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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (c) 2022 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
#
import os
import shutil
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from collections import defaultdict
def copy_files_src_to_tgt(samples, fns_dict, src_folder, tgt_folder):
for sample in samples:
files_to_copy = fns_dict.get(sample)
for _file in files_to_copy:
src_fn = os.path.join(src_folder, _file)
tgt_fn = os.path.join(tgt_folder, _file)
shutil.copy2(src_fn, tgt_fn)
def split_images(src_folder, tgt_folder):
labels = os.listdir(src_folder)
print("Number of labels = ", len(labels))
print("Labels are: \n", labels)
for label in labels:
fns = os.listdir(os.path.join(src_folder, label))
fns.sort()
fns_root = ['_'.join(x.split('_')[:2]) for x in fns]
# Convert list of tuples to dictionary value lists
print("\nCreating default dict for stratifying the subject in {}.".format(label))
fns_dict = defaultdict(list)
for i, j in zip(fns_root, fns):
fns_dict[i].append(j)
train_samples, test_samples = train_test_split(list(fns_dict.keys()), test_size=0.2, random_state=100)
src_dir = os.path.join(src_folder, label)
tgt_dir = os.path.join(tgt_folder, 'train', label)
os.makedirs(tgt_dir, exist_ok=True)
copy_files_src_to_tgt(train_samples, fns_dict, src_dir, tgt_dir)
tgt_dir = os.path.join(tgt_folder, 'test', label)
os.makedirs(tgt_dir, exist_ok=True)
copy_files_src_to_tgt(test_samples, fns_dict, src_dir, tgt_dir)
print("Done splitting the files for label = {}\n".format(label))
print("Done splitting the data. Output data is here: ", tgt_folder)
def get_subject_id(image_name):
image_name = image_name.split("/")[-1]
patient_id = "".join(image_name.split("_")[:2])[1:]
return patient_id
def create_patient_id_list(image_data_folder, folder):
folder_pth = os.path.join(folder, image_data_folder)
patient_id_list = []
for fldr in os.listdir(folder_pth):
for f in os.listdir(os.path.join(folder_pth, fldr)):
patient_id_list.append(get_subject_id(f))
return np.unique(patient_id_list)
def read_annotation_file(
folder,
file_name,
label_column,
data_column,
patient_id,
patient_id_list,
image_data_folder
):
df_path = os.path.join(folder, file_name)
df = pd.read_csv(df_path)
label_map, reverse_label_map = label2map(df, label_column)
if patient_id_list is not None:
df = df[df[patient_id].isin(patient_id_list)]
else:
image_name_list = []
for label in os.listdir(image_data_folder):
image_name_list.extend(os.listdir(os.path.join(image_data_folder, label)))
df = df[df[patient_id].isin(np.unique([get_subject_id(i) for i in image_name_list]))]
df_new = pd.DataFrame(columns=[label_column, data_column, patient_id])
for i in df[patient_id].unique():
annotation = " ".join(df[df[patient_id].isin([i])][data_column].to_list())
temp_labels = df[df[patient_id] == i][label_column].unique()
if len(temp_labels) == 1:
df_new.loc[len(df_new)] = [temp_labels[0], annotation, i]
else:
if patient_id_list is not None:
# this is the case only shows for inference
# label assigne as a place holder
df_new.loc[len(df_new)] = ["Normal", annotation, i]
else:
Warning("Conflict in labelling ....")
return df_new, label_map, reverse_label_map
def label2map(df, label_column):
label_map, reverse_label_map = {}, {}
for i, v in enumerate(df[label_column].unique().tolist()):
label_map[v] = i
reverse_label_map[i] = v
return label_map, reverse_label_map
def create_train_test_set(df, patient_id, patient_id_list):
train_label, test_label = train_test_split(
patient_id_list, test_size=0.33, random_state=42
)
df_test = df[df[patient_id].isin(test_label)]
df_train = df[df[patient_id].isin(train_label)]
return df_train, df_test
def split_annotation(folder, file_name, image_data_folder):
label_column = "label"
data_column = "symptoms"
patient_id = "Patient_ID"
patient_id_list = None
df, label_map, reverse_label_map = read_annotation_file(
folder,
file_name,
label_column,
data_column,
patient_id,
patient_id_list,
image_data_folder
)
patient_id_list = create_patient_id_list(image_data_folder, folder)
df_train, df_test = create_train_test_set(df, patient_id, patient_id_list)
return df_train
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