def create_video_results_wrapper(top_video_fullpath,classifier_path,
progress_bar_signal,
view='top',
doOverwrite=0,output_suffix=''):
try:
video_fullpath = top_video_fullpath
video_path = os.path.dirname(video_fullpath)
video_name = os.path.basename(video_fullpath)
model_type = mof.get_clf_type(classifier_path=classifier_path)
# Get the output folder for this specific mouse.
output_folder = mof.get_mouse_output_dir(dir_output_should_be_in=video_path, video_name=video_name,
output_suffix=output_suffix)
# Get the pose and feature files' names.
pose_basename = mof.get_pose_no_ext(video_fullpath=top_video_fullpath,
output_folder=output_folder,
view='top',
output_suffix=output_suffix)
top_pose_fullpath = pose_basename + '.json'
# Get the name of the text file we're going to save to.
classifier_savename = mof.get_classifier_savename(video_fullpath=top_video_fullpath,
output_folder=output_folder,
view=view,
classifier_path=classifier_path,
output_suffix=output_suffix,model_type=model_type)
# print(classifier_savename)
predictions_exists=os.path.exists(classifier_savename)
top_pose_exist = os.path.exists(top_pose_fullpath)
video_savename = classifier_savename[:-3]+'mp4'
video_exists=os.path.exists(video_savename)
# print(video_savename)
if not top_pose_exist:
raise ValueError("No pose has been extracted for this video!")
if not predictions_exists:
raise ValueError("No behavior classified for this video!")
if (not video_exists) | doOverwrite:
pool = mp.Pool(1)
manager = mp.Manager()
queue_for_progress_bar = manager.Queue(20)
result = pool.apply_async(create_mp4_prediction, (video_fullpath,top_pose_fullpath,classifier_savename,video_savename, queue_for_progress_bar))
still_frames_left = True
while still_frames_left:
progress_bar_input = queue_for_progress_bar.get()
if progress_bar_input:
still_frames_left = True
progress_bar_signal.emit(progress_bar_input[0], progress_bar_input[1])
else:
still_frames_left = False
break
pool.close()
pool.join()
result.get()
else:
print("4 - Video already exists")
return
except Exception as e:
print(e)
raise (e)
return
def classify_actions_wrapper(top_video_fullpath,
front_video_fullpath,
doOverwrite,
view,
classifier_path='',
output_suffix=''):
try:
video_fullpath = top_video_fullpath
video_path = os.path.dirname(video_fullpath)
video_name = os.path.basename(video_fullpath)
model_type = mof.get_clf_type(classifier_path=classifier_path)
# Get the output folder for this specific mouse.
output_folder = mof.get_mouse_output_dir(
dir_output_should_be_in=video_path,
video_name=video_name,
output_suffix=output_suffix)
# Get the name of the features you should be loading.
front_feat_basename = mof.get_feat_no_ext(
video_fullpath=top_video_fullpath,
output_folder=output_folder,
view='front',
output_suffix=output_suffix)
top_feat_basename = mof.get_feat_no_ext(
video_fullpath=top_video_fullpath,
output_folder=output_folder,
view='top',
output_suffix=output_suffix)
# Get the name of the text file we're going to save to.
classifier_savename = mof.get_classifier_savename(
video_fullpath=top_video_fullpath,
output_folder=output_folder,
view=view,
classifier_path=classifier_path,
output_suffix=output_suffix,
model_type=model_type)
# Make their matfile names.
if 'pcf' in classifier_path:
# more hiding pcf features shenanigans, hopefully the last time?
# top_feat_basename = top_feat_basename[:-4] + 'pcf_'+top_feat_basename[-4:]
front_feat_name = front_feat_basename + '_wnd.npz'
top_feat_name = top_feat_basename + '_wnd.npz'
else:
front_feat_name = front_feat_basename + '_wnd.npz'
top_feat_name = top_feat_basename + '_wnd.npz'
# Check that features exist.
top_feats_exist = os.path.exists(top_feat_name)
front_feats_exist = os.path.exists(front_feat_name)
classifier_savename_exist = os.path.exists(classifier_savename)
# if False:
if (not classifier_savename_exist) | doOverwrite:
# If the proper features don't exist, raise an exception. Otherwise, load them.
if (view == 'top') or (view == 'toppcf'):
if top_feats_exist:
print("loading top features")
features = mcm.load_features_from_filename(
top_feat_name=top_feat_name)
else:
print(
"Top features don't exist in the proper format/location. Aborting..."
)
raise ValueError(
top_feat_name.split('/')[-1] + " doesn't exist.")
elif view == 'topfront':
if not top_feats_exist:
print(
"Top features don't exist in the proper format/location. Aborting..."
)
raise ValueError(
top_feat_name.split('/')[-1] + " doesn't exist.")
elif not front_feats_exist:
print(
"Front features don't exist in the proper format/location. Aborting..."
)
raise ValueError(
front_feat_name.split('/')[-1] + " doesn't exist.")
else: # Both featuresets exist.
print("loading top and front features")
features = mcm.load_features_from_filename(
top_feat_name=top_feat_name,
front_feat_name=front_feat_name)
else:
print("Classifier available for top or top and front view")
raise ValueError('Classifier not available for only fron view')
# Classify the actions (get the labels back).
print("predicting labels")
predicted_labels, predicted_labels_interaction = mcm.predict_labels(
features, classifier_path)
# Dump the labels into the Caltech Behavior Annotator format.
mcm.dump_labels_CBA(predicted_labels, predicted_labels_interaction,
classifier_savename)
else:
print("3 - Predictions already exist")
return
except Exception as e:
print(e)
raise (e)
return