Question

import cv2
image = cv2.imread("cat.1.jpg",0)
winSize = (64,64)
blockSize = (16,16)
blockStride = (8,8)
cellSize = (8,8)
nbins = 9
derivAperture = 1
winSigma = 4.
histogramNormType = 0
L2HysThreshold = 2.0000000000000001e-01
gammaCorrection = 0
nlevels = 64
hog = cv2.HOGDescriptor(winSize,blockSize,blockStride,cellSize,nbins,derivAperture,winSigma,histogramNormType,L2HysThreshold,gammaCorrection,nlevels)
winStride = (8,8)
padding = (8,8)
locations = ((10,20),)
hist = hog.compute(image,winStride,padding,locations)
print (hist)
print (len(hist))
print (len(hist[0]))

我有近50000只狗（25K）和猫（25K）的图像。我想训练SVM分类器，以便正确预测特定图像是狗还是猫。通过使用HOG描述符，我得到了特定图像的大小为1764 * 1的特征向量。

如何使用所有图像的特征向量？还可以如何提供标签（例如1表示猫或-1表示狗）。注意图像文件名格式为at.1.jpg，cat。 2.JPG ............. cat.25000.jpg

Answer 1

import cv2
import numpy as np
winSize = (64,64)
blockSize = (16,16)
blockStride = (8,8)
cellSize = (8,8)
nbins = 9
derivAperture = 1
winSigma = 4.
histogramNormType = 0
L2HysThreshold = 2.0000000000000001e-01
gammaCorrection = 0
nlevels = 64
hog = cv2.HOGDescriptor(winSize,blockSize,blockStride,cellSize,nbins,derivAperture,winSigma,histogramNormType,L2HysThreshold,gammaCorrection,nlevels)
#compute(img[, winStride[, padding[, locations]]]) -> descriptors
winStride = (8,8)
padding = (8,8)
locations = ((10,20),)
out = []
for x in range(0,12500):
    image = cv2.imread("cat or dog.{}.jpg".format(x),0)
    hist = hog.compute(image,winStride,padding,locations))
    out.append(hist)
np.savetxt("hog.txt",out)

在每次生猪功能之后，您可以根据自己的选择指定标签。

Answer 2

请参阅此链接https://docs.opencv.org/3.0-beta/doc/tutorials/ml/introduction_to_svm/introduction_to_svm.html

SVM的语法略有不同，比如训练它变成svm-＆gt; train（training_mat，ROW_SAMPLE，labels_mat）

正确标签的分配使用1表示正样本，-1表示负样本。然后将这些标签输入labels_mat，然后用于SVM训练程序

为HOG功能分配标签以训练SVM分类器

2 个答案: