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# -*- coding: utf-8 -*-
"""Copy of extract_colors_from_image.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1Zx45R30-L2sIBh8VU_Fnbv9G6v4445TD
"""
# Commented out IPython magic to ensure Python compatibility.
from sklearn.cluster import KMeans
from collections import Counter
import numpy as np
import cv2
import gradio as gr
def get_image(pil_image):
#image = cv2.imread(image_path)
nimg = np.array(pil_image)
image = cv2.cvtColor(nimg, cv2.COLOR_RGB2BGR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
def get_labels(rimg):
clf = KMeans(n_clusters = 5)
labels = clf.fit_predict(rimg)
return labels , clf
def RGB2HEX(color):
return "#{:02x}{:02x}{:02x}".format(int(color[0]), int(color[1]), int(color[2]))
def get_colors(pimg):
img = get_image(pimg)
reshaped_img = img.reshape(img.shape[0]*img.shape[1], img.shape[2])
labels, clf = get_labels(reshaped_img)
counts = Counter(labels)
center_colors = clf.cluster_centers_
# We get ordered colors by iterating through the keys
ordered_colors = [center_colors[i] for i in counts.keys()]
hex_colors = [RGB2HEX(ordered_colors[i]) for i in counts.keys()]
#rgb_colors = [ordered_colors[i] for i in counts.keys()]
return hex_colors
demo = gr.Blocks()
with demo:
gr.Markdown(
"""
# Extract Colors from an image using KMeans clustering
"""
)
inputs = [gr.Image(type="pil", label="Image to extract colors from")]
with gr.Row():
outputs = [gr.ColorPicker(label="color 1"), gr.ColorPicker(label="color 2"),gr.ColorPicker(label="color 3"),gr.ColorPicker(label="color 4"),gr.ColorPicker(label="color 5")]
btn = gr.Button("Extract colors")
btn.click(fn=get_colors, inputs=inputs, outputs=outputs)
demo.queue()
demo.launch()
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