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November 29, 2020 13:52
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Use clustering algorithms to generate photo filter
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| from sklearn.cluster import KMeans, AgglomerativeClustering, DBSCAN, Birch | |
| from sklearn.mixture import GaussianMixture | |
| from PIL import Image | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| # load picture | |
| img = Image.open('./thor4.jpg') | |
| arr = np.array(img) | |
| # function to transform the image | |
| def create_image(arr, n_center=3, method='kmeans'): | |
| arr_flat = arr.reshape([arr.shape[0] * arr.shape[1], 3]) | |
| if method == 'kmeans': | |
| cluster_obj = KMeans(n_clusters=n_center, random_state=42) | |
| elif method == 'agglo': | |
| cluster_obj = AgglomerativeClustering(n_clusters=n_center) | |
| elif method == 'em': | |
| cluster_obj = GaussianMixture(n_components=n_center, random_state=42) | |
| elif method == 'dbscan': | |
| cluster_obj = DBSCAN() | |
| elif method == 'birch': | |
| cluster_obj = Birch(n_clusters=n_center) | |
| else: | |
| raise Exception('unknown clustering method') | |
| cluster = cluster_obj.fit_predict(arr_flat) | |
| arr_df = pd.DataFrame(arr_flat, columns=['r', 'g', 'b']) | |
| arr_df['cluster'] = cluster | |
| cluster_dict = dict() | |
| for c in arr_df['cluster'].unique(): | |
| c_ave = arr_df.loc[arr_df['cluster'] == c, ['r', 'g', 'b']].mean(axis=0) | |
| cluster_dict[c] = c_ave.tolist() | |
| return cluster_dict | |
| new_arr = [cluster_dict[c] for c in cluster] | |
| new_arr = np.array(new_arr, dtype=np.uint8) | |
| new_arr = new_arr.reshape(arr.shape[0], arr.shape[1], 3) | |
| new_img = Image.fromarray(new_arr, mode='RGB') | |
| return new_img | |
| # different experimentations | |
| kmeans_3 = create_image(arr, 3, method='kmeans') | |
| kmeans_5 = create_image(arr, 5, method='kmeans') | |
| kmeans_10 = create_image(arr, 10, method='kmeans') | |
| em_3 = create_image(arr, 3, method='em') | |
| em_5 = create_image(arr, 5, method='em') | |
| em_10 = create_image(arr, 10, method='em') | |
| dbscan = create_image(arr, method='dbscan') | |
| # plot the final photos | |
| f, axarr = plt.subplots(3,3, figsize=(15,12)) | |
| axarr[0,0].imshow(arr) | |
| axarr[0,0].axis('off') | |
| axarr[0,0].set_title('Original') | |
| axarr[0,1].imshow(dbscan) | |
| axarr[0,1].axis('off') | |
| axarr[0,1].set_title('DBSCAN') | |
| axarr[0,2].axis('off') | |
| axarr[1,0].imshow(kmeans_3) | |
| axarr[1,0].axis('off') | |
| axarr[1,0].set_title('KMeans - 3 centres') | |
| axarr[1,1].imshow(kmeans_5) | |
| axarr[1,1].axis('off') | |
| axarr[1,1].set_title('KMeans - 5 centres') | |
| axarr[1,2].imshow(kmeans_10) | |
| axarr[1,2].axis('off') | |
| axarr[1,2].set_title('KMeans - 10 centres') | |
| axarr[2,0].imshow(em_3) | |
| axarr[2,0].axis('off') | |
| axarr[2,0].set_title('EM - 3 centres') | |
| axarr[2,1].imshow(em_5) | |
| axarr[2,1].axis('off') | |
| axarr[2,1].set_title('EM - 5 centres') | |
| axarr[2,2].imshow(em_10) | |
| axarr[2,2].axis('off') | |
| axarr[2,2].set_title('EM - 10 centres') |
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