app.py

from io import BytesIO

import PIL.Image as Image
import numpy as np
import openvino.runtime as ov
import streamlit as st
from cellpose import models

model = models.Cellpose(
    gpu=False,
    model_type="cyto2",
    net_avg=False,
)

uploaded_file = st.file_uploader("Choose a file")
if uploaded_file is not None:
    bytes_data = uploaded_file.getvalue()
    image = Image.open(BytesIO(bytes_data))
    img_np = np.asarray(image)
    img_input = img_np.copy()

    if len(img_np.shape) <= 2:
        img_np = np.expand_dims(img_np, axis=-1)
    img_np = np.expand_dims(img_np, axis=0)

    img_np = img_np / (2**16 - 1)

    mask, *_ = model.eval(
        img_np,
        batch_size=64,
        normalize=False,
        diameter=None,
        flow_threshold=0.4,
        channels=(0, 0),
    )

    st.image(img_np)
    st.image((mask > 0) * 255)

    image = np.expand_dims(img_input[:224, :224], axis=0)
    image = np.expand_dims(
        np.concatenate(
            [
                image,
                np.zeros_like(image),
            ],
            axis=0,
        ),
        axis=0,
    ).astype(float)
    image /= 2**16 - 1

    core = ov.Core()
    model = core.read_model("./converted_unet_cellpose/cyto2.xml")
    compiled_model = core.compile_model(model, "CPU")

    input_layer = compiled_model.input(0)
    output_layer = compiled_model.output(0)

    result = compiled_model(image)
    result = {k.get_any_name(): v for k, v in result.items()}
    outputs = result["gradients"], result["styles"]

    st.write("OPENVINO OK")