OpenCV3.0+Python3.6实现特定颜色的物体追踪

站长资源 2024/11/25 佚名

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一、环境

win10、Python3.6、OpenCV3.x；编译器：pycharm5.0.3

二、实现目标

根据需要追踪的物体颜色，设定阈值，在视频中框选出需要追踪的物体。

三、实现步骤

1）根据需要追踪的物体颜色，设定颜色阈值，获取追踪物体的掩膜

代码：generate_threshold.py

# -*- coding : utf-8 -*-
# Author: Tom Yu
import cv2
import numpy as np
 
cap = cv2.VideoCapture(0)#获取摄像头图像
# img = cv2.imread("timg1.jpg")
# hsv_img = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
 
def nothing(x):
  pass
def createbars():
  """
  实现创建六个滑块的作用，分别控制H、S、V的最高值与最低值
  """
  cv2.createTrackbar("H_l","image",0,180,nothing)
  cv2.createTrackbar("H_h","image",0,180,nothing)
  cv2.createTrackbar("S_l","image",0,255,nothing)
  cv2.createTrackbar("S_h","image",0,255,nothing)
  cv2.createTrackbar("V_l","image",0,255,nothing)
  cv2.createTrackbar("V_h","image",0,255,nothing)
cv2.namedWindow("image")
createbars()#创建六个滑块
 
lower = np.array([0,0,0])#设置初始值
upper = np.array([0,0,0])
while True:
  ret,frame = cap.read()
  hsv_frame = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)#将图片由BGR颜色空间转化成HSV空间，HSV可以更好地分割颜色图形
  lower[0]=cv2.getTrackbarPos("H_l","image")#获取"H_l"滑块的实时值
  upper[0]=cv2.getTrackbarPos("H_h","image")#获取"H_h"滑块的实时值
  lower[1]=cv2.getTrackbarPos("S_l","image")
  upper[1]=cv2.getTrackbarPos("S_h","image")
  lower[2]=cv2.getTrackbarPos("V_l","image")
  upper[2]=cv2.getTrackbarPos("V_h","image")
 
  mask = cv2.inRange(hsv_frame,lower,upper)#cv2.inrange()函数通过设定的最低、最高阈值获得图像的掩膜
  cv2.imshow("img",frame)
  cv2.imshow("mask",mask)
  if cv2.waitKey(1)&0xff == 27:
    break
 
cv2.destroyAllWindows()

实现效果：获取需要追踪的物体颜色阈值

2）根据获取到的阈值，设定阈值范围，在视频中追踪特定颜色的物体并用框选框出所需追踪的物体

代码：tracking_object.py

# -*- coding : utf-8 -*-
# Author: Tom Yu
import cv2
import numpy as np
 
cap = cv2.VideoCapture(0)#获取摄像头视频
 
while True:
  ret,frame = cap.read()#读取每一帧图片
  hsv_frame = cv2.cvtColor(frame,cv2.COLOR_BGR2HSV)#将每一帧图片转化HSV空间颜色
  """
  依据之前的脚本获取的阈值设置最高值与最低值
  """
  lower = np.array([0,104,205])
  upper = np.array([15,208,255])
 
  mask = cv2.inRange(hsv_frame,lower,upper)
  img,conts,hier = cv2.findContours(mask,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)#找出边界
  cv2.drawContours(frame,conts,-1,(0,255,0),3)#画出边框
  dst = cv2.bitwise_and(frame,frame,mask=mask)#对每一帧进行位与操作，获取追踪图像的颜色
  #cv2.imshow("mask",mask)
  #cv2.imshow("dst",dst)
  cv2.imshow("frame",frame)
  if cv2.waitKey(1)&0xff == 27:
    break
 
cv2.destroyAllWindows()

实现效果：