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import gradio as gr
from collections import Counter
from sklearn.cluster import KMeans
from matplotlib import colors
import matplotlib.pyplot as plt
import numpy as np
import cv2
def rgb_to_hex(rgb_color):
hex_color = "#"
for i in rgb_color:
hex_color += ("{:02x}".format(int(i)))
return hex_color
def preprocess(raw):
image = cv2.resize(raw, (900, 600), interpolation = cv2.INTER_AREA)
image = image.reshape(image.shape[0]*image.shape[1], 3)
return image
def analyze(img,n_cluster ):
modified_image = preprocess(img)
clf = KMeans(n_clusters = n_cluster)
color_labels = clf.fit_predict(modified_image)
center_colors = clf.cluster_centers_
counts = Counter(color_labels)
ordered_colors = [center_colors[i] for i in counts.keys()]
hex_colors = [rgb_to_hex(ordered_colors[i]) for i in counts.keys()]
plot = plt.figure(figsize = (12, 8))
plt.pie(counts.values(), labels = hex_colors, autopct='%1.1f%%', colors = hex_colors)
plt.savefig("color_classifier_pie.png")
print(str(n_cluster) + " the most dominant colors:\n")
for color in hex_colors:
print(color)
return plot
color_picker = gr.Interface(fn=analyze, inputs=["image", gr.inputs.Slider(minimum=2, maximum=10, step=1, label="Number of claster")], outputs="plot")
color_picker.launch() |