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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):
# 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 = 8
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_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
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>You can upload an image and choose from two sampling methods - *uniform* and *kmeans*.</p>""",
fn=infer,
inputs=[gr.Image(image_mode="L", label="Input Image"),
gr.Dropdown(choices=["uniform", "kmeans"], label="Sampling Method")],
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"],
["examples/racoon.png", "kmeans"]]).launch()
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