def __init__(self,
dataset_name='SumMe',
split='train',
clip_size=100,
sum_budget=0.15,
sample_rate=None,
overlap_ratio=0.25):
if dataset_name.lower() not in ['summe', 'tvsum']:
print('Unrecognized dataset {:s}'.format(dataset_name))
self.dataset_name = dataset_name
self.eval_metrics = eval_metrics[self.dataset_name.lower()]
self.split = split
self.sum_budget = sum_budget
self.filenames = datasetpaths[self.dataset_name.lower()].file_names
self.data_loader = data_loaders[self.dataset_name.lower()]
n_files = len(self.filenames)
if self.split == 'train':
self.filenames = self.filenames[:int(0.8 * n_files)]
elif self.split == 'val':
self.filenames = self.filenames[int(0.8 * n_files):]
else:
print("Unrecognized split:{:s}".format(self.split))
self.overlap_ratio = overlap_ratio
self.clip_size = clip_size
if sample_rate is None:
self.sample_rate = [1, 5, 10]
else:
if isinstance(sample_rate, list):
self.sample_rate = sample_rate
else:
self.sample_rate = [sample_rate]
self.videofeatures = []
self.groundtruthscores = []
self.segments = []
self.videonames = []
Kydataset = KyLoader.loadKyDataset(self.dataset_name.lower())
conversion = KyLoader.loadConversion(self.dataset_name.lower())
self.raw2Ky = conversion[0]
self.Ky2raw = conversion[1]
for s_video_idx, s_filename in enumerate(self.filenames):
KyKey = self.raw2Ky[s_filename]
s_video_features, _, _ = self.data_loader.load_by_name(s_filename)
s_scores = Kydataset[KyKey]['user_summary'][...]
s_scores = s_scores.transpose()
s_segments = LoaderUtils.convertlabels2segs(s_scores)
self.groundtruthscores.append(s_scores)
self.videofeatures.append(s_video_features)
self.segments.append(s_segments)
self.videonames.append(s_filename)
self.dataset_size = len(self.videofeatures)
print("{:s}\tEvaluator: {:s}\t{:d} Videos".format(
self.dataset_name, self.split, self.dataset_size))
def __init__(self, dataset_name='SumMe',split='train', clip_size=50, output3=False):
if dataset_name.lower() not in ['summe', 'tvsum']:
print('Unrecognized dataset {:s}'.format(dataset_name))
sys.exit(-1)
self.dataset_name = dataset_name
self.split = split
self.filenames = datasetpaths[self.dataset_name.lower()].file_names
self.data_loader = data_loaders[self.dataset_name.lower()]
n_files = len(self.filenames)
if self.split == 'train':
self.filenames = self.filenames[:int(0.8*n_files)]
elif self.split == 'val':
self.filenames = self.filenames[int(0.8*n_files):]
else:
print("Unrecognized split:{:s}".format(self.split))
sys.exit(-1)
self.clip_size = clip_size
self.output3 = output3
Kydataset = KyLoader.loadKyDataset(self.dataset_name.lower())
conversion = KyLoader.loadConversion(self.dataset_name.lower())
self.raw2Ky = conversion[0]
self.Ky2raw = conversion[1]
self.features = {}
self.avg_summary = {}
self.annotations = []
pbar = progressbar.ProgressBar(max_value=len(self.filenames))
print("Processing {:s}\t{:s} data".format(self.dataset_name, self.split))
for file_dix, s_filename in enumerate(self.filenames):
pbar.update(file_dix)
Kykey = self.raw2Ky[s_filename]
s_features, _, _ = self.data_loader.load_by_name(s_filename)
s_usersummaries = Kydataset[Kykey]['user_summary'][...]
s_usersummaries = s_usersummaries.transpose()
self.features[s_filename] = s_features
s_avg_summary = np.mean(s_usersummaries, axis=1)
self.avg_summary[s_filename] = s_avg_summary
s_video_len = len(s_avg_summary)
s_segments = LoaderUtils.convertlabels2segs(s_usersummaries)
for s_seg in s_segments:
sSegment = LoaderUtils.Segment(s_filename)
s_score = np.mean(s_avg_summary[s_seg[0]:s_seg[1]])
sSegment.initId(s_seg[0], s_seg[1], s_video_len, score=s_score)
self.annotations.append(sSegment)
self.augmented_ratio = 10
self.dataset_size = len(self.annotations)
print("{:s}\t{:s}\t{:d}".format(self.dataset_name, self.split, self.dataset_size))