title: Apply HOG+SVM Training to Webcam for Object Detection
body: <p>I have trained my SVM classifier by extracting HOG features from a positive and negative dataset</p>
  
  <pre><code>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) 
  </code></pre>
  
  <p>I want to apply that training so that the program will be able to detect my custom object (chairs).</p>
  
  <p>I have added labels to each set already; what needs to be done next? </p>