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| # -*- coding:utf-8 -*-
# import the necessary packages
import argparse
import datetime
#import imutils
import time
import cv2
import json
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to the video file")
ap.add_argument("-c", "--conf" , required=True,help="path to the JSON config file")
args = vars(ap.parse_args())
conf = json.load(open(args["conf"]))
# 开启优化
if (not cv2.useOptimized()):
cv2.setUseOptimized(True)
# if the video argument is None, then we are reading from webcam
if args.get("video", None) is None:
camera = cv2.VideoCapture(0)
time.sleep(3.5)
# otherwise, we are reading from a video file
else:
camera = cv2.VideoCapture(args["video"])
# find opencv version
print cv2.__version__
(major_ver,minor_ver,subminor_ver,fin_ver) = (cv2.__version__).split('.')
if int(major_ver) < 3 :
fps = camera.get(cv2.cv.CV_CAP_PROP_FPS)
else:
fps = camera.get(cv2.CV_CAP_PROP_FPS)
print fps
print "Frames per second :{0}".format(fps)
# initialize the first frame in the video stream
firstFrame = None
avg = None
#avg1 = None
t = time.time()
decay = conf["decay-time"]
num_frames = 0
# loop over the frames of the video
while True:
start = time.time()
# grab the current frame
(grabbed, frame) = camera.read()
text = "safe"
# if the frame could not be grabbed, reached the end of the video
if not grabbed:
break
# resize the frame, convert it to grayscale, and blur it
# frame = imutils.resize(frame, width=500)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# gray = cv2.GaussianBlur(gray, (21, 21), 0)
# if the first frame is None, initialize it
if firstFrame is None:
firstFrame = gray.copy().astype("float")
#avg1 = firstFrame
avg = firstFrame
continue
now = time.time()
if (now -t >= decay):
t = now
# compute the absolute diff between current and first frame
cv2.accumulateWeighted(avg,firstFrame,0.4)
# firstFrame = (1-alpha)*firstFrame+alpha*avg
# firstFrame = 0.6*firstFrame + 0.4*avg
cv2.accumulateWeighted(gray,avg,0.05)
# 更新一个动态平均数 avg = (1-alpha)*avg+alpha*gray
# avg = 0.95 * avg + 0.05 * gary
frameDelta = cv2.absdiff(gray,cv2.convertScaleAbs(avg))
# 基于加权平均数的帧，当前帧减去加权平均值，得到帧变化量
thresh = cv2.threshold(frameDelta, conf["delta-thresh"], 255, cv2.THRESH_BINARY)[1]
# 阈值化 >25 255
# dilate the thresholded image to fill in holes, then find contours
# on thresholded image
thresh = cv2.dilate(thresh, None, iterations=2)
(cnts, _) = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
# loop over the contours
for c in cnts:
# if the contour is too small, ignore it
if cv2.contourArea(c) < conf["min-area"]:
continue
# compute the bounding box for the contour, draw it on the frame,
# and update the text
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
text = "someone"
num_frames += 1
fps = num_frames /(time.time() - start)
num_frames = 0
# draw the text and timestamp on the frame
cv2.putText(frame, "Room Status: {}".format(text.encode('utf-8')), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(frame, datetime.datetime.now().strftime("%A %d %B %Y %I:%M:%S%p" + " FPS:{}".format(str(fps))),
(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 0, 255), 1)
# show the frame and record if the user presses a key
cv2.imshow("安全监控画面", frame)
if conf["show-thresh"]:
cv2.imshow("阈值化画面-Thresh", thresh)
if conf["show-frameDelta"]:
cv2.imshow("畸变画面-Frame Delta", frameDelta)
key = cv2.waitKey(1) & 0xFF
# if the `q` key is pressed, break from the lop
if key == ord("q"):
break
# cleanup the camera and close any open windows
camera.release()
cv2.destroyAllWindows()
|