cv(二)

1.cv读视频的时候会分析内容，然后对每一帧进行处理，cv并不是音视频的编码解码的工具，cv是要对读过来的内容进行对象检测，对象跟踪，音视频的编码解码是由ffmg来做的，所以cv读出来的视频是没有声音的，对视频大小是有一定限制的。

2.

import cv2 as cv
import numpy as np


def access_pixels(image):
    print(image.shape)
    height = image.shape[0]  # 高
    width = image.shape[1]  # 宽
    channels = image.shape[2]  # 通道数
    print('widti: %s, height: %s, channels: %s' %(height, width, channels))
    # 获取像素值
    for row in range(height):
        for col in range(width):
            for c in range(channels):
                pv = image[row, col, c]
                image[row, col, c] = 255 - pv  # 图像反转
    cv.imshow('pixels_demo', image)


def create_image():
    img = np.zeros((400, 400, 3), np.uint8)  # 创建一张三通道图
    img[:, :, 0] = np.ones((400, 400))*255  # 修改第一通道

    img2 = np.zeros((400, 400, 1), np.uint8)  # 创建单通道图像
    img2[:, :, 0] = np.ones((400, 400))*127

    m1 = np.ones((3, 3), np.uint8)
    m1.fill(12222.388)
    print(m1)
    m2 = m1.reshape((1, 9))  # 改变图像形状
    print(m2)

    cv.imshow('new image', img)


def inverse(image):
    dst = cv.bitwise_not(image)  # 像素取反,因为底层是c实现的，所以要比自己实现的access_pixels(src)要快很多
    cv.imshow('inverse image', dst)

    n3 = np.array([[2, 3, 4], [4, 5, 6], [7, 8, 9]], np.int32)
    n3.fill(9)
    print(n3)

print('--------Hello Python------------')
src = cv.imread('')  # 读图
t1 = cv.getTickCount()  # 获取当前cpu转的时钟圈数
access_pixels(src)
t2 = cv.getTickCount()  # 获取当前cpu转的时钟圈数
# 计算性能
time = (t2-t1)/cv.getTickFrequency()
print('time: %s ms'%(time * 1000))  # t2-t1:总圈数 (t2-t1)/cv.getTickFrequency()：每秒的圈数

3.色彩空间

import cv2 as cv
import numpy as np

'''
常见的色彩空间：RGB HSV HIS YCrCb(常用于皮肤特征提取) YUV
HSV: H(0-100) S(0-255) V(0-255)
'''
# 色彩空间转换，从bgr到gray，hsv，yuv，ycrcb
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)


# 从视频中提取指定颜色范围，并将其置为白，其余置为黑
def extract_object_demo():
    capture = cv.VideoCapture("cv.gif")
    while True:
        ret, frame = capture.read()

        if ret is False:  # 如果没有获取到视频帧则返回false
            break
        hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)  # 首先转换成hsv空间
        # lower_hsv和upper_hsv是根据表查出来的，不是随便填的
        lower_hsv = np.array([35, 43, 46])  # hsv中h，s，v的最小值
        upper_hsv = np.array([77, 255, 255])  # hsv中的h，s，v最大值

        # 提取指定颜色区域，保留指定颜色区域, 其余置为黑(0)
        mask = cv.inRange(hsv, lowerb=lower_hsv, upperb=upper_hsv)  # 用inRange函数提取指定颜色范围，这里对hsv来处理
        cv.imshow("video", frame)
        cv.imshow("mask", mask)

        c = cv.waitKey(40)
        if c == 27:  # 27是esc键
            break


# 对图片三个通道颜色提取并放在三张图片中
def channels_split_merge(image):
    # 通道分离
    b, g, r = cv.split(image)  # b或g或r通道取时，对该通道颜色保留，其余通道置为0
    cv.imshow("blue", b)
    cv.imshow("green", g)
    cv.imshow("red", r)

    changed_image = image.copy()

    changed_image[:, :, 2] = 0  # 将r通道颜色全部置为0
    cv.imshow("changed_image", changed_image)

    # 通道合并
    merge_image = cv.merge([b, g, r])
    cv.imshow("merge_image", merge_image)

if __name__ == '__main__':

    src = cv.imread("../images/CrystalLiu1.jpg")  # 读入图片放进src中
    cv.namedWindow("Crystal Liu", cv.WINDOW_AUTOSIZE)  # 创建窗口, 窗口尺寸自动调整
    cv.imshow("Crystal Liu", src)  # 将src图片放入该创建的窗口中
    # color_space_demo(src)
    # extract_object_demo()
    channels_split_merge(src)
    cv.waitKey(0)
    cv.destroyAllWindows()

4.像素运算

import cv2 as cv
import numpy as np

'''像素运算: 运算前需要保证两个图像大小必须一样
        算术运算：加减乘除，调节亮度，调节对比度
        逻辑运算：与或非'''

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


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

# 图像的逻辑运算，：AND，OR，NOT，XOR
def logic_demo(m1, m2):
    dst1 = cv.bitwise_and(m1, m2)  # 与  dst = cv.bitwise_and(frame, frame, mask=mask)
    dst2 = cv.bitwise_not(m1)  # 非
    dst3 = cv.bitwise_or(m1, m2)  # 或
    cv.imshow("logic_demo", dst1)

# 调节亮度和对比度
def contrast_brightness_demo(image, c, b):
    h, w, ch = image.shape
    blank = np.zeros([h, w, ch], image.dtype)  # 创建一个空白图像

    # 图像混合，c：对比度, 1-c为这两张图片的权重 b:亮度
    dst = cv.addWeighted(image, c, blank, 1-c, b)
    cv.imshow("contrast_brightness_demo", dst)


# 对照片像素做均值方差
def others(m1, m2):
    M1 = cv.mean(m1)  # 计算均值
    M2 = cv.mean(m2)
    print(M1)
    print(M2)

    mean1, dev1 = cv.meanStdDev(m1)  # 返回均值和方差，通过方差可以判断除像素之间的差异性
    print("均值：",mean1,"方差：", dev1)


if __name__ == '__main__':
    print("----------Hello World!----------")
    src1 = cv.imread("../images/01.jpg")
    src2 = cv.imread("../images/02.jpg")
    print(src1.shape)
    print(src2.shape)

    cv.namedWindow("image1", cv.WINDOW_AUTOSIZE)  # 创建窗口, 窗口尺寸自动调整
    cv.imshow("image1", src1)
    cv.imshow("image2", src2)

    # add_demo(src1, src2)
    # subtract_demo(src1, src2)
    # divide_demo(src1, src2)
    # multiply_demo(src1,src2)
    # others(src1, src2)

    # logic_demo(src1, src2)
    src = cv.imread("../images/CrystalLiu2.jpg")
    contrast_brightness_demo(src,1.3, 80)
    cv.waitKey(0)

    cv.destroyAllWindows()