类型错误:只能将长度为1的数组转换为Python标量 [英] Type Error: Only length-1 arrays can be converted to Python Scalars

问题描述

我是openCV的初学者，正在尝试分析数独求解器的现有代码.代码的这一部分会引发错误.

I am a beginner to openCV and am trying to analyse an existing code for a sudoku solver. There is this section of code which throws an error.

samples = np.float32(np.loadtxt('feature_vector_pixels.data'))
responses = np.float32(np.loadtxt('samples_pixels.data'))

model = cv2.ml.KNearest_create()
model.train(samples, responses)

错误如下Type Error: Only length-1 arrays can be converted to Python Scalars.

完整的回溯如下:

C:\Study stuff\FinalProject>c:\Python27\python.exe Sudoku.py
Traceback (most recent call last):
  File "Sudoku.py", line 15, in <module>
    model.train(samples, responses)
TypeError: only length-1 arrays can be converted to Python scalars

对这个问题有什么想法吗?

Any idea as to what the issue is?

推荐答案

您收到的错误消息:

TypeError: Only length-1 arrays can be converted to Python Scalars

从字面上看是指:您在单个值或单个元素数组多个元素的数组>是预期的.

literally means: you have provided an array of more than one element in a place where a single value or an array of a single element was expected.

所以传递给调用model.train(samples, responses)的参数之一是标量和标量...但是，哪个?

So one of the arguments passed to call model.train(samples, responses) requires and scalar... but which?

看看 KNearest类的最新文档，请允许我们查看 StatsModel.train方法:

A look at the latest documentation of KNearest class, allow us to see the signature of the StatsModel.train method:

虚拟布尔cv :: ml :: StatModel :: train(InputArray示例，int布局，InputArray响应)

virtual bool cv::ml::StatModel::train ( InputArray samples, int layout, InputArray responses )

显然，添加了一个新的 layout 参数.但这含义有点晦涩从文档中获取.

Apparently, a new layout argument was added. But it's meaning is a little obscure from the documentation.

在不了解文件内容的情况下，我无法确定您是否需要传递ROW_SAMPLE或COL_SAMPLE，但是有了这些信息，我可以找到

Without knowledge of the contents of you file, I can't tell if you need to pass ROW_SAMPLE or COL_SAMPLE, but armed with that information, I could find a similar question, whose solution was to add cv2.ml.ROW_SAMPLE as second argument to the train method:

model.train(samples, cv2.ml.ROW_SAMPLE, responses) 

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