'model_file': os.path.join(model_dir, 'Xception-004-0.984.hdf5'),
'input_shape': (299, 299, 3),
'model_weight': 1
}
dicts_models.append(dict_model1)
filename_csv = os.path.join(dir_dest, 'LaserSpot_predict_dir.csv')
if GEN_CSV:
os.makedirs(os.path.dirname(filename_csv), exist_ok=True)
write_csv_dir_nolabel(filename_csv, dir_preprocess)
df = pd.read_csv(filename_csv)
all_files, all_labels = get_images_labels(filename_csv_or_pd=df)
prob_total, y_pred_total, prob_list, pred_list = \
do_predict(dicts_models, filename_csv, argmax=True)
import pickle
os.makedirs(os.path.dirname(pkl_prob), exist_ok=True)
with open(pkl_prob, 'wb') as file:
pickle.dump(prob_total, file)
if COMPUTE_DIR_FILES:
op_files_multiclass(filename_csv,
prob_total,
dir_preprocess=dir_preprocess,
dir_dest=dir_dest,
dir_original=dir_original,
keep_subdir=True)
print('OK')
# prob_total = pickle.load(pkl_file)
if COMPUTE_CONFUSIN_MATRIX:
(cf_list, not_match_list, cf_total, not_match_total) = \
my_confusion_matrix.compute_confusion_matrix(prob_list, dir_dest_confusion,
all_files, all_labels, dir_preprocess=dir_crop_optic_disc, dir_original=dir_original)
if not os.path.exists(os.path.dirname(pkl_confusion_matrix)):
os.makedirs(os.path.dirname(pkl_confusion_matrix))
with open(pkl_confusion_matrix, 'wb') as file:
pickle.dump(cf_total, file)
if COMPUTE_DIR_FILES:
my_multi_class.op_files_multiclass(filename_csv,
prob_total,
dir_preprocess=dir_crop_optic_disc,
dir_dest=dir_dest_predict_dir,
dir_original=dir_original,
keep_subdir=True)
print('OK')
'''
confusion
train:
[1102,73]
[46,956]
validation:
[203,12]
[9,161]
'''
