Python使用OPENCV的目标跟踪算法实现自动视频标注效果

时间：2022-10-06 19:59:34

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Python使用OPENCV的目标跟踪算法实现自动视频标注效果

Python使用OPENCV的目标跟踪算法实现自动视频标注效果

1.首先，要使用opencv的目标跟踪算法，必须要有opencv环境

使用：opencv==4.4.0 和opencv-contrib-python==4.4.0.46，lxml这三个环境包。

也可以使用以下方法进行下载：

pip install opencv-python==4.4.0
pip install opencv-contrib-python==4.4.0.4

pip installlxml

2.使用方法：

（1）：英文状态下的 “s” 是进行标注

（2）：使用小键盘 1-9 按下对应的标签序号，标签序号和标签可自定义（需要提前定义）

（3）：对目标进行绘制

（4）：按空格键继续

重复进行 （1）（2）（3）（4）步骤，可实现多个目标的跟踪绘制

英文状态下的 “r” 是所有清除绘制

英文状态下的 “q” 是退出

当被跟踪目标丢失时，自动清除所有绘制

import cv2
import os
import time
from lxml import etree

#视频路径
Vs = cv2.VideoCapture('peaple.avi')
#自定义标签
Label = {1:"people",2:"car",3:"Camera"}
#图片保存路径 ，一定使用要用绝对路径！！
imgpath = r"C:\Users\BGT\Desktop\opencv\img"
#xml保存路径 ，一定使用要用绝对路径！！
xmlpath = r"C:\Users\BGT\Desktop\opencv\xml"
#设置视频缩放
cv2.namedWindow("frame", 0)
#设置视频宽高
cv2.resizeWindow("frame", 618, 416)

#定义生成xml类
class Gen_Annotations:
  def __init__(self, json_info):
      self.root = etree.Element("annotation")

      child1 = etree.SubElement(self.root, "folder")
      child1.text = str(json_info["pic_dirname"])

      child2 = etree.SubElement(self.root, "filename")
      child2.text = str(json_info["filename"])

      child3 = etree.SubElement(self.root, "path")
      child3.text = str(json_info["pic_path"])

      child4 = etree.SubElement(self.root, "source")

      child5 = etree.SubElement(child4, "database")
      child5.text = "My name is BGT"

  def set_size(self, witdh, height, channel):
      size = etree.SubElement(self.root, "size")
      widthn = etree.SubElement(size, "width")
      widthn.text = str(witdh)
      heightn = etree.SubElement(size, "height")
      heightn.text = str(height)
      channeln = etree.SubElement(size, "depth")
      channeln.text = str(channel)
      segmented = etree.SubElement(self.root, "segmented")
      segmented.text = "0"

  def savefile(self, filename):
      tree = etree.ElementTree(self.root)
      tree.write(filename, pretty_print=True, xml_declaration=False, encoding='utf-8')

  def add_pic_attr(self, label, x0, y0, x1, y1):
      object = etree.SubElement(self.root, "object")
      namen = etree.SubElement(object, "name")
      namen.text = label
      pose = etree.SubElement(object, "pose")
      pose.text = "Unspecified"
      truncated = etree.SubElement(object, "truncated")
      truncated.text = "0"
      difficult = etree.SubElement(object, "difficult")
      difficult.text = "0"
      bndbox = etree.SubElement(object, "bndbox")
      xminn = etree.SubElement(bndbox, "xmin")
      xminn.text = str(x0)
      yminn = etree.SubElement(bndbox, "ymin")
      yminn.text = str(y0)
      xmaxn = etree.SubElement(bndbox, "xmax")
      xmaxn.text = str(x1)
      ymaxn = etree.SubElement(bndbox, "ymax")
      ymaxn.text = str(y1)

#定义生成xml的方法
def voc_opencv_xml(a,b,c,d,e,f,boxes,Label,Label_a,save="1.xml"):
  json_info = {}
  json_info["pic_dirname"] = a
  json_info["pic_path"] = b
  json_info["filename"] = c
  anno = Gen_Annotations(json_info)

  anno.set_size(d, e, f)

  for box in range(len(boxes)):
      x,y,w,h = [int(v) for v in boxes[box]]
      anno.add_pic_attr(Label[Label_a[box]],x,y,x+w,y+h)
  anno.savefile(save)

if __name__ == '__main__':
  Label_a = []
  contents = os.path.split(imgpath)[1]
  trackers = cv2.MultiTracker_create()
  while True:
      Filename_jpg = str(time.time()).split(".")[0] + "_" + str(time.time()).split(".")[1] + ".jpg"
      Filename_xml = str(time.time()).split(".")[0] + "_" + str(time.time()).split(".")[1] + ".xml"

      path_Filename_jpg = os.path.join(imgpath,Filename_jpg)
      path_Filename_xml = os.path.join(xmlpath,Filename_xml)

      ret,frame = Vs.read()
      if not ret:
          break

      success,boxes = trackers.update(frame)
      if len(boxes)>0:
          cv2.imwrite(path_Filename_jpg, frame)
          judge = True
      else:
          judge = False

       if success==False:
          print("目标丢失")
          trackers = cv2.MultiTracker_create()
          Label_a = []
          judge = False
      if judge:
          voc_opencv_xml(contents,Filename_jpg,path_Filename_jpg,frame.shape[1],frame.shape[0],frame.shape[2],boxes,Label,Label_a,path_Filename_xml)
      if judge:
          for box in range(len(boxes)):
              x,y,w,h = [int(v) for v in boxes[box]]
              cv2.putText(frame, Label[Label_a[box]], (x, y), cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 1)
              cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2)

      cv2.imshow('frame',frame)

      var = cv2.waitKey(30)

      if var == ord('s'):
          imgzi = cv2.putText(frame, str(Label), (50, 50), cv2.FONT_HERSHEY_TRIPLEX, 1, (0, 255, 0), 2)
          cv2.imshow('frame', frame)
          var = cv2.waitKey(0)
          if var-48<len(Label) or var-48<=len(Label):
              Label_a.append(int(var-48))
          box = cv2.selectROI("frame", frame, fromCenter=False,showCrosshair=True)
          tracker = cv2.TrackerCSRT_create()
          trackers.add(tracker,frame,box)
      elif var == ord("r"):
          trackers = cv2.MultiTracker_create()
          Label_a = []
      elif var == ord('q'): #退出
          break

  Vs.release()
  cv2.destroyAllWindows()

3.得到xml和img数据是VOC格式，img和xml文件以时间戳进行命名。防止同名覆盖。

Python使用OPENCV的目标跟踪算法实现自动视频标注效果

4.最后使用 labelImg软件 对获取到的img和xml进行最后的检查和微调

Python使用OPENCV的目标跟踪算法实现自动视频标注效果

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原文链接：https://blog.csdn.net/qq_44619703/article/details/120210729

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