Image Colorization with OpenVINO

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This notebook demonstrates how to colorize images with OpenVINO using the Colorization model colorization-v2 or colorization-siggraph from Open Model Zoo based on the paper Colorful Image Colorization models from Open Model Zoo.

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 repository for more details.

Table of contents:

%pip install "openvino-dev>=2023.1.0"
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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
Note: you may need to restart the kernel to use updated packages.

Imports

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.

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

import ipywidgets as widgets

core = ov.Core()

device = widgets.Dropdown(
    options=core.available_devices + ["AUTO"],
    value='AUTO',
    description='Device:',
    disabled=False,
)

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

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 or colorization-siggraph from Open Model Zoo and saves it under MODEL_DIR, as specified in the configuration above.

download_command = (
    f"omz_downloader "
    f"--name {MODEL_NAME} "
    f"--output_dir {MODEL_DIR} "
    f"--cache_dir {MODEL_DIR}"
)
! $download_command
################|| Downloading colorization-v2 ||################

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