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import gradio as gr
import numpy as np
import cv2
from tqdm import trange
from sklearn.cluster import KMeans
class KMeansClustering():
def __init__(self, n_clusters=8, max_iter=300):
self.n_clusters = n_clusters
self.max_iter = max_iter
def fit(self, X):
self.inertia_ = float('inf')
# random init of clusters
idx = np.random.choice(range(X.shape[0]), self.n_clusters, replace=False)
self.cluster_centers_ = X[idx]
print(f'Training for {self.max_iter} epochs')
epochs = trange(self.max_iter)
for i in epochs:
distances = X[:, np.newaxis, :] - self.cluster_centers_[np.newaxis, :, :]
distances = np.linalg.norm(distances, axis=2)
self.labels_ = np.argmin(distances, axis=1)
new_inertia = np.sum(np.min(distances, axis=1) ** 2)
epochs.set_description(f'Epoch-{i+1}, Inertia-{new_inertia}')
if new_inertia < self.inertia_:
self.inertia_ = new_inertia
else:
epochs.close()
print('Early Stopping. Inertia has converged.')
break
self.cluster_centers_ = np.empty_like(self.cluster_centers_)
for cluster in range(self.n_clusters):
in_cluster = (self.labels_ == cluster)
if np.any(in_cluster):
self.cluster_centers_[cluster] = np.mean(X[in_cluster], axis=0)
else:
# cluster is empty, pick random point as next centroid
self.cluster_centers_[cluster] = X[np.random.randint(0, X.shape[0])]
return self
def predict(self, X):
distances = X[:, np.newaxis, :] - self.cluster_centers_[np.newaxis, :, :]
distances = np.linalg.norm(distances, axis=2)
labels = np.argmin(distances, axis=1)
return labels
def fit_predict(self, X):
return self.fit(X).labels_
def segment_image(image, model: KMeansClustering):
w, b, c = image.shape
image = image.reshape(w*b, c) / 255
idx = np.random.choice(range(image.shape[0]), image.shape[0]//5, replace=False)
image_subset = image[idx]
model.fit(image_subset) # fit model on 20% sample of image
labels = model.predict(image)
return labels.reshape(w,b), model
def generate_outputs(image, implementation, num_colours):
if implementation == 'custom':
model = KMeansClustering(n_clusters=num_colours, max_iter=10)
elif implementation == 'sk-learn':
model = KMeans(n_clusters=num_colours, n_init='auto')
label_map, model = segment_image(image, model)
clustered_image = model.cluster_centers_[label_map]
clustered_image = (clustered_image * 255).astype('uint8')
clustered_image = cv2.medianBlur(clustered_image,5)
edges = 255 - cv2.Canny(clustered_image, 0, 1)
edges = cv2.cvtColor(edges, cv2.COLOR_GRAY2RGB)
return [(edges, 'Coloring Page'), (clustered_image, 'Filled Picture')]
with gr.Blocks() as demo:
gr.Markdown(
"""
# image2coloringbook
(image2coloringbook)[https://github.com/ShawnLJW/image2coloringbook] is a simple tool that converts an image into a coloring book.
""")
with gr.Row():
with gr.Column():
image = gr.Image()
submit = gr.Button('Generate')
with gr.Column():
num_colours = gr.Slider(
minimum=1,
maximum=40,
value=24,
step=1,
label='Number of colours'
)
implementation = gr.Dropdown(
choices=['sk-learn','custom'],
value='sk-learn',
label='Implementation'
)
with gr.Row():
output = gr.Gallery(preview=True)
submit.click(
generate_outputs,
inputs=[image, implementation, num_colours],
outputs=[output]
)
if __name__ == '__main__':
demo.launch() |