From 21bf6909e63591ad6d1b09ebaf43bfdca3caa53c Mon Sep 17 00:00:00 2001
From: Farzaneh Labbaf <f.labbaf97@gmail.com>
Date: Fri, 17 Mar 2023 13:24:11 +0100
Subject: [PATCH] Make launch NN completely independent from GUI
---
Launch_NN_command_line.py | 25 +++++++++++++++++++++----
1 file changed, 21 insertions(+), 4 deletions(-)
diff --git a/Launch_NN_command_line.py b/Launch_NN_command_line.py
index 0ede315..640e0a1 100644
--- a/Launch_NN_command_line.py
+++ b/Launch_NN_command_line.py
@@ -15,18 +15,35 @@ sys.path.append("./disk")
#this file handles the interaction with the disk, so loading/saving images
#and masks and it also runs the neural network.
-from GUI_main import App
+
from segment import segment
import Reader as nd
import argparse
import skimage
import neural_network as nn
+def LaunchPrediction(im, is_pc, pretrained_weights=None):
+ """It launches the neural neutwork on the current image and creates
+ an hdf file with the prediction for the time T and corresponding FOV.
+ """
+ im = skimage.exposure.equalize_adapthist(im)
+ im = im*1.0;
+ pred = nn.prediction(im, is_pc, pretrained_weights)
+ return pred
+
+
+
+def ThresholdPred(thvalue, pred):
+ """Thresholds prediction with value"""
+ if thvalue == None:
+ thresholdedmask = nn.threshold(pred)
+ else:
+ thresholdedmask = nn.threshold(pred, thvalue)
+ return thresholdedmask
def LaunchInstanceSegmentation(reader, image_type, fov_indices=[0], time_value1=0, time_value2=0, thr_val=None, min_seed_dist=5, path_to_weights=None):
"""
"""
-
# cannot have both path_to_weights and image_type supplied
if (image_type is not None) and (path_to_weights is not None):
print("image_type and path_to_weights cannot be both supplied.")
@@ -58,7 +75,7 @@ def LaunchInstanceSegmentation(reader, image_type, fov_indices=[0], time_value1=
im = reader.LoadOneImage(t, fov_ind)
try:
- pred = App.LaunchPrediction(im, is_pc, pretrained_weights=path_to_weights)
+ pred = LaunchPrediction(im, is_pc, pretrained_weights=path_to_weights)
except ValueError:
print('Error! ',
'The neural network weight files could not '
@@ -67,7 +84,7 @@ def LaunchInstanceSegmentation(reader, image_type, fov_indices=[0], time_value1=
'the folder unet, or specify a path to a custom weights file with -w argument.')
return
- thresh = App.ThresholdPred(thr_val, pred)
+ thresh = ThresholdPred(thr_val, pred)
seg = segment(thresh, pred, min_seed_dist)
reader.SaveMask(t, fov_ind, seg)
print('--------- Finished segmenting.')
--
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