Python 使用OpenCV 2.4.11打开图像以用作sklearn的数据集

我目前正在从事以下工作：

import numpy as np
import cv2
import os
from matplotlib import pyplot as plt
from os import listdir
from os.path import isfile, join
from sklearn import datasets
from sklearn import svm
  
digits = datasets.load_digits()

imageFolderPath ='C:/PathToFolderContainingMyImages/'

# Getting all the paths for each image
individualImagePaths = [ imageFolderPath + f for f in listdir(imageFolderPath) if isfile(join(imageFolderPath,f))]
individualImagePaths = sorted(individualImagePaths)

logos = []
logoLabels = []

for x in individualImagePaths:
    filename = os.path.basename(x).split(" ")
    filename = filename[0]

    logos.append(np.array(cv2.imread(x,0)))
    logoLabels.append(filename)

logos = np.asarray(logos)
logoLabels = np.asarray(logoLabels)

print type(logos)
print type(logoLabels)
print logos[0]
print logoLabels[0]

print type(digits.images)
print type(digits.target)
print digits.images[0]
print digits.target[0]

clf = svm.SVC(gamma=0.001, C=100.)
clf.fit(logos[:-1], logoLabels[:-1])

• Python 2.7
• OpenCV 2.4.11
• Sklearn 0.16.1

我在用电脑

---

我的目标是加载自己的数据集，而不是使用预定义的数据集。我正试图通过以下方式实现这一目标：

import numpy as np
import cv2
import os
from matplotlib import pyplot as plt
from os import listdir
from os.path import isfile, join
from sklearn import datasets
from sklearn import svm
  
digits = datasets.load_digits()

imageFolderPath ='C:/PathToFolderContainingMyImages/'

# Getting all the paths for each image
individualImagePaths = [ imageFolderPath + f for f in listdir(imageFolderPath) if isfile(join(imageFolderPath,f))]
individualImagePaths = sorted(individualImagePaths)

logos = []
logoLabels = []

for x in individualImagePaths:
    filename = os.path.basename(x).split(" ")
    filename = filename[0]

    logos.append(np.array(cv2.imread(x,0)))
    logoLabels.append(filename)

logos = np.asarray(logos)
logoLabels = np.asarray(logoLabels)

print type(logos)
print type(logoLabels)
print logos[0]
print logoLabels[0]

print type(digits.images)
print type(digits.target)
print digits.images[0]
print digits.target[0]

clf = svm.SVC(gamma=0.001, C=100.)
clf.fit(logos[:-1], logoLabels[:-1])

运行此脚本时出现以下错误：

array = np.array(array, dtype=dtype, order=order, copy=copy)
ValueError: setting an array element with a sequence.

我的图像名为：

“1（1）.png”表示1位数的图像

“2（1）.png”表示2位数的图像

“2（2）.png”表示2位数的图像

---

返回：

<type 'numpy.ndarray'>
<type 'numpy.ndarray'>
[[255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]
 ..., 
 [255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]]
0

<type 'numpy.ndarray'>
<type 'numpy.ndarray'>
[[  0.   0.   5.  13.   9.   1.   0.   0.]
 [  0.   0.  13.  15.  10.  15.   5.   0.]
 [  0.   3.  15.   2.   0.  11.   8.   0.]
 [  0.   4.  12.   0.   0.   8.   8.   0.]
 [  0.   5.   8.   0.   0.   9.   8.   0.]
 [  0.   4.  11.   0.   1.  12.   7.   0.]
 [  0.   2.  14.   5.  10.  12.   0.   0.]
 [  0.   0.   6.  13.  10.   0.   0.   0.]]
0

返回：

<type 'numpy.ndarray'>
<type 'numpy.ndarray'>
[[255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]
 ..., 
 [255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]
 [255 255 255 ..., 255 255 255]]
0

<type 'numpy.ndarray'>
<type 'numpy.ndarray'>
[[  0.   0.   5.  13.   9.   1.   0.   0.]
 [  0.   0.  13.  15.  10.  15.   5.   0.]
 [  0.   3.  15.   2.   0.  11.   8.   0.]
 [  0.   4.  12.   0.   0.   8.   8.   0.]
 [  0.   5.   8.   0.   0.   9.   8.   0.]
 [  0.   4.  11.   0.   1.  12.   7.   0.]
 [  0.   2.  14.   5.  10.  12.   0.   0.]
 [  0.   0.   6.  13.  10.   0.   0.   0.]]
0

[[  0.   0.   5.  13.   9.   1.   0.   0.]
[  0.   0.  13.  15.  10.  15.   5.   0.]
[  0.   3.  15.   2.   0.  11.   8.   0.]
[  0.   4.  12.   0.   0.   8.   8.   0.]
[  0.   5.   8.   0.   0.   9.   8.   0.]
[  0.   4.  11.   0.   1.  12.   7.   0.]
[  0.   2.  14.   5.  10.  12.   0.   0.]
[  0.   0.   6.  13.  10.   0.   0.   0.]]
0

---

关于如何创建/加载自己的数据集并使用sklearn

fit

函数使用数据集，我有什么想法吗？

如果其他人看到了这一点并遇到了问题，我会反向工作，将数字数据集保存到单个图像中，重新加载图像，训练分类器，然后预测图像

我最终没有使用opencv，如下代码所示：

import matplotlib.pyplot as plt
import numpy as np
import scipy
from sklearn import datasets, svm, metrics
import uuid
import os
from os import listdir
from os.path import isfile, join

def scipySaveImage(path,name,image):
    scipy.misc.imsave(path + name,image)

def scipyLoadImage(path,flatten=0):
        return scipy.misc.imread(path,flatten)

def saveAsUniqueImage(path,image,target):
        # Defining our parts
        target = str(target)
        unique = str(uuid.uuid1())
        extension = '.png'
        name = target + '-' + unique + extension
        scipySaveImage(path,name,image)

def saveDataSet(path,dataset):
    for i,image in enumerate(dataset.images):
        target = dataset.target[i]
        saveAsUniqueImage(path,image,target)

def predict(classifier,data):
        return classifier.predict(data[:len(data)])

def shape_data(data):
        n_samples = len(data)
        return data.reshape((n_samples, -1))

def train_classifer(data):
        n_samples = len(data)
        data = shape_data(data)
        classifier = svm.SVC(gamma=0.001)
        classifier.fit(data[:n_samples], digits.target[:n_samples])
        return classifier

# A Dataset Object
class dataset:
        def __init_():
                return

def loadTestDataset(path):
        data = dataset()
        targets = []
        filenames = []
        images = []

        imagePaths = [ path + f for f in listdir(path) if isfile(join(path,f))]
        imagePaths = sorted(imagePaths)

        for x in imagePaths:
                filename = os.path.basename(x)
                target = filename.split("-")
                target = target[0]
                target = int(target)

                targets.append(target)
                filenames.append(filename)
                images.append(scipy.misc.imread(x,1))

        data.target = np.asarray(targets)
        data.images = np.asarray(images)
        data.filenames = filenames

        return data

# The folder where my digit images will go in
training_path = 'Digits/'

# Saving the images in the digit dataset into the Digit folder
# Comment out this line if you already have the digits
saveDataSet(training_path,datasets.load_digits())

# Loading all the images from a folder into a dataset
digits = loadTestDataset(training_path)

# Reloads the images from our training folder
test_digits = loadTestDataset(training_path)

# Shaping the images, I believe this just makes the rows 1 pixel so an 8x8 image will now be 64x1
test_digits.images = shape_data(test_digits.images)

# Training our classifer so it knows how to classify digits
digits_model = train_classifer(digits.images)

# The target that our model thinks is being represented
prediction = predict(digits_model,test_digits.images[300])

# Printing the filename ( which includes the target ) and our models prediction
print test_digits.filenames[300]
print prediction