# 我在训练自己的人脸分类模型的时候发现图片的维度不能太高，经过很多次测试过后觉得一般人脸图片分为28*28大小训练的效果比较好。建议在使用其训练自己的物体识别模型的时候，尽量把图片压缩到28*28# coding:utf-8
import timeimport matplotlib.pyplot as plt
from autokeras import ImageClassifier
from keras.engine.saving import load_model
from keras.utils import plot_model
from scipy.misc import imresize
import numpy as np
import pandas as pd
import random
import osfrom sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_scoreos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# 导入图片的函数def read_img(path):nameList = os.listdir(path)n = len(nameList)# indexImg,columnImg = plt.imread(path+'/'+nameList[0]).shapex_train = np.zeros([n,28,28,1]);y_train=[]for i in range(n):x_train[i,:,:,0] = imresize(plt.imread(path+'/'+nameList[i]),[28,28])y_train.append(np.int(nameList[i].split('.')[1]))return x_train,y_trainx_train,y_train = read_img('./dataset')
y_train = pd.DataFrame(y_train)
n = len(y_train[y_train.iloc[:,0]==2])x_train = np.array(x_train)x_wzp = np.random.choice(y_train[y_train.iloc[:,0]==1].index.tolist(),n,replace=False)x_train_w = x_train[x_wzp,:].copy()
x_train_l = x_train[y_train[y_train.iloc[:,0]==2].index.tolist()].copy()
x_train = np.concatenate([x_train_w,x_train_l],axis=0)print(x_train.shape)y_train = y_train.iloc[-208:,:].copy()# 对两组数据进行洗牌
index = random.sample(range(len(y_train)),len(y_train))
index = np.array(index)
y_train = y_train.iloc[index,:]
# y_train.plot()
# plt.show()
x_train = x_train[index,:,:,:]# x_train,x_test,y_train,y_test = train_test_split(x_train,y_train,test_size=0.2)
# print(x_train.shape,y_train.shape,x_test.shape,y_test.shape)
# y_test = y_test.values.reshape(-1)
y_train = y_train.values.reshape(-1)# 数据测试print(y_train)
for i in range(5):n = i*20img = x_train[n,:,:,:].reshape((28,28))print(y_train[n])plt.figure()plt.imshow(img,cmap='gray')plt.xticks([])plt.yticks([])
plt.show()if __name__=='__main__':start = time.time()# 模型构建model = ImageClassifier(verbose=True)# 搜索网络模型model.fit(x_train,y_train,time_limit=1*60)# 验证最优模型model.final_fit(x_train,y_train,x_train,y_train,retrain=True)# 给出评估结果score = model.evaluate(x_train,y_train)# 识别结果y_predict = model.predict(x_train)# y_pred = np.argmax(y_predict,axis=1)# 精确度accuracy = accuracy_score(y_train,y_predict)# 打印出score与accuracyprint('score:',score,'  accuracy:',accuracy)print(y_predict,y_train)model_dir = r'./trainer/auto_learn_Model.h5'model_img = r'./trainer/imgModel_ST.png'# 保存可视化模型# model.load_searcher().load_best_model().produce_keras_model().save(model_dir)# 加载模型# automodel = load_model(model_dir)# 输出模型 structure 图# plot_model(automodel, to_file=model_img)
end = time.time()print('time:',end-start)