Samuel Schmidt commited on
Commit
9a5647a
·
1 Parent(s): 5a5b371

Update src/app.py

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Files changed (1) hide show
  1. src/app.py +7 -7
src/app.py CHANGED
@@ -65,7 +65,7 @@ def get_neighbors(query_image, selected_descriptor, top_k=5):
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  Returns:
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  A list of the top_k most similar images as PIL objects.
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  """
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- if "Color Descriptor" in selected_descriptor:
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  cd = ColorDescriptor((8, 12, 3))
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  qi_embedding = cd.describe(query_image)
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  qi_np = np.array(qi_embedding)
@@ -73,14 +73,14 @@ def get_neighbors(query_image, selected_descriptor, top_k=5):
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  'color_embeddings', qi_np, k=top_k)
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  images = retrieved_examples['image'] #retrieved images is a dict, with images and embeddings
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  return images
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- if "CLIP" in selected_descriptor:
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  clip_model = CLIPImageEncoder()
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  qi_embedding = clip_model.encode_image(query_image)
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  scores, retrieved_examples = dataset_with_embeddings.get_nearest_examples(
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  'clip_embeddings', qi_embedding, k=top_k)
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  images = retrieved_examples['image']
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  return images
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- if "LBP" in selected_descriptor:
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  lbp_model = LBPImageEncoder(8,2)
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  qi_embedding = lbp_model.describe(query_image)
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  scores, retrieved_examples = dataset_with_embeddings.get_nearest_examples(
@@ -117,14 +117,14 @@ with gr.Blocks() as demo:
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  """)
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  with gr.Row():
 
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  checkboxes_descr = gr.CheckboxGroup(["Color Descriptor", "LBP", "CLIP"], label="Please choose an descriptor")
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  dataset_dropdown = gr.Dropdown(
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  ["huggan/CelebA-faces", "EIT/cbir-eit"],
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- value=["huggan/CelebA-faces"]
 
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  )
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- btn_index = gr.Button(value="Switch Dataset")
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- btn_index.click(load_cbir_dataset, inputs=[dataset_dropdown])
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- btn.click(get_neighbors, inputs=[image_input, checkboxes_descr], outputs=[gallery_output])
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  # gr.Markdown(
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  # """
 
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  Returns:
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  A list of the top_k most similar images as PIL objects.
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  """
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+ if "Color Descriptor" == selected_descriptor:
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  cd = ColorDescriptor((8, 12, 3))
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  qi_embedding = cd.describe(query_image)
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  qi_np = np.array(qi_embedding)
 
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  'color_embeddings', qi_np, k=top_k)
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  images = retrieved_examples['image'] #retrieved images is a dict, with images and embeddings
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  return images
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+ if "CLIP" == selected_descriptor:
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  clip_model = CLIPImageEncoder()
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  qi_embedding = clip_model.encode_image(query_image)
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  scores, retrieved_examples = dataset_with_embeddings.get_nearest_examples(
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  'clip_embeddings', qi_embedding, k=top_k)
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  images = retrieved_examples['image']
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  return images
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+ if "LBP" == selected_descriptor:
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  lbp_model = LBPImageEncoder(8,2)
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  qi_embedding = lbp_model.describe(query_image)
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  scores, retrieved_examples = dataset_with_embeddings.get_nearest_examples(
 
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  """)
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  with gr.Row():
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+ descr_dropdown = gr.Dropdown(["Color Descriptor", "LBP", "CLIP"], value="LBP", label="Please choose an descriptor")
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  checkboxes_descr = gr.CheckboxGroup(["Color Descriptor", "LBP", "CLIP"], label="Please choose an descriptor")
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  dataset_dropdown = gr.Dropdown(
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  ["huggan/CelebA-faces", "EIT/cbir-eit"],
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+ value="huggan/CelebA-faces",
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+ label="Please select a dataset"
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  )
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+ btn.click(get_neighbors, inputs=[image_input, descr_dropdown], outputs=[gallery_output])
 
 
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  # gr.Markdown(
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  # """