backup

README.md

@@ -4,7 +4,7 @@ emoji: 🐢
 colorFrom: yellow
 colorTo: green
 sdk: gradio
-sdk_version: 4.22.0
+sdk_version: 4.36.0
 app_file: app.py
 pinned: false
 ---

app.py

@@ -15,10 +15,6 @@ csv.field_size_limit(sys.maxsize)
 
 
 def compute_spatial_similarity(conv1, conv2):
-    """
-    Takes in the last convolutional layer from two images, computes the pooled output
-    feature, and then generates the spatial similarity map for both images.
-    """
     conv1 = conv1.reshape(-1, 7 * 7).T
     conv2 = conv2.reshape(-1, 7 * 7).T
 
@@ -37,8 +33,6 @@ def compute_spatial_similarity(conv1, conv2):
     return similarity1, similarity2
 
 
-# Get Layer 4
-
 display_transform = transforms.Compose(
     [transforms.Resize(256), transforms.CenterCrop((224, 224))]
 )
@@ -68,19 +62,14 @@ class Wrapper(torch.nn.Module):
         _ = self.model(input)
         return self.layer4_ouputs
 
-    def __repr__(self):
-        return "Wrapper"
-
 
 def get_layer4(input_image):
     l4_model = models.resnet50(pretrained=True)
-    # l4_model = l4_model.cuda()
     l4_model.eval()
     wrapped_model = Wrapper(l4_model)
 
     with torch.no_grad():
         data = imagenet_transform(input_image).unsqueeze(0)
-        # data = data.cuda()
         reference_layer4 = wrapped_model(data)
 
     return reference_layer4.data.to("cpu").numpy()
@@ -91,10 +80,10 @@ def NormalizeData(data):
 
 
 # Visualization
-def visualize_similarities(q, n):
-    image1 = Image.fromarray(q)
-    image2 = Image.fromarray(n)
-
+def visualize_similarities(image1, image2):
+    print(f"image1: {image1}")
+    print(f"image2: {image2}")
+    print(type(image1))
     a = get_layer4(image1).squeeze()
     b = get_layer4(image2).squeeze()
     sim1, sim2 = compute_spatial_similarity(a, b)
@@ -128,60 +117,57 @@ def visualize_similarities(q, n):
     fig.colorbar(im2, ax=axes[1])
     plt.tight_layout()
 
-    q_image = display_transform(image1)
-    nearest_image = display_transform(image2)
+    # q_image = display_transform(image1)
+    # nearest_image = display_transform(image2)
 
-    # make a binarized veruin of the Q
-    fig2, ax = plt.subplots(1, figsize=(5, 5))
-    ax.imshow(display_transform(image1))
+    # # make a binarized veruin of the Q
+    # fig2, ax2 = plt.subplots(1, figsize=(5, 5))
+    # ax2.imshow(display_transform(image1))
 
-    # create a binarized version of  sim1 , for value below 0.5 set to 0 and above 0.5 set to 1
-    sim1_bin = np.where(sim1 > 0.5, 1, 0)
-    print(sim1_bin)
-    # create a binarized version of  sim2 , for value below 0.5 set to 0 and above 0.5 set to 1
-    sim2_bin = np.where(sim2 > 0.5, 1, 0)
+    # # create a binarized version of  sim1 , for value below 0.5 set to 0 and above 0.5 set to 1
+    # sim1_bin = np.where(sim1 > 0.5, 1, 0)
+    # # create a binarized version of  sim2 , for value below 0.5 set to 0 and above 0.5 set to 1
+    # sim2_bin = np.where(sim2 > 0.5, 1, 0)
 
-    ax.imshow(
-        skimage.transform.resize(sim1_bin, (224, 224)),
-        alpha=1,
-        cmap="binary",
-        vmin=0,
-        vmax=1,
-    )
+    # ax2.imshow(
+    #     skimage.transform.resize(sim1_bin, (224, 224)),
+    #     alpha=1,
+    #     cmap="binary",
+    #     vmin=0,
+    #     vmax=1,
+    # )
 
-    return fig, q_image, nearest_image, fig2
-
-
-# GRADIO APP
-main = gr.Interface(
-    fn=visualize_similarities,
-    inputs=["image", "image"],
-    allow_flagging="never",
-    outputs=["plot", "image", "image", "plot"],
-    cache_examples=True,
-    enable_queue=False,
-    examples=[
-        [
-            "./examples/Red_Winged_Blackbird_0012_6015.jpg",
-            "./examples/Red_Winged_Blackbird_0025_5342.jpg",
-        ],
-    ],
-)
+    return fig
 
-# iface.launch()
 
 blocks = gr.Blocks()
-with blocks:
 
+with blocks as demo:
+    gr.Markdown("# Visualizing Deep Similarity Networks")
+    gr.Markdown("A quick demo to visualize the similarity between two images.")
     gr.Markdown(
-        """
-# Visualizing Deep Similarity Networks
-A quick demo to visualize the similarity between two images.
-[Original Paper](https://arxiv.org/pdf/1901.00536.pdf) - [Github Page](https://github.com/GWUvision/Similarity-Visualization)
-    """
+        "[Original Paper](https://arxiv.org/pdf/1901.00536.pdf) - [Github Page](https://github.com/GWUvision/Similarity-Visualization)"
     )
 
-    gr.TabbedInterface([main], ["Main"])
+    with gr.Row():
+        with gr.Column():
+            image1 = gr.Image(label="Image 1", type="pil")
+            image2 = gr.Image(label="Image 2", type="pil")
+        with gr.Column():
+            sim1_output = gr.Plot()
+
+    examples = gr.Examples(
+        examples=[
+            [
+                "./examples/Red_Winged_Blackbird_0012_6015.jpg",
+                "./examples/Red_Winged_Blackbird_0025_5342.jpg",
+            ],
+        ],
+        inputs=[image1, image2],
+    )
 
+    btn = gr.Button("Compute Similarity")
+    btn.click(visualize_similarities, inputs=[image1, image2], outputs=[sim1_output])
 
-blocks.launch(debug=True)
+    demo.launch(debug=True)
+# blocks.launch(debug=True)

requirements.txt

@@ -1,7 +1,6 @@
-gradio==3.0.5
-matplotlib==3.5.2
-numpy==1.21.5
-Pillow==9.0.1
-scikit-image==0.19.2
-torch==1.11.0
-torchvision==0.12.0
+matplotlib
+numpy
+Pillow
+scikit-image
+torch
+torchvision