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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.fromarray(image) # np to pil
print(image)
print("-----------")
image = image.resize((640, 640))
print(image)
print("-----------")
image = np.asarray(image, dtype=np.float32)/255
print(image)
print("-----------")
image = image[np.newaxis, ...].transpose((0,3,1,2))
ort_sess = ort.InferenceSession("M-Raw.onnx")
output = ort_sess.run(["output0"], {"images": image})
print(output)
return [output]
demo = gr.Interface(
snap,
[gr.Image(source="webcam", tool=None, streaming=True)],
["image"],
)
if __name__ == "__main__":
demo.launch()
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