| import numpy as np | |
| from PIL import Image | |
| import gradio as gr | |
| from onnx import hub | |
| import onnxruntime as ort | |
| import onnx | |
| onnx_model = onnx.load("M-Raw.onnx") | |
| text = onnx.checker.check_model(onnx_model) | |
| print("The model is checked") | |
| def snap(image): | |
| image = image.resize((400, 400)) | |
| ort_sess = ort.InferenceSession("M-Raw.onnx") | |
| output = ort_sess.run(["output0"], {"images": image}) | |
| return [output] | |
| demo = gr.Interface( | |
| snap, | |
| [gr.Image(source="webcam", tool=None, streaming=True)], | |
| ["image"], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |