Python进阶——OpenCV之GUI

文章目录

• 图像处理（Getting Started with Images）

• 读取图像

• 显示图像

• 保存图像

• 使用Matplotlib

• 视频处理（Getting Started with Videos）

• 读取摄像头播放视频

• 播放视频文件

• 保存视频文件

• OpenCV画图函数

• 画线

• 画矩形

• 画圆

• 画椭圆

• 画多边形

• 添加文字

• 以上步骤最终图像

• 鼠标作为画图刷

• 简单示例

• 高级示例

• 移动条调色板

有感于人工智能发展，现在开始学习Opencv关于计算机视觉的知识，又不想捣鼓C++代码，因此决定用Python来搞，此篇开始按照官网的教程开始学习，记录自己的学习历程，写一点笔记，方便以后查阅。
官方的教程：https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html
GUI部分主要包括图像处理、视频处理、画图工具、GUI工具4个部分。

图像处理（Getting Started with Images）

学习使用cv2.imread(), cv2.imshow() , cv2.imwrite()这三个函数，以及Matplotlib库显示图像。

读取图像

cv2.imread(filename, flags=None)

import cv2img = cv2.imread(r'/home/luke/图片/2018-08-09 11-58-52.jpg',cv2.IMREAD_COLOR)# filename参数u用于定位图片，可以是当前路径或绝对路径，注意windows平台，字符串前加 r# 第二项参数的默认值是：cv2.IMREAD_COLOR，读取色彩图像，还可以是# cv2.IMREAD_GRAYSCALE : 读取灰度图像# cv2.IMREAD_UNCHANGED : 读取图像包含 alpha channel

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显示图像

cv2.imshow() 函数将图像显示到窗口，窗口大小自动适应图像大小。

cv2.imshow('image',img)#参数1：窗口标题名称#参数2：已读取的显示图像cv2.waitKey(0)# cv2.waitKey()函数等待任意按键， # 参数是等待超时时间，时间单位为ms；参数为0时，等待直到任意键按下，# 返回值是按下的键值cv2.destroyAllWindows()# 销毁所有打开的窗口123456789123456789

保存图像

img = cv2.imread('/home/luke/图片/2018-07-19 19-47-33屏幕截图.png',0)cv2.imshow('image',img)k = cv2.waitKey(0)#k = cv2.waitKey(0) & 0xFFif k == 27: # wait for ESC key to exit cv2.destroyAllWindows()elif k == ord('s'): # wait for 's' key to save and exit cv2.imwrite('/home/luke/图片/test.png',img) cv2.destroyAllWindows()

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使用Matplotlib

使用Matplotlib库显示图像、缩放图像，保存图像，关于这个库的说明，需要另写文章。以下是简单示例

import numpy as npimport cv2from matplotlib import pyplot as pltimg = cv2.imread('messi5.jpg',0)plt.imshow(img, cmap = 'gray', interpolation = 'bicubic')plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axisplt.show()# 注意：OpenCV图像存储格式为BGR模式，但是 Matplotlib 使用RGB模式；因此要想正确使用 Matplotlib库必须先进行转换。# convert BGR image to RGB image# cv2.cvtColor(img, cv2.COLOR_BGR2RGB)# img2 = img[:,:,::-1]123456789101112123456789101112

视频处理（Getting Started with Videos）

主要学习函数：

• cv2.VideoCapture(),

• cv2.VideoWriter()

读取摄像头播放视频

要播放摄像头视频，需要先捕捉摄像头视频流，创造一个VideoCapture对象。

import numpy as npimport cv2cap = cv2.VideoCapture(0)#cap = cv2.VideoCapture(/dev/video0)#cap.read() returns a bool (True/False). If frame is read correctly, #it will be True. So you can check end of the video by checking this return valuewhile(True): # Capture frame-by-frame ret, frame = cap.read() # Our operations on the frame come here gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # Display the resulting frame cv2.imshow('frame',gray) if cv2.waitKey(1) & 0xFF == ord('q'): break# When everything done, release the capturecap.release()cv2.destroyAllWindows()

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• cv2.VideoCapture()用于创建VideoCapture对象，参数可以使video设备序号，从0开始，也可以是设备名称/dev/video0cap 对象代表的摄像头节点可能还未被初始化，直接读取可能会报错可以使用cap.isOpened()函数判断，返回值为True，表示已打开；否则用cap.open()函数打开。

• cap.get(propId)函数可以获取视频相关参数

• cap.set(propId, vaule),设置视频相关参数

• propId取值范围是0-21

• CV_CAP_PROP_POS_MSEC Current position of the video file in milliseconds or video capture timestamp.

• CV_CAP_PROP_POS_FRAMES 0-based index of the frame to be decoded/captured next.

• CV_CAP_PROP_POS_AVI_RATIO Relative position of the video file: 0 - start of the film, 1 - end of the film.

• CV_CAP_PROP_FRAME_WIDTH Width of the frames in the video stream.

• CV_CAP_PROP_FRAME_HEIGHT Height of the frames in the video stream.

• CV_CAP_PROP_FPS Frame rate.

• CV_CAP_PROP_FOURCC 4-character code of codec.

• CV_CAP_PROP_FRAME_COUNT Number of frames in the video file.

• CV_CAP_PROP_FORMAT Format of the Mat objects returned by retrieve() .

• CV_CAP_PROP_MODE Backend-specific value indicating the current capture mode.

• CV_CAP_PROP_BRIGHTNESS Brightness of the image (only for cameras).

• CV_CAP_PROP_CONTRAST Contrast of the image (only for cameras).

• CV_CAP_PROP_SATURATION Saturation of the image (only for cameras).

• CV_CAP_PROP_HUE Hue of the image (only for cameras).

• CV_CAP_PROP_GAIN Gain of the image (only for cameras).

• CV_CAP_PROP_EXPOSURE Exposure (only for cameras).

• CV_CAP_PROP_CONVERT_RGB Boolean flags indicating whether images should be converted to RGB.

• CV_CAP_PROP_WHITE_BALANCE_U The U value of the whitebalance setting (note: only supported by DC1394 v 2.x backend currently)

• CV_CAP_PROP_WHITE_BALANCE_V The V value of the whitebalance setting (note: only supported by DC1394 v 2.x backend currently)

• CV_CAP_PROP_RECTIFICATION Rectification flag for stereo cameras (note: only supported by DC1394 v 2.x backend currently)

• CV_CAP_PROP_ISO_SPEED The ISO speed of the camera (note: only supported by DC1394 v 2.x backend currently)

• CV_CAP_PROP_BUFFERSIZE Amount of frames stored in internal buffer memory (note: only supported by DC1394 v 2.x backend currently)

播放视频文件

import numpy as npimport cv2cap = cv2.VideoCapture('vtest.avi')while(cap.isOpened()):    ret, frame = cap.read()    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)    cv2.imshow('frame',gray)    if cv2.waitKey(1) & 0xFF == ord('q'):        breakcap.release()cv2.destroyAllWindows()123456789101112123456789101112

注意：Make sure proper versions of ffmpeg or gstreamer is installed. Sometimes, it is a headache to work with Video Capture mostly due to wrong installation of ffmpeg/gstreamer.

保存视频文件

以下代码一遍播放一遍保存视频文件

import numpy as npimport cv2cap = cv2.VideoCapture(0)# Define the codec and create VideoWriter objectfourcc = cv2.VideoWriter_fourcc(*'XVID')out = cv2.VideoWriter('output.avi',fourcc, 20.0, (640,480))while(cap.isOpened()): ret, frame = cap.read() if ret==True: frame = cv2.flip(frame,0) # write the flipped frame，垂直方向翻转 out.write(frame) cv2.imshow('frame',frame) if cv2.waitKey(1) & 0xFF == ord('q'): break else: break# Release everything if job is finishedcap.release()out.release()cv2.destroyAllWindows()

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cv2.VideoWriter()参数说明：

• 参数1：存储的视频文件名称

• 参数2：视频文件格式FourCC， 4-byte 的编码格式，In Fedora: DIVX, XVID, MJPG, X264, WMV1, WMV2. (XVID is more preferable. MJPG results in high size video. X264 gives very small size video)
  In Windows: DIVX (More to be tested and added)

• 参数3：视频文件的帧率大小，每秒多少帧

• 参数4：元组格式(640, 320)，表示视频文件画面的大小

OpenCV画图函数

基本函数

• cv2.line(),

• cv2.circle() ,

• cv2.rectangle(),

• cv2.ellipse(),

• cv2.putText()

基本参数

• img : 被作图的图像载体

• color : Color of the shape. for BGR, pass it as a tuple, eg: (255,0,0) for blue. For grayscale, just pass the scalar value.

• thickness : Thickness of the line or circle etc. If -1 is passed for closed figures like circles, it will fill the shape. default thickness = 1

• lineType : Type of line, whether 8-connected, anti-aliased line etc. By default, it is 8-connected. cv2.LINE_AA gives anti-aliased line which looks great for curves.

画线

首先创建全黑图像，然后给出起点与终点坐标，线的颜色、厚度
以下函数画一条从左上角到右下角的蓝色线

import numpy as npimport cv2# Create a black imageimg = np.zeros((512,512,3), np.uint8)# Draw a diagonal blue line with thickness of 5 pximg = cv2.line(img,(0,0),(511,511),(255,0,0),5)1234567812345678

画矩形

声明矩形的左上角坐标、右下角坐标

img = cv2.rectangle(img,(384,0),(510,128),(0,255,0),3)

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画圆

声明圆心点坐标、半径、颜色

img = cv2.circle(img,(447,63), 63, (0,0,255), -1)11

画椭圆

椭圆参数：

• 圆心坐标 (x,y)

• a、b长短轴长度 (major axis length, minor axis length)

• 作图方向，逆时针，起点角度与终点角度 0 到 360 g画出完整椭圆

img = cv2.ellipse(img,(256,256),(100,50),0,0,180,255,-1)

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画多边形

多边形参数：

• 多边形定点坐标

pts = np.array([[10,5],[20,30],[70,20],[50,10]], np.int32)pts = pts.reshape((-1,1,2))img = cv2.polylines(img,[pts],True,(0,255,255))123123

注意：If third argument is False, you will get a polylines joining all the points, not a closed shape.
注意：cv2.polylines() can be used to draw multiple lines. Just create a list of all the lines you want to draw and pass it to the function. All lines will be drawn individually. It is more better and faster way to draw a group of lines than calling cv2.line() for each line.

添加文字

向图像添加文字需要声明以下参数

• 文字内容

• 文字起点坐标，起始位置为左下角

• 字体类型

• 字体大小

• 其它参数：color, thickness, lineType， lineType = cv2.LINE_AA 推荐使用.

font = cv2.FONT_HERSHEY_SIMPLEXcv2.putText(img,'OpenCV',(10,500), font, 4,(255,255,255),2,cv2.LINE_AA)

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以上步骤最终图像

鼠标作为画图刷

学习函数：cv2.setMouseCallback()

简单示例

• 1、首先创建鼠标事件回调函数，每一个鼠标事件给出一个坐标(x,y)，根据此坐标与对应的鼠标EVENT，灰回调函数可以实现任何事情

• 2、鼠标事件EVENT：['EVENT_FLAG_ALTKEY', 'EVENT_FLAG_CTRLKEY', 'EVENT_FLAG_LBUTTON', 'EVENT_FLAG_MBUTTON', 'EVENT_FLAG_RBUTTON', 'EVENT_FLAG_SHIFTKEY', 'EVENT_LBUTTONDBLCLK', 'EVENT_LBUTTONDOWN', 'EVENT_LBUTTONUP', 'EVENT_MBUTTONDBLCLK', 'EVENT_MBUTTONDOWN', 'EVENT_MBUTTONUP', 'EVENT_MOUSEHWHEEL', 'EVENT_MOUSEMOVE', 'EVENT_MOUSEWHEEL', 'EVENT_RBUTTONDBLCLK', 'EVENT_RBUTTONDOWN', 'EVENT_RBUTTONUP']

• 3、以下代码，设置鼠标双击左键事件，以此事件的坐标点换一个圆

import cv2import numpy as np# mouse callback functiondef draw_circle(event,x,y,flags,param):    if event == cv2.EVENT_LBUTTONDBLCLK:        cv2.circle(img,(x,y),100,(255,0,0),-1)# Create a black image, a window and bind the function to windowimg = np.zeros((512,512,3), np.uint8)cv2.namedWindow('image')cv2.setMouseCallback('image',draw_circle)while(1):    cv2.imshow('image',img)    if cv2.waitKey(20) & 0xFF == 27:        breakcv2.destroyAllWindows()123456789101112131415161718123456789101112131415161718

高级示例

鼠标回调函数绑定多个事件，既可以画圆也可以画矩形

import cv2import numpy as npdrawing = False # true if mouse is pressedmode = True # if True, draw rectangle. Press 'm' to toggle to curveix,iy = -1,-1# mouse callback functiondef draw_circle(event,x,y,flags,param): global ix,iy,drawing,mode if event == cv2.EVENT_LBUTTONDOWN: drawing = True ix,iy = x,y elif event == cv2.EVENT_MOUSEMOVE: if drawing == True: if mode == True: cv2.rectangle(img,(ix,iy),(x,y),(0,255,0),-1) else: cv2.circle(img,(x,y),5,(0,0,255),-1) elif event == cv2.EVENT_LBUTTONUP: drawing = False if mode == True: cv2.rectangle(img,(ix,iy),(x,y),(0,255,0),-1) else: cv2.circle(img,(x,y),5,(0,0,255),-1)

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主循环检测“m”键，是否按下，用以切换画圆还是画矩形
鼠标左键按下使能画图，并拖动开始画图

img = np.zeros((512,512,3), np.uint8)cv2.namedWindow('image')cv2.setMouseCallback('image',draw_circle)while(1):    cv2.imshow('image',img)    k = cv2.waitKey(1) & 0xFF    if k == ord('m'):        mode = not mode    elif k == 27:        breakcv2.destroyAllWindows()1234567891011121312345678910111213

移动条调色板

主要学习函数

• cv2.createTrackbar()

• cv2.getTrackbarPos()，参数1，trackbar名称，参数2，附着窗口名称，参数3，默认值，参数4，最大值，参数5，回调函数，移动条value变化时出发执行，此示例中，只需trackbar的value值，因此回调函数什么也不做

• 此示例有4个移动条，分别用来选择R、G、B；以及On/Off选择

• trackbar的value取值只有0与1时，可以作为switch开关，此示例中用来决定是否显色调色的后的颜色，value为0 时，调色板为黑色

import cv2import numpy as npdef nothing(x): pass# Create a black image, a windowimg = np.zeros((300,512,3), np.uint8)cv2.namedWindow('image')# create trackbars for color changecv2.createTrackbar('R','image',0,255,nothing)cv2.createTrackbar('G','image',0,255,nothing)cv2.createTrackbar('B','image',0,255,nothing)# create switch for ON/OFF functionalityswitch = '0 : OFF \n1 : ON'cv2.createTrackbar(switch, 'image',0,1,nothing)while(1): cv2.imshow('image',img) k = cv2.waitKey(1) & 0xFF if k == 27: break # get current positions of four trackbars r = cv2.getTrackbarPos('R','image') g = cv2.getTrackbarPos('G','image') b = cv2.getTrackbarPos('B','image') s = cv2.getTrackbarPos(switch,'image') if s == 0: img[:] = 0 else: img[:] = [b,g,r]cv2.destroyAllWindows()

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好了，本片就这么多，继续学习！