关于Python对交通路口的红绿灯进行颜色检测

转自|Python联盟

1.视频读取

首先把视频读取进来，因为我测试的视频是4k的所以我用resize调整了一下视频的分辨大小

cap = cv2.VideoCapture('video/小路口.mp4')while True:    ret,frame = cap.read()    if ret == False:        break    frame = cv2.resize(frame,(1920,1080))    cv2.imshow('frame',frame)    c = cv2.waitKey(10)    if c==27:break

imshow()

2.截取roi区域

截取roi的区域，也就是说，为了避免多余的干扰因素我们要把红绿灯的位置给截取出来

截取后的roi

3.转换hsv颜色空间

HSV颜色分量范围

（详细参考：https://www.cnblogs.com/wangyblzu/p/5710715.html）
一般对颜色空间的图像进行有效处理都是在HSV空间进行的，然后对于基本色中对应的HSV分量需要给定一个严格的范围，下面是通过实验计算的模糊范围（准确的范围在网上都没有给出）。

H: 0— 180

S: 0— 255

V: 0— 255

此处把部分红色归为紫色范围（如下图所示）：

上面是已给好特定的颜色值，如果你的颜色效果不佳，可以通过python代码来对min和max值的微调，用opencv中的api来获取你所需理想的颜色，可以复制以下代码来进行颜色的调整。
1.首先你要截取roi区域的一张图片
2.读取这张图然后调整颜色值

颜色调整代码如下：

（详细参考：https://www.bilibili.com/video/BV16K411W7x9）

import cv2import numpy as np
def empty(a):    pass
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
#读取的图片路径path = './green.jpg'cv2.namedWindow("TrackBars")cv2.resizeWindow("TrackBars",640,240)cv2.createTrackbar("Hue Min","TrackBars",0,179,empty)cv2.createTrackbar("Hue Max","TrackBars",19,179,empty)cv2.createTrackbar("Sat Min","TrackBars",110,255,empty)cv2.createTrackbar("Sat Max","TrackBars",240,255,empty)cv2.createTrackbar("Val Min","TrackBars",153,255,empty)cv2.createTrackbar("Val Max","TrackBars",255,255,empty)
while True:    img = cv2.imread(path)    imgHSV = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)    h_min = cv2.getTrackbarPos("Hue Min","TrackBars")    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)    lower = np.array([h_min,s_min,v_min])    upper = np.array([h_max,s_max,v_max])    mask = cv2.inRange(imgHSV,lower,upper)    imgResult = cv2.bitwise_and(img,img,mask=mask)

    imgStack = stackImages(0.6,([img,imgHSV],[mask,imgResult]))    cv2.imshow("Stacked Images", imgStack)cv2.waitKey(1)

运行代码后调整的结果（如下图所示），很明显可以看到绿色已经被获取到。

4.二值图像颜色判定

因为图像是二值的图像，所以如果图像出现白点，也就是255，那么就取他的max最大值255，视频帧的不断变化然后遍历每个颜色值

red_color = np.max(red_blur)green_color = np.max(green_blur)if red_color == 255:  print('red')elif green_color == 255:print('green')

5.颜色结果画在图像上

用矩形框来框选出红绿灯区域

cv2.rectangle(frame,(1020,50),(1060,90),(0,0,255),2) #按坐标画出矩形框cv2.putText(frame,"red",(1020,40),cv2.FONT_HERSHEY_COMPLEX,1,(0,0,255),2)#显示red文本信息

6.完整代码

import cv2import numpy as np
cap = cv2.VideoCapture('video/小路口.mp4')while True:    ret,frame = cap.read()    if ret == False:        break    frame = cv2.resize(frame,(1920,1080))    #截取roi区域    roiColor = frame[50:90,950:1100]    #转换hsv颜色空间    hsv = cv2.cvtColor(roiColor,cv2.COLOR_BGR2HSV)
    #red    lower_hsv_red = np.array([157,177,122])    upper_hsv_red = np.array([179,255,255])    mask_red = cv2.inRange(hsv,lowerb=lower_hsv_red,upperb=upper_hsv_red)    #中值滤波    red_blur = cv2.medianBlur(mask_red, 7)    #green    lower_hsv_green = np.array([49,79,137])    upper_hsv_green = np.array([90,255,255])    mask_green = cv2.inRange(hsv,lowerb=lower_hsv_green,upperb=upper_hsv_green)    #中值滤波    green_blur = cv2.medianBlur(mask_green, 7)
    #因为图像是二值的图像，所以如果图像出现白点，也就是255，那么就取他的max最大值255    red_color = np.max(red_blur)    green_color = np.max(green_blur)    #在red_color中判断二值图像如果数值等于255，那么就判定为red    if red_color == 255:        print('red')                        #。。。这是我经常会混淆的坐标。。。就列举出来记一下。。。                        #      y  y+h x  x+w                        #frame[50:90,950:1100]
                        #     x   y    x+w  y+h        cv2.rectangle(frame,(1020,50),(1060,90),(0,0,255),2) #按坐标画出矩形框        cv2.putText(frame, "red", (1020, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 0, 255),2)#显示red文本信息    #在green_color中判断二值图像如果数值等于255，那么就判定为green    elif green_color == 255:        print('green')        cv2.rectangle(frame,(1020,50),(1060,90),(0,255,0),2)        cv2.putText(frame, "green", (1020, 40), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0),2)
    cv2.imshow('frame',frame)    red_blur = cv2.resize(red_blur,(300,200))    green_blur = cv2.resize(green_blur,(300,200))    cv2.imshow('red_window',red_blur)    cv2.imshow('green_window',green_blur)
    c = cv2.waitKey(10)    if c==27:break

编辑：jq