rel_prop, combined_scores[:, video_cls][:, None], loc_scores, dur_scores), axis=1)
except:
print(video_cls, rel_prop.shape, combined_scores.shape, reg_scores.shape, loc_scores.shape, dur_scores.shape)
raise
print("Preprocessing detections...")
for k, v in detection_scores.items():
gen_detection_results(k, v)
print('Done.')
# perform NMS
print("Performing nms...")
for cls in range(num_class):
dataset_detections[cls] = {
k: temporal_nms(v, nms_threshold) for k,v in dataset_detections[cls].items()
}
print("NMS Done.")
def perform_regression(detections):
t0 = detections[:, 0]
t1 = detections[:, 1]
center = (t0 + t1) / 2
duration = (t1 - t0)
new_center = center + duration * detections[:, 3]
new_duration = duration * np.exp(detections[:, 4])
new_detections = np.concatenate((
np.clip(new_center - new_duration / 2, 0, 1)[:, None], np.clip(new_center + new_duration / 2, 0, 1)[:, None], detections[:, 2:]
except:
print(video_cls, rel_prop.shape, combined_scores.shape,
reg_scores.shape, loc_scores.shape, dur_scores.shape)
raise
print("Preprocessing detections...")
for k, v in detection_scores.items():
gen_detection_results(k, v)
print('Done.')
# perform NMS
print("Performing nms...")
for cls in range(num_class):
dataset_detections[cls] = {
k: temporal_nms(v, nms_threshold)
for k, v in dataset_detections[cls].items()
}
print("NMS Done.")
def perform_regression(detections):
t0 = detections[:, 0]
t1 = detections[:, 1]
center = (t0 + t1) / 2
duration = (t1 - t0)
new_center = center + duration * detections[:, 3]
new_duration = duration * np.exp(detections[:, 4])
new_detections = np.concatenate(
rel_prop, combined_scores[:, video_cls][:, None], loc_scores, dur_scores), axis=1)
except:
print(video_cls, rel_prop.shape, combined_scores.shape, reg_scores.shape, loc_scores.shape, dur_scores.shape)
raise
print("Preprocessing detections...")
for k, v in detection_scores.items():
gen_detection_results(k, v)
print('Done.')
# perform NMS
print("Performing nms...")
for cls in range(num_class):
dataset_detections[cls] = {
k: temporal_nms(v, nms_threshold) for k,v in dataset_detections[cls].items()
}
print("NMS Done.")
def perform_regression(detections):
t0 = detections[:, 0]
t1 = detections[:, 1]
center = (t0 + t1) / 2
duration = (t1 - t0)
new_center = center + duration * detections[:, 3]
new_duration = duration * np.exp(detections[:, 4])
new_detections = np.concatenate((
np.clip(new_center - new_duration / 2, 0, 1)[:, None], np.clip(new_center + new_duration / 2, 0, 1)[:, None], detections[:, 2:]
