4. OpenCV轨迹栏

会用到以下主要两个函数

cv2.createTrackbar(trackbarName, windowName, value, count, onChange)创建轨迹栏

主要参数：

• trackbarName：轨迹栏名称
• windowName：附加到的窗口名称
• value：默认值
• count：最大值
• onChange：执行的回调函数每次跟踪栏值更改，下面例子函数什么都不做

getTrackbarPos(trackbarname, winname)获取轨迹栏的位置

• trackbarname：轨迹栏名称
• winname：附加到的窗口名称

4.1 轨迹栏作为调色板

创建一个黑色图像，通过轨迹栏改变BGR三通道值来显示不同颜色

import numpy as np
import cv2


def nothing(x):
    pass


# 创建一个黑色的图像，一个窗口
img = np.zeros((512, 512, 3), np.uint8)
cv2.namedWindow('image')
# 创建颜色变化的轨迹栏
cv2.createTrackbar('R', 'image', 0, 255, nothing)
cv2.createTrackbar('G', 'image', 0, 255, nothing)
cv2.createTrackbar('B', 'image', 0, 255, nothing)
# 为 ON/OFF 功能创建开关
switch = '0 : OFF \n1 : ON'
cv2.createTrackbar(switch, 'image', 0, 1, nothing)
while (1):
    cv2.imshow('image', img)
    k = cv2.waitKey(1) & 0xFF
    if k == 27:
        break
    # 得到四条轨迹的当前位置
    r = cv2.getTrackbarPos('R', 'image')
    g = cv2.getTrackbarPos('G', 'image')
    b = cv2.getTrackbarPos('B', 'image')
    s = cv2.getTrackbarPos(switch, 'image')
    if s == 0:
        img[:] = 0
    else:
        img[:] = [b, g, r]
cv2.destroyAllWindows()

4.2 轨迹栏显示不同通道图像

OpenCV读取的是BGR形式，通过轨迹栏可以显示不同通道的图像，例如：只有R单通道的图像、将两通道合并的图像、将三通道合并为原图像。

• split()： 通道分离，(b, g, r)形式
• merge() ：通道合并，输入1通道或者3通道

import numpy as np
import cv2


def nothing(x):
    pass


# 将选择的通道设置为零矩阵，不显示
def zeros_channels(channel):
    zeros = np.zeros_like(channel)
    return zeros


img = cv2.imread('lena.jpg')
# 将3通道分离
b, g, r = cv2.split(img)
cv2.namedWindow('image')
# 创建三通道开关的轨迹栏
switch_r = 'R_channel'
cv2.createTrackbar(switch_r, 'image', 0, 1, nothing)
switch_g = 'G_channel'
cv2.createTrackbar(switch_g, 'image', 0, 1, nothing)
switch_b = 'B_channel'
cv2.createTrackbar(switch_b, 'image', 0, 1, nothing)

while (1):
    cv2.imshow('image', img)
    k = cv2.waitKey(1) & 0xFF
    if k == 27:
        break
    # 得到三条轨迹的当前位置
    s_r = cv2.getTrackbarPos(switch_r, 'image')
    s_g = cv2.getTrackbarPos(switch_g, 'image')
    s_b = cv2.getTrackbarPos(switch_b, 'image')

    if s_r == 1:
        if s_g == 1:
            if s_b == 1:
                img = cv2.merge([b, g, r])  # 对通道按照BGR的顺序合并生成图像bgr
            else:
                img = cv2.merge([zeros_channels(b), g, r]) # 不显示b通道
        else:
            if s_b == 1:
                img = cv2.merge([b, zeros_channels(g), r])  # 对通道按照BGR的顺序合并生成图像bgr
            else:
                img = cv2.merge([zeros_channels(b), zeros_channels(g), r]) # 不显示b, g通道
    else:
        if s_g == 1:
            if s_b == 1:
                img = cv2.merge([b, g, zeros_channels(r)])  # 对通道按照BGR的顺序合并生成图像bgr
            else:
                img = cv2.merge([zeros_channels(b), g, zeros_channels(r)]) # 不显示b, r通道
        else:
            if s_b == 1:
                img = cv2.merge([b, zeros_channels(g), zeros_channels(r)])  # 对通道按照BGR的顺序合并生成图像bgr
            else:
                img = cv2.merge([zeros_channels(b), zeros_channels(g), zeros_channels(r)]) # 不显示b, g, r通道


cv2.destroyAllWindows()