Duplicate from sklearn-docs/sklearn_vector_quantization

Co-authored-by: Johannes Kolbe <[email protected]>

.gitattributes

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README.md

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+---
+title: sklearn Vector Quantization
+emoji: 📊
+colorFrom: red
+colorTo: green
+sdk: gradio
+sdk_version: 3.27.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: sklearn-docs/sklearn_vector_quantization
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import KBinsDiscretizer
+import numpy as np
+from typing import Tuple
+
+
+def build_init_plot(img_array: np.ndarray) -> Tuple[str, plt.Figure]:
+    init_text = (f"The dimension of the image is {img_array.shape}\n"
+          f"The data used to encode the image is of type {img_array.dtype}\n"
+          f"The number of bytes taken in RAM is {img_array.nbytes}")
+
+    fig, ax = plt.subplots(ncols=2, figsize=(12, 4))
+
+    ax[0].imshow(img_array, cmap=plt.cm.gray)
+    ax[0].axis("off")
+    ax[0].set_title("Rendering of the image")
+    ax[1].hist(img_array.ravel(), bins=256)
+    ax[1].set_xlabel("Pixel value")
+    ax[1].set_ylabel("Count of pixels")
+    ax[1].set_title("Distribution of the pixel values")
+    _ = fig.suptitle("Original image")
+
+    return init_text, fig
+
+
+def build_compressed_plot(compressed_image, img_array, sampling: str) -> plt.Figure:
+    compressed_text = (f"The number of bytes taken in RAM is {compressed_image.nbytes}\n"
+          f"Compression ratio: {compressed_image.nbytes / img_array.nbytes}\n"
+          f"Type of the compressed image: {compressed_image.dtype}")
+    
+    sampling = sampling if sampling == "uniform" else "K-Means"
+    
+    fig, ax = plt.subplots(ncols=2, figsize=(12, 4))
+    ax[0].imshow(compressed_image, cmap=plt.cm.gray)
+    ax[0].axis("off")
+    ax[0].set_title("Rendering of the image")
+    ax[1].hist(compressed_image.ravel(), bins=256)
+    ax[1].set_xlabel("Pixel value")
+    ax[1].set_ylabel("Count of pixels")
+    ax[1].set_title("Sub-sampled distribution of the pixel values")
+    _ = fig.suptitle(f"Original compressed using 3 bits and a {sampling} strategy")
+
+    return compressed_text, fig
+
+
+def infer(img_array: np.ndarray, sampling: str, number_of_bins: int):
+    # greyscale_image = input_image.convert("L")
+    # img_array = np.array(greyscale_image)
+
+    #raccoon_face = face(gray=True)
+    init_text, init_fig = build_init_plot(img_array)
+    
+    n_bins = number_of_bins
+    encoder = KBinsDiscretizer(
+        n_bins=n_bins, encode="ordinal", strategy=sampling, random_state=0
+    )
+    compressed_image = encoder.fit_transform(img_array.reshape(-1, 1)).reshape(
+        img_array.shape
+    )
+    compressed_image = compressed_image.astype(np.uint8)
+    compressed_text, compressed_fig = build_compressed_plot(compressed_image,
+                                                            img_array,
+                                                            sampling)
+
+    bin_edges = encoder.bin_edges_[0]
+    bin_center = bin_edges[:-1] + (bin_edges[1:] - bin_edges[:-1]) / 2
+
+    comparison_fig, ax = plt.subplots()
+    ax.hist(img_array.ravel(), bins=256)
+    color = "tab:orange"
+    for center in bin_center:
+        ax.axvline(center, color=color)
+        ax.text(center - 10, ax.get_ybound()[1] + 100, f"{center:.1f}", color=color)
+
+    return init_text, init_fig, compressed_text, compressed_fig, comparison_fig
+
+
+article = """<center>
+Demo by <a href='https://huggingface.co/johko' target='_blank'>Johannes (johko) Kolbe</a>"""
+
+
+gr.Interface(
+    title="Vector Quantization with scikit-learn",
+    description="""<p style="text-align: center;">This is an interactive demo for the <a href="https://scikit-learn.org/stable/auto_examples/cluster/plot_face_compress.html">Vector Quantization Tutorial</a> from scikit-learn.
+    </br><b>Vector Quantization</b> is a compression technique to reduce the number of color values that are used in an image and with this save memory while trying to keep a good quality.
+    In this demo this can be done naively via <i>uniform</i> sampling, which just uses <i>N</i> color values (specified via slider) uniformly sampled from the whole spectrum or via <i>k-means</i> which pays closer attention 
+    to the actual pixel distribution and potentially leads to a better quality of the compressed image.
+    In this demo we actually won't see a compression effect, because we cannot go smaller than <i>uint8</i> in datatype size here.
+    </br>
+    </br><b>Usage</b>: To run the demo you can simply upload an image and choose from two sampling methods - <i>uniform</i> and <i>kmeans</i>. Choose the number of bins and then click 'submit'. 
+    You will get information about the histogram, pixels distribution and other image statistics for your orginial image as grayscale and the quantized version of it.
+    </p>""",
+    article=article,
+    fn=infer, 
+    inputs=[gr.Image(image_mode="L", label="Input Image"),
+            gr.Dropdown(choices=["uniform", "kmeans"], label="Sampling Method"),
+            gr.Slider(minimum=2, maximum=50, value=8, step=1, label="Number of Bins")], 
+    outputs=[gr.Text(label="Original Image Stats"),
+             gr.Plot(label="Original Image Histogram"),
+             gr.Text(label="Compressed Image Stats"), 
+             gr.Plot(label="Compressed Image Histogram"),
+             gr.Plot(label="Pixel Distribution Comparison")],
+    examples=[["examples/hamster.jpeg", "uniform", 8],
+              ["examples/racoon.png", "kmeans", 8]]).launch()

requirements.txt

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+matplotlib==3.6.3
+scikit-learn==1.2.1
+scipy