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¶
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:
Imports ⇑¶
import os
import sys
from pathlib import Path
import cv2
import matplotlib.pyplot as plt
import numpy as np
from openvino.runtime import Core
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 = 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')
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
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 ||################
========== Downloading models/public/colorization-v2/ckpt/colorization-v2-eccv16.pth
========== Downloading models/public/colorization-v2/model/__init__.py
========== Downloading models/public/colorization-v2/model/base_color.py
========== Downloading models/public/colorization-v2/model/eccv16.py
========== Replacing text in models/public/colorization-v2/model/__init__.py
========== 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
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
========== Converting colorization-v2 to ONNX
Conversion to ONNX command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-475/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-475/.workspace/scm/ov-notebook/.venv/lib/python3.8/site-packages/openvino/model_zoo/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
ONNX check passed successfully.
========== Converting colorization-v2 to IR (FP16)
Conversion command: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-475/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-475/.workspace/scm/ov-notebook/.venv/bin/mo --framework=onnx --output_dir=/tmp/tmp7wsuasz7 --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
[ INFO ] Generated IR will be compressed to FP16. If you get lower accuracy, please consider disabling compression by removing argument --compress_to_fp16 or set it to false --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 ] The model was converted to IR v11, the latest model format that corresponds to the source DL framework input/output format. While IR v11 is backwards compatible with OpenVINO Inference Engine API v1.0, please use API v2.0 (as of 2022.1) to take advantage of the latest improvements in IR v11.
Find more information about API v2.0 and IR v11 at https://docs.openvino.ai/2023.0/openvino_2_0_transition_guide.html
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /tmp/tmp7wsuasz7/colorization-v2.xml
[ SUCCESS ] BIN file: /tmp/tmp7wsuasz7/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.
core = 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 ⇑¶
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 ⇑¶
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")
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
color_img_0 = colorize(test_img_0)
color_img_1 = colorize(test_img_1)
