3677dda457
* 2023.1 link fix * 2023.1 link fix * 2023.1 link fix * 2023.1 link fix * 2023.1 link fix
167 lines
4.2 KiB
ReStructuredText
167 lines
4.2 KiB
ReStructuredText
Hello Image Classification
|
|
==========================
|
|
|
|
|
|
|
|
This basic introduction to OpenVINO™ shows how to do inference with an
|
|
image classification model.
|
|
|
|
A pre-trained `MobileNetV3
|
|
model <https://docs.openvino.ai/2023.1/omz_models_model_mobilenet_v3_small_1_0_224_tf.html>`__
|
|
from `Open Model
|
|
Zoo <https://github.com/openvinotoolkit/open_model_zoo/>`__ is used in
|
|
this tutorial. For more information about how OpenVINO IR models are
|
|
created, refer to the `TensorFlow to
|
|
OpenVINO <101-tensorflow-classification-to-openvino-with-output.html>`__
|
|
tutorial.
|
|
|
|
|
|
|
|
.. _top:
|
|
|
|
**Table of contents**:
|
|
|
|
- `Imports <#imports>`__
|
|
- `Download the Model and data samples <#download-the-model-and-data-samples>`__
|
|
- `Select inference device <#select-inference-device>`__
|
|
- `Load the Model <#load-the-model>`__
|
|
- `Load an Image <#load-an-image>`__
|
|
- `Do Inference <#do-inference>`__
|
|
|
|
Imports `⇑ <#top>`__
|
|
############################################
|
|
|
|
.. code:: ipython3
|
|
|
|
from pathlib import Path
|
|
import sys
|
|
|
|
import cv2
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
from openvino.runtime import Core
|
|
|
|
sys.path.append("../utils")
|
|
from notebook_utils import download_file
|
|
|
|
Download the Model and data samples `⇑ <#top>`__
|
|
########################################################################
|
|
|
|
.. code:: ipython3
|
|
|
|
base_artifacts_dir = Path('./artifacts').expanduser()
|
|
|
|
model_name = "v3-small_224_1.0_float"
|
|
model_xml_name = f'{model_name}.xml'
|
|
model_bin_name = f'{model_name}.bin'
|
|
|
|
model_xml_path = base_artifacts_dir / model_xml_name
|
|
|
|
base_url = 'https://storage.openvinotoolkit.org/repositories/openvino_notebooks/models/mobelinet-v3-tf/FP32/'
|
|
|
|
if not model_xml_path.exists():
|
|
download_file(base_url + model_xml_name, model_xml_name, base_artifacts_dir)
|
|
download_file(base_url + model_bin_name, model_bin_name, base_artifacts_dir)
|
|
else:
|
|
print(f'{model_name} already downloaded to {base_artifacts_dir}')
|
|
|
|
|
|
|
|
.. parsed-literal::
|
|
|
|
artifacts/v3-small_224_1.0_float.xml: 0%| | 0.00/294k [00:00<?, ?B/s]
|
|
|
|
|
|
|
|
.. parsed-literal::
|
|
|
|
artifacts/v3-small_224_1.0_float.bin: 0%| | 0.00/4.84M [00:00<?, ?B/s]
|
|
|
|
|
|
Select inference device `⇑ <#top>`__
|
|
############################################################
|
|
|
|
Select device from dropdown list for running inference using OpenVINO:
|
|
|
|
.. code:: ipython3
|
|
|
|
import ipywidgets as widgets
|
|
|
|
core = Core()
|
|
device = widgets.Dropdown(
|
|
options=core.available_devices + ["AUTO"],
|
|
value='AUTO',
|
|
description='Device:',
|
|
disabled=False,
|
|
)
|
|
|
|
device
|
|
|
|
|
|
|
|
|
|
.. parsed-literal::
|
|
|
|
Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')
|
|
|
|
|
|
|
|
Load the Model `⇑ <#top>`__
|
|
###################################################
|
|
|
|
.. code:: ipython3
|
|
|
|
core = Core()
|
|
model = core.read_model(model=model_xml_path)
|
|
compiled_model = core.compile_model(model=model, device_name=device.value)
|
|
|
|
output_layer = compiled_model.output(0)
|
|
|
|
Load an Image `⇑ <#top>`__
|
|
##################################################
|
|
|
|
.. code:: ipython3
|
|
|
|
# The MobileNet model expects images in RGB format.
|
|
image = cv2.cvtColor(cv2.imread(filename="../data/image/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, 0)
|
|
plt.imshow(image);
|
|
|
|
|
|
|
|
.. image:: 001-hello-world-with-output_files/001-hello-world-with-output_10_0.png
|
|
|
|
|
|
Do Inference `⇑ <#top>`__
|
|
#################################################
|
|
|
|
.. code:: ipython3
|
|
|
|
result_infer = compiled_model([input_image])[output_layer]
|
|
result_index = np.argmax(result_infer)
|
|
|
|
.. code:: ipython3
|
|
|
|
# Convert the inference result to a class name.
|
|
imagenet_classes = open("../data/datasets/imagenet/imagenet_2012.txt").read().splitlines()
|
|
|
|
# The model description states that for this model, class 0 is a background.
|
|
# Therefore, a background must be added at the beginning of imagenet_classes.
|
|
imagenet_classes = ['background'] + imagenet_classes
|
|
|
|
imagenet_classes[result_index]
|
|
|
|
|
|
|
|
|
|
.. parsed-literal::
|
|
|
|
'n02099267 flat-coated retriever'
|
|
|
|
|