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import os
from PIL import Image
import streamlit as st
import ast
import numpy as np
import pandas as pd
from cellpose import models, io, plot
from pathlib import Path
def rle_decode(mask_rle, shape=(520, 704)):
"""
mask_rle: run-length as string formated (start length)
shape: (height,width) of array to return
Returns numpy array, 1 - mask, 0 - background
"""
s = mask_rle.split()
starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]
starts -= 1
ends = starts + lengths
img = np.zeros(shape[0] * shape[1], dtype=np.uint8)
for lo, hi in zip(starts, ends):
img[lo:hi] = 1
return img.reshape(shape)
def rle_encode(img):
pixels = img.flatten()
pixels = np.concatenate([[0], pixels, [0]])
runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
runs[1::2] -= runs[::2]
return " ".join(str(x) for x in runs)
def inference(image, model_path, **model_params):
img = image
model_inference = models.CellposeModel(gpu=False, pretrained_model=model_path)
preds, flows, _ = model_inference.eval([img], **model_params)
return preds, flows
if __name__ == "__main__":
st.title("Sartorius Cell Segmentation")
uploaded_img = st.file_uploader(label="Upload neuronal cell image")
if uploaded_img is not None:
st.image(uploaded_img)
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(
image=img,
model_path="./cellpose_residual_on_style_on_concatenation_off_fold1_ep_649_cv_0.2834",
**model_params
)
print(preds)
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