Detecting People: Python & OpenCV

The latest SVN version of OpenCV contains an (undocumented) implementation of HOG-based pedestrian detection. It even comes with a pre-trained detector and a python wrapper. The basic usage is as follows:

from cv import *

storage = CreateMemStorage(0)
img = LoadImage(file)  # or read from camera

found = list(HOGDetectMultiScale(img, storage, win_stride=(8,8),
                padding=(32,32), scale=1.05, group_threshold=2))

An example from the examples folder:

import sys
from cv import *

def inside(r, q):
    (rx, ry), (rw, rh) = r
    (qx, qy), (qw, qh) = q
    return rx > qx and ry > qy and rx + rw < qx + qw and ry + rh < qy + qh

try:
    img = LoadImage(sys.argv[1])
except:
    try:
        f = open(sys.argv[1], "rt")
    except:
        print "cannot read " + sys.argv[1]
        sys.exit(-1)
    imglist = list(f.readlines())
else:
    imglist = [sys.argv[1]]

NamedWindow("people detection demo", 1)
storage = CreateMemStorage(0)

for name in imglist:
    n = name.strip()
    print n
    try:
        img = LoadImage(n)
    except:
        continue
    
    #ClearMemStorage(storage)
    found = list(HOGDetectMultiScale(img, storage, win_stride=(8,8),
        padding=(32,32), scale=1.05, group_threshold=2))
    found_filtered = []
    for r in found:
        insidef = False
        for q in found:
            if inside(r, q):
                insidef = True
                break
        if not insidef:
            found_filtered.append(r)
    for r in found_filtered:
        (rx, ry), (rw, rh) = r
        tl = (rx + int(rw*0.1), ry + int(rh*0.07))
        br = (rx + int(rw*0.9), ry + int(rh*0.87))
        Rectangle(img, tl, br, (0, 255, 0), 3)
        
    ShowImage("people detection demo", img)
    c = WaitKey(0)
    if c == ord('q'):
        break

Reference:

• https://code.ros.org/trac/opencv/browser/trunk/opencv/samples/python/peopledetect.py
• http://stackoverflow.com/questions/2188646/how-can-i-detect-and-track-people-using-opencv