重新整理文件夹结构，并添加CNN训练代码。

tools/TrainCNN/generate.py

@@ -0,0 +1,49 @@
+import numpy as np
+import os
+import cv2
+import random
+from forward import OUTPUT_NODES
+
+
+class DataSet:
+    def __init__(self, folder):
+        self.train_sets = []
+        self.test_sets = []
+        self.generate_data_sets(folder)
+
+    def generate_data_sets(self, folder):
+        def file2nparray(name):
+            image = cv2.imread(name)
+            return image[:, :, 0]
+
+        def id2label(id):
+            a = np.zeros([OUTPUT_NODES, 1])
+            a[id] = 1
+            return a[:]
+
+        sets = []
+        for i in range(OUTPUT_NODES):
+            dir = "%s/%d" % (folder, i)
+            files = os.listdir(dir)
+            for file in files:
+                sets.append([file2nparray("%s/%s" % (dir, file)), id2label(i)])
+        sets = np.array(sets)
+        np.random.shuffle(sets)
+        length = len(sets)
+        self.train_sets = sets[:length*3//4]
+        self.test_sets = sets[length*3//4:]
+
+    def sample_train_sets(self, length):
+        samples = []
+        labels = []
+        for i in range(length):
+            id = random.randint(0, length-1)
+            samples.append(self.train_sets[id][0])
+            labels.append(self.train_sets[id][1])
+        return np.array(samples), np.array(labels)
+
+    def all_train_sets(self):
+        return self.train_sets[:, 0, :, :], self.train_sets[:, 1, :, :]
+
+    def all_test_sets(self):
+        return self.test_sets[:, 0, :, :], self.test_sets[:, 1, :, :]