【python-opencv】opencv基础操作之二

opencv_2

 

1 图像的基本操作

Python的版本是Python3.6，openCV使用的是cv2

 

1.4 图像的基本信息

图像的基本信息有：行、列、通道、像素数目和图像数据类型

In [1]:

import cv2
import requests
import time
from PIL import Image
from io import BytesIO
import numpy as np
from matplotlib import pyplot as plt

%matplotlib inline
# set None proxy
import os
os.environ['no_proxy'] = '*' 

In [2]:

# draw a cvMat using plt.
def draw(image):
        plt.figure() # 设置画布
        plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
        plt.show()

In [3]:

# get file(of Internet picture)
class InternetPicture:
    def __init__(self, url):
        # attribute
        self.url = url
        self.img = None
        # initial method
        self.get() # self代表类的实例， 而非类本身，所以self.get(self)--> self.get()
        self.show()
        self.info()

    # get Internet Picture
    def get(self):
        for i in range(5): # 多获取几次，增加成功率
            start=time.time()
            file = requests.get(self.url)
            img = cv2.imdecode(np.fromstring(file.content, np.uint8), 1)    #file.content 是读取的远程文件的字节流
            print(i + 1, ' time of Requesting Time:', time.time()-start)
        self.img = img
        print('get a file of:', type(img))
    
    # show image
    def show(self):
        #using plt draw picture
        plt.figure() # 设置画布
        plt.imshow(cv2.cvtColor(self.img, cv2.COLOR_BGR2RGB))
        plt.show()
    
    def info(self):
        # using cv2 to read Internet file of picture
        color_img = self.img
        gray_img = cv2.cvtColor(self.img, cv2.COLOR_BGR2GRAY)

        # 获取信息：行，列，通道，像素数目，图像数据类型
        print('获取信息：行，列，通道，像素数目，图像数据类型')
        print(color_img.shape, color_img.size, color_img.dtype)
        print(gray_img.shape, gray_img.size, gray_img.dtype)
        print('self.img: get a numpy image')
        print('self.show(): show a numpy image using plt')

        # using PIL.Image to read Internet file of picture
        ## image = Image.open(BytesIO(file.content))
        ## image.show()

In [4]:

url = "https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1565165195&di=0ae43289971ee5b3cdc36eb9c9612a0a&imgtype=jpg&er=1&src=http%3A%2F%2Fvpic.video.qq.com%2F3388556%2Fx0540ujyh6i_ori_3.jpg"
pic = InternetPicture(url)

 

1  time of Requesting Time: 0.04800081253051758
2  time of Requesting Time: 0.06759953498840332
3  time of Requesting Time: 0.043440818786621094
4  time of Requesting Time: 0.040445566177368164
5  time of Requesting Time: 0.040354013442993164
get a file of: <class 'numpy.ndarray'>

 

 

获取信息：行，列，通道，像素数目，图像数据类型
(360, 640, 3) 691200 uint8
(360, 640) 230400 uint8
self.img: get a numpy image
self.show(): show a numpy image using plt

 

1.5 在图像上输出文本

在处理图片的时候，我们经常会需要把一些信息直接以文字的形式输出在图片上，我们可以用cv2.putText函数实现

函数原型：putText(img, text, org, fontFace, fontScale, color[, thickness[, lineType[, bottomLeftOrigin]]])

参数依次是：图像、文本信息、位置、字体、颜色（RGB）、线粗（缺省值为1）、等等

In [5]:

url = "https://timgsa.baidu.com/timg?image&quality=80&size=b9999_10000&sec=1565234351&di=92352e792456d5f5b366cf9a7f9ba266&imgtype=jpg&er=1&src=http%3A%2F%2Fvpic.video.qq.com%2F3388556%2Fo0398rwyrwy_ori_3.jpg"
pic = InternetPicture(url)

 

1  time of Requesting Time: 0.03394651412963867
2  time of Requesting Time: 0.03303980827331543
3  time of Requesting Time: 0.03547406196594238
4  time of Requesting Time: 0.035376787185668945
5  time of Requesting Time: 0.044396400451660156
get a file of: <class 'numpy.ndarray'>

 

 

获取信息：行，列，通道，像素数目，图像数据类型
(426, 640, 3) 817920 uint8
(426, 640) 272640 uint8
self.img: get a numpy image
self.show(): show a numpy image using plt

In [6]:

img = pic.img
cv2.putText(img, 'Hello World', (500, 350), 0, 0.5, (255, 255, 255), 1, 100)  # 最后一个参数是100，让字体更平滑
draw(img)

 

 

1.6 图像平移

图像的平移其实就是仿射变换的特殊形式，下一节会仔细讲仿射变换。

示例代码（将图像平移到到点（200,100）处）：

In [7]:

img = pic.img
print(type(img))
rows, cols , _= img.shape # 默认读取了３个通道
M = np.float32([[1, 0, 200], [0, 1, 100]])
dst = cv2.warpAffine(img, M, (cols, rows))
draw(dst)

 

<class 'numpy.ndarray'>

 

 

1.6 图像平移

有时需要将图像对R、G、B三个通道分别进行操作

示例代码：

In [8]:

img = pic.img
img2 = cv2.resize(img, None, fx=0.5, fy=0.5, interpolation=cv2.INTER_CUBIC)
b, g, r = cv2.split(img2)


# 创建画布, 把四个图画在一个画布里面
plt.figure(figsize=(15,10))
# （1,1）
ax1 = plt.subplot(2, 2, 1)
ax2 = plt.subplot(2, 2, 2)
ax3 = plt.subplot(2, 2, 3)
ax4 = plt.subplot(2, 2, 4)

# 选择ax1
plt.sca(ax1)
plt.imshow(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB))
# 选择ax2
plt.sca(ax2)
plt.imshow(cv2.cvtColor(b, cv2.COLOR_BGR2RGB))
# 选择ax3
plt.sca(ax3)
plt.imshow(cv2.cvtColor(g, cv2.COLOR_BGR2RGB))
# 选择ax4
plt.sca(ax4)
plt.imshow(cv2.cvtColor(r, cv2.COLOR_BGR2RGB))

Out[8]:

<matplotlib.image.AxesImage at 0x7f0eb7616b70>

 

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