Python调用OpenCV实现摄像头的运动检测[树莓派版]

# USAGE
# python pi_surveillance.py --conf conf.json

# import the necessary packages
from pyimagesearch.tempimage import TempImage
from picamera.array import PiRGBArray # picamera(CANT BE IMPORTED ON WINDOWS PLATFORM)
from picamera import PiCamera # picamera(CANT BE IMPORTED ON WINDOWS PLATFORM)
import argparse
import warnings
import datetime
import imutils
import json
import time
import cv2

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-c", "--conf", required=True, help="path to the JSON configuration file")
args = vars(ap.parse_args())

# filter warnings, load the configuration and initialize the Dropbox client
warnings.filterwarnings("ignore")
conf = json.load(open(args["conf"]))
client = None

# initialize the camera and grab a reference to the raw camera capture
camera = PiCamera()
camera.resolution = tuple(conf["resolution"])
camera.framerate = conf["fps"]
rawCapture = PiRGBArray(camera, size=tuple(conf["resolution"]))

# allow the camera to warmup, then initialize the average frame, last
# uploaded timestamp, and frame motion counter
print "[INFO] warming up..."
time.sleep(conf["camera_warmup_time"])
avg = None
lastUploaded = datetime.datetime.now()
motionCounter = 0

# capture frames from the camera
for f in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):
    # grab the raw NumPy array representing the image and initialize
    # the timestamp and occupied/unoccupied text
    frame = f.array
    timestamp = datetime.datetime.now()
    text = "Unoccupied"

    # 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 average frame is None, initialize it
    if avg is None:
        print "[INFO] starting background model..."
        avg = gray.copy().astype("float")
        rawCapture.truncate(0)
        continue

    # accumulate the weighted average between the current frame and
    # previous frames, then compute the difference between the current
    # frame and running average
    cv2.accumulateWeighted(gray, avg, 0.5)
    frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg))

    # threshold the delta image, dilate the thresholded image to fill
    # in holes, then find contours on thresholded image
    thresh = cv2.threshold(frameDelta, conf["delta_thresh"], 255,
        cv2.THRESH_BINARY)[1]
    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 = "Occupied"

    # draw the text and timestamp on the frame
    ts = timestamp.strftime("%A %d %B %Y %I:%M:%S%p")
    cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
    cv2.putText(frame, ts, (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX,
        0.35, (0, 0, 255), 1)

    # check to see if the room is occupied
    if text == "Occupied":
        # check to see if enough time has passed between uploads
        if (timestamp - lastUploaded).seconds >= conf["min_upload_seconds"]:
            # increment the motion counter
            motionCounter += 1

            # check to see if the number of frames with consistent motion is
            # high enough
            if motionCounter >= conf["min_motion_frames"]:
                # check to see if dropbox sohuld be used
                if conf["use_dropbox"]:
                    # write the image to temporary file
                    t = TempImage()
                    cv2.imwrite(t.path, frame)

                    # upload the image to Dropbox and cleanup the tempory image
                    print "[UPLOAD] {}".format(ts)
                    path = "{base_path}/{timestamp}.jpg".format(
                        base_path=conf["dropbox_base_path"], timestamp=ts)
                    client.put_file(path, open(t.path, "rb"))
                    t.cleanup()

                # update the last uploaded timestamp and reset the motion
                # counter
                lastUploaded = timestamp
                motionCounter = 0

    # otherwise, the room is not occupied
    else:
        motionCounter = 0

    # check to see if the frames should be displayed to screen
    if conf["show_video"]:
        # display the security feed
        cv2.imshow("Security Feed", frame)
        key = cv2.waitKey(1) & 0xFF

        # if the `q` key is pressed, break from the lop
        if key == ord("q"):
            break

    # clear the stream in preparation for the next frame
    rawCapture.truncate(0)

{
    "show_video": true,
    "use_dropbox": true,
    "dropbox_key": "YOUR_DROPBOX_KEY",
    "dropbox_secret": "YOUR_DROPBOX_SECRET",
    "dropbox_base_path": "YOUR_DROPBOX_APP_PATH",
    "min_upload_seconds": 3.0,
    "min_motion_frames": 8,
    "camera_warmup_time": 2.5,
    "delta_thresh": 5,
    "resolution": [640, 480],
    "fps": 16,
    "min_area": 5000
}

# import the necessary packages
import uuid
import os

class TempImage:
    def __init__(self, basePath="./", ext=".jpg"):
        # construct the file path
        self.path = "{base_path}/{rand}{ext}".format(base_path=basePath,
            rand=str(uuid.uuid4()), ext=ext)

    def cleanup(self):
        # remove the file
        os.remove(self.path)