我们可以用python实现很多功能，那么如何用python读取一个图像呢？下面我给大家分享一下。

CMD命令行

首先按下Win+R组合键打开运行界面，输入CMD，如下图所示

接下来在弹出的CMD界面中导入Python的图像处理模块PIL,如下图所示

接下来利用PIL模块的open方法读取图像，只要在括号里传入图像地址即可，如下图所示

最后执行程序我们就可以看到程序自动调用图像识别软件进行图像的读取了，如下图所示

# -*- coding: utf-8 -*-

import sys#, time

from PyQt5 import QtWidgets

from PyQt5.QtCore import QTimer, QThread, pyqtSignal

from Ui_cv2ui_thread import Ui_MainWindow

import cv2 as cv

from PyQt5.QtGui import QImage, QPixmap

from PyQt5.QtWidgets import (QApplication, QDialog, QFileDialog, QGridLayout,

QLabel, QPushButton, QColorDialog)

import numpy as np

class MainWindow(QtWidgets.QMainWindow, Ui_MainWindow):

def __init__(self, parent=None):

super(MainWindow, self).__init__(parent=parent)

self.setupUi(self) #这个一定要在这个最前面位置

# define the slot for pushbutton to save the merged image

self.pushButton.clicked.connect(self.savemergeimage)

self.img = np.ndarray(())#空的numpy array

self.img1 = np.ndarray(())

self.img2= np.ndarray(())

self.img3= np.ndarray(())

self.img4= np.ndarray(())

self.img4= np.empty([960,1280, 3], int)

self.cap = cv.VideoCapture(3) #注意，由大开到小,很重要

self.cap.set(3, 640) # setup the resolution of CCD

self.cap.set(4, 480)

ret, self.img=self.cap.read()

self.cap1 = cv.VideoCapture(2)

self.cap1.set(3, 640)

self.cap1.set(4, 480)

ret, self.img1=self.cap1.read()

self.cap2 = cv.VideoCapture(1)

self.cap2.set(3, 640)

self.cap2.set(4, 480)

ret, self.img2=self.cap2.read()

self.cap3 = cv.VideoCapture(0)

self.cap3.set(3, 640)

self.cap3.set(4, 480)

ret, self.img3=self.cap3.read()

#time.sleep(1)也许需要延迟，等他准备好

# 初始化一个定时器，在其他条件下用的

#self.timer = QTimer(self)

# 实例化一个线程

self.work0= WorkThread()

self.work0.trigger.connect(self.ccd2)

# 定义时间任务是一次性任务就设定下一行

#self.timer.setSingleShot(True)

# 启动时间任务，注意一致性

self.work0.start()

# 实例化一个线程

self.work= WorkThread()

# 多线程的信号触发连接到ccd3

self.work.trigger.connect(self.ccd3)

self.work.start()

# 实例化一个线程

self.work2 = WorkThread()

# 多线程的信号触发连接到ccd4

self.work2.trigger.connect(self.ccd4)

self.work2.start()

# 实例化一个线程

self.work3 = WorkThread()

# 多线程的信号触发连接到ccd1

self.work3.trigger.connect(self.ccdmerge)

self.work3.start()

self.work4 = WorkThread()

# 多线程的信号触发连接到ccd1

self.work4.trigger.connect(self.ccd1)

self.work4.start()

def refreshShowa(self):#显示ccd1到label1

# 提取图像的尺寸和通道, 用于将opencv下的image转换成Qimage

height, width, channel = self.img.shape

bytesPerLine = 3 * width

self.qImg = QImage(self.img.data, width, height, bytesPerLine,

QImage.Format_RGB888).rgbSwapped()

# 将Qimage显示出来

self.label.setPixmap(QPixmap.fromImage(self.qImg))

def refreshShowb(self):#显示ccd2到label2

# 提取图像的尺寸和通道, 用于将opencv下的image转换成Qimage

height, width, channel = self.img1.shape

bytesPerLine = 3 * width

self.qImg1 = QImage(self.img1.data, width, height, bytesPerLine,

QImage.Format_RGB888).rgbSwapped()

# 将Qimage显示出来

self.label_2.setPixmap(QPixmap.fromImage( self.qImg1))

def refreshShowc(self):#显示ccd3到label3

# 提取图像的尺寸和通道, 用于将opencv下的image转换成Qimage

height, width, channel = self.img2.shape

bytesPerLine = 3 * width

self.qImg2 = QImage(self.img2.data, width, height, bytesPerLine,

QImage.Format_RGB888).rgbSwapped()

# 将Qimage显示出来

self.label_3.setPixmap(QPixmap.fromImage( self.qImg2))

def refreshShowd(self):#显示ccd4到label4

# 提取图像的尺寸和通道, 用于将opencv下的image转换成Qimage

height, width, channel = self.img3.shape

bytesPerLine = 3 * width

self.qImg3 = QImage(self.img3.data, width, height, bytesPerLine,

QImage.Format_RGB888).rgbSwapped()

# 将Qimage显示出来

self.label_4.setPixmap(QPixmap.fromImage( self.qImg3))

def refreshShowe(self):#显示合并的影像到label6

# 提取图像的尺寸和通道, 用于将opencv下的image转换成Qimage

height, width, channel = self.img4.shape

bytesPerLine = 3 * width

self.qImg4 = QImage(self.img4.data, width, height, bytesPerLine,

QImage.Format_RGB888).rgbSwapped()

# 将Qimage显示出来

self.label_6.setPixmap(QPixmap.fromImage( self.qImg4))

def ccd1(self):

self.cap.set(3, 640)

self.cap.set(4, 480)

ret, self.img = self.cap.read()

self.refreshShowa()

# 启动另一个线程

self.work0.start()#注意一致性

def ccd2(self, str):

self.cap1.set(3, 640)

self.cap1.set(4, 480)

ret, self.img1 = self.cap1.read()

self.refreshShowb()

self.work.start()#注意一致性

def ccd3(self, str):

self.cap2.set(3, 640)

self.cap2.set(4, 480)

ret, self.img2= self.cap2.read()

self.refreshShowc()

self.work2.start()#注意一致性

def ccd4(self, str):

self.cap3.set(3, 640)

self.cap3.set(4, 480)

ret, self.img3 = self.cap3.read()

self.refreshShowd()

self.work3.start()#注意一致性

def ccdmerge(self, str):

self.img4=np.hstack((self.img, self.img1))

self.img4=np.vstack((self.img4, np.hstack((self.img2, self.img3))))

#print ('here is a merge process') 可以用来判断多线程的执行

self.refreshShowe() #later to remove the remark

self.work4.start()#注意一致性

def savemergeimage(self):

# 调用存储文件dialog

fileName, tmp = QFileDialog.getSaveFileName(

self, 'Save Image', './__data', '*.png *.jpg *.bmp', '*.png')

if fileName == '':

return

if self.img.size == 1:

return

# 调用opencv写入图像

cv.imwrite(fileName,self.img4)

class WorkThread(QThread): #多线程核心，非常重要

# 定义一个信号

trigger = pyqtSignal(str)

def __int__(self):

# 初始化函数，默认

super(WorkThread, self).__init__()

def run(self):

self.trigger.emit('')

if __name__ == "__main__":

app = QtWidgets.QApplication(sys.argv)

w = MainWindow()

w.show()

sys.exit(app.exec_())