原标题：机器学习：用SVM和局部二值模式进行图像识别

来源：今日头条         链接：https://www.toutiao.com/a6563500474505363971/

在本文中，我将告诉你如何做一个简单的性别预测。这里我用了一些库

• Flask

• sklearn

• matplotlib

• Local binary pattern

• 其他

机器只知道数字,所以我们需要将图像像素转换成数字。使用局部二值模式是一件好事，因为它提供了一个简单的概念来将图像转换成数字，尽管这对进一步的研究没有好处。

LBP将图像分割到一些区域，并计算每个区域的梯度密度，然后处理成直方图

LBP

# import the necessary packages

from skimage import feature

import numpy as np

class LocalBinaryPatterns:

def __init__(self, numPoints, radius):

# store the number of points and radius

self.numPoints = numPoints

self.radius = radius

def describe(self, image, eps=1e-7):

# compute the Local Binary Pattern representation

# of the image, and then use the LBP representation

# to build the histogram of patterns

lbp = feature.local_binary_pattern(image, self.numPoints,

self.radius, method="uniform")

(hist, _) = np.histogram(lbp.ravel(),

bins=np.arange(0, self.numPoints + 3),

range=(0, self.numPoints + 2))

# normalize the histogram

hist = hist.astype("float")

hist /= (hist.sum() + eps)

# return the histogram of Local Binary Patterns

return hist

这是Pyth实现的LBP代码，结果将是描述的直方图，或者我们可以说它是一组数组。

你从上面的代码中得到的结果可以称之为数据集。

Dataset

[0.021636221875666023,0.01754288260189137,0.009927043885038529,0.007963911784350686,0.007880374248151202,0.008311984851848529,0.007031075963456462,0.009189128981943098,0.01198763644462577,0.016122744486500164,0.023543662285554212,0.038496881265261615,0.05056805524608687,0.04409389619062696,0.029669748273516275,0.023641122744453607,0.014465916685210422,0.01357484963241594,0.008311984851848529,0.010581421251934477,0.008854978837145167,0.01077634216973327,0.012377478280223356,0.019659166852278264,0.02316774337265654,0.5506237469361903]

这是我从一个图像中得到的数据集的一个例子，我使用了LBP，这个数据集现在可以用于训练。别忘了给它贴上数字标签。我用1表示男性，0代表女性。

首先，您应该定义标签和数据变量。

E.g i have three datasets

datas = [[dataset],[dataset],[dataset]]

label = [1,0,0]

1= male, 0=female

sklearn将帮助您做一个SVM预测器，只需要几行代码。

model = LinearSVC(C=100.0, random_state=42)

model.fit(datas, label)

一切都设置好了。您的训练代码已经准备好使用，现在您只需要制作测试代码。很简单

testing = [dataset]

prediction = model.predict(testing)[0]

在执行代码之后，您将看到基于数据训练中的标签的结果。

result = print(prediction)

一THE END一

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