上绘制轴的图形，但没有得到任何结果，而试图基于HOG特征进行分类图像(Plotting a grap

我需要使用级联提升培训在一些图像分类scikit学习。我想根据HOG特征进行分类。

我下面的代码是改编自这个例子。

在这部分代码是，我真的做的唯一的事情：

import sys
from scipy import misc, ndimage
from skimage import data, io, filter, color, exposure
from skimage.viewer import ImageViewer
from skimage.feature import hog
from skimage.transform import resize
import matplotlib.pyplot as plt
from sklearn.datasets import make_gaussian_quantiles
from sklearn.ensemble import AdaBoostClassifier
from sklearn.externals.six.moves import xrange
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
import pylab as pl
from sklearn.externals.six.moves import zip

f = open("PATH_TO_SAMPLES_LIST\\samples.txt",'r')
out = f.read().splitlines()
import numpy as np

### THIS IS THE MAIN CHANGES I MADE TO THE CODE
### THE CHANGES ARE ONLY IN ORDER TO GET HOG FEATURES OUT OF IMAGES TO PASS ON TO THE CLASSIFIERS

imgs = []
tmp_hogs = []

# I've omitted the code where I populate an array called "labels", but it's just a 1D #array with 528 elements, either 1 or 0

i=0
for file in out:
        filepath = "C:\\work_asaaki\\caltech\\cars_brad\\resized\\"
        readfile = filepath + file
        curr_img = color.rgb2gray(io.imread(readfile))
        imgs.append(curr_img)
        fd, hog_image = hog(curr_img, orientations=8, pixels_per_cell=(8, 8),
                 cells_per_block=(1, 1), visualise=True, normalise=True)
        tmp_hogs.append(fd) 
        i+=1

img_hogs = np.array(tmp_hogs, dtype =float) 
print img_hogs.shape

n_split = 508
X_train, X_test = np.array(img_hogs[:n_split]), np.array(img_hogs[n_split:])
y_train, y_test = np.array(labels[:n_split]), np.array(labels[n_split:])

代码的其余部分是从链路上的例子：

#### THE CODE BELOW IS TAKEN DIRECTLY FROM THE EXAMPLE


bdt_real = AdaBoostClassifier(
    DecisionTreeClassifier(max_depth=2),
    n_estimators=600,
    learning_rate=1)

bdt_discrete = AdaBoostClassifier(
    DecisionTreeClassifier(max_depth=2),
    n_estimators=600,
    learning_rate=1.5,
    algorithm="SAMME")

bdt_real.fit(X_train, y_train)
bdt_discrete.fit(X_train, y_train)

real_test_errors = []
discrete_test_errors = []

for real_test_predict, discrete_train_predict in zip(
        bdt_real.staged_predict(X_test), bdt_discrete.staged_predict(X_test)):
    real_test_errors.append(
        1. - accuracy_score(real_test_predict, y_test))
    discrete_test_errors.append(
        1. - accuracy_score(discrete_train_predict, y_test))

n_trees_discrete = len(bdt_discrete)
n_trees_real = len(bdt_real)

# Boosting might terminate early but the following arrays are always
# n_estimators long. We crop them to the actual number of tree here:
discrete_estimator_errors = bdt_discrete.estimator_errors_[:n_trees_discrete]
real_estimator_errors = bdt_real.estimator_errors_[:n_trees_real]
discrete_estimator_weights = bdt_discrete.estimator_weights_[:n_trees_discrete]

plt.figure(figsize=(15, 5))

plt.subplot(131)
plt.plot(xrange(1, n_trees_discrete + 1),
         discrete_test_errors, c='black', label='SAMME')
plt.plot(xrange(1, n_trees_real + 1),
         real_test_errors, c='black',
         linestyle='dashed', label='SAMME.R')
plt.legend()
plt.ylim(0.18, 0.62)
plt.ylabel('Test Error')
plt.xlabel('Number of Trees')
print "n trees"
print n_trees_discrete
print "discrete_test_errors"
print bdt_discrete.estimator_errors_.shape

plt.subplot(132)
plt.plot(xrange(1, n_trees_discrete + 1), discrete_estimator_errors,
         "b", label='SAMME', alpha=.5)
plt.plot(xrange(1, n_trees_real + 1), real_estimator_errors,
         "r", label='SAMME.R', alpha=.5)
plt.legend()
plt.ylabel('Error')
plt.xlabel('Number of Trees')
plt.ylim((.2,
         max(real_estimator_errors.max(),
             discrete_estimator_errors.max()) * 1.2))
plt.xlim((-20, len(bdt_discrete) + 20))

print  "plotting..."

plt.subplot(133)
plt.plot(xrange(1, n_trees_discrete + 1), discrete_estimator_weights,
         "b", label='SAMME')
plt.legend()
plt.ylabel('Weight')
plt.xlabel('Number of Trees')
plt.ylim((0, discrete_estimator_weights.max() * 1.2))
plt.xlim((-20, n_trees_discrete + 20))
# prevent overlapping y-axis labels
plt.subplots_adjust(wspace=0.25)
plt.show()

我的问题是，这是根据HOG特征图像进行分类的正确方法？总共有528图像。首先他们是240x360。但是，当我打印的形状img_hogs ，我得到：