Update app.py

app.py

@@ -53,65 +53,6 @@ def Retrieval(image, candidate_labels):
     return results
 
 
-def Get_Densefeature(image, candidate_labels):
-    """
-    Takes an image and a comma-separated string of candidate labels,
-    and returns the classification scores.
-    """
-    candidate_labels = [label.lstrip(" ") for label in candidate_labels.split(",") if label !=""]
-    # print(candidate_labels)
-
-    image_size=224
-    image = image.convert("RGB")
-    image = image.resize((image_size,image_size))
-    image_input = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].to(device)
-
-    with torch.no_grad():
-        dense_image_feature = model.get_image_dense_features(image_input)
-        captions = [candidate_labels[0]]
-        caption_input = torch.tensor(tokenizer(captions, max_length=77, padding="max_length", truncation=True).input_ids, dtype=torch.long, device=device)
-        text_feature = model.get_text_features(caption_input,walk_short_pos=True)
-        text_feature = text_feature / text_feature.norm(p=2, dim=-1, keepdim=True)
-        dense_image_feature = dense_image_feature / dense_image_feature.norm(p=2, dim=-1, keepdim=True)
-
-    similarity = dense_image_feature.squeeze() @ text_feature.squeeze().T
-    similarity = similarity.cpu().numpy()
-    patch_size = int(math.sqrt(similarity.shape[0]))
-
-
-    original_shape = (patch_size, patch_size)
-    show_image = similarity.reshape(original_shape) 
-    # normalized = (show_image - show_image.min()) / (show_image.max() - show_image.min())
-
-    # def viridis_colormap(x):
-
-    #     r = np.clip(1.1746 * x - 0.1776, 0, 1)
-    #     g = np.clip(2.0 * x - 0.7, 0, 1)
-    #     b = np.clip(-2.0 * x + 1.7, 0, 1)
-    #     return np.stack([r, g, b], axis=-1)
-
-    # color_mapped = viridis_colormap(normalized)
-
-    # color_mapped_uint8 = (color_mapped * 255).astype(np.uint8)
-
-    # pil_img = Image.fromarray(color_mapped_uint8)
-    # pil_img = pil_img.resize((512,512))
-
-    fig = plt.figure(figsize=(6, 6))
-    plt.imshow(show_image)
-    plt.title('similarity Visualization')
-    plt.axis('off')  
-
-    buf = io.BytesIO()
-    plt.savefig(buf, format='png')
-    buf.seek(0)  
-    plt.close(fig)
-
-    pil_img = Image.open(buf)
-    # buf.close()
-    return  pil_img
-
-
 
 
 def infer(image, candidate_labels):
@@ -126,33 +67,28 @@ with gr.Blocks() as demo:
         
         "This app uses the FG-CLIP model (qihoo360/fg-clip-base) for retrieval on CPU :"
     )
-    gr.Markdown(
-        "(Run Densefeature) only support one class!"
-    )
-    
+
     with gr.Row():
         with gr.Column():
             image_input = gr.Image(type="pil")
             text_input = gr.Textbox(label="Input a list of labels (comma seperated)")
             run_button = gr.Button("Run Retrieval", visible=True)
-            dfs_button = gr.Button("Run Densefeature", visible=True)
         with gr.Column():
             fg_output = gr.Label(label="FG-CLIP Output", num_top_classes=11)
-            dfs_output = gr.Image(label="Similarity Visualization", type="pil")
+
             
     examples = [
-        # ["./baklava.jpg", "dessert on a plate, a serving of baklava, a plate and spoon"],
-        # ["./dog.jpg", "A light brown wood stool, A bucket with a body made of dark brown plastic, A black velvet back cover for a cellular telephone, A green ball with a perforated pattern, A light blue plastic helmet made of plastic, A grey slipper made of wool, A newspaper with white and black perforated printed on a paper texture, A blue dog with a white colored head, A yellow sponge with a dark green rough surface, A book with white, dark orange and brown pages made of paper, A black ceramic scarf with a body made of fabric."],
+        
         ["./Landscape.jpg", "red grass, yellow grass, green grass"],
         ["./cat.jpg", "two sleeping cats, two cats playing, three cats laying down"],
-        ["./cat_dfclor.jpg", "white cat,"],
+        
     ]
     gr.Examples(
         examples=examples,
         inputs=[image_input, text_input],
-        # outputs=fg_output,
-        # fn=infer,
+        outputs=fg_output,
+        fn=infer,
     )
     run_button.click(fn=infer, inputs=[image_input, text_input], outputs=fg_output)
-    dfs_button.click(fn=Get_Densefeature, inputs=[image_input, text_input], outputs=dfs_output)
+
 demo.launch()