fixing bugs with redcell detection

suite2p/detection/anatomical.py

@@ -34,7 +34,7 @@ def patch_detect(patches, diam):
     print("refining masks using cellpose")
     npatches = len(patches)
     ly = patches[0].shape[0]
-    model = Cellpose(net_avg=False)
+    model = Cellpose()
     imgs = np.zeros((npatches, ly, ly, 2), np.float32)
     for i, m in enumerate(patches):
         imgs[i, :, :, 0] = transforms.normalize99(m)
@@ -101,8 +101,9 @@ def refine_masks(stats, patches, seeds, diam, Lyc, Lxc):
 
 def roi_detect(mproj, diameter=None, cellprob_threshold=0.0, flow_threshold=1.5,
                pretrained_model=None):
+    pretrained_model = "cyto3" if pretrained_model is None else pretrained_model
     if not os.path.exists(pretrained_model):
-        model = CellposeModel(model_type=pretrained_model)
+        model = Cellpose(model_type=pretrained_model)
     else:
         model = CellposeModel(pretrained_model=pretrained_model)
     masks = model.eval(mproj, channels=[0, 0], diameter=diameter,

suite2p/detection/chan2detect.py

@@ -93,8 +93,8 @@ def cellpose_overlap(stats, mimg2):
         iou = ious.max()
         redstats[
             i,
-        ] = np.array([iou > 0.5, iou])  #this had the wrong dimension
-    return redstats
+        ] = np.array([iou > 0.25, iou])  #this had the wrong dimension
+    return redstats, masks
 
 
 def detect(ops, stats):
@@ -111,13 +111,15 @@ def detect(ops, stats):
     if ops.get("anatomical_red", True):
         try:
             print(">>>> CELLPOSE estimating masks in anatomical channel")
-            redstats = cellpose_overlap(stats, mimg2)
+            redstats, masks = cellpose_overlap(stats, mimg2)
         except:
             print(
                 "ERROR importing or running cellpose, continuing without anatomical estimates"
             )
 
     if redstats is None:
         redstats = intensity_ratio(ops, stats)
+    else:
+        ops["chan2_masks"] = masks
 
     return ops, redstats

suite2p/io/save.py

@@ -110,6 +110,7 @@ def combined(save_folder, save=True):
     LX = int(np.amax(dx + Lx))
     meanImg = np.zeros((LY, LX))
     meanImgE = np.zeros((LY, LX))
+    print(ops1[0]["nchannels"], plane_folders)
     if ops1[0]["nchannels"] > 1:
         meanImg_chan2 = np.zeros((LY, LX))
     if any(["meanImg_chan2_corrected" in ops for ops in ops1]):
@@ -177,6 +178,7 @@ def combined(save_folder, save=True):
                 redcell = np.concatenate((redcell, redcell0))
         ii += 1
         print("appended plane %d to combined view" % k)
+    print(meanImg_chan2.shape)
     ops["meanImg"] = meanImg
     ops["meanImgE"] = meanImgE
     if ops["nchannels"] > 1:
@@ -191,13 +193,7 @@ def combined(save_folder, save=True):
     ops["xrange"] = [0, ops["Lx"]]
     ops["yrange"] = [0, ops["Ly"]]
 
-    if save:
-        if len(ops["save_folder"]) > 0:
-            fpath = os.path.join(ops["save_path0"], ops["save_folder"], "combined")
-        else:
-            fpath = os.path.join(ops["save_path0"], "suite2p", "combined")
-    else:
-        fpath = os.path.join(save_folder, "combined")
+    fpath = os.path.join(save_folder, "combined")
 
     if not os.path.isdir(fpath):
         os.makedirs(fpath)