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import gradio as gr
import numpy as np
import colorizers as c
from colorizers.util import postprocess_tens, preprocess_img
def interface(image, model: str = "siggraph17"):
if model == "eccv16":
img = siggraph17(pretrained=True).eval()
else:
img = c.siggraph17(pretrained=True).eval()
oimg = np.asarray(image)
if(oimg.ndim == 2):
oimg = np.tile(oimg[:,:,None], 3)
(tens_l_orig, tens_l_rs) = preprocess_img(oimg)
output_img = postprocess_tens(
tens_l_orig,
img(tens_l_rs).cpu()
)
return output_img
css='''
.Box {
background-color: var(--color-canvas-default);
border-color: var(--color-border-default);
border-style: solid;
border-width: 1px;
border-radius: 6px;
}
.d-flex {
display: flex !important;
}
.flex-md-row {
flex-direction: row !important;
}
.flex-column {
flex-direction: column !important;
}
'''
title = "Image Colorization Using AI Models"
description = r"""<center>An automatic colorization functionality for Real-Time User-Guided Image Colorization with Learned Deep Priors,ECCV16 & SIGGRAPH 2017 Models!<br>
Practically the algorithm is used to COLORIZE your **old BLACK & WHITE / GRAYSCALE photos**.<br>
To use it, simply just upload the concerned image.<br>
"""
article = r"""
"""
#with gr.Interface(css=css) as mainBody:
gr.HTML("""<style>""" + css+ """</Style>""")
mainBody = gr.Interface(
interface,
[
gr.components.Image(type="pil", label="image"),
gr.components.Radio(
["siggraph17"],
type="value",
label="model"
)
],
[
gr.components.Image(label="output")
],
#inputs="sketchpad",
#outputs="label",
theme="huggingface",
title=title,
description=description,
article=article,
live=True,
)
mainBody.launch() |