OpenCV的色彩空间——RGB与BGR

主要用于物理硬件显示器中

OpenCV的色彩空间——HSV与HSL

HSV主要用于OpenCV中

HSL

OpenCV色彩空间转换

YUV主要用于视频中

详情见官网手册


cvtColor函数

# -*- coding: utf-8 -*-
import cv2def callback():pass# 创建窗口
cv2.namedWindow('color', cv2.WINDOW_NORMAL)img = cv2.imread('E://pic//love_girl_guy_beach_sea_peter_smile_couple_5760x3840.jpg')colorspaces = [cv2.COLOR_BGR2RGBA, cv2.COLOR_BGR2BGRA, cv2.COLOR_BGR2GRAY, cv2.COLOR_BGR2HSV, cv2.COLOR_BGR2YUV]cv2.createTrackbar('curcolor', 'color', 0, len(colorspaces), callback)while True:index = cv2.getTrackbarPos('curcolor', 'color')# 颜色空间转换APIcvt_img = cv2.cvtColor(img, colorspaces[index])cv2.imshow('color', cvt_img)key = cv2.waitKey(10)if key & 0xff == ord('q'):breakcv2.destroyAllWindows()

题目

A

图像操作的基石Numpy【基础操作】

np.array

np.zeros

np.ones

np.full

np.identity

np.eye

Numpy基本操作之矩阵的检索与赋值

# -*- coding: utf-8 -*-
import cv2
import numpy as npimg = np.zeros((480, 640), np.uint8)cnt = 0
while cnt < 200:img[cnt, 100] = 255cnt += 1cv2.imshow('img', img)
key = cv2.waitKey(0)
if key & 0xff == ord('q'):cv2.destroyAllWindows()

# -*- coding: utf-8 -*-
import cv2
import numpy as npimg = np.zeros((480, 640, 3), np.uint8)cnt = 0
while cnt < 200:# BGRimg[cnt, 100, 0] = 255cnt += 1cv2.imshow('img', img)
key = cv2.waitKey(0)
if key & 0xff == ord('q'):cv2.destroyAllWindows()

# -*- coding: utf-8 -*-
import cv2
import numpy as npimg = np.zeros((480, 640, 3), np.uint8)cnt = 0
while cnt < 200:img[cnt, 100] = [0, 0, 255]cnt += 1cv2.imshow('img', img)
key = cv2.waitKey(0)
if key & 0xff == ord('q'):cv2.destroyAllWindows()

Numpy基本操作三——ROI(Region of Image)

# -*- coding: utf-8 -*-
import cv2
import numpy as npimg = np.zeros((480, 640, 3), np.uint8)# cnt = 0
# while cnt < 200:
#     img[cnt, 100] = [0, 0, 255]
#     cnt += 1
roi = img[100:400, 200:600]
roi[:] = [0, 255, 0]cv2.imshow('img', roi)
key = cv2.waitKey(0)
if key & 0xff == ord('q'):cv2.destroyAllWindows()

# -*- coding: utf-8 -*-
import cv2
import numpy as npimg = np.zeros((480, 640, 3), np.uint8)# cnt = 0
# while cnt < 200:
#     img[cnt, 100] = [0, 0, 255]
#     cnt += 1
roi = img[100:400, 100:600]
roi[:] = [0, 255, 0]
roi[:, 100] = [0, 0, 0]
roi[10:200, 10:200] = [255, 0, 0]cv2.imshow('img', roi)
key = cv2.waitKey(0)
if key & 0xff == ord('q'):cv2.destroyAllWindows()

OpenCV的重要结构体Mat

Mat属性

Mat的深拷贝与浅拷贝

红色是我们原先创建的mat，蓝色是我们之后创建的mat，我们把A赋给B,默认浅拷贝

Mat浅拷贝

拷贝的是header，并没有拷贝data

Mat深拷贝

让Mat B独立，拷贝了一份data，完全和Mat A独立开来

# -*- coding: utf-8 -*-
import cv2
import numpy as npimg = cv2.imread('E://pic//2.jpg')# 默认浅拷贝
img2 = img# 深拷贝
img3 = img.copy()img[10:100, 10:100] = [0, 255, 66]
cv2.imshow('img', img)
cv2.imshow('img2', img2)
cv2.imshow('img3', img3)
key = cv2.waitKey(0)
if key & 0xff == ord('q'):cv2.destroyAllWindows()

图像的多种属性

# -*- coding: utf-8 -*-
import cv2
import numpy as npimg = cv2.imread('E://pic//2.jpg')
# shape属性中包含三个信息
# 高度、长度、通道数
print(img.shape)# 图像占用多大空间
# 高度 * 长度 * 通道数
print(img.size)# 图像中每个元素的位深
print(img.dtype)

通道的分割与合并

变成一个颜色的二维数组就没有颜色了，就只有黑白色之分了

# -*- coding: utf-8 -*-
import cv2
import numpy as npimg = cv2.imread('E://pic//2.jpg')b, g, r = cv2.split(img)
b[10:100, 10:100] = 255
g[10:100, 10:100] = 255
r[10:100, 10:100] = 255cv2.imshow('img', img)
cv2.imshow('b', b)
cv2.imshow('g', g)
cv2.imshow('r', r)key = cv2.waitKey(0)
if key & 0xff == ord('q'):cv2.destroyAllWindows()

# -*- coding: utf-8 -*-
import cv2
import numpy as npimg = cv2.imread('E://pic//2.jpg')b, g, r = cv2.split(img)
b[10:100, 10:100] = 255
g[10:100, 10:100] = 255
# r[10:100, 10:100] = 255img2 = cv2.merge((b, g, r))cv2.imshow('img', img)
cv2.imshow('b', b)
cv2.imshow('g', g)
cv2.imshow('r', r)
cv2.imshow('img2', img2)key = cv2.waitKey(0)
if key & 0xff == ord('q'):cv2.destroyAllWindows()

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