import os

import cv2

import numpy as np

import matplotlib.pyplot as plt

from sklearn.naive_bayes import BernoulliNB

from sklearn.model_selection 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, 12):

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

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

Y.append(i)

X = np.array(X)

Y = np.array(Y)

print(X[:2])

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:

image = cv2.imread(i)

img = cv2.resize(image, (32,32),

interpolation=cv2.INTER_CUBIC)

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:

image = cv2.imread(i)

img = cv2.resize(image, (32,32),

interpolation=cv2.INTER_CUBIC)

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

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

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

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

predictions_labels = clf.predict(XX_test)

print('预测结果:')

print(predictions_labels)

print('算法评价:')

print(classification_report(y_test, predictions_labels,digits=4))

k = 0

while k<10:

print(X_test[k])

image = cv2.imread(X_test[k])

print(predictions_labels[k])

cv2.imshow("img", image)

cv2.waitKey(0)

cv2.destroyAllWindows()

k = k + 1