Hello Image Classification

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A very basic introduction to OpenVINO that shows how to perform inference with an image classification model.

We use a pre-trained MobileNetV3 model from the Open Model Zoo. See the TensorFlow to OpenVINO tutorial to learn more about how this OpenVINO IR model was created.

Imports

import cv2
import matplotlib.pyplot as plt
import numpy as np
from openvino.inference_engine import IECore

Load the Model

ie = IECore()
net = ie.read_network(
    model="model/v3-small_224_1.0_float.xml", weights="model/v3-small_224_1.0_float.bin"
)
exec_net = ie.load_network(network=net, device_name="CPU")

input_key = next(iter(exec_net.input_info))
output_key = next(iter(exec_net.outputs.keys()))

Load an Image

# The MobileNet network expects images in RGB format
image = cv2.cvtColor(cv2.imread(filename="data/coco.jpg"), code=cv2.COLOR_BGR2RGB)

# resize to MobileNet image shape
input_image = cv2.resize(src=image, dsize=(224, 224))

# reshape to network input shape
input_image = np.expand_dims(input_image.transpose(2, 0, 1), 0)
plt.imshow(image);
../_images/001-hello-world-with-output_6_0.png

Do Inference

result = exec_net.infer(inputs={input_key: input_image})[output_key]
result_index = np.argmax(result)

# Convert the inference result to a class name.
imagenet_classes = open("utils/imagenet_2012.txt").read().splitlines()

# The model description states that for this model, class 0 is background,
# so we add background at the beginning of imagenet_classes
imagenet_classes = ['background'] + imagenet_classes

imagenet_classes[result_index]

'n02099267 flat-coated retriever'
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