fix tif load gui

Author: kevinjohncutler

docs/examples/mono_channel_bact.ipynb

@@ -375,7 +375,7 @@ def compute_model(self):
 
     ### will either have to put in edge cases for worm etc or just generalize model loading to respect what is there 
     # try:
-    omni_model = 'omni' in self.current_model
+    omni_model = 'omni' in self.current_model or 'affinity' in self.current_model
     bacterial = 'bact' in self.current_model
     if omni_model or bacterial:
         self.NetAvg.setCurrentIndex(1) #one run net
@@ -386,11 +386,11 @@ def compute_model(self):
     # allow omni to be togged manually or forced by model
     if OMNI_INSTALLED:
         if omni_model:
-            logger.info('turning on Omnipose mask recontruction version for Omnipose models (see menu)')
+            logger.info('turning on Omnipose mask reconstruction version for Omnipose models (see menu)')
             if not self.omni.isChecked():
-                print('WARNING: Omnipose models require Omnipose mask recontruction (toggle back on in menu)')
+                print('WARNING: Omnipose models require Omnipose mask reconstruction (toggle back on in menu)')
             if not self.cluster.isChecked():
-                print(('NOTE: clutering algorithm can help with over-segmentation in thin cells.'
+                print(('NOTE: clustering algorithm can help with over-segmentation in thin cells.'
                         'Default is ON with omnipose models (see menu)'))
 
         elif self.omni.isChecked():

src/.DS_Store

@@ -39,8 +39,15 @@ def read_image_with_channel_axis(filename):
         meta: The metadata dictionary.
     """
     img = iio.imread(filename)
-    meta = iio.immeta(filename)
+    # meta = iio.immeta(filename)
+    try:
+        meta = iio.immeta(filename)  
+    except Exception as e:
+        print(f"Warning: could not load metadata for {filename}: {e}")
+        meta = {}
 
+    print('image shape', img.shape)
+    # Check if the image is grayscale saved as RGB
 
     if 'axes' in meta:
         axes = meta['axes']
@@ -170,6 +177,7 @@ def _load_image(parent, filename=None, load_seg=True):
 
     # from here, we now will just be loading an image and a mask image file format, not npy 
     try:
+    
         logger.info(f'[_load image] loading image: {filename}')
         image, channel_axis, meta = read_image_with_channel_axis(filename)
         logger.info(f'[_load image] image shape: {image.shape}, channel_axis: {channel_axis}')
@@ -178,7 +186,6 @@ def _load_image(parent, filename=None, load_seg=True):
         # if image.ndim ==3 and image.shape[-1] == 3:
         #     print('Assuming RGB image, converting to CYX')
         #     image = np.transpose(image, (2,0,1))
-            
         _initialize_images(parent, image, channel_axis)
         parent.reset()
         parent.recenter()

src/cellpose_omni/gui/MainWindowModules/models.py

@@ -136,10 +136,13 @@ def get_niter(dists):
         of the distance field relaxation method
     
     """
-    isarray = type(dists) == np.ndarray
-    module = np if isarray else torch
+    # isarray = type(dists) == np.ndarray
+    # module = np if isarray else torch
+    module = utils.get_module(dists)
     c = module.ceil(module.max(dists)*1.16)+1
-    return c.astype(int) if isarray else c.int()
+    return c.astype(int) if module==np else c.int()
+    
+    # deprecated? only called during training flow generation it seems, not inference 
 
     # m = module.max(dists)
     # c = module.ceil(m*1.16)
@@ -1486,7 +1489,11 @@ def compute_masks(dP, dist, affinity_graph=None, bd=None, p=None, coords=None, i
                         affinity_graph = affinity_graph.squeeze().cpu().numpy()
                         # print(affinity_graph.shape,affinity_graph[(Ellipsis,)+tuple(coords)].shape)
                         affinity_graph = affinity_graph[(Ellipsis,)+tuple(coords)]
-                        
+                    
+                    
+                    # despur really not needed anymore, handled by the affinity grpah torch version
+                    
+                    
                     # elif despur:
                         # if it is passed in, we need the neigh_inds to compute masks 
                         # (though eventually we will want this to also be in parallel on GPU...)