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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() | |