# !usr/bin/env python
# -*- coding:utf-8 _*-
"""
@Author:tzting
@File: 色彩空间.py
@Time: 2020/11/11 21：20
"""
import tensorflow as tf
import cv2 as cv
import numpy as np
"""
色彩空间
常见色彩空间  RGB，HSV（H：0-180，S：0-255，V:0-255)归一化，主要用来找某一特征颜色，明显差异化，HIS，YCrCb检测皮肤颜色
代码层面知识点：
1、色彩空间转换API
2、学会用inRange色彩空间分离
3、通道分离与合并

"""

# 色彩空间转换
def color_space_demo(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    cv.imshow("gray",gray)
    hsv = cv.cvtColor(image,cv.COLOR_BGR2HSV)  # 颜色物体的跟踪非常有用，关键一步
    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)


# 读视频
def extract_object_demo():
    capture = cv.VideoCapture("C:/Users/tzt/Desktop/opencv-python/ex.mp4")
    while(True):
        ret, frame = capture.read()  # 如果有的话返回真，如果没有的话，返回flase,frame返回图像，ret返回值
        if ret == False:
            break;
        # 用inrange色彩空间分离，不过要先转化为hsv图像空间！！！
        hsv = cv.cvtColor(frame,cv.COLOR_BGR2HSV)
        lower_hsv = np.array([35,43,46])  # 绿色的低值
        higher_hsv = np.array([77,255,255])  # 绿色的高值
        mask = cv.inRange(hsv, lower_hsv,higher_hsv)  # 得到二值图像
        cv.imshow("video",frame)
        cv.imshow("mask",mask)
        c = cv.waitKey(40)
        if c == 27:  # braek的值
            break;


src = cv.imread("C:/Users/tzt/Desktop/opencv-python/girl.jpg")
cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("input image", src)
# extract_object_demo()
# 通道分离与合并
b, g, r =cv.split(src)  # 分离
cv.imshow("blue",b)
cv.imshow("green",g)
cv.imshow("red", r)

# 对某一通道进行赋值
# src[:,:, 2] = 0  # 将红色通道赋值为0
src = cv.merge([b, g, r])  # 通道合并
src[:,:, 2] = 0  # 将红色通道赋值为0
cv.imshow("changed image", src)

cv.waitKey(0)
cv.destroyAllWindows()

附一张hsv空间色彩对应的图

image.png