Create app.py

app.py

@@ -0,0 +1,68 @@
+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)
+
+    print(preds.shape)
+    print(flows.shape)
+    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:
+        img = Image.open(uploaded_img)
+        st.image(img)
+
+        model_params = {
+            "diameter": 19.0,
+            "channels": [0, 0],
+            "augment": True,
+            "resample": True,
+        }
+        preds, flows = inference(
+            image=img,
+            model_path="cellpose_residual_on_style_on_concatenation_off_fold1_ep_649_cv_0.2834",
+            **model_params
+        )
+
+        print(preds)