changing how size works to try to fix mac pyinstaller version

cellpose/gui.py

@@ -657,13 +657,13 @@ def get_channels(self):
 
     def calibrate_size(self):
         self.initialize_model()
-        diams, _ = self.model.sz.eval([self.stack[self.currentZ].copy()], invert=self.invert.isChecked(),
+        diams, _ = self.model.sz.eval(self.stack[self.currentZ].copy(), invert=self.invert.isChecked(),
                                    channels=self.get_channels(), progress=self.progress)
         diams = np.maximum(5.0, diams)
         print('estimated diameter of cells using %s model = %0.1f pixels'%
                 (self.current_model, diams))
-        self.Diameter.setText('%0.1f'%diams[0])
-        self.diameter = diams[0]
+        self.Diameter.setText('%0.1f'%diams)
+        self.diameter = diams
         self.compute_scale()
         self.progress.setValue(100)
 

cellpose/models.py

@@ -183,22 +183,22 @@ def eval(self, x, batch_size=8, channels=None, invert=False, normalize=True, dia
                 diams = diameter * np.ones(nimg, np.float32)
             else:
                 diams = diameter
-            rescale = self.diam_mean / diams.copy() 
+            rescale = self.diam_mean / diams
         else:
             if rescale is not None and (not isinstance(rescale, list) or len(rescale)==1):
                 rescale = rescale * np.ones(nimg, np.float32)
             if self.pretrained_size is not None and rescale is None and not do_3D:
                 tic = time.time()
                 diams, _ = self.sz.eval(x, channels=channels, invert=invert, batch_size=batch_size, augment=augment, tile=tile)
-                rescale = self.diam_mean / diams.copy()
+                rescale = self.diam_mean / diams
                 print('estimated cell diameters for %d image(s) in %0.2f sec'%(nimg, time.time()-tic))
             else:
                 if rescale is None:
                     if do_3D:
                         rescale = np.ones(1)
                     else:
                         rescale = np.ones(nimg, np.float32)
-                diams = self.diam_mean / rescale.copy() 
+                diams = self.diam_mean / rescale
 
         tic = time.time()
         masks, flows, styles = self.cp.eval(x, batch_size=batch_size, invert=invert, rescale=rescale, anisotropy=anisotropy, 
@@ -1347,15 +1347,14 @@ def eval(self, imgs=None, styles=None, channels=None, normalize=True, invert=Fal
         """
         if styles is None and imgs is None:
             raise ValueError('no image or features given')
-
-        nimg = len(imgs)
 
         if progress is not None:
             progress.setValue(10)
 
         if imgs is not None:
             x, nolist = convert_images(imgs.copy(), channels, False, normalize, invert)
-
+            nimg = len(x)
+
         if styles is None:
             styles = self.cp.eval(x, channels=channels, net_avg=False, augment=augment, tile=tile, compute_masks=False)[-1]
             if progress is not None:
@@ -1385,7 +1384,10 @@ def eval(self, imgs=None, styles=None, channels=None, normalize=True, invert=Fal
         else:
             diam = diam_style
             print('no images provided, using diameters estimated from styles alone')
-        return diam, diam_style
+        if nolist:
+            return diam[0], diam_style[0]
+        else:
+            return diam, diam_style
 
     def _size_estimation(self, style):
         """ linear regression from style to size