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| import os | |
| from typing import Tuple | |
| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| 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) \ | |
| -> Tuple[Image.Image, np.ndarray, np.ndarray, np.ndarray]: | |
| 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) | |
| colors = kmeans.cluster_centers_.round().astype(np.uint8) | |
| prediction = kmeans.predict(raw) | |
| new_data = colors[prediction].reshape((height, width, 3)) | |
| new_image = Image.fromarray(new_data, mode='RGB') | |
| cids, counts = np.unique(prediction, return_counts=True) | |
| counts = np.asarray(list(map(lambda x: x[1], sorted(zip(cids, counts))))) | |
| return new_image, colors, counts, prediction.reshape((height, width)) | |
| def main_func(image: Image.Image, n: int, pixels: int, fixed_width: bool, width: int): | |
| if fixed_width: | |
| _width, _height = image.size | |
| r = width / _width | |
| new_width, new_height = int(round(_width * r)), int(round(_height * r)) | |
| image = image.resize((new_width, new_height)) | |
| new_image, colors, counts, predictions = get_main_colors(image, n, pixels) | |
| table = pd.DataFrame({ | |
| 'r': colors[:, 0], | |
| 'g': colors[:, 1], | |
| 'b': colors[:, 2], | |
| 'count': counts, | |
| }) | |
| table['ratio'] = table['count'] / table['count'].sum() | |
| hexes = [] | |
| for r, g, b in zip(table['r'], table['g'], table['b']): | |
| hexes.append(f'#{r:02x}{g:02x}{b:02x}') | |
| table['hex'] = hexes | |
| new_table = pd.DataFrame({ | |
| 'Hex': table['hex'], | |
| 'Pixels': table['count'], | |
| 'Ratio': table['ratio'], | |
| 'Red': table['r'], | |
| 'Green': table['g'], | |
| 'Blue': table['b'], | |
| }).sort_values('Pixels', ascending=False) | |
| return new_image, new_table | |
| if __name__ == '__main__': | |
| pd.set_option("display.precision", 3) | |
| 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(): | |
| with gr.Tabs(): | |
| with gr.Tab('Output Image'): | |
| ch_output = gr.Image(type='pil', label='Output Image') | |
| with gr.Tab('Color Map'): | |
| ch_color_map = gr.Dataframe( | |
| headers=['Hex', 'Pixels', 'Ratio', 'Red', 'Green', 'Blue'], | |
| label='Color Map' | |
| ) | |
| ch_submit.click( | |
| main_func, | |
| inputs=[ch_image, ch_clusters, ch_pixels, ch_fixed_width, ch_width], | |
| outputs=[ch_output, ch_color_map], | |
| ) | |
| demo.queue(os.cpu_count()).launch() | |