在OpenCV中通過圖片的URL地址獲取圖片：

# -*- coding: utf-8 -*-
import numpy as np
import urllib
import cv2

# URL到圖片
def url_to_image(url):
    # download the image, convert it to a NumPy array, and then read
    # it into OpenCV format
    resp = urllib.urlopen(url)
    # bytearray將資料轉換成（返回）一個新的位元組陣列
    # asarray 複製資料，將結構化資料轉換成ndarray
    image = np.asarray(bytearray(resp.read()), dtype="uint8")
    # cv2.imdecode()函式將資料解碼成Opencv影象格式
    image = cv2.imdecode(image, cv2.IMREAD_COLOR)
    # return the image
    return image

# initialize the list of image URLs to download
urls = [
    "http://www.pyimagesearch.com/wp-content/uploads/2015/01/opencv_logo.png",
    "http://www.pyimagesearch.com/wp-content/uploads/2015/01/google_logo.png"
]

# loop over the image URLs
for url in urls:
    # download the image URL and display it
    print "downloading %s" % (url)
    image = url_to_image(url)
    cv2.imshow("Image", image)
    cv2.waitKey(0)
 

以上過程用到bytearray、numpy.asarray、cv2.imdecode

Python bytearray()

bytearray是Python內的一個類，bytearray()返回一個新位元組陣列，可以看做是array的位元組表示。bytearray()需要一個數組作為引數，這個數組裡的元素是可變的，並且數組裡每個元素的值範圍是: 0 <= x < 256。

bytearray()使用：

# -*- coding: utf-8 -*-
a = bytearray()
b = bytearray([1,2,3,4,5])
c = bytearray('ABC','UTF-8')
d = bytearray(u'中文','UTF-8')
print type(a)
print type(b)
print c
print d
print(len(a))
print(len(b))
print(len(c))
print(len(d))
 

<type 'bytearray'>
<type 'bytearray'>
ABC
中文
0
5
3
6

numpy.asarray()

numpy.asarray()函式的作用是將輸入資料（列表的列表，元組的元組，元組的列表等結構化資料）轉換為numpy中矩陣(ndarray、多維陣列物件)的形式。

# -*- coding: utf-8 -*-
import numpy as np

data1=[[1,2,3],[1,2,3],[1,2,3]]
data2=[(1,2,3),(1,2,3),(1,2,3)]
data3=[(1,2,3,1,2,3,1,2,3)]
data4=[1,2,3,1,2,3,1,2,3]

arr1=np.array(data1)
arr2=np.array(data1)
arr3=np.array(data3)
arr4=np.array(data4)

print arr1
print arr2
print arr3
print arr4
 

輸出：

[[1 2 3]
 [1 2 3]
 [1 2 3]]
[[1 2 3]
 [1 2 3]
 [1 2 3]]
[[1 2 3 1 2 3 1 2 3]]
[1 2 3 1 2 3 1 2 3]

指定轉換型別：

# -*- coding: utf-8 -*-
import numpy as np

data1=[[1,2,3],[1,2,3],[1,2,3]]
data2=[(1,2,3),(1,2,3),(1,2,3)]
data3=[(1,2,3,1,2,3,1,2,3)]
data4=[1,2,3,1,2,3,1,2,3]

arr1=np.array(data1,'f')
arr2=np.array(data1,'f')
arr3=np.array(data3,'f')
arr4=np.array(data4,'f')

print arr1
print arr2
print arr3
print arr4

[[ 1.  2.  3.]
 [ 1.  2.  3.]
 [ 1.  2.  3.]]
[[ 1.  2.  3.]
 [ 1.  2.  3.]
 [ 1.  2.  3.]]
[[ 1.  2.  3.  1.  2.  3.  1.  2.  3.]]
[ 1.  2.  3.  1.  2.  3.  1.  2.  3.]