In this section, we will demonstrate how the python-opencv library functions can be used to detect people in an image using HOG-SVM. The following code shows how to compute the HOG descriptors from an image and use the descriptors to feed into a pre-trained SVM classifier (with cv2's HOGDescriptor_getDefaultPeopleDetector()), which will predict the presence or absence of a person in from an image block at multiple scales with the detectMultiScale() function from python-opencv:
import numpy as npimport cv2import matplotlib.pylab as pylabimg = cv2.imread("../images/me16.jpg")# create HOG descriptor using default people (pedestrian) detectorhog = cv2.HOGDescriptor()hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) ...
