Duplicate from keras-io/metric-learning-image-similarity-search

Co-authored-by: Vrinda Prabhu <[email protected]>

.gitattributes

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README.md

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+---
+title: Metric Learning Image Similarity Search
+emoji: 🌍
+colorFrom: red
+colorTo: blue
+sdk: gradio
+sdk_version: 3.0.13
+app_file: app.py
+pinned: false
+duplicated_from: keras-io/metric-learning-image-similarity-search
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import numpy as np
+from PIL import Image
+
+from tensorflow.keras.datasets import cifar10
+
+from huggingface_hub import from_pretrained_keras
+import gradio as gr
+
+
+def prepare_output(neighbours):
+    """Function to return the image grid based on the nearest neighbours
+    @params neighbours: List of indices of the nearest neighbours"""
+    anchor_near_neighbours = reversed(neighbours)
+    img_grid = Image.new("RGB", (HEIGHT_WIDTH * 5, HEIGHT_WIDTH * 2))
+
+    # Image Grid of top-10 neighbours
+    for idx, nn_idx in enumerate(anchor_near_neighbours):
+        img_arr = (np.array(x_test[nn_idx]) * 255).astype(np.uint8)
+        img_grid.paste(
+            Image.fromarray(img_arr, "RGB"),
+            ((idx % 5) * HEIGHT_WIDTH, (idx // 5) * HEIGHT_WIDTH),
+        )
+
+    return img_grid
+
+
+def get_nearest_neighbours(img):
+    """Has the inference code to get the nearest neighbours from the model
+    @params img: Image to be fed to the model"""
+
+    # Pre-process image
+    img = np.expand_dims(img / 255, axis=0)
+    img_x_test = np.append(x_test, img, axis=0)
+
+    # Get the embeddings and check the cosine distance
+    embeddings = model.predict(img_x_test)
+    gram_matrix = np.einsum("ae,be->ab", embeddings, embeddings)
+    near_neighbours = np.argsort(gram_matrix.T)[:, -(NEAR_NEIGHBOURS + 1) :]
+
+    # Make image grid output
+    img_grid = prepare_output(near_neighbours[-1][:-1])
+    return np.array(img_grid)
+
+
+if __name__ == "__main__":
+    # Constants
+    HEIGHT_WIDTH = 32
+    NEAR_NEIGHBOURS = 10
+
+    (x_train, y_train), (x_test, y_test) = cifar10.load_data()
+    x_test = x_test.astype("float32") / 255.0
+
+    model = from_pretrained_keras("keras-io/cifar10_metric_learning")
+
+    examples = ["examples/yatch.jpeg", "examples/horse.jpeg", "examples/car.jpeg"]
+    title = "Metric Learning for Image Similarity Search"
+
+    more_text = """Embeddings for the input image are computed using the model. The nearest neighbours are then calculated 
+                   using cosine distance. These are shown here as an image grid."""
+
+    description = f"This space uses model trained on CIFAR10 dataset using metric learning technique.\n{more_text}\n\n"
+
+    article = """
+<p style='text-align: center'>
+            <a href='https://keras.io/examples/vision/metric_learning/' target='_blank'>Keras Example given by Mat Kelcey</a>
+            <br>
+            Space by Vrinda Prabhu
+        </p>
+    """
+
+    gr.Interface(
+        fn=get_nearest_neighbours,
+        inputs=gr.Image(shape=(32, 32)),  # Resize to CIFAR
+        outputs=gr.Image(),
+        examples=examples,
+        article=article,
+        allow_flagging="never",
+        analytics_enabled=False,
+        title=title,
+        description=description,
+    ).launch(enable_queue=True)

requirements.txt

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+tensorflow