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132 lines
4.0 KiB
Python
132 lines
4.0 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# USAGE
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# python motion_detector.py
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# python motion_detector.py --video videos/example_01.mp4
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# import the necessary packages
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# sudo pip install PiCamera[array]
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#This script is used to detect motion from camera connected to raspberry pi
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#LED light strip lights when motion is detected
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#Gaussian blur is used in motion detection, sensitivity of motion detection
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#to adjust change "minimum area size"
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import imutils
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from imutils.video import VideoStream
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import argparse
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import datetime
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import time, sys
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from time import sleep
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import cv2
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from LEDfunctions import *
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#LED for motion detection, when motion is detected, LED is lighted
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def vu_2_leds(color):
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while True:
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data = play_process.stdout.readline()
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if not data:
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pixels.clear() # make LEDs dark
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pixels.show()
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break
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data = data.rstrip()
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if data.endswith("%"):
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vu = float(data[:-1][-3:])/100 # 0-100
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leds_color_intensity(color, vu)
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def leds_start_stop(color): # for pirate
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while True:
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data = play_process.stdout.readline()
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data = data.rstrip()
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print('data:',data)
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print('process:', play_process.stdout.readline()) #_handle_exitstatus
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leds_pirate_bounce(color_pirate)
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# construct the argument parser and parse the arguments
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ap = argparse.ArgumentParser()
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ap.add_argument("-a", "--min-area", type=int, default=6000, help="minimum area size")
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args = vars(ap.parse_args())
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# we are reading from the pi camera
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vs = VideoStream(usePiCamera=True).start()
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sleep(0.5)
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# initialize the first frame in the video stream
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firstFrame = None
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occupied = False
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# loop over the frames of the video
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while True:
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# grab the current frame and initialize the occupied/unoccupied
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# text
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frame = vs.read()
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frame = frame if args.get("video", None) is None else frame[1]
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text = "Unoccupied"
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# if the frame could not be grabbed, then we have reached the end of the video
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if frame is None:
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break
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# resize the frame, convert it to grayscale, and blur it
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frame = imutils.resize(frame, width=500)
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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gray = cv2.GaussianBlur(gray, (21, 21), 0)
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# if the first frame is None, initialize it
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if firstFrame is None:
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firstFrame = gray
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continue
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# compute the absolute difference between the current frame and the first frame
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frameDelta = cv2.absdiff(firstFrame, gray)
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thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
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# dilate the thresholded image to fill in holes, then find contours on thresholded image
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thresh = cv2.dilate(thresh, None, iterations=2)
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cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_SIMPLE)
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cnts = cnts[0] if imutils.is_cv2() else cnts[1]
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# loop over the contours
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for c in cnts:
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# if the contour is too small, ignore it
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if cv2.contourArea(c) < args["min_area"]:
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continue
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# compute the bounding box for the contour, draw it on the frame,
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# and update the text
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(x, y, w, h) = cv2.boundingRect(c)
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cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
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text = "Occupied"
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if not occupied:
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occupied = True
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print ('occupied')
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# led activation bounce will preform
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play_process = leds_activation(color_activation)
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# break the while true loop, this will allow the motion.sh loop to preform the second part -> guru-pyrate.py
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sys.exit()
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if occupied and text == "Unoccupied":
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occupied = False
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print ("Unoccupied")
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#check images for debugging
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# # show the frame and record if the user presses a key
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# # cv2.imshow("Security Feed", frame)
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# cv2.imwrite('RegularCamera.jpg', frame)
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# #cv2.imshow("Thresh", thresh)
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# cv2.imwrite('threshold.jpg', thresh)
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# #cv2.imshow("Frame Delta", frameDelta)
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# cv2.imwrite('blured.jpg', frameDelta)
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# #key = cv2.waitKey(1) & 0xFF
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# # if the `q` key is pressed, break from the lop
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# #if key == ord("q"):
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# # break
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# cleanup the camera and close any open windows
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vs.stop() if args.get("video", None) is None else vs.release()
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#cv2.destroyAllWindows()
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