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CV2识别图片A,B,C,D

2019年10月11日  | 移动技术网科技  | 我要评论

舒淇早期作品,波兰足球队,花都十二钗

这个模型是经过简单训练，只能识别四个英文字母：abcd，是我为了提取答案才训练的，因为科大，百度，以及腾讯的识别不是很准，所以自己训练自己使用，希望能够给你带来方便。下面是训练好的model,如多你感觉图片的类型相似，可直接使用。

1、图片形式，尽量png

2、模型

如果需要可以加我qq：1851999452

 

3、

  1 import os
  2 import numpy as np
  4 import cv2
  5 import matplotlib.pyplot as plt
  6 import tensorflow as tf
  7 import keras
  8 from keras.models import model,load_model
  9 from keras.layers import conv2d,dense,activation,input,maxpooling2d,flatten
 10 from sklearn.model_selection import train_test_split
 11 # file_dir = '/home/youxue/bord'
 12 # b = []
 13 # a =[]
 14 # c =[]
 15 # label_a=[]
 16 # label_c=[]
 17 # label_b = []
 18 # d = []
 19 # label_d = []
 20 # for file in os.listdir(file_dir):
 21 #     if file == 'a':
 22 #         for img in os.listdir(os.path.join(file_dir, file)):
 23 #             a.append(file_dir + '/' + file + '/' + img)
 24 #             label_a.append(0)
 25 #     elif file == 'b':
 26 #         for img in os.listdir(os.path.join(file_dir, file)):
 27 #             b.append(file_dir + '/' + file + '/' + img)
 28 #             label_b.append(1)
 29 #     elif file == 'c':
 30 #         for img in os.listdir(os.path.join(file_dir, file)):
 31 #             c.append(file_dir + '/' + file + '/' + img)
 32 #             label_c.append(2)
 33 #     else:
 34 #         for img in os.listdir(os.path.join(file_dir, file)):
 35 #             d.append(file_dir + '/' + file + '/' + img)
 36 #             label_d.append(3)
 37 #
 38 # print('there are %d b\n there are %d d' % (len(b), len(d)))
 39 #
 40 # image_list = np.hstack((a,b,c,d))
 41 # label_list = np.hstack((label_a,label_b,label_c, label_d))
 42 # temp = np.array([image_list, label_list])
 43 # temp = temp.transpose()
 44 #
 45 #
 46 # np.random.shuffle(temp)
 47 # png_list=[]
 48 # image_list = list(temp[:, 0])
 49 # label_list = list(temp[:, 1])
 50 # label_list = [int(i) for i in label_list]
 51 # for png in image_list:
 52 #     png_list.append(cv2.imread(png))
 53 # print(png_list[100].shape)
 54 # png = np.array(png_list)
 55 # label = np.array(label_list)
 56 # x_train,x_test,y_train,y_test=train_test_split(png,label,test_size=0.1)
 57 #
 58 # # plt.imshow(x_train[11])
 59 # # plt.show()
 60 #
 61 # x_train = x_train/255.
 62 # x_test = x_test/255.
 63 #
 64 # y_train = np.eye(4)[y_train.reshape(-1)]
 65 # y_test = np.eye(4)[y_test.reshape(-1)]
 66 # print(x_train.shape,y_train.shape)
 67 
 68 # input_shape= (24,810,3)
 69 # input_data = input(input_shape)
 70 # layer1 = conv2d(32,(3,3),padding='same',activation='relu')(input_data)
 71 # layer2 = maxpooling2d(pool_size=(2,2))(layer1)
 72 # layer3 = conv2d(32,(3,3),padding='same',activation='relu')(layer2)
 73 # layer4 = maxpooling2d(pool_size=(2,2))(layer3)
 74 # layer5 = conv2d(32,(3,3),padding='same',activation='relu')(layer4)
 75 # layer6 = flatten()(layer5)
 76 # layer7 = dense(4)(layer6)
 77 # pred= activation('softmax')(layer7)
 78 # model= model(input=input_data,output=pred)
 79 # model.summary()
 80 # model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['accuracy'])
 81 # model.fit(x_train,y_train,batch_size=32,epochs=20,validation_data=(x_test,y_test))
 82 # model.save('abcd.model')
 83 model=load_model('abcd.model')
 84 #这里填写自己的存放路径
 85 with open('id.txt','a+') as f:
 86     #这里填写你需要识别图片的路径
 87     path=''
 88     for png in os.listdir(path):
 89         pn= cv2.imread(os.path.join(path,png))
 90         ids_1 = png.split('answer')[0]
 91         print(type(ids_1))
 92     #png2= cv2.imread('/home/youxue/4907988answer.png')
 93         # plt.imshow(pn)
 94         # plt.show()
 95         img=[]
 96         img.append(pn)
 97     #img.append(png2)
 98         img=np.array(img)
 99         img = img/255.
100 
101     # print(img.shape)
102         pre=model.predict(img)
103         for c in pre:
104             temp= max(c[0],c[1],c[2],c[3])
105             if temp == c[0]:
106                 abcd = 'a'
107             elif temp == c[1]:
108                 abcd = 'b'
109             elif temp == c[2]:
110                 abcd = 'c'
111             else:
112                 abcd = 'd'
113        
114         f.write(ids_1 + '--' + abcd + '\n')

 

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