Opencv基于python的基本操作(一)

一.OpenCV在win7系统的安装

 

测试代码 (读入图片并展示):

import cv2 as cv

src= cv.imread("D:\opencv-test\EBK7%HI(P%)(FV1(`J034D3.png")
cv.namedWindow("input image",cv.WINDOW_AUTOSIZE)
cv.imshow("input image",src)
cv.waitKey(0)
cv.destroyAllWindows()

二.图片的加载和保存

代码分析:

'''
图像加载与保存
'''
import numpy as np
import cv2 as cv

def video_demo():
    #开视频
    capture = cv.VideoCapture(r'D:\opencv-test\007、Tensorflow案例实战视频课程07 逻辑回归框架.mp4')
     #开摄像头
    # capture = cv.VideoCapture(0)

    while(True):
       ret,frame= capture.read()
       # frame = cv.flip(frame,1)
       cv.imshow("video",frame)
       c = cv.waitKey(50)
       #ESC退出
       if c == 27:
         break

def get_image_info(image):
    print(type(image))
    print(image.shape)
    print(image.size)
    print(image.dtype)
    pixel_data = np.array(image)
    print(pixel_data)

src= cv.imread("D:\opencv-test\EBK7%HI(P%)(FV1(`J034D3.png")
cv.namedWindow("input image",cv.WINDOW_AUTOSIZE)
# 展示图片
cv.imshow("input image",src)
# get_image_info(src)
#展示视频
# video_demo()
cv.waitKey(0)
#写入保存
# cv.imwrite(r'D:\opencv-test\result.png',src)
cv.destroyAllWindows()

三.numpy修改图片像素等操作

代码实现:

'numpy修改图片像素等操作'

import numpy as np
import cv2 as cv
import time

def get_image_info(image):
    print(image.shape)
    height = image.shape[0]
    width= image.shape[1]
    channels = image.shape[2]
    print("height:%s,width:%s,channels:%s"%(height,width,channels))
    #取反操作
    for i in range(height):
        for j in range(width):
            for k in range(channels):
                m = image[i,j,k]
                image[i,j,k]=255-m
    cv.imshow('new image',image)
#取反函数
def inverse(image):
    inv = cv.bitwise_not(image)
    cv.imshow('inv',inv)
#创建新像片
def create_image():
    #多通道
    # img = np.zeros([400,400,3],np.uint8)
    # img[:,:,0] = np.ones([400,400])*255
    # img[:,:,2] = np.ones([400,400])*255
    #单通道
    # img = np.zeros([400, 400,1], np.uint8)
    # img[:,:,0]=np.ones([400,400])*127
    img = np.ones([400, 400, 1], np.uint8)
    img = img*127
    cv.imshow('black',img)
src= cv.imread(r"D:\opencv-test\9807657b93fbe2067c1f394af8fd674.png")
# cv.namedWindow("input image",cv.WINDOW_AUTOSIZE)
t1 = time.time()
# cv.imshow("input image",src)
inverse(src)
t2= time.time()
print(t2-t1)
# get_image_info(src)
# create_image()

cv.waitKey(0)


cv.destroyAllWindows()

四.颜色空间:

HSV(Hue, Saturation, Value)是根据颜色的直观特性由A. R. Smith在1978年创建的一种颜色空间, 也称六角锥体模型(Hexcone Model)。

这个模型中颜色的参数分别是：色调（H），饱和度（S），亮度（V）。

除了HSV,还有其他色彩空间:

代码如下(选取一个颜色HSV范围,视频中的该颜色变为白,其他颜色为黑):

import cv2 as cv
import numpy as np


def extract():
    capture = cv.VideoCapture(r'D:\opencv-test\007、Tensorflow案例实战视频课程07 逻辑回归框架.mp4')
    while True:
        ret,frame=capture.read()
        if not ret:
            break
        hsv = cv.cvtColor(frame,cv.COLOR_BGR2HSV)
        lower_hsv = np.array([37,43,46])
        upper_hsv = np.array([77,255,255])
        mask = cv.inRange(hsv,lowerb=lower_hsv,upperb=upper_hsv)
        #黑白视频
        cv.imshow('mask',mask)
        #本色视频
        cv.imshow('video',frame)
        c = cv.waitKey(40)
        if c ==27:
            break

def color_space_demo(image):
    gray = cv.cvtColor(image,cv.COLOR_BGR2GRAY)
    cv.imshow("gray",gray)
    hsv = cv.cvtColor(image,cv.COLOR_BGR2HSV)
    cv.imshow('hsv',hsv)
    yuv = cv.cvtColor(image, cv.COLOR_BGR2YUV)
    cv.imshow('yuv',yuv)
    ycrcb= cv.cvtColor(image, cv.COLOR_BGR2YCrCb)
    cv.imshow('YCRCB', ycrcb)

src= cv.imread("D:\opencv-test\9807657b93fbe2067c1f394af8fd674.png")
# cv.namedWindow("input image",cv.WINDOW_AUTOSIZE)
# cv.imshow("input image",src)
# color_space_demo(src)
extract()
#三元色分割
# b,g,r =cv.split(src)
# cv.imshow('blue',b)
# cv.imshow('green',g)
# cv.imshow('red',r)
#三原色合并
# src = cv.merge([b,g,r])
#更改其中一个元色
# src[:,:,0]=0
# cv.imshow('changed image',src)



cv.waitKey(0)

cv.destroyAllWindows()