Merge pull request #5 from nicewang/original-draft

cmt-20230412-1: +cnn_new_gray +rnn_new_gray

cnn_new_gray/.gitignore

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+.idea
+**/*.txt
+**/*.png
+**/*.jpg
+**/model
+**/*.local

cnn_new_gray/cnn_test1.py

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+# -*- coding: utf-8 -*-
+from skimage import io,transform
+import tensorflow as tf
+from travel_path import *
+import sys
+
+os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
+
+signal_dict = {0:'正常无风舞信号',1:'异常有风舞信号'}
+
+w = 5
+h = 50
+c = 1
+
+# 测试集地址
+path = 'labeledTimeFreq/test_set/'
+files = travel_txt(path)
+
+data = []
+labels = []
+for i in range(files.shape[0]):
+    data_tmp = np.genfromtxt(files[i])
+    data_tmp1 = data_tmp[:250,:]
+    for j in range(data_tmp1.shape[1]):
+        data_tmp2 = data_tmp1[:,j]
+        data_tmp2 = data_tmp2.reshape(5, 50)
+        b = np.zeros((5, 50, 1))
+        for i1 in range(5):
+            for j1 in range(50):
+                b[i1, j1, 0] = data_tmp2[i1, j1]
+                # b[i1, j1, 1] = data_tmp2[i1, j1]
+                # b[i1, j1, 2] = data_tmp2[i1, j1]
+        # img = transform.resize(b,(10,24,1))
+        img = b
+        data.append(img)
+    label_tmp1 = data_tmp[250,:]
+    label_tmp1 = label_tmp1.reshape(1,label_tmp1.shape[0])
+    # if data == []:
+    #     data = data_tmp1
+    # else:
+    #     data = np.column_stack((data, data_tmp1))
+    if labels == []:
+        labels = label_tmp1
+    else:
+        labels = np.column_stack((labels, label_tmp1))
+
+data = np.asarray(data,np.float32)
+label = labels.reshape(labels.shape[0]*labels.shape[1],)
+# print(data.shape)
+# print(label.shape)
+
+with tf.Session() as sess:
+    saver = tf.train.import_meta_graph('model/model.ckpt-' + sys.argv[1] + '.meta')
+    saver.restore(sess,tf.train.latest_checkpoint('model/'))
+
+    graph = tf.get_default_graph()
+    x = graph.get_tensor_by_name("x:0")
+    feed_dict = {x:data}
+
+    logits = graph.get_tensor_by_name("logits_eval:0")
+
+    classification_result = sess.run(logits,feed_dict)
+
+    #打印出预测矩阵
+    # print(classification_result)
+    #打印出预测矩阵每一行最大值的索引
+    # print(tf.argmax(classification_result,1).eval())
+    #根据索引通过字典对应花的分类
+    output = []
+    output = tf.argmax(classification_result,1).eval()
+    count = 0
+    count_abnormal = 0
+    total_abnormal = 0
+    for i in range(len(output)):
+        # print("第",i+1,"帧信号识别:"+signal_dict[output[i]])
+        if label[i] == 1:
+            total_abnormal = total_abnormal + 1
+        if output[i] == label[i]:
+            count = count + 1
+            if label[i] == 1:
+                count_abnormal = count_abnormal + 1
+    print(count_abnormal)
+    print(total_abnormal)
+    print(count)
+    # print("信号识别准确率：",count/label.shape[0])
+    # print("异常信号识别准确率：",count_abnormal/total_abnormal)
+    # 记录下每一步的损失函数和训练之后的准确率
+    # if os.path.exists('test_acc/test acc in every step training model.txt'):
+    #     data_tmp = np.genfromtxt('test_acc/test acc in every step training model.txt')
+    #     data_tmp = np.array([data_tmp])
+    #     if data_tmp.shape != (1,):
+    #         data_tmp = data_tmp.reshape(data_tmp.shape[0]*data_tmp.shape[1],1)
+    #     data_i = np.array([[count/label.shape[0]]],dtype=np.float64)
+    #     data = np.row_stack((data_tmp, data_i))
+    #     np.savetxt('test_acc/test acc in every step training model.txt', data)
+    # else:
+    #     data = np.array([count/label.shape[0]],dtype=np.float64)
+    #     np.savetxt('test_acc/test acc in every step training model.txt', data)
+    # if os.path.exists('test_acc/test abnormal acc in every step training model.txt'):
+    #     data_tmp = np.genfromtxt('test_acc/test abnormal acc in every step training model.txt')
+    #     data_tmp = np.array([data_tmp])
+    #     if data_tmp.shape != (1,):
+    #         data_tmp = data_tmp.reshape(data_tmp.shape[0]*data_tmp.shape[1],1)
+    #     data_i = np.array([[count_abnormal/total_abnormal]],dtype=np.float64)
+    #     data = np.row_stack((data_tmp, data_i))
+    #     np.savetxt('test_acc/test abnormal acc in every step training model.txt', data)
+    # else:
+    #     data = np.array([count_abnormal/total_abnormal],dtype=np.float64)
+    #     np.savetxt('test_acc/test abnormal acc in every step training model.txt', data)
+    if os.path.exists('test_acc/count.txt'):
+        data_tmp = np.genfromtxt('test_acc/count.txt')
+        data_tmp = np.array([data_tmp])
+        if data_tmp.shape != (1,):
+            data_tmp = data_tmp.reshape(data_tmp.shape[0]*data_tmp.shape[1],1)
+        data_i = np.array([[count]])
+        data = np.row_stack((data_tmp, data_i))
+        np.savetxt('test_acc/count.txt', data)
+    else:
+        data = np.array([count])
+        np.savetxt('test_acc/count.txt', data)
+    if os.path.exists('test_acc/count_abnormal.txt'):
+        data_tmp = np.genfromtxt('test_acc/count_abnormal.txt')
+        data_tmp = np.array([data_tmp])
+        if data_tmp.shape != (1,):
+            data_tmp = data_tmp.reshape(data_tmp.shape[0]*data_tmp.shape[1],1)
+        data_i = np.array([[count_abnormal]])
+        data = np.row_stack((data_tmp, data_i))
+        np.savetxt('test_acc/count_abnormal.txt', data)
+    else:
+        data = np.array([count_abnormal])
+        np.savetxt('test_acc/count_abnormal.txt', data)