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import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from cellpose import models
@st.cache()
def load_model(model_path):
inf_model = models.CellposeModel(gpu=False, pretrained_model=model_path)
return inf_model
#def inference(img, model_path, **model_params):
# inf_model = models.CellposeModel(gpu=False, pretrained_model=model_path)
# preds, flows, _ = inf_model.eval([img], **model_params)
# return preds, flows
if __name__ == "__main__":
st.title("Sartorius Neuronal Cell Segmentation")
model_path = "./cellpose_residual_on_style_on_concatenation_off_fold1_ep_649_cv_0.2834"
inf_model = models.CellposeModel(gpu=False, pretrained_model=model_path)
uploaded_img = st.file_uploader(label="Upload neuronal cell image")
with st.expander("View input image"):
if uploaded_img is not None:
st.image(uploaded_img)
else:
st.warning("Please upload an image")
segment = st.button("Perform segmentation")
if uploaded_img is not None and segment:
img = Image.open(uploaded_img)
img = np.array(img)
model_params = {
"diameter": 19.0,
"channels": [0, 0],
"augment": True,
"resample": True,
}
with st.spinner("Performing segmentation. This might take a while..."):
#preds, flows = inference(
# img=img,
# model_path="./cellpose_residual_on_style_on_concatenation_off_fold1_ep_649_cv_0.2834",
# **model_params
#)
preds, flows, _ = inf_model.eval([img], **model_params)
fig, (ax1, ax2, ax3) = plt.subplots(1, 3)
ax1.axis("off")
ax2.axis("off")
ax3.axis("off")
ax1.set_title("Original Image")
ax1.imshow(img, cmap="gray")
ax2.set_title("Segmented image")
ax2.imshow(preds[0])
ax3.set_title("Image flows")
ax3.imshow(flows[0][0])
st.pyplot(fig)
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