def extract_pose_wrapper(video_fullpath,
view,
doOverwrite,
progress_bar_signal='',
verbose=0,
output_suffix='',
mars_opts={},
max_frames=999999):
video_path = os.path.dirname(video_fullpath)
video_name = os.path.basename(video_fullpath)
output_folder = mof.get_mouse_output_dir(
dir_output_should_be_in=video_path,
video_name=video_name,
output_suffix=output_suffix)
extract_pose(video_fullpath=video_fullpath,
output_folder=output_folder,
output_suffix=output_suffix,
view=view,
doOverwrite=doOverwrite,
progress_bar_signal=progress_bar_signal,
mars_opts=mars_opts,
verbose=verbose,
max_frames=max_frames)
return
def dump_bento(video_fullpath, output_suffix='', pose_file='', basepath=''):
if not output_suffix:
# Default suffix is just the version number.
output_suffix = mof.get_version_suffix()
video_path = os.path.dirname(video_fullpath)
video_name = os.path.basename(video_fullpath)
# 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)
mouse_name = output_folder.split('/')[-1]
# if not movie_file:
# movie_name = mouse_name + '_Top_J85.seq'
#
# movie_location = output_folder
# movie_location = os.path.split(movie_location)[0]
# movie_location = os.path.split(movie_location)[0]
#
# movie_file = os.path.join(movie_location, movie_name)
# else:
# movie_file = os.path.basename(movie_file)
if not pose_file:
pose_basename = mof.get_pose_no_ext(video_fullpath=video_fullpath,
output_folder=output_folder,
view='top',
output_suffix=output_suffix)
top_pose_fullpath = pose_basename + '.mat'
# else:
# pose_file = os.path.basename(pose_file)
""" This function writes an xls with information for bento in it."""
wb = xlwt.Workbook(encoding='utf-8')
ws1 = wb.add_sheet('Sheet1', cell_overwrite_ok=True)
ws1.write(0, 0, basepath) # A1
ws1.write(0, 1, 'Ca framerate:') # B1
ws1.write(0, 2, 0) # C1
ws1.write(0, 3, 'Annot framerate:') # D1
ws1.write(0, 4, 30) # E1
ws1.write(0, 5, 'Multiple trials/Ca file:') # F1
ws1.write(0, 6, 0) # G1
ws1.write(0, 7, 'Multiple trails/annot file') # H1
ws1.write(0, 8, 0) # I1
ws1.write(0, 9, 'Includes behavior movies:') # J1
ws1.write(0, 10, 1) # K1
ws1.write(
0, 11,
'Offset (in seconds; positive values = annot starts before Ca):') # L1
ws1.write(0, 12, 0) # M1
ws1.write(1, 0, 'Mouse') # A2
ws1.write(1, 1, 'Sessn') # B2
ws1.write(1, 2, 'Trial') # C2
ws1.write(1, 3, 'Stim') # D2
ws1.write(1, 4, 'Calcium imaging file') # E2
ws1.write(1, 5, 'Start Ca') # F2
ws1.write(1, 6, 'Stop Ca') # G2
ws1.write(1, 7, 'FR Ca') # H2
ws1.write(1, 8, 'Alignments') # I2
ws1.write(1, 9, 'Annotation file') # J2
ws1.write(1, 10, 'Start Anno') # K2
ws1.write(1, 11, 'Stop Anno') # L2
ws1.write(1, 12, 'FR Anno') # M2
ws1.write(1, 13, 'Offset') # N2
ws1.write(1, 14, 'Behavior movie') # O2
ws1.write(1, 15, 'Tracking') # P2
ws1.write(2, 0, 1) # A2
ws1.write(2, 1, 1) # B2
ws1.write(2, 2, 1) # C2
ws1.write(2, 3, '') # D2
ws1.write(2, 4, '') # E2
ws1.write(2, 5, '') # F2
ws1.write(2, 6, '') # G2
ws1.write(2, 7, '') # H2
ws1.write(2, 8, '') # I2
ann = [
os.path.join(output_folder, f) for f in os.listdir(output_folder)
if is_gt_annotation(f) | ('pred' in f)
]
ann = sorted(ann)
ann = [get_rel_path(annot_path, basepath) for annot_path in ann]
ws1.write(2, 9, ';'.join(ann)) # J2
ws1.write(2, 10, '') # K2
ws1.write(2, 11, '') # L2
ws1.write(2, 12, '') # M2
ws1.write(2, 13, '') # N2
ws1.write(2, 14, get_rel_path(video_fullpath, basepath)) # O2
ws1.write(2, 15, get_rel_path(top_pose_fullpath, basepath)) # P2
bento_name = 'bento_' + output_suffix + '.xls'
wb.save(os.path.join(output_folder, bento_name))
return 1
def dump_bento_across_dir(root_path):
''' This function makes a bento file for a specific directory.'''
wb = xlwt.Workbook(encoding='utf-8')
ws1 = wb.add_sheet('Sheet1', cell_overwrite_ok=True)
ws1.write(0, 0, os.path.abspath(root_path)) # A1
ws1.write(0, 1, 'Ca framerate:') # B1
ws1.write(0, 2, 0) # C1
ws1.write(0, 3, 'Annot framerate:') # D1
ws1.write(0, 4, 30) # E1
ws1.write(0, 5, 'Multiple trials/Ca file:') # F1
ws1.write(0, 6, 0) # G1
ws1.write(0, 7, 'Multiple trails/annot file') # H1
ws1.write(0, 8, 0) # I1
ws1.write(0, 9, 'Includes behavior movies:') # J1
ws1.write(0, 10, 1) # K1
ws1.write(
0, 11,
'Offset (in seconds; positive values = annot starts before Ca):') # L1
ws1.write(0, 12, 0) # M1
ws1.write(0, 13, 'Includes tracking data:')
ws1.write(0, 14, 0)
ws1.write(0, 15, 'Includes audio files:')
ws1.write(0, 16, 0)
ws1.write(1, 0, 'Mouse') # A2
mouse_col_num = 0
session_col_num = 1
trial_col_num = 2
ws1.write(1, 1, 'Sessn') # B2
ws1.write(1, 2, 'Trial') # C2
ws1.write(1, 3, 'Stim') # D2
ws1.write(1, 4, 'Calcium imaging file') # E2
ws1.write(1, 5, 'Start Ca') # F2
ws1.write(1, 6, 'Stop Ca') # G2
ws1.write(1, 7, 'FR Ca') # H2
ws1.write(1, 8, 'Alignments') # I2
ws1.write(1, 9, 'Annotation file') # J2
annot_file_col_num = 9
ws1.write(1, 10, 'Start Anno') # K2
ws1.write(1, 11, 'Stop Anno') # L2
ws1.write(1, 12, 'FR Anno') # M2
ws1.write(1, 13, 'Offset') # N2
ws1.write(1, 14, 'Behavior movie') # O2
behavior_movie_col_num = 14
ws1.write(1, 15, 'Tracking') # P2
tracking_file_col_num = 15
ws1.write(1, 16, 'Audio file')
audio_file_col_num = 16
ws1.write(1, 17, 'tSNE')
# ws1.write(2, 0, 1) # A2
# ws1.write(2, 1, 1) # B2
# ws1.write(2, 2, 1) # C2
# ws1.write(2, 3, '') # D2
# ws1.write(2, 4, '') # E2
# ws1.write(2, 5, '') # F2
# ws1.write(2, 6, '') # G2
# ws1.write(2, 7, '') # H2
# ws1.write(2, 8, '') # I2
mouse_number = 0
row_num = 2
# Going through everything in this directory.
audio_filenames = []
add_audio_count = 0
nonaudio_filenames = []
for path, subdirs, filenames in os.walk(root_path):
for fname in sorted(filenames):
fname = os.path.join(path, fname)
if fname.endswith('.wav'):
audio_filenames.append(fname)
else:
nonaudio_filenames.append(fname)
audio_filenames = sorted(audio_filenames)
nonaudio_filenames = sorted(nonaudio_filenames)
for fname in nonaudio_filenames:
try:
cond1 = '.seq' not in fname
cond2 = 'skipped' in path
if (cond1) | cond2:
continue
if 'Top' in fname:
front_fname, top_fname, mouse_name = mof.get_names(fname)
fullpath_to_front = os.path.join(path, front_fname)
fullpath_to_top = os.path.join(path, top_fname)
else:
# This is a seq file, but doesnt have "Top" or "Front" in it. Let's skip it.
continue
# Add their info to the bento file at the appropriate level.
video_fullpath = fullpath_to_top
output_suffix = ''
video_path = os.path.dirname(video_fullpath)
video_name = os.path.basename(video_fullpath)
# 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)
_, _, mouse_name = mof.get_names(video_name=video_name)
pose_basename = mof.get_pose_no_ext(video_fullpath=video_fullpath,
output_folder=output_folder,
view='top',
output_suffix=output_suffix)
top_pose_fullpath = pose_basename + '.json'
same_path_ann = [
os.path.join(root_path, f) for f in os.listdir(root_path)
if is_annotation_file(f, mouse_name)
]
ann = [
os.path.join(output_folder, f)
for f in os.listdir(output_folder)
if is_annotation_file(f, mouse_name)
]
ann = sorted(ann)
ann = [get_normrel_path(f, root_path) for f in ann]
pose_cond = os.path.exists(top_pose_fullpath)
video_cond = os.path.exists(video_fullpath)
should_write = (pose_cond and video_cond)
if should_write:
old_mouse_number = mouse_number
mouse_number = get_mouse_number(video_fullpath)
mouse_cond = (old_mouse_number == mouse_number)
# TODO: Session condition
sess_cond = (True)
if mouse_cond and sess_cond:
trial_count += 1
else:
trial_count = 1
ws1.write(row_num, mouse_col_num, mouse_number) # A2
ws1.write(row_num, session_col_num, 1) # B2
ws1.write(row_num, trial_col_num, trial_count) # C2
ws1.write(row_num, annot_file_col_num, ';'.join(ann)) # J2
ws1.write(row_num, 10, '') # K2
ws1.write(row_num, 11, '') # L2
ws1.write(row_num, 12, '') # M2
ws1.write(row_num, 13, '') # N2
track_file = get_normrel_path(top_pose_fullpath, root_path)
ws1.write(row_num, behavior_movie_col_num,
get_normrel_path(fullpath_to_top, root_path)) # O2
ws1.write(row_num, tracking_file_col_num, track_file) # P2
row_num += 1
except Exception as e:
print(e)
error_msg = 'ERROR: ' + fname + ' has failed. ' + str(e)
continue
# End of try-except block
# End of particular fname
# End of the particular root_path
last_row = row_num
row_num = 2
for audio_file_count, audio_file in enumerate(audio_filenames):
# Write the files in order.
ws1.write(row_num + audio_file_count, audio_file_col_num + 2,
get_normrel_path(audio_file, root_path))
bento_name = 'bento_' + mof.get_version_suffix() + '.xls'
wb.save(os.path.join(root_path, bento_name))
return
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