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preTrained2Kmean_clustering.py
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preTrained2Kmean_clustering.py
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# -*- coding: utf-8 -*-
# @Time : 2020/3/18 上午10:29
# @Author : LuoLu
# @FileName: preTrained2Kmean_clustering.py
# @Software: PyCharm
# @Github ：https://github.com/luolugithub
# @E-mail ：[email protected]
import csv
# Misc
import os
# Function to get the first mode number when there are multiple mode numbers
from collections import Counter
import matplotlib.cm as cm
# Matplot
import matplotlib.pyplot as plt
import numpy as np
# from keras.applications.resnet50 import ResNet50
from keras.applications.densenet import DenseNet201
# from keras.applications.resnet50 import preprocess_input
from keras.applications.densenet import preprocess_input
from keras.preprocessing import image
# Kmeans
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
def get_first_mode(a):
c = Counter(a)
mode_count = max(c.values())
mode = {key for key, count in c.items() if count == mode_count}
first_mode = next(x for x in a if x in mode)
return first_mode
# Part 1 - Feature Extraction using CNN
# Define CNN model
# For feature extraction, include_top has to be False
# Otherwise, with the top 3 fully-connected layers, result is all grouped to 1000 defined categories
# model = VGG16(weights='imagenet', include_top=False)
model = DenseNet201(weights='imagenet', include_top=False)
img_path = 'result/'
dirs = os.listdir(img_path)
ResNet50_feature_list = []
# Pass all the images to CNN model
for file in dirs:
# Image preprocessing
# Input layer of CNN model only takes image in sie of 224 x 224. Resize the image to fit the size.
img = image.load_img(img_path + file, target_size=(224, 224), color_mode='rgb')
img_arr = image.img_to_array(img)
img_arr = np.expand_dims(img_arr, axis=0)
img_arr = preprocess_input(img_arr)
# Image feature
ResNet50_feature = model.predict(img_arr)
ResNet50_feature_arr = np.array(ResNet50_feature)
ResNet50_feature_list.append(ResNet50_feature_arr.squeeze())
# print('ResNet50 predict Finish！！')
# Convert the feature list for 5011 images into array
ResNet50_feature_list_arr = np.array(ResNet50_feature_list)
# print("ResNet50_feature_list_arr shape: ", ResNet50_feature_list_arr.shape)
# img_r = img.reshape((shape[0], shape[1] * shape[2]))
ResNet50_feature_list_arr = ResNet50_feature_list_arr.reshape(ResNet50_feature_list_arr.shape[0],
ResNet50_feature_list_arr.shape[1] *
ResNet50_feature_list_arr.shape[2] *
ResNet50_feature_list_arr.shape[3])
print("ResNet50_feature_list_arr reshape shape: ", ResNet50_feature_list_arr.shape)
# Part 2 - Clustering using Kmeans
# range_n_clusters = list(range(2, 10))
range_n_clusters = [2, 3, 4, 5, 6, 7, 8, 9, 10]
# range_n_clusters = [2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000]
cluster_silhouette_score = []
# Try different clusters for comparison
for n_clusters in range_n_clusters:
# Fit features into Kmeans
# print('enter kmeans fit:')
kmeans = KMeans(n_clusters=n_clusters, random_state=0).fit(ResNet50_feature_list_arr)
# print('enter kmeans fit Finish!!')
cluster_labels = kmeans.labels_
# print('cluster_labels Finish!!')
silhouette_avg = silhouette_score(ResNet50_feature_list_arr, cluster_labels)
# print('silhouette_avg Finish!!')
cluster_silhouette_score.append(silhouette_avg)
print("For n_clusters =", n_clusters,
"The average silhouette_score is :", silhouette_avg)
# Graph Plot - Code reference scikit-learn.org
# Create a subplot with 1 row and 2 columns
fig, (ax1, ax2) = plt.subplots(1, 2)
fig.set_size_inches(18, 7)
# The 1st subplot is the silhouette plot
# The silhouette coefficient can range from -1, 1 but in this example all
# lie within [-0.1, 1]
ax1.set_xlim([-0.1, 1])
# The (n_clusters+1)*10 is for inserting blank space between silhouette
# plots of individual clusters, to demarcate them clearly.
ax1.set_ylim([0, len(ResNet50_feature_list_arr) + (n_clusters + 1) * 10])
# Compute the silhouette scores for each sample
sample_silhouette_values = silhouette_samples(ResNet50_feature_list_arr, cluster_labels)
y_lower = 10
for i in range(n_clusters):
# Aggregate the silhouette scores for samples belonging to
# cluster i, and sort them
ith_cluster_silhouette_values = \
sample_silhouette_values[cluster_labels == i]
ith_cluster_silhouette_values.sort()
size_cluster_i = ith_cluster_silhouette_values.shape[0]
y_upper = y_lower + size_cluster_i
color = cm.nipy_spectral(float(i) / n_clusters)
ax1.fill_betweenx(np.arange(y_lower, y_upper),
0, ith_cluster_silhouette_values,
facecolor=color, edgecolor=color, alpha=0.7)
# Label the silhouette plots with their cluster numbers at the middle
ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
# Compute the new y_lower for next plot
y_lower = y_upper + 10 # 10 for the 0 samples
ax1.set_title("The silhouette plot for the various clusters.")
ax1.set_xlabel("The silhouette coefficient values")
ax1.set_ylabel("Cluster label")
# The vertical line for average silhouette score of all the values
ax1.axvline(x=silhouette_avg, color="red", linestyle="--")
ax1.set_yticks([]) # Clear the yaxis labels / ticks
ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
# 2nd Plot showing the actual clusters formed
colors = cm.nipy_spectral(cluster_labels.astype(float) / n_clusters)
ax2.scatter(ResNet50_feature_list_arr[:, 0], ResNet50_feature_list_arr[:, 1], marker='.', s=30, lw=0, alpha=0.7,
c=colors, edgecolor='k')
# Labeling the clusters
centers = kmeans.cluster_centers_
# Draw white circles at cluster centers
ax2.scatter(centers[:, 0], centers[:, 1], marker='o',
c="white", alpha=1, s=200, edgecolor='k')
for i, c in enumerate(centers):
ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,
s=50, edgecolor='k')
ax2.set_title("The visualization of the clustered data.")
ax2.set_xlabel("Feature space for the 1st feature")
ax2.set_ylabel("Feature space for the 2nd feature")
plt.suptitle(("Silhouette analysis for KMeans clustering on sample data "
"with n_clusters = %d" % n_clusters),
fontsize=16, fontweight='bold')
plt.savefig("ReadMeImg/" + str(n_clusters) + ".png")
# Part 3 - Pick the best number of cluster of choosing the largest Silhouette Score
best_n_cluster = range_n_clusters[cluster_silhouette_score.index(max(cluster_silhouette_score))]
print("Best number of clusters is =", best_n_cluster,
"The best silhouette_score is :", max(cluster_silhouette_score))
kmeans = KMeans(n_clusters=best_n_cluster, random_state=0).fit(ResNet50_feature_list_arr)
cluster_labels = kmeans.labels_
# Part 4 - Reformat Output
clustered_images = [[] for x in range(len(set(cluster_labels)))]
for index in range(len(dirs)):
clustered_images[cluster_labels[index]].append(dirs[index])
max_row = cluster_labels.tolist().count(get_first_mode(cluster_labels))
header = []
result = [[] for x in range(max_row)]
for col in range(max_row):
for row in range(len(clustered_images)):
if len(clustered_images[row]) <= col:
result[col].append('')
else:
result[col].append("'" + clustered_images[row][col][:-4] + "'")
for row in range(len(clustered_images)):
header.append('Cluster_%d' % row)
result.insert(0, header)
# plot result
plt.subplots(1, 1)
plt.title("Cluster Silhouette Score Visualization")
plt.xlabel('Range N Clusters')
plt.ylabel('Cluster Silhouette Score')
plt.plot(range_n_clusters, cluster_silhouette_score, marker='o', linewidth=3.0, color='red')
plt.grid(True)
plt.savefig("ReadMeImg/cluster_silhouette_score_DenseNet201.png")
with open('cluster_result.csv', 'w', newline='') as csvFile:
writer = csv.writer(csvFile)
writer.writerows(result)
csvFile.close()