Streaming to website instead of Window OpenCV

Human Recogition Program

class PeopleTracker:

hog = cv2.HOGDescriptor()
caps = cv2.VideoCapture(r'C:/Users/Emyr/Documents/Jupyter/pedestrian-detection/video/Ped4.MOV')
count = int(caps.get(cv2.CAP_PROP_FRAME_COUNT))
center = []
recCount = 0
pick = 0
#          Red       Yellow      Blue      Green     Purple 
colors = [(255,0,0),(255,255,0),(0,0,255),(0,128,0),(128,0,128)]

def BBoxes(self, frame):
    #frame = imutils.resize(frame, width = min(frame.shape[0], frame.shape[1]))
    frame = imutils.resize(frame, width= 1000,height = 1000)

    # detect people in the image
    (rects, weights) = self.hog.detectMultiScale(frame, winStride=(1,1), padding=(3, 3), scale=0.5)

    # apply non-maxima suppression to the bounding boxes using a
    # fairly large overlap threshold to try to maintain overlapping
    # boxes that are still people

    rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])

    self.pick = non_max_suppression(rects, probs=None, overlapThresh=0.7)

    # draw the final bounding boxes
    self.recCount  = 0

    for (xA, yA, xB, yB) in self.pick:

        #cv2.rectangle(frame, (xA, yA), (xB, yB), (0, 255, 0), 2)

        CentxPos = int((xA + xB)/2)
        CentyPos = int((yA + yB)/2)

        cv2.circle(frame,(CentxPos, CentyPos), 5, (0,255,0), -1)
        self.recCount += 1

        if len(rects) >1:
               self.center.append([CentxPos, CentyPos])


    return frame


def Clustering(self, frame):

    db = DBSCAN(eps= 70, min_samples = 2).fit(self.center)

    labels = db.labels_

    # Number of clusters in labels, ignoring noise if present.
    n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
    n_noise_ = list(labels).count(-1)
    #print("Labels: ", labels)
    # Black removed and is used for noise instead.
    unique_labels = set(labels)
    #print("Unique Labels: ", unique_labels)

    #colors = plt.cm.rainbow(np.linspace(0, 255, len(unique_labels)))

    #colors = [(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) for k in range(len(unique_labels)) ]

    #print(self.colors)

    i = 0

    for (xA, yA, xB, yB) in self.pick:

        if labels[i] == -1:
            cv2.rectangle(frame, (xA, yA), (xB, yB), (0, 0, 0), 2)
            i += 1
        else:

            cv2.rectangle(frame, (xA, yA), (xB, yB), (self.colors[labels[i]][0], self.colors[labels[i]][1], self.colors[labels[i]][2]), 2)
            i += 1


    #print("Colours: ", colors)
    center = np.asarray(self.center)

    #fig, ax = plt.subplots()

    #ax.set_xlim(0,frame.shape[1])
    #ax.set_ylim(frame.shape[0], 0)

    #for k, col in zip(unique_labels, colors):

        #if k == -1:
             #Black used for noise.
             #col = [0, 0, 0, 1]

        #class_member_mask = (labels == k)
        #xy = center[class_member_mask]
        #plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col), markeredgecolor='k', markersize=8)

def main():

PT = PeopleTracker()
PT.hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())



while PT.count > 1:

    PT.center = []

    ret, frame = PT.caps.read()

    frame = PT.BBoxes(frame)

    if PT.recCount >= 2:

        PT.Clustering(frame)


        #plt.title('Estimated number of clusters: %d' % n_clusters_)
        #plt.show()   
        cv2.imshow("Tracker", frame)
        cv2.waitKey(1)
        #cv2.destroyAllWindows()
        PT.count = PT.count - 1

    else:

        cv2.imshow("Tracker", frame)
        cv2.waitKey(1)
        #cv2.destroyAllWindows()
        PT.count = PT.count - 1

enter image description here

The code I currently have here displays the stream of an existing human recognition video to a window (as shown in the picture in the link), if possible I was wondering is there a way in which I can send that video feed to a website that im developing instead of using a window?

Thank You in advance :)