def get_humans_from_dataset(dataset, path=None):
videos_processed = {}
outfile = osp.curdir() if path is None else path
print('processing videos wait...')
process_dataset(dataset, videos_processed, outfile)
outfile = osp.join(outfile, 'positions.npz')
np.savez(outfile, **videos_processed)
print('the file is saved in ', outfile)
def change_player_list():
player_list_path = join(curdir(), 'data', 'player_lists')
list_choices = [
f for f in listdir(player_list_path)
if isfile(join(player_list_path, f))
]
for i in range(len(list_choices)):
print('{:3}- {}'.format(i + 1,
list_choices[i][:-4].replace('_',
' ').title()))
choice = san.sanitize_int("Choose the player list you would like to use: ",
1, len(list_choices))
return list_choices[choice - 1][:-4]
def _default_output_directory(data_source, default_output_dirname):
source_dir = path.dirname(data_source)
default_dir = path.join(source_dir, default_output_dirname)
if not path.isdir(default_dir):
try:
mkdir(default_dir)
except OSError:
print(
'could not create default directory {}. Using current directory instead'
.format(default_dir))
default_dir = path.curdir()
return default_dir
def get_humans_from_dataset(dataset, path=None):
from tf_pose.estimator import TfPoseEstimator
from tf_pose.networks import get_graph_path
e = TfPoseEstimator(get_graph_path('cmu'), target_size=(432, 368))
videos_processed = {}
outfile = osp.curdir() if path is None else path
print('processing videos wait...')
for video in dataset:
video_name = video[1]['class']+'_' + \
video[1]['consultant']+'_'+video[1]['repetition']
try:
videos_processed[video_name] = process_video(video[0], e)
except InferenceError as ie:
videos_processed[video_name] = ie.args
error_handle(ie.args, outfile, video_name)
outfile = osp.join(outfile, 'dataset_humans.npz')
np.savez(outfile, **videos_processed)
print('the file is saved in ', outfile)