python - 使用python-opencv进行质心跟踪以进行背景子跟踪

Question

我正在使用backgoundsubtraction进行对象定位，并且将本教程https://www.pyimagesearch.com/2018/07/23/simple-object-tracking-with-opencv/应用于实时流视频，并正确输出了视频。但是当我给出图像帧序列时，所有ID都将被打印在新图像上，并且旧ID和OLD ID不会被删除

下一帧将获得新的ID，并且先前的ID也将被打印在其上
我该如何解决

def detection():
    backsub = cv2. createBackgroundSubtractorMOG2(128, cv2.THRESH_BINARY, 1)
    minarea = 50 
    counter = 0
    counter = 0
    counter1 = 0
    ct = CentroidTracker()
    rects = []
    #cx = 0
    #cy = 0
    (H, W) = (None, None)    
    filenames = [img for img in glob.glob("img location/*.jpg")]
    filenames.sort()
    print("start2")
    for img in filenames:
        frame = cv2.imread(img)

        t = time.localtime()
        timestamp = int(round(time.time() * 1000))
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        blur = cv2.medianBlur(frame, 21)
        #blur = cv2.GaussianBlur(frame, (21,21), 0)
        fgmask = backsub.apply(blur)
        fgmask[fgmask==127] = 0
        thresh = cv2.threshold(fgmask, 25, 255, cv2.THRESH_BINARY)[1]
        thresh = cv2.dilate(thresh, None, iterations=4)
        #cv2.line(frame, (138, 265), (344, 640), (0, 255, 0),1) 
        cv2.line(frame, (103, 209), (332, 607), (0, 255, 0),1) 

        #pts = np.array([[0, 640], [0, 264], [138, 265], [344, 640]], np.int32)
        pts = np.array([[0, 607], [0, 215], [103, 209], [332, 607]], np.int32)

        #bbPath = mplPath.Path(np.array([[0, 640], [0, 264], [138, 265], [344, 640]]))
        bbPath = mplPath.Path(np.array([[0, 607], [0, 215], [103, 209], [332, 607]]))

        frame = cv2.polylines(frame, [pts], True, (0, 255,0))    
        cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
        cnts = imutils.grab_contours(cnts)
        for c in cnts:
            M = cv2.moments(c)
            A = cv2.contourArea(c)
            if cv2.contourArea(c) >= minarea:
                (x, y, w, h) = cv2.boundingRect(c)
                cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
                rectang = [x,y,(x + w),(y + h)]

                rects.append(rectang)

                if M["m00"] != 0:
                    cx = int(M["m10"] / M["m00"])
                    cy = int(M["m01"] / M["m00"])
                    cv2.circle(frame, (cx, cy),5, (255, 255, 255), -1)
                    centroid1 = (cx, cy)

                    cv2.imwrite("Writelocation/I%s.jpg"%timestamp,frame)

                    print("Area is : ",A)
        objects = ct.update(rects)
        if object is not None:
            for (objectID, centroid) in objects.items():
            # draw both the ID of the object and the centroid of the
            # object on the output frame
                text = "ID:{}".format(objectID)
                cv2.putText(frame, text, (centroid[0], centroid[1] - 10),
                cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
                cv2.circle(frame, (centroid[0], centroid[1]), 4, (0, 255, 0), -1)
                cv2.imshow("fgmask", thresh)
                cv2.imshow("img",frame)

            if cv2.waitKey(1) & 0xFF == ord('q'):
                break
            cv2.imwrite("Writelocation/V%s.jpg"%timestamp,frame)


    print("Done")
detection()

上面是BackgroundSubtraction pgm
以下是质心跟踪pgm

    from scipy.spatial import distance as dist
from collections import OrderedDict
import numpy as np

class CentroidTracker():
    def __init__(self, maxDisappeared=3):
        self.nextObjectID = 0
        self.objects = OrderedDict()
        self.disappeared = OrderedDict()

        self.maxDisappeared = maxDisappeared

    def register(self, centroid):
        self.objects[self.nextObjectID] = centroid
        self.disappeared[self.nextObjectID] = 0
        self.nextObjectID += 1

    def deregister(self, objectID):
        del self.objects[objectID]
        del self.disappeared[objectID]

    def update(self, rects):
        if len(rects) == 0:
            for objectID in list(self.disappeared.keys()):
                self.disappeared[objectID] += 1

                if self.disappeared[objectID] > self.maxDisappeared:
                    self.deregister(objectID)

            return self.objects

        inputCentroids = np.zeros((len(rects), 2), dtype="int")

        for (i, (startX, startY, endX, endY)) in enumerate(rects):
            cX = int((startX + endX) / 2.0)
            cY = int((startY + endY) / 2.0)
            inputCentroids[i] = (cX, cY)

        if len(self.objects) == 0:
            for i in range(0, len(inputCentroids)):
                self.register(inputCentroids[i])

        else:
            objectIDs = list(self.objects.keys())
            objectCentroids = list(self.objects.values())

            D = dist.cdist(np.array(objectCentroids), inputCentroids)

            rows = D.min(axis=1).argsort()

            cols = D.argmin(axis=1)[rows]

            usedRows = set()
            usedCols = set()

            for (row, col) in zip(rows, cols):
                if row in usedRows or col in usedCols:
                    continue

                objectID = objectIDs[row]
                self.objects[objectID] = inputCentroids[col]
                self.disappeared[objectID] = 0

                usedRows.add(row)
                usedCols.add(col)

            unusedRows = set(range(0, D.shape[0])).difference(usedRows)
            unusedCols = set(range(0, D.shape[1])).difference(usedCols)

            if D.shape[0] >= D.shape[1]:
                for row in unusedRows:
                    objectID = objectIDs[row]
                    self.disappeared[objectID] += 1

                    if self.disappeared[objectID] > self.maxDisappeared:
                        self.deregister(objectID)

            else:
                for col in unusedCols:
                    self.register(inputCentroids[col])

        return self.objects

Answer 1

尝试将rects = []行放在第一个for循环内的detection()中，即在for img in filenames:行之后