Apply HOG+SVM Training to Webcam for Object Detection

I have trained my SVM classifier by extracting HOG features from a positive and negative dataset

from sklearn.svm import SVC
import cv2
import numpy as np

hog = cv2.HOGDescriptor()


def hoggify(x,z):

    data=[]

    for i in range(1,int(z)):
        image = cv2.imread("/Users/munirmalik/cvprojek/cod/"+x+"/"+"file"+str(i)+".jpg", 0)
        dim = 128
        img = cv2.resize(image, (dim,dim), interpolation = cv2.INTER_AREA)
        img = hog.compute(img)
        img = np.squeeze(img)
        data.append(img)

    return data

def svmClassify(features,labels):
    clf=SVC(C=10000,kernel="linear",gamma=0.000001)
    clf.fit(features,labels)

    return clf

def list_to_matrix(lst):
    return np.stack(lst) 

I want to apply that training so that the program will be able to detect my custom object (chairs).

I have added labels to each set already; what needs to be done next?