openvino之图像分类

 1 import cv2 as cv
 2 import numpy as np
 3 from openvino.inference_engine import IECore
 4 
 5 
 6 '''
 7 1、读取模型、声明推理器
 8 '''
 9 ie = IECore()
10 for device in ie.available_devices:
11     print(device)
12 model_xml = "resnet18.xml"
13 model_bin = "resnet18.bin"
14 # read the net
15 net = ie.read_network(model=model_xml, weights=model_bin)
16 # 获取网络的输入、输出
17 input_blob = next(iter(net.inputs))
18 out_blob = next(iter(net.outputs))
19 n, c, h, w = net.inputs[input_blob].shape
20 print(n, c, h, w) # 1 3 224 224 xml文件前面就有
21 # 声明推理器
22 exec_net = ie.load_network(network=net, device_name="CPU")
23 '''
24 2、读取类别文件
25 '''
26 with open('imagenet_classes.txt') as f:
27     labels = [line.strip() for line in f.readlines()]
28 '''
29 3、读取图像和预处理
30 '''
31 src = cv.imread("d:/3.png")
32 # resize
33 image = cv.resize(src, (w, h))
34 # 归一化
35 image = np.float32(image) / 255.0
36 # 减去均值
37 image[:, :, ] -= (np.float32(0.485), np.float32(0.456), np.float32(0.406))
38 image[:, :, ] /= (np.float32(0.229), np.float32(0.224), np.float32(0.225))
39 # -> c, h, w
40 image = image.transpose(2, 0, 1)
41 '''
42 4、推理 & 解析结果
43 '''
44 res = exec_net.infer(inputs={input_blob:[image]}) #[]：使得c, h, w -> 1, c, h, w;其实就是batch_size = 1
45 # 获取网络输出
46 res = res[out_blob] # 1000个分类的置信度
47 label_index = np.argmax(res, 1) # 最大置信度索引
48 print(labels[label_index[0]])
49 '''
50 可视化
51 '''
52 cv.putText(src, labels[label_index[0]], (50, 50), cv.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2, 8)
53 cv.imshow("image classification", src)
54 cv.waitKey(0)