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main.py
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main.py
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"""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 json
import cv2
import logging as log
import paho.mqtt.client as mqtt
from argparse import ArgumentParser
from inference import Network
#import imutils
# MQTT server environment variables
HOSTNAME = socket.gethostname()
IPADDRESS = socket.gethostbyname(HOSTNAME)
MQTT_HOST = IPADDRESS
MQTT_PORT = 3001
MQTT_KEEPALIVE_INTERVAL = 60
CPU_EXTENSION = "/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so"
labels = ["background","person", "bicycle", "car", "motorcycle", "airplane", "bus",
"train", "truck", "boat", "traffic light", "fire hydrant",
"stop sign", "parking meter", "bench", "bird", "cat", "dog",
"horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe",
"backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
"skis", "snowboard", "sports ball", "kite", "baseball bat",
"baseball glove", "skateboard", "surfboard", "tennis racket",
"bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl",
"banana", "apple", "sandwich", "orange", "broccoli", "carrot",
"hot dog", "pizza", "donut", "cake", "chair", "couch",
"potted plant", "bed", "dining table", "toilet", "tv", "laptop",
"mouse", "remote", "keyboard", "cell phone", "microwave", "oven",
"toaster", "sink", "refrigerator", "book", "clock", "vase",
"scissors", "teddy bear", "hair drier", "toothbrush"]
def build_argparser():
"""
Parse command line arguments.
:return: command line arguments
"""
parser = ArgumentParser()
parser.add_argument("-m", "--model", required=True, type=str,
help="Path to an xml file with a trained model.")
parser.add_argument("-i", "--input", required=True, type=str,
help="Path to image or video file")
parser.add_argument("-l", "--cpu_extension", required=False, type=str,
default=CPU_EXTENSION,
help="MKLDNN (CPU)-targeted custom layers."
"Absolute path to a shared library with the"
"kernels impl.")
parser.add_argument("-d", "--device", type=str, default="CPU",
help="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)")
parser.add_argument("-pt", "--prob_threshold", type=float, default=0.5,
help="Probability threshold for detections filtering"
"(0.5 by default)")
return parser
# extract bounding boxes and stats
def extract_stats(frame, result, args, width, height):
'''
Draw bounding boxes onto the frame.
'''
count = 0
for box in result[0][0]: # Output shape is 1x1x100x7
conf = box[2]
detected_object = labels[int(box[1])]
#print(detected_object)
#print(conf)
if conf >= args.prob_threshold and "person" in detected_object:
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), (255,0,0), 1)
count =+1
return frame, count
def connect_mqtt():
### TODO: Connect to the MQTT client ###
client = mqtt.Client()
client.connect(MQTT_HOST,MQTT_PORT,MQTT_KEEPALIVE_INTERVAL)
return client
def preprocess_frame(frame, dsize):
p_frame = cv2.resize(frame, dsize)
p_frame = p_frame.transpose((2,0,1))
p_frame = p_frame.reshape(1,*p_frame.shape)
return p_frame
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
"""
present_count=0
preceding_count=0
total_count=0
start_time=0
duration=0
frame_count=0
wait_time=57
single_image_mode= False
# Initialise the class
infer_network = Network()
# Set Probability threshold for detections
args.prob_threshold = float(args.prob_threshold)
### TODO: Load the model through `infer_network` ###
infer_network.load_model(args.model,args.device, args.cpu_extension)
rfcnn_input_shape = infer_network.get_input_shape()
print(rfcnn_input_shape)
# width and height input to the model
dsize = (rfcnn_input_shape[3],rfcnn_input_shape[2])
# single image mode
single_image_format = ['jpg','tif','png','jpeg', 'bmp']
if args.input.split(".")[-1].lower() in single_image_format:
single_image_mode= True
frame = cv2.imread(args.input)
height, width, channel = frame.shape
p_frame = preprocess_frame(frame, dsize)
infer_network.exec_net(p_frame)
if infer_network.wait()==0:
### TODO: Get the results of the inference request ###
infer_result = infer_network.get_output()
### TODO: Extract any desired stats from the results ###
single_frame, present_count = extract_stats(frame, infer_result, args, width, height)
### TODO: Write an output image if `single_image_mode` ###
cv2.imwrite("image.jpg", single_frame)
### TODO: Handle the input stream ###
input_stream = cv2.VideoCapture(args.input)
input_stream.open(args.input)
width = int(input_stream.get(3))
height = int(input_stream.get(4))
# Create a video output to see your result
#out = cv2.VideoWriter('out.mp4',0x00000021,30,(width,height))
### TODO: Loop until stream is over ###
while input_stream.isOpened() and not single_image_mode:
### TODO: Read from the video capture ###
flag, frame = input_stream.read()
if not flag:
break
key_pressed = cv2.waitKey(60)
### TODO: Pre-process the image as needed ###
p_frame = preprocess_frame(frame, dsize)
### TODO: Start asynchronous inference for specified request ###
infer_network.exec_net(p_frame)
### TODO: Wait for the result ###
if infer_network.wait()==0:
### TODO: Get the results of the inference request ###
infer_result = infer_network.get_output()
### TODO: Extract any desired stats from the results ###
out_frame, present_count = extract_stats(frame, infer_result, args, width, height)
### 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" ###
# when a person is in the video
if present_count>preceding_count:
start_time=time.time()
total_count+=present_count - preceding_count
frame_count = 0
payload_total_count = {
"total" : total_count
}
client.publish("person", json.dumps(payload_total_count))
# when there is one less person
if present_count<preceding_count and frame_count < wait_time:
present_count=preceding_count
frame_count+=1
# when there is one less person for up to 30 frames
if present_count<preceding_count and frame_count == wait_time:
duration = int(time.time() - start_time)
payload_duration = {
"duration": duration
}
client.publish("person/duration", json.dumps(payload_duration))
preceding_count=present_count
payload_present_count = {
"count" : present_count
}
client.publish("person", json.dumps(payload_present_count))
### TODO: Send the frame to the FFMPEG server ###
sys.stdout.buffer.write(out_frame)
sys.stdout.flush()
if key_pressed == 27:
break
# -- release the out writer, capture and destroy any opencv windows
input_stream.release()
cv2.destroyAllWindows()
client.disconnect()
def main():
"""
Load the network and parse the output.
:return: None
"""
# Grab command line args
args = build_argparser().parse_args()
# Connect to the MQTT server
client = connect_mqtt()
# Perform inference on the input stream
infer_on_stream(args, client)
if __name__ == '__main__':
main()