opencv 图像处理

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opencv 图像

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读取图像资源

import cv2 as cv

# opencv读取图片资源 numpy 类型
src = cv.imread("./tim.jpeg")

# 设置窗口
cv.namedWindow('img window', cv.WINDOW_AUTOSIZE)

# 显示图像
cv.imshow("img window", src)

# 任意键退出
cv.waitKey(0)

# 销毁窗口
cv.destroyAllWindows()

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图像信息

• 宽和高
• 通道数量
• 像素数据
• 图像类型

import cv2 as cv

# 获取视频
def get_video():
    # 获取第几号摄像头
    video = cv.VideoCapture(0)
    while(True):
        # 读取摄像头帧数据
        ret, frame = video.read()
        # 翻转帧数据
        frame = cv.flip(frame, 1)
        # 显示视频
        cv.imshow("video view", frame)
        c = cv.waitKey(50)
        if  c == 27 :
            break


# 保存图像
def save_img(img):
    # 图像灰度处理
    gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
    # 写入磁盘
    cv.imwrite('./copy.jpg', gray)

# 读取图像信息
def get_img_info(img):
    # 图像资源类型
    print(type(img))
    # 图像长、宽、通道数量
    print(img.shape)
    # 图像大小
    print(img.size)
    # 每个通道占字节
    print(img.dtype)

src = cv.imread("./tim.jpeg")

save_img(src)

get_img_info(src)

get_video();

cv.waitKey(0)

cv.destroyAllWindows()

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numpy 处理图像数据

import cv2 as cv

def show_img(src):
    shape = src.shape
    print(shape)
    # 图像高度
    height = shape[0]
    # 图像宽度
    width = shape[1]
    # 色彩通道
    channels = shape[2]

    # 反向rgb色彩
    for row  in range(height):
        for col in range(width):
            for c in range(channels):
                src[row, col, c] = 255 - src[row, col, c]
    cv.imshow('test', src)


time1 = cv.getTickCount()
src = cv.imread('./src.jpg')
time2 = cv.getTickCount()

# 获取修改图像信息消耗的时间
print( (time2-time1)/cv.getTickFrequency())

show_img(src)
cv.waitKey(0)
cv.destroyAllWindows()

import numpy as np
import cv2 as cv

# row300 col200 通道3 的三位数组，默认值为0 值类型为int8
d = np.zeros([300, 200, 3], np.uint8)

# 默认值为1 ， 修改通道
d[:, :, 0] = np.ones([300,200]) * 255  # blue green red


cv.imshow("test", d)

cv.waitKey(0)

cv.destroyAllWindows()

https://docs.scipy.org/doc/numpy/user/quickstart.html

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色彩空间

像素取反

import cv2 as cv

src = cv.imread('./src.jpg')
# 像素取反
dst = cv.bitwise_not(src)

cv.imshow("test", dst)

cv.waitKey(0)

cv.destroyAllWindows()

色彩之间相互转换

# RGB
# HSV
# HIS
# YCRCB
# YUV

grap = cv.cvtColor(src, cv.COLOR_BGR2GRAY)

cv.imshow('grap', grap)


hsv = cv.cvtColor(src, cv.COLOR_RGB2HSV)
cv.imshow('hsv', hsv)


his = cv.cvtColor(src, cv.COLOR_BGR2HLS)

cv.imshow('his', his)


ycrcb = cv.cvtColor(src, cv.COLOR_BGR2YCrCb)

cv.imshow('ycrcb', ycrcb)


yuv = cv.cvtColor(src, cv.COLOR_BGR2YUV)

cv.imshow('yuv', yuv)

HSV 色彩空间取值表

利用 inrange 对 HSV 图像使用二值化处理

import cv2  as cv
import numpy as np

video = cv.VideoCapture(0)
while(True):
    ret, frame = video.read()

    frame = cv.flip(frame, 1)

    lowerb = np.array([37, 43, 46])
    upperb = np.array([77, 255, 255])

    hsv = cv.inRange(frame, lowerb=lowerb, upperb=upperb)

    cv.imshow("video", frame)

    cv.imshow("hsv", hsv)

    key = cv.waitKey(50)
    
    if(key == 27):
        break

cv.destroyAllWindows()

通道拆分与合并

import cv2 as cv
import numpy as np

src = cv.imread("./src.jpg")

# 通道拆分
b, g, r = cv.split(src)

cv.imshow('blue', b)

cv.imshow('green', g)

cv.imshow('red', r)


# 通道合并
merge = cv.merge([b, g, r])

cv.imshow('merge', merge)

cv.waitKey(0)


cv.destroyAllWindows()

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像素运算

import cv2 as cv

# 加
cv.add()
# 减
cv.subtract()
# 乘
cv.multiply()
# 除
cv.divide()

# 均值
cv.mean()

# 方差
cv.meanStdDev()

# 与
cv.bitwise_and()
# 或
cv.bitwise_or()
# 非
cv.bitwise_not()

# 亮度对比度
cv.addWeighted()

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ROI 操作 和 泛洪填充

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模糊操作

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人脸检测

import cv2 as cv
face_cascade = cv.CascadeClassifier("/usr/local/lib/python3.6/site-packages/cv2/data/haarcascade_frontalface_default.xml")


video = cv.VideoCapture(0)

while(True):
    ret, frame = video.read()

    src = cv.flip(frame, 1)
    gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)

    faces = face_cascade.detectMultiScale(gray, 1.3, 1)
    for (x, y, w, h) in faces:
        cv.rectangle(src, (x, y), (x + w, y + h), (255, 0, 0), 2)
    cv.imshow('face', src)
    key = cv.waitKey(50)
    if key == 27:
        break


cv.destroyAllWindows()

Face++ 人脸识别

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https://blog.csdn.net/manyouxianfeng/article/details/78049648