From 42e3f8dcbb01d275a1e915f47cf02cf6c6f67a66 Mon Sep 17 00:00:00 2001
From: lpbsscientist <lpbsscientist@gmail.com>
Date: Sun, 15 Aug 2021 21:02:00 +0200
Subject: [PATCH] SJR: allow running NN from command line.
---
GUI_main.py | 10 ++--
Launch_NN_command_line.py | 101 ++++++++++++++++++++++++++++++++++++++
unet/neural_network.py | 23 +++++----
3 files changed, 118 insertions(+), 16 deletions(-)
create mode 100644 Launch_NN_command_line.py
diff --git a/GUI_main.py b/GUI_main.py
index a571ac1..b1dd775 100644
--- a/GUI_main.py
+++ b/GUI_main.py
@@ -748,22 +748,24 @@ class App(QMainWindow):
print('--------- Finished segmenting.')
- def LaunchPrediction(self, im, is_pc):
+ @staticmethod
+ 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)
+ pred = nn.prediction(im, is_pc, pretrained_weights)
return pred
- def ThresholdPred(self, thvalue, pred):
+ @staticmethod
+ def ThresholdPred(thvalue, pred):
"""Thresholds prediction with value"""
if thvalue == None:
thresholdedmask = nn.threshold(pred)
else:
- thresholdedmask = nn.threshold(pred,thvalue)
+ thresholdedmask = nn.threshold(pred, thvalue)
return thresholdedmask
diff --git a/Launch_NN_command_line.py b/Launch_NN_command_line.py
new file mode 100644
index 0000000..0ede315
--- /dev/null
+++ b/Launch_NN_command_line.py
@@ -0,0 +1,101 @@
+# Run:
+#
+# python Launch_NN_command_line.py -i DIRECTORY/IMAGE_FILE -m OUTPUT_MASK_FILE --path_to_weights PATH_TO_HDF5_FILE --fov N --range_of_frames n1 n2 --min_seed_dist 5 --threshold 0.5
+#
+# or:
+#
+# python Launch_NN_command_line.py -i DIRECTORY/IMAGE_FILE -m OUTPUT_MASK_FILE --image_type pc_OR_bf --fov N --range_of_frames n1 n2 --min_seed_dist 5 --threshold 0.5
+
+
+import sys
+sys.path.append("./unet")
+sys.path.append("./disk")
+
+#Import all the other python files
+#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 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.")
+ return
+
+
+ # check if correct imaging value
+ if (image_type not in ['bf', 'pc']) and (path_to_weights is None):
+ print("Wrong imaging type value ('{}')!".format(image_type),
+ "imaging type must be either 'bf' or 'pc'")
+ return
+ is_pc = image_type == 'pc'
+
+ # check range_of_frames constraint
+ if time_value1 > time_value2 :
+ print("Error", 'Invalid Time Constraints')
+ return
+
+ # displays that the neural network is running
+ print('Running the neural network...')
+
+ for fov_ind in fov_indices:
+
+ #iterates over the time indices in the range
+ for t in range(time_value1, time_value2+1):
+ print('--------- Segmenting field of view:',fov_ind,'Time point:',t)
+
+ #calls the neural network for time t and selected fov
+ im = reader.LoadOneImage(t, fov_ind)
+
+ try:
+ pred = App.LaunchPrediction(im, is_pc, pretrained_weights=path_to_weights)
+ except ValueError:
+ print('Error! ',
+ 'The neural network weight files could not '
+ 'be found. \nMake sure to download them from '
+ 'the link in the readme and put them into '
+ 'the folder unet, or specify a path to a custom weights file with -w argument.')
+ return
+
+ thresh = App.ThresholdPred(thr_val, pred)
+ seg = segment(thresh, pred, min_seed_dist)
+ reader.SaveMask(t, fov_ind, seg)
+ print('--------- Finished segmenting.')
+
+ # apply tracker if wanted and if not at first time
+ temp_mask = reader.CellCorrespondence(t, fov_ind)
+ reader.SaveMask(t, fov_ind, temp_mask)
+
+def main(args):
+
+ if '.h5' in args.mask_path:
+ args.mask_path = args.mask_path.replace('.h5','')
+
+ reader = nd.Reader("", args.mask_path, args.image_path)
+
+ LaunchInstanceSegmentation(reader, args.image_type, args.fov,
+ args.range_of_frames[0], args.range_of_frames[1], args.threshold, args.min_seed_dist, args.path_to_weights)
+
+if __name__ == '__main__':
+
+ parser = argparse.ArgumentParser(description='', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+ parser.add_argument('-i', '--image_path', type=str, help="Specify the path to a single image or to a folder of images", required=True)
+ parser.add_argument('-m', '--mask_path', type=str, help="Specify where to save predicted masks", required=True)
+ parser.add_argument('--image_type', type=str, help="Specify the imaging type, possible 'bf' and 'pc'. Supersedes path_to_weights.")
+ parser.add_argument('--path_to_weights', default=None, type=str, help="Specify weights path.")
+ parser.add_argument('--fov', default=[0], nargs='+', type=int, help="Specify field of view index.")
+ parser.add_argument('--range_of_frames', nargs=2, default=[0,0], type=int, help="Specify start and end in range of frames.")
+ parser.add_argument('--threshold', default=None, type=float, help="Specify threshold value.")
+ parser.add_argument('--min_seed_dist', default=5, type=int, help="Specify minimum distance between seeds.")
+ args = parser.parse_args()
+ main(args)
diff --git a/unet/neural_network.py b/unet/neural_network.py
index 813f730..68debb8 100644
--- a/unet/neural_network.py
+++ b/unet/neural_network.py
@@ -46,7 +46,7 @@ def threshold(im,th = None):
return bi
-def prediction(im, is_pc):
+def prediction(im, is_pc, pretrained_weights=None):
"""
Calculate the prediction of the label corresponding to image im
Param:
@@ -60,19 +60,18 @@ def prediction(im, is_pc):
col_add = 16-ncol%16
padded = np.pad(im, ((0, row_add), (0, col_add)))
- # WHOLE CELL PREDICTION
- model = unet(pretrained_weights = None,
- input_size = (None,None,1))
-
- if is_pc:
- path = path_weights + 'unet_weights_batchsize_25_Nepochs_100_SJR0_10.hdf5'
- else:
- path = path_weights + 'weights_budding_BF_multilab_0_1.hdf5'
+ if pretrained_weights is None:
+ if is_pc:
+ pretrained_weights = path_weights + 'unet_weights_batchsize_25_Nepochs_100_SJR0_10.hdf5'
+ else:
+ pretrained_weights = path_weights + 'weights_budding_BF_multilab_0_1.hdf5'
- if not os.path.exists(path):
+ if not os.path.exists(pretrained_weights):
raise ValueError('Path does not exist')
-
- model.load_weights(path)
+
+ # WHOLE CELL PREDICTION
+ model = unet(pretrained_weights = pretrained_weights,
+ input_size = (None,None,1))
results = model.predict(padded[np.newaxis,:,:,np.newaxis], batch_size=1)
--
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