|
import gradio as gr |
|
import numpy as np |
|
from PIL import Image |
|
from sklearn.cluster import KMeans |
|
|
|
|
|
def _image_resize(image: Image.Image, pixels: int = 90000, **kwargs): |
|
rt = (image.size[0] * image.size[1] / pixels) ** 0.5 |
|
if rt > 1.0: |
|
small_image = image.resize((int(image.size[0] / rt), int(image.size[1] / rt)), **kwargs) |
|
else: |
|
small_image = image.copy() |
|
return small_image |
|
|
|
|
|
def get_main_colors(image: Image.Image, n: int = 28, pixels: int = 90000) -> Image.Image: |
|
image = image.copy() |
|
if image.mode != 'RGB': |
|
image = image.convert('RGB') |
|
small_image = _image_resize(image, pixels) |
|
|
|
few_raw = np.asarray(small_image).reshape(-1, 3) |
|
kmeans = KMeans(n_clusters=n) |
|
kmeans.fit(few_raw) |
|
|
|
width, height = image.size |
|
raw = np.asarray(image).reshape(-1, 3) |
|
new_data = kmeans.cluster_centers_[kmeans.predict(raw)] |
|
new_data = new_data.round().astype(np.uint8).reshape((height, width, 3)) |
|
|
|
return Image.fromarray(new_data, mode='RGB') |
|
|
|
|
|
def main_func(image: Image.Image, n: int, pixels: int, fixed_width: bool, width: int): |
|
new_image = get_main_colors(image, n, pixels) |
|
if fixed_width: |
|
_width, _height = new_image.size |
|
r = width / _width |
|
new_width, new_height = int(round(_width * r)), int(round(_height * r)) |
|
new_image = new_image.resize((new_width, new_height), resample=Image.NEAREST) |
|
|
|
return new_image |
|
|
|
|
|
if __name__ == '__main__': |
|
with gr.Blocks() as demo: |
|
with gr.Row(): |
|
with gr.Column(): |
|
ch_image = gr.Image(type='pil', label='Original Image') |
|
with gr.Row(): |
|
ch_clusters = gr.Slider(value=8, minimum=2, maximum=256, step=2, label='Clusters') |
|
ch_pixels = gr.Slider(value=100000, minimum=10000, maximum=1000000, step=10000, |
|
label='Pixels for Clustering') |
|
ch_fixed_width = gr.Checkbox(value=True, label='Width Fixed') |
|
ch_width = gr.Slider(value=200, minimum=12, maximum=2048, label='Width') |
|
|
|
ch_submit = gr.Button(value='Submit', variant='primary') |
|
|
|
with gr.Column(): |
|
ch_output = gr.Image(type='pil', label='Output Image') |
|
|
|
ch_submit.click( |
|
main_func, |
|
inputs=[ch_image, ch_clusters, ch_pixels, ch_fixed_width, ch_width], |
|
outputs=[ch_output], |
|
) |
|
|
|
demo.launch() |
|
|