From sztu 自动化专业的小菜鸡。
自计算机视觉入门系列结束,这是第一个利用先前所学的知识来做的一个项目
1.用画笔来实现与摄像头沟通并画画。import cv2
import numpy as np
frameWidth = 640
frameHeight = 480
cap = cv2.VideoCapture(0)
cap.set(3,frameWidth)
cap.set(4,frameHeight)
cap.set(10,300)#右往高亮,往低暗
myColors = [[5,107,0,19,255,255],[133,56,0,159,156,255],[57,76,0,100,255,255]]
myColorsValues = [[51,153,255],[255,0,255],[0,255,0]]
myPoints = [] #[x,y,colorId]
def drawOnCanvas(myPoints,myColorsValues):
for point in myPoints:
cv2.circle(imgResult, (point[0], point[1]), 10, myColorsValues[point[2]], cv2.FILLED)
def getContours(img):
contours,hierarchy = cv2.findContours(img,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
x,y,w,h =0,0,0,0
for cnt in contours:
area = cv2.contourArea(cnt)
if area>500:
#cv2.drawContours(imgResult, cnt, -1, (255, 0, 0), 3)
peri = cv2.arcLength(cnt,True)
approx = cv2.approxPolyDP(cnt,0.02*peri,True)
x , y , w , h =cv2.boundingRect(approx)
return x+w//2,y
def findColor(img,myColors,myColorsValues):
imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) # 将BGR转换为HSV
count = 0
newPoints=[]
for color in myColors:
lower = np.array(myColors[0][0:3])#确定最小
upper = np.array(myColors[0][3:6])#确定最大
mask = cv2.inRange(imgHSV,lower,upper)#蒙盖
x,y=getContours(mask)
cv2.circle(imgResult,(x,y),10,myColorsValues[count],cv2.FILLED)
if x !=0 and y !=0:
newPoints.append([x,y,count])
count += 1
#cv2.imshow(str(color[0]),mask)
return newPoints
while True:
success,img = cap.read()
imgResult = img.copy()
newPoints = findColor(img,myColors,myColorsValues)
if len(newPoints)!=0:
for newP in newPoints:
myPoints.append(newP)
if len(myPoints)!=0:
drawOnCanvas(myPoints,myColorsValues)
cv2.imshow("Result",imgResult)
if cv2.waitKey(1)&0xFF==ord('q'):
break
上代码,具体每一段代码博主这也不再一一解析了,这儿就简单说一下原理。简单来说,就是利用opencv里的hsv识别来识别对应的颜色,比如我这识别的是橙黄色,那么我就可以利用识别到的地方给它画圆,以小圆就可以代替笔尖,然后让其残留在上面,这就达到了我们的目的。
运行结果:
用opencv结合摄像头画画



