SaratahKumar cDhananjay Patil
Created July 15, 2020 © CERN-OHL

Personal Protective Body Camera (PPBC)

Introducing Personal Protective Body Camera (PPBC). An AI-Powred device for a user before COVID attack & after COVID attack.

IntermediateFull instructions provided10 hours25
Personal Protective Body Camera (PPBC)

Things used in this project

Hardware components

Raspberry Pi 3 Model B
Raspberry Pi 3 Model B
×1
Webcam, Logitech® HD Pro
Webcam, Logitech® HD Pro
×1
u-blox Ublox GPS
×1
Solar Cockroach Vibrating Disc Motor
Brown Dog Gadgets Solar Cockroach Vibrating Disc Motor
×1

Software apps and online services

Raspbian
Raspberry Pi Raspbian
Amazon Web Services AWS IoT Greengrass
Amazon Web Services neo-ai-dlr

Hand tools and fabrication machines

Soldering iron (generic)
Soldering iron (generic)
Solder Wire, Lead Free
Solder Wire, Lead Free

Story

Read more

Code

resnet18a.py

Python
Raspberry Pi code for running DLR model
# import libraries
import time
import os
from PIL import Image
from dlr import DLRModel
import numpy as np
import utils
import logging
from sys import argv
import gps
import requests
from datetime import datetime
#now = datetime.now()
#dt_string = now.strftime("%d/%m/%Y %H:%M:%S")
#print("date and time =", dt_string)

#Listen on port 2947 of gpsd
session = gps.gps("localhost", "2947")
session.stream(gps.WATCH_ENABLE | gps.WATCH_NEWSTYLE)

list = ['ATM','automated teller machine', 'sliding door', 'dog', 'bicycle','bikini','cab','hack','taxi','taxicab','cellphone','cellular telephone','coffee mug','computer keyboard','keypad','desktop computer','dial telephone','school bus','gasmask','gas helmet','jeep','landrover','laptop','mailbox','mask','motor scooter','scooter','oxygen mask','pay-phone','restaurant','eating place','ski mask','toilet seat','trailer truck','vending machine','washbasin','handbasin','washbowl','wash-hand basin','toilet tissue','bathroom tissue','laptop','mouse'] 

import RPi.GPIO as GPIO # Import Raspberry Pi GPIO library
from time import sleep # Import the sleep function from the time module
GPIO.setwarnings(False) # Ignore warning for now
GPIO.setmode(GPIO.BOARD) # Use physical pin numbering
GPIO.setup(12, GPIO.OUT, initial=GPIO.LOW) # Set pin 8 to be an output pin and set initial value to low (off)
global locate

logging.basicConfig(filename='test-dlr.log', level=logging.DEBUG)

#current_milli_time = lambda: int(round(time.time() * 1000))


def run_inference():
    now = datetime.now()
    dt_string = now.strftime("%d/%m/%Y %H:%M:%S")
    print("date and time =", dt_string)
    
    for x in range(4):
 
       rep = session.next()
       try :
            if (rep["class"] == "TPV") :
                    print(str(rep.lat) + "," + str(rep.lon))
                    locate=("Timestamp "+ dt_string  +","+" Latitude: " + str(rep.lat) + "," +"longitudes: " + str(rep.lon))

       except Exception as e :
            print("Got exception " + str(e))
    print(time)
    #os.system('fswebcam -r 1024x768 --no-banner --scale 224x224 output.jpg -S 7 --save /home/pi/Photos/std.jpg') # uses Fswebcam to take picture
    image = Image.open('output.jpg')
    #data = np.array(image,dtype='float64')
    #data=data1.reshape((1,data1.shape[2],data1.shape[0],data1.shape[1]))

    #np.save( 'flamingo.npy', data)
 
    image_data = utils.transform_image(image)
    #print(image_data)
    flattened_data = image_data.astype(np.float32).flatten()
    #np.save( 'puppi.npy',flattened_data)
    #print("Start Prinring Flattern") 
    #print(flattened_data)
    #run_inference(image_data)
    #time.sleep(15) # this line creates a 15 second delay before repeating the loop
   

    model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), '../model-rasp3b')
    batch_size = 1
    channels = 3
    height = width = 224
    input_shape = {'input0': [batch_size, channels, height, width]}
    classes = 1000
    output_shape = [batch_size, classes]
    device = 'cpu'
    model = DLRModel(model_path, input_shape, output_shape, device)

    synset_path = os.path.join(model_path, 'imagenet1000_clsidx_to_labels.txt')
    with open(synset_path, 'r') as f:
        synset = eval(f.read())

    #image = np.load(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'dog.npy')).astype(np.float32)
    #input_data = {'data': image_data}
 
    # Predict
    out = model.run({'input0' : flattened_data}).squeeze()
    top1 = np.argmax(out)
    prob = np.max(out)
	
	# Using for loop 
    for i in list:
        # How to use find() 
        if (synset[top1].find(i) != -1): 
	         print ("Contains given substring ")
                 GPIO.output(12, GPIO.HIGH) # Turn on
                 sleep(10) # Sleep for 1 second
                 GPIO.output(12, GPIO.LOW) # Turn off
                 #sleep(10)
 
        #else: 
	         #print ("Doesn't contains given substring") 	
	         #print(i) 
    print("Class: %s, probability: %f" % (synset[top1], prob))
    #while True: # Run forever
      #GPIO.output(8, GPIO.HIGH) # Turn on
      #sleep(10) # Sleep for 1 second
      #GPIO.output(8, GPIO.LOW) # Turn off
      #sleep(10)

    #for rep in range(4):
        #t1 = current_milli_time()
        #out = model.run({'input0' : flattened_data}).squeeze()
        #t2 = current_milli_time()

        #logging.debug('done m.run(), time (ms): {}'.format(t2 - t1))

    #top1 = np.argmax(out)
    
    print(locate)
    logging.debug('Inference result: {}, {}'.format(locate, synset[top1]))
    
    #import resource
    #logging.debug("peak memory usage (bytes on OS X, kilobytes on Linux) {}".format(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss))

    #return {
        #'synset_id': top1,
        #'prediction': synset[top1],
        #'time': t2 - t1
    #}

if __name__ == '__main__':
 while True :
    res = run_inference()
    #cls_id = res['synset_id'] 
    print("All tests PASSED!")

Complete Project file

Sagemaker notebook and compiled model doc

Credits

SaratahKumar c

SaratahKumar c

4 projects • 1 follower
Dhananjay Patil

Dhananjay Patil

0 projects • 1 follower

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