利用python打开摄像头及颜色检测方法

lmport cv2
import numpy as np#添加模块和矩阵模块
cap=cv2.VideoCapture(0)
＃打开摄像头，若打开本地视频，同opencv一样，只需将０换成("×××.avi")
while(1): # get a frame 
 ret, frame = cap.read() # show a frame 
 cv2.imshow("capture", frame) 
 if cv2.waitKey(1) & 0xFF == ord('q'):  
  break
cap.release()
cv2.destroyAllWindows()
#释放并销毁窗口

import cv2
import numpy as np
cap = cv2.VideoCapture(0)# set blue thresh
lower_blue=np.array([78,43,46])
upper_blue=np.array([110,255,255])
while(1): # get a frame and show 
 ret, frame = cap.read() 
 cv2.imshow('Capture', frame) # change to hsv model 
 hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV) # get mask 
 mask = cv2.inRange(hsv, lower_blue, upper_blue) 
 cv2.imshow('Mask', mask) # detect blue 
 res = cv2.bitwise_and(frame, frame, mask=mask) 
 cv2.imshow('Result', res) 
 if cv2.waitKey(1) & 0xFF == ord('q'):  
  breakcap.release()
cv2.destroyAllWindows()

#!/usr/bin/python
# -*- coding: utf-8 -*-
import cv2
import numpy as np
import time
readlower=np.array([156,179,144])
readupper=np.array([180,255,255])
readlower1 = np.array([0, 128, 146])
readupper2 = np.array([5, 255, 255])
lowerarry=[[readlower,readupper,'red'],[readlower1,readupper2,'red1']]
capture=cv2.VideoCapture('4.mp4')
while True:
 ret,frame=capture.read()
 print frame.shape
 frame=cv2.resize(frame,(640,480))
 if ret==False:
  print("video is erro")
 #cv2.imshow('xiaorun',frame)
 hsv=cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)
 for colormin,colermax,name in lowerarry:
  mask=cv2.inRange(hsv,colormin,colermax)
  #res = cv2.bitwise_and(frame, frame, mask=mask)
 #mask=cv2.erode(mask,None,iterations=1)
 mask=cv2.dilate(mask,None,iterations=25)
 ret, binary = cv2.threshold(mask,15, 255, cv2.THRESH_BINARY)
 cv2.imshow('result',binary)
 kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 7))
 closed = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
 cv2.imshow('closed', closed)
 #erode = cv2.erode(closed, None, iterations=4)
 #cv2.imshow('erode', erode)
 dilate = cv2.dilate(closed, None, iterations=50)
 cv2.imshow('dilate', dilate)
 _,contours, hierarchy = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 #res=_.copy()
 for con in contours:
  x, y, w, h = cv2.boundingRect(con) # 将轮廓分解为识别对象的左上角坐标和宽、高
  # 在图像上画上矩形（图片、左上角坐标、右下角坐标、颜色、线条宽度）
  cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0,0), 3)

 cv2.imshow('res',frame)
 key=cv2.waitKey(1)
 if key==ord('q'):
  break