python-opencv3.0新增了一些比较有用的追踪器算法,这里根据官网示例写了一个追踪器类
程序只能运行在安装有opencv3.0以上版本和对应的contrib模块的python解释器
主要代码
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
|
#encoding=utf-8 import cv2from items import MessageItemimport timeimport numpy as np'''监视者模块,负责入侵检测,目标跟踪'''class WatchDog(object): #入侵检测者模块,用于入侵检测 def __init__(self,frame=None): #运动检测器构造函数 self._background = None if frame is not None: self._background = cv2.GaussianBlur(cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY),(21,21),0) self.es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10, 10)) def isWorking(self): #运动检测器是否工作 return self._background is not None def startWorking(self,frame): #运动检测器开始工作 if frame is not None: self._background = cv2.GaussianBlur(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), (21, 21), 0) def stopWorking(self): #运动检测器结束工作 self._background = None def analyze(self,frame): #运动检测 if frame is None or self._background is None: return sample_frame = cv2.GaussianBlur(cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY),(21,21),0) diff = cv2.absdiff(self._background,sample_frame) diff = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1] diff = cv2.dilate(diff, self.es, iterations=2) image, cnts, hierarchy = cv2.findContours(diff.copy(),cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) coordinate = [] bigC = None bigMulti = 0 for c in cnts: if cv2.contourArea(c) < 1500: continue (x,y,w,h) = cv2.boundingRect(c) if w * h > bigMulti: bigMulti = w * h bigC = ((x,y),(x+w,y+h)) if bigC: cv2.rectangle(frame, bigC[0],bigC[1], (255,0,0), 2, 1) coordinate.append(bigC) message = {"coord":coordinate} message['msg'] = None return MessageItem(frame,message) class Tracker(object): ''' 追踪者模块,用于追踪指定目标 ''' def __init__(self,tracker_type = "BOOSTING",draw_coord = True): ''' 初始化追踪器种类 ''' #获得opencv版本 (major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.') self.tracker_types = ['BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN'] self.tracker_type = tracker_type self.isWorking = False self.draw_coord = draw_coord #构造追踪器 if int(minor_ver) < 3: self.tracker = cv2.Tracker_create(tracker_type) else: if tracker_type == 'BOOSTING': self.tracker = cv2.TrackerBoosting_create() if tracker_type == 'MIL': self.tracker = cv2.TrackerMIL_create() if tracker_type == 'KCF': self.tracker = cv2.TrackerKCF_create() if tracker_type == 'TLD': self.tracker = cv2.TrackerTLD_create() if tracker_type == 'MEDIANFLOW': self.tracker = cv2.TrackerMedianFlow_create() if tracker_type == 'GOTURN': self.tracker = cv2.TrackerGOTURN_create() def initWorking(self,frame,box): ''' 追踪器工作初始化 frame:初始化追踪画面 box:追踪的区域 ''' if not self.tracker: raise Exception("追踪器未初始化") status = self.tracker.init(frame,box) if not status: raise Exception("追踪器工作初始化失败") self.coord = box self.isWorking = True def track(self,frame): ''' 开启追踪 ''' message = None if self.isWorking: status,self.coord = self.tracker.update(frame) if status: message = {"coord":[((int(self.coord[0]), int(self.coord[1])),(int(self.coord[0] + self.coord[2]), int(self.coord[1] + self.coord[3])))]} if self.draw_coord: p1 = (int(self.coord[0]), int(self.coord[1])) p2 = (int(self.coord[0] + self.coord[2]), int(self.coord[1] + self.coord[3])) cv2.rectangle(frame, p1, p2, (255,0,0), 2, 1) message['msg'] = "is tracking" return MessageItem(frame,message) class ObjectTracker(object): def __init__(self,dataSet): self.cascade = cv2.CascadeClassifier(dataSet) def track(self,frame): gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY) faces = self.cascade.detectMultiScale(gray,1.03,5) for (x,y,w,h) in faces: cv2.rectangle(frame,(x,y),(x+w,y+h),(255,0,0),2) return frame if __name__ == '__main__' : a = ['BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN'] tracker = Tracker(tracker_type="KCF") video = cv2.VideoCapture(0) ok, frame = video.read() bbox = cv2.selectROI(frame, False) tracker.initWorking(frame,bbox) while True: _,frame = video.read(); if(_): item = tracker.track(frame); cv2.imshow("track",item.getFrame()) k = cv2.waitKey(1) & 0xff if k == 27: break |
信息封装类
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
|
#encoding=utf-8import jsonfrom utils import IOUtil'''信息封装类'''class MessageItem(object): #用于封装信息的类,包含图片和其他信息 def __init__(self,frame,message): self._frame = frame self._message = message def getFrame(self): #图片信息 return self._frame def getMessage(self): #文字信息,json格式 return self._message def getBase64Frame(self): #返回base64格式的图片,将BGR图像转化为RGB图像 jepg = IOUtil.array_to_bytes(self._frame[...,::-1]) return IOUtil.bytes_to_base64(jepg) def getBase64FrameByte(self): #返回base64格式图片的bytes return bytes(self.getBase64Frame()) def getJson(self): #获得json数据格式 dicdata = {"frame":self.getBase64Frame().decode(),"message":self.getMessage()} return json.dumps(dicdata) def getBinaryFrame(self): return IOUtil.array_to_bytes(self._frame[...,::-1]) |
运行之后在第一帧图像上选择要追踪的部分,这里测试了一下使用KCF算法的追踪器
更新utils
|
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
|
#encoding=utf-8import timeimport numpyimport base64import osimport loggingimport sysfrom settings import *from PIL import Imagefrom io import BytesIO #工具类class IOUtil(object): #流操作工具类 @staticmethod def array_to_bytes(pic,formatter="jpeg",quality=70): ''' 静态方法,将numpy数组转化二进制流 :param pic: numpy数组 :param format: 图片格式 :param quality:压缩比,压缩比越高,产生的二进制数据越短 :return: ''' stream = BytesIO() picture = Image.fromarray(pic) picture.save(stream,format=formatter,quality=quality) jepg = stream.getvalue() stream.close() return jepg @staticmethod def bytes_to_base64(byte): ''' 静态方法,bytes转base64编码 :param byte: :return: ''' return base64.b64encode(byte) @staticmethod def transport_rgb(frame): ''' 将bgr图像转化为rgb图像,或者将rgb图像转化为bgr图像 ''' return frame[...,::-1] @staticmethod def byte_to_package(bytes,cmd,var=1): ''' 将每一帧的图片流的二进制数据进行分包 :param byte: 二进制文件 :param cmd:命令 :return: ''' head = [ver,len(byte),cmd] headPack = struct.pack("!3I", *head) senddata = headPack+byte return senddata @staticmethod def mkdir(filePath): ''' 创建文件夹 ''' if not os.path.exists(filePath): os.mkdir(filePath) @staticmethod def countCenter(box): ''' 计算一个矩形的中心 ''' return (int(abs(box[0][0] - box[1][0])*0.5) + box[0][0],int(abs(box[0][1] - box[1][1])*0.5) +box[0][1]) @staticmethod def countBox(center): ''' 根据两个点计算出,x,y,c,r ''' return (center[0][0],center[0][1],center[1][0]-center[0][0],center[1][1]-center[0][1]) @staticmethod def getImageFileName(): return time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())+'.png' #构造日志logger = logging.getLogger(LOG_NAME)formatter = logging.Formatter(LOG_FORMATTER)IOUtil.mkdir(LOG_DIR);file_handler = logging.FileHandler(LOG_DIR + LOG_FILE,encoding='utf-8')file_handler.setFormatter(formatter)console_handler = logging.StreamHandler(sys.stdout)console_handler.setFormatter(formatter)logger.addHandler(file_handler)logger.addHandler(console_handler)logger.setLevel(logging.INFO) |
以上就是python基于opencv实现目标追踪示例的详细内容，更多关于python opencv目标追踪的资料请关注服务器之家其它相关文章！
原文链接：https://blog.csdn.net/qq_35488769/article/details/79103264
