import numpy as np
import cv2 as cv

# 读取图片
im = cv.imread('test_img.png')
imgray = cv.cvtColor(im,cv.COLOR_BGR2GRAY)

# 获取轮廓
ret, thresh = cv.threshold(imgray,127,255,0)
contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE,cv.CHAIN_APPROX_SIMPLE)

# 注释以下代码，或 调整数值 “0.1” 以获得不同的精度
epsilon = 0.01*cv.arcLength(contours[0],True)
contours = cv.approxPolyDP(contours[0],epsilon,True)


# 展示
cv.drawContours(im,contours, -1,(0,255,0),3)
cv.imshow('result',im)
cv.waitKey(0)

# 释放资源
cv.destroyAllWindows()

import numpy as np
import cv2 as cv

# 读取图片
im = cv.imread('test.png')
imgray = cv.cvtColor(im,cv.COLOR_BGR2GRAY)

# 获取轮廓
ret, thresh = cv.threshold(imgray,127,255,0)
contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE,cv.CHAIN_APPROX_SIMPLE)

# 展示
cv.drawContours(im,contours, -1,(0,255,0),3)
cv.imshow('result',im)
cv.waitKey(0)

# 释放资源
cv.destroyAllWindows()

import cv2 as cv
import numpy as np

cap = cv.VideoCapture(0)

while(1):
    # 获取每一帧数据
    _, frame = cap.read()

    # 将图像从 BGR 转 HSV
    hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)

    # 定义 HSV 中的黄色范围
    lower_blue = np.array([110,50,50])
    upper_blue = np.array([130,255,255])

    # 对 HSV 图像设置阈值，使其只显示蓝色
    mask = cv.inRange(hsv, lower_blue, upper_blue)

    # 使用 mask 遮罩层显示图像
    res = cv.bitwise_and(frame,frame, mask=mask)

    cv.imshow('frame', frame)
    cv.imshow('mask', mask)
    cv.imshow('res',res)
    k = cv.waitKey(5) % 0xFF
    if k == 27:
        break

cv.destroyAllWindows()

>>> green = np.uint8([[[0,255,0 ]]])
>>> hsv_green = cv.cvtColor(green,cv.COLOR_BGR2HSV)
>>> print( hsv_green )
[[[ 60 255 255]]]

import cv2 as cv

# 获取当前时间（1）
e1 = cv.getTickCount()

# 你的一些耗时的代码
for i in range(10000):
    print(i)

# 获取当前时间（2）
e2 = cv.getTickCount()
# 计算时间差
time = (e2 - e1) / cv.getTickFrequency()

# 打印耗时
print('耗时（秒）',time)

....
9996
9997
9998
9999
耗时（秒） 0.6926358

import cv2 as cv

# 加载两张图片
img1 = cv.imread('messi.jpeg')
img2 = cv.imread('opencv-logo.png')

# 我希望把 LOGO 防止到左上角，所以创建了一个区域（roi)
rows,cols,channels = img2.shape
roi = img1[0:rows, 0:cols]

# 将 LOGO 图转换为灰度图，阈值为 10， 最大值为 255 
img2gray = cv.cvtColor(img2,cv.COLOR_BGR2GRAY)
ret, mask = cv.threshold(img2gray, 10, 255, cv.THRESH_BINARY)
mask_inv = cv.bitwise_not(mask)

# 现在将 ROI 抠出黑色区域
img1_bg = cv.bitwise_and(roi,roi,mask = mask_inv)

# 抠出 LOGO 图像
img2_fg = cv.bitwise_and(img2,img2,mask = mask)

# 将抠出的 LOGO 防入 ROI 区域中
dst = cv.add(img1_bg,img2_fg)
img1[0:rows, 0:cols ] = dst

# 显示图像
cv.imshow('res',img1)

# 退出，并释放资源
cv.waitKey(0)
cv.destroyAllWindows()

import cv2 as cv

# 读取图像文件
img1 = cv.imread('ml.png')
img2 = cv.imread('opencv-logo.png')

# 图像重叠
dst = cv.addWeighted(img1,0.3,img2,0.7,0)

# 显示图像
cv.imshow('dst',dst)
cv.waitKey(0)

# 退出程序，释放资源
cv.destroyAllWindows()