def localizer_train(ibs, species_list=None, **kwargs):
from pydarknet import Darknet_YOLO_Detector
data_path = ibs.export_to_xml(species_list=species_list, **kwargs)
output_path = join(ibs.get_cachedir(), 'training', 'localizer')
ut.ensuredir(output_path)
dark = Darknet_YOLO_Detector()
model_path = dark.train(data_path, output_path)
del dark
return model_path
def train_part_detector():
"""
Problem:
healthy sharks usually have a mostly whole body shot
injured sharks usually have a close up shot.
This distribution of images is likely what the injur-shark net is picking up on.
The goal is to train a detector that looks for things that look
like the distribution of injured sharks.
We will run this on healthy sharks to find the parts of
"""
import wbia
ibs = wbia.opendb('WS_ALL')
imgset = ibs.imagesets(text='Injured Sharks')
injured_annots = imgset.annots[0] # NOQA
# config = {
# 'dim_size': (224, 224),
# 'resize_dim': 'wh'
# }
from pydarknet import Darknet_YOLO_Detector
data_path = ibs.export_to_xml()
output_path = join(ibs.get_cachedir(), 'training', 'localizer')
ut.ensuredir(output_path)
dark = Darknet_YOLO_Detector()
results = dark.train(data_path, output_path)
del dark
localizer_weight_path, localizer_config_path, localizer_class_path = results
classifier_model_path = ibs.classifier_train()
labeler_model_path = ibs.labeler_train()
output_path = join(ibs.get_cachedir(), 'training', 'detector')
ut.ensuredir(output_path)
ut.copy(localizer_weight_path, join(output_path, 'localizer.weights'))
ut.copy(localizer_config_path, join(output_path, 'localizer.config'))
ut.copy(localizer_class_path, join(output_path, 'localizer.classes'))
ut.copy(classifier_model_path, join(output_path, 'classifier.npy'))
ut.copy(labeler_model_path, join(output_path, 'labeler.npy'))