def main():
selectors = [
FPSReductionKS(n=3),
MaxHistDiffKS(frames_per_window=1),
]
extractors = [
ColorLayoutFE(),
AutoEncoderFE(dummy=True),
]
indexes = [
LinearIndex(dummy=True),
KDTreeIndex(dummy=True, trees=5),
SGHIndex(dummy=True, projections=10),
]
lshs = [
LSHIndex(dummy=True, projections=5, tables=10), # color layout
LSHIndex(dummy=True, projections=3, tables=10), # auto encoder
]
for selector in selectors:
read_selection_log(selector, extractors)
for extractor in extractors:
read_extraction_log(extractor, selectors)
for index in indexes:
read_index_logs(selectors, extractors, index)
for i in range(len(lshs)):
read_index_logs(selectors, [extractors[i]], lshs[i])
read_detection_log()
return
def main():
selector = FPSReductionKS(n=3)
extractors = [
ColorLayoutFE(),
AutoEncoderFE.load_autoencoder(model_name='../features/model'),
]
for extractor in extractors:
tags, features = group_features(selector=selector, extractor=extractor)
x = range(features.shape[1])
mean = np.mean(features, axis=0)
std = np.std(features, axis=0)
mins = np.min(features, axis=0)
maxs = np.max(features, axis=0)
plt.figure()
plt.title(f'{selector.name()} - {extractor.name()}')
plt.errorbar(x, mean, std, fmt='.b', ecolor='r', capsize=3)
plt.plot(x, maxs, 'g')
plt.plot(x, mins, 'y')
plt.legend([
'max',
'min',
])
plt.xlabel('features')
plt.ylabel('value')
plt.show()
return
def main():
selectors = [
FPSReductionKS(n=3),
# MaxHistDiffKS(frames_per_window=1),
]
extractors = [
ColorLayoutFE(),
AutoEncoderFE(dummy=True, model_name='model'),
]
indexes = [
# LinearIndex(dummy=True, k=100),
KDTreeIndex(dummy=True, trees=5, k=100),
# SGHIndex(dummy=True, projections=14, tables=2, k=100),
# LSHIndex(dummy=True, projections=14, k=100),
]
for selector in selectors:
for extractor in extractors:
for index in indexes:
find_copies(
video_name='385',
selector=selector,
extractor=extractor,
index=index,
)
return
def main():
numpy.random.seed(1209)
load = False
# input shape
shape = (64, 64, 3)
# select keyframes and shuffle
selector = FPSReductionKS(n=1)
videos = ['003', '071', '118', '209', '258', '385', '437']
videos_path = get_videos_dir()
frames, _, _ = selector.select_keyframes(f'{videos_path}/{videos[0]}.mp4')
for video in videos[1:]:
frames_2, _, _ = selector.select_keyframes(
f'{videos_path}/{video}.mp4')
frames = numpy.concatenate([frames, frames_2])
numpy.random.shuffle(frames)
if load:
autoencoder = AutoEncoderFE.load_autoencoder(model_name='model')
shape = autoencoder.input_shape
print(f'input size: {shape}')
else:
# best: tanh-tanh, second: tanh-relu
autoencoder = AutoEncoderFE(input_shape=shape,
cells=4,
convs=2,
activation='tanh',
output_activation='tanh',
model_name='model2')
autoencoder.train(frames, epochs=50)
autoencoder.save()
print(f'descriptor size: {autoencoder.output_size}')
autoencoder.test(frames[:100])
return
def main():
videos = ['417', '143', '215', '385', '178', '119-120', ]
selectors = [
FPSReductionKS(n=3),
MaxHistDiffKS(frames_per_window=1),
]
extractors = [
ColorLayoutFE(),
AutoEncoderFE(dummy=True),
]
indexes = [
LinearIndex(dummy=True),
KDTreeIndex(dummy=True, trees=5),
SGHIndex(dummy=True, projections=10),
LSHIndex(dummy=True, projections=5, tables=10), # color layout
LSHIndex(dummy=True, projections=3, tables=10), # auto encoder
]
datasets = [
(selectors[0], extractors[0], indexes[0],), # linear
# (selectors[0], extractors[0], indexes[1],), # kdtree
# (selectors[0], extractors[0], indexes[2],), # sgh
# (selectors[0], extractors[0], indexes[3],), # lsh
# (selectors[0], extractors[1], indexes[0],), # linear
# (selectors[0], extractors[1], indexes[1],), # kdtree
# (selectors[0], extractors[1], indexes[2],), # sgh
# (selectors[0], extractors[1], indexes[4],), # lsh
# (selectors[1], extractors[0], indexes[1],), # kdtree
# (selectors[1], extractors[0], indexes[2],), # sgh
# (selectors[1], extractors[0], indexes[3],), # lsh
# (selectors[1], extractors[1], indexes[1],), # kdtree
# (selectors[1], extractors[1], indexes[2],), # sgh
# (selectors[1], extractors[1], indexes[4],), # lsh
]
for video in videos:
for selector, extractor, index in datasets:
evaluate_duplicates(
video_name=video,
selector=selector,
extractor=extractor,
index=index,
)
return
def main():
selectors = [
FPSReductionKS(n=3),
MaxHistDiffKS(frames_per_window=1),
]
extractors = [
ColorLayoutFE(),
AutoEncoderFE.load_autoencoder(model_name='features/model'),
]
for selector in selectors:
for extractor in extractors:
extract_features_videos(selector=selector, extractor=extractor, force=True)
return
def main():
np.random.seed(1209)
videos = [
'119-120',
'143',
'178',
'215',
'385',
'417',
] # ['190', '227', ]
k = 100
selectors = [
MaxHistDiffKS(frames_per_window=1),
FPSReductionKS(n=3),
]
extractors = [
ColorLayoutFE(),
# AutoEncoderFE.load_autoencoder(model_name='features/model'),
]
indexes = [
[LinearIndex, {}],
[KDTreeIndex, {
'trees': 5,
'checks': 1000
}],
[SGHIndex, {
'projections': 10,
'training_split': 0.3
}],
[LSHIndex, {
'projections': 5,
'tables': 10,
'bin_width': 20
}], # for color layout
# [LSHIndex, {'projections': 3, 'tables': 10, 'bin_width': 20}], # for autoencoder
]
for selector in selectors:
for extractor in extractors:
print('\n')
all_tags, all_features = group_features(selector=selector,
extractor=extractor)
print(f'loaded {all_features.shape[0]:,} features')
for index_class, kwargs in indexes:
print(f'\nstarting index construction')
index = index_class(data=all_features,
labels=all_tags,
k=k,
**kwargs)
print(
f'{index.name()} construction took {index.build_time:.1f} seconds\n'
)
try:
index.engine.analize_storage()
except:
pass
neighbours_path = get_neighbours_dir(selector=selector,
extractor=extractor,
index=index)
if not os.path.isdir(neighbours_path):
os.makedirs(neighbours_path)
log_path = f'{neighbours_path}/constructions.txt'
log_persistent(
f'{index.name()}\t{index.build_time:.2f}\t{all_features.shape[0]}\n',
log_path=log_path)
for video_name in videos:
nearest_neighbours(video_name=video_name,
selector=selector,
extractor=extractor,
index=index,
analize=True,
force=True)
return
def add_timestamp(frames, timestamps):
for i in range(len(frames)):
cv2.putText(frames[i], f'{timestamps[i]:.2f}', (10, 30), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 0),
thickness=5)
cv2.putText(frames[i], f'{timestamps[i]:.2f}', (10, 30), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255),
thickness=2)
if __name__ == '__main__':
from keyframes import FPSReductionKS, MaxHistDiffKS
chapter = '../../videos/Shippuden_low/178.mp4'
selector1 = FPSReductionKS()
keyframes1, timestamps1, _ = selector1.select_keyframes(chapter)
selector2 = MaxHistDiffKS(frames_per_window=1)
keyframes2, timestamps2, _ = selector2.select_keyframes(chapter)
print(len(keyframes1), len(keyframes2), int(timestamps1[-1]))
keyframes1 = resize_frames(keyframes1)
add_timestamp(keyframes1, timestamps1)
keyframes2 = resize_frames(keyframes2)
add_timestamp(keyframes2, timestamps2)
# show keyframes
i1 = 0