[英]Resize HOG feature for Scikit-Learn classifier
I'm trying to execute this code that processes 70 images and extracts Histogram of Oriented Gradients (HOG) features. 我正在尝试执行此代码来处理70个图像并提取直方图梯度(HOG)功能。 These are passed to a classifier (Scikit-Learn).
这些传递给分类器(Scikit-Learn)。
However, an error is raised: 但是,会出现错误:
hog_image = hog_image_rescaled.resize((200, 200), Image.ANTIALIAS)
TypeError: an integer is required
I do not understand why, because with attempting with a single image works correctly. 我不明白为什么,因为尝试使用单个图像正常工作。
#Hog Feature
from skimage.feature import hog
from skimage import data, color, exposure
import cv2
import matplotlib.pyplot as plt
from PIL import Image
import os
import glob
import numpy as np
from numpy import array
listagrigie = []
path = 'img/'
for infile in glob.glob( os.path.join(path, '*.jpg') ):
print("current file is: " + infile )
colorato = Image.open(infile)
greyscale = colorato.convert('1')
#hog feature
fd, hog_image = hog(greyscale, orientations=8, pixels_per_cell=(16, 16),
cells_per_block=(1, 1), visualise=True)
plt.figure(figsize=(8, 4))
print(type(fd))
plt.subplot(121).set_axis_off()
plt.imshow(grigiscala, cmap=plt.cm.gray)
plt.title('Input image')
# Rescale histogram for better display
hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02))
print("hog 1 immagine shape")
print(hog_image_rescaled.shape)
hog_image = hog_image_rescaled.resize((200, 200), Image.ANTIALIAS)
listagrigie.append(hog_image)
target.append(i)
print("ARRAY of gray matrices")
print(len(listagrigie))
grigiume = np.dstack(listagrigie)
print(grigiume.shape)
grigiume = np.rollaxis(grigiume, -1)
print(grigiume.shape)
from sklearn import svm, metrics
n_samples = len(listagrigie)
data = grigiume.reshape((n_samples, -1))
# Create a classifier: a support vector classifier
classifier = svm.SVC(gamma=0.001)
# We learn the digits on the first half of the digits
classifier.fit(data[:n_samples / 2], target[:n_samples / 2])
# Now predict the value of the digit on the second half:
expected = target[n_samples / 2:]
predicted = classifier.predict(data[n_samples / 2:])
print("expected")
print("predicted")
You should rescale the source image (named colorato
in your example) to (200, 200)
, then extract the HOG features and then pass the list of fd
vectors to your machine learning models. 您应该将源图像(在您的示例中名为
colorato
)重新缩放为(200, 200)
,然后提取HOG colorato
,然后将fd
向量列表传递给您的机器学习模型。 The hog_image
are just meant to visualize the feature descriptors in a user friendly manner. hog_image
只是用户友好的方式可视化特征描述符。 The actual features are returned in the fd
variable. 实际的功能在
fd
变量中返回。
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