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CPU Upgrade
File size: 3,490 Bytes
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# created with great guidance from https://github.com/NimaBoscarino
import gradio as gr
import kornia as K
from kornia.core import Tensor
def filters_without(file, blur_pool2d, box_blur, gaussian_blur2d, max_blur_pool2d, median_blur):
# load the image using the rust backend
img: Tensor = K.io.load_image(file.name, K.io.ImageLoadType.RGB32)
img = img[None] # 1xCxHxW / fp32 / [0, 1]
# apply tensor image enhancement
x_out: Tensor = K.filters.box_blur(img, (int(box_blur), int(box_blur)))
x_out = K.filters.gaussian_blur2d(x_out,
(int(gaussian_blur2d), int(gaussian_blur2d)),
(float(gaussian_blur2d), float(gaussian_blur2d)))
x_out = K.filters.max_blur_pool2d(x_out, int(max_blur_pool2d))
x_out = K.filters.median_blur(x_out, (int(median_blur), int(median_blur)))
return K.utils.tensor_to_image(x_out)
def filters_with(file, blur_pool2d, box_blur, gaussian_blur2d, max_blur_pool2d, median_blur):
# load the image using the rust backend
img: Tensor = K.io.load_image(file.name, K.io.ImageLoadType.RGB32)
img = img[None] # 1xCxHxW / fp32 / [0, 1]
# apply tensor image enhancement
x_out: Tensor = K.filters.blur_pool2d(x_out, int(blur_pool2d))
x_out = K.filters.box_blur(img, (int(box_blur), int(box_blur)))
x_out = K.filters.gaussian_blur2d(x_out,
(int(gaussian_blur2d), int(gaussian_blur2d)),
(float(gaussian_blur2d), float(gaussian_blur2d)))
x_out = K.filters.max_blur_pool2d(x_out, int(max_blur_pool2d))
x_out = K.filters.median_blur(x_out, (int(median_blur), int(median_blur)))
return K.utils.tensor_to_image(x_out)
examples = [
["examples/monkey.jpg", 1, 1, 1, 1, 1],
["examples/pikachu.jpg", 1, 1, 1, 1, 1],
]
without_downsampling_demo = gr.Interface(
filters_without,
[
gr.inputs.Image(type="file"),
gr.inputs.Slider(minimum=1, maximum=10, step=1, default=1, label="Box Blur"),
gr.inputs.Slider(minimum=1, maximum=21, step=2, default=1, label="Gaussian Blur"),
gr.inputs.Slider(minimum=1, maximum=20, step=1, default=1, label="Max Pool"),
gr.inputs.Slider(minimum=1, maximum=5, step=2, default=1, label="Median Blur"),
],
"image",
examples=examples,
# title=title,
description= 'If you want to use the filters with downsampled image, use tab "With image downsampling"',
# article=article,
live=True
)
with_downsampling_demo = gr.Interface(
filters_with,
[
gr.inputs.Image(type="file"),
gr.inputs.Slider(minimum=1, maximum=10, step=1, default=1, label="Blur Pool"),
gr.inputs.Slider(minimum=1, maximum=10, step=1, default=1, label="Box Blur"),
gr.inputs.Slider(minimum=1, maximum=21, step=2, default=1, label="Gaussian Blur"),
gr.inputs.Slider(minimum=1, maximum=20, step=1, default=1, label="Max Pool"),
gr.inputs.Slider(minimum=1, maximum=5, step=2, default=1, label="Median Blur"),
],
"image",
examples=examples,
# title=title,
description = 'Blur Pooling downsamples the image in the default setting!',
# article=article,
live=True
)
demo = gr.TabbedInterface(
[
without_downsampling_demo,
with_downsampling_demo
],
[
"Without image downsampling",
"With image downsampling"
]
)
demo.launch() |