如何使用Python將給定的圖像集進行聚類?
<a onclick="parent.postMessage({'referent':'.kaggle.usercode.12234793.44545592.ShowMeWhatYouLearnt..featureMaps'}, '*')">featureMaps = <a onclick="parent.postMessage({'referent':'.kaggle.usercode.12234793.44545592.ShowMeWhatYouLearnt..model'}, '*')">model.predict(<a onclick="parent.postMessage({'referent':'.kaggle.usercode.12234793.44545592.ShowMeWhatYouLearnt..img'}, '*')">img)
## Plotting Features
for a onclick="parent.postMessage({'referent':'.kaggle.usercode.12234793.44545592.ShowMeWhatYouLearnt..maps'}, '*')">maps in <a onclick="parent.postMessage({'referent':'.kaggle.usercode.12234793.44545592.ShowMeWhatYouLearnt..featureMaps'}, '*')">featureMaps:
plt.<a onclick="parent.postMessage({'referent':'.matplotlib.pyplot.figure'}, '*')">figure(figsize=(20,20))
<a onclick="parent.postMessage({'referent':'.kaggle.usercode.12234793.44545592.ShowMeWhatYouLearnt..pltNum'}, '*')">pltNum = 1
for <a onclick="parent.postMessage({'referent':'.kaggle.usercode.12234793.44545592.ShowMeWhatYouLearnt..a(chǎn)'}, '*')">a in range(8):
for <a onclick="parent.postMessage({'referent':'.kaggle.usercode.12234793.44545592.ShowMeWhatYouLearnt..b'}, '*')">b in <a onclick="parent.postMessage({'referent':'.kaggle.usercode.12234793.44545592.ShowMeWhatYouLearnt..range'}, '*')">range(8):
plt.<a onclick="parent.postMessage({'referent':'.matplotlib.pyplot.subplot'}, '*')">subplot(8, 8, <a onclick="parent.postMessage({'referent':'.kaggle.usercode.12234793.44545592.ShowMeWhatYouLearnt..pltNum'}, '*')">pltNum)
plt.<a onclick="parent.postMessage({'referent':'.matplotlib.pyplot.imshow'}, '*')">imshow(<a onclick="parent.postMessage({'referent':'.kaggle.usercode.12234793.44545592.ShowMeWhatYouLearnt..maps'}, '*')">maps[: ,: ,<a onclick="parent.postMessage({'referent':'.kaggle.usercode.12234793.44545592.ShowMeWhatYouLearnt..pltNum'}, '*')">pltNum - 1], cmap='gray')
<a onclick="parent.postMessage({'referent':'.kaggle.usercode.12234793.44545592.ShowMeWhatYouLearnt..pltNum'}, '*')">pltNum += 1
plt.<a onclick="parent.postMessage({'referent':'.matplotlib.pyplot.show'}, '*')">show()
接下來我們將重點介紹如何來創(chuàng)建我們的聚類算法。設計圖像聚類算法在本節(jié)中,我們使用Kaggle上的 keep-babies-safe 數(shù)據(jù)集。https://www.kaggle.com/akash14/keep-babies-safe首先,我們創(chuàng)建一個圖像聚類模型,來將給定的圖像分為兩類,即玩具或消費品,以下是來自該數(shù)據(jù)集的一些圖像。
以下代碼實現(xiàn)我們的聚類算法:##################### Making Essential Imports ############################
import sklearn
import os
import sys
import matplotlib.pyplot as plt
import cv2
import pytesseract
import numpy as np
import pandas as pd
import tensorflow as tf
conf = r'-- oem 2'
#####################################
# Defining a skeleton for our #
# DataFrame #
#####################################
DataFrame = {
'photo_name' : [],
'flattenPhoto' : [],
'text' : [],
}
#######################################################################################
# The Approach is to apply transfer learning hence using Resnet50 as my #
# pretrained model #
#######################################################################################
MyModel = tf.keras.models.Sequential()
MyModel.a(chǎn)dd(tf.keras.a(chǎn)pplications.ResNet50(
include_top = False, weights='imagenet', pooling='avg',
))
# freezing weights for 1st layer
MyModel.layers[0].trainable = False
### Now defining dataloading Function
def LoadDataAndDoEssentials(path, h, w):
img = cv2.imread(path)
DataFrame['text'].a(chǎn)ppend(pytesseract.image_to_string(img, config = conf))
img = cv2.resize(img, (h, w))
## Expanding image dims so this represents 1 sample
img = img = np.expand_dims(img, 0)
img = tf.keras.a(chǎn)pplications.resnet50.preprocess_input(img)
extractedFeatures = MyModel.predict(img)
extractedFeatures = np.a(chǎn)rray(extractedFeatures)
DataFrame['flattenPhoto'].a(chǎn)ppend(extractedFeatures.flatten())
### with this all done lets write the iterrrative loop
def ReadAndStoreMyImages(path):
list_ = os.listdir(path)
for mem in list_:
DataFrame['photo_name'].a(chǎn)ppend(mem)
imagePath = path + '/' + mem
LoadDataAndDoEssentials(imagePath, 224, 224)
### lets give the address of our Parent directory and start
path = 'enter your data's path here'
ReadAndStoreMyImages(path)
######################################################
# lets now do clustering #
######################################################
Training_Feature_vector = np.a(chǎn)rray(DataFrame['flattenPhoto'], dtype = 'float64')
from sklearn.cluster import AgglomerativeClustering
kmeans = AgglomerativeClustering(n_clusters = 2)
kmeans.fit(Training_Feature_vector)
A little explanation for the above code:
上面的代碼使用Resnet50(一種經(jīng)過預先訓練的CNN)進行特征提取,我們只需移除其頭部或用于預測類別的神經(jīng)元的最后一層,然后將圖像輸入到CNN并獲得特征向量作為輸出,實際上,這是我們的CNN在Resnet50的倒數(shù)第二層學習到的所有特征圖的扁平數(shù)組。可以將此輸出向量提供給進行圖像聚類的任何聚類算法。讓我向你展示通過這種方法創(chuàng)建的簇。
該可視化的代碼如下## lets make this a dataFrame
import seaborn as sb
import matplotlib.pyplot as plt
dimReducedDataFrame = pd.DataFrame(Training_Feature_vector)
dimReducedDataFrame = dimReducedDataFrame.rename(columns = { 0: 'V1', 1 : 'V2'})
dimReducedDataFrame['Category'] = list (df['Class_of_image'])
plt.figure(figsize = (10, 5))
sb.scatterplot(data = dimReducedDataFrame, x = 'V1', y = 'V2',hue = 'Category')
plt.grid(True)
plt.show()
結(jié)論本文通過解釋如何使用深度學習和聚類將視覺上相似的圖像聚在一起形成簇,而無需創(chuàng)建數(shù)據(jù)集并在其上訓練CNN。
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