预测验证码

Author: makelove

ch46-机器学习-K近邻/2-使用kNN对手写数字OCR.py

@@ -43,9 +43,10 @@
 accuracy = correct * 100.0 / result.size
 print('准确率', accuracy)  # 准确率91.76%
 
-''''''
+
 # save the data
 np.savez('knn_data.npz', train=train, train_labels=train_labels,test=test,test_labels=test_labels)
+
 # Now load the data
 with np.load('knn_data.npz') as data:
     print(data.files)
@@ -56,5 +57,8 @@
 
 
 #TODO 怎样预测数字？
-retval, results=knn.predict(test[3:5])
+retval, results=knn.predict(test[1003:1005])
 # Docstring: predict(samples[, results[, flags]]) -> retval, results
+print(retval, results)#(4.0, array([[ 4.],[ 4.]], dtype=float32))
+#对比
+cv2.imwrite('test[1005].jpg',test[1005].reshape((20,20)))

ch46-机器学习-K近邻/2.png

@@ -19,6 +19,28 @@
     test = data['test']
     test_labels = data['test_labels']
 
+print('加载KNN,数据')
 knn = cv2.ml.KNearest_create()
 knn.train(train, cv2.ml.ROW_SAMPLE, train_labels)
-ret, result, neighbours, dist = knn.findNearest(test, k=5)
+ret, result, neighbours, dist = knn.findNearest(test, k=5)
+
+# 加载相片
+print('加载相片')
+img2 = cv2.imread('2.png', 0)
+gray2 = cv2.resize(img2, (20, 20))
+# gray2=gray2.reshape((400,))
+gray21 = gray2.reshape((-1, 400)).astype(np.float32)
+
+img6 = cv2.imread('6.png', 0)
+gray6 = cv2.resize(img6, (20, 20))
+# gray2=gray2.reshape((400,))
+gray61 = gray6.reshape((-1, 400)).astype(np.float32)
+
+g2 = np.append(gray21, gray61)
+g3 = g2.reshape((2, 400))
+
+# 预测
+retval, results = knn.predict(g3)
+print(retval, results)  # 不准确
+# (0.0, array([[ 0.],
+#         [ 5.]], dtype=float32))