def __init__(self, project, step):
self.project = Path(project)
self.project_status = status.read_status(project)
if DEBUG:
status.show_status(self.project)
self.cfg = util.read_yaml(self.project / 'config.yaml')
self.auto = 1 # For a while loop with all the commands later
self.step = step # Could be auto or any of the steps
self.steps = [
"Created", "CheckDirConfig", "CropAdjust", "FrameExtraction",
"Annotated", "TrainingLabelsDrawn", "LabelsCollected", "Shuffled",
"DeepCutReady", "Training", "Evaluated", "Inference"
]
self.extracommands = ["GetImgSet"]
def step(project):
# TODO: Allow returning to specific points in the wizard without having to edit status.yml
project = Path(project)
project_status = status.read_status(project)
if DEBUG:
status.show_status(project)
cfg = util.read_yaml(project / 'config.yaml')
# Control for succesful project creation
# -------------------------------------------------------------------------------------------
if 'Created' not in project_status: # sanity check
_echo(
"Whoopsie, this ain't Kansas no more! Something went terribly wrong during project creation."
)
else:
_echo('Project creation OK')
# Check that dataset list in config file and in data directory match
# -------------------------------------------------------------------------------------------
data_path = project / 'data'
datasets = [
d for d in data_path.glob('*')
if d.is_dir() and 'labeled' not in d.name
]
if len(datasets) != len(cfg['image_sets']):
Warning(
'Dataset directory not matching configuration file image set list.'
)
# a lot of string wrangling just to have the output not be too confusing...
print(
'Config:', ''.join(
['\n\t' + k['img_path'] for k in cfg['image_sets'].values()]))
print('data :',
''.join(['\n\t' + '/'.join(k.parts[-2:]) for k in datasets]))
print('')
# TODO: Update command to add new image sets to existing project
# Check cropping configuration for videos
# -------------------------------------------------------------------------------------------
if 'CropAdjust' not in project_status or not project_status['CropAdjust']:
_echo('Checking video cropping configuration...')
# Create directory for image sets
if not len(cfg['image_sets']):
print('No videos given to create image sets. Stopping.')
return
for video, metadata in cfg['image_sets'].items():
video_path = project / video
img_path = project / 'data' / video_path.with_suffix('').name
img_path.mkdir(exist_ok=True)
# check cropping
crop = list(map(int, metadata['crop'].split(',')))
extract.crop_examples(video_path, crop, img_path)
_echo('Check cropping of "{}" in "{}"'.format(video, img_path))
util.update_yaml(project / 'status.yaml', {'CropAdjust': True})
_echo(
'Check image set directories for original and cropped example frames and '
'adjust as needed.\n---------------------------------------------------'
)
return
else:
_echo('Crop adjustment OK')
# Extract images from all videos
# -------------------------------------------------------------------------------------------
if 'FrameExtraction' not in project_status or not project_status[
'FrameExtraction']:
_echo('Next up: Creating image sets...')
num_frames = max(2, int(cfg['num_frames']) // len(cfg['image_sets']))
# Loop over all video files specified in the project configuration
# Total number of frames is evenly divided between the videos.
# Does not take length of the video into account... worse, a very short
# video might have duplicates extracted!
# TODO: Distribute extracted frames according to video length
seed = int(cfg['random_seed']) #+ n
extract.extract_frames(cfg['image_sets'],
video_root=project,
num_frames=num_frames,
destination=project / 'data/ExtractedFrames.h5',
seed=seed)
# for n, (video, metadata) in enumerate(cfg['image_sets'].items()):
# Path.mkdir(project / metadata['img_path'])
# video_path = Path(video)
# print('Extracting {} frames with seed {} from {}'.format(num_frames, seed, video_path.name))
util.update_yaml(project / 'status.yaml', {'FrameExtraction': True})
print(
'\nYou can now annotate frames in e.g. ImageJ and store the results in the '
'image set directories as `Results.csv`.')
return
else:
_echo('Frame Extraction OK')
# User annotation of joint position in image set
# -------------------------------------------------------------------------------------------
if 'Annotated' not in project_status or not project_status['Annotated']:
do_labeling = input('Run labeling GUI? [Y/n]')
if not do_labeling in ['N', 'n']:
GUI_utils.run_labeler(cfg, root=project)
util.update_yaml(project / 'status.yaml', {'Annotated': True})
_echo('Minimized {} image set label file(s).'.format(
len(cfg['image_sets'])))
else:
# TODO: Check existence of csv files
_echo('Image set annotations OK')
# # Draw labels on images for verification
# # -------------------------------------------------------------------------------------------
# if 'TrainingLabelsDrawn' not in project_status or not project_status['TrainingLabelsDrawn']:
# _echo('Drawing labels on images in data sets for verification...')
# for n, (video, metadata) in enumerate(cfg['image_sets'].items()):
# print(video,metadata)
# label.draw_image_labels(project / metadata['img_path'] / 'multijoint.csv', cfg['joints'],
# cmap_name=cfg['cmap'] if 'cmap' in cfg else None)
# util.update_yaml(project / 'status.yaml', {'TrainingLabelsDrawn': True})
# _echo('Labels drawn. Check labeled images in image set directories')
# return
# else:
# _echo('Drawing Labels OK')
# # Join csv files of all image sets
# # -------------------------------------------------------------------------------------------
# if 'LabelsCollected' not in project_status or not project_status['LabelsCollected']:
# _echo('Preparing combined label file...')
# label.combine_labels(project / 'data')
# util.update_yaml(project / 'status.yaml', {'LabelsCollected': True})
# else:
# _echo('Label Collection OK')
# # Create shuffles
# # -------------------------------------------------------------------------------------------
# if 'Shuffled' not in project_status or not project_status['Shuffled']:
# _echo('Shuffling and splitting training set')
# for n in trange(cfg['num_shuffles'], leave=False):
# num_frames = int(cfg['num_frames'])
# f_train = float(cfg['train_fraction'])
# num_train = int(num_frames * f_train)
# num_testing = num_frames - num_train
# shuffle_name = 'shuffle{:03d}_{:.0f}pct-{}'.format(n, 100 * f_train, cfg['experimenter'])
# tqdm.write(shuffle_name)
# labels_csv = project / 'data' / 'joint_labels.csv'
# shuffle_path = project / 'shuffles' / shuffle_name
# # Create shuffle, training and test directories
# _ = [shuffle_path.joinpath(d).mkdir(exist_ok=True) for d in ['', 'train', 'test']]
# # Shuffle all images from the labeled example set with the specified fraction of training
# # to test images. The result is a list of labels for chosen images in a .mat or .csv file
# # to be used during training
# shuffle.shuffle(csv_file=labels_csv, train_fraction=f_train, destination=shuffle_path, joints=cfg['joints'],
# boundary=cfg['boundary'])
# tqdm.write('Shuffling #{} w/ {}:{} train and test images'.format(n, num_train, num_testing))
# util.update_yaml(project / 'status.yaml', {'Shuffled': True})
# else:
# _echo('Shuffling OK')
# # Create directories and configuration files for pose-tensorflow (DeeperCut)
# # -------------------------------------------------------------------------------------------
# if 'DeepCutReady' not in project_status or not project_status['DeepCutReady']:
# _echo('Training preparation...')
# # Create training and testing directories for each shuffle
# shuffles = [d.resolve() for d in project.joinpath('shuffles').glob('*') if d.is_dir()]
# if not len(shuffles):
# util.update_yaml(project / 'status.yaml', {'Shuffled': False})
# print('No training sets found. Rerun wizard!')
# for shuffle_path in shuffles:
# _echo(str(shuffle_path))
# # Create training yaml
# train_set_path = shuffle_path / 'training.mat'
# if not train_set_path.resolve().exists():
# raise FileNotFoundError('Could not find "training.mat" file for shuffle "{}"'.format(shuffle_path))
# joints = cfg['joints']
# items2change = {'dataset': '../training.mat',
# "num_joints": len(joints),
# "all_joints": [[i] for i in range(len(joints))],
# "all_joints_names": joints}
# resource_package = __name__ # Could be any module/package name
# resource_path = '/'.join(('..', 'resources', 'templates', 'training_pose_cfg.yaml'))
# pose_cfg_template = pkg_resources.resource_filename(resource_package, resource_path)
# # Create configuration yaml for training, and keep configuration data for the test configuration
# trainingdata = shuffle.training_pose_yaml(pose_cfg_template, items2change,
# shuffle_path / 'train' / 'pose_cfg.yaml')
# # Keys to keep for the test configuration yaml
# keys2save = ['dataset', 'num_joints', 'all_joints', 'all_joints_names', 'net_type', 'init_weights',
# 'global_scale', 'location_refinement', 'locref_stdev']
# shuffle.test_pose_yaml(trainingdata, keys2save, shuffle_path / 'test/pose_cfg.yaml')
# util.update_yaml(project / 'status.yaml', {'DeepCutReady': True})
# else:
# _echo('Training prep OK')
# Training
# -------------------------------------------------------------------------------------------
if 'Trained' not in project_status or not project_status['Trained']:
_echo('Start training...')
shuffles = [
d.resolve() for d in project.joinpath('shuffles').glob('*')
if d.is_dir()
]
for shfl in shuffles:
cfg_yaml_path = shfl / 'train/pose_cfg.yaml'
_echo('Training starting for: {}'.format(cfg_yaml_path))
training.train(cfg_yaml_path)
return
else:
_echo('Training completed (to some degree')
# Evaluation
if 'Evaluated' not in project_status or not project_status['Evaluated']:
_echo('Evaluation trained models')
return
else:
_echo('Eavluation complete')
# Inference
if 'ReadyForUse' not in project_status or not project_status['ReadForUse']:
_echo('What is left to do?')
return
else:
_echo('Use me!')
def read_status(path):
path = Path(path)
yaml_path = path / 'status.yaml' if path.is_dir() else path
return read_yaml(yaml_path)