import os

import cv2

import numpy as np

from sklearn.cross_validation import train_test_split

from sklearn.metrics import confusion_matrix, classification_report

X = [] #定义图像名称

Y = [] #定义图像分类类标

Z = [] #定义图像像素

for i in range(0, 10):

#遍历文件夹，读取图片

for f in os.listdir("photo/%s" % i):

#获取图像名称

X.append("photo//" +str(i) + "//" + str(f))

#获取图像类标即为文件夹名称

Y.append(i)

X = np.array(X)

Y = np.array(Y)

X_train, X_test, y_train, y_test = train_test_split(X, Y,

test_size=0.3, random_state=1)

print len(X_train), len(X_test), len(y_train), len(y_test)

XX_train = []

for i in X_train:

#读取图像

#print i

image = cv2.imread(i)

#图像像素大小一致

img = cv2.resize(image, (256,256),

interpolation=cv2.INTER_CUBIC)

#计算图像直方图并存储至X数组

hist = cv2.calcHist([img], [0,1], None,

[256,256], [0.0,255.0,0.0,255.0])

XX_train.append(((hist/255).flatten()))

XX_test = []

for i in X_test:

#读取图像

#print i

image = cv2.imread(i)

#图像像素大小一致

img = cv2.resize(image, (256,256),

interpolation=cv2.INTER_CUBIC)

#计算图像直方图并存储至X数组

hist = cv2.calcHist([img], [0,1], None,

[256,256], [0.0,255.0,0.0,255.0])

XX_test.append(((hist/255).flatten()))

from sklearn.naive_bayes import BernoulliNB

clf = BernoulliNB().fit(XX_train, y_train)

predictions_labels = clf.predict(XX_test)

print u'预测结果:'

print predictions_labels

print u'算法评价:'

print (classification_report(y_test, predictions_labels))

k = 0

while k<10:

#读取图像

print X_test[k]

image = cv2.imread(X_test[k])

print predictions_labels[k]

#显示图像

cv2.imshow("img", image)

cv2.waitKey(0)

cv2.destroyAllWindows()

k = k + 1