detect_faces.py

#modified by Fidelis

from utils.tempimage import TempImage
from imutils.video import VideoStream
from utils.notifications import TwillioDropbox
import numpy as np
#import argparse
import warnings
import datetime
import imutils
import json
import time
import cv2
import config

# construct the argument parser and parse the arguments
#ap = argparse.ArgumentParser()
#ap.add_argument("-p", "--prototxt", required=True,
#	help="path to Caffe 'deploy' prototxt file")
#ap.add_argument("-m", "--model", required=True,
#	help="path to Caffe pre-trained model")
#ap.add_argument("-co", "--confidence", type=float, default=0.5,
#	help="minimum probability to filter weak detections")
#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(config.conf_path))


# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(config.prototxt_path, config.caffee_path)

# initialize the camera and grab a reference to the raw camera capture
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)

# 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"])
lastUploaded = datetime.datetime.now()
motionCounter = 0

# capture frames from the camera
while True:
	# grab the raw NumPy array representing the image and initialize
	# the timestamp and occupied/unoccupied text
    frame = vs.read()
    frame = imutils.resize(frame,width=400)
    (h,w) = frame.shape[:2]
    blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0,(300, 300), (104.0, 177.0, 123.0))
    net.setInput(blob)
    detections = net.forward()
    
    timestamp = datetime.datetime.now()
    text = "Tidak Ada Orang"
    notifSent = False

    for i in range(0, detections.shape[2]):
        confidence = detections[0, 0, i, 2]
        
        
        if confidence < 0.5:
            continue

		# compute the bounding box for the contour, draw it on the frame,
		# and update the text
        box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
        (startX, startY, endX, endY) = box.astype("int")
 
		# draw the bounding box of the face along with the associated
		# probability
        
        y = startY - 10 if startY - 10 > 10 else startY + 10
        cv2.rectangle(frame, (startX, startY), (endX, endY),
			(0, 0, 255), 2)
        text = "Ada Orang"
    
    #text = "{:.2f}%".format(confidence * 100)
    
    ts = timestamp.strftime("%A %d %B %Y %I:%M:%S%p")
    # draw the text and timestamp on the frame
    cv2.putText(frame, "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 == "Ada Orang":
		# 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
                    tn = TwillioDropbox.TwilioNotifier(conf)
                
                    print("[UPLOAD] file {}.jpg".format(ts))
                    #path = "/{base_path}/{timestamp}.jpg".format(
					  # base_path=conf["dropbox_base_path"], timestamp=ts)
                    
                    #client.files_upload(open(t.path, "rb").read(), path)
                    #url = client.files_get_temporary_link(path)
                    if not notifSent:    
                        msg = "Ada Orang Di Luar Rumah"
                        tn.send(t,msg,ts)
                        #time.sleep(3.0)
                        #remove the file after some delays or looking for 
                        t.cleanup()
                        notifSent = True
                        
                    else :
                        notiSent = False
                        
                    #remove the file
                    
                    #t.cleanup()
                 
                    #msg= "Ada Orang Di Luar Rumah"
                
                #send the image
                      
                    #tn.send_message(msg,url.link)
                    #print("[SEND] file {}.jpg".format(ts))
                    #client.files_delete(path)
                    #print("[DELETE] file {}.jpg".format(ts))

				# 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
cv2.destroyAllWindows()
vs.stop()