--- a
+++ b/drunet/performance.py
@@ -0,0 +1,148 @@
+import os
+import time
+import math
+import pathlib
+from functools import reduce
+from collections import Counter
+
+import utils
+import cv2 as cv
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+import matplotlib.pyplot as plt
+
+
+def prepro_image(img_path, img_resize, threshold=128):
+    image = cv.imread(img_path, 0)
+    if len(image.shape) != 2:
+        image = cv.cvtColor(image, cv.COLOR_RGB2GRAY)
+    image = cv.resize(image, img_resize)
+    _, bin_image = cv.threshold(image, threshold, 255, cv.THRESH_BINARY)
+    bin_image = np.array(bin_image / 255, dtype=np.int)
+    return bin_image
+
+
+def true_positive(pred, gt):
+    assert pred.shape == gt.shape
+    tp_bool = np.logical_and(pred, gt)
+    tp_int = np.array(tp_bool, dtype=np.int)
+    tp = np.sum(tp_int)
+    return tp
+
+
+def true_negative(pred, gt):
+    assert pred.shape == gt.shape
+    no_pred = np.array(np.logical_not(pred), dtype=np.int)
+    no_gt = np.array(np.logical_not(gt), dtype=np.int)
+    tn = true_positive(no_pred, no_gt)
+    return tn
+
+
+def false_positive(pred, gt):
+    assert pred.shape == gt.shape
+    no_gt = np.array(np.logical_not(gt), dtype=np.int)
+    fp = true_positive(pred, no_gt)
+    return fp
+
+
+def false_negative(pred, gt):
+    assert pred.shape == gt.shape
+    no_pred = np.array(np.logical_not(pred), dtype=np.int)
+    fn = true_positive(no_pred, gt)
+    return fn
+
+
+def f1_score(precision, recall):
+    return (2 * precision * recall) / (precision + recall)
+
+
+def calc_performance(pred_path, gt_path, img_resize, threshold=128):
+    """ Used to calculate the difference between the segmented image and Ground Truth for statistical prediction
+        to evaluate the performance of the model, but currently only gray-scale images can be calculated
+     :param pred_path: path of predicted image
+     :param gt_path: real mask path
+     :param img_resize: the size to resize the image
+     :param threshold: Used to binaries images
+     :return: pix-accuracy, precision, recall, VOE, RVD, Dice, IOU evaluation indicators
+     """
+    total_pix = reduce(lambda x, y: x * y, img_resize)
+    pred_image = prepro_image(pred_path, img_resize, threshold)
+    mask_image = prepro_image(gt_path, img_resize, threshold)
+
+    tp = true_positive(pred_image, mask_image)
+    tn = true_negative(pred_image, mask_image)
+    fp = false_positive(pred_image, mask_image)
+    fn = false_negative(pred_image, mask_image)
+
+    accuracy = (tp + tn) / total_pix
+    precision = tp / (tp + fp + 1e-10)
+    recall = tp / (tp + fn + 1e-10)
+    iou = tp / (tp + fp + fn + 1e-10)
+    dice = 2 * tp / (fn + tp + tp + fp + 1e-10)
+    voe = 1 - tp / (tp + fn + fp + 1e-10)
+    rvd = (fp - fn) / (fn + tp + 1e-10)
+    specificity = tn / (tn + fp + 1e-10)
+    return tp, tn, fp, fn, accuracy, precision, recall, iou, dice, voe, rvd, specificity
+
+
+def save_performance_to_csv(pred_dir, gt_dir, img_resize, csv_save_name, csv_save_path='', threshold=128):
+    gt_paths, gt_names = utils.get_path(gt_dir)
+    pred_paths, pred_names = utils.get_path(pred_dir)
+
+    record_pd = pd.DataFrame(columns=[
+        'pred_name', 'gt_name', 'TP', 'FP', 'FN', 'TN',
+        'accuracy', 'precision', 'recall', 'IOU', 'DICE', 'VOE', 'RVD', 'specificity',
+    ])
+
+    total_file_nums = len(gt_paths)
+    for file_index in tqdm(range(total_file_nums), total=total_file_nums):
+        TP, TN, FP, FN, accuracy, precision, recall, IOU, DICE, VOE, RVD, specificity = calc_performance(
+            pred_paths[file_index], gt_paths[file_index], img_resize, threshold)
+
+        record_pd = record_pd.append({
+            'pred_name': pred_names[file_index],
+            'gt_name': gt_names[file_index],
+            'accuracy': accuracy,
+            'precision': precision,
+            'recall': recall,
+            'specificity': specificity,
+            'IOU': IOU,
+            'DICE': DICE,
+            'VOE': VOE,
+            'RVD': RVD,
+            'TP': TP, 'FP': FP, 'FN': FN, 'TN': TN
+        }, ignore_index=True)
+
+    record_pd.to_csv(
+        os.path.join(csv_save_path, '{}.csv'.format(csv_save_name)), index=True, header=True)
+
+    m_accuracy, m_precision, m_recall, m_iou, m_dice, m_voe, m_rvd, m_spec = analysis_performance(
+        os.path.join(csv_save_path, '{}.csv'.format(csv_save_name)))
+    analysis_pd = pd.DataFrame(columns=[
+        'm_accu', 'm_prec', 'm_recall', 'm_iou', 'm_dice', 'm_voe', 'm_rvd', 'm_spec'
+    ])
+    analysis_pd = analysis_pd.append({
+        'm_accu': m_accuracy, 'm_prec': m_precision, 'm_recall': m_recall, 'm_iou': m_iou,
+        'm_dice': m_dice, 'm_voe': m_voe, 'm_rvd': m_rvd, 'm_spec': m_spec,
+    }, ignore_index=True)
+    analysis_pd.to_csv(
+        os.path.join(csv_save_path, 'analysis_{}.csv'.format(csv_save_name)), index=True, header=True)
+    return m_dice, m_iou, m_precision, m_recall
+
+
+def analysis_performance(csv_file_path):
+    data_frame = pd.read_csv(csv_file_path, header=None)
+
+    m_accuracy = np.mean(np.array(data_frame.loc[1:, 7], dtype=np.float32))
+    m_precision = np.mean(np.array(data_frame.loc[1:, 8], dtype=np.float32))
+    m_recall = np.mean(np.array(data_frame.loc[1:, 9], dtype=np.float32))
+    m_iou = np.mean(np.array(data_frame.loc[1:, 10], dtype=np.float32))
+    m_dice = np.mean(np.array(data_frame.loc[1:, 11], dtype=np.float32))
+    m_voe = np.mean(np.array(data_frame.loc[1:, 12], dtype=np.float32))
+    m_rvd = np.mean(np.array(data_frame.loc[1:, 13], dtype=np.float32))
+    m_spec = np.mean(np.array(data_frame.loc[1:, 14], dtype=np.float32))
+    print(
+        ' accuracy: {},\n precision: {},\n recall: {},\n iou: {},\n dice: {},\n voe: {},\n rvd: {},\n spec: {}.\n'.format(
+            m_accuracy, m_precision, m_recall, m_iou, m_dice, m_voe, m_rvd, m_spec))
+    return m_accuracy, m_precision, m_recall, m_iou, m_dice, m_voe, m_rvd, m_spec