基于Python对交通路口的红绿灯进行颜色检测
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2021-09-18 02:19
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视觉/图像重磅干货,第一时间送达
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()(如下图所示)
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(如下图所示)
截取后的roi(如下图所示)
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 cv2
import 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 cv2
import 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
检测红灯的效果(如下图所示)
检测绿灯的效果(如下图所示)
最后!!!
第一次接触opencv!所以请各位视觉领域的大佬们勿喷我这个小菜鸡!(/狗头)
代码量非常少,无泛化能力,很low的一种做法。。。不过对于小白的我来说学习hsv颜色空间还是很有帮助滴!干就完了!奥利给!
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