Inference_Dogs.py

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

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("-c", "--confidence", type=float, default=0.2, help="minimum probability to filter weak detections")
ap.add_argument("-u", "--movidius", type=bool, default=0, help="boolean indicating if the Movidius should be used")
args = vars(ap.parse_args())

CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
	"bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
	"dog", "horse", "motorbike", "person", "pottedplant", "sheep",
	"sofa", "train", "tvmonitor"]
COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))


import smtplib 
from email.mime.text import MIMEText
from email.mime.multipart import MIMEMultipart

email = ""
pas = ""
sms_gateway = ''
smtp = "smtp.gmail.com" 
port = 587

def send_sms():
    server = smtplib.SMTP(smtp,port)
    server.starttls()
    server.login(email,pas)
    msg = MIMEMultipart()
    msg['From'] = email
    msg['To'] = sms_gateway
    msg['Subject'] = "Object detected\n"
    body = "Dog Identified\n"
    msg.attach(MIMEText(body, 'plain'))
    sms = msg.as_string()
    server.sendmail(email,sms_gateway,sms)
    server.quit()


# load serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# specify the target device as the Myriad processor on the NCS
net.setPreferableTarget(cv2.dnn.DNN_TARGET_MYRIAD)

# initialize the video stream

print("[INFO] starting video stream...")
vs = VideoStream(usePiCamera=True).start()
time.sleep(2.0)
fps = FPS().start()
# loop over the frames from the video stream
while True:
	# grab the frame from the threaded video stream and resize it
	# to have a maximum width of 400 pixels
	frame = vs.read()
	frame = imutils.resize(frame, width=400)
    
	# grab the frame dimensions and convert it to a blob
	(h, w) = frame.shape[:2]
	blob = cv2.dnn.blobFromImage(frame, 0.007843, (300, 300), 127.5)
    
	# pass the blob through the network and obtain the detections and predictions
	net.setInput(blob)
	detections = net.forward()
    
	# loop over the detections
	for i in np.arange(0, detections.shape[2]):
		# extract the confidence (i.e., probability) associated with prediction

		confidence = detections[0, 0, i, 2]
		# ensuring the confidence is greater than the minimum threshold
        
		if confidence > args["confidence"]:
			# extract the index of the class label from detections and
			#  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)
			idlabel=CLASSES[idx]
            
            #if Dog is detected in frame, call SMS function:
			if idlabel == "dog": send_sms()
            
			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)
            
	# show the output frame
	cv2.imshow("Frame", frame)
	key = cv2.waitKey(1) & 0xFF
    
	# if the `q` key was pressed, break from the loop
	if key == ord("q"):
		break
	fps.update()


fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

cv2.destroyAllWindows()
vs.stop()