如何在 scikit-learn(用于计算机视觉)中使用我自己的数据集? [英] How can I work with my own dataset in scikit-learn (for computer vision)?

问题描述

如何在 scikit-learn 中使用我自己的数据集?Scikit Tutorial总是以加载他的数据集(数字数据集，花卉数据集...)为例.

How can I work with my own dataset in scikit-learn? Scikit Tutorial always take as example to load his dataset (digit dataset, flower dataset...)

http://scikit-learn.org/stable/datasets/index.html即:从 sklearn.datasets 导入 load_iris

http://scikit-learn.org/stable/datasets/index.html ie: from sklearn.datasets import load_iris

我有我的图像，但我不知道如何创建新图像.

I have my images and I have no idea how create new one.

特别是，对于开始，我使用我找到的这个例子(我使用库 opencv):

Particularly, for starting, i use this example i found (i use library opencv):

img =cv2.imread('telamone.jpg')

# Convert them to grayscale
imgg =cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

# SURF extraction
surf = cv2.SURF()
kp, descritors = surf.detect(imgg,None,useProvidedKeypoints = False)

# Setting up samples and responses for kNN
samples = np.array(descritors)
responses = np.arange(len(kp),dtype = np.float32)

我想提取一组图像的特征，以一种对实现机器学习算法有用的方式！

I would like to extract features of a set of images, in a way useful to implement a machine learning algorithm!

推荐答案

您首先需要明确定义您要实现的目标:将特征提取到一组图像中，以一种对实现机器学习算法有用的方式！"太模糊了，无法给你任何指导.

You would first need to clearly define what you are trying to achieve: "extract feature to a set of images, in a way useful to implement a machine learning algorithm!" is much too vague to give you any guidance.

你想做什么:

• 图片整体的图像分类(例如室内场景 vs 室外场景)?

• image classification of the picture as a whole (e.g. indoor scene vs outdoor scene)?

在一组图片的子部分内进行对象识别(例如识别不同图片中同一对象的多个实例)，可能使用具有各种大小窗口的扫描程序?

object recognition (e.g. recognizing several instances of the same object in different pictures) inside sub-parts of a set of pictures, maybe using a scan procedures with windows of various sizes?

对象检测和基于类的分类(例如，查找图片中所有出现的汽车或行人，以及这些类的每个实例周围的边界框)?

object detection and class-based categorization (e.g. finding all occurrences of cars or pedestrians in pictures and a bounding box around each occurrence of instances of those classes)?

全图语义解析，也就是像素的分割+每个片段的类别分类(建筑、道路、人、树)...

full picture semantic parsing a.k.a. segmentation of the pixels + class categorization of each segment (build, road, people, trees)...

这些任务中的每一个都需要不同的管道(特征提取 + 机器学习模型组合).

Each of those tasks will require different pipelines (feature extraction + machine learning models combo).

您可能应该从阅读有关该主题的书籍开始，例如:http://szeliski.org/Book/

You should probably start by reading a book on the subject, for instance: http://szeliski.org/Book/

另外作为旁注，stackoverflow 可能不是提出此类开放式问题的最佳场所.

Also as a side note, stackoverflow is probably not the best place to ask such open ended questions.

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