openvino/docs/notebooks/222-vision-image-colorization-with-output.rst

Image Colorization with OpenVINO
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

This notebook demonstrates how to colorize images with OpenVINO using
the Colorization model
`colorization-v2 <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/colorization-v2/README.md>`__
or
`colorization-siggraph <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/colorization-siggraph>`__
from `Open Model
Zoo <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/index.md>`__
based on the paper `Colorful Image
Colorization <https://arxiv.org/abs/1603.08511>`__ models from Open
Model Zoo.

.. figure:: https://user-images.githubusercontent.com/18904157/180923280-9caefaf1-742b-4d2f-8943-5d4a6126e2fc.png
   :alt: Let there be color

   Let there be color

Given a grayscale image as input, the model generates colorized version
of the image as the output.

About Colorization-v2
^^^^^^^^^^^^^^^^^^^^^

-  The colorization-v2 model is one of the colorization group of models
   designed to perform image colorization.
-  Model trained on the ImageNet dataset.
-  Model consumes L-channel of LAB-image as input and produces predict
   A- and B-channels of LAB-image as output.

About Colorization-siggraph
^^^^^^^^^^^^^^^^^^^^^^^^^^^

-  The colorization-siggraph model is one of the colorization group of
   models designed to real-time user-guided image colorization.
-  Model trained on the ImageNet dataset with synthetically generated
   user interaction.
-  Model consumes L-channel of LAB-image as input and produces predict
   A- and B-channels of LAB-image as output.

See the `colorization <https://github.com/richzhang/colorization>`__
repository for more details.

Table of contents:
^^^^^^^^^^^^^^^^^^

-  `Imports <#imports>`__
-  `Configurations <#configurations>`__

   -  `Select inference device <#select-inference-device>`__

-  `Download the model <#download-the-model>`__
-  `Convert the model to OpenVINO
   IR <#convert-the-model-to-openvino-ir>`__
-  `Loading the Model <#loading-the-model>`__
-  `Utility Functions <#utility-functions>`__
-  `Load the Image <#load-the-image>`__
-  `Display Colorized Image <#display-colorized-image>`__

.. code:: ipython3

    %pip install "openvino-dev>=2024.0.0"


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.. parsed-literal::

    DEPRECATION: pytorch-lightning 1.6.5 has a non-standard dependency specifier torch>=1.8.*. pip 24.1 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063


.. parsed-literal::

    Note: you may need to restart the kernel to use updated packages.


Imports
-------



.. code:: ipython3

    import os
    import sys
    from pathlib import Path

    import cv2
    import matplotlib.pyplot as plt
    import numpy as np
    import openvino as ov

    sys.path.append("../utils")
    import notebook_utils as utils

Configurations
--------------



-  ``PRECISION`` - {FP16, FP32}, default: FP16.
-  ``MODEL_DIR`` - directory where the model is to be stored, default:
   public.
-  ``MODEL_NAME`` - name of the model used for inference, default:
   colorization-v2.
-  ``DATA_DIR`` - directory where test images are stored, default: data.

.. code:: ipython3

    PRECISION = "FP16"
    MODEL_DIR = "models"
    MODEL_NAME = "colorization-v2"
    # MODEL_NAME="colorization-siggraph"
    MODEL_PATH = f"{MODEL_DIR}/public/{MODEL_NAME}/{PRECISION}/{MODEL_NAME}.xml"
    DATA_DIR = "data"

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



select device from dropdown list for running inference using OpenVINO

.. code:: ipython3

    import ipywidgets as widgets

    core = ov.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')



Download the model
------------------



``omz_downloader`` downloads model files from online sources and, if
necessary, patches them to make them more usable with Model Converter.

In this case, ``omz_downloader`` downloads the checkpoint and pytorch
model of
`colorization-v2 <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/colorization-v2/README.md>`__
or
`colorization-siggraph <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/colorization-siggraph>`__
from `Open Model
Zoo <https://github.com/openvinotoolkit/open_model_zoo/blob/master/models/public/index.md>`__
and saves it under ``MODEL_DIR``, as specified in the configuration
above.

.. code:: ipython3

    download_command = (
        f"omz_downloader "
        f"--name {MODEL_NAME} "
        f"--output_dir {MODEL_DIR} "
        f"--cache_dir {MODEL_DIR}"
    )
    ! $download_command


.. parsed-literal::

    ################|| Downloading colorization-v2 ||################

    ========== Downloading models/public/colorization-v2/ckpt/colorization-v2-eccv16.pth


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.. parsed-literal::

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.. parsed-literal::

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.. parsed-literal::

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.. parsed-literal::

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.. parsed-literal::

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.. parsed-literal::

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.. parsed-literal::

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.. parsed-literal::

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.. parsed-literal::

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.. parsed-literal::

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.. parsed-literal::

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.. parsed-literal::

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.. parsed-literal::

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.. parsed-literal::

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.. parsed-literal::

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.. parsed-literal::

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    ========== Replacing text in models/public/colorization-v2/model/__init__.py
    ========== Replacing text in models/public/colorization-v2/model/eccv16.py



Convert the model to OpenVINO IR
--------------------------------



``omz_converter`` converts the models that are not in the OpenVINO™ IR
format into that format using model conversion API.

The downloaded pytorch model is not in OpenVINO IR format which is
required for inference with OpenVINO runtime. ``omz_converter`` is used
to convert the downloaded pytorch model into ONNX and OpenVINO IR format
respectively

.. code:: ipython3

    if not os.path.exists(MODEL_PATH):
        convert_command = (
            f"omz_converter "
            f"--name {MODEL_NAME} "
            f"--download_dir {MODEL_DIR} "
            f"--precisions {PRECISION}"
        )
        ! $convert_command


.. parsed-literal::

    ========== Converting colorization-v2 to ONNX
    Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/omz_tools/internal_scripts/pytorch_to_onnx.py --model-path=models/public/colorization-v2 --model-name=ECCVGenerator --weights=models/public/colorization-v2/ckpt/colorization-v2-eccv16.pth --import-module=model --input-shape=1,1,256,256 --output-file=models/public/colorization-v2/colorization-v2-eccv16.onnx --input-names=data_l --output-names=color_ab



.. parsed-literal::

    ONNX check passed successfully.


.. parsed-literal::


    ========== Converting colorization-v2 to IR (FP16)
    Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=models/public/colorization-v2/FP16 --model_name=colorization-v2 --input=data_l --output=color_ab --input_model=models/public/colorization-v2/colorization-v2-eccv16.onnx '--layout=data_l(NCHW)' '--input_shape=[1, 1, 256, 256]' --compress_to_fp16=True



.. parsed-literal::

    [ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression explicitly by adding argument --compress_to_fp16=False.
    Find more information about compression to FP16 at https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_FP16_Compression.html
    [ INFO ] MO command line tool is considered as the legacy conversion API as of OpenVINO 2023.2 release. Please use OpenVINO Model Converter (OVC). OVC represents a lightweight alternative of MO and provides simplified model conversion API.
    Find more information about transition from MO to OVC at https://docs.openvino.ai/2023.2/openvino_docs_OV_Converter_UG_prepare_model_convert_model_MO_OVC_transition.html


.. parsed-literal::

    [ SUCCESS ] Generated IR version 11 model.
    [ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/222-vision-image-colorization/models/public/colorization-v2/FP16/colorization-v2.xml
    [ SUCCESS ] BIN file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-632/.workspace/scm/ov-notebook/notebooks/222-vision-image-colorization/models/public/colorization-v2/FP16/colorization-v2.bin



Loading the Model
-----------------



Load the model in OpenVINO Runtime with ``ie.read_model`` and compile it
for the specified device with ``ie.compile_model``.

.. code:: ipython3

    core = ov.Core()
    model = core.read_model(model=MODEL_PATH)
    compiled_model = core.compile_model(model=model, device_name=device.value)
    input_layer = compiled_model.input(0)
    output_layer = compiled_model.output(0)
    N, C, H, W = list(input_layer.shape)

Utility Functions
-----------------



.. code:: ipython3

    def read_image(impath: str) -> np.ndarray:
        """
        Returns an image as ndarra, given path to an image reads the
        (BGR) image using opencv's imread() API.

            Parameter:
                impath (string): Path of the image to be read and returned.

            Returns:
                image (ndarray): Numpy array representing the read image.
        """

        raw_image = cv2.imread(impath)
        if raw_image.shape[2] > 1:
            image = cv2.cvtColor(
                cv2.cvtColor(raw_image, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2RGB
            )
        else:
            image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)

        return image


    def plot_image(image: np.ndarray, title: str = "") -> None:
        """
        Given a image as ndarray and title as string, display it using
        matplotlib.

            Parameters:
                image (ndarray): Numpy array representing the image to be
                                 displayed.
                title (string): String representing the title of the plot.

            Returns:
                None

        """

        plt.imshow(image)
        plt.title(title)
        plt.axis("off")
        plt.show()


    def plot_output(gray_img: np.ndarray, color_img: np.ndarray) -> None:
        """
        Plots the original (bw or grayscale) image and colorized image
        on different column axes for comparing side by side.

            Parameters:
                gray_image (ndarray): Numpy array representing the original image.
                color_image (ndarray): Numpy array representing the model output.

            Returns:
                None
        """

        fig = plt.figure(figsize=(12, 12))

        ax1 = fig.add_subplot(1, 2, 1)
        plt.title("Input", fontsize=20)
        ax1.axis("off")

        ax2 = fig.add_subplot(1, 2, 2)
        plt.title("Colorized", fontsize=20)
        ax2.axis("off")

        ax1.imshow(gray_img)
        ax2.imshow(color_img)

        plt.show()

Load the Image
--------------



.. code:: ipython3

    img_url_0 = "https://user-images.githubusercontent.com/18904157/180923287-20339d01-b1bf-493f-9a0d-55eff997aff1.jpg"
    img_url_1 = "https://user-images.githubusercontent.com/18904157/180923289-0bb71e09-25e1-46a6-aaf1-e8f666b62d26.jpg"

    image_file_0 = utils.download_file(
        img_url_0, filename="test_0.jpg", directory="data", show_progress=False, silent=True, timeout=30
    )
    assert Path(image_file_0).exists()

    image_file_1 = utils.download_file(
        img_url_1, filename="test_1.jpg", directory="data", show_progress=False, silent=True, timeout=30
    )
    assert Path(image_file_1).exists()

    test_img_0 = read_image("data/test_0.jpg")
    test_img_1 = read_image("data/test_1.jpg")

.. code:: ipython3

    def colorize(gray_img: np.ndarray) -> np.ndarray:

        """
        Given an image as ndarray for inference convert the image into LAB image,
        the model consumes as input L-Channel of LAB image and provides output
        A & B - Channels of LAB image. i.e returns a colorized image

            Parameters:
                gray_img (ndarray): Numpy array representing the original
                                    image.

            Returns:
                colorize_image (ndarray): Numpy arrray depicting the
                                          colorized version of the original
                                          image.
        """

        # Preprocess
        h_in, w_in, _ = gray_img.shape
        img_rgb = gray_img.astype(np.float32) / 255
        img_lab = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2Lab)
        img_l_rs = cv2.resize(img_lab.copy(), (W, H))[:, :, 0]

        # Inference
        inputs = np.expand_dims(img_l_rs, axis=[0, 1])
        res = compiled_model([inputs])[output_layer]
        update_res = np.squeeze(res)

        # Post-process
        out = update_res.transpose((1, 2, 0))
        out = cv2.resize(out, (w_in, h_in))
        img_lab_out = np.concatenate((img_lab[:, :, 0][:, :, np.newaxis],
                                      out), axis=2)
        img_bgr_out = np.clip(cv2.cvtColor(img_lab_out, cv2.COLOR_Lab2RGB), 0, 1)
        colorized_image = (cv2.resize(img_bgr_out, (w_in, h_in))
                           * 255).astype(np.uint8)
        return colorized_image

.. code:: ipython3

    color_img_0 = colorize(test_img_0)
    color_img_1 = colorize(test_img_1)

Display Colorized Image
-----------------------



.. code:: ipython3

    plot_output(test_img_0, color_img_0)



.. image:: 222-vision-image-colorization-with-output_files/222-vision-image-colorization-with-output_21_0.png


.. code:: ipython3

    plot_output(test_img_1, color_img_1)



.. image:: 222-vision-image-colorization-with-output_files/222-vision-image-colorization-with-output_22_0.png