Category: Tutorial
The Blending Brush
Blending is the process of mixing differents colours in order to archieve a more omogeneus and balanced artwork, I use it dozens of time on Krita
First we need to load our dataset, I've done this by uploading a CSV file containing the URL path of all the images that I want to classify together with the features. The best thing is that we can customize how we want the features to be based on the artwork. (Es: we can determinate a scale of Greyscale that ranges from 0 to 1, 0 meaning total Black and 1 total White)
imageClassifier.csv
Image_ID,Color_R,Color_G,Color_B,ColourDogde_UP1,ColourDogde_UP2,ColourDogde_UP3,ColourDogde_UP4,Hue,Saturation, composition_Level....
elevation.jpg,0.23,0.45,0.12,0.56,0.72,0.41,0.33,0.29,0.75, 0.34....
image2.jpg,0.56,0.32,0.78,0.44,0.38,0.89,0.65,0.21,0.54, 0.42....
image3.jpg,0.11,0.67,0.34,0.61,0.49,0.73,0.77,0.43,0.48, 0.23....
image4.jpg,0.88,0.54,0.27,0.39,0.21,0.60,0.53,0.62,0.35, 0.4....
We train then asimple Clustering model on Google Colab:
from sklearn.cluster import AgglomerativeClustering
from scipy.cluster.hierarchy import dendrogram, linkage
import matplotlib.pyplot as plt
import pandas as pd
#Importing the image dataset
df = pd.read_csv('imageClassifier.csv')
Z = linkage(features_list, method='ward')
# Dendogram
plt.figure(figsize=(10, 7))
dendrogram(Z)
plt.show()
# Hierarchical Clustering
cluster = AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='ward')
cluster_labels = cluster.fit_predict(features_list)
Example Output:
The Speedpainting(elevation.jpg) was very bright compared to other images of the same dataset. Its scale of Colour Dogde was of 0.82, very high compared to other(That had less than 0.60)
Published:14 September 2024
