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inference.py
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inference.py
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#!/usr/bin/env python3
#python main.py -i /home/workspace/resources/Pedestrian_Detect_2_1_1.mp4 | ffmpeg -v warning -f rawvideo -pixel_format bgr24 -video_size 768x432 -framerate 24 -i - http://0.0.0.0:3004/fac.ffm
#python3 main.py -i /home/adrian-estelio/Documents/vision/PeopleCounterApp/resources/Pedestrian_Detect_2_1_1.mp4
"""
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 persons 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 logging as log
from openvino.inference_engine import IENetwork, IECore
class Network:
"""
Load and configure inference plugins for the specified target devices
and performs synchronous and asynchronous modes for the specified infer requests.
"""
def __init__(self):
### TODO: Initialize any class variables desired ###
self.exec_net = None
self.plugin = None
self.net = None
self.input_blob = None
self.output_blob = None
return
def load_model(self, model, device = "CPU", cpu_extension = None):
model_xml = model
model_bin = os.path.splitext(model_xml)[0]+".bin"
self.plugin = IECore()
### TODO: Load the model ###
#if cpu_extension and "CPU" in device:
#self.plugin.add_extension(cpu_extension,device)
self.net = IENetwork(model = model_xml, weights = model_bin)
self.exec_net= self.plugin.load_network(self.net,device)
### TODO: Check for supported layers ###
if device == "CPU":
supported_layers = self.plugin.query_network(self.net, device)
not_supported_layers = [l for l in self.net.layers.keys() if l not in supported_layers ]
if len(not_supported_layers) != 0:
log.error("It seems that there are layer that are not supported by the device {}:\n {}".format(self.plugin.device, ', '.join(not_supported_layers)))
sys.exit(1)
### TODO: Add any necessary extensions ###
### TODO: Return the loaded inference plugin ###
### Note: You may need to update the function parameters. ###
return self.plugin
def get_input_shape(self):
### TODO: Return the shape of the input layer ###
self.input_blob = next(iter(self.net.inputs))
shape = self.net.inputs[self.input_blob].shape
return shape
def exec_network(self, image):
### TODO: Start an asynchronous request ###
self.exec_net.start_async(request_id = 0, inputs = {self.input_blob: image})
### TODO: Return any necessary information ###
### Note: You may need to update the function parameters. ###
return
def wait(self):
### TODO: Wait for the request to be complete. ###
status = self.exec_net.requests[0].wait(-1)
### TODO: Return any necessary information ###
### Note: You may need to update the function parameters. ###
return status
def get_output(self):
### TODO: Extract and return the output results
self.output_blob = next(iter(self.net.outputs))
output = self.exec_net.requests[0].outputs[self.output_blob]
### Note: You may need to update the function parameters. ###
return output