smarthome-presence-detect/camera/video_presence.py

from imutils.video import VideoStream
from imutils.video import FPS
import argparse
import imutils
import time
import cv2
from datetime import datetime, time
import numpy as np
import time as time2

""" Arguments """
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to the video file")
ap.add_argument("-a", "--min-area", type=int, default=500, help="minimum area size")
ap.add_argument("-t", "--tracker", type=str, default="csrt", help="OpenCV object tracker type")
args = vars(ap.parse_args())

""" Determine opencv version and select tracker """
# extract the OpenCV version info
(major, minor) = cv2.__version__.split(".")[:2]
# if we are using OpenCV 3.2 or an earlier version, we can use a special factory
# function to create the entity that tracks objects 
if int(major) == 3 and int(minor) < 3:
    tracker = cv2.Tracker_create(args["tracker"].upper())
    #tracker = cv2.TrackerGOTURN_create()   
# otherwise, for OpenCV 3.3 or newer, 
# we need to explicity call the respective constructor that contains the tracker object:
else:
    # initialize a dictionary that maps strings to their corresponding
    # OpenCV object tracker implementations
    OPENCV_OBJECT_TRACKERS = {
            "csrt": cv2.TrackerCSRT_create,
            "kcf": cv2.TrackerKCF_create,
            "boosting": cv2.TrackerBoosting_create,
            "mil": cv2.TrackerMIL_create,
            "tld": cv2.TrackerTLD_create,
            "medianflow": cv2.TrackerMedianFlow_create,
            "mosse": cv2.TrackerMOSSE_create
    }
# grab the appropriate object tracker using our dictionary of
# OpenCV object tracker objects
    tracker = OPENCV_OBJECT_TRACKERS[args["tracker"]]()
    #tracker = cv2.TrackerGOTURN_create()   
# if the video argument is None, then the code will read from webcam (work in progress)
if args.get("video", None) is None:
    vs = VideoStream(src=0).start()
    time2.sleep(2.0)
# otherwise, we are reading from a video file
else:
    vs = cv2.VideoCapture(args["video"])

"""" Analyzing video frames """
# loop over the frames of the video, and store corresponding information from each frame
firstFrame = None
initBB2 = None
fps = None
differ = None
now = ''
framecounter = 0
trackeron = 0

while True:
    frame = vs.read()
    frame = frame if args.get("video", None) is None else frame[1]
    # if the frame can not be grabbed, then we have reached the end of the video
    if frame is None:
            break

    # resize the frame to 500
    frame = imutils.resize(frame, width=500)

    framecounter = framecounter+1
    if framecounter > 1:

        (H, W) = frame.shape[:2]
        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)
        cnts = cnts[0] if imutils.is_cv2() else cnts[1]

        # loop over the contours identified
        contourcount = 0
        for c in cnts:
            contourcount =  contourcount + 1

           # 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,
            (x, y, w, h) = cv2.boundingRect(c)
            initBB2 =(x,y,w,h)

            prott1 = r'ML-Models\MobileNetSSD_deploy.prototxt'
            prott2 = r'ML-Models\MobileNetSSD_deploy.caffemodel'
            net = cv2.dnn.readNetFromCaffe(prott1, prott2)

            CLASSES = ["person"]
            COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))

            trackbox = frame[y:y+h, x:x+w]
            trackbox = cv2.resize(trackbox, (224, 224))
            cv2.imshow('image',trackbox)
            blob = cv2.dnn.blobFromImage(cv2.resize(trackbox, (300, 300)),0.007843, (300, 300), 127.5)
            net.setInput(blob)
            detections = net.forward()

            for i in np.arange(0, detections.shape[2]):
                confidence = detections[0, 0, i, 2]

                confidence_level = 0.7 

                if confidence > confidence_level:
                    # extract the index of the class label from the `detections`, then compute the (x, y)-coordinates of
                    # the bounding box for the object
                    idx = int(detections[0, 0, i, 1])
                    box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
                    (startX, startY, endX, endY) = box.astype("int")
                    # draw the prediction on the frame
                    label = "{}: {:.2f}%".format(CLASSES[idx],
                                                 confidence * 100)
                    cv2.rectangle(frame, (startX, startY), (endX, endY),
                                  COLORS[idx], 2)
                    y = startY - 15 if startY - 15 > 15 else startY + 15
                    cv2.putText(frame, label, (startX, y),
                                cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)

            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 0), 2)
            # Start tracker
            now = datetime.now()
            if differ == None or differ > 9:
                tracker.init(frame, initBB2)
                fps = FPS().start()


    # check to see if we are currently tracking an object, if so, ignore other boxes
    # this code is relevant if we want to identify particular persons (section 2 of this tutorial)
    if initBB2 is not None:

        # grab the new bounding box coordinates of the object
        (success, box) = tracker.update(frame)

        # check to see if the tracking was a success
        differ = 10
        if success:
            (x, y, w, h) = [int(v) for v in box]
            cv2.rectangle(frame, (x, y), (x + w, y + h),(0, 255, 0), 2)
            differ = abs(initBB2[0]-box[0]) + abs(initBB2[1]-box[1])
            i = tracker.update(lastframe)
            if i[0] != True:
                time2.sleep(4000)
        else:
            trackeron = 1

        # update the FPS counter
        fps.update()
        fps.stop()

        # initialize the set of information we'll be displaying on
        # the frame
        info = [
            ("Success", "Yes" if success else "No"),
            ("FPS", "{:.2f}".format(fps.fps())),
        ]

        # loop over the info tuples and draw them on our frame
        for (i, (k, v)) in enumerate(info):
            text = "{}: {}".format(k, v)
            cv2.putText(frame, text, (10, H - ((i * 20) + 20)), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)

        # draw the text and timestamp on the frame
        now2 = datetime.now()
        time_passed_seconds = str((now2-now).seconds)
        cv2.putText(frame, 'Detecting persons',(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("Video stream", frame)
    key = cv2.waitKey(1) & 0xFF

    # if the `q` key is pressed, break from the lop
    if key == ord("q"):
        break
    if key == ord("d"):
        firstFrame = None
    lastframe = frame

# finally, stop the camera/stream and close any open windows
vs.stop() if args.get("video", None) is None else vs.release()
cv2.destroyAllWindows()