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(详细参考原文链接)
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:(详细参考视频教程链接)
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)
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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原文地址:https://blog.csdn.net/weixin_44912902