如何将目录中的一组图像输入python以用作训练集？

Question

我已经能够提取URL数据集和链接以用作训练/测试数据集，但是我想将其扩展为图像。基本上，如果我有150张猫的图像，如何输入该图像并进行分类？使用IRIS数据集从URL提取的当前代码import pandasfrom pandas.plotting import scatter_matriximport matplotlib.pyplot as pltfrom sklearn import model_selectionfrom sklearn.metrics import classification_reportfrom sklearn.metrics import confusion_matrixfrom sklearn.metrics import accuracy_scorefrom sklearn.neighbors import KNeighborsClassifierurl = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']dataset = pandas.read_csv(url, names=names)print(dataset.shape)print(dataset.head(20))print(dataset.loc[1])print(dataset.describe())print(dataset.loc[1][0])plt.show()dataset.hist()plt.show()scatter_matrix(dataset)plt.show()array = dataset.valuesX = array[:,0:4]Y = array[:,4]validation_size = 0.20seed = 7X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)seed = 7scoring = 'accuracy'models = []models.append(('KNN', KNeighborsClassifier()))# evaluate each model in turnresults = []names = []for name, model in models:    kfold = model_selection.KFold(n_splits=10, random_state=seed)    cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)    results.append(cv_results)    names.append(name)    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())    print(msg)fig = plt.figure()fig.suptitle('Algorithm Comparison')ax = fig.add_subplot(111)plt.boxplot(results)ax.set_xticklabels(names)plt.show()knn = KNeighborsClassifier()knn.fit(X_train, Y_train)predictions = knn.predict(X_validation)print(accuracy_score(Y_validation, predictions))print(confusion_matrix(Y_validation, predictions))print(classification_report(Y_validation, predictions))

Answer 1

您可以使用选择的库读取具有顺序文件名的图像

import skimage as ski

filenames = ['image-%03d.jpg'%n for n in range(150)]

images = []

for f in filenames:

    im = ski.imread(f)

    images.append(im)

然后images是图像列表。

您还可以遍历任何类型的文件名，或使用该os模块仅从具有特定扩展名的目录中提取文件。原理是一样的。只是filenames根据需要构造。

但是，我建议使用pims，可能与处理管道一起使用

import pims

import numpy as np

images = pims.ImageSequence('images-*.jpg')

@pims.pipeline

def grayarr(im):

    return np.array(im)[:,:,0]

images = grayarr(images)

在这一点上，您可以images使用类似numpy的切片进行索引。pims当您处理太多无法将其保存在RAM中的图像时，此功能特别有用。您可以在pims文档中阅读有关这些内容的信息。

Answer 2

您可以使用Glob并从目录中提取数据

from PIL import Image

import glob

list_of_images = []

for filename in glob.glob('file_directory/.jpg'): #assuming you are dealing with #jpg

    training_set = Image.open(filename)

    list_of_images.append(training_set)