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import numpy as np |
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from PIL import Image |
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import gradio as gr |
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from onnx import hub |
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import onnxruntime as ort |
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import onnx |
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onnx_model = onnx.load("M-Raw.onnx") |
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text = onnx.checker.check_model(onnx_model) |
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print("The model is checked") |
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def snap(image): |
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image = Image.fromarray(image) |
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print(image) |
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print("-----------") |
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image = image.resize((640, 640)) |
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print(image) |
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print("-----------") |
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image = np.asarray(image, dtype=np.float32)/255 |
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print(image) |
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print("-----------") |
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image = image[np.newaxis, ...].transpose((0,3,1,2)) |
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ort_sess = ort.InferenceSession("M-Raw.onnx") |
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output = ort_sess.run(["output0"], {"images": image}) |
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print(output) |
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return [output] |
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demo = gr.Interface( |
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snap, |
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[gr.Image(source="webcam", tool=None, streaming=True)], |
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["image"], |
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) |
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if __name__ == "__main__": |
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demo.launch() |
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