python opencv load image and splite train/test set

from sklearn import cross_validation

import cv2

def load_image_data():
dir_path=r'E:\'
# class_1_path=dir_path+r'\no'
# class_2_path=dir_path+r'\other'
# class_3_path=dir_path+r'\yes'
X=[]
Y=[]
testX=[]
testY=[]

#print(class_1_path)
filepathvec = []
for rt, dirs, files in os.walk(dir_path):  # =pathDir
# print('rt:',rt)
# print('dirs:',dirs)
for dir in dirs:
# print(dir)
# if dir.find('-')<0:
#     continue
for rt1, dirs, filenames in os.walk(rt+'\\'+dir):
file_count=0
for filename in filenames:
file_count+=1
if file_count>200:
break
# print (filename)
if filename.find('.') >= 0:
(shotname, extension) = os.path.splitext(filename)
# print shotname,extension
if extension == '.png' or extension == '.tif':
filepathvec.append(os.path.join('%s\\%s' % (rt+'\\'+dir, filename)))
# print (filename)
break
# exit()
print(len(filepathvec))
for file in filepathvec:
# if len(X)>3200:
#     break
print(file)
img=cv2.imread(file,0)
# print(np.shape(img))
#cv2.namedWindow("the window")
#cv2.imshow("the window",img)
image_width,image_height=np.shape(img)
print(image_width)
fzoom=20.0/float(image_width)
img=cv2.resize(img,(int(float(image_height)*fzoom),20), interpolation=cv2.INTER_CUBIC)

img=cv2.rotate(img,cv2.ROTATE_90_CLOCKWISE)
img=255-img #反色处理
# cv2.imshow('xx', img)
# cv2.waitKey()

#print(img)
#print(len(img))
#print(np.shape(img))
X.append(img)
Y.append(get_label(file))

#cv2.namedWindow("the window")
#cv2.imshow("the window",img)
#break
#多块平移提取处理
# img_src_height=np.shape(img)[0]
# img_src_width=np.shape(img)[1]
# for i in range(1,3):
#     img_crop=np.array(img[i:-1][i:-1])
#     img_crop = cv2.resize(img_crop, (28, 28), interpolation=cv2.INTER_CUBIC)
#     img_crop = 255 - img_crop  # 反色处理
#     X.append(img_crop)
#     Y.append(get_label(file))
#
# for i in range(1,3):
#     img_crop = img[0:i][0:i]
#     img_crop = cv2.resize(img_crop, (28, 28), interpolation=cv2.INTER_CUBIC)
#     img_crop = 255 - img_crop  # 反色处理
#     X.append(img_crop)
#     Y.append(get_label(file))

# print(X[0:5])
# print(Y[0:5])
print(len(X),len(Y))
X=np.array(X)
Y=np.array(Y)
# testX=X
# testY=Y

X, testX, Y, testY = cross_validation.train_test_split(X, Y, test_size=0.2)
print('train set:',len(X))
print('test set:',len(testX))

return X,Y,testX,testY