_directory)
logging.info('Writing scene list to CSV file:\n %s', scene_list_path)
with open(scene_list_path, 'wt') as scene_list_file:
write_scene_list(scene_list_file, scene_list, cut_list)
# Handle `list-scenes`.
if self.print_scene_list:
logging.info("""Scene List:
-----------------------------------------------------------------------
| Scene # | Start Frame | Start Time | End Frame | End Time |
-----------------------------------------------------------------------
%s
-----------------------------------------------------------------------
""", '\n'.join(
[' | %5d | %11d | %s | %11d | %s |' % (
i+1,
start_time.get_frames(), start_time.get_timecode(),
end_time.get_frames(), end_time.get_timecode())
for i, (start_time, end_time) in enumerate(scene_list)]))
if cut_list:
logging.info('Comma-separated timecode list:\n %s',
','.join([cut.get_timecode() for cut in cut_list]))
# Handle save-images command.
if self.save_images:
self._generate_images(scene_list=scene_list, video_name=video_name,
image_name_template=self.image_name_format,
output_dir=self.image_directory)
# Handle export-html command.
if self.export_html:
html_filename = Template(self.html_name_format).safe_substitute(
VIDEO_NAME=video_name)
if not html_filename.lower().endswith('.html'):
html_filename += '.html'
html_path = self.get_output_file_path(
html_filename, self.image_directory)
logging.info('Exporting to html file:\n %s:', html_path)
if not self.html_include_images:
self.image_filenames = None
write_scene_list_html(html_path, scene_list, cut_list,
image_filenames=self.image_filenames,
image_width=self.image_width,
image_height=self.image_height)
# Handle split-video command.
if self.split_video:
# Add proper extension to filename template if required.
dot_pos = self.split_name_format.rfind('.')
if self.split_mkvmerge and not self.split_name_format.endswith('.mkv'):
self.split_name_format += '.mkv'
# Don't add if we find an extension between 2 and 4 characters
elif not (dot_pos >= 0) or (
dot_pos >= 0 and not
((len(self.split_name_format) - (dot_pos+1) <= 4 >= 2))):
self.split_name_format += '.mp4'
output_file_prefix = self.get_output_file_path(
self.split_name_format, output_dir=self.split_directory)
mkvmerge_available = is_mkvmerge_available()
ffmpeg_available = is_ffmpeg_available()
if mkvmerge_available and (self.split_mkvmerge or not ffmpeg_available):
if not self.split_mkvmerge:
logging.warning(
'ffmpeg not found, falling back to fast copy mode (split-video -c/--copy).')
split_video_mkvmerge(video_paths, scene_list, output_file_prefix, video_name,
suppress_output=self.quiet_mode or self.split_quiet)
elif ffmpeg_available:
if self.split_mkvmerge:
logging.warning('mkvmerge not found, falling back to normal splitting'
' mode (split-video).')
split_video_ffmpeg(video_paths, scene_list, output_file_prefix,
video_name, arg_override=self.split_args,
hide_progress=self.quiet_mode,
suppress_output=self.quiet_mode or self.split_quiet)
else:
if not (mkvmerge_available or ffmpeg_available):
error_strs = ["ffmpeg/mkvmerge is required for split-video [-c/--copy]."]
else:
error_strs = [
"{EXTERN_TOOL} is required for split-video{EXTRA_ARGS}.".format(
EXTERN_TOOL='mkvmerge' if self.split_mkvmerge else 'ffmpeg',
EXTRA_ARGS=' -c/--copy' if self.split_mkvmerge else '')]
error_strs += ["Install one of the above tools to enable the split-video command."]
error_str = '\n'.join(error_strs)
logging.debug(error_str)
raise click.BadParameter(error_str, param_hint='split-video')
if scene_list:
logging.info('Video splitting completed, individual scenes written to disk.')
def check_input_open(self):
# type: () -> None
""" Check Input Open: Ensures that the CliContext's VideoManager was initialized,
started, and at *least* one input video was successfully opened - otherwise, an
exception is raised.
Raises:
click.BadParameter
"""
if self.video_manager is None or not self.video_manager.get_num_videos() > 0:
error_strs = ["No input video(s) specified.",
"Make sure '--input VIDEO' is specified at the start of the command."]
error_str = '\n'.join(error_strs)
logging.debug(error_str)
raise click.BadParameter(error_str, param_hint='input video')
def add_detector(self, detector):
""" Add Detector: Adds a detection algorithm to the CliContext's SceneManager. """
self.check_input_open()
options_processed_orig = self.options_processed
self.options_processed = False
try:
self.scene_manager.add_detector(detector)
except scenedetect.stats_manager.FrameMetricRegistered:
raise click.BadParameter(message='Cannot specify detection algorithm twice.',
param_hint=detector.cli_name)
self.options_processed = options_processed_orig
def _init_video_manager(self, input_list, framerate, downscale):
self.base_timecode = None
logging.debug('Initializing VideoManager.')
video_manager_initialized = False
try:
self.video_manager = VideoManager(
video_files=input_list, framerate=framerate, logger=logging)
video_manager_initialized = True
self.base_timecode = self.video_manager.get_base_timecode()
self.video_manager.set_downscale_factor(downscale)
except VideoOpenFailure as ex:
error_strs = [
'could not open video%s.' % get_plural(ex.file_list),
'Failed to open the following video file%s:' % get_plural(ex.file_list)]
error_strs += [' %s' % file_name[0] for file_name in ex.file_list]
dll_okay, dll_name = check_opencv_ffmpeg_dll()
if not dll_okay:
error_strs += [
'Error: OpenCV dependency %s not found.' % dll_name,
'Ensure that you installed the Python OpenCV module, and that the',
'%s file can be found to enable video support.' % dll_name]
logging.debug('\n'.join(error_strs[1:]))
if not dll_okay:
click.echo(click.style(
'\nOpenCV dependency missing, video input/decoding not available.\n', fg='red'))
raise click.BadParameter('\n'.join(error_strs), param_hint='input video')
except VideoFramerateUnavailable as ex:
error_strs = ['could not get framerate from video(s)',
'Failed to obtain framerate for video file %s.' % ex.file_name]
error_strs.append('Specify framerate manually with the -f / --framerate option.')
logging.debug('\n'.join(error_strs))
raise click.BadParameter('\n'.join(error_strs), param_hint='input video')
except VideoParameterMismatch as ex:
error_strs = ['video parameters do not match.', 'List of mismatched parameters:']
for param in ex.file_list:
if param[0] == cv2.CAP_PROP_FPS:
param_name = 'FPS'
if param[0] == cv2.CAP_PROP_FRAME_WIDTH:
param_name = 'Frame width'
if param[0] == cv2.CAP_PROP_FRAME_HEIGHT:
param_name = 'Frame height'
error_strs.append(' %s mismatch in video %s (got %.2f, expected %.2f)' % (
param_name, param[3], param[1], param[2]))
error_strs.append(
'Multiple videos may only be specified if they have the same framerate and'
' resolution. -f / --framerate may be specified to override the framerate.')
logging.debug('\n'.join(error_strs))
raise click.BadParameter('\n'.join(error_strs), param_hint='input videos')
except InvalidDownscaleFactor as ex:
error_strs = ['Downscale value is not > 0.', str(ex)]
logging.debug('\n'.join(error_strs))
raise click.BadParameter('\n'.join(error_strs), param_hint='downscale factor')
return video_manager_initialized
def parse_options(self, input_list, framerate, stats_file, downscale, frame_skip):
# type: (List[str], float, str, int, int) -> None
""" Parse Options: Parses all global options/arguments passed to the main
scenedetect command, before other sub-commands (e.g. this function processes
the [options] when calling scenedetect [options] [commands [command options]].
This method calls the _init_video_manager(), _open_stats_file(), and
check_input_open() methods, which may raise a click.BadParameter exception.
Raises:
click.BadParameter
"""
if not input_list:
return
logging.debug('Parsing program options.')
self.frame_skip = frame_skip
video_manager_initialized = self._init_video_manager(
input_list=input_list, framerate=framerate, downscale=downscale)
# Ensure VideoManager is initialized, and open StatsManager if --stats is specified.
if not video_manager_initialized:
self.video_manager = None
logging.info('VideoManager not initialized.')
else:
logging.debug('VideoManager initialized.')
self.stats_file_path = self.get_output_file_path(stats_file)
if self.stats_file_path is not None:
self.check_input_open()
self._open_stats_file()
# Init SceneManager.
self.scene_manager = SceneManager(self.stats_manager)
self.options_processed = True
def time_command(self, start=None, duration=None, end=None):
# type: (Optional[str], Optional[str], Optional[str]) -> None
""" Time Command: Parses all options/arguments passed to the time command,
or with respect to the CLI, this function processes [time options] when calling:
scenedetect [global options] time [time options] [other commands...].
Raises:
click.BadParameter, VideoDecodingInProgress
"""
logging.debug('Setting video time:\n start: %s, duration: %s, end: %s',
start, duration, end)
self.check_input_open()
if duration is not None and end is not None:
raise click.BadParameter(
'Only one of --duration/-d or --end/-e can be specified, not both.',
param_hint='time')
self.video_manager.set_duration(start_time=start, duration=duration, end_time=end)
if start is not None:
self.start_frame = start.get_frames()
def list_scenes_command(self, output_path, filename_format, no_output_mode, quiet_mode):
# type: (str, str, bool, bool) -> None
""" List Scenes Command: Parses all options/arguments passed to the list-scenes command,
or with respect to the CLI, this function processes [list-scenes options] when calling:
scenedetect [global options] list-scenes [list-scenes options] [other commands...].
Raises:
click.BadParameter
"""
self.check_input_open()
self.print_scene_list = True if quiet_mode is None else not quiet_mode
self.scene_list_directory = output_path
self.scene_list_name_format = filename_format
if self.scene_list_name_format is not None and not no_output_mode:
logging.info('Scene list CSV file name format:\n %s', self.scene_list_name_format)
self.scene_list_output = False if no_output_mode else True
if self.scene_list_directory is not None:
logging.info('Scene list output directory set:\n %s', self.scene_list_directory)
def export_html_command(self, filename, no_images, image_width, image_height):
# type: (str, bool) -> None
"""Export HTML command: Parses all options/arguments passed to the export-html command,
or with respect to the CLI, this function processes [export-html] options when calling:
scenedetect [global options] export-html [export-html options] [other commands...].
Raises:
click.BadParameter
"""
self.check_input_open()
self.html_name_format = filename
if self.html_name_format is not None:
logging.info('Scene list html file name format:\n %s', self.html_name_format)
self.html_include_images = False if no_images else True
self.image_width = image_width
self.image_height = image_height
def save_images_command(self, num_images, output, name_format, jpeg, webp, quality,
png, compression):
# type: (int, str, str, bool, bool, int, bool, int) -> None
""" Save Images Command: Parses all options/arguments passed to the save-images command,
or with respect to the CLI, this function processes [save-images options] when calling:
scenedetect [global options] save-images [save-images options] [other commands...].
Raises:
click.BadParameter
"""
self.check_input_open()
num_flags = sum([True if flag else False for flag in [jpeg, webp, png]])
if num_flags <= 1:
# Ensure the format exists.
extension = 'jpg' # Default is jpg.
if png:
extension = 'png'
elif webp:
extension = 'webp'
if not extension in self.imwrite_params or self.imwrite_params[extension] is None:
error_strs = [
'Image encoder type %s not supported.' % extension.upper(),
'The specified encoder type could not be found in the current OpenCV module.',
'To enable this output format, please update the installed version of OpenCV.',
'If you build OpenCV, ensure the the proper dependencies are enabled. ']
logging.debug('\n'.join(error_strs))
raise click.BadParameter('\n'.join(error_strs), param_hint='save-images')
self.save_images = True
self.image_directory = output
self.image_extension = extension
self.image_param = compression if png else quality
self.image_name_format = name_format
self.num_images = num_images
image_type = 'JPEG' if self.image_extension == 'jpg' else self.image_extension.upper()
image_param_type = ''
if self.image_param:
image_param_type = 'Compression' if image_type == 'PNG' else 'Quality'
image_param_type = ' [%s: %d]' % (image_param_type, self.image_param)
logging.info('Image output format set: %s%s', image_type, image_param_type)
if self.image_directory is not None:
logging.info('Image output directory set:\n %s',
os.path.abspath(self.image_directory))
else:
self.options_processed = False
logging.error('Multiple image type flags set for save-images command.')
raise click.BadParameter(
'Only one image type (JPG/PNG/WEBP) can be specified.', param_hint='save-images')
