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dev(hansbug): update code
Browse files
app.py
CHANGED
@@ -1,4 +1,5 @@
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import os
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import gradio as gr
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import numpy as np
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@@ -16,7 +17,8 @@ def _image_resize(image: Image.Image, pixels: int = 90000, **kwargs):
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return small_image
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def get_main_colors(image: Image.Image, n: int = 28, pixels: int = 90000)
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image = image.copy()
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if image.mode != 'RGB':
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image = image.convert('RGB')
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@@ -33,20 +35,27 @@ def get_main_colors(image: Image.Image, n: int = 28, pixels: int = 90000) -> Ima
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new_data = colors[prediction].reshape((height, width, 3))
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new_image = Image.fromarray(new_data, mode='RGB')
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def main_func(image: Image.Image, n: int, pixels: int, fixed_width: bool, width: int):
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new_image = get_main_colors(image, n, pixels)
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if fixed_width:
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_width, _height =
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r = width / _width
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new_width, new_height = int(round(_width * r)), int(round(_height * r))
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-
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-
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-
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-
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table['ratio'] = table['count'] / table['count'].sum()
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hexes = []
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for r, g, b in zip(table['r'], table['g'], table['b']):
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@@ -60,7 +69,7 @@ def main_func(image: Image.Image, n: int, pixels: int, fixed_width: bool, width:
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'Red': table['r'],
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'Green': table['g'],
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'Blue': table['b'],
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})
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return new_image, new_table
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import os
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from typing import Tuple
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import gradio as gr
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import numpy as np
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return small_image
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def get_main_colors(image: Image.Image, n: int = 28, pixels: int = 90000) \
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-> Tuple[Image.Image, np.ndarray, np.ndarray, np.ndarray]:
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image = image.copy()
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if image.mode != 'RGB':
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image = image.convert('RGB')
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new_data = colors[prediction].reshape((height, width, 3))
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new_image = Image.fromarray(new_data, mode='RGB')
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cids, counts = np.unique(prediction, return_counts=True)
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counts = np.asarray(list(map(lambda x: x[1], sorted(zip(cids, counts)))))
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return new_image, colors, counts, prediction.reshape((height, width))
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def main_func(image: Image.Image, n: int, pixels: int, fixed_width: bool, width: int):
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if fixed_width:
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_width, _height = image.size
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r = width / _width
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new_width, new_height = int(round(_width * r)), int(round(_height * r))
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image = image.resize((new_width, new_height))
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new_image, colors, counts, predictions = get_main_colors(image, n, pixels)
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table = pd.DataFrame({
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'r': colors[:, 0],
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'g': colors[:, 1],
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'b': colors[:, 2],
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'count': counts,
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})
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table['ratio'] = table['count'] / table['count'].sum()
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hexes = []
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for r, g, b in zip(table['r'], table['g'], table['b']):
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'Red': table['r'],
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'Green': table['g'],
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'Blue': table['b'],
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}).sort_values('Pixels', ascending=False)
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return new_image, new_table
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