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OpenCV 入门

article 2024/12/28 10:54:57

OpenCV_Python 入门

创建/显示窗口

方法说明参数返回
namedWindow创建一个窗口窗口名称,WINDOW_NORMAL(窗口属性)
resizeWindow设置窗口大小窗口名称,宽,高
imshow显示窗口窗口名称,要显示的图像
destroyAllWindows关闭所有窗口
waitKey延时监听键盘按下0无限 / 单位毫秒键盘按下键的ASCII码
import cv2

cv2.namedWindow("img",cv2.WINDOW_NORMAL)
cv2.resizeWindow("img",800,600)

while True:
    cv2.imshow("img",0)
    key = cv2.waitKey(0)
    if key & 0xFF == ord('q'):
        break
cv2.destroyAllWindows()

显示图片

方法说明参数返回
imread读取图片路径,IMREAD_GRAYSCALE(灰)/IMREAD_COLOR(彩)/其它读取到的图片资源
import cv2

while True:
    # cv2.imshow("img",cv2.imread("../1.jpg",cv2.IMREAD_COLOR))
    cv2.imshow("img",cv2.imread("../1.jpg",cv2.IMREAD_GRAYSCALE))

    key = cv2.waitKey(0)
    if key & 0xFF == ord('q'):
        break
cv2.destroyAllWindows()

保存图片

方法说明参数返回
imwrite保存图片保存的文件名称,图片资源
import  cv2

img = cv2.imread("../1.jpg",cv2.IMREAD_COLOR)

while True:
    cv2.imshow("img",img)
    key = cv2.waitKey(0)
    if key & 0xFF == ord('q'):
        break
    elif key & 0xFF == ord('s'):
        cv2.imwrite("11.png",img)
cv2.destroyAllWindows()

视频采集

方法说明参数返回
VideoCapture摄像机0(开启本机摄像头)
VideoCapture.read获取视频帧ret(读取到帧为true),frame(视频帧)
VideoCapture.release释放资源
import cv2

cv2.namedWindow("frame",cv2.WINDOW_NORMAL)
cv2.resizeWindow("frame",800,600)
cap = cv2.VideoCapture(0)
while True:
    ret,frame = cap.read()
    cv2.imshow('frame',frame)
    key = cv2.waitKey(1)
    if key & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

读取视频文件

方法说明参数返回
VideoCapture摄像机本机视频文件路径
import cv2

cv2.namedWindow("frame",cv2.WINDOW_NORMAL)
cv2.resizeWindow("frame",800,600)
cap = cv2.VideoCapture("../2.mp4")
while True:
    ret,frame = cap.read()
    cv2.imshow('frame',frame)
    key = cv2.waitKey(1)
    if key & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

视频录制

方法说明参数返回
VideoWriter_fourcc编码器*MJPG / 其它
VideoWriter视频输出文件名称,编码器,帧数,(宽,高)
VideoWriter.write视频写入帧
VideoWriter.release释放资源
import cv2
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
vw = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))
cv2.namedWindow("frame",cv2.WINDOW_NORMAL)
cv2.resizeWindow("frame",800,600)
cap = cv2.VideoCapture(0)
while True:
    ret,frame = cap.read()
    cv2.imshow('frame',frame)
    vw.write(frame)
    key = cv2.waitKey(1)
    if key & 0xFF == ord('q'):
        break
cap.release()
vw.release()
cv2.destroyAllWindows()

import cv2
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
vw = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))

cv2.namedWindow("frame",cv2.WINDOW_NORMAL)
cv2.resizeWindow("frame",640,480)
cap = cv2.VideoCapture(0)
while cap.isOpened(): # 判断摄像头是否为打开状态
    ret,frame = cap.read()
    if not ret: break # 判断帧是否打开
    cv2.imshow('frame',frame)
    # 窗口撑开后重新设置
    cv2.resizeWindow("frame", 640, 480)
    vw.write(frame)
    key = cv2.waitKey(1)
    if key & 0xFF == ord('q'):
        break
cap.release()
vw.release()
cv2.destroyAllWindows()

鼠标控制

方法说明参数返回
setMouseCallback监听鼠标窗口名称,监听事件,额外参数
import cv2

# 鼠标操作    x    y    组合操作    额外参数 
def mouse_callback(event, x, y, flags, param):
    print(event, x, y, flags, param)
cv2.namedWindow("img",cv2.WINDOW_NORMAL)
cv2.resizeWindow("img",800,600)
cv2.setMouseCallback("img",mouse_callback,"123")
img = cv2.imread("../1.jpg")
while True:
    cv2.imshow("img",img)
    key = cv2.waitKey(0)
    if key & 0xFF == ord('q'):
        break
cv2.destroyAllWindows()

TrackBar 滑块

方法说明参数返回
createTrackbar创建滑块名称,窗口名称,value 当前值,count 最大值,callback 回调,额外参数
getTrackbarPos获取滑块名称,窗口名称当前值
import  cv2
import numpy as np

def callback():
    pass

cv2.namedWindow("img",cv2.WINDOW_NORMAL)

cv2.createTrackbar("R","img",0,255,callback)
cv2.createTrackbar("G","img",0,255,callback)
cv2.createTrackbar("B","img",0,255,callback)

img = np.zeros((480,640,3),np.uint8)

while True:

    r = cv2.getTrackbarPos("R","img")
    g = cv2.getTrackbarPos("G","img")
    b = cv2.getTrackbarPos("B","img")

    img[:] = [b,g,r]

    cv2.imshow("img",img)
    key = cv2.waitKey(10)
    if key & 0xFF == ord('q'):
        break
cv2.destroyAllWindows()

色彩

  • opencv 采用 BGR 格式的颜色显示
  • HSV 色相 / 饱和度 / 明度
  • HSL 色相 / 饱和度 / 明度
  • 色彩空间转换
方法说明参数返回
cvtColor色彩转换获取到的图片资源,色彩转换
import cv2

def callback():
    pass
cv2.namedWindow('image',cv2.WINDOW_NORMAL)
cv2.resizeWindow('image',800,600)
img = cv2.imread("../1.jpg",1)
colorspaces = [cv2.COLOR_BGR2RGB,cv2.COLOR_BGR2BGRA,
               cv2.COLOR_BGR2GRAY,cv2.COLOR_BGR2HSV_FULL,
               cv2.COLOR_BGR2YUV]
cv2.createTrackbar('curcolor','image',0,len(colorspaces)-1,callback)
while True:
    v = cv2.getTrackbarPos('curcolor','image')
    # 颜色空间转换
    cvt_img = cv2.cvtColor(img,colorspaces[v])
    cv2.imshow('image',cvt_img)
    key = cv2.waitKey(10)
    if key & 0xFF == ord('q'):
        break
cv2.destroyAllWindows()

Numpy 基本操作

  • 创建矩阵
方法参数说明
array[]
zeros(行,列,通道数),类型值为0
ones(行,列,通道数),类型值为1
full(行,列,通道数),值,类型
identity3三行三列
eye3,5,k=1三行五列, 从第 1-1列开始
import numpy as np

a = np.array([1,2,3])
b = np.array([[1,2,3],[4,5,6]])
print(a)
print(b)

c = np.zeros((8,8,3),np.uint8)
print(c)

d = np.ones((8,8,3),np.uint8)
print(d)

e = np.full((8,8,3),10,np.uint8)
print(e)

f = np.identity(4)
print(f)

y = np.eye(5,7,k=1)
print(y)
  • 赋值 [y,x]
  • 赋值 [y,x,channel]
import numpy as np
import cv2

img = np.zeros((640,480,3),np.uint8)
# 检索
print(img[100,100])
count = 0
while count < 100:
    # 赋值      通道  0,1,2
    # img[count,100,0] = 255
    img[count, 100] = [0,0,255]
    count = count+1
cv2.imshow("img",img)
key = cv2.waitKey(0)
if key & 0xFF == ord('q'):
    cv2.destroyAllWindows()
  • ROI 获取子矩阵 [y1:y2,x1:x2]
  • [:,:] 整个矩阵 [:]
import numpy as np
import cv2

img = np.zeros((640,480,3),np.uint8)
roi = img[100:200,100:200]
img[:] = [255,0,0]
roi[:,:] = [0,0,255]
roi[10:20,10:20] = [255,255,255]
cv2.imshow("img",img)
key = cv2.waitKey(0)
if key & 0xFF == ord('q'):
    cv2.destroyAllWindows()

Mat

  • 深浅拷贝
import numpy as np
import cv2

img = cv2.imread('../1.jpg')
# 浅拷贝
img2 = img
# 深拷贝
img3 = img.copy()
img[10:100,10:100] = [0,0,255]
cv2.imshow('img',img)
cv2.imshow('img2',img2)
cv2.imshow('img3',img3)
cv2.waitKey(0)
  • 属性
import numpy as np
import cv2

img = cv2.imread('../1.jpg')
# shape 高,长度,通道
print(img.shape)

# 图像占用多大空间
# 高度 * 长度 * 通道数
print(img.size)

# 图像中每个元素的位深
print(img.dtype)
  • 分割 / 合并
import numpy as np
import cv2

img = np.zeros((480,640,3),np.uint8)

# 分割
b,g,r = cv2.split(img)

b[10:100,10:100] = 255
g[10:100,10:100] = 255

# 合并
img2 = cv2.merge([b,g,r])

cv2.imshow("img",img)
cv2.imshow("b",b)
cv2.imshow("g",g)
cv2.imshow("img2",img2)
cv2.waitKey(0)

图形绘制

  • 线
  • 矩形
  • 圆
  • 椭圆
  • 多边形
  • 文本
import numpy as np
import cv2

img = np.zeros((480,640,3),np.uint8)

# 画线 坐标点 x,y
# 起始点     结束点    bgr     线条宽     线型:平滑
cv2.line(img,(10,20),(300,400),(0,0,255),2,16)

# 绘制矩形
# 图像   起始点     结束点    bgr     线条宽 -1为填充
cv2.rectangle(img, (100, 120), (160, 200), (0, 255, 0), 2)
cv2.rectangle(img, (180, 300), (160, 200), (0, 255, 0), -1)

# 圆
# 图像   圆点   半径   bgr     线条宽 -1为填充
cv2.circle(img,(300,300), 30, (255,0,0), 5)
cv2.circle(img,(360,360), 30, (255,0,0), -1)

# 椭圆
# 图像   圆点   长宽一半  起始角度  终止角度  角度     bgr  线条宽 -1为填充
cv2.ellipse(img,(300,420), (100,30),0,0,360, (255,0,0), 5)
cv2.ellipse(img,(300,420), (100,30),0,0,360, (255,0,0), -1)

pts = np.array([(300,10),(150,100),(450,100)],np.int32)
# 多边形
# 图像   点集  是否闭合   bgr  线条宽 
cv2.polylines(img,[pts],True,(0,255,0))
# 多边形填充
# 图像   点集    bgr
cv2.fillPoly(img,[pts],(0,255,0))

# 文本
# 图像    文本    起始点    字体    字号    brg
cv2.putText(img,"Hello World!!!",(100,100),cv2.FONT_HERSHEY_PLAIN,3,(255,255,255))

cv2.imshow("img",img)
cv2.waitKey(0)

import numpy as np
import cv2

# 通过鼠标进行图形的绘制
# 1. l键 画线
# 2. r键 画矩形
# 3. c键 画圆

current = 0
startpos = (0,0)
def mouse_callback(event, x, y, flags, param):
    # print(event, x, y, flags, param)
    global current,startpos
    if event & cv2.EVENT_LBUTTONDOWN == cv2.EVENT_LBUTTONDOWN:
        startpos = (x,y)
    elif event & cv2.EVENT_LBUTTONUP == cv2.EVENT_LBUTTONUP:
        if current == 0:
            cv2.line(img,startpos,(x,y),(0,0,255))
        elif current == 1:
            cv2.rectangle(img,startpos,(x,y),(0,0,255))
        elif current == 2:
            a = (x-startpos[0])
            b = (y-startpos[1])
            r = (a**2 + b**2)**0.5
            cv2.circle(img,startpos,int(r),(0,0,255))
        else:
            print("无其它")

cv2.namedWindow("img",cv2.WINDOW_NORMAL)

cv2.setMouseCallback("img",mouse_callback,"123")

img = np.zeros((480,640,3),np.uint8)
while True:
    cv2.imshow("img",img)
    key = cv2.waitKey(1)
    if key & 0xFF == ord('q'):
        break
    elif key  == ord('l'):
        current =0
    elif key  == ord('r'):
        current =1
    elif key  == ord('c'):
        current =2

cv2.destroyAllWindows()

图像运算

  • 加
  • 减
  • 乘
  • 除
  • 溶合
import numpy as np
import cv2

img = cv2.imread('../1.jpg')
img2 = np.ones(img.shape,np.uint8) * 50
# 加    原始图像   相加的图像
img11 = cv2.add(img, img2)
# 减    原始图像   相减的图像
img22 = cv2.subtract(img, img2)
# 乘    原始图像   相乘的图像
img33 = cv2.multiply(img, img2)
# 除    原始图像   相除的图像
img44 = cv2.divide(img, img2)
# 溶合    原始图像 原始图像权重    溶合的图像 溶合的图像权重  静态权重
img55 = cv2.addWeighted(img,0.1, img2,0.9,0)
cv2.imshow('img', img)
cv2.imshow('img11', img11)
cv2.imshow('img22', img22)
cv2.imshow('img33', img33)
cv2.imshow('img44', img44)
cv2.imshow('img55', img55)
cv2.waitKey(0)
  • 非运算
  • 与运算
  • 或运算
  • 异或运算
import numpy as np
import cv2

img1 = np.zeros((200,200),np.uint8)
img1[50:150,50:150] = 255
# 非运算
img11 = cv2.bitwise_not(img1)

img2 = np.zeros((200,200),np.uint8)
img22 = np.zeros((200,200),np.uint8)
img2[20:120,20:120] = 255
img22[80:150,80:150] = 255
# 与运算
# img222 = cv2.bitwise_and(img2,img22)
# 或运算
# img222 = cv2.bitwise_or(img2,img22)
# 异或运算
img222 = cv2.bitwise_xor(img2,img22)

cv2.imshow('img1',img1)
cv2.imshow('img11',img11)
cv2.imshow('img222',img222)
cv2.waitKey(0)

import numpy as np
import cv2

img = cv2.imread('../1.jpg')

# roi 获取图标在原始图像的位置
roi = img[0:200,0:200]

# 图标创建
logo = np.zeros((200,200, 3), np.uint8)
logo[20:120,20:120] = [0,0,255]
logo[80:180,80:180] = [0,255,0]

# 创建一个与图标相同的图像
# 使用非运算取相反的图像
# 图标在原始图像的位置与当前获取相反图标进行与运算得到原始图像中显示图标的位置
# logo与原始图像中显示图标相加得到原始图像显示logo的图像
mask = np.zeros((200,200), np.uint8)
mask[20:120,20:120] = 255
mask[80:180,80:180] = 255
m = cv2.bitwise_not(mask)
tmp = cv2.bitwise_and(roi,roi,mask=m)
dst = cv2.add(tmp,logo)

# 更改原始图像显示logo位置的图像
img[0:200,0:200] = dst

cv2.imshow('logo', dst)
cv2.waitKey(0)

图像变换

  • 缩放
  • 翻转
  • 旋转
import numpy as np
import cv2

img = cv2.imread('../1.jpg')

# 缩放
# 图像  (长,宽)   fx 长缩放比例  fy 宽缩放比例   interpolation 插值算法
img1 = cv2.resize(img, (224, 224))
img2 = cv2.resize(img, None, fx=0.6, fy=0.6, interpolation=cv2.INTER_AREA)
img3 = cv2.resize(img, None, fx=1.6, fy=1.6, interpolation=cv2.INTER_AREA)

cv2.imshow('image', img)
cv2.imshow('image1', img1)
cv2.imshow('image2', img2)
cv2.imshow('image3', img3)
cv2.waitKey(0)

import numpy as np
import cv2

img = cv2.imread('../1.jpg')

# 翻转
# 图像  0 上下翻转   >0 左右翻转    <0 上下左右翻转
img1 = cv2.flip(img, 0)
img2 = cv2.flip(img, 1)
img3 = cv2.flip(img, -1)

cv2.imshow('image', img)
cv2.imshow('image1', img1)
cv2.imshow('image2', img2)
cv2.imshow('image3', img3)
cv2.waitKey(0)

import numpy as np
import cv2

img = cv2.imread('../1.jpg')

# 旋转
img1 = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)

cv2.imshow('image', img1)
cv2.waitKey(0)
  • 仿射变换
import numpy as np
import cv2

img = cv2.imread('../1.jpg')
h,w,ch = img.shape
print(h,w)
# 平移
# M = np.float32([[1,0,100],[0,1,200]])
# 中心点   旋转角度(逆时针)   缩放
# M = cv2.getRotationMatrix2D((w/2,h/2),15,1.0)
src = np.float32([[200,150],[400,150],[400,500]])
dst = np.float32([[100,200],[300,250],[150,600]])
M = cv2.getAffineTransform(src,dst)
# 修改新图的尺寸dsize(w/2,h/2)
img2 = cv2.warpAffine(img,M,(w,h))

cv2.imshow('img',img)
cv2.imshow('img2',img2)
cv2.waitKey(0)
  • 透视变换
import numpy as np
import cv2


img = cv2.imread('../1.jpg')

src = np.float32([[10,300],[100,300],[20,500],[150,450]])
dst = np.float32([[0,0],[120,0],[0,300],[120,300]])
M = cv2.getPerspectiveTransform(src,dst)
img1 = cv2.warpPerspective(img,M,(300,120))

cv2.imshow('img',img)
cv2.imshow('img1',img1)
cv2.waitKey(0)

图像滤波

import numpy as np
import cv2

img = cv2.imread('../1.jpg')
kernel = np.ones((5,5),np.uint32) / 25
dst = cv2.filter2D(img,-1,kernel)
cv2.imshow('img',img)
cv2.imshow('dst',dst)
cv2.waitKey(0)
  • 方盒滤波
  • 均值滤波
import numpy as np
import cv2

img = cv2.imread('../1.jpg')

dst = cv2.blur(img,(5,5))

cv2.imshow('img',img)
cv2.imshow('dst',dst)
cv2.waitKey(0)
  • 高斯滤波
import numpy as np
import cv2

img = cv2.imread('../1.jpg')

dst = cv2.GaussianBlur(img,(5,5),sigmaX=1)

cv2.imshow('img',img)
cv2.imshow('dst',dst)
cv2.waitKey(0)
  • 中值滤波
import numpy as np
import cv2

img = cv2.imread('../1.jpg')

dst = cv2.medianBlur(img,5)

cv2.imshow('img',img)
cv2.imshow('dst',dst)
cv2.waitKey(0)
  • 双边滤波
import numpy as np
import cv2

img = cv2.imread('../1.jpg')

dst = cv2.bilateralFilter(img,5,20,50)

cv2.imshow('img',img)
cv2.imshow('dst',dst)
cv2.waitKey(0)
  • 高通滤波
import numpy as np
import cv2

img = cv2.imread('./12.png')

# 索贝尔算子x方向边缘
dst = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5)
# 索贝尔算子y方向边缘
dst1 = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5)
dst2 = cv2.add(dst,dst1)
cv2.imshow('img',img)
cv2.imshow('dst',dst2)
cv2.waitKey(0)

import numpy as np
import cv2

img = cv2.imread('./12.png')

# 沙尔算子x方向边缘
dst = cv2.Scharr(img,cv2.CV_64F,1,0)
# 沙尔算子y方向边缘
dst1 = cv2.Scharr(img,cv2.CV_64F,0,1)
dst2 = cv2.add(dst,dst1)
cv2.imshow('img',img)
cv2.imshow('dst',dst2)
cv2.waitKey(0)

import numpy as np
import cv2

img = cv2.imread('./12.png')

# 拉普拉斯算子边缘,需先进行降噪
dst = cv2.Laplacian(img,cv2.CV_64F,ksize=5)

cv2.imshow('img',img)
cv2.imshow('dst',dst)
cv2.waitKey(0)

边缘检测

import numpy as np
import cv2

img = cv2.imread('../1.jpg')

# Canny
dst = cv2.Canny(img,100,200)

cv2.imshow('img',img)
cv2.imshow('dst',dst)
cv2.waitKey(0)

形态学图像处理

  • 图像二值化
import numpy as np
import cv2

img = cv2.imread('../1.jpg')
img1 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

ret,img2 = cv2.threshold(img1,180,255,cv2.THRESH_BINARY)

cv2.imshow('img',img)
cv2.imshow('img1',img1)
cv2.imshow('img2',img2)

cv2.waitKey(0)

import numpy as np
import cv2

img = cv2.imread('../1.jpg')
img1 = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

dst = cv2.adaptiveThreshold(img1,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,3,0)

cv2.imshow('img',img)
cv2.imshow('img1',img1)
cv2.imshow('img2',dst)

cv2.waitKey(0)
  • 腐蚀
  • 膨胀
import numpy as np
import cv2

img = cv2.imread('./img.png')

# kernel = np.ones((3,3),np.uint8)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(3,3))
# 腐蚀
img1 = cv2.erode(img,kernel,iterations = 1)
# 膨胀
img2 = cv2.dilate(img1,kernel,iterations = 1)

cv2.imshow('img',img)
cv2.imshow('img1',img1)
cv2.imshow('img2',img2)

cv2.waitKey(0)
  • 开运算
    • 腐蚀 + 膨胀
  • 闭运算
    • 膨胀 + 腐蚀
  • 梯度
  • 顶帽
    • 原图 - 开运算
  • 黑帽
    • 原图 - 闭运算
import numpy as np
import cv2

img = cv2.imread('./img_1.png')
img3 = cv2.imread('./img_3.png')
img6 = cv2.imread('./img_2.png')
imgd = cv2.imread('./img_4.png')

# kernel = np.ones((3,3),np.uint8)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(7,7))
# 开运算
img2 = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
# 闭运算
img33 = cv2.morphologyEx(img3, cv2.MORPH_CLOSE, kernel)
# 梯度
img66 = cv2.morphologyEx(img6, cv2.MORPH_GRADIENT, kernel)
# 顶帽
imgdd = cv2.morphologyEx(imgd, cv2.MORPH_TOPHAT, cv2.getStructuringElement(cv2.MORPH_RECT,(19,19)))
# 黑帽
imgh = cv2.morphologyEx(img3, cv2.MORPH_BLACKHAT, kernel)

cv2.imshow('img2',img2)
cv2.imshow('img33',img33)
cv2.imshow('img66',img66)
cv2.imshow('imgdd',imgdd)
cv2.imshow('imgh',imgh)
cv2.waitKey(0)

图像轮廓

import numpy as np
import cv2

img = cv2.imread('./img_5.png')

# 转为单通道
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 二值化
ret,img1 = cv2.threshold(gray,150,255,cv2.THRESH_BINARY)
# 查找轮廓
contours,hierarchy = cv2.findContours(img1,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# 绘制轮廓
cv2.drawContours(img,contours,-1,(0,0,255),1)
# 计算面积
area = cv2.contourArea(contours[0])
print(area)
# 计算周长
len = cv2.arcLength(contours[0],True)
print(len)

cv2.imshow('img', img)
cv2.waitKey(0)

import numpy as np
import cv2

img = cv2.imread('./img_6.png')

# 转为单通道
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 二值化
ret,img1 = cv2.threshold(gray,150,255,cv2.THRESH_BINARY)
# 查找轮廓
contours,hierarchy = cv2.findContours(img1,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
# 绘制轮廓
cv2.drawContours(img,contours,0,(255,0,0),1)
# 多边形逼近
approx = cv2.approxPolyDP(contours[0],20,True)
# 多边形凸包
hull = cv2.convexHull(contours[0])

def drawShape(img, approx):
    i = 0
    while i < len(approx):
        if i == len(approx) - 1:
            x, y = approx[i][0]
            x1, y1 = approx[0][0]
            cv2.line(img, (x, y), (x1, y1), (0, 0, 255), 1)
        else:
            x, y = approx[i][0]
            x1, y1 = approx[i + 1][0]
            cv2.line(img, (x, y), (x1, y1), (0, 0, 255), 1)
        i = i+1

drawShape(img,approx)
drawShape(img,hull)

cv2.imshow('img', img)
cv2.waitKey(0)

import numpy as np
import cv2


img = cv2.imread('./img_7.png')

# 转为单通道
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 二值化
ret,img1 = cv2.threshold(gray,150,255,cv2.THRESH_BINARY)
# 查找轮廓
contours,hierarchy = cv2.findContours(img1,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)

# 最小外接矩形
r = cv2.minAreaRect(contours[1]) 
box = cv2.boxPoints(r)
box = np.int8(box)
cv2.drawContours(img,[box],0,(0,0,255),2)

# 最大外接矩形
x,y,w,h = cv2.boundingRect(contours[1])
cv2.rectangle(img,(x,y),(x+w,y+h),(0,255,0),2)

cv2.imshow("img",img)
cv2.waitKey(0)

特征点检测

  • 哈里斯角点检测
  • Shi-Tomasi 角点检测
  • SIFT
  • SURF
  • ORB
import numpy as np
import cv2

img = cv2.imread('./12.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# dst = cv2.cornerHarris(gray, 2, 3, 0.04)
# img[dst>0.01*dst.max()] = [0,0,255]

# corners = cv2.goodFeaturesToTrack(gray, 1000, 0.01, 10)
# corners = np.int8(corners)
# for i in corners:
#     x,y = i.ravel()
#     cv2.circle(img,(x,y),3,(255,0,0),-1)

# sift = cv2.SIFT.create()
# kp,des = sift.detectAndCompute(gray, None)
# cv2.drawKeypoints(gray,kp,img)

orb = cv2.ORB.create()
kp,des = orb.detectAndCompute(gray,None)
cv2.drawKeypoints(gray,kp,img)

cv2.imshow("img",img)
cv2.waitKey(0)

特征匹配

  • BF
import numpy as np
import cv2

img1 = cv2.imread('./img.png')
img2 = cv2.imread('./img_1.png')
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

sift = cv2.SIFT.create()
kp1,des1 = sift.detectAndCompute(gray1, None)
kp2,des2 = sift.detectAndCompute(gray2, None)

bf = cv2.BFMatcher(cv2.NORM_L1)
matches = bf.match(des1, des2)
img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches, None, flags=2)

cv2.imshow("img",img3)
cv2.waitKey(0)
  • FLANN
import numpy as np
import cv2

img1 = cv2.imread('./img.png')
img2 = cv2.imread('./img_1.png')
gray1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)

sift = cv2.SIFT.create()
kp1,des1 = sift.detectAndCompute(gray1, None)
kp2,des2 = sift.detectAndCompute(gray2, None)


flann = cv2.FlannBasedMatcher( dict(trees=5,algorithm=1),dict(checks=50))
matches = flann.knnMatch(des1, des2,k=2)

good = []
for i,(m,n) in enumerate(matches):
    if m.distance < 0.7*n.distance:
        good.append(m)

ret = cv2.drawMatchesKnn(img1,kp1,img2,kp2,[good],None)

cv2.imshow("img",ret)
cv2.waitKey(0)

图像分割

import numpy as np
import cv2

img = cv2.imread("./img.png")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)

kernel = np.ones((3,3),np.uint8)
open1 = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel,iterations=2)

bg = cv2.dilate(open1,kernel,iterations=2)

dist = cv2.distanceTransform(open1,cv2.DIST_L2,5)
re1t,fg = cv2.threshold(dist,0.7*dist.max(),255,cv2.THRESH_BINARY)

fg = np.uint8(fg)
unknow = cv2.subtract(bg,fg)

ret3,marker = cv2.connectedComponents(fg)
marker = marker + 1
marker[unknow == 255] = 0

result = cv2.watershed(img,marker)

img[result == -1] = [0,0,255]

cv2.imshow("img",img)
cv2.waitKey(0)

import numpy as np
import cv2


class App:
    rect = (0,0,0,0)
    flag_rect = False
    startX = 0
    startY = 0

    def onmouse(self,event,x,y,flags,param):
        print("onmouse")

        if event == cv2.EVENT_LBUTTONDOWN:
            self.flag_rect = True
            self.startX = x
            self.startY = y
        elif event == cv2.EVENT_LBUTTONUP:
            self.flag_rect = False
            cv2.rectangle(self.img,(self.startX,self.startY),(x,y),(0,0,255),2)

            self.rect = (min(self.startX,x),min(self.startY,y),abs(self.startX - x),abs(self.startY - y))
            print("左键抬起")
        elif event == cv2.EVENT_MOUSEMOVE:
            if self.flag_rect:
                self.img = self.img2.copy()
                cv2.rectangle(self.img,(self.startX,self.startY),(x,y),(255,0,0),2)
            print("移动")


    def run(self):
        print("run")
        cv2.namedWindow('input')
        cv2.setMouseCallback('input',self.onmouse)

        self.img = cv2.imread('./img.png')
        self.img2 = self.img.copy()
        self.mask = np.zeros(self.img.shape[:2],np.uint8)
        self.output = np.zeros(self.img.shape,np.uint8)

        while 1:
            cv2.imshow('input',self.img)
            cv2.imshow('output',self.output)
            k = cv2.waitKey(100)
            if k == 27:
                break
            if k == ord('g'):
                bgmodel = np.zeros((1,65),np.float64)
                fgmodel = np.zeros((1,65),np.float64)
                cv2.grabCut(self.img2,self.mask,self.rect,bgmodel,fgmodel,1,cv2.GC_INIT_WITH_RECT)

            mask2 = np.where((self.mask == 1)|(self.mask == 3),255,0).astype('uint8')
            self.output = cv2.bitwise_and(self.img2,self.img2,mask=mask2)


App().run()


import numpy as np
import cv2

img = cv2.imread("./img.png")

img2 = cv2.pyrMeanShiftFiltering(img, 20, 30)

img3 = cv2.Canny(img2, 150, 300)

contours,_ = cv2.findContours(img3, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img, contours, -1, (0,0,255), 2)

cv2.imshow("img", img)
cv2.imshow("img2", img2)
cv2.imshow("img3", img3)

cv2.waitKey(0)

import numpy as np
import cv2

cap = cv2.VideoCapture('../2.mp4')

mog = cv2.createBackgroundSubtractorMOG2()

while cap.isOpened():
    ret, frame = cap.read()
    fgmask = mog.apply(frame)

    cv2.imshow('frame', fgmask)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

图像修复

import numpy as np
import cv2

img = cv2.imread("./img_1.png")
mask = cv2.imread("./img_2.png",0)
mask = cv2.resize(mask,(img.shape[1],img.shape[0]))
print(img.shape)
print(mask.shape)
dst = cv2.inpaint(img,mask,3,cv2.INPAINT_TELEA)

cv2.imshow("img",dst)
cv2.waitKey(0)

哈尔级联法

人脸识别

import numpy as np
import cv2

# 创建haar级联器
facer = cv2.CascadeClassifier(
    'D:\SoftWare\py\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml'
)

img = cv2.imread('./img_3.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

faces = facer.detectMultiScale(gray,1.1,5)

for (x, y, w, h) in faces:
    cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)

cv2.imshow('img', img)
cv2.waitKey(0)

import numpy as np
import cv2

# 创建haar级联器
facer = cv2.CascadeClassifier(
    'D:\SoftWare\py\Lib\site-packages\cv2\data\haarcascade_frontalface_default.xml'
)

img = cv2.VideoCapture(0)

while True:
    ret,frame = img.read()

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    faces = facer.detectMultiScale(gray,1.1,5)

    for (x, y, w, h) in faces:
        cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 2)

    cv2.imshow('img', frame)
    k = cv2.waitKey(10)
    if k == 27:
        break
img.release()
cv2.destroyAllWindows()
  • 眼
import numpy as np
import cv2

# 眼
facer = cv2.CascadeClassifier(
    'D:\SoftWare\py\Lib\site-packages\cv2\data\haarcascade_eye.xml'
)

img = cv2.imread('./img_3.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

faces = facer.detectMultiScale(gray,1.1,5)


for (x, y, w, h) in faces:
    cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)

cv2.imshow('img', img)
cv2.waitKey(0)

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