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#TEST PROGRAM
# import the necessary packages
from __future__ import print_function
from sklearn.cluster import DBSCAN
import matplotlib.pyplot as plt
from imutils.object_detection import non_max_suppression
from imutils import paths
import numpy as np
import imutils
import cv2
import sys
import matplotlib.pyplot as plt
from itertools import cycle
import random


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

cv2.destroyAllWindows()


main()

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 :)

I'm not skilled enough to understand all that code but try Selenium. It's really good for interacting with websites

Don't use the IDE because the commands on there are limited so you would want to import it into python. I'm pretty sure pip can be used to install it

SDGRIFFUSW

SheeppOSU