Save button is now inactive until there are available labels in curre…

…nt image

app/backend/backend.py

@@ -157,112 +157,113 @@ def get_saved_masks_and_labels(self, image_id, dataset_id):
             return masks, labels
 
     def save_masks(self, masks, labels):
-        # Save the masks and labels in the h5 file
-        with h5py.File(self.user_dataset, 'a') as f:
-            # Check if the dataset_id group exists, if not, create it
-            if self.dataset_id not in f:
-                self._create_user_dataset()
-
-            dataset_group = f[self.dataset_id]
-
-            image_ids_dataset = dataset_group['image_ids']
-
-             # Check if the image_id already exists in the file
-            if self.image_id in image_ids_dataset:
-
-                start_occur = np.where(image_ids_dataset[:] == self.image_id)[0][0]
-                end_occur = np.where(image_ids_dataset[:] == self.image_id)[0][-1]
-                len_occur = (end_occur - start_occur) + 1
-                len_masks = len(masks)
-                init_len = dataset_group['masks'].shape[0]
-                new_masks = len_occur - len_masks
-
-                if len_masks == len_occur:
-                    for mask, label in zip(masks, labels):
-                        # Append the mask, label, and image_id to the corresponding datasets
+        if len(masks) != 0 and len(labels) != 0:
+            # Save the masks and labels in the h5 file
+            with h5py.File(self.user_dataset, 'a') as f:
+                # Check if the dataset_id group exists, if not, create it
+                if self.dataset_id not in f:
+                    self._create_user_dataset()
+
+                dataset_group = f[self.dataset_id]
+
+                image_ids_dataset = dataset_group['image_ids']
+
+                 # Check if the image_id already exists in the file
+                if self.image_id in image_ids_dataset:
+
+                    start_occur = np.where(image_ids_dataset[:] == self.image_id)[0][0]
+                    end_occur = np.where(image_ids_dataset[:] == self.image_id)[0][-1]
+                    len_occur = (end_occur - start_occur) + 1
+                    len_masks = len(masks)
+                    init_len = dataset_group['masks'].shape[0]
+                    new_masks = len_occur - len_masks
+
+                    if len_masks == len_occur:
+                        for mask, label in zip(masks, labels):
+                            # Append the mask, label, and image_id to the corresponding datasets
+                            mask_dataset = dataset_group['masks']
+                            mask_dataset[start_occur] = mask
+
+                            label_dataset = dataset_group['labels']
+                            label_dataset[start_occur] = label
+
+                            start_occur += 1
+
+                    elif len_masks > len_occur:
+                        # Resize the arrays 
                         mask_dataset = dataset_group['masks']
-                        mask_dataset[start_occur] = mask
+                        mask_dataset.resize(init_len + (len_masks-len_occur), axis=0)
 
                         label_dataset = dataset_group['labels']
-                        label_dataset[start_occur] = label
+                        label_dataset.resize(init_len + (len_masks-len_occur), axis=0)
 
-                        start_occur += 1
+                        image_id_dataset = dataset_group['image_ids']
+                        image_id_dataset.resize(init_len + (len_masks-len_occur), axis=0)
 
-                elif len_masks > len_occur:
-                    # Resize the arrays 
-                    mask_dataset = dataset_group['masks']
-                    mask_dataset.resize(init_len + (len_masks-len_occur), axis=0)
+                        # Shift all the data after the Image-ID in question to the right
+                        for k in range(init_len-(end_occur+1)):
+                            mask_dataset[-1-k] = mask_dataset[init_len-1-k]
+                            label_dataset[-1-k] = label_dataset[init_len-1-k]
+                            image_id_dataset[-1-k] = image_id_dataset[init_len-1-k]
 
-                    label_dataset = dataset_group['labels']
-                    label_dataset.resize(init_len + (len_masks-len_occur), axis=0)
+                        for mask, label in zip(masks, labels):
+                            # Append the mask, label, and image_id to the corresponding datasets
+                            mask_dataset[start_occur] = mask
 
-                    image_id_dataset = dataset_group['image_ids']
-                    image_id_dataset.resize(init_len + (len_masks-len_occur), axis=0)
+                            label_dataset[start_occur] = label
 
-                    # Shift all the data after the Image-ID in question to the right
-                    for k in range(init_len-(end_occur+1)):
-                        mask_dataset[-1-k] = mask_dataset[init_len-1-k]
-                        label_dataset[-1-k] = label_dataset[init_len-1-k]
-                        image_id_dataset[-1-k] = image_id_dataset[init_len-1-k]
+                            image_id_dataset[start_occur] = self.image_id
 
-                    for mask, label in zip(masks, labels):
-                        # Append the mask, label, and image_id to the corresponding datasets
-                        mask_dataset[start_occur] = mask
+                            start_occur += 1
+                    # len_masks < len_ocurr
+                    else:
+                        mask_dataset = dataset_group['masks']
+                        label_dataset = dataset_group['labels']
+                        image_id_dataset = dataset_group['image_ids']
 
-                        label_dataset[start_occur] = label
+                        # Shift all the data after the Image-ID in question to the left
+                        for k in range(init_len-(end_occur+1)):
+                            mask_dataset[end_occur+(len_masks-len_occur)+1+k] = mask_dataset[end_occur+1+k]
+                            label_dataset[end_occur+(len_masks-len_occur)+1+k] = label_dataset[end_occur+1+k]
+                            image_id_dataset[end_occur+(len_masks-len_occur)+1+k] = image_id_dataset[end_occur+1+k]
 
-                        image_id_dataset[start_occur] = self.image_id
+                        for mask, label in zip(masks, labels):
+                            # Append the mask, label, and image_id to the corresponding datasets
+                            mask_dataset = dataset_group['masks']
+                            mask_dataset[start_occur] = mask
 
-                        start_occur += 1
-                # len_masks < len_ocurr
-                else:
-                    mask_dataset = dataset_group['masks']
-                    label_dataset = dataset_group['labels']
-                    image_id_dataset = dataset_group['image_ids']
+                            label_dataset = dataset_group['labels']
+                            label_dataset[start_occur] = label
 
-                    # Shift all the data after the Image-ID in question to the left
-                    for k in range(init_len-(end_occur+1)):
-                        mask_dataset[end_occur+(len_masks-len_occur)+1+k] = mask_dataset[end_occur+1+k]
-                        label_dataset[end_occur+(len_masks-len_occur)+1+k] = label_dataset[end_occur+1+k]
-                        image_id_dataset[end_occur+(len_masks-len_occur)+1+k] = image_id_dataset[end_occur+1+k]
+                            image_id_dataset = dataset_group['image_ids']
+                            image_id_dataset[start_occur] = self.image_id
 
+                            start_occur += 1
+
+                        # Resize the arrays
+                        mask_dataset.resize(init_len + (len_masks-len_occur), axis=0)
+                        label_dataset.resize(init_len + (len_masks-len_occur), axis=0)
+                        image_id_dataset.resize(init_len + (len_masks-len_occur), axis=0)
+                    self.cell_counter += new_masks
+                else:
+                    # Save the masks, labels, and image_ids as separate datasets
                     for mask, label in zip(masks, labels):
                         # Append the mask, label, and image_id to the corresponding datasets
                         mask_dataset = dataset_group['masks']
-                        mask_dataset[start_occur] = mask
+                        mask_dataset.resize(mask_dataset.shape[0] + 1, axis=0)
+                        mask_dataset[-1] = mask
 
                         label_dataset = dataset_group['labels']
-                        label_dataset[start_occur] = label
+                        label_dataset.resize(label_dataset.shape[0] + 1, axis=0)
+                        label_dataset[-1] = label
 
                         image_id_dataset = dataset_group['image_ids']
-                        image_id_dataset[start_occur] = self.image_id
-
-                        start_occur += 1
-
-                    # Resize the arrays
-                    mask_dataset.resize(init_len + (len_masks-len_occur), axis=0)
-                    label_dataset.resize(init_len + (len_masks-len_occur), axis=0)
-                    image_id_dataset.resize(init_len + (len_masks-len_occur), axis=0)
-                self.cell_counter += new_masks
-            else:
-                # Save the masks, labels, and image_ids as separate datasets
-                for mask, label in zip(masks, labels):
-                    # Append the mask, label, and image_id to the corresponding datasets
-                    mask_dataset = dataset_group['masks']
-                    mask_dataset.resize(mask_dataset.shape[0] + 1, axis=0)
-                    mask_dataset[-1] = mask
-
-                    label_dataset = dataset_group['labels']
-                    label_dataset.resize(label_dataset.shape[0] + 1, axis=0)
-                    label_dataset[-1] = label
-
-                    image_id_dataset = dataset_group['image_ids']
-                    image_id_dataset.resize(image_id_dataset.shape[0] + 1, axis=0)
-                    image_id_dataset[-1] = self.image_id
-
-                self.cell_counter += len(masks)
-            # Increment the image_counter
-            self.image_counter += 1
+                        image_id_dataset.resize(image_id_dataset.shape[0] + 1, axis=0)
+                        image_id_dataset[-1] = self.image_id
+
+                    self.cell_counter += len(masks)
+                    # Increment the image_counter
+                    self.image_counter += 1
 
 
     def get_masks_from_polygons(self, polygons):

app/frontend/my-app2/src/App.js

@@ -137,6 +137,7 @@ const AnnotationArea = () => {
   // Preview line management
   const [previewLine, setPreviewLine] = useState(null)
   const isDrawing = React.useRef(false)
+  const [isClassified, setIsClassified] = useState(false)
 
   // Context Menu for Polygon-editing
   const [contextMenu, setContextMenu] = useState({ visible: false, x: 0, y: 0, polygonID: -1 });
@@ -263,6 +264,7 @@ async function downloadMasksAndLabels() {
       })
     }
     setPolygons(transformedPolygons)
+    setIsClassified(false)
 
   }
 
@@ -281,6 +283,7 @@ function classifyCallback(labels) {
   });
 
   setPolygons(transformedPolygons);
+  setIsClassified(true)
 }
 
   function setImageCallback(response_json) {
@@ -309,6 +312,10 @@ function classifyCallback(labels) {
         }
         transformedPolygons.push(currentPolygon)
       })
+      setIsClassified(true)
+    }
+    else {
+      setIsClassified(false)
     }
     setPolygons(transformedPolygons)
   }
@@ -603,6 +610,7 @@ function classifyCallback(labels) {
           onClassify={classify}
           onSave={saveMasksAndLabels}
           onDownload={download}
+          isClassified={isClassified}
         />
       </MenuContainer>
       <StageContainer>

app/frontend/my-app2/src/components/Menu/Menu.css

@@ -28,6 +28,21 @@
   background-color: #606268;
 }
 
+
+.menu-button-disabled {
+  margin-bottom: 10px;
+  width: 250%;
+  padding: 10px 10px;
+  font-size: 14px;
+  border-radius: 5px;
+  border: none;
+  color: #fff;
+  transition: background-color 0.3s ease;
+  opacity: 0.6; /* Lower the opacity to visually indicate disabled state */
+  cursor: not-allowed; /* Change the cursor to indicate unclickable */
+  background-color: #9e9e9e; /* Change the background color for disabled state */
+}
+
 .selector-container {
   width: 250%;
   margin-bottom: 20px;

app/frontend/my-app2/src/components/Menu/Menu.js

@@ -71,6 +71,7 @@ function Menu({
   onClassify,
   onSave,
   onDownload,
+  isClassified
 }) {
   return (
     <div className="menu-container">
@@ -92,7 +93,15 @@ function Menu({
       <SegmentationMethodsSelector onChange={onSegmentationMethodChange} />
       <DatasetSelector onChange={onDatasetChange} />
       <Button className="menu-button" onClick={onClassify}>Classify</Button>
-      <Button className="menu-button" onClick={onSave}>Save Masks and Labels</Button>
+{isClassified ? (
+  <Button className="menu-button" onClick={onSave}>
+    Save Masks and Labels
+  </Button>
+) : (
+  <Button className="menu-button-disabled" disabled>
+    Save Masks and Labels
+  </Button>
+)}
       <Button className="menu-button" onClick={onDownload}>Download Masks and Labels</Button>
     </div>
   )