update label

Author: Zhang-Yang-Sustech

models/image_segmentation_efficientsam/demo.py

@@ -105,7 +105,7 @@ def select(event, x, y, flags, param):
             # receive click
             if clicked_left:
                 # put the click point (x,y) into the model to predict
-                result = model.infer(image=image, points=point, lables=[1])
+                result = model.infer(image=image, points=point, labels=[1])
                 # get the visualized result
                 vis_result = visualize(image, result)
                 # create window to show visualized result

models/image_segmentation_efficientsam/efficientSAM.py

@@ -27,7 +27,7 @@ def setBackendAndTarget(self, backendId, targetId):
         self._model.setPreferableBackend(self._backendId)
         self._model.setPreferableTarget(self._targetId)
 
-    def _preprocess(self, image, points, lables):
+    def _preprocess(self, image, points, labels):
 
         image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
         # record the input image size, (width, height)
@@ -46,17 +46,17 @@ def _preprocess(self, image, points, lables):
 
         points_blob = np.array([[points]], dtype=np.float32)
 
-        lables_blob = np.array([[[lables]]])
+        labels_blob = np.array([[[labels]]])
 
-        return image_blob, points_blob, lables_blob
+        return image_blob, points_blob, labels_blob
 
-    def infer(self, image, points, lables):
+    def infer(self, image, points, labels):
         # Preprocess
-        imageBlob, pointsBlob, lablesBlob = self._preprocess(image, points, lables)
+        imageBlob, pointsBlob, labelsBlob = self._preprocess(image, points, labels)
         # Forward
         self._model.setInput(imageBlob, self._inputNames[0])
         self._model.setInput(pointsBlob, self._inputNames[1])
-        self._model.setInput(lablesBlob, self._inputNames[2])
+        self._model.setInput(labelsBlob, self._inputNames[2])
         outputBlob = self._model.forward()
         # Postprocess
         results = self._postprocess(outputBlob)