From 176aa03f28da04cc130a2adeea98525d55323612 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Al=C3=A1n=20Mu=C3=B1oz?= <alan.munoz@ed.ac.uk>
Date: Mon, 9 Jan 2023 09:12:07 +0000
Subject: [PATCH] remove(ext): delete faiss and k2_major_median
---
src/extraction/core/functions/cell.py | 32 ---------------------------
1 file changed, 32 deletions(-)
diff --git a/src/extraction/core/functions/cell.py b/src/extraction/core/functions/cell.py
index f367c40f..c3d99d27 100644
--- a/src/extraction/core/functions/cell.py
+++ b/src/extraction/core/functions/cell.py
@@ -13,7 +13,6 @@ import math
import typing as t
import bottleneck as bn
-import faiss
import numpy as np
from scipy import ndimage
@@ -121,37 +120,6 @@ def std(cell_mask, trap_image):
return np.std(trap_image[cell_mask])
-def k2_major_median(cell_mask, trap_image):
- """
- Finds the medians of the major cluster after clustering the pixels in the cell into two clusters.
-
- Parameters
- ----------
- cell_mask: 2d array
- Segmentation mask for the cell
- trap_image: 2d array
-
- Returns
- -------
- median: float
- The median of the major cluster of two clusters
- """
- if bn.anynan(trap_image):
- cell_mask[np.isnan(trap_image)] = False
- X = trap_image[cell_mask].reshape(-1, 1).astype(np.float32)
- # cluster pixels in cell into two clusters
- indices = faiss.IndexFlatL2(X.shape[1])
- # (n_clusters=2, random_state=0).fit(X)
- _, indices = indices.search(X, k=2)
- high_indices = np.argmax(indices, axis=1).astype(bool)
- # find the median of pixels in the largest cluster
- # high_masks = np.logical_xor( # Use casting to obtain masks
- # high_indices.reshape(-1, 1), np.tile((0, 1), X.shape[0]).reshape(-1, 2)
- # )
- major_median = bn.median(X[high_indices])
- return major_median
-
-
def volume(cell_mask) -> float:
"""
Estimates the volume of the cell assuming it is an ellipsoid with the mask providing a cross-section through the median plane of the ellipsoid.
--
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