OpenCV——Python：图像颜色检测与轨迹栏5_opencv识别图像上的线条轨迹

# 颜色检测
import cv2
import numpy as np

# 图像排列处理函数
def stackImages(scale, imgArray):
    rows = len(imgArray)
    cols = len(imgArray[0])
    rowsAvailable = isinstance(imgArray[0], list)
    width = imgArray[0][0].shape[1]
    height = imgArray[0][0].shape[0]
    if rowsAvailable:
        for x in range(0, rows):
            for y in range(0, cols):
                if imgArray[x][y].shape[:2] == imgArray[0][0].shape[:2]:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
                else:
                    imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
                if len(imgArray[x][y].shape) == 2: imgArray[x][y] = cv2.cvtColor(imgArray[x][y], cv2.COLOR_GRAY2BGR)
        imageBlank = np.zeros((height, width, 3), np.uint8)
        hor = [imageBlank]*rows
        hor_con = [imageBlank]*rows
        for x in range(0, rows):
            hor[x] = np.hstack(imgArray[x])
        ver = np.vstack(hor)
    else:
        for x in range(0, rows):
            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
            else:
                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
        hor= np.hstack(imgArray)
        ver = hor
    return ver

# 弄一个可以在程序里调的轨迹栏42-49行
def empty(a):
    pass

path = '3.png' # 设置资源文件夹
cv2.namedWindow("TrackBars")  # 创建窗口
cv2.resizeWindow("TrackBars", 640, 240)  # 创建其窗口大小
cv2.createTrackbar("Hue Min", "TrackBars", 0, 179, empty)  # 创建跟踪栏(0-180个值)
cv2.createTrackbar("Hue Max", "TrackBars", 179, 179, empty)  # Hue为色调
cv2.createTrackbar("Sat Min", "TrackBars", 52, 255, empty)
cv2.createTrackbar("Sat Max", "TrackBars", 222, 255, empty)  # sat为饱和度
cv2.createTrackbar("Val Min", "TrackBars", 95, 255, empty)
cv2.createTrackbar("Val Max", "TrackBars", 255, 255, empty)  # val为亮度，前为默认值，后为最大取到哪里

while True:
    img = cv2.imread(path)  # 读取图片
    imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)  # H色调，S饱和度，V明度
    h_min = cv2.getTrackbarPos("Hue Min", "TrackBars")  # 读取设置的bar值
    h_max = cv2.getTrackbarPos("Hue Max", "TrackBars")
    s_min = cv2.getTrackbarPos("Sat Min", "TrackBars")
    s_max = cv2.getTrackbarPos("Sat Max", "TrackBars")
    v_min = cv2.getTrackbarPos("Val Min", "TrackBars")
    v_max = cv2.getTrackbarPos("Val Max", "TrackBars")
    print(h_min, h_max, s_min, s_max, v_min, v_max)  # 打印值查看变化
    lower = np.array([h_min, s_min, v_min])  # 创建最小数组
    upper = np.array([h_max, s_max, v_max])  # 创建最大数组
    mask = cv2.inRange(imgHSV, lower, upper)    # 添加一个蒙版，HSV，并给定范围（执行此操作将滤除并提供改颜色的滤除图像）
    imgResult = cv2.bitwise_and(img, img, mask=mask)  # 按位操作, 掩模

#    cv2.imshow("ImageStack", img)  # 显示图像
#    cv2.imshow("HSV", imgHSV)
#    cv2.imshow("MASK", mask)
#    cv2.imshow("Mask", imgResult)
    imgStack = stackImages(0.6, ([img, imgHSV], [mask, imgResult]))
    cv2.imshow("Stacked Images", imgStack) # 显示图像
    cv2.waitKey(1)  # 延迟显示
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