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import PIL.Image |
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import PIL.ImageOps |
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import gradio as gr |
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import numpy as np |
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import tensorflow as tf |
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from poser import draw_bones, movenet |
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def predict(image: PIL.Image): |
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input_size = 256 |
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size = (1280, 1280) |
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image = PIL.ImageOps.fit(image, size, PIL.Image.LANCZOS) |
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image_tf = tf.keras.preprocessing.image.img_to_array(image) |
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input_image = tf.expand_dims(image_tf, axis=0) |
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input_image = tf.image.resize_with_pad(input_image, input_size, input_size) |
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keypoints = movenet(input_image) |
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keypoints = np.array(keypoints) |
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image = tf.keras.preprocessing.image.array_to_img(image_tf) |
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joints = draw_bones(image, keypoints) |
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points = [f"{x}#{y}" for p, x, y in joints] |
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return image, joints, points |
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footer = r""" |
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<center> |
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<b> |
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Demo for <a href='https://www.tensorflow.org/hub/tutorials/movenet'>MoveNet</a> |
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</b> |
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</center> |
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""" |
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with gr.Blocks(title="MoveNet") as app: |
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gr.HTML("<center><h1>Human Pose Estimation with MoveNet</h1></center>") |
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gr.HTML("<center><h3>MoveNet: Ultra fast and accurate pose detection model</h3></center>") |
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with gr.Row().style(equal_height=False): |
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with gr.Column(): |
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input_img = gr.Image(type="pil", label="Input image") |
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run_btn = gr.Button(variant="primary") |
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with gr.Column(): |
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output_img = gr.Image(type="numpy", label="Output image") |
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with gr.Accordion("See Positions", open=False): |
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positions = gr.Dataframe( |
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interactive=True, |
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headers=["x", "y", "label"], |
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datatype=["str", "number", "number"], |
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row_count=16, |
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col_count=(3, "fixed"), |
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) |
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data = gr.Textbox(label="Positions", lines=17) |
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gr.ClearButton(components=[input_img, output_img, positions, data], variant="stop") |
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run_btn.click(predict, [input_img], [output_img, positions, data]) |
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with gr.Row(): |
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blobs = [[f"examples/{x:02d}.jpg"] for x in range(1, 4)] |
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examples = gr.Dataset(components=[input_img], samples=blobs) |
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examples.click(lambda x: x[0], [examples], [input_img]) |
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with gr.Row(): |
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gr.HTML(footer) |
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app.launch(share=False, debug=True, show_error=True) |
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app.queue() |
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