forked from lllyasviel/ControlNet
/
gradio_annotator.py
160 lines (125 loc) · 5.58 KB
/
gradio_annotator.py
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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 apply_canny
model_canny = apply_canny
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 apply_hed
model_hed = apply_hed
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 apply_mlsd
model_mlsd = apply_mlsd
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 apply_midas
model_midas = apply_midas
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 apply_openpose
model_openpose = apply_openpose
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 apply_uniformer
model_uniformer = apply_uniformer
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')