一、模型转换流程：
(以mxnet框架在openvino上的转换为例）
1、安装相关openvino toolkit: (以镜像的方式为例）
sudo docker search openvino
docker pull cortexica/openvino:latest
docker run -i -t cortexica/openvino:latest /bin/bash
cd /opt/intel/openvino/deployment_tools/model_optimizer
pip3 install -r requirements_mxnet.txt
2、将相关mxnet模型文件及网络结构参数文件放到同一目录下
3、模型转换
python3 mo_mxnet.py --input_shape [1,3,112,112] --input_model model-0000.params --output_dir openvino_models
cd openvino_models/
二、openvino推理流程:（ubuntu16.04, openvino-2020.3为例）
1、设置环境变量：
vi ~/.bashrc
source <INSTALL_DIR>/bin/setupvars.sh
source ~/.bashrc
#如果想要设置具体版本的python
PYTHONPATH=<INSTALL_DIR>/deployment_tools/inference_engine/python_api/<desired_python_version
2、导入IE/OpenCV/numpy/time模块
from openvino.inference_engine import IECore, IENetwork
import cv2
import numpy as np
from time import time
3、配置推断计算设备，IR文件路径，图片（视频）路径
DEVICE = 'CPU'
model_xml = 'model-0000.xml'
model_bin = 'model-0000.bin'
image_file = 'test.jpg'
cpu_extension_lib = "/inference_engine/bin/intel64/Release/cpu_extension.dll"
labels_map = ["fake", "cat", "dog"]
4、初始化插件
ie = IECore()
5、读取IR模型文件
net = IENetwork(model=model_xml, weights=model_bin)
6、准备输入输出张量
print("Preparing input blobs")
input_blob = next(iter(net.inputs))
out_blob = next(iter
7、载入模型到AI推断计算设备
print("Loading IR to the plugin...")
exec_net = ie.load_network(network=net, num_requests=1, device_name=DEVICE)
8、读入图片，执行推断计算
n, c, h, w = net.inputs[input_blob].shape
frame = cv2.imread(image_file)
initial_h, initial_w, channels = frame.shape
# 按照AI模型要求放缩图片
image = cv2.resize(frame, (w, h))
# 按照AI模型要求将图像数据结构从HWC转为CHW
image = image.transpose((2, 0, 1))
print("Batch size is {}".format(n))
print("Starting inference in synchronous mode")
start = time()
res = exec_net.infer(inputs={input_blob: image})
end = time()
print("Infer Time:{}ms".format((end - start) * 1000))
9、处理输出，显示结果
print("Processing output blob")
res = res[out_blob]
print(res)
for obj in res:
print(obj)
# 当信心指数大于0.7时，显示检测结果
if obj[2] > 0.7:
xmin = int(obj[3] * initial_w)
ymin = int(obj[4] * initial_h)
xmax = int(obj[5] * initial_w)
ymax = int(obj[6] * initial_h)
class_id = int(obj[1])
# 显示信心指数，物体标签和边界框
color = (0, 255, 0) if class_id > 1 else (255, 0, 0)
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color, 2)
det_label = labels_map[class_id] if labels_map else str(class_id)
cv2.putText(frame, det_label + ' ' + str(round(obj[2] * 100, 1)) + ' %', (xmin, ymin - 7),
cv2.FONT_HERSHEY_COMPLEX, 0.6, color, 1)
print("Inference is completed")
cv2.imshow("Detection results", frame)
cv2.waitKey(0)
cv2.destroyAllWindows()
