Use clip scaler

app.py

@@ -36,6 +36,10 @@ imgs_tensor = torch.zeros(4, 3, patch_h * 14, patch_w * 14)
 # PCA
 pca = PCA(n_components=3)
 
+# Min-Max Scaler
+from sklearn.preprocessing import MinMaxScaler
+scaler = MinMaxScaler(clip=True)
+
 def query_image(
     img1, img2, img3, img4,
     background_threshold,
@@ -57,7 +61,8 @@ def query_image(
     # PCA Feature
     pca.fit(features)
     pca_features = pca.transform(features)
-    pca_feature = sklearn.preprocessing.minmax_scale(pca_features)
+    scaler.fit(pca_features)
+    pca_feature = scaler.transform(pca_features)
 
     # Foreground/Background
     if is_foreground_larger_than_threshold:
@@ -71,7 +76,8 @@ def query_image(
     pca_features_rem = pca.transform(features[pca_features_fg])
 
     # Min Max Normalization
-    pca_features_rem = sklearn.preprocessing.minmax_scale(pca_features_rem)
+    scaler.fit(pca_features_rem)
+    pca_features_rem = scaler.transform(pca_features_rem)
 
     pca_features_rgb = np.zeros((4 * patch_h * patch_w, 3))
     pca_features_rgb[pca_features_bg] = 0
@@ -92,7 +98,7 @@ Method:
 1. Compute the features of patches from 4 images. We can get a feature that have (4 * patch_w * patch_h, feature_dim) shape.
 2. PCA the feature with 3 dims. After PCA, Min-Max normalization is performed.
 3. Use first component to split foreground and background. (threshold and checkbox)
-4. All the feature of patches included in the background are set to 0.=
+4. All the feature of patches included in the background are set to 0.
 5. PCA is performed based on the remaining features. Afer PCA, Min-Max normalization is performed.
 6. Visualize
 """