detect.py

#-*- coding: UTF-8 -*-   
# import the necessary packages
from __future__ import print_function
from imutils.object_detection import non_max_suppression
from imutils import paths
import numpy as np
import argparse
import imutils
import cv2

cap = cv2.VideoCapture(0)
fourcc = cv2.cv.CV_FOURCC(*'XVID')
#opencv3的话用:fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi',fourcc,20.0,(640,480))#保存视频

hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())


while True:
	
	ret, image = cap.read()
	image = imutils.resize(image, width=min(400, image.shape[1]))
	orig = image.copy()
 
	# detect people in the image
	(rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),
		padding=(8, 8), scale=1.05)
 
	# draw the original bounding boxes
	for (x, y, w, h) in rects:
		cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)
 
	# 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])
	pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)
 
	# draw the final bounding boxes
	for (xA, yA, xB, yB) in pick:
		cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)
 
	# show some information on the number of bounding boxes
	# filename = imagePath[imagePath.rfind("/") + 1:]
	# print("[INFO] {}: {} original boxes, {} after suppression".format(
	# 	filename, len(rects), len(pick)))
 
	# show the output images
	#cv2.imshow("Before NMS", orig)
	cv2.imshow("After NMS", image)
	cv2.waitKey(1)