main.py

"""People Counter."""
"""
 Copyright (c) 2018 Intel Corporation.
 Permission is hereby granted, free of charge, to any person obtaining
 a copy of this software and associated documentation files (the
 "Software"), to deal in the Software without restriction, including
 without limitation the rights to use, copy, modify, merge, publish,
 distribute, sublicense, and/or sell copies of the Software, and to
 permit person to whom the Software is furnished to do so, subject to
 the following conditions:
 The above copyright notice and this permission notice shall be
 included in all copies or substantial portions of the Software.
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
 EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
 MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
 NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
 LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
 OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
 WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
"""



import os
import sys
import time
import socket
import cv2
import json
import time


import logging as log
import paho.mqtt.client as mqtt

import argparse
from inference import Network

# MQTT server environment variables
HOSTNAME = socket.gethostname()
IPADDRESS = socket.gethostbyname(HOSTNAME)
MQTT_HOST = IPADDRESS
MQTT_PORT = 3001
MQTT_KEEPALIVE_INTERVAL = 120


def build_argparser():
    
    
        
    parser = argparse.ArgumentParser("People Counter App")
    m_desc = "Path to an xml file with a trained model."
    i_desc = "Path to image or video file"
    l_desc = "MKLDNN (CPU)-targeted custom layers. Absolute path to a shared library with the kernels impl."
    d_desc = "Specify the target device to infer on: CPU, GPU, FPGA or MYRIAD is acceptable. Sample will look for a suitable plugin for device specified (CPU by default)"
    pt_desc = "Probability threshold for detections filtering (0.5 by default)"
    
     # -- Add required and optional groups
    parser._action_groups.pop()
    required = parser.add_argument_group('required arguments')
    optional = parser.add_argument_group('optional arguments')

    
    required.add_argument("-m", required=True, type=str,help=m_desc)
    required.add_argument("-i", required=True, type=str, help=i_desc)
    optional.add_argument("-l", required=False, type=str, default=None, help=l_desc)
    optional.add_argument("-d", type=str, default="CPU", help=d_desc)
    optional.add_argument("-pt", type=float, default=0.5, help=pt_desc)
    args = parser.parse_args()
    return args
 

def connect_mqtt():
    ### TODO: Connect to the MQTT client ###
    
    client = mqtt.Client()
    client.connect(MQTT_HOST, MQTT_PORT, MQTT_KEEPALIVE_INTERVAL)

    return client


def draw_boxes(frame, result,probability,color, width, height):
    count = 0
    for box in result[0][0]:
        conf = box[2]
        output_class = box[1]
        if conf>=probability and output_class==1 : #output_class  = 1 is for person, as per coco.names file
            xmin = int(box[3] * width)
            ymin = int(box[4] * height)
            xmax = int(box[5] * width)
            ymax = int(box[6] * height)
            cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color, 1)
            count = count+1
    return frame,count
def infer_on_stream(args, client):
    """
    Initialize the inference network, stream video to network,
    and output stats and video.

    :param args: Command line arguments parsed by `build_argparser()`
    :param client: MQTT client
    :return: None
    """
    
    # Initializing Variables
    global width,height,prob_threshold
    image_mode = False
    last_count = 0
    total_count = 0
    start_time = 0
    last_last_count = 0
    
    # Initialise the class
    infer_network = Network()
    
    # Set Probability threshold for detections
    prob_threshold = args.pt

    ### TODO: Load the model through `infer_network` ###
    infer_network.load_model(args.m, args.d, args.l)
    net_input_shape = infer_network.get_input_shape()

    ### TODO: Handle the input stream ###
    
    # Checking Image File
    if args.i.endswith('.jpg') or args.i.endswith('.png'):
        image_mode = True
        Input_stream = args.i
    
    # Checking for input video from the camera
    elif args.i == 'CAM':
        Input_stream = 0
        
    # Checking for Video File   
    else:
        Input_stream = args.i
     
    
    cap = cv2.VideoCapture(Input_stream)
    
    if Input_stream:
        cap.open(args.i)

    if not cap.isOpened():
        log.error("ERROR! Unable to open the media")
    
    width = int(cap.get(3))
    height = int(cap.get(4))
    
    ### TODO: Loop until stream is over ###
    while cap.isOpened():

        ### TODO: Read from the video capture ###
        flag,frame = cap.read()
        if not flag:
            break
        inf_start = time.time()
        
        key_pressed = cv2.waitKey(60)

        ### TODO: Pre-process the image as needed ###
        p_frame = cv2.resize(frame, (net_input_shape[3], net_input_shape[2]))
        p_frame = p_frame.transpose((2,0,1))
        p_frame = p_frame.reshape(1, *p_frame.shape)

        ### TODO: Start asynchronous inference for specified request ###
        infer_network.exec_net(p_frame)

        ### TODO: Wait for the result ###
        status = infer_network.wait()
        if status==0:
            det_time = time.time() - inf_start
        ### TODO: Get the results of the inference request ###
        ### TODO: Extract any desired stats from the results ###
            result = infer_network.get_output()
            frame,current_count = draw_boxes(frame, result,prob_threshold,(0,0,255), width, height)
            inf_time_message = "Inference time: {:.3f}ms".format(det_time * 1000)
            cv2.putText(frame, inf_time_message, (15, 15), cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
            
        ### TODO: Calculate and send relevant information on ###
        ### current_count, total_count and duration to the MQTT server ###
        ### Topic "person": keys of "count" and "total" ###
        ### Topic "person/duration": key of "duration" ###
            
            if current_count > last_count:
                start_time = time.time()
                
            if current_count < last_count: #To avoid False Negatives
                if(last_last_count>=last_count):                   
                    duration = int(time.time() - start_time)
                    total_count = total_count + last_count - current_count
                    client.publish("person", json.dumps({"total": total_count}))
                    client.publish("person/duration",json.dumps({"duration": duration}))

            client.publish("person", json.dumps({"count": current_count}))
            last_last_count = last_count
            last_count = current_count
            
        if key_pressed==27:
            break
        
        ### TODO: Send the frame to the FFMPEG server ###
        sys.stdout.buffer.write(frame)
        sys.stdout.flush()
        if image_mode:
            cv2.imwrite('output_image.jpg', frame)
    
  
        
        ### TODO: Write an output image if `single_image_mode` ###
        
        
    
    cap.release()
    cv2.destroyAllWindows()
    client.disconnect()
    
        

def main():
    """
    Load the network and parse the output.

    :return: None
    """
    # Grab command line args
    args = build_argparser()
    
    # Connect to the MQTT server
    client = connect_mqtt()
    # Perform inference on the input stream
    infer_on_stream(args, client)


if __name__ == '__main__':
    main()