001-hello-world: Hello Image Classification using OpenVINO™ toolkit

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Thu Jun 09 2022 14:58:38 GMT+0000 (UTC)

Saved by @OpenVINOtoolkit #python #openvino #tensorflow

# Imports
import cv2
import matplotlib.pyplot as plt
import numpy as np
from openvino.runtime import Core

# Load the model
ie = Core()
model = ie.read_model(model="model/v3-small_224_1.0_float.xml")
compiled_model = ie.compile_model(model=model, device_name="CPU")
output_layer = compiled_model.output(0)

# Load an Image
# The MobileNet model 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 model input shape
input_image = np.expand_dims(input_image.transpose(2, 0, 1), 0)
plt.imshow(image);

# Do Inference
result_infer = compiled_model([input_image])[output_layer]
result_index = np.argmax(result_infer)

# 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]
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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 OpenVINO IR model like this one is created. If you have not yet installed OpenVINO™, please follow the Installation Guide to install all required dependencies. https://github.com/openvinotoolkit/openvino_notebooks/blob/main/README.md#-installation-guide Link to .bin and .xml files: https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/mobilenet-v3-small-1.0-224-tf/model.yml

https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/001-hello-world/001-hello-world.ipynb