Normalize Range

app.py

@@ -1,3 +1,6 @@
+import csv
+import sys
+
 import gradio as gr
 import numpy as np
 import skimage.transform
@@ -7,8 +10,6 @@ import torchvision.transforms as transforms
 from matplotlib import pyplot as plt
 from numpy import matlib as mb
 from PIL import Image
-import csv
-import sys
 
 csv.field_size_limit(sys.maxsize)
 
@@ -85,24 +86,37 @@ def get_layer4(input_image):
     return reference_layer4.data.to("cpu").numpy()
 
 
+def NormalizeData(data):
+    return (data - np.min(data)) / (np.max(data) - np.min(data))
+
+
 # Visualization
 def visualize_similarities(image1, image2):
     a = get_layer4(image1).squeeze()
     b = get_layer4(image2).squeeze()
     sim1, sim2 = compute_spatial_similarity(a, b)
 
+    sim1 = NormalizeData(sim1)
+    sim2 = NormalizeData(sim2)
+
     fig, axes = plt.subplots(1, 2, figsize=(12, 5))
     axes[0].imshow(display_transform(image1))
     im1 = axes[0].imshow(
-        skimage.transform.resize(sim1, (224, 224)), alpha=0.5, cmap="jet"
+        skimage.transform.resize(sim1, (224, 224)),
+        alpha=0.5,
+        cmap="jet",
+        vmin=0,
+        vmax=1,
     )
-    # axes[0].colorbar()
 
     axes[1].imshow(display_transform(image2))
     im2 = axes[1].imshow(
-        skimage.transform.resize(sim2, (224, 224)), alpha=0.5, cmap="jet"
+        skimage.transform.resize(sim2, (224, 224)),
+        alpha=0.5,
+        cmap="jet",
+        vmin=0,
+        vmax=1,
     )
-    # axes[1].colorbar()
 
     fig.colorbar(im1, ax=axes[0])
     fig.colorbar(im2, ax=axes[1])
@@ -114,8 +128,8 @@ def visualize_similarities(image1, image2):
 iface = gr.Interface(
     fn=visualize_similarities,
     inputs=[
-        gr.Image(type="pil"),
-        gr.Image(type="pil"),
+        gr.inputs.Image(type="pil"),
+        gr.inputs.Image(type="pil"),
     ],
     allow_flagging="never",
     outputs=[gr.Plot(type="matplotlib")],