From 27f388b42a4463a03ed3432060b1f3356bb51750 Mon Sep 17 00:00:00 2001
From: lpbsscientist <lpbsscientist@gmail.com>
Date: Sat, 14 Aug 2021 20:40:38 +0200
Subject: [PATCH] SJR: latest
---
readme.md | 11 ++++++++++-
unet/neural_network.py | 12 ++----------
2 files changed, 12 insertions(+), 11 deletions(-)
diff --git a/readme.md b/readme.md
index f2147b9..98df231 100644
--- a/readme.md
+++ b/readme.md
@@ -4,6 +4,15 @@ This is the user manual for the graphical interface for segmenting yeast images
Want to try out the neural network without installing any software? Check out our online segmentation tool at https://lpbs-nn.epfl.ch/.
+## Latest updates
+
+14.08.2021:
+
+1. Updated the bright-field neural network parameters based on the multi-lab bright-field training set.
+2. Added a keyboard short-cut for the cell change ID function.
+3. Fixed a bug in the cell-cell boundary test.
+4. Turn off cell numbers while using the brush of eraser to avoid significant system slow-down.
+
## Installation
### System requirements
@@ -21,7 +30,7 @@ Installation time is less than 5 minutes.
1. Clone this repository ("git clone https://github.com/lpbsscientist/YeaZ-GUI").
2. Download the parameters for the neural network:
3. Download the parameters for segmenting phase contrast images from: https://drive.google.com/file/d/1UTivmx_aEMpeGdOkCZO1CS9mcdJ3zmw2. Put the file in the folder `/unet`.
-4. Download the parameters for segmenting bright-field images from: https://drive.google.com/file/d/1oxZpbJOwXxH6g453lCrBkdPB_2KD6rNo. Put the file in the folder `/unet`.
+4. Download the parameters for segmenting bright-field images from: https://drive.google.com/file/d/1VYBzUtgLQcS-w6S9XpjGcSBacKyItYJ_. Put the file in the folder `/unet`.
5. If you don't have conda or miniconda installed, download it from https://docs.conda.io/en/latest/miniconda.html.
6. In the command line, create a virtual environment with python 3.6.8 with the command `conda create -n YeaZ python=3.6.8`.
7. Activate that environment using `conda activate YeaZ`.
diff --git a/unet/neural_network.py b/unet/neural_network.py
index 543af68..813f730 100644
--- a/unet/neural_network.py
+++ b/unet/neural_network.py
@@ -65,17 +65,9 @@ def prediction(im, is_pc):
input_size = (None,None,1))
if is_pc:
- path = '/home/sjrahi/Research/YeaZ/unet_weights/2021_05_27_budding_BF_all/files-p-G/unet_weights_budding_BF_multilab-p-G_SJR_0_1_batchsize_10_Nepochs_250.hdf5'
- # '/home/sjrahi/Research/YeaZ/unet_weights/2021_04_15_Silke_pombe/unet_weights_pombe_Silke_all_SJR_0_1_batchsize_5_Nepochs_500.hdf5'
- # path_weights + 'unet_weights_batchsize_25_Nepochs_100_SJR0_10.hdf5'
+ path = path_weights + 'unet_weights_batchsize_25_Nepochs_100_SJR0_10.hdf5'
else:
- path = '/home/sjrahi/Research/YeaZ/unet_weights/2021_05_09_pombe_all/unet_weights_pombe_all_SJR_0_2_batchsize_8_Nepochs_400.hdf5'
- # '/home/sjrahi/Research/YeaZ/unet_weights/2021_04_15_Silke_pombe/unet_weights_BF_batchsize_15_Nepochs_400_SJR_0_1.hdf5'
- # '/home/sjrahi/Research/YeaZ/unet_weights/2021_04_15_Silke_pombe/unet_weights_BF_batchsize_25_Nepochs_200_SJR_0_1.hdf5'
- # '/home/sjrahi/Research/YeaZ/unet_weights/2021_04_15_Silke_pombe/unet_weights_epoch_600_batchsize_50_fold_1_4.hdf5'
- # '/home/sjrahi/Research/YeaZ/unet_weights/2021_04_15_Silke_pombe/unet_weights_epoch_2000_batchsize_50_fold_1.hdf5'
- # '/home/sjrahi/Research/YeaZ/unet_weights/2021_04_15_Silke_pombe/unet_weights_epoch_600_batchsize_50_fold_1_4.hdf5'
- # path_weights + 'weights_budding_BF_02.hdf5'
+ path = path_weights + 'weights_budding_BF_multilab_0_1.hdf5'
if not os.path.exists(path):
raise ValueError('Path does not exist')
--
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