t_acc = 0
t_acc_max = 0
for video_idx, s_filename in enumerate(video_file_stem_names):
_, user_labels, feature_sizes = SumMeMultiViewFeatureLoader.load_by_name(s_filename, doSoftmax=False)
s_seg = pdefined_segs[s_filename]
# s_F1_scores = []
avg_labels = np.mean(user_labels, axis=1)
n_frames = len(avg_labels)
# avg_indices = np.argsort(-avg_labels)
# endpoints = int(0.15*len(avg_labels))
# for i in range(len(avg_labels)):
# avg_labels[avg_indices[i]] = 0 if i <= endpoints else 1
s_frame01scores = rep_conversions.framescore2frame01score(avg_labels, s_seg)
user_scores_list = [user_labels[:, i] for i in range(user_labels.shape[1])]
s_F1_score = metrics.averaged_F1_score(y_trues=user_scores_list, y_score=s_frame01scores.tolist())
# s_F1_scores.append(s_F1_score)
print "[{:02d} | {:02d}]\t{:s}: \t:{:.04f}, n_frames:{:d}".format(video_idx, len(video_file_stem_names), s_filename, s_F1_score, user_labels.shape[0])
t_acc += (s_F1_score)
print "Total MinAcc: {:.04f}".format(t_acc/len(video_file_stem_names))
from vsSummDevs.SumEvaluation import rep_conversions
dataset_name = 'tvsum'
tvsum_gt = LoadLabels.load_annotations()
tv_segments = getShotBoundariesECCV2016.getTVSumShotBoundaris()
KY_dataset_path = os.path.join(os.path.expanduser('~'),
'datasets/KY_AAAI18/datasets')
h5f_path = os.path.join(
KY_dataset_path,
'eccv16_dataset_{:s}_google_pool5.h5'.format(dataset_name))
dataset = h5py.File(h5f_path, 'r')
dataset_keys = dataset.keys()
n_videos = len(dataset_keys)
index_dict = getShotBoundariesECCV2016.getTVSumCorrespondecesKZ()
for s_videostem in tvsum_gt.keys():
user_annotations = tvsum_gt[s_videostem]
s_segments = tv_segments[s_videostem]
# key = dataset_keys[index_dict[s_videostem]]
key = 'video_{:d}'.format(index_dict[s_videostem] + 1)
s_user_score = user_annotations['video_user_scores'][:, 0]
s_user_score01 = rep_conversions.framescore2frame01score(
s_user_score.tolist(), s_segments.tolist())
ky_user_summary = dataset[key]['user_summary'][...]
ky_user_summary0 = ky_user_summary[0]
cps = dataset[key]['change_points'][...]
print "DEBUG"
print "DEBUG"
F1_scores = 0
for video_idx, s_filename in enumerate(videofile_stems):
video_features, user_labels, _ = SumMeMultiViewFeatureLoader.load_by_name(
s_filename, doSoftmax=False)
sklearn.preprocessing.normalize(video_features)
pca = PCA(whiten=True, svd_solver='auto')
pca.fit(video_features.transpose())
matrix = pca.components_
frame_contrib = np.sum(pca.components_, axis=0)
frame_contrib = (frame_contrib - np.min(frame_contrib)) / (
np.max(frame_contrib) - np.mean(frame_contrib))
s_seg = pdefined_segs[s_filename]
s_frame01scores = rep_conversions.framescore2frame01score(
frame_contrib, s_seg)
# s_frame01scores = rep_conversions.framescore2frame01score_sort(frame_contrib)
user_scores_list = [user_labels[:, i] for i in range(user_labels.shape[1])]
s_F1_score = metrics.max_F1_score(y_trues=user_scores_list,
y_score=s_frame01scores.tolist())
avg_labels = np.mean(user_labels, axis=1)
s_scorr, s_p = pearsonr(frame_contrib, avg_labels)
print "[{:d} | {:d}] \t {:s} \t{:.04f}\t correlation:{:.04f}".format(
video_idx, len(videofile_stems), s_filename, s_F1_score, s_scorr)
F1_scores += s_F1_score
print "overall F1 score: {:.04f}".format(F1_scores / len(videofile_stems))
for video_idx, s_filename in enumerate(video_file_stem_names):
# s_filename = video_file_stem_names[0]
s_segments = pdefined_segs[s_filename].tolist()
video_features, user_labels, feature_sizes = SumMeMultiViewFeatureLoader.load_by_name(
s_filename, doSoftmax=True)
# original_nFrames = video_features.shape[0]
# video_features = video_features[::sample_rate, :]
# avg_labels = np.mean(user_labels, axis=1)
frame_entropy = SumMeMultiViewFeatureLoader.feature_entropy(
video_features, feature_sizes)
frame_entropy = (frame_entropy - np.min(frame_entropy)) / (
np.max(frame_entropy) - np.min(frame_entropy))
s_frame01scores = rep_conversions.framescore2frame01score(
frame_entropy, s_segments)
user_scores_list = [user_labels[:, i] for i in range(user_labels.shape[1])]
s_F1_score = metrics.averaged_F1_score(y_trues=user_scores_list,
y_score=s_frame01scores.tolist())
# print "{:s} \t{:.04f}\t".format(s_filename, s_F1_score)
print "[{:d} | {:d}] \t {:s} \t{:.04f}".format(video_idx,
len(video_file_stem_names),
s_filename, s_F1_score)
totalF1 += s_F1_score
print "overall F1 score: {:.04f}".format(totalF1 / len(video_file_stem_names))
# print "DEBUG"
s_seg = pdefined_segs[s_filename]
ntrain = int(percentage*video_features.shape[0])
selected_train_features = video_features[:ntrain, :]
selected_train_labels = reg_labels[:ntrain]
# val_features = video_features[ntrain:,:]
# val_labels = reg_labels[ntrain:]
# clc_labels = np.max(user_labels, axis=1)
clf = svm.LinearSVR(C=C)
clf.fit(selected_train_features, selected_train_labels)
predicted_video_labels = clf.predict(video_features)
s_frame01scores = rep_conversions.framescore2frame01score_sort(predicted_video_labels)
user_scores_list = [user_labels[:, i] for i in range(user_labels.shape[1])]
s_scorr, s_p = pearsonr(predicted_video_labels, reg_labels)
s_F1_score_sort = metrics.averaged_F1_score(y_trues=user_scores_list, y_score=s_frame01scores.tolist())
t_acc_sort += s_F1_score_sort
s_frame01scores = rep_conversions.framescore2frame01score(predicted_video_labels, s_seg)
s_F1_score_seg = metrics.averaged_F1_score(y_trues=user_scores_list, y_score=s_frame01scores.tolist())
t_acc_seg += s_F1_score_seg
print "[{:d} | {:d}] \t {:s} \t{:.04f}\t correlation:{:.04f}\t{:.04f}".format(video_idx, len(video_file_stem_names),
s_filename, s_F1_score_sort, s_scorr, s_F1_score_seg)
print "Total Acc: {:.04f}\t{:.04f}".format(t_acc_sort / len(video_file_stem_names), t_acc_seg/(len(video_file_stem_names)))
t_acc = 0
for video_idx, s_filename in enumerate(video_file_stem_names):
_, user_labels, feature_sizes = SumMeMultiViewFeatureLoader.load_by_name(
s_filename, doSoftmax=False)
s_seg = pdefined_segs[s_filename]
avg_labels = np.mean(user_labels, axis=1)
nframes = user_labels.shape[0]
intevals = rep_conversions.convert_seg2interval(s_seg, nframes)
intevals = np.asarray(intevals)
nfps = []
for i in range(1, len(s_seg)):
nfps.append(s_seg[i] - s_seg[i - 1])
generated_summary = vsum_tools.generate_summary(avg_labels,
intevals,
nframes,
nfps,
positions=np.asarray(
range(nframes)))
my_generated_summary = rep_conversions.framescore2frame01score(
avg_labels, s_seg)
s_F1_score, _, _ = vsum_tools.evaluate_summary(generated_summary,
user_labels.transpose(),
eval_metric='max')
t_acc += (s_F1_score)
print "Total MinAcc: {:.04f}".format(t_acc / len(video_file_stem_names))
sample_rate = 5
pdefined_segs = getSegs.getSumMeShotBoundaris()
video_file_stem_names = dataset_pathvars.file_names
video_file_stem_names.sort()
t_acc_min = 0
t_acc_max = 0
for video_idx, s_filename in enumerate(video_file_stem_names):
_, user_labels, feature_sizes = SumMeMultiViewFeatureLoader.load_by_name(
s_filename, doSoftmax=False)
s_seg = pdefined_segs[s_filename]
s_F1_scores = []
for user_idx in range(user_labels.shape[1]):
selected_labels = user_labels[:, user_idx]
s_frame01scores = rep_conversions.framescore2frame01score(
selected_labels, s_seg)
user_scores_list = [
user_labels[:, i] for i in range(user_labels.shape[1])
]
s_F1_score = metrics.averaged_F1_score(
y_trues=user_scores_list, y_score=s_frame01scores.tolist())
s_F1_scores.append(s_F1_score)
print "[{:02d} | {:02d}]\t{:s}: \tMin:{:.04f}\tMax:{:.04f}".format(
video_idx, len(video_file_stem_names), s_filename, min(s_F1_scores),
max(s_F1_scores))
t_acc_min += min(s_F1_scores)
t_acc_max += max(s_F1_scores)
print "Total MinAcc: {:.04f}\t MaxAcc{:.04f}".format(
t_acc_min / len(video_file_stem_names),
t_acc_max / len(video_file_stem_names))