ControlNet/gradio_annotator.py at main · tinypeter/ControlNet · GitHub

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import gradio as gr

from annotator.util import resize_image, HWC3

model_canny = None

def canny(img, res, l, h):

img = resize_image(HWC3(img), res)

global model_canny

if model_canny is None:

from annotator.canny import CannyDetector

model_canny = CannyDetector()

result = model_canny(img, l, h)

return [result]

model_hed = None

def hed(img, res):

img = resize_image(HWC3(img), res)

global model_hed

if model_hed is None:

from annotator.hed import HEDdetector

model_hed = HEDdetector()

result = model_hed(img)

return [result]

model_mlsd = None

def mlsd(img, res, thr_v, thr_d):

img = resize_image(HWC3(img), res)

global model_mlsd

if model_mlsd is None:

from annotator.mlsd import MLSDdetector

model_mlsd = MLSDdetector()

result = model_mlsd(img, thr_v, thr_d)

return [result]

model_midas = None

def midas(img, res, a):

img = resize_image(HWC3(img), res)

global model_midas

if model_midas is None:

from annotator.midas import MidasDetector

model_midas = MidasDetector()

results = model_midas(img, a)

return results

model_openpose = None

def openpose(img, res, has_hand):

img = resize_image(HWC3(img), res)

global model_openpose

if model_openpose is None:

from annotator.openpose import OpenposeDetector

model_openpose = OpenposeDetector()

result, _ = model_openpose(img, has_hand)

return [result]

model_uniformer = None

def uniformer(img, res):

img = resize_image(HWC3(img), res)

global model_uniformer

if model_uniformer is None:

from annotator.uniformer import UniformerDetector

model_uniformer = UniformerDetector()

result = model_uniformer(img)

return [result]

block = gr.Blocks().queue()

with block:

with gr.Row():

gr.Markdown("## Canny Edge")

with gr.Row():

with gr.Column():

input_image = gr.Image(source='upload', type="numpy")

low_threshold = gr.Slider(label="low_threshold", minimum=1, maximum=255, value=100, step=1)

high_threshold = gr.Slider(label="high_threshold", minimum=1, maximum=255, value=200, step=1)

resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)

run_button = gr.Button(label="Run")

with gr.Column():

gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")

run_button.click(fn=canny, inputs=[input_image, resolution, low_threshold, high_threshold], outputs=[gallery])

with gr.Row():

gr.Markdown("## HED Edge")

with gr.Row():

with gr.Column():

input_image = gr.Image(source='upload', type="numpy")

resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)

run_button = gr.Button(label="Run")

with gr.Column():

gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")

run_button.click(fn=hed, inputs=[input_image, resolution], outputs=[gallery])

with gr.Row():

gr.Markdown("## MLSD Edge")

with gr.Row():

with gr.Column():

input_image = gr.Image(source='upload', type="numpy")

value_threshold = gr.Slider(label="value_threshold", minimum=0.01, maximum=2.0, value=0.1, step=0.01)

distance_threshold = gr.Slider(label="distance_threshold", minimum=0.01, maximum=20.0, value=0.1, step=0.01)

resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=384, step=64)

run_button = gr.Button(label="Run")

with gr.Column():

gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")

run_button.click(fn=mlsd, inputs=[input_image, resolution, value_threshold, distance_threshold], outputs=[gallery])

with gr.Row():

gr.Markdown("## MIDAS Depth and Normal")

with gr.Row():

with gr.Column():

input_image = gr.Image(source='upload', type="numpy")

alpha = gr.Slider(label="alpha", minimum=0.1, maximum=20.0, value=6.2, step=0.01)

resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=384, step=64)

run_button = gr.Button(label="Run")

with gr.Column():

gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")

run_button.click(fn=midas, inputs=[input_image, resolution, alpha], outputs=[gallery])

with gr.Row():

gr.Markdown("## Openpose")

with gr.Row():

with gr.Column():

input_image = gr.Image(source='upload', type="numpy")

hand = gr.Checkbox(label='detect hand', value=False)

resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)

run_button = gr.Button(label="Run")

with gr.Column():

gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")

run_button.click(fn=openpose, inputs=[input_image, resolution, hand], outputs=[gallery])

with gr.Row():

gr.Markdown("## Uniformer Segmentation")

with gr.Row():

with gr.Column():

input_image = gr.Image(source='upload', type="numpy")

resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)

run_button = gr.Button(label="Run")

with gr.Column():

gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")

run_button.click(fn=uniformer, inputs=[input_image, resolution], outputs=[gallery])

block.launch(server_name='0.0.0.0')

ControlNet/gradio_annotator.py at main · tinypeter/ControlNet · GitHub

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