def main(params, debug_export=DEBUG_EXPORT):
""" the main entry point
:param dict params: segmentation parameters
:param bool debug_export: whether export visualisations
"""
logging.getLogger().setLevel(logging.DEBUG)
params = tl_expt.create_experiment_folder(
params, dir_name=NAME_EXPERIMENT, stamp_unique=EACH_UNIQUE_EXPERIMENT)
tl_expt.set_experiment_logger(params['path_exp'])
logging.info(tl_expt.string_dict(params, desc='PARAMETERS'))
# tl_expt.create_subfolders(params['path_exp'], [FOLDER_IMAGE])
df_paths = pd.read_csv(params['path_list'], index_col=0)
logging.info('loaded %i items with columns: %r', len(df_paths),
df_paths.columns.tolist())
df_paths.dropna(how='any', inplace=True)
# create sub-folders if required
tl_expt.create_subfolders(params['path_exp'], ['input', 'simple'])
dict_segment = create_dict_segmentation(params, None, None, None, None)
dirs_center = [n + DIR_CENTRE_POSIX for n in dict_segment]
dirs_visu = [n + DIR_VISUAL_POSIX for n in dict_segment]
tl_expt.create_subfolders(params['path_exp'], [n for n in dict_segment] +
dirs_center + dirs_visu)
if debug_export:
list_dirs = [n + DIR_DEBUG_POSIX for n in dict_segment if 'rg2sp' in n]
tl_expt.create_subfolders(params['path_exp'], list_dirs)
_wrapper_segment = partial(image_segmentation, params=params)
iterate = tl_expt.WrapExecuteSequence(_wrapper_segment,
df_paths.iterrows(),
nb_workers=params['nb_workers'])
list(iterate)
def main(params):
""" PIPELINE for new detections
:param dict params:
"""
params['path_expt'] = os.path.join(
params['path_output'], run_detect.FOLDER_EXPERIMENT % params['name'])
tl_expt.set_experiment_logger(params['path_expt'])
# tl_expt.create_subfolders(params['path_expt'], LIST_SUBDIRS)
logging.info(tl_expt.string_dict(params, desc='PARAMETERS'))
path_csv = os.path.join(params['path_expt'], NAME_CSV_TRIPLES)
df_paths = run_detect.get_csv_triplets(params['path_list'], path_csv,
params['path_images'],
params['path_segms'],
params['path_centers'],
FORCE_RELOAD)
df_eval = df_paths.copy(deep=True)
for stage in params['stages']:
df_eval = evaluate_detection_stage(df_eval, stage,
params['path_infofile'],
params['path_expt'],
params['nb_workers'])
if not df_eval.empty and 'image' in df_eval.columns:
df_eval.set_index('image', inplace=True)
df_eval.to_csv(os.path.join(params['path_expt'],
NAME_CSV_TRIPLES_STAT))
gc.collect()
time.sleep(1)
if not df_eval.empty:
df_stat = df_eval.describe().transpose()
logging.info('STATISTIC: \n %r', df_stat)
df_stat.to_csv(os.path.join(params['path_expt'], NAME_CSV_STATISTIC))
def main(params):
""" the main body containgn two approches:
1) segment each image indecently
2) estimate model over whole image sequence and estimate
:param dict params:
:return dict:
"""
logging.getLogger().setLevel(logging.DEBUG)
show_visual = params.get('visual', False)
reload_dir_config = os.path.isfile(params['path_config']) or FORCE_RELOAD
stamp_unique = params.get('unique', EACH_UNIQUE_EXPERIMENT)
params = tl_expt.create_experiment_folder(params,
dir_name=NAME_EXPERIMENT,
stamp_unique=stamp_unique,
skip_load=reload_dir_config)
tl_expt.set_experiment_logger(params['path_exp'])
logging.info(tl_expt.string_dict(params, desc='PARAMETERS'))
tl_expt.create_subfolders(params['path_exp'], LIST_FOLDERS_BASE)
if show_visual:
tl_expt.create_subfolders(params['path_exp'], LIST_FOLDERS_DEBUG)
paths_img = load_path_images(params)
assert paths_img, 'missing images'
def _path_expt(n):
return os.path.join(params['path_exp'], n)
# Segment as single model per image
path_visu = _path_expt(FOLDER_SEGM_GMM_VISU) if show_visual else None
dict_segms_gmm = experiment_single_gmm(params,
paths_img,
_path_expt(FOLDER_SEGM_GMM),
path_visu,
show_debug_imgs=show_visual)
gc.collect()
time.sleep(1)
# Segment as model ober set of images
if params.get('run_groupGMM', False):
path_visu = _path_expt(FOLDER_SEGM_GROUP_VISU) if show_visual else None
dict_segms_group = experiment_group_gmm(params,
paths_img,
_path_expt(FOLDER_SEGM_GROUP),
path_visu,
show_debug_imgs=show_visual)
else:
write_skip_file(_path_expt(FOLDER_SEGM_GROUP))
# write_skip_file(_path_expt(FOLDER_SEGM_GROUP_VISU))
dict_segms_group = None
if dict_segms_group is not None:
df_ars = compare_segms_metric_ars(dict_segms_gmm,
dict_segms_group,
suffix='_gmm-group')
df_ars.to_csv(_path_expt(NAME_CSV_ARS_CORES))
logging.info(df_ars.describe())
return params
def main_predict(path_classif,
path_pattern_imgs,
path_out,
name='SEGMENT___',
params_local=None):
""" given trained classifier segment new images
:param str path_classif:
:param str path_pattern_imgs:
:param str path_out:
:param str name:
"""
logging.getLogger().setLevel(logging.INFO)
logging.info('running PREDICTION...')
assert path_pattern_imgs is not None
dict_classif = seg_clf.load_classifier(path_classif)
classif = dict_classif['clf_pipeline']
params = dict_classif['params']
if params_local is not None:
params.update({
k: params_local[k]
for k in params_local
if k.startswith('path_') or k.startswith('gc_')
})
path_out, path_visu = prepare_output_dir(path_pattern_imgs,
path_out,
name,
visual=params.get(
'visual', False))
tl_expt.set_experiment_logger(path_out)
logging.info(tl_expt.string_dict(params, desc='PARAMETERS'))
paths_img = sorted(glob.glob(path_pattern_imgs))
logging.info('found %i images on path "%s"', len(paths_img),
path_pattern_imgs)
logging.debug('run prediction...')
show_debug_imgs = params.get('visual', False)
_wrapper_segment = partial(
try_segment_image,
params=params,
classif=classif,
path_out=path_out,
path_visu=path_visu,
show_debug_imgs=show_debug_imgs,
)
list_img_path = list(zip([None] * len(paths_img), paths_img))
iterate = tl_expt.WrapExecuteSequence(
_wrapper_segment,
list_img_path,
nb_workers=params['nb_workers'],
desc='segmenting images',
)
for _ in iterate:
gc.collect()
time.sleep(1)
logging.info('prediction DONE')
def main(params):
""" PIPELINE for new detections
:param {str: str} params:
"""
params = run_train.prepare_experiment_folder(params, FOLDER_EXPERIMENT)
# run_train.check_pathes_patterns(paths)
tl_expt.set_experiment_logger(params['path_expt'])
logging.info('COMPUTER: \n%r', platform.uname())
logging.info(tl_expt.string_dict(params, desc='PARAMETERS'))
tl_expt.create_subfolders(params['path_expt'], LIST_SUBFOLDER)
path_csv = os.path.join(params['path_expt'], NAME_CSV_TRIPLES)
df_paths = get_csv_triplets(params['path_list'],
path_csv,
params['path_images'],
params['path_segms'],
force_reload=FORCE_RERUN)
dict_classif = seg_clf.load_classifier(params['path_classif'])
params_clf = dict_classif['params']
params_clf.update(params)
logging.info(tl_expt.string_dict(params, desc='UPDATED PARAMETERS'))
# perform on new images
df_stat = pd.DataFrame()
_wrapper_detection = partial(
load_compute_detect_centers,
params=params_clf,
path_classif=params['path_classif'],
path_output=params['path_expt'],
)
iterate = tl_expt.WrapExecuteSequence(_wrapper_detection,
df_paths.iterrows(),
nb_workers=params['nb_workers'])
for dict_center in iterate:
df_stat = df_stat.append(dict_center, ignore_index=True)
df_stat.to_csv(os.path.join(params['path_expt'],
NAME_CSV_TRIPLES_TEMP))
df_stat.set_index(['image'], inplace=True)
df_stat.to_csv(os.path.join(params['path_expt'], NAME_CSV_TRIPLES))
logging.info('STATISTIC: \n %r', df_stat.describe())
def main_train(params):
""" PIPELINE for training
0) load triplets or create triplets from path to images, annotations
1) load precomputed data or compute them now
2) train classifier with hyper-parameters
3) perform Leave-One-Out experiment
:param {str: any} params:
"""
params = prepare_experiment_folder(params, FOLDER_EXPERIMENT)
tl_expt.set_experiment_logger(params['path_expt'])
logging.info(tl_expt.string_dict(params, desc='PARAMETERS'))
tl_expt.save_config_yaml(
os.path.join(params['path_expt'], NAME_YAML_PARAMS), params)
tl_expt.create_subfolders(params['path_expt'], LIST_SUBDIRS)
df_paths, _ = load_df_paths(params)
path_dump_data = os.path.join(params['path_expt'], NAME_DUMP_TRAIN_DATA)
if not os.path.isfile(path_dump_data) or FORCE_RECOMP_DATA:
(dict_imgs, dict_segms, dict_slics, dict_points, dict_centers,
dict_features, dict_labels, feature_names) = \
dataset_load_images_segms_compute_features(params, df_paths, params['nb_workers'])
assert len(dict_imgs) > 0, 'missing images'
save_dump_data(
path_dump_data,
dict_imgs,
dict_segms,
dict_slics,
dict_points,
dict_centers,
dict_features,
dict_labels,
feature_names,
)
else:
(dict_imgs, dict_segms, dict_slics, dict_points, dict_centers,
dict_features, dict_labels,
feature_names) = load_dump_data(path_dump_data)
if is_drawing(params['path_expt']) and EXPORT_TRAINING_DATA:
export_dataset_visual(params['path_expt'], dict_imgs, dict_segms,
dict_slics, dict_points, dict_labels,
params['nb_workers'])
# concentrate features, labels
features, labels, sizes = seg_clf.convert_set_features_labels_2_dataset(
dict_features,
dict_labels,
drop_labels=[-1],
balance_type=params['balance'])
# remove all bad values from features space
features[np.isnan(features)] = 0
features[np.isinf(features)] = -1
assert np.sum(sizes) == len(labels), \
'not equal sizes (%d) and labels (%i)' \
% (int(np.sum(sizes)), len(labels))
# feature norm & train classification
nb_holdout = int(np.ceil(len(sizes) * CROSS_VAL_LEAVE_OUT_SEARCH))
cv = seg_clf.CrossValidateGroups(sizes, nb_holdout)
classif, params[
'path_classif'] = seg_clf.create_classif_search_train_export(
params['classif'],
features,
labels,
cross_val=cv,
params=params,
feature_names=feature_names,
nb_search_iter=params['nb_classif_search'],
pca_coef=params.get('pca_coef', None),
nb_workers=params['nb_workers'],
path_out=params['path_expt'],
)
nb_holdout = int(np.ceil(len(sizes) * CROSS_VAL_LEAVE_OUT_EVAL))
cv = seg_clf.CrossValidateGroups(sizes, nb_holdout)
seg_clf.eval_classif_cross_val_scores(params['classif'],
classif,
features,
labels,
cross_val=cv,
path_out=params['path_expt'])
seg_clf.eval_classif_cross_val_roc(params['classif'],
classif,
features,
labels,
cross_val=cv,
path_out=params['path_expt'])
if RUN_LEAVE_ONE_OUT:
experiment_loo(classif, dict_imgs, dict_segms, dict_centers,
dict_slics, dict_points, dict_features, feature_names,
params)
def main_train(params):
""" the main composed from following steps:
1) load already computed data (features and labels) or compute them now
2) visualise labeled superpixels aka annotation
3) load or train classifier with hyper-parameters search
4) perform Leave-One-Out and Leave-P-Out experiments on images
:param {str: ...} params:
:return {str: ...}:
"""
logging.getLogger().setLevel(logging.DEBUG)
logging.info('running TRAINING...')
show_visual = params.get('visual', False)
reload_dir_config = (os.path.isfile(params.get('path_config', ''))
or FORCE_RELOAD)
params = tl_expt.create_experiment_folder(params,
dir_name=NAME_EXPERIMENT,
stamp_unique=params.get(
'unique',
EACH_UNIQUE_EXPERIMENT),
skip_load=reload_dir_config)
tl_expt.set_experiment_logger(params['path_exp'])
logging.info(tl_expt.string_dict(params, desc='PARAMETERS'))
tl_expt.create_subfolders(params['path_exp'], LIST_FOLDERS_BASE)
if show_visual:
tl_expt.create_subfolders(params['path_exp'], LIST_FOLDERS_DEBUG)
df_stat = pd.DataFrame()
path_dump = os.path.join(params['path_exp'], NAME_DUMP_TRAIN_DATA)
if os.path.isfile(path_dump) and not FORCE_RECOMP_DATA:
dict_imgs, dict_annot, dict_slics, dict_features, dict_labels, \
dict_label_hist, feature_names = load_dump_data(path_dump)
else:
dict_imgs, dict_annot, dict_slics, dict_features, dict_labels, \
dict_label_hist, feature_names = \
dataset_load_images_annot_compute_features(params,
show_visual)
save_dump_data(path_dump, dict_imgs, dict_annot, dict_slics,
dict_features, dict_labels, dict_label_hist,
feature_names)
assert len(dict_imgs) > 1, 'training require at least 2 images'
dict_annot_slic = {
n: np.asarray(dict_labels[n])[dict_slics[n]]
for n in dict_annot
}
df = eval_segment_with_annot(params, dict_annot, dict_annot_slic,
dict_label_hist,
NAME_CSV_SEGM_STAT_SLIC_ANNOT,
params.get('drop_labels',
None), params['nb_jobs'])
df_stat = df_stat.append(get_summary(df, 'SLIC-annot'), ignore_index=True)
path_csv_stat = os.path.join(params['path_exp'],
NAME_CSV_SEGM_STAT_RESULTS)
df_stat.set_index(['name']).to_csv(path_csv_stat)
if params['gc_use_trans']:
params['label_transitions'] = \
seg_gc.count_label_transitions_connected_segments(dict_slics,
dict_labels)
logging.info('summary on edge-label transitions: \n %s',
repr(params['label_transitions']))
path_purity_visu = os.path.join(params['path_exp'], FOLDER_SLIC_ANNOT) \
if show_visual else None
dict_labels = filter_train_with_purity(dict_imgs,
dict_labels,
dict_label_hist,
params['label_purity'],
dict_slics,
drop_labels=params.get(
'drop_labels', None),
path_visu=path_purity_visu,
nb_jobs=params['nb_jobs'])
logging.info('prepare features...')
# concentrate features, labels
features, labels, sizes = seg_clf.convert_set_features_labels_2_dataset(
dict_features,
dict_labels,
balance_type=params['balance'],
drop_labels=[-1, np.nan] + params.get('drop_labels', []))
# drop "do not care" label which are -1
features = np.nan_to_num(features)
nb_holdout = params.get('cross_val', CROSS_VAL_LEAVE_OUT_SEARCH)
nb_holdout = max(1, int(round(len(sizes) * nb_holdout))) # minimum is 1
nb_holdout = min(nb_holdout, int(len(sizes) / 2)) # max is half of the set
params, classif, path_classif = load_train_classifier(
params, features, labels, feature_names, sizes, nb_holdout)
def _path_expt(n):
return os.path.join(params['path_exp'], n)
# test classif. on training images
df_paths = pd.read_csv(params['path_train_list'], index_col=0)
df_paths.reset_index(inplace=True)
paths_img = df_paths['path_image'].tolist()
if RUN_TRAIN_PREDICT:
perform_train_predictions(params,
paths_img,
classif,
show_debug_imgs=show_visual)
else:
write_skip_file(_path_expt(FOLDER_TRAIN))
gc.collect()
time.sleep(1)
# LEAVE P OUT
if params.get('run_LPO', True):
idx_paths_img = list(
zip(df_paths.index.tolist(), df_paths['path_image'].tolist()))
df_stat = experiment_lpo(params,
df_stat,
dict_annot,
idx_paths_img,
path_classif,
path_dump,
nb_holdout,
show_debug_imgs=show_visual)
else:
write_skip_file(_path_expt(FOLDER_LPO))
# write_skip_file(_path_expt(FOLDER_LPO_VISU))
logging.info('Statistic: \n %s', repr(df_stat.describe()))
logging.info('training DONE')
return params