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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

先上效果


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 install lxml
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 cv2import osimport timefrom 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 = labelpose = 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"] = ajson_info["pic_path"] = bjson_info["filename"] = canno = 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 = Trueelse:judge = False if success==False:print("目标丢失")trackers = cv2.MultiTracker_create()Label_a = []judge = Falseif 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
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
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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

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