def init_new_project(project,
videos,
working_directory=None,
videotype='.mp4'):
from datetime import datetime as dt
from vame.util import auxiliary
date = dt.today()
month = date.strftime("%B")
day = date.day
year = date.year
d = str(month[0:3] + str(day))
date = dt.today().strftime('%Y-%m-%d')
if working_directory == None:
working_directory = '.'
wd = Path(working_directory).resolve()
project_name = '{pn}-{date}'.format(pn=project, date=d + '-' + str(year))
project_path = wd / project_name
if project_path.exists():
print('Project "{}" already exists!'.format(project_path))
return
video_path = project_path / 'videos'
data_path = project_path / 'data'
results_path = project_path / 'results'
model_path = project_path / 'model'
for p in [video_path, data_path, results_path, model_path]:
p.mkdir(parents=True)
print('Created "{}"'.format(p))
vids = []
for i in videos:
#Check if it is a folder
if os.path.isdir(i):
vids_in_dir = [
os.path.join(i, vp) for vp in os.listdir(i) if videotype in vp
]
vids = vids + vids_in_dir
if len(vids_in_dir) == 0:
print("No videos found in", i)
print(
"Perhaps change the videotype, which is currently set to:",
videotype)
else:
videos = vids
print(len(vids_in_dir), " videos from the directory", i,
"were added to the project.")
else:
if os.path.isfile(i):
vids = vids + [i]
videos = vids
videos = [Path(vp) for vp in videos]
video_names = []
dirs_data = [data_path / Path(i.stem) for i in videos]
for p in dirs_data:
"""
Creates directory under data
"""
p.mkdir(parents=True, exist_ok=True)
video_names.append(p.stem)
dirs_results = [results_path / Path(i.stem) for i in videos]
for p in dirs_results:
"""
Creates directory under results
"""
p.mkdir(parents=True, exist_ok=True)
destinations = [video_path.joinpath(vp.name) for vp in videos]
os.mkdir(str(project_path) + '/' + 'videos/pose_estimation/')
os.mkdir(str(project_path) + '/model/pretrained_model')
print("Copying the videos \n")
for src, dst in zip(videos, destinations):
shutil.copy(os.fspath(src), os.fspath(dst))
cfg_file, ruamelFile = auxiliary.create_config_template()
cfg_file
cfg_file['Project'] = str(project)
cfg_file['project_path'] = str(project_path) + '/'
cfg_file['test_fraction'] = 0.1
cfg_file['video_sets'] = video_names
cfg_file['all_data'] = 'yes'
cfg_file['load_data'] = '-PE-seq-clean'
cfg_file['anneal_function'] = 'linear'
cfg_file['batch_size'] = 256
cfg_file['max_epochs'] = 500
cfg_file['transition_function'] = 'GRU'
cfg_file['beta'] = 1
cfg_file['zdims'] = 30
cfg_file['learning_rate'] = 5e-4
cfg_file['time_window'] = 30
cfg_file['prediction_decoder'] = 1
cfg_file['prediction_steps'] = 15
cfg_file['model_convergence'] = 50
cfg_file['model_snapshot'] = 50
cfg_file['num_features'] = 12
cfg_file['savgol_filter'] = True
cfg_file['savgol_length'] = 5
cfg_file['savgol_order'] = 2
cfg_file['hidden_size_layer_1'] = 256
cfg_file['hidden_size_layer_2'] = 256
cfg_file['dropout_encoder'] = 0
cfg_file['hidden_size_rec'] = 256
cfg_file['dropout_rec'] = 0
cfg_file['hidden_size_pred'] = 256
cfg_file['dropout_pred'] = 0
cfg_file['kl_start'] = 2
cfg_file['annealtime'] = 4
cfg_file['mse_reconstruction_reduction'] = 'sum'
cfg_file['mse_prediction_reduction'] = 'sum'
cfg_file['kmeans_loss'] = cfg_file['zdims']
cfg_file['kmeans_lambda'] = 0.1
cfg_file['scheduler'] = 1
cfg_file['length_of_motif_video'] = 1000
cfg_file['noise'] = False
cfg_file['scheduler_step_size'] = 100
cfg_file['legacy'] = False
cfg_file['individual_parameterization'] = False
cfg_file['random_state_kmeans'] = 42
cfg_file['n_init_kmeans'] = 15
cfg_file['model_name'] = 'VAME'
cfg_file['n_cluster'] = 15
cfg_file['pretrained_weights'] = False
cfg_file['pretrained_model'] = 'None'
cfg_file['min_dist'] = 0.1
cfg_file['n_neighbors'] = 200
cfg_file['random_state'] = 42
cfg_file['num_points'] = 30000
cfg_file['scheduler_gamma'] = 0.2
cfg_file['softplus'] = False
cfg_file['pose_confidence'] = 0.99
projconfigfile = os.path.join(str(project_path), 'config.yaml')
# Write dictionary to yaml config file
auxiliary.write_config(projconfigfile, cfg_file)
print('A VAME project has been created. \n')
print(
'Now its time to prepare your data for VAME. '
'The first step is to move your pose .csv file (e.g. DeepLabCut .csv) into the '
'//YOUR//VAME//PROJECT//videos//pose_estimation folder. From here you can call '
'either the function vame.egocentric_alignment() or if your data is by design egocentric '
'call vame.csv_to_numpy(). This will prepare the data in .csv into the right format to start '
'working with VAME.')
return projconfigfile
def init_new_project(project,
videos,
working_directory=None,
videotype='.mp4'):
from datetime import datetime as dt
from vame.util import auxiliary
date = dt.today()
month = date.strftime("%B")
day = date.day
year = date.year
d = str(month[0:3] + str(day))
date = dt.today().strftime('%Y-%m-%d')
if working_directory == None:
working_directory = '.'
wd = Path(working_directory).resolve()
project_name = '{pn}-{date}'.format(pn=project, date=d + '-' + str(year))
project_path = wd / project_name
if project_path.exists():
print('Project "{}" already exists!'.format(project_path))
return
video_path = project_path / 'videos'
data_path = project_path / 'data'
results_path = project_path / 'results'
model_path = project_path / 'model'
for p in [video_path, data_path, results_path, model_path]:
p.mkdir(parents=True)
print('Created "{}"'.format(p))
vids = []
for i in videos:
#Check if it is a folder
if os.path.isdir(i):
vids_in_dir = [
os.path.join(i, vp) for vp in os.listdir(i) if videotype in vp
]
vids = vids + vids_in_dir
if len(vids_in_dir) == 0:
print("No videos found in", i)
print(
"Perhaps change the videotype, which is currently set to:",
videotype)
else:
videos = vids
print(len(vids_in_dir), " videos from the directory", i,
"were added to the project.")
else:
if os.path.isfile(i):
vids = vids + [i]
videos = vids
videos = [Path(vp) for vp in videos]
video_names = []
dirs_data = [data_path / Path(i.stem) for i in videos]
for p in dirs_data:
"""
Creates directory under data
"""
p.mkdir(parents=True, exist_ok=True)
video_names.append(p.stem)
dirs_results = [results_path / Path(i.stem) for i in videos]
for p in dirs_results:
"""
Creates directory under results
"""
p.mkdir(parents=True, exist_ok=True)
destinations = [video_path.joinpath(vp.name) for vp in videos]
os.mkdir(str(project_path) + '/' + 'videos/pose_estimation/')
os.mkdir(str(project_path) + '/model/pretrained_model')
print("Copying the videos \n")
for src, dst in zip(videos, destinations):
shutil.copy(os.fspath(src), os.fspath(dst))
cfg_file, ruamelFile = auxiliary.create_config_template()
cfg_file
cfg_file['Project'] = str(project)
cfg_file['project_path'] = str(project_path) + '/'
cfg_file['test_fraction'] = 0.2
cfg_file['video_sets'] = video_names
cfg_file['all_data'] = 'yes'
cfg_file['load_data'] = '-PE-seq-clean'
cfg_file['snapshot'] = 'no'
cfg_file['snapshot_epoch'] = 50
cfg_file['median_filter'] = 5
cfg_file['anneal_function'] = 'linear'
cfg_file['batch_size'] = 256
cfg_file['max_epochs'] = 500
cfg_file['transition_function'] = 'GRU'
cfg_file['beta'] = 1
cfg_file['zdims'] = 30
cfg_file['learning_rate'] = 5e-4
cfg_file['time_window'] = 30
cfg_file['prediction_decoder'] = 1
cfg_file['prediction_steps'] = 15
cfg_file['model_convergence'] = 50
cfg_file['model_snapshot'] = 50
cfg_file['num_features'] = 12
cfg_file['savgol_filter'] = True
cfg_file['savgol_length'] = 5
cfg_file['savgol_order'] = 2
cfg_file['hidden_size_layer_1'] = 256
cfg_file['hidden_size_layer_2'] = 256
cfg_file['dropout_encoder'] = 0
cfg_file['hidden_size_rec'] = 256
cfg_file['dropout_rec'] = 0
cfg_file['hidden_size_pred'] = 256
cfg_file['dropout_pred'] = 0
cfg_file['kl_start'] = 3
cfg_file['annealtime'] = 8
cfg_file['mse_reconstruction_reduction'] = 'sum'
cfg_file['mse_prediction_reduction'] = 'sum'
cfg_file['kmeans_loss'] = cfg_file['zdims']
cfg_file['kmeans_lambda'] = 0.1
cfg_file['scheduler'] = 1
cfg_file['lenght_of_motif_video'] = 1000
projconfigfile = os.path.join(str(project_path), 'config.yaml')
# Write dictionary to yaml config file
auxiliary.write_config(projconfigfile, cfg_file)
print(
'A VAME project has been created. \n'
'\n'
'Next use vame.create_trainset(config) to split your data into a train and test set. \n'
'Afterwards you can use vame.rnn_model() to train the model on your data.'
)
return projconfigfile