def test_minus_against_nothing(self):
intrvl_long1 = Interval(1., 10., 1)
intrvl_long2 = Interval(3., 15., 2)
intrvlslong = IntervalList([intrvl_long2, intrvl_long1])
intrvlshort1 = Interval(20., 20.5, 3)
intrvlsshort = IntervalList([intrvlshort1])
intrvlsminusrec = intrvlslong.minus(intrvlsshort)
self.assertEqual(len(intrvlsminusrec.intrvls), 2)
self.assertEqual(intrvlsminusrec.intrvls[0].__repr__(),
"<Interval start:1.0 end:10.0 payload:1>")
self.assertEqual(intrvlsminusrec.intrvls[1].__repr__(),
"<Interval start:3.0 end:15.0 payload:2>")
def test_minus(self):
intrvl_long1 = Interval(1., 10., 1)
intrvl_long2 = Interval(3., 15., 2)
intrvlshort1 = Interval(2., 2.5, 3)
intrvlshort2 = Interval(2., 2.7, 4)
intrvlshort3 = Interval(2.9, 3.5, 5)
intrvlshort4 = Interval(5., 7., 6)
intrvlshort5 = Interval(9., 12., 7)
intrvlshort6 = Interval(14., 16., 8)
intrvlslong = IntervalList([intrvl_long2, intrvl_long1])
intrvlsshort = IntervalList([
intrvlshort2, intrvlshort5, intrvlshort3, intrvlshort1,
intrvlshort4, intrvlshort6
])
intrvlsminusrec = intrvlslong.minus(intrvlsshort)
self.assertEqual(len(intrvlsminusrec.intrvls), 7)
self.assertEqual(intrvlsminusrec.intrvls[0].__repr__(),
"<Interval start:1.0 end:2.0 payload:1>")
self.assertEqual(intrvlsminusrec.intrvls[1].__repr__(),
"<Interval start:2.7 end:2.9 payload:1>")
self.assertEqual(intrvlsminusrec.intrvls[2].__repr__(),
"<Interval start:3.5 end:5.0 payload:1>")
self.assertEqual(intrvlsminusrec.intrvls[3].__repr__(),
"<Interval start:3.5 end:5.0 payload:2>")
self.assertEqual(intrvlsminusrec.intrvls[4].__repr__(),
"<Interval start:7.0 end:9.0 payload:1>")
self.assertEqual(intrvlsminusrec.intrvls[5].__repr__(),
"<Interval start:7.0 end:9.0 payload:2>")
self.assertEqual(intrvlsminusrec.intrvls[6].__repr__(),
"<Interval start:12.0 end:14.0 payload:2>")
intrvlsminusnonrec = intrvlslong.minus(intrvlsshort,
recursive_diff=False)
self.assertEqual(len(intrvlsminusnonrec.intrvls), 15)
intrvlsminusrec = intrvlslong.minus(intrvlsshort,
payload_merge_op=payload_second)
self.assertEqual(len(intrvlsminusrec.intrvls), 7)
self.assertEqual(intrvlsminusrec.intrvls[0].__repr__(),
"<Interval start:1.0 end:2.0 payload:3>")
self.assertEqual(intrvlsminusrec.intrvls[1].__repr__(),
"<Interval start:2.7 end:2.9 payload:4>")
self.assertEqual(intrvlsminusrec.intrvls[2].__repr__(),
"<Interval start:3.5 end:5.0 payload:5>")
self.assertEqual(intrvlsminusrec.intrvls[3].__repr__(),
"<Interval start:3.5 end:5.0 payload:5>")
self.assertEqual(intrvlsminusrec.intrvls[4].__repr__(),
"<Interval start:7.0 end:9.0 payload:6>")
self.assertEqual(intrvlsminusrec.intrvls[5].__repr__(),
"<Interval start:7.0 end:9.0 payload:6>")
self.assertEqual(intrvlsminusrec.intrvls[6].__repr__(),
"<Interval start:12.0 end:14.0 payload:7>")
intrvlsminusrec = intrvlslong.minus(intrvlsshort,
predicate=overlaps_before())
self.assertEqual(len(intrvlsminusrec.intrvls), 2)
self.assertEqual(intrvlsminusrec.intrvls[0].__repr__(),
"<Interval start:1.0 end:9.0 payload:1>")
self.assertEqual(intrvlsminusrec.intrvls[1].__repr__(),
"<Interval start:3.0 end:14.0 payload:2>")
def compute_shots(microshot_boundaries, faces_scanner, frames, video):
print('Number of microshots: ', len(microshot_boundaries))
faces_per_frame = IntervalList([
(frame, frame, facelist)
for frame, facelist in zip(frames, faces_scanner)
])
transitions = IntervalList([(boundary - 1, boundary, 0)
for boundary in microshot_boundaries])
faces_at_boundaries = faces_per_frame.filter_against(
transitions,
predicate=overlaps()).filter(payload_satisfies(length_at_least(1)))
# Get all transitions where there are faces before and after the transition
# This IntervalList's payload is stil 0
transitions_with_faces = transitions.filter_against(
faces_at_boundaries, predicate=starts_inv()).filter_against(
transitions.filter_against(faces_at_boundaries,
predicate=finishes_inv()),
predicate=equal())
# Annotate transitions_with_faces with the list of faces before and after
# every transition
transitions_with_faces_at_start_of_transition = transitions_with_faces.merge(
faces_at_boundaries,
predicate=starts_inv(),
payload_merge_op=payload_second)
transitions_with_faces_at_end_of_transition = transitions_with_faces.merge(
faces_at_boundaries,
predicate=finishes_inv(),
payload_merge_op=payload_second)
transitions_with_faces = transitions_with_faces_at_start_of_transition.merge(
transitions_with_faces_at_end_of_transition,
predicate=equal(),
payload_merge_op=lambda starting_faces, ending_faces: {
'starts': starting_faces,
'finishes': ending_faces
})
# Get all the transitions where the faces at the start and the end are
# the same
def face_list_stays_the_same(start_finishes_payload):
""" Define a scene graph by the face positions at the start and check
if the face positions at the end satisfy it. """
graph = {
'nodes': [{
'name':
'face{}'.format(idx),
'predicates': [
position(face.x1,
face.y1,
face.x2,
face.y2,
epsilon=POSITION_EPSILON),
lambda face: face['score'] > MINIMUM_FACE_PROBABILITY
]
} for idx, face in enumerate(start_finishes_payload['starts'])
if face.score > MINIMUM_FACE_PROBABILITY],
'edges': []
}
return scene_graph(graph, exact=True)([{
'x1': face.x1,
'y1': face.y1,
'x2': face.x2,
'y2': face.y2,
'score': face.score
} for face in start_finishes_payload['finishes']])
bad_transitions = transitions_with_faces.filter(
payload_satisfies(face_list_stays_the_same))
print(bad_transitions.size())
# Finally, compute shot boundaries
def convert_shot_boundaries_to_shots(shot_boundary_list):
"""
Helper function to convert an IntervalList of shot boundaries to an
IntervalList of shots.
shot_boundary_list should have the start and end of the movie as
boundaries.
"""
def fold_boundaries_to_shots(acc, frame):
if acc == []:
return [frame.copy()]
top = acc[-1]
top.end = frame.start - 1
if top.length() > 0:
acc.append(frame.copy())
else:
top.end = frame.start
return acc
return shot_boundary_list.fold_list(fold_boundaries_to_shots, [])
# Convert microshot boundaries to IntervalList
shot_boundaries = IntervalList([
(boundary, boundary, 0)
for boundary in list(set([0, video.num_frames] + microshot_boundaries))
])
microshots = convert_shot_boundaries_to_shots(shot_boundaries)
# Filter out short microshots
short_microshots = microshots.filter_length(
max_length=math.floor(MINIMUM_SHOT_DURATION * video.fps))
shots = microshots.set_union(
short_microshots.map(
lambda i: (i.start, i.end + 1, i.payload)).coalesce()).coalesce()
# Remove shots that start with the bad boundaries we found earlier
bad_shots = shots.filter_against(
bad_transitions.map(lambda i: (i.start + 1, i.end, i.payload)),
predicate=starts_inv())
shot_boundaries = shots.map(lambda i: (i.start, i.start, i.payload))
shot_boundaries_without_bad_shots = shot_boundaries.minus(bad_shots)
shots = convert_shot_boundaries_to_shots(shot_boundaries_without_bad_shots)
return shots
def detect_commercial_rekall(video,
transcript_path,
blackframe_list=None,
histogram=None,
verbose=True):
"""
API for detecting commercial blocks from TV news video using rekall
@video: django query set
@transcript_path: transcript_path
@blackframe_list: list of black frames index
@histogram: list of histogram 16x3 bin for each frame, not used if blackframe_list is provided
Return: commercial_list (list of tuple((start_fid, start_sec), (end_fid, end_sec)), None if failed)
"""
transcript = load_transcript(transcript_path)
if blackframe_list is None:
blackframe_intervallist = get_blackframe_list(histogram)
else:
blackframe_intervallist = IntervalList([
(fid2second(fid, video.fps), fid2second(fid + 1, video.fps), 0)
for fid in blackframe_list
])
black_windows = blackframe_intervallist \
.dilate(1. / video.fps) \
.coalesce() \
.dilate(-1. / video.fps) \
.filter_length(min_length=MIN_BLACKWINDOW * 1. / video.fps)
# if verbose:
# print("black window: ({})\n".format(black_windows.size()))
# for idx, win in enumerate(black_windows.get_intervals()):
# print(idx, win)
# get all instances of >>, Announcer:, and >> Announcer: in transcript
arrow_text = get_text_intervals(">>", transcript)
announcer_text = get_text_intervals("Announcer:", transcript)
arrow_announcer_text = get_text_intervals(">> Announcer:", transcript)
# if verbose:
# print('arrow_text', arrow_text)
# print('announcer_text', announcer_text)
# print('arrow_announcer_text', arrow_announcer_text)
# get an interval for the whole video
whole_video = IntervalList([(0., video.num_frames / video.fps, 0)])
# whole video minus black windows to get segments in between black windows
# then filter out anything that overlaps with ">>" as long as it's not
# ">> Announcer:"
# then coalesce, as long as it doesn't get too long
def fold_fn(stack, interval):
if len(stack) == 0:
stack.append(interval)
else:
last = stack.pop()
if or_pred(overlaps(), after(max_dist=1), arity=2)(interval, last):
if last.merge(interval).length() > MAX_COMMERCIAL_TIME:
if last.length() > MAX_COMMERCIAL_TIME:
stack.append(
Interval(last.start,
last.start + MAX_COMMERCIAL_TIME,
last.payload))
else:
stack.append(last)
stack.append(interval)
else:
stack.append(last.merge(interval))
else:
stack.append(last)
stack.append(interval)
return stack
all_blocks = whole_video.minus(black_windows)
non_commercial_blocks = all_blocks.filter_against(
arrow_text.minus(arrow_announcer_text), predicate=overlaps())
commercial_blocks = whole_video.minus(non_commercial_blocks)
if verbose:
print("commercial blocks candidates: ({})\n".format(
commercial_blocks.size()))
for idx, win in enumerate(commercial_blocks.get_intervals()):
print(idx, win)
commercials = commercial_blocks \
.fold_list(fold_fn, []) \
.filter_length(min_length = MIN_COMMERCIAL_TIME)
# commercials = whole_video \
# .minus(black_windows) \
# .filter_against(
# arrow_text.filter_against(arrow_announcer_text,
# predicate=not_pred(overlaps(), arity=2)),
# predicate=not_pred(overlaps(), arity=2)
# ) \
# .set_union(black_windows) \
# .fold_list(fold_fn, []) \
# .filter_length(min_length = MIN_COMMERCIAL_TIME)
if verbose:
print("commercials from blackwindow:\n", commercials)
# add in lowercase intervals
lowercase_intervals = get_lowercase_intervals(transcript)
if verbose:
print("lowercase intervals:\n", lowercase_intervals)
commercials = commercials \
.set_union(lowercase_intervals) \
.dilate(MIN_COMMERCIAL_GAP / 2) \
.coalesce() \
.dilate(MIN_COMMERCIAL_GAP / 2)
if verbose:
print("commercials merge with lowercase:\n", commercials)
# if verbose:
# print(whole_video)
# print(IntervalList([
# (start_sec - TRANSCRIPT_DELAY, end_sec - TRANSCRIPT_DELAY, 0)
# for text, start_sec, end_sec in transcript
# ]).coalesce().size())
# get blank intervals
blank_intervals = whole_video.minus(
IntervalList([
(start_sec - TRANSCRIPT_DELAY, end_sec - TRANSCRIPT_DELAY, 0)
for text, start_sec, end_sec in transcript
]).coalesce()).coalesce().filter_length(min_length=MIN_BLANKWINDOW,
max_length=MAX_BLANKWINDOW)
if verbose:
print("blank intervals:\n", blank_intervals)
# add in blank intervals, but only if adding in the new intervals doesn't
# get too long
commercials = commercials.merge(blank_intervals,
predicate=or_pred(before(max_dist=MAX_MERGE_GAP),
after(max_dist=MAX_MERGE_GAP), arity=2),
working_window=MAX_MERGE_GAP
) \
.filter_length(max_length=MAX_MERGE_DURATION) \
.set_union(commercials) \
.dilate(MIN_COMMERCIAL_GAP / 2) \
.coalesce() \
.dilate(MIN_COMMERCIAL_GAP / 2)
if verbose:
print("commercials merge with lowercase:\n", commercials)
# post-process commercials to get rid of gaps, small commercials, and
# islated blocks
small_gaps = whole_video \
.minus(commercials) \
.filter_length(max_length = MAX_COMMERCIAL_GAP) \
.filter_against(
arrow_text.filter_against(
announcer_text,
predicate=not_pred(overlaps()),
working_window=1.0
), predicate=not_pred(overlaps()),
working_window=1.0)
# merge with small gaps, but only if that doesn't make things too long
commercials = commercials \
.set_union(small_gaps.dilate(0.1)) \
.coalesce() \
.filter_length(max_length=MAX_COMMERCIAL_TIME) \
.set_union(commercials) \
.coalesce()
# get isolated commercials
not_isolated_commercials = commercials.filter_against(
commercials,
predicate=or_pred(before(max_dist=MAX_COMMERCIAL_TIME),
after(max_dist=MAX_COMMERCIAL_TIME),
arity=2),
working_window=MAX_COMMERCIAL_TIME)
isolated_commercials = commercials.minus(not_isolated_commercials)
commercials_to_delete = isolated_commercials \
.filter_length(max_length=MIN_COMMERCIAL_TIME_FINAL) \
.set_union(isolated_commercials \
.filter_against(blank_intervals, predicate=equal()) \
.filter_length(max_length=MAX_ISOLATED_BLANK_TIME))
commercials = commercials.minus(commercials_to_delete)
return commercials
def detect_commercial_rekall(video, transcript_path, blackframe_list=None, histogram=None, debug=True, verbose=False):
"""
API for detecting commercial blocks from TV news video using rekall
@video: django query set
@transcript_path: transcript_path
@blackframe_list: list of black frames index
@histogram: list of histogram 16x3 bin for each frame, not used if blackframe_list is provided
Return: commercial_list (list of tuple((start_fid, start_sec), (end_fid, end_sec)), None if failed)
"""
if type(video) == dict:
fps = video['fps']
video_length = video['num_frames'] / fps
else:
fps = video.fps
video_length = video.num_frames / video.fps
transcript = load_transcript(transcript_path)
if blackframe_list is None:
blackframe_intervallist = get_blackframe_list(histogram)
else:
blackframe_intervallist = IntervalList([(fid2second(fid, fps),
fid2second(fid + 1, fps),
0) for fid in blackframe_list])
# get black windows
black_windows = blackframe_intervallist \
.dilate(1. / fps) \
.coalesce() \
.dilate(-1. / fps)
# .filter_length(min_length=MIN_BLACKWINDOW * 1. / video.fps)
if verbose:
print("black window: ({})\n".format(black_windows.size()))
for idx, win in enumerate(black_windows.get_intervals()):
print(idx, win)
# get all instances of >>
arrow_intervals = get_text_intervals(">>", transcript)
arrow_announcer_intervals = get_text_intervals(">> Announcer:", transcript)
arrow_having_intervals = get_text_intervals(">> HAVING", transcript)
if verbose:
print("arrow_text: ({})\n".format(arrow_intervals.size()), arrow_intervals)
print("arrow_announcer_text: ({})\n".format(arrow_announcer_intervals.size()), arrow_announcer_intervals)
# get intervals for the whole transcript
transcript_intervals = IntervalList([
(start_sec, end_sec, 0)
for text, start_sec, end_sec in transcript
if not '{' in text
]).dilate(1) \
.coalesce() \
.dilate(-1) \
# get an interval for the whole video
whole_video = IntervalList([(0., video_length, 0)])
# whole video minus black windows to get segments in between black windows
# then filter out anything that overlaps with ">>" as long as it's not ">> Announcer:"
# then coalesce, as long as it doesn't get too long
def fold_fn(stack, interval):
if interval.length() > MAX_COMMERCIAL_TIME:
interval = Interval(interval.start, interval.start + MAX_COMMERCIAL_TIME, interval.payload)
if len(stack) == 0:
stack.append(interval)
else:
last = stack.pop()
if or_pred(overlaps(), after(max_dist=5), arity=2)(interval, last):
if last.merge(interval).length() > MAX_COMMERCIAL_TIME:
stack.append(Interval(
last.start,
last.start + MAX_COMMERCIAL_TIME,
last.payload))
else:
stack.append(last.merge(interval))
else:
stack.append(last)
stack.append(interval)
return stack
# get reliable double arrow intervals
reliable_transcripts = transcript_intervals.filter_length(min_length=RELIABLE_TEXT_DURATION)
arrow_intervals = arrow_intervals \
.minus(arrow_announcer_intervals) \
.minus(arrow_having_intervals) \
.filter_against(
reliable_transcripts,
predicate=overlaps()
)
# get non-commercial blocks by filtering out intervals overlaps with >>
all_blocks = whole_video.minus(black_windows)
non_commercial_blocks = all_blocks.filter_against(
arrow_intervals,
predicate=overlaps()
)
commercial_blocks = whole_video.minus(non_commercial_blocks.set_union(black_windows))
if verbose:
print("commercial blocks candidates: ({})\n".format(commercial_blocks.size()))
for idx, win in enumerate(commercial_blocks.get_intervals()):
print(idx, win)
commercials = commercial_blocks \
.fold_list(fold_fn, []) \
.filter_length(min_length = MIN_COMMERCIAL_TIME)
commercials_raw = copy.deepcopy(commercials)
if verbose:
print("commercials from blackwindow:\n", commercials)
# add in lowercase intervals
lowercase_intervals = get_lowercase_intervals(transcript)
if verbose:
print("lowercase intervals:\n", lowercase_intervals)
commercials = commercials.set_union(lowercase_intervals)
if verbose:
print("commercials merge with lowercase:\n", commercials)
# get blank intervals
blank_intervals = whole_video \
.minus(transcript_intervals) \
.filter_length(min_length=MIN_BLANKWINDOW, max_length=MAX_BLANKWINDOW)
# remove last one minute segment due to no aligned transcripts
blank_intervals = blank_intervals \
.minus(IntervalList([(video_length-60, video_length, 0)])) \
.filter_length(min_length=MIN_BLANKWINDOW)
if verbose:
print("blank intervals:\n", blank_intervals)
# add in blank intervals
commercials = commercials.set_union(blank_intervals)
# commercials = commercials.merge(blank_intervals,
# predicate=or_pred(before(max_dist=MAX_MERGE_GAP),
# after(max_dist=MAX_MERGE_GAP), arity=2),
# working_window=MAX_MERGE_GAP
# ) \
# .filter_length(max_length=MAX_MERGE_DURATION) \
# .set_union(commercials) \
# .dilate(MIN_COMMERCIAL_GAP / 2) \
# .coalesce() \
# .dilate(-MIN_COMMERCIAL_GAP / 2)
if verbose:
print("commercials merge with blank intervals:\n", commercials)
# merge with small gaps, but only if that doesn't make things too long
commercials = commercials \
.dilate(MAX_MERGE_GAP / 2) \
.coalesce() \
.dilate(-MAX_MERGE_GAP / 2) \
.filter_length(max_length=MAX_COMMERCIAL_TIME) \
.set_union(commercials_raw) \
.set_union(lowercase_intervals) \
.set_union(blank_intervals) \
.coalesce()
# # post-process commercials to get rid of gaps, small commercials, and
# # islated blocks
# small_gaps = whole_video \
# .minus(commercials) \
# .filter_length(max_length = MAX_COMMERCIAL_GAP) \
# .filter_against(
# arrow_text.filter_against(
# announcer_text,
# predicate=not_pred(overlaps()),
# working_window=1.0
# ), predicate=not_pred(overlaps()),
# working_window=1.0)
# # merge with small gaps, but only if that doesn't make things too long
# commercials = commercials \
# .set_union(small_gaps.dilate(0.1)) \
# .coalesce() \
# .filter_length(max_length=MAX_COMMERCIAL_TIME) \
# .set_union(commercials) \
# .coalesce()
# # get isolated commercials
# not_isolated_commercials = commercials.filter_against(commercials,
# predicate=or_pred(before(max_dist=MAX_COMMERCIAL_TIME),
# after(max_dist=MAX_COMMERCIAL_TIME), arity=2),
# working_window=MAX_COMMERCIAL_TIME)
# isolated_commercials = commercials.minus(not_isolated_commercials)
# commercials_to_delete = isolated_commercials \
# .filter_length(max_length=MIN_COMMERCIAL_TIME_FINAL) \
# .set_union(isolated_commercials \
# .filter_against(blank_intervals, predicate=equal()) \
# .filter_length(max_length=MAX_ISOLATED_BLANK_TIME))
# commercials = commercials.minus(commercials_to_delete)
if debug:
result = {'black': black_windows.dilate(2),
'arrow': arrow_intervals.dilate(2),
'commercials_raw': commercials_raw,
'lowercase': lowercase_intervals,
'blank': blank_intervals,
'commercials': commercials,
}
return result
else:
result = [(i.start, i.end) for i in commercials.get_intervals()]
return result