Boundary selection bug and peogress check setup fixed

MedSAM/MedSAMLite/MedSAMLite.py

@@ -280,7 +280,7 @@ def setup(self) -> None:
 
         # Buttons
         self.ui.pbUpgrade.connect('clicked(bool)', lambda: self.logic.run_on_background(self.logic.upgrade, (True,), 'Checking for updates...'))
-        self.ui.pbSendImage.connect('clicked(bool)', lambda: self.logic.sendImage())
+        self.ui.pbSendImage.connect('clicked(bool)', lambda: self.logic.sendImage(partial=False))
         self.ui.pbSegment.connect('clicked(bool)', lambda: self.logic.applySegmentation())
 
         self.ui.pbCTprep.setIcon(QIcon(os.path.join(self.logic.server_dir, 'CT.jpg')))
@@ -744,9 +744,8 @@ def sendImage(self, partial=False, serverUrl='http://127.0.0.1:5555', numpyServe
 
         ###########################
         # Timer
-        if self.timer is None:
-            self.timer = QTimer()
-            self.timer.timeout.connect(lambda: self.progressCheck(partial))
+        self.timer = QTimer()
+        self.timer.timeout.connect(lambda: self.progressCheck(partial))
         ###########################
         self.progressbar = slicer.util.createProgressDialog(autoClose=False)
         self.progressbar.minimum = 0
@@ -805,7 +804,7 @@ def singleSegmentation(self, serverUrl):
 
     def applySegmentation(self, serverUrl='http://127.0.0.1:5555'):
         if self.widget.ui.pbSegment.text == 'Single Segmentation':
-            continueSingle = QMessageBox.question(None,'', "You are using single segmentation option which is faster but is not advised if you want multiple regions be segmented in one image. In that case click 'Send Image' button. Do you wish to continue with single segmentation?", QMessageBox.Yes | QMessageBox.No)
+            continueSingle = QMessageBox.question(None,'', "You are using single segmentation option which is faster but is not advised if you want large or multiple regions be segmented in one image. In that case click 'Send Image' button. Do you wish to continue with single segmentation?", QMessageBox.Yes | QMessageBox.No)
             if continueSingle == QMessageBox.No: return
             self.singleSegmentation(serverUrl)
 

server/server.py

@@ -251,7 +251,7 @@ def get_image(wmin: int, wmax: int, zmin: int, zmax: int):
         img_1024_tensor = (
                 torch.tensor(img_1024).float().permute(2, 0, 1).unsqueeze(0).to(device)
         )
-        if (zmax == -1) or (zmin <= slice_idx <= zmax):
+        if (zmax == -1) or ((zmin-1) <= slice_idx <= (zmax+1)):
             with torch.no_grad():
                 embedding = medsam_model.image_encoder(img_1024_tensor)  # (1, 256, 64, 64)
         else:
@@ -312,8 +312,8 @@ class InferenceParams(BaseModel):
 def infer(params: InferenceParams):
     print(params.slice_idx, params.bbox, params.zrange)
     zmin, zmax = params.zrange
-    zmax += 1
-    zmin -= 1
+    zmax = min(zmax+1, len(embeddings))
+    zmin = max(zmin-1, 0)
     bbox_1024_prev = np.array([params.bbox]) / np.array([W, H, W, H]) * 256
     res = {}