--- a
+++ b/LoadBatches1D.py
@@ -0,0 +1,57 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Apr 21 13:46:44 2019
+
+@author: Winham
+
+LoadBatches1D.py: 迭代生成训练时的batch
+
+实现参考：https://github.com/divamgupta/image-segmentation-keras/blob/master/LoadBatches.py
+
+"""
+
+import os
+import itertools
+import numpy as np
+from sklearn import preprocessing as prep
+
+
+def getSigArr(path, sigNorm='scale'):
+    sig = np.load(path)
+    if sigNorm == 'scale':
+        sig = prep.scale(sig)
+    elif sigNorm == 'minmax':
+        min_max_scaler = prep.MinMaxScaler()
+        sig = min_max_scaler.fit_transform(sig)
+    return np.expand_dims(sig, axis=1)
+
+
+def getSegmentationArr(path, nClasses=3, output_length=1800, class_value=[0, 0.5, 1]):
+    # class_value是在generate_labels.py中定义的，背景0，正常0.5，早搏1，必须要保持一致
+    seg_labels = np.zeros([output_length, nClasses])
+    seg = np.load(path)
+    for i in range(nClasses):
+        seg_labels[:, i] = (seg == class_value[i]).astype(float)
+    return seg_labels
+
+
+def SigSegmentationGenerator(sigs_path, segs_path, batch_size, n_classes, output_length=1800):
+    sigs = os.listdir(sigs_path)
+    segmentations = os.listdir(segs_path)
+    sigs.sort()
+    segmentations.sort()
+    for i in range(len(sigs)):
+        sigs[i] = sigs_path + sigs[i]
+        segmentations[i] = segs_path + segmentations[i]
+    assert len(sigs) == len(segmentations)
+    for sig, seg in zip(sigs, segmentations):
+        assert (sig.split('/')[-1].split(".")[0] == seg.split('/')[-1].split(".")[0])
+    zipped = itertools.cycle(zip(sigs, segmentations))
+    while True:
+        X = []
+        Y = []
+        for _ in range(batch_size):
+            sig, seg = next(zipped)
+            X.append(getSigArr(sig))
+            Y.append(getSegmentationArr(seg, n_classes, output_length))
+        yield np.array(X), np.array(Y)