def create_histogrames(path=None):
log_path = data_functions.create_path(LOG_PATH, 'extract_logs')
configure_logger(name="stem_extractor",
console_level='INFO',
file_level='INFO',
out_path=log_path)
logger = logging.getLogger(__name__)
try:
params_dict = data_functions.load_json(path)
stem_extract.create_test_train_obj(**params_dict)
except:
message = 'Error loading Model'
logger.exception(message)
raise Exception
def train_unet(params_dict_path, src_path, update_dict_path, steps):
log_path = data_functions.create_path(LOG_PATH, 'unet_logs')
configure_logger(name="cherry_stem",
console_level='INFO',
file_level='INFO',
out_path=log_path)
logger = logging.getLogger(__name__)
try:
if params_dict_path is not None:
if src_path is not None:
message = 'Ambiguous loading paths, input either' \
' "params_dict_path or "src_path", not both '
raise UnetModelException(message)
else:
params_dict = data_functions.load_json(params_dict_path)
model = unet_model_functions.ClarifruitUnet(**params_dict)
else:
if update_dict_path is not None:
update_dict = data_functions.load_json(update_dict_path)
else:
update_dict = None
model = unet_model_functions.ClarifruitUnet.load_model(
src_path, update_dict, steps)
keras_logs_path = model.set_model_for_train()
logger.info(f"for tensorboard use \n "
f"tensorboard --logdir={keras_logs_path}")
model.fit_unet()
except:
message = 'Error loading Model'
logger.exception(message)
raise Exception
import os
from work.unet import unet_model_functions
from keras.callbacks import ReduceLROnPlateau
from work.auxiliary import data_functions
from work.auxiliary.logger_settings import configure_logger
import logging
LOG_PATH = os.path.abspath('logs')
DATA_PATH = os.path.abspath('data')
log_path = data_functions.create_path(LOG_PATH, 'unet_logs')
configure_logger(name="cherry_stem",
console_level='INFO',
file_level='INFO',
out_path=log_path)
logger = logging.getLogger(__name__)
def main():
"""
for model debugging
:return:
"""
# train_path = os.path.join(DATA_PATH, r'raw_data\with_maskes')
#train_path = os.path.join(DATA_PATH, r'segmentation\augmented')
train_path = os.path.join(DATA_PATH, r'segmentation\augmented')
dest_path = os.path.join(DATA_PATH, r'unet_data\training')
from work.stem_classifier import classify
from work.auxiliary import data_functions
import os
from work.stem_classifier.model_functions import *
from work.auxiliary.logger_settings import configure_logger
LOG_PATH = os.path.abspath('logs')
DATA_PATH = os.path.abspath('data')
log_path = data_functions.create_path(LOG_PATH, 'classifier')
configure_logger(name="classifier",
console_level='INFO',
file_level='INFO',
out_path=log_path)
logger = logging.getLogger(__name__)
def func():
train_path = r'D:\Clarifruit\cherry_stem\data\classification_data\from_all\set1\train'
dest_path = r'D:\Clarifruit\cherry_stem\data\unet_data\training\2019-10-07_20-12-39'
test_path = r'D:\Clarifruit\cherry_stem\data\classification_data\from_all\set1\test'
params_dict = dict(train_path=train_path,
data_gen_args=dict(rescale=1. / 255,
rotation_range=180,
width_shift_range=0.25,
height_shift_range=0.25,
shear_range=0.2,
zoom_range=[0.5, 1.0],
from work.auxiliary import data_functions
import cv2
from work.segmentation import segmentation
import os
from work.auxiliary.logger_settings import configure_logger
import logging
LOG_PATH = os.path.abspath('logs')
DATA_PATH = os.path.abspath('data')
log_path = data_functions.create_path(LOG_PATH, 'segmentation_logs')
configure_logger(name="segmentation",
console_level='INFO',
file_level='INFO',
out_path=log_path)
logger = logging.getLogger(__name__)
def main():
orig_path = os.path.join(DATA_PATH, r'raw_data\with_maskes\image')
#orig_path = os.path.join(DATA_PATH, r'segmentation\src2')
mask_path = os.path.join(DATA_PATH,r'raw_data\with_maskes\label')
dest_path = os.path.join(DATA_PATH,r'raw_data\with_maskes\label-augmented')
# mask_path = os.path.join(DATA_PATH,
# r'unet_data\training\2019-10-20_19-26-14\raw_pred')
# dest_path = os.path.join(DATA_PATH,
# r'unet_data\training\2019-10-20_19-26-14')
from work.auxiliary.logger_settings import configure_logger
from work.auxiliary import data_functions
import os
import logging
LOG_PATH = os.path.abspath('logs')
DATA_PATH = os.path.abspath('data')
log_path = data_functions.create_path(LOG_PATH, 'stem_extract')
configure_logger(name="stem_extract",
console_level='INFO',
file_level='INFO',
out_path=log_path)
logger = logging.getLogger(__name__)
def get_test_train():
"""
use the src_path to create a test train split of data seperated to clases,
where each class has it's own folder.
save the test and train results in dest_path
:return:
"""
src_path = os.path.join(DATA_PATH,
r'classification_data\from_all\set3\All')
dest_path = os.path.join(DATA_PATH, r'classification_data\from_all\set3')
data_functions.get_train_test_split(src_path,
dest_path,
train_name='train',