1.图像的基本操作

import cv2
import matplotlib.pyplot as plt
import numpy as np

# 图像的基本操作
if __name__ == '__main__':
    img = cv2.imread('aa.jpg')  # cv2 默认读取格式为GBR
    img_color = cv2.imread('bb.jpg', cv2.IMREAD_COLOR)  # 读取彩色图片
    img_grey = cv2.imread('cc.jpg', cv2.IMREAD_GRAYSCALE)  # 读取灰色图片
    print(img)
    print(type(img))
    print(img.size)
    print(img.shape)
    cv2.imshow('img', img)
    cv2.imshow('img', img_color)
    cv2.imshow('img', img_grey)
    cv2.waitKey(0)  # 等待时间 毫秒，0表示任意按键终止
    cv2.imwrite('grey.jpg', img_grey)  # 图片保存
    cv2.destroyAllWindows()

2.视频读取与处理

import cv2
import matplotlib.pyplot as plt
import numpy as np

# 视频读取与处理
if __name__ == '__main__':
    vc = cv2.VideoCapture('w1.mp4')
    if vc.isOpened:
        open, frame = vc.read()  # 读取视频的第一帧
    else:
        open = False
    print(open)

    while open:
        ret, frame = vc.read()
        if frame is None:
            break
        if ret:
            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            cv2.imshow('grey', gray)
            if cv2.waitKey(10) & 0XFF == 27:
                break
    vc.release()
    cv2.destroyAllWindows()

3.ROI (region of interest) 感兴趣的区域

import cv2
import matplotlib.pyplot as plt
import numpy as np

# ROI (region of interest) 感兴趣的区域
if __name__ == '__main__':
    img = cv2.imread('aa.jpg')
    roi = img[0:100, 0:200]  # w, h
    b, g, r = cv2.split(img)  # 提取颜色通道
    print(type(b))
    print(b)
    print(b.size)
    print(b.shape)

    img = cv2.merge((b, g, r))  # 重新组合

    # 只保留B
    b_img = img.copy()
    b_img[:, :, 1] = 0
    b_img[:, :, 2] = 0
    # cv2.imshow('b_img', b_img)

    # 只保留G
    g_img = img.copy()
    g_img[:, :, 0] = 0
    g_img[:, :, 2] = 0
    cv2.imshow('g_img', g_img)

    # 只保留R
    r_img = img.copy()
    r_img[:, :, 0] = 0
    r_img[:, :, 1] = 0
    cv2.imshow('r_img', r_img)

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

4.边界填充

import cv2
import matplotlib.pyplot as plt
import numpy as np

# 边界填充
if __name__ == '__main__':
    img = cv2.imread('aa.jpg')
    top_size, bottom_size, left_size, right_size = (50, 50, 50, 50)
    replicate = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_REPLICATE)
    reflect = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_REFLECT)
    reflect101 = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_REFLECT_101)
    wrap = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_WRAP)
    constant = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_CONSTANT, value=0)

    '''
    BORDER_REPLICATE:复制法
                                                      <--          -->
    BORDER_REFLECT:反射法，对ROI的两侧像素进行复制，例如：fedcba|abcdefgh|hgfedcb
                                                      <--          -->
    BORDER_REFLECT_101: 反射法，以最边缘像素为轴，例如：gfedcb|abcdefgh|gfedcba
                           
    BORDER_WRAP:外包装法，cdefgh|abcdefgh|abcdefg
    
    BORDER_CONSTANT:常量发，常数值填充
    '''

    plt.subplot(231), plt.imshow(img, 'gray'), plt.title('ORIGINAL')
    plt.subplot(232), plt.imshow(replicate, 'gray'), plt.title('REPLICATE')
    plt.subplot(233), plt.imshow(reflect, 'gray'), plt.title('REFLECT')
    plt.subplot(234), plt.imshow(reflect101, 'gray'), plt.title('REFLECT_101')
    plt.subplot(235), plt.imshow(wrap, 'gray'), plt.title('WRAP')
    plt.subplot(236), plt.imshow(constant, 'gray'), plt.title('CONSTANT')

bjtc.png