def create_labeled_videos(folders_to_analyze, marked_vids_parent, view_config_paths, scorernames,
cropped_vid_type='.avi',
skipdirect=False,
skipmirror=False,
view_list=('direct', 'leftmirror', 'rightmirror')
):
'''
:param folders_to_analyze:
:param view_config_paths:
:param scorernames: dictionary with keys 'direct' and 'mirror'
:param cropped_vid_type:
:param move_to_new_folder: if True, create a new folder in which the marked videos and analysis files are stored
to make it easier to move them to another computer without taking the original videos with them
:return:
'''
# in case there are some previously cropped videos that need to be analyzed
folders_to_analyze = navigation_utilities.find_folders_to_analyze(cropped_videos_parent, view_list=view_list)
# view_list = folders_to_analyze.keys()
for view in view_list:
if 'direct' in view:
if skipdirect:
continue
dlc_network = 'direct'
elif 'mirror' in view:
if skipmirror:
continue
dlc_network = 'mirror'
else:
print(view + ' does not contain the keyword "direct" or "mirror"')
continue
config_path = view_config_paths[dlc_network]
scorername = scorernames[dlc_network]
current_view_folders = folders_to_analyze[view]
for current_folder in current_view_folders:
cropped_video_list = glob.glob(current_folder + '/*' + cropped_vid_type)
deeplabcut.create_video_with_all_detections(config_path, cropped_video_list, scorername)
# current_basename = os.path.basename(current_folder)
new_dir = navigation_utilities.create_marked_vids_folder(current_folder, cropped_videos_parent, marked_vids_parent)
# os.path.join(marked_vids_parent, current_basename + '_marked')
test_name = os.path.join(current_folder, '*' + scorername + '*.mp4')
marked_vid_list = glob.glob(test_name)
pickle_list = glob.glob(os.path.join(current_folder, '*.pickle'))
for marked_vid in marked_vid_list:
# if the file already exists in the marked_vid directory, don't move it
_, marked_vid_name = os.path.split(marked_vid)
if not os.path.isfile(os.path.join(new_dir, marked_vid_name)):
shutil.move(marked_vid, new_dir)
for pickle_file in pickle_list:
# if the file already exists in the marked_vid directory, don't move it
_, pickle_name = os.path.split(pickle_file)
if not os.path.isfile(os.path.join(new_dir, pickle_name)):
shutil.move(pickle_file, new_dir)
def create_labeled_optitrack_videos(folders_to_analyze, parent_directories, config_path, scorername,
cropped_vid_type='.avi'):
'''
:param folders_to_analyze:
:param view_config_paths:
:param scorernames: dictionary with keys 'direct' and 'mirror'
:param cropped_vid_type:
:param move_to_new_folder: if True, create a new folder in which the marked videos and analysis files are stored
to make it easier to move them to another computer without taking the original videos with them
:return:
'''
marked_vids_parent = parent_directories['marked_vids_parent']
if cropped_vid_type[0]=='.':
pass
else:
cropped_vid_type = '.' + cropped_vid_type
# in case there are some previously cropped videos that need to be analyzed
cam_list = folders_to_analyze.keys()
for cam_name in cam_list:
current_cam_folders = folders_to_analyze[cam_name]
for current_folder in current_cam_folders:
_, folder_name = os.path.split(current_folder)
if current_folder[:8] == 'dLight36':
continue
cropped_video_list = glob.glob(current_folder + '/*' + cropped_vid_type)
try:
deeplabcut.create_video_with_all_detections(config_path, cropped_video_list, scorername)
except:
pass
# current_basename = os.path.basename(current_folder)
new_dir = navigation_utilities.create_marked_vids_folder(current_folder, cropped_vids_parent,
marked_vids_parent)
# os.path.join(marked_vids_parent, current_basename + '_marked')
test_name = os.path.join(current_folder, '*' + scorername + '*.mp4')
marked_vid_list = glob.glob(test_name)
pickle_list = glob.glob(os.path.join(current_folder, '*.pickle'))
for marked_vid in marked_vid_list:
# if the file already exists in the marked_vid directory, don't move it
_, marked_vid_name = os.path.split(marked_vid)
if not os.path.isfile(os.path.join(new_dir, marked_vid_name)):
shutil.move(marked_vid, new_dir)
for pickle_file in pickle_list:
# if the file already exists in the marked_vid directory, don't move it
_, pickle_name = os.path.split(pickle_file)
if not os.path.isfile(os.path.join(new_dir, pickle_name)):
shutil.move(pickle_file, new_dir)
deeplabcut.analyze_videos(
config_path,
[new_video_path],
"mp4",
robust_nframes=True,
allow_growth=True,
use_shelve=USE_SHELVE,
)
print("Video analyzed.")
print("Create video with all detections...")
scorer, _ = auxiliaryfunctions.GetScorerName(cfg, 1, TRAIN_SIZE)
deeplabcut.create_video_with_all_detections(
config_path, [new_video_path], shuffle=1, displayedbodyparts=["bodypart1"]
)
print("Video created.")
print("Convert detections to tracklets...")
deeplabcut.convert_detections2tracklets(
config_path,
[new_video_path],
"mp4",
track_method=TESTTRACKER
)
print("Tracklets created...")
deeplabcut.stitch_tracklets(
config_path,
def analyze_videos(self, event):
shuffle = self.shuffle.GetValue()
trainingsetindex = self.trainingset.GetValue()
if self.cfg.get("multianimalproject", False):
print("Analyzing ... ")
else:
if self.csv.GetStringSelection() == "Yes":
save_as_csv = True
else:
save_as_csv = False
if self.dynamic.GetStringSelection() == "No":
dynamic = (False, 0.5, 10)
else:
dynamic = (True, 0.5, 10)
if self.filter.GetStringSelection() == "No":
filter = None
else:
filter = True
if self.cfg["cropping"] == "True":
crop = self.cfg["x1"], self.cfg["x2"], self.cfg["y1"], self.cfg[
"y2"]
else:
crop = None
if self.cfg.get("multianimalproject", False):
if self.robust.GetStringSelection() == "No":
robust = False
else:
robust = True
scorername = deeplabcut.analyze_videos(
self.config,
self.filelist,
videotype=self.videotype.GetValue(),
shuffle=shuffle,
trainingsetindex=trainingsetindex,
gputouse=None,
cropping=crop,
robust_nframes=robust,
)
if self.create_video_with_all_detections.GetStringSelection(
) == "Yes":
trainFrac = self.cfg["TrainingFraction"][trainingsetindex]
scorername, DLCscorerlegacy = auxiliaryfunctions.GetScorerName(
self.cfg, shuffle, trainFraction=trainFrac)
print(scorername)
deeplabcut.create_video_with_all_detections(
self.config, self.filelist, DLCscorername=scorername)
else:
scorername = deeplabcut.analyze_videos(
self.config,
self.filelist,
videotype=self.videotype.GetValue(),
shuffle=shuffle,
trainingsetindex=trainingsetindex,
gputouse=None,
save_as_csv=save_as_csv,
cropping=crop,
dynamic=dynamic,
)
if self.filter.GetStringSelection() == "Yes":
deeplabcut.filterpredictions(
self.config,
self.filelist,
videotype=self.videotype.GetValue(),
shuffle=shuffle,
trainingsetindex=trainingsetindex,
filtertype="median",
windowlength=5,
save_as_csv=True,
)
if self.trajectory.GetStringSelection() == "Yes":
if self.showfigs.GetStringSelection() == "No":
showfig = False
else:
showfig = True
deeplabcut.plot_trajectories(
self.config,
self.filelist,
displayedbodyparts=self.bodyparts,
videotype=self.videotype.GetValue(),
shuffle=shuffle,
trainingsetindex=trainingsetindex,
filtered=True,
showfigures=showfig,
)
def analyze_videos(self, event):
shuffle = self.shuffle.GetValue()
videotype = self.videotype.GetValue(),
if self.cfg.get("multianimalproject", False):
print("DLC network loading and video analysis starting ... ")
auto_track = True
else:
if self.csv.GetStringSelection() == "Yes":
save_as_csv = True
else:
save_as_csv = False
if self.dynamic.GetStringSelection() == "No":
dynamic = (False, 0.5, 10)
else:
dynamic = (True, 0.5, 10)
if self.filter.GetStringSelection() == "No":
_filter = False
else:
_filter = True
if self.cfg["cropping"] == "True":
crop = self.cfg["x1"], self.cfg["x2"], self.cfg["y1"], self.cfg[
"y2"]
else:
crop = None
if self.cfg.get("multianimalproject", False):
if self.robust.GetStringSelection() == "No":
robust = False
else:
robust = True
scorername = deeplabcut.analyze_videos(
self.config,
self.filelist,
self.videotype.GetValue(),
shuffle=shuffle,
gputouse=None,
cropping=crop,
robust_nframes=robust,
auto_track=True,
n_tracks=self.ntracks.GetValue(),
calibrate=self.calibrate.GetStringSelection() == "Yes",
identity_only=self.identity_toggle.GetStringSelection() ==
"Yes",
)
if self.create_video_with_all_detections.GetStringSelection(
) == "Yes":
deeplabcut.create_video_with_all_detections(
self.config,
self.filelist,
self.videotype.GetValue(),
shuffle=shuffle,
)
if self.filter.GetStringSelection() == "Yes":
deeplabcut.filterpredictions(
self.config,
self.filelist,
self.videotype.GetValue(),
)
if self.csv.GetStringSelection() == "Yes":
deeplabcut.analyze_videos_converth5_to_csv(self.filelist,
listofvideos=True)
if self.nwb.GetStringSelection() == "Yes":
deeplabcut.analyze_videos_converth5_to_nwb(
self.config,
self.filelist,
listofvideos=True,
)
if self.trajectory.GetStringSelection() == "Yes":
if self.showfigs.GetStringSelection() == "No":
showfig = False
else:
showfig = True
deeplabcut.plot_trajectories(self.config,
self.filelist,
self.videotype.GetValue(),
showfigures=showfig)
else:
scorername = deeplabcut.analyze_videos(
self.config,
self.filelist,
self.videotype.GetValue(),
shuffle=shuffle,
gputouse=None,
save_as_csv=save_as_csv,
cropping=crop,
dynamic=dynamic,
)
if _filter:
deeplabcut.filterpredictions(
self.config,
self.filelist,
self.videotype.GetValue(),
shuffle=shuffle,
filtertype="median",
windowlength=5,
save_as_csv=save_as_csv,
)
if self.trajectory.GetStringSelection() == "Yes":
if self.showfigs.GetStringSelection() == "No":
showfig = False
else:
showfig = True
deeplabcut.plot_trajectories(
self.config,
self.filelist,
self.videotype.GetValue(),
displayedbodyparts=self.bodyparts,
shuffle=shuffle,
filtered=_filter,
showfigures=showfig,
)
new_video_path = deeplabcut.ShortenVideo(
video_path,
start="00:00:00",
stop="00:00:01",
outsuffix="short",
outpath=os.path.join(cfg["project_path"], "videos"),
)
print("Analyzing video...")
deeplabcut.analyze_videos(config_path, [new_video_path], "mov", robust_nframes=True)
print("Video analyzed.")
print("Create video with all detections...")
scorer, _ = auxiliaryfunctions.GetScorerName(cfg, 1, TRAIN_SIZE)
deeplabcut.create_video_with_all_detections(config_path, [new_video_path], scorer)
print("Video created.")
edgewisecondition = True
print("Convert detections...")
deeplabcut.convert_detections2tracklets(
config_path,
[new_video_path],
"mov",
track_method="box",
edgewisecondition=edgewisecondition,
)
deeplabcut.convert_detections2tracklets(
config_path, [new_video_path], "mov", track_method="skeleton"
)
import os
os.environ["DLClight"] = "True"
import sys
sys.path.append('/home/jma819/DeepLabCut/')
import deeplabcut
#path_config_file = '/projects/p30771/dlc_analysis/GRIN_rotarod_rearview-JJM-2020-04-02/config.yaml'
path_config_file = sys.argv[1]
behav_cam_directory = sys.argv[2]
avi_list = [
os.path.join(dp, f)
for dp, dn, fn in os.walk(os.path.expanduser(behav_cam_directory))
for f in fn if f.endswith('.avi')
]
scorername = 'DLC_resnet50_rotarod_side_view_multi_animalAug25shuffle1_200000'
deeplabcut.create_video_with_all_detections(path_config_file,
avi_list,
DLCscorername=scorername)
print('finished creating labeled videos!')
# os.environ["DLClight"]="True"
import deeplabcut
config_path = '/home/felix/biteOscope/aedes-bbb-2020-11-16_05/config.yaml'
videos = glob.glob('/home/felix/test/WT*cropTrim.mp4')
# # #
print('analyzing ' + str(len(videos)) + ' videos:')
print(*videos, sep = "\n")
# # #
# # #
deeplabcut.analyze_videos(config_path, videos, shuffle=1, batchsize=8, dynamic=(True, 0.5, 100))
# # # # #
deeplabcut.create_video_with_all_detections(config_path, videos, 'DLC_resnet50_aedesNov16shuffle1_80000')
# # #
# deeplabcut.convert_detections2tracklets(config_path, videos, shuffle=1, videotype='mp4', trainingsetindex=0, track_method='box')
# # #
# # # #
# trackletPickles = glob.glob('/home/felix/Dropbox/lisaBaik/*_bx.pickle')
# #
# print('Converting ' + str(len(trackletPickles)) + ' tracklets:')
# print(trackletPickles)
# #
# for tracklet in trackletPickles:
# deeplabcut.convert_raw_tracks_to_h5(config_path, tracklet)
#
# deeplabcut.create_labeled_video(config_path, videos, videotype='.mp4', track_method='box', filtered=False, color_by='individual')
print('ZzzZzz.. Sleeping for', sleeptime, 'seconds.. zzzZZzzZz')
sleep(sleeptime)
edits = {'snapshotindex': snapshotindex}
dlc.auxiliaryfunctions.edit_config(config_path, edits)
print('\nediting the config file... ')
for item in edits.items():
print(item)
print('edit completed!')
dlc.create_video_with_all_detections(
config=config_path,
videos=videos_path_list[SLURM_ARRAY_TASK_ID:SLURM_ARRAY_TASK_ID + 1],
shuffle=shuffleindex,
trainingsetindex=0,
displayedbodyparts='all',
destfolder=None,
modelprefix='',
)
print("dlc_create_video_with_all_detections.py with the call", str(sys.argv),
"is done!")
print("returning snapshotindex back to 'all'...!")
edits = {'snapshotindex': 'all'}
dlc.auxiliaryfunctions.edit_config(config_path, edits)
print("snapshotindex is set back to 'all'")
