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d5606d9 08dbfe6 d5606d9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | import gradio as gr
import func as fu
def clear_all(image1, image2, output_image, output_json):
return None, None, None, None
with gr.Blocks() as demo:
gr.Markdown("Choose or upload a dog image and press cpmpare!! the system will retutn the 2 detected faces with the recognition result")
with gr.Row():
with gr.Column():
image1_input = gr.Image(type="pil", label="Image 1")
examples_image1 = gr.Examples(examples=["./images/dob1.jpg", "./images/p1.jpg", "./images/dob3.jpg", "./images/d1.jpg"], inputs=image1_input)
with gr.Column():
image2_input = gr.Image(type="pil", label="Image 2")
examples_image2 = gr.Examples(examples=["./images/dob2.jpg", "./images/p2.jpg", "./images/dob4.jpg", "./images/d2.jpg"], inputs=image2_input)
threshold_input = gr.Slider(minimum=0.0, maximum=1.0, value=0.55, step=0.05, label="Threshold")
# detect_input = gr.Radio(["Yes", "No"], label="Detect dog face", info="Detect dog face on compare!"),
compare_button = gr.Button("Compare")
clear_button = gr.Button("Clear")
output_image = gr.Image(type="pil", label="Stacked Image", interactive=False)
output_json = gr.JSON(label="Result")
compare_button.click(
fn=fu.compare_faces,
inputs=[image1_input, image2_input, threshold_input],
outputs=[output_image, output_json]
)
clear_button.click(
fn=clear_all,
inputs=[image1_input, image2_input, output_image, output_json],
outputs=[image1_input, image2_input,output_image, output_json]
)
demo.launch() |