OpenVINO による画像のカラー化¶
この Jupyter ノートブックはオンラインで起動でき、ブラウザーのウィンドウで対話型環境を開きます。ローカルにインストールすることもできます。次のオプションのいずれかを選択します。
このノートブックは、Open Model Zoo の論文 Colorful Image Colorization models に基づいて、Open Model Zoo のカラー化モデル colorization-v2 または colorization-siggraph を使用して、OpenVINO で画像を色付けする方法を示します。
カラー化します¶
グレースケール画像を入力として指定すると、モデルはカラー化された画像を出力として生成します。
Colorization-v2 について¶
colorization-v2 モデルは、画像の色付けを実行するように設計されたモデルのカラー化グループの 1 つです。
モデルは ImageNet データセットでトレーニングされています。
モデルは LAB 画像の L チャネルを入力として受け取り、LAB 画像の予測 A チャネルと B チャネルを出力として生成します。
Colorization-siggraph について¶
colorization-siggraph モデルは、リアルタイムのユーザーガイドによる画像の色付けを目的として設計されたモデルの色付けグループの 1 つです。
モデルは、合成的に生成されたユーザー・インタラクションにより ImageNet データセット上でトレーニングされました。
モデルは LAB 画像の L チャネルを入力として受け取り、LAB 画像の予測 A チャネルと B チャネルを出力として生成します。
詳細については、カラー化リポジトリーを参照してください。
目次¶
%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.
インポート¶
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
構成¶
PRECISION- {FP16, FP32}、デフォルト: FP16。MODEL_DIR- モデルが保存されるディレクトリー、デフォルト: パブリック。MODEL_NAME- 推論に使用されるモデルの名前、デフォルト: colorization-v2。DATA_DIR- テスト画像が保存されるディレクトリー、デフォルト: 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"
推論デバイスの選択¶
OpenVINO を使用して推論を実行するためにドロップダウン・リストからデバイスを選択します。
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 は、オンラインソースからモデルファイルをダウンロードし、必要に応じてモデル・オプティマイザーでさらに使いやすくするためにパッチを適用します。
この場合、omz_downloader は、Open Model Zoo から colorization-v2 または colorization-siggraph のチェックポイントと pytorch モデルをダウンロードし、上記の設定で指定されている MODEL_DIR に保存します。
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
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========== Downloading models/public/colorization-v2/model/__init__.py
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========== Replacing text in models/public/colorization-v2/model/__init__.py
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omz_converter モデル変換 API を使用して、OpenVINO™ IR 形式ではないモデルをその形式に変換します。
ダウンロードされた pytorch モデルは、OpenVINO ランタイムの推論に必要な OpenVINO IR 形式ではありません。omz_converter は、ダウンロードした pytorch モデルをそれぞれ ONNX および OpenVINO IR 形式に変換するために使用されます。
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-609/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.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-609/.workspace/scm/ov-notebook/.venv/bin/python -- /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.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
[ 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 ] 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
[ 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
[ SUCCESS ] Generated IR version 11 model.
[ SUCCESS ] XML file: /opt/home/k8sworker/ci-ai/cibuilds/ov-notebook/OVNotebookOps-609/.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-609/.workspace/scm/ov-notebook/notebooks/222-vision-image-colorization/models/public/colorization-v2/FP16/colorization-v2.bin
ie.read_model を使用してモデルを OpenVINO ランタイムにロードし、ie.compile_model を使用して指定されたデバイス用にコンパイルします。
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)
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()
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)
color_img_0 = colorize(test_img_0)
color_img_1 = colorize(test_img_1)
plot_output(test_img_0, color_img_0)
plot_output(test_img_1, color_img_1)
