Convert and Optimize YOLOv8 real-time object detection with OpenVINO™
=====================================================================

Real-time object detection is often used as a key component in computer
vision systems. Applications that use real-time object detection models
include video analytics, robotics, autonomous vehicles, multi-object
tracking and object counting, medical image analysis, and many others.

This tutorial demonstrates step-by-step instructions on how to run and
optimize PyTorch YOLOv8 with OpenVINO. We consider the steps required
for object detection scenario.

The tutorial consists of the following steps:

- Prepare the PyTorch model.
- Download and prepare a dataset.
- Validate the original model.
- Convert the PyTorch model to OpenVINO IR.
- Validate the converted model.
- Prepare and run optimization pipeline.
- Compare performance of the FP32 and quantized models.
- Compare accuracy of the FP32 and quantized models.
- Other optimization possibilities with OpenVINO api
- Live demo

**Table of contents:**


-  `Get PyTorch model <#get-pytorch-model>`__

   -  `Prerequisites <#prerequisites>`__

-  `Instantiate model <#instantiate-model>`__

   -  `Convert model to OpenVINO IR <#convert-model-to-openvino-ir>`__
   -  `Verify model inference <#verify-model-inference>`__
   -  `Select inference device <#select-inference-device>`__
   -  `Test on single image <#test-on-single-image>`__

-  `Check model accuracy on the
   dataset <#check-model-accuracy-on-the-dataset>`__

   -  `Download the validation
      dataset <#download-the-validation-dataset>`__
   -  `Define validation function <#define-validation-function>`__
   -  `Configure Validator helper and create
      DataLoader <#configure-validator-helper-and-create-dataloader>`__

-  `Optimize model using NNCF Post-training Quantization
   API <#optimize-model-using-nncf-post-training-quantization-api>`__

   -  `Validate Quantized model
      inference <#validate-quantized-model-inference>`__

-  `Compare the Original and Quantized
   Models <#compare-the-original-and-quantized-models>`__

   -  `Compare performance object detection
      models <#compare-performance-object-detection-models>`__
   -  `Validate quantized model
      accuracy <#validate-quantized-model-accuracy>`__

-  `Next steps <#next-steps>`__

   -  `Async inference pipeline <#async-inference-pipeline>`__
   -  `Integration preprocessing to
      model <#integration-preprocessing-to-model>`__

      -  `Initialize PrePostProcessing
         API <#initialize-prepostprocessing-api>`__
      -  `Define input data format <#define-input-data-format>`__
      -  `Describe preprocessing
         steps <#describe-preprocessing-steps>`__
      -  `Integrating Steps into a
         Model <#integrating-steps-into-a-model>`__
      -  `Postprocessing <#postprocessing>`__

-  `Live demo <#live-demo>`__

   -  `Run Live Object Detection <#run-live-object-detection>`__

Installation Instructions
~~~~~~~~~~~~~~~~~~~~~~~~~

This is a self-contained example that relies solely on its own code.

We recommend running the notebook in a virtual environment. You only
need a Jupyter server to start. For details, please refer to
`Installation
Guide <https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/README.md#-installation-guide>`__.

Get PyTorch model
-----------------



Generally, PyTorch models represent an instance of the
`torch.nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html>`__
class, initialized by a state dictionary with model weights. We will use
the YOLOv8 nano model (also known as ``yolov8n``) pre-trained on a COCO
dataset, which is available in this
`repo <https://github.com/ultralytics/ultralytics>`__. Similar steps are
also applicable to other YOLOv8 models. Typical steps to obtain a
pre-trained model: 1. Create an instance of a model class. 2. Load a
checkpoint state dict, which contains the pre-trained model weights. 3.
Turn the model to evaluation for switching some operations to inference
mode.

In this case, the creators of the model provide an API that enables
converting the YOLOv8 model to ONNX and then to OpenVINO IR. Therefore,
we do not need to do these steps manually.

Prerequisites
^^^^^^^^^^^^^



Install necessary packages.

.. code:: ipython3

    %pip install -q "openvino>=2024.0.0" "nncf>=2.9.0"
    %pip install -q "torch>=2.1" "torchvision>=0.16" "ultralytics==8.2.24" onnx tqdm opencv-python --extra-index-url https://download.pytorch.org/whl/cpu

Import required utility functions. The lower cell will download the
``notebook_utils`` Python module from GitHub.

.. code:: ipython3

    from pathlib import Path

    # Fetch `notebook_utils` module
    import requests

    r = requests.get(
        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py",
    )

    open("notebook_utils.py", "w").write(r.text)

    from notebook_utils import download_file, VideoPlayer, device_widget, quantization_widget

.. code:: ipython3

    # Download a test sample
    IMAGE_PATH = Path("./data/coco_bike.jpg")
    download_file(
        url="https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/image/coco_bike.jpg",
        filename=IMAGE_PATH.name,
        directory=IMAGE_PATH.parent,
    )

Instantiate model
-----------------



There are `several
models <https://docs.ultralytics.com/tasks/detect/>`__ available in the
original repository, targeted for different tasks. For loading the
model, required to specify a path to the model checkpoint. It can be
some local path or name available on models hub (in this case model
checkpoint will be downloaded automatically).

Making prediction, the model accepts a path to input image and returns
list with Results class object. Results contains boxes for object
detection model. Also it contains utilities for processing results, for
example, ``plot()`` method for drawing.

Let us consider the examples:

.. code:: ipython3

    models_dir = Path("./models")
    models_dir.mkdir(exist_ok=True)

.. code:: ipython3

    from PIL import Image
    from ultralytics import YOLO

    DET_MODEL_NAME = "yolov8n"

    det_model = YOLO(models_dir / f"{DET_MODEL_NAME}.pt")
    label_map = det_model.model.names

    res = det_model(IMAGE_PATH)
    Image.fromarray(res[0].plot()[:, :, ::-1])


.. parsed-literal::

    Downloading https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8n.pt to 'models/yolov8n.pt'...


.. parsed-literal::

    100%|██████████████████████████████████████████████████████████████████████████████| 6.23M/6.23M [00:01<00:00, 3.73MB/s]


.. parsed-literal::


    image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/coco_bike.jpg: 480x640 2 bicycles, 2 cars, 1 dog, 43.2ms
    Speed: 1.9ms preprocess, 43.2ms inference, 0.9ms postprocess per image at shape (1, 3, 480, 640)




.. image:: yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_9_3.png



Convert model to OpenVINO IR
~~~~~~~~~~~~~~~~~~~~~~~~~~~~



YOLOv8 provides API for convenient model exporting to different formats
including OpenVINO IR. ``model.export`` is responsible for model
conversion. We need to specify the format, and additionally, we can
preserve dynamic shapes in the model.

.. code:: ipython3

    # object detection model
    det_model_path = models_dir / f"{DET_MODEL_NAME}_openvino_model/{DET_MODEL_NAME}.xml"
    if not det_model_path.exists():
        det_model.export(format="openvino", dynamic=True, half=True)


.. parsed-literal::

    Ultralytics YOLOv8.1.42 🚀 Python-3.10.12 torch-2.2.2+cpu CPU (Intel Core(TM) i9-10980XE 3.00GHz)

    PyTorch: starting from 'models/yolov8n.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)

    OpenVINO: starting export with openvino 2024.0.0-14509-34caeefd078-releases/2024/0...
    OpenVINO: export success ✅ 1.8s, saved as 'models/yolov8n_openvino_model/' (6.4 MB)

    Export complete (3.0s)
    Results saved to /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/models
    Predict:         yolo predict task=detect model=models/yolov8n_openvino_model imgsz=640 half
    Validate:        yolo val task=detect model=models/yolov8n_openvino_model imgsz=640 data=coco.yaml half
    Visualize:       https://netron.app


Verify model inference
~~~~~~~~~~~~~~~~~~~~~~



We can reuse the base model pipeline for pre- and postprocessing just
replacing the inference method where we will use the IR model for
inference.

Select inference device
~~~~~~~~~~~~~~~~~~~~~~~



Select device from dropdown list for running inference using OpenVINO

.. code:: ipython3

    device = device_widget()

    device




.. parsed-literal::

    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')



Test on single image
~~~~~~~~~~~~~~~~~~~~



Now, once we have defined preprocessing and postprocessing steps, we are
ready to check model prediction for object detection.

.. code:: ipython3

    import torch
    import openvino as ov

    core = ov.Core()

    det_ov_model = core.read_model(det_model_path)

    ov_config = {}
    if device.value != "CPU":
        det_ov_model.reshape({0: [1, 3, 640, 640]})
    if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
        ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
    det_compiled_model = core.compile_model(det_ov_model, device.value, ov_config)


    def infer(*args):
        result = det_compiled_model(args)
        return torch.from_numpy(result[0])


    det_model.predictor.inference = infer
    det_model.predictor.model.pt = False

    res = det_model(IMAGE_PATH)
    Image.fromarray(res[0].plot()[:, :, ::-1])


.. parsed-literal::


    image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/coco_bike.jpg: 640x640 2 bicycles, 2 cars, 1 dog, 27.5ms
    Speed: 3.2ms preprocess, 27.5ms inference, 1.2ms postprocess per image at shape (1, 3, 640, 640)




.. image:: yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_16_1.png



Check model accuracy on the dataset
-----------------------------------



For comparing the optimized model result with the original, it is good
to know some measurable results in terms of model accuracy on the
validation dataset.

Download the validation dataset
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



YOLOv8 is pre-trained on the COCO dataset, so to evaluate the model
accuracy we need to download it. According to the instructions provided
in the YOLOv8 repo, we also need to download annotations in the format
used by the author of the model, for use with the original model
evaluation function.

   **Note**: The initial dataset download may take a few minutes to
   complete. The download speed will vary depending on the quality of
   your internet connection.

.. code:: ipython3

    from zipfile import ZipFile

    from ultralytics.data.utils import DATASETS_DIR


    DATA_URL = "http://images.cocodataset.org/zips/val2017.zip"
    LABELS_URL = "https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels-segments.zip"
    CFG_URL = "https://raw.githubusercontent.com/ultralytics/ultralytics/v8.1.0/ultralytics/cfg/datasets/coco.yaml"

    OUT_DIR = DATASETS_DIR

    DATA_PATH = OUT_DIR / "val2017.zip"
    LABELS_PATH = OUT_DIR / "coco2017labels-segments.zip"
    CFG_PATH = OUT_DIR / "coco.yaml"

    download_file(DATA_URL, DATA_PATH.name, DATA_PATH.parent)
    download_file(LABELS_URL, LABELS_PATH.name, LABELS_PATH.parent)
    download_file(CFG_URL, CFG_PATH.name, CFG_PATH.parent)

    if not (OUT_DIR / "coco/labels").exists():
        with ZipFile(LABELS_PATH, "r") as zip_ref:
            zip_ref.extractall(OUT_DIR)
        with ZipFile(DATA_PATH, "r") as zip_ref:
            zip_ref.extractall(OUT_DIR / "coco/images")

Define validation function
~~~~~~~~~~~~~~~~~~~~~~~~~~



.. code:: ipython3

    import numpy as np

    from tqdm.notebook import tqdm
    from ultralytics.utils.metrics import ConfusionMatrix


    def test(
        model: ov.Model,
        core: ov.Core,
        data_loader: torch.utils.data.DataLoader,
        validator,
        num_samples: int = None,
    ):
        """
        OpenVINO YOLOv8 model accuracy validation function. Runs model validation on dataset and returns metrics
        Parameters:
            model (Model): OpenVINO model
            data_loader (torch.utils.data.DataLoader): dataset loader
            validator: instance of validator class
            num_samples (int, *optional*, None): validate model only on specified number samples, if provided
        Returns:
            stats: (Dict[str, float]) - dictionary with aggregated accuracy metrics statistics, key is metric name, value is metric value
        """
        validator.seen = 0
        validator.jdict = []
        validator.stats = dict(tp=[], conf=[], pred_cls=[], target_cls=[])
        validator.batch_i = 1
        validator.confusion_matrix = ConfusionMatrix(nc=validator.nc)
        model.reshape({0: [1, 3, -1, -1]})
        compiled_model = core.compile_model(model)
        for batch_i, batch in enumerate(tqdm(data_loader, total=num_samples)):
            if num_samples is not None and batch_i == num_samples:
                break
            batch = validator.preprocess(batch)
            results = compiled_model(batch["img"])
            preds = torch.from_numpy(results[compiled_model.output(0)])
            preds = validator.postprocess(preds)
            validator.update_metrics(preds, batch)
        stats = validator.get_stats()
        return stats


    def print_stats(stats: np.ndarray, total_images: int, total_objects: int):
        """
        Helper function for printing accuracy statistic
        Parameters:
            stats: (Dict[str, float]) - dictionary with aggregated accuracy metrics statistics, key is metric name, value is metric value
            total_images (int) -  number of evaluated images
            total objects (int)
        Returns:
            None
        """
        print("Boxes:")
        mp, mr, map50, mean_ap = (
            stats["metrics/precision(B)"],
            stats["metrics/recall(B)"],
            stats["metrics/mAP50(B)"],
            stats["metrics/mAP50-95(B)"],
        )
        # Print results
        print("    Best mean average:")
        s = ("%20s" + "%12s" * 6) % (
            "Class",
            "Images",
            "Labels",
            "Precision",
            "Recall",
            "mAP@.5",
            "mAP@.5:.95",
        )
        print(s)
        pf = "%20s" + "%12i" * 2 + "%12.3g" * 4  # print format
        print(pf % ("all", total_images, total_objects, mp, mr, map50, mean_ap))
        if "metrics/precision(M)" in stats:
            s_mp, s_mr, s_map50, s_mean_ap = (
                stats["metrics/precision(M)"],
                stats["metrics/recall(M)"],
                stats["metrics/mAP50(M)"],
                stats["metrics/mAP50-95(M)"],
            )
            # Print results
            print("    Macro average mean:")
            s = ("%20s" + "%12s" * 6) % (
                "Class",
                "Images",
                "Labels",
                "Precision",
                "Recall",
                "mAP@.5",
                "mAP@.5:.95",
            )
            print(s)
            pf = "%20s" + "%12i" * 2 + "%12.3g" * 4  # print format
            print(pf % ("all", total_images, total_objects, s_mp, s_mr, s_map50, s_mean_ap))

Configure Validator helper and create DataLoader
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



The original model repository uses a ``Validator`` wrapper, which
represents the accuracy validation pipeline. It creates dataloader and
evaluation metrics and updates metrics on each data batch produced by
the dataloader. Besides that, it is responsible for data preprocessing
and results postprocessing. For class initialization, the configuration
should be provided. We will use the default setup, but it can be
replaced with some parameters overriding to test on custom data. The
model has connected the ``ValidatorClass`` method, which creates a
validator class instance.

.. code:: ipython3

    from ultralytics.utils import DEFAULT_CFG
    from ultralytics.cfg import get_cfg
    from ultralytics.data.converter import coco80_to_coco91_class
    from ultralytics.data.utils import check_det_dataset

    args = get_cfg(cfg=DEFAULT_CFG)
    args.data = str(CFG_PATH)

.. code:: ipython3

    det_validator = det_model.task_map[det_model.task]["validator"](args=args)

.. code:: ipython3

    det_validator.data = check_det_dataset(args.data)
    det_validator.stride = 32
    det_data_loader = det_validator.get_dataloader(OUT_DIR / "coco", 1)


.. parsed-literal::

    val: Scanning /home/maleksandr/test_notebooks/ultrali/datasets/coco/labels/val2017.cache... 4952 images, 48 backgrounds,


.. code:: ipython3

    det_validator.is_coco = True
    det_validator.class_map = coco80_to_coco91_class()
    det_validator.names = det_model.model.names
    det_validator.metrics.names = det_validator.names
    det_validator.nc = det_model.model.model[-1].nc

After definition test function and validator creation, we are ready for
getting accuracy metrics >\ **Note**: Model evaluation is time consuming
process and can take several minutes, depending on the hardware. For
reducing calculation time, we define ``num_samples`` parameter with
evaluation subset size, but in this case, accuracy can be noncomparable
with originally reported by the authors of the model, due to validation
subset difference. *To validate the models on the full dataset set
``NUM_TEST_SAMPLES = None``.*

.. code:: ipython3

    NUM_TEST_SAMPLES = 300

.. code:: ipython3

    fp_det_stats = test(det_ov_model, core, det_data_loader, det_validator, num_samples=NUM_TEST_SAMPLES)



.. parsed-literal::

      0%|          | 0/300 [00:00<?, ?it/s]


.. code:: ipython3

    print_stats(fp_det_stats, det_validator.seen, det_validator.nt_per_class.sum())


.. parsed-literal::

    Boxes:
        Best mean average:
                   Class      Images      Labels   Precision      Recall      mAP@.5  mAP@.5:.95
                     all         300        2145       0.594       0.542       0.579       0.417


``print_stats`` reports the following list of accuracy metrics:

-  ``Precision`` is the degree of exactness of the model in identifying
   only relevant objects.
-  ``Recall`` measures the ability of the model to detect all ground
   truths objects.
-  ``mAP@t`` - mean average precision, represented as area under the
   Precision-Recall curve aggregated over all classes in the dataset,
   where ``t`` is the Intersection Over Union (IOU) threshold, degree of
   overlapping between ground truth and predicted objects. Therefore,
   ``mAP@.5`` indicates that mean average precision is calculated at 0.5
   IOU threshold, ``mAP@.5:.95`` - is calculated on range IOU thresholds
   from 0.5 to 0.95 with step 0.05.

Optimize model using NNCF Post-training Quantization API
--------------------------------------------------------



`NNCF <https://github.com/openvinotoolkit/nncf>`__ provides a suite of
advanced algorithms for Neural Networks inference optimization in
OpenVINO with minimal accuracy drop. We will use 8-bit quantization in
post-training mode (without the fine-tuning pipeline) to optimize
YOLOv8.

The optimization process contains the following steps:

1. Create a Dataset for quantization.
2. Run ``nncf.quantize`` for getting an optimized model.
3. Serialize OpenVINO IR model, using the ``openvino.runtime.serialize``
   function.

Please select below whether you would like to run quantization to
improve model inference speed.

.. code:: ipython3

    int8_model_det_path = models_dir / f"{DET_MODEL_NAME}_openvino_int8_model/{DET_MODEL_NAME}.xml"
    quantized_det_model = None

    to_quantize = quantization_widget()

    to_quantize




.. parsed-literal::

    Checkbox(value=True, description='Quantization')



Let’s load ``skip magic`` extension to skip quantization if
``to_quantize`` is not selected

.. code:: ipython3

    # Fetch skip_kernel_extension module
    r = requests.get(
        url="https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py",
    )
    open("skip_kernel_extension.py", "w").write(r.text)

    %load_ext skip_kernel_extension

Reuse validation dataloader in accuracy testing for quantization. For
that, it should be wrapped into the ``nncf.Dataset`` object and define a
transformation function for getting only input tensors.

.. code:: ipython3

    %%skip not $to_quantize.value

    import nncf
    from typing import Dict


    def transform_fn(data_item:Dict):
        """
        Quantization transform function. Extracts and preprocess input data from dataloader item for quantization.
        Parameters:
           data_item: Dict with data item produced by DataLoader during iteration
        Returns:
            input_tensor: Input data for quantization
        """
        input_tensor = det_validator.preprocess(data_item)['img'].numpy()
        return input_tensor


    quantization_dataset = nncf.Dataset(det_data_loader, transform_fn)


.. parsed-literal::

    INFO:nncf:NNCF initialized successfully. Supported frameworks detected: torch, onnx, openvino


The ``nncf.quantize`` function provides an interface for model
quantization. It requires an instance of the OpenVINO Model and
quantization dataset. Optionally, some additional parameters for the
configuration quantization process (number of samples for quantization,
preset, ignored scope, etc.) can be provided. YOLOv8 model contains
non-ReLU activation functions, which require asymmetric quantization of
activations. To achieve a better result, we will use a ``mixed``
quantization preset. It provides symmetric quantization of weights and
asymmetric quantization of activations. For more accurate results, we
should keep the operation in the postprocessing subgraph in floating
point precision, using the ``ignored_scope`` parameter.

   **Note**: Model post-training quantization is time-consuming process.
   Be patient, it can take several minutes depending on your hardware.

.. code:: ipython3

    %%skip not $to_quantize.value


    ignored_scope = nncf.IgnoredScope(
        names=[
            "__module.model.22.cv3.0.0.conv/aten::_convolution/Convolution",  # in the post-processing subgraph
        	"__module.model.16.conv/aten::_convolution/Convolution",
        	"__module.model.22.cv2.0.0.conv/aten::_convolution/Convolution",
        	"__module.model.6.cv1.conv/aten::_convolution/Convolution",
        	"__module.model.22.cv3.1.1.conv/aten::_convolution/Convolution",
        	"__module.model.21.cv2.conv/aten::_convolution/Convolution",
        	"__module.model.21.m.0.cv1.conv/aten::_convolution/Convolution",
        	"__module.model.22/aten::add/Add_6",
        	"__module.model.22/aten::sub/Subtract",
        	"__module.model.7.conv/aten::_convolution/Convolution",
        	"__module.model.12.cv1.conv/aten::_convolution/Convolution",
        	"__module.model.4.cv1.conv/aten::_convolution/Convolution",
        	"__module.model.22.cv2.2.1.conv/aten::_convolution/Convolution",
        	"__module.model.22.cv2.0.1.conv/aten::_convolution/Convolution",
        	"__module.model.22.dfl.conv/aten::_convolution/Convolution",
        	"__module.model.22.cv3.2.2/aten::_convolution/Convolution",
        	"__module.model.22.cv3.0.2/aten::_convolution/Convolution",
        	"__module.model.15.cv1.conv/aten::_convolution/Convolution",
        	"__module.model.5.conv/aten::_convolution/Convolution",
        	"__module.model.0.conv/aten::_convolution/Convolution"
        ]
    )


    # Detection model
    quantized_det_model = nncf.quantize(
        det_ov_model,
        quantization_dataset,
        preset=nncf.QuantizationPreset.MIXED,
        ignored_scope=ignored_scope
    )

.. code:: ipython3

    %%skip not $to_quantize.value

    print(f"Quantized detection model will be saved to {int8_model_det_path}")
    ov.save_model(quantized_det_model, str(int8_model_det_path))


.. parsed-literal::

    Quantized detection model will be saved to models/yolov8n_openvino_int8_model/yolov8n.xml


Validate Quantized model inference
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



``nncf.quantize`` returns the OpenVINO Model class instance, which is
suitable for loading on a device for making predictions. ``INT8`` model
input data and output result formats have no difference from the
floating point model representation. Therefore, we can reuse the same
``detect`` function defined above for getting the ``INT8`` model result
on the image.

.. code:: ipython3

    %%skip not $to_quantize.value

    device

.. code:: ipython3

    %%skip not $to_quantize.value

    ov_config = {}
    if device.value != "CPU":
        quantized_det_model.reshape({0: [1, 3, 640, 640]})
    if "GPU" in device.value or ("AUTO" in device.value and "GPU" in core.available_devices):
        ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
    quantized_det_compiled_model = core.compile_model(quantized_det_model, device.value, ov_config)


    def infer(*args):
        result = quantized_det_compiled_model(args)
        return torch.from_numpy(result[0])

    det_model.predictor.inference = infer

    res = det_model(IMAGE_PATH)
    display(Image.fromarray(res[0].plot()[:, :, ::-1]))


.. parsed-literal::


    image 1/1 /home/maleksandr/test_notebooks/update_ultralytics/openvino_notebooks/notebooks/yolov8-optimization/data/coco_bike.jpg: 640x640 2 bicycles, 2 cars, 1 dog, 18.4ms
    Speed: 2.1ms preprocess, 18.4ms inference, 0.9ms postprocess per image at shape (1, 3, 640, 640)



.. image:: yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_43_1.png


Compare the Original and Quantized Models
-----------------------------------------



Compare performance object detection models
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



Finally, use the OpenVINO `Benchmark
Tool <https://docs.openvino.ai/2024/learn-openvino/openvino-samples/benchmark-tool.html>`__
to measure the inference performance of the ``FP32`` and ``INT8``
models.

   **Note**: For more accurate performance, it is recommended to run
   ``benchmark_app`` in a terminal/command prompt after closing other
   applications. Run
   ``benchmark_app -m <model_path> -d CPU -shape "<input_shape>"`` to
   benchmark async inference on CPU on specific input data shape for one
   minute. Change ``CPU`` to ``GPU`` to benchmark on GPU. Run
   ``benchmark_app --help`` to see an overview of all command-line
   options.

.. code:: ipython3

    %%skip not $to_quantize.value

    device

.. code:: ipython3

    if int8_model_det_path.exists():
        # Inference FP32 model (OpenVINO IR)
        !benchmark_app -m $det_model_path -d $device.value -api async -shape "[1,3,640,640]"


.. parsed-literal::

    [Step 1/11] Parsing and validating input arguments
    [ INFO ] Parsing input parameters
    [Step 2/11] Loading OpenVINO Runtime
    [ WARNING ] Default duration 120 seconds is used for unknown device AUTO
    [ INFO ] OpenVINO:
    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
    [ INFO ]
    [ INFO ] Device info:
    [ INFO ] AUTO
    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
    [ INFO ]
    [ INFO ]
    [Step 3/11] Setting device configuration
    [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
    [Step 4/11] Reading model files
    [ INFO ] Loading model files
    [ INFO ] Read model took 14.59 ms
    [ INFO ] Original model I/O parameters:
    [ INFO ] Model inputs:
    [ INFO ]     x (node: x) : f32 / [...] / [?,3,?,?]
    [ INFO ] Model outputs:
    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_7) : f32 / [...] / [?,84,16..]
    [Step 5/11] Resizing model to match image sizes and given batch
    [ INFO ] Model batch size: 1
    [ INFO ] Reshaping model: 'x': [1,3,640,640]
    [ INFO ] Reshape model took 8.72 ms
    [Step 6/11] Configuring input of the model
    [ INFO ] Model inputs:
    [ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
    [ INFO ] Model outputs:
    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_7) : f32 / [...] / [1,84,8400]
    [Step 7/11] Loading the model to the device
    [ INFO ] Compile model took 272.15 ms
    [Step 8/11] Querying optimal runtime parameters
    [ INFO ] Model:
    [ INFO ]   NETWORK_NAME: Model0
    [ INFO ]   EXECUTION_DEVICES: ['CPU']
    [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
    [ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
    [ INFO ]   CPU:
    [ INFO ]     AFFINITY: Affinity.CORE
    [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
    [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
    [ INFO ]     ENABLE_CPU_PINNING: True
    [ INFO ]     ENABLE_HYPER_THREADING: True
    [ INFO ]     EXECUTION_DEVICES: ['CPU']
    [ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
    [ INFO ]     INFERENCE_NUM_THREADS: 36
    [ INFO ]     INFERENCE_PRECISION_HINT: <Type: 'float32'>
    [ INFO ]     KV_CACHE_PRECISION: <Type: 'float16'>
    [ INFO ]     LOG_LEVEL: Level.NO
    [ INFO ]     NETWORK_NAME: Model0
    [ INFO ]     NUM_STREAMS: 12
    [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
    [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
    [ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
    [ INFO ]     PERF_COUNT: NO
    [ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
    [ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
    [ INFO ]   LOADED_FROM_CACHE: False
    [Step 9/11] Creating infer requests and preparing input tensors
    [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
    [ INFO ] Fill input 'x' with random values
    [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 120000 ms duration)
    [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
    [ INFO ] First inference took 41.00 ms
    [Step 11/11] Dumping statistics report
    [ INFO ] Execution Devices:['CPU']
    [ INFO ] Count:            21300 iterations
    [ INFO ] Duration:         120060.45 ms
    [ INFO ] Latency:
    [ INFO ]    Median:        67.21 ms
    [ INFO ]    Average:       67.48 ms
    [ INFO ]    Min:           31.90 ms
    [ INFO ]    Max:           143.04 ms
    [ INFO ] Throughput:   177.41 FPS


.. code:: ipython3

    if int8_model_det_path.exists():
        # Inference INT8 model (OpenVINO IR)
        !benchmark_app -m $int8_model_det_path -d $device.value -api async -shape "[1,3,640,640]" -t 15


.. parsed-literal::

    [Step 1/11] Parsing and validating input arguments
    [ INFO ] Parsing input parameters
    [Step 2/11] Loading OpenVINO Runtime
    [ INFO ] OpenVINO:
    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
    [ INFO ]
    [ INFO ] Device info:
    [ INFO ] AUTO
    [ INFO ] Build ................................. 2024.0.0-14509-34caeefd078-releases/2024/0
    [ INFO ]
    [ INFO ]
    [Step 3/11] Setting device configuration
    [ WARNING ] Performance hint was not explicitly specified in command line. Device(AUTO) performance hint will be set to PerformanceMode.THROUGHPUT.
    [Step 4/11] Reading model files
    [ INFO ] Loading model files
    [ INFO ] Read model took 21.34 ms
    [ INFO ] Original model I/O parameters:
    [ INFO ] Model inputs:
    [ INFO ]     x (node: x) : f32 / [...] / [1,3,?,?]
    [ INFO ] Model outputs:
    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_7) : f32 / [...] / [1,84,21..]
    [Step 5/11] Resizing model to match image sizes and given batch
    [ INFO ] Model batch size: 1
    [ INFO ] Reshaping model: 'x': [1,3,640,640]
    [ INFO ] Reshape model took 11.86 ms
    [Step 6/11] Configuring input of the model
    [ INFO ] Model inputs:
    [ INFO ]     x (node: x) : u8 / [N,C,H,W] / [1,3,640,640]
    [ INFO ] Model outputs:
    [ INFO ]     ***NO_NAME*** (node: __module.model.22/aten::cat/Concat_7) : f32 / [...] / [1,84,8400]
    [Step 7/11] Loading the model to the device
    [ INFO ] Compile model took 478.52 ms
    [Step 8/11] Querying optimal runtime parameters
    [ INFO ] Model:
    [ INFO ]   NETWORK_NAME: Model0
    [ INFO ]   EXECUTION_DEVICES: ['CPU']
    [ INFO ]   PERFORMANCE_HINT: PerformanceMode.THROUGHPUT
    [ INFO ]   OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
    [ INFO ]   MULTI_DEVICE_PRIORITIES: CPU
    [ INFO ]   CPU:
    [ INFO ]     AFFINITY: Affinity.CORE
    [ INFO ]     CPU_DENORMALS_OPTIMIZATION: False
    [ INFO ]     CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0
    [ INFO ]     DYNAMIC_QUANTIZATION_GROUP_SIZE: 0
    [ INFO ]     ENABLE_CPU_PINNING: True
    [ INFO ]     ENABLE_HYPER_THREADING: True
    [ INFO ]     EXECUTION_DEVICES: ['CPU']
    [ INFO ]     EXECUTION_MODE_HINT: ExecutionMode.PERFORMANCE
    [ INFO ]     INFERENCE_NUM_THREADS: 36
    [ INFO ]     INFERENCE_PRECISION_HINT: <Type: 'float32'>
    [ INFO ]     KV_CACHE_PRECISION: <Type: 'float16'>
    [ INFO ]     LOG_LEVEL: Level.NO
    [ INFO ]     NETWORK_NAME: Model0
    [ INFO ]     NUM_STREAMS: 12
    [ INFO ]     OPTIMAL_NUMBER_OF_INFER_REQUESTS: 12
    [ INFO ]     PERFORMANCE_HINT: THROUGHPUT
    [ INFO ]     PERFORMANCE_HINT_NUM_REQUESTS: 0
    [ INFO ]     PERF_COUNT: NO
    [ INFO ]     SCHEDULING_CORE_TYPE: SchedulingCoreType.ANY_CORE
    [ INFO ]   MODEL_PRIORITY: Priority.MEDIUM
    [ INFO ]   LOADED_FROM_CACHE: False
    [Step 9/11] Creating infer requests and preparing input tensors
    [ WARNING ] No input files were given for input 'x'!. This input will be filled with random values!
    [ INFO ] Fill input 'x' with random values
    [Step 10/11] Measuring performance (Start inference asynchronously, 12 inference requests, limits: 15000 ms duration)
    [ INFO ] Benchmarking in inference only mode (inputs filling are not included in measurement loop).
    [ INFO ] First inference took 35.17 ms
    [Step 11/11] Dumping statistics report
    [ INFO ] Execution Devices:['CPU']
    [ INFO ] Count:            4104 iterations
    [ INFO ] Duration:         15062.51 ms
    [ INFO ] Latency:
    [ INFO ]    Median:        43.53 ms
    [ INFO ]    Average:       43.85 ms
    [ INFO ]    Min:           24.58 ms
    [ INFO ]    Max:           70.57 ms
    [ INFO ] Throughput:   272.46 FPS


Validate quantized model accuracy
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



As we can see, there is no significant difference between ``INT8`` and
float model result in a single image test. To understand how
quantization influences model prediction precision, we can compare model
accuracy on a dataset.

.. code:: ipython3

    %%skip not $to_quantize.value

    int8_det_stats = test(quantized_det_model, core, det_data_loader, det_validator, num_samples=NUM_TEST_SAMPLES)



.. parsed-literal::

      0%|          | 0/300 [00:00<?, ?it/s]


.. code:: ipython3

    %%skip not $to_quantize.value

    print("FP32 model accuracy")
    print_stats(fp_det_stats, det_validator.seen, det_validator.nt_per_class.sum())

    print("INT8 model accuracy")
    print_stats(int8_det_stats, det_validator.seen, det_validator.nt_per_class.sum())


.. parsed-literal::

    FP32 model accuracy
    Boxes:
        Best mean average:
                   Class      Images      Labels   Precision      Recall      mAP@.5  mAP@.5:.95
                     all         300        2153       0.594       0.542       0.579       0.417
    INT8 model accuracy
    Boxes:
        Best mean average:
                   Class      Images      Labels   Precision      Recall      mAP@.5  mAP@.5:.95
                     all         300        2153       0.597       0.509       0.562       0.389


Great! Looks like accuracy was changed, but not significantly and it
meets passing criteria.

Next steps
----------

This section contains
suggestions on how to additionally improve the performance of your
application using OpenVINO.

Async inference pipeline
~~~~~~~~~~~~~~~~~~~~~~~~

The key advantage of the Async
API is that when a device is busy with inference, the application can
perform other tasks in parallel (for example, populating inputs or
scheduling other requests) rather than wait for the current inference to
complete first. To understand how to perform async inference using
openvino, refer to `Async API tutorial <async-api-with-output.html>`__

Integration preprocessing to model
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~



Preprocessing API enables making preprocessing a part of the model
reducing application code and dependency on additional image processing
libraries. The main advantage of Preprocessing API is that preprocessing
steps will be integrated into the execution graph and will be performed
on a selected device (CPU/GPU etc.) rather than always being executed on
CPU as part of an application. This will improve selected device
utilization.

For more information, refer to the overview of `Preprocessing
API <https://docs.openvino.ai/2024/openvino-workflow/running-inference/optimize-inference/optimize-preprocessing/preprocessing-api-details.html>`__.

For example, we can integrate converting input data layout and
normalization defined in ``image_to_tensor`` function.

The integration process consists of the following steps: 1. Initialize a
PrePostProcessing object. 2. Define the input data format. 3. Describe
preprocessing steps. 4. Integrating Steps into a Model.

Initialize PrePostProcessing API
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^



The ``openvino.preprocess.PrePostProcessor`` class enables specifying
preprocessing and postprocessing steps for a model.

.. code:: ipython3

    from openvino.preprocess import PrePostProcessor

    ppp = PrePostProcessor(quantized_det_model if quantized_det_model is not None else det_model)

Define input data format
^^^^^^^^^^^^^^^^^^^^^^^^



To address particular input of a model/preprocessor, the
``input(input_id)`` method, where ``input_id`` is a positional index or
input tensor name for input in ``model.inputs``, if a model has a single
input, ``input_id`` can be omitted. After reading the image from the
disc, it contains U8 pixels in the ``[0, 255]`` range and is stored in
the ``NHWC`` layout. To perform a preprocessing conversion, we should
provide this to the tensor description.

.. code:: ipython3

    ppp.input(0).tensor().set_shape([1, 640, 640, 3]).set_element_type(ov.Type.u8).set_layout(ov.Layout("NHWC"))
    pass

To perform layout conversion, we also should provide information about
layout expected by model

Describe preprocessing steps
^^^^^^^^^^^^^^^^^^^^^^^^^^^^



Our preprocessing function contains the following steps: \* Convert the
data type from ``U8`` to ``FP32``. \* Convert the data layout from
``NHWC`` to ``NCHW`` format. \* Normalize each pixel by dividing on
scale factor 255.

``ppp.input(input_id).preprocess()`` is used for defining a sequence of
preprocessing steps:

.. code:: ipython3

    ppp.input(0).preprocess().convert_element_type(ov.Type.f32).convert_layout(ov.Layout("NCHW")).scale([255.0, 255.0, 255.0])

    print(ppp)


.. parsed-literal::

    Input "x":
        User's input tensor: [1,640,640,3], [N,H,W,C], u8
        Model's expected tensor: [1,3,?,?], [N,C,H,W], f32
        Pre-processing steps (3):
          convert type (f32): ([1,640,640,3], [N,H,W,C], u8) -> ([1,640,640,3], [N,H,W,C], f32)
          convert layout [N,C,H,W]: ([1,640,640,3], [N,H,W,C], f32) -> ([1,3,640,640], [N,C,H,W], f32)
          scale (255,255,255): ([1,3,640,640], [N,C,H,W], f32) -> ([1,3,640,640], [N,C,H,W], f32)



Integrating Steps into a Model
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^



Once the preprocessing steps have been finished, the model can be
finally built. Additionally, we can save a completed model to OpenVINO
IR, using ``openvino.runtime.serialize``.

.. code:: ipython3

    quantized_model_with_preprocess = ppp.build()

    with_preprocess_path = (
        int8_model_det_path.with_name(f"{DET_MODEL_NAME}_with_preprocess.xml")
        if quantized_det_model is not None
        else det_model_path.with_name(f"{DET_MODEL_NAME}_with_preprocess.xml")
    )
    ov.save_model(quantized_model_with_preprocess, str(with_preprocess_path))

The model with integrated preprocessing is ready for loading to a
device.

.. code:: ipython3

    from typing import Tuple, Dict
    import cv2
    import numpy as np
    from ultralytics.utils.plotting import colors
    import random


    def plot_one_box(
        box: np.ndarray,
        img: np.ndarray,
        color: Tuple[int, int, int] = None,
        label: str = None,
        line_thickness: int = 5,
    ):
        """
        Helper function for drawing single bounding box on image
        Parameters:
            x (np.ndarray): bounding box coordinates in format [x1, y1, x2, y2]
            img (no.ndarray): input image
            color (Tuple[int, int, int], *optional*, None): color in BGR format for drawing box, if not specified will be selected randomly
            label (str, *optonal*, None): box label string, if not provided will not be provided as drowing result
            line_thickness (int, *optional*, 5): thickness for box drawing lines
        """
        # Plots one bounding box on image img
        tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
        color = color or [random.randint(0, 255) for _ in range(3)]
        c1, c2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
        cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
        if label:
            tf = max(tl - 1, 1)  # font thickness
            t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
            c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
            cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
            cv2.putText(
                img,
                label,
                (c1[0], c1[1] - 2),
                0,
                tl / 3,
                [225, 255, 255],
                thickness=tf,
                lineType=cv2.LINE_AA,
            )

        return img


    def draw_results(results: Dict, source_image: np.ndarray, label_map: Dict):
        """
        Helper function for drawing bounding boxes on image
        Parameters:
            image_res (np.ndarray): detection predictions in format [x1, y1, x2, y2, score, label_id]
            source_image (np.ndarray): input image for drawing
            label_map; (Dict[int, str]): label_id to class name mapping
        Returns:
            Image with boxes
        """
        boxes = results["det"]
        for idx, (*xyxy, conf, lbl) in enumerate(boxes):
            label = f"{label_map[int(lbl)]} {conf:.2f}"
            source_image = plot_one_box(xyxy, source_image, label=label, color=colors(int(lbl)), line_thickness=1)
        return source_image

Postprocessing
''''''''''''''



The model output contains detection boxes candidates, it is a tensor
with the [-1,84,-1] shape in the B,84,N format, where:

B - batch size N - number of detection boxes For getting the final
prediction, we need to apply a non-maximum suppression algorithm and
rescale box coordinates to the original image size.

Finally, detection box has the [x, y, h, w, class_no_1, …, class_no_80]
format, where:

(x, y) - raw coordinates of box center h, w - raw height and width of
the box class_no_1, …, class_no_80 - probability distribution over the
classes.

.. code:: ipython3

    from typing import Tuple
    from ultralytics.utils import ops
    import torch
    import numpy as np


    def letterbox(
        img: np.ndarray,
        new_shape: Tuple[int, int] = (640, 640),
        color: Tuple[int, int, int] = (114, 114, 114),
        auto: bool = False,
        scale_fill: bool = False,
        scaleup: bool = False,
        stride: int = 32,
    ):
        """
        Resize image and padding for detection. Takes image as input,
        resizes image to fit into new shape with saving original aspect ratio and pads it to meet stride-multiple constraints

        Parameters:
          img (np.ndarray): image for preprocessing
          new_shape (Tuple(int, int)): image size after preprocessing in format [height, width]
          color (Tuple(int, int, int)): color for filling padded area
          auto (bool): use dynamic input size, only padding for stride constrins applied
          scale_fill (bool): scale image to fill new_shape
          scaleup (bool): allow scale image if it is lower then desired input size, can affect model accuracy
          stride (int): input padding stride
        Returns:
          img (np.ndarray): image after preprocessing
          ratio (Tuple(float, float)): hight and width scaling ratio
          padding_size (Tuple(int, int)): height and width padding size


        """
        # Resize and pad image while meeting stride-multiple constraints
        shape = img.shape[:2]  # current shape [height, width]
        if isinstance(new_shape, int):
            new_shape = (new_shape, new_shape)

        # Scale ratio (new / old)
        r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
        if not scaleup:  # only scale down, do not scale up (for better test mAP)
            r = min(r, 1.0)

        # Compute padding
        ratio = r, r  # width, height ratios
        new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
        dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
        if auto:  # minimum rectangle
            dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
        elif scale_fill:  # stretch
            dw, dh = 0.0, 0.0
            new_unpad = (new_shape[1], new_shape[0])
            ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios

        dw /= 2  # divide padding into 2 sides
        dh /= 2

        if shape[::-1] != new_unpad:  # resize
            img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
        top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
        left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
        img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
        return img, ratio, (dw, dh)


    def postprocess(
        pred_boxes: np.ndarray,
        input_hw: Tuple[int, int],
        orig_img: np.ndarray,
        min_conf_threshold: float = 0.25,
        nms_iou_threshold: float = 0.7,
        agnosting_nms: bool = False,
        max_detections: int = 300,
    ):
        """
        YOLOv8 model postprocessing function. Applied non maximum supression algorithm to detections and rescale boxes to original image size
        Parameters:
            pred_boxes (np.ndarray): model output prediction boxes
            input_hw (np.ndarray): preprocessed image
            orig_image (np.ndarray): image before preprocessing
            min_conf_threshold (float, *optional*, 0.25): minimal accepted confidence for object filtering
            nms_iou_threshold (float, *optional*, 0.45): minimal overlap score for removing objects duplicates in NMS
            agnostic_nms (bool, *optiona*, False): apply class agnostinc NMS approach or not
            max_detections (int, *optional*, 300):  maximum detections after NMS
        Returns:
           pred (List[Dict[str, np.ndarray]]): list of dictionary with det - detected boxes in format [x1, y1, x2, y2, score, label]
        """
        nms_kwargs = {"agnostic": agnosting_nms, "max_det": max_detections}
        preds = ops.non_max_suppression(torch.from_numpy(pred_boxes), min_conf_threshold, nms_iou_threshold, nc=80, **nms_kwargs)

        results = []
        for i, pred in enumerate(preds):
            shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
            if not len(pred):
                results.append({"det": [], "segment": []})
                continue
            pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round()
            results.append({"det": pred})

        return results

Now, we can skip these preprocessing steps in detect function:

.. code:: ipython3

    def detect_without_preprocess(image: np.ndarray, model: ov.Model):
        """
        OpenVINO YOLOv8 model with integrated preprocessing inference function. Preprocess image, runs model inference and postprocess results using NMS.
        Parameters:
            image (np.ndarray): input image.
            model (Model): OpenVINO compiled model.
        Returns:
            detections (np.ndarray): detected boxes in format [x1, y1, x2, y2, score, label]
        """
        output_layer = model.output(0)
        img = letterbox(image)[0]
        input_tensor = np.expand_dims(img, 0)
        input_hw = img.shape[:2]
        result = model(input_tensor)[output_layer]
        detections = postprocess(result, input_hw, image)
        return detections


    compiled_model = core.compile_model(quantized_model_with_preprocess, device.value)
    input_image = np.array(Image.open(IMAGE_PATH))
    detections = detect_without_preprocess(input_image, compiled_model)[0]
    image_with_boxes = draw_results(detections, input_image, label_map)

    Image.fromarray(image_with_boxes)




.. image:: yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_70_0.png



Live demo
---------



The following code runs model inference on a video:

.. code:: ipython3

    import collections
    import time
    from IPython import display

    det_ov_model


    # Main processing function to run object detection.
    def run_object_detection(
        source=0,
        flip=False,
        use_popup=False,
        skip_first_frames=0,
        model=det_model,
        device=device.value,
    ):
        player = None
        ov_config = {}
        if device != "CPU":
            model.reshape({0: [1, 3, 640, 640]})
        if "GPU" in device or ("AUTO" in device and "GPU" in core.available_devices):
            ov_config = {"GPU_DISABLE_WINOGRAD_CONVOLUTION": "YES"}
        compiled_model = core.compile_model(model, device, ov_config)

        def infer(*args):
            result = compiled_model(args)
            return torch.from_numpy(result[0])

        det_model.predictor.inference = infer

        try:
            # Create a video player to play with target fps.
            player = VideoPlayer(source=source, flip=flip, fps=30, skip_first_frames=skip_first_frames)
            # Start capturing.
            player.start()
            if use_popup:
                title = "Press ESC to Exit"
                cv2.namedWindow(winname=title, flags=cv2.WINDOW_GUI_NORMAL | cv2.WINDOW_AUTOSIZE)

            processing_times = collections.deque()
            while True:
                # Grab the frame.
                frame = player.next()
                if frame is None:
                    print("Source ended")
                    break
                # If the frame is larger than full HD, reduce size to improve the performance.
                scale = 1280 / max(frame.shape)
                if scale < 1:
                    frame = cv2.resize(
                        src=frame,
                        dsize=None,
                        fx=scale,
                        fy=scale,
                        interpolation=cv2.INTER_AREA,
                    )
                # Get the results.
                input_image = np.array(frame)

                start_time = time.time()
                detections = det_model(input_image, verbose=False)
                stop_time = time.time()
                frame = detections[0].plot()

                processing_times.append(stop_time - start_time)
                # Use processing times from last 200 frames.
                if len(processing_times) > 200:
                    processing_times.popleft()

                _, f_width = frame.shape[:2]
                # Mean processing time [ms].
                processing_time = np.mean(processing_times) * 1000
                fps = 1000 / processing_time
                cv2.putText(
                    img=frame,
                    text=f"Inference time: {processing_time:.1f}ms ({fps:.1f} FPS)",
                    org=(20, 40),
                    fontFace=cv2.FONT_HERSHEY_COMPLEX,
                    fontScale=f_width / 1000,
                    color=(0, 0, 255),
                    thickness=1,
                    lineType=cv2.LINE_AA,
                )
                # Use this workaround if there is flickering.
                if use_popup:
                    cv2.imshow(winname=title, mat=frame)
                    key = cv2.waitKey(1)
                    # escape = 27
                    if key == 27:
                        break
                else:
                    # Encode numpy array to jpg.
                    _, encoded_img = cv2.imencode(ext=".jpg", img=frame, params=[cv2.IMWRITE_JPEG_QUALITY, 100])
                    # Create an IPython image.
                    i = display.Image(data=encoded_img)
                    # Display the image in this notebook.
                    display.clear_output(wait=True)
                    display.display(i)
        # ctrl-c
        except KeyboardInterrupt:
            print("Interrupted")
        # any different error
        except RuntimeError as e:
            print(e)
        finally:
            if player is not None:
                # Stop capturing.
                player.stop()
            if use_popup:
                cv2.destroyAllWindows()

Run Live Object Detection
~~~~~~~~~~~~~~~~~~~~~~~~~



Use a webcam as the video input. By default, the primary webcam is set
with \ ``source=0``. If you have multiple webcams, each one will be
assigned a consecutive number starting at 0. Set \ ``flip=True`` when
using a front-facing camera. Some web browsers, especially Mozilla
Firefox, may cause flickering. If you experience flickering,
set \ ``use_popup=True``.

   **NOTE**: To use this notebook with a webcam, you need to run the
   notebook on a computer with a webcam. If you run the notebook on a
   remote server (for example, in Binder or Google Colab service), the
   webcam will not work. By default, the lower cell will run model
   inference on a video file. If you want to try live inference on your
   webcam set ``WEBCAM_INFERENCE = True``

Run the object detection:

.. code:: ipython3

    WEBCAM_INFERENCE = False

    if WEBCAM_INFERENCE:
        VIDEO_SOURCE = 0  # Webcam
    else:
        VIDEO_SOURCE = "https://storage.openvinotoolkit.org/repositories/openvino_notebooks/data/data/video/people.mp4"

.. code:: ipython3

    device




.. parsed-literal::

    Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')



.. code:: ipython3

    run_object_detection(
        source=VIDEO_SOURCE,
        flip=True,
        use_popup=False,
        model=det_ov_model,
        device=device.value,
    )



.. image:: yolov8-object-detection-with-output_files/yolov8-object-detection-with-output_76_0.png


.. parsed-literal::

    Source ended

