文章目录
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 cv2
img = 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
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
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) & 0xFF
if 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()
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 np
import cv2
from matplotlib import pyplot as plt
img = cv2.imread('messi5.jpg',0)
plt.imshow(img, cmap = 'gray', interpolation = 'bicubic')
plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis
plt.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]
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 np
import cv2
cap = 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 value
while(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 capture
cap.release()
cv2.destroyAllWindows()
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/video0
cap 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 np
import cv2
cap = 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'):
break
cap.release()
cv2.destroyAllWindows()
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 np
import cv2
cap = cv2.VideoCapture(0)
# Define the codec and create VideoWriter object
fourcc = 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 finished
cap.release()
out.release()
cv2.destroyAllWindows()
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 np
import cv2
# Create a black image
img = np.zeros((512,512,3), np.uint8)
# Draw a diagonal blue line with thickness of 5 px
img = cv2.line(img,(0,0),(511,511),(255,0,0),5)
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)
画圆
声明圆心点坐标、半径、颜色
img = cv2.circle(img,(447,63), 63, (0,0,255), -1)
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)
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))
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_SIMPLEX
cv2.putText(img,'OpenCV',(10,500), font, 4,(255,255,255),2,cv2.LINE_AA)
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 cv2
import numpy as np
# mouse callback function
def 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 window
img = 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:
break
cv2.destroyAllWindows()
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 cv2
import numpy as np
drawing = False # true if mouse is pressed
mode = True # if True, draw rectangle. Press 'm' to toggle to curve
ix,iy = -1,-1
# mouse callback function
def 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)
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:
break
cv2.destroyAllWindows()
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 cv2
import numpy as np
def nothing(x):
pass
# Create a black image, a window
img = np.zeros((300,512,3), np.uint8)
cv2.namedWindow('image')
# create trackbars for color change
cv2.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 functionality
switch = '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()
好了,本片就这么多,继续学习!