deepflash2 - pypi Package Compare versions

Comparing version 0.2.0 to 0.2.1

deepflash2.egg-info/entry_points.txt

+[console_scripts]
+
+

deepflash2.egg-info/PKG-INFO

 Metadata-Version: 2.1
 Name: deepflash2
-Version: 0.2.0
+Version: 0.2.1
 Summary: A Deep learning pipeline for segmentation of fluorescent labels in microscopy images
@@ -10,2 +10,3 @@ Home-page: https://github.com/matjesg/deepflash2
 Keywords: unet,deep learning,semantic segmentation,microscopy,fluorescent labels
+Platform: UNKNOWN
 Classifier: Development Status :: 3 - Alpha
@@ -56,3 +57,8 @@ Classifier: Intended Audience :: Developers
 > Get started in less than a minute. Watch the <a href="https://matjesg.github.io/deepflash2/tutorial.html" target="_blank">tutorials</a> for help.
+#### Demo on Hugging Face Spaces
 
+Go to the [demo space](https://huggingface.co/spaces/matjesg/deepflash2) -- inference only (no training possible).
+
+#### Demo usage with Google Colab
+
 For a quick start, run *deepflash2* in Google Colaboratory (Google account required).
@@ -63,4 +69,2 @@
 
-
-#### Demo usage
 The GUI provides a build-in use for our [sample data](https://github.com/matjesg/deepflash2/releases/tag/sample_data).
@@ -178,1 +182,3 @@
 The ImagJ-Macro is available [here](https://raw.githubusercontent.com/matjesg/DeepFLaSH/master/ImageJ/Macro_create_maps.ijm).
+
+

deepflash2.egg-info/SOURCES.txt

@@ -23,4 +23,5 @@ CONTRIBUTING.md
 deepflash2.egg-info/dependency_links.txt
+deepflash2.egg-info/entry_points.txt
 deepflash2.egg-info/not-zip-safe
 deepflash2.egg-info/requires.txt
 deepflash2.egg-info/top_level.txt

deepflash2/__init__.py

@@ -1,1 +0,1 @@
-__version__ = "0.2.0"
+__version__ = "0.2.1"

deepflash2/_nbdev.py

@@ -71,5 +71,3 @@ # AUTOGENERATED BY NBDEV! DO NOT EDIT!
          "GRID_COLS": "08_gui.ipynb",
-         "COLS_PRED_KEEP": "08_gui.ipynb",
-         "COLS_PRED_KEEP_DS": "08_gui.ipynb",
-         "COLS_PRED_KEEP_CP": "08_gui.ipynb",
+         "COLS_PRED_REMOVE": "08_gui.ipynb",
          "SAMPLE_DATA_URL": "08_gui.ipynb",
@@ -76,0 +74,0 @@ "exception_handler": "08_gui.ipynb",

deepflash2/config.py

@@ -80,2 +80,3 @@ # AUTOGENERATED! DO NOT EDIT! File to edit: nbs/00_config.ipynb (unless otherwise specified).
     cellpose_export_class:int=1
+    cellpose_flow_threshold:float=0.4
     instance_segmentation_metrics:bool=False
@@ -82,0 +83,0 @@

deepflash2/data.py

@@ -11,2 +11,3 @@ # AUTOGENERATED! DO NOT EDIT! File to edit: nbs/02_data.ipynb (unless otherwise specified).
 
+import matplotlib.pyplot as plt
 from matplotlib.patches import Rectangle
@@ -23,3 +24,4 @@ from skimage.measure import label
 
-from fastai.vision.all import *
+#from fastai.vision.all import *
+from fastai.data.transforms import get_image_files
 from fastcore.all import *
@@ -113,18 +115,2 @@ from fastprogress import progress_bar
 # Cell
-@typedispatch
-def show_batch(x:TensorImage, y:tuple, samples, max_n=6, figsize=None, **kwargs):
-    "Show one batch (image, mask, and weights) from a `DataLoader`"
-    max_n = np.min((max_n, len(x)))
-    if figsize is None: figsize = (12, max_n * 5)
-    for i in range(max_n): show(x[i], y[0][i], y[1][i], figsize=figsize, **kwargs)
-
-# Cell
-@typedispatch
-def show_results(x:TensorImage, y:tuple, samples, outs, max_n=4, figsize=None, **kwargs):
-    "Show image, mask, and weights from `max_n` items"
-    max_n = np.min((max_n, len(x)))
-    if figsize is None: figsize = (12, max_n * 5)
-    for i in range(max_n): show(x[i], y[0][i], outs[i][0], pred=True, figsize=figsize, **kwargs)
-
-# Cell
 # adapted from Falk, Thorsten, et al. "U-Net: deep learning for cell counting, detection, and morphometry." Nature methods 16.1 (2019): 67-70.
@@ -131,0 +117,0 @@ def preprocess_mask(clabels=None, instlabels=None, remove_connectivity=True, num_classes = 2):

deepflash2/inference.py

@@ -229,3 +229,3 @@ # AUTOGENERATED! DO NOT EDIT! File to edit: nbs/04_inference.ipynb (unless otherwise specified).
             #softmax[ox0:ox1, oy0:oy1] += batch_smx.permute(0,2,3,1)[0][ix0:ix1, iy0:iy1].to(softmax)
-            softmax[..., ox0:ox1, oy0:oy1] += batch_smx[0, ..., ix0:ix1, iy0:iy1].to(softmax)
+            softmax[0:self.num_classes, ox0:ox1, oy0:oy1] += batch_smx[0, 0:self.num_classes, ix0:ix1, iy0:iy1].to(softmax)
             merge_map[ox0:ox1, oy0:oy1] += self.mw[ix0:ix1, iy0:iy1].to(merge_map)
@@ -232,0 +232,0 @@

deepflash2/learner.py

@@ -24,2 +24,3 @@ # AUTOGENERATED! DO NOT EDIT! File to edit: nbs/03_learner.ipynb (unless otherwise specified).
 from fastai.learner import Learner
+from fastai.callback.all import *
 from fastai.callback.tracker import SaveModelCallback
@@ -500,3 +501,3 @@ from fastai.callback.progress import CSVLogger
 
-    def get_cellpose_results(self, export_dir=None):
+    def get_cellpose_results(self, export_dir=None, check_missing=True):
         'Get instance segmentation results using the cellpose integration'
@@ -518,4 +519,12 @@ assert self.df_ens is not None, "Please run `get_ensemble_results` first."
                                 min_size=self.min_pixel_export,
+                                flow_threshold=self.cellpose_flow_threshold,
                                 gpu=torch.cuda.is_available())
 
+        # Check for missing pixels in cellpose masks
+        if check_missing:
+            for i, _ in self.df_ens.iterrows():
+                cp_mask_bin = (cp_masks[i]>0).astype('uint8')
+                n_diff = np.sum(masks[i]!=cp_mask_bin, dtype='uint8')
+                self.df_ens.at[i,f'cellpose_removed_pixels_class{cl}'] = n_diff
+
         if export_dir:
@@ -522,0 +531,0 @@ export_dir = Path(export_dir)/'instance_labels'

deepflash2/models.py

@@ -132,3 +132,3 @@ # AUTOGENERATED! DO NOT EDIT! File to edit: nbs/01_models.ipynb (unless otherwise specified).
 # Cell
-def run_cellpose(probs, masks, model_type='nuclei', diameter=0, min_size=-1, gpu=True):
+def run_cellpose(probs, masks, model_type='nuclei', diameter=0, min_size=-1, gpu=True, flow_threshold=0.4):
     'Run cellpose on deepflash2 predictions'
@@ -148,7 +148,11 @@ check_cellpose_installation()
             p = dynamics.follow_flows(-1 * dP * mask / 5., niter=niter, interp=interp, use_gpu=self.gpu)
-        maski = dynamics.get_masks(p, iscell=mask, flows=dP, threshold=flow_threshold if not do_3D else None)
+        maski = dynamics.get_masks(p, iscell=mask, flows=dP, threshold=flow_threshold)
+
+        # remove postpreocessing?
         maski = utils.fill_holes_and_remove_small_masks(maski, min_size=min_size)
-        if resize is not None:
-            maski = transforms.resize_image(maski, resize[0], resize[1],
-                                            interpolation=cv2.INTER_NEAREST)
+
+        # resizing does not work
+        #if resize is not None:
+        #    maski = transforms.resize_image(maski, resize[0], resize[1], interpolation=cv2.INTER_NEAREST)
+
         return maski, p
@@ -165,2 +169,3 @@
                                        min_size=min_size,
+                                       flow_threshold=flow_threshold,
                                        resample=True,
@@ -167,0 +172,0 @@ channels=[0,0])

PKG-INFO

 Metadata-Version: 2.1
 Name: deepflash2
-Version: 0.2.0
+Version: 0.2.1
 Summary: A Deep learning pipeline for segmentation of fluorescent labels in microscopy images
@@ -10,2 +10,3 @@ Home-page: https://github.com/matjesg/deepflash2
 Keywords: unet,deep learning,semantic segmentation,microscopy,fluorescent labels
+Platform: UNKNOWN
 Classifier: Development Status :: 3 - Alpha
@@ -56,3 +57,8 @@ Classifier: Intended Audience :: Developers
 > Get started in less than a minute. Watch the <a href="https://matjesg.github.io/deepflash2/tutorial.html" target="_blank">tutorials</a> for help.
+#### Demo on Hugging Face Spaces
 
+Go to the [demo space](https://huggingface.co/spaces/matjesg/deepflash2) -- inference only (no training possible).
+
+#### Demo usage with Google Colab
+
 For a quick start, run *deepflash2* in Google Colaboratory (Google account required).
@@ -63,4 +69,2 @@
 
-
-#### Demo usage
 The GUI provides a build-in use for our [sample data](https://github.com/matjesg/deepflash2/releases/tag/sample_data).
@@ -178,1 +182,3 @@
 The ImagJ-Macro is available [here](https://raw.githubusercontent.com/matjesg/DeepFLaSH/master/ImageJ/Macro_create_maps.ijm).
+
+

README.md

@@ -36,3 +36,8 @@ # Welcome to
 > Get started in less than a minute. Watch the <a href="https://matjesg.github.io/deepflash2/tutorial.html" target="_blank">tutorials</a> for help.
+#### Demo on Hugging Face Spaces
 
+Go to the [demo space](https://huggingface.co/spaces/matjesg/deepflash2) -- inference only (no training possible).
+
+#### Demo usage with Google Colab
+
 For a quick start, run *deepflash2* in Google Colaboratory (Google account required).
@@ -43,4 +48,2 @@
 
-
-#### Demo usage
 The GUI provides a build-in use for our [sample data](https://github.com/matjesg/deepflash2/releases/tag/sample_data).
@@ -47,0 +50,0 @@

settings.ini

@@ -11,3 +11,3 @@ [DEFAULT]
 branch = master
-version = 0.2.0
+version = 0.2.1
 min_python = 3.7
@@ -31,2 +31,3 @@ audience = Developers
 tst_flags = slow
-cell_spacing = 1
+cell_spacing = 1
+

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Improved metrics

Total package byte prevSize

237414

0.27%

Number of package files

28

3.7%

Worsened metrics

Lines of code

3771

-0.11%