如何将目录中的一组图像输入python以用作训练集?

时间:2018-08-14 09:30:40

标签: python python-3.x machine-learning deep-learning

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,我将如何输入它并对其进行分类?

使用IRIS数据集从URL中提取的当前代码

import pandas
from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
from sklearn import model_selection
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
url = "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.values
X = array[:,0:4]
Y = array[:,4]
validation_size = 0.20
seed = 7

X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)
seed = 7
scoring = 'accuracy'
models = []
models.append(('KNN', KNeighborsClassifier()))
# evaluate each model in turn
results = []
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))

2 个答案:

答案 0 :(得分:0)

您可以使用选择的库读取具有连续文件名的图像

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。只需根据需要构造filenames

但是,我建议您使用pims,并且可能使用处理管道

import pims
import numpy as np
images = pims.ImageSequence('images-*.jpg')

@pims.pipeline
def grayarr(im):
    return np.array(im)[:,:,0]

images = grayarr(images)

此时,您可以使用类似numpy的切片索引到images中。 pims在处理太多无法将其保存在RAM中的图像时特别有用。您可以在pims文档中了解这些内容。

答案 1 :(得分:0)

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)