# USAGE

# python motion_detector.py

# python motion_detector.py --video videos/example_01.mp4

# import the necessary packages

import argparse

import datetime

import imutils

import time

import cv2

from skimage.io import imread

from skimage.feature import hog

from sklearn.externals import joblib

import argparse as ap

from nms import nms

from config import *

from PIL import Image

from resizeimage import resizeimage

import numpy as np

# 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("-a", "--min-area", type=int, default=5000, help="minimum area size")

ap.add_argument('-t', "--tempfolder", help="Folder for temp files", required=True)

args = vars(ap.parse_args())

# 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(0.25)

# otherwise, we are reading from a video file

else:

camera = cv2.VideoCapture(args["video"])

# initialize the first frame in the video stream

firstFrame = None

_object_type = "Car"

_temp_path = args["tempfolder"]

# Load the classifier

clf = joblib.load(model_path + "/" + _object_type + "/" + _object_type + ".model")

# function to do the prediction

def getLabel(image_path):

return (0, clf.decision_function(fd))

car_count = 0

# loop over the frames of the video

while True:

# grab the current frame and initialize the occupied/unoccupied

# text

(grabbed, frame) = camera.read()

text = "Testing Video"

# if the frame could not be grabbed, then we have 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

continue

# compute the absolute difference between the current frame and

# first frame

frameDelta = cv2.absdiff(firstFrame, gray)

thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]

# 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) < args["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)

roi = frame[y:y+h, x:x+w]

cv2.imwrite(_temp_path + "/" + _object_type + ".png", roi)

output, label_dis = getLabel(_temp_path + "/" + _object_type + ".png")

if output == 1 and label_dis > 0.2:

car_count += 1

cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

cv2.putText(frame, "Car " + str(label_dis), (x - 3 ,y - 3), cv2.FONT_HERSHEY_SIMPLEX, 0.35, (0, 255, 0),2)

# text = "Occupied"

# draw the text and timestamp on the frame

cv2.putText(frame, text, (10, 20),

cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)

# show the frame and record if the user presses a key

cv2.imshow("Security Feed", frame)

# cv2.imshow("Thresh", thresh)

# cv2.imshow("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()

print car_count, " cars detected!!!"