# -*- coding:utf-8 _*-
"""
@Author:tzting
@File: 像素运算06与07.py
@Time: 2020/11/12 11:14
"""
"""
像素运算：算术运算与逻辑运算
算术运算：加、减、乘、除；应用它可以调节亮度；调整对比度
逻辑运算：与、或、非；应用-遮罩层控制
代码层面知识点：
常见图像混合
算法运算与几何运算
"""
import tensorflow as tf
import cv2 as cv
import numpy as np


# 算术运算
def add_demo(m1,m2):  # 加
    dst = cv.add(m1,m2)
    cv.imshow("add_demo",dst)


def subtract_demo(m1,m2):  # 减
    dst = cv.subtract(m1,m2)
    cv.imshow("subtract_demo",dst)


def multipy_demo(m1, m2):  # 乘
    dst = cv.multiply(m1, m2)
    cv.imshow("multipy_demo", dst)


def divide_demo(m1, m2):  # 乘
    dst = cv.divide(m1, m2)
    cv.imshow("divide_demo", dst)


def others(m1, m2):
    # M1 = cv.mean(m1)  # 均值 图像是数组
    # M2 = cv.mean(m2)
    M1, dev1 = cv.meanStdDev(m1)  # 得到均值和方差 ,方差越大，说明图像之间差异性越大
    M2, dev2 = cv.meanStdDev(m2)
    h,w = m1.shape[:2]
    print(M1)
    print(M2)
    print(dev1)
    print(dev2)
    img = np.zeros([h,w],np.uint8)
    M, dev = cv.meanStdDev(img)
    print(M)
    print(dev)


# 逻辑运算
def logic_demo(m1):  # ,m2):
    #dst = cv.bitwise_and(m1,m2)  # 与运算
    #dst = cv.bitwise_or(m1, m2)  # 或运算
    dst = cv.bitwise_not(m1)    # 或对一张图像而言
    cv.imshow("logic_demo", dst)


def extract_object_demo():  # 对颜色对象很快的提取出来
    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
        dst = cv.bitwise_and(frame, frame, mask=mask)
        cv.imshow("video",frame)
        cv.imshow("mask",dst)
        c = cv.waitKey(40)
        if c == 27:  # braek的值
            break;


def contrast_brightness_demo(image,c,b):  # 调整亮度b和对比度c
    h,w,ch = image.shape
    blank = np.zeros([h,w,ch],image.dtype)
    dst = cv.addWeighted(image,c,blank,1-c,b)
    cv.imshow("contrast_brightness_demo",dst)


src2 = cv.imread("C:/Users/tzt/Desktop/opencv-python/lena.png")
src1 = cv.imread("C:/Users/tzt/Desktop/opencv-python/contours.png")
print(src1.shape)
print(src2.shape)
# cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
cv.imshow("sc1", src1)
cv.imshow("sc2", src2)
"""
add_demo(src1,src2)   # 大于255截断为255，黑色为0
subtract_demo(src1,src2) # 小于0截断为0
multipy_demo(src1,src2)
divide_demo(src1,src2)
others(src1,src2)
logic_demo(src2)
extract_object_demo()
"""
contrast_brightness_demo(src2,1.2,100)

cv.waitKey(0)
cv.destroyAllWindows()