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| import os | |
| 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.queue(os.cpu_count()).launch() | |