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import gradio as gr
from delf import DeepLocalFeatures
delf = DeepLocalFeatures()
def predict(image_a, image_b):
return delf.match(image_a, image_b)
footer = r"""
<center>
<b>
Demo for <a href='https://www.tensorflow.org/hub/tutorials/tf_hub_delf_module'>DELF</a>
</b>
</center>
"""
coffe = r"""
<center>
<a href="https://www.buymeacoffee.com/leonelhs"> <img
src="https://img.buymeacoffee.com/button-api/?text=Buy me a
coffee&emoji=&slug=leonelhs&button_colour=FFDD00&font_colour=000000&font_family=Cookie&outline_colour=000000
&coffee_colour=ffffff" /></a>
</center>
"""
with gr.Blocks(title="DELF") as app:
gr.HTML("<center><h1>Match images using DELF</h1></center>")
gr.HTML("<center><h3>Neural network and logic for processing images to identify keypoints and their "
"descriptors.</h3></center>")
with gr.Row(equal_height=False):
with gr.Column():
with gr.Row(equal_height=True):
with gr.Column():
input_img_a = gr.Image(type="pil", label="Input image A")
with gr.Column():
input_img_b = gr.Image(type="pil", label="Input image B")
run_btn = gr.Button(variant="primary")
with gr.Column():
output_img = gr.Image(type="pil", label="Output image")
gr.ClearButton(components=[input_img_a, input_img_b, output_img], variant="stop")
run_btn.click(predict, [input_img_a, input_img_b], [output_img])
with gr.Row():
blobs_a = [[f"examples/image_a/{x:02d}.jpg"] for x in range(1, 5)]
examples_a = gr.Dataset(components=[input_img_a], samples=blobs_a)
examples_a.click(lambda x: x[0], [examples_a], [input_img_a])
with gr.Row():
blobs_b = [[f"examples/image_b/{x:02d}.jpg"] for x in range(1, 5)]
examples_b = gr.Dataset(components=[input_img_b], samples=blobs_b)
examples_b.click(lambda x: x[0], [examples_b], [input_img_b])
with gr.Row():
gr.HTML(footer)
with gr.Row():
gr.HTML(coffe)
app.launch(share=False, debug=True, show_error=True)
app.queue()
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