在绘制轴的曲线，但得到没有任何结果，而试图基于HOG特征进行分类图像 [英] Plotting a graph on axes but getting no results while trying to classify image based on HoG features

问题描述

我需要使用级联提升培训在scikit学习一些图片进行分类。
我想根据HOG特征进行分类。

I need to use boosted cascaded training to classify some images in scikit-learn. I want to classify according to HoG features.

我的code以下是从<一个适应href=\"http://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_multiclass.html#example-ensemble-plot-adaboost-multiclass-py\"相对=nofollow>这个例子。

My code below is adapted from this example.

在code的这部分是我真的做的唯一的事情：

This part of the code is the only thing that I've really done:

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:])

在code的其余部分是从链路上的例子：

The rest of the code is from the example on the link:

#### 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特征的图像分类的正确方法？
总共有528图像。首先他们是240x360。
但是，当我打印 img_hogs 的形状，我得到了：

(528L, 2640L)

有人告诉我，有没有图形，将因为分类是提前终止，因为有许多比形象更多的功能。所以我减少了我的图像，20×。

I was told there are no graphs to plot because the classification is terminating early since there are many more features than images. So I reduced my images to 20x30.

现在当我打印 img_hogs 的形状，我得到：

Now when I print the shape of img_hogs, I get:

(528L, 48L)

不过，我仍然没有得到结果。在这两种情况下，轴绘制，但图形是空的。

But I'm still getting no results. In either case, the axes are plotted but the graph is empty.

推荐答案

我已经删除了这个问题，但它发生，我认为在分类另一新手可能具有相同的问题，所以我只是回答这个问题。

I would have deleted this question but it occurred to me that another newbie at classification might have the same issue so I'm just answering the question.

问题是因为一个非常愚蠢的错误 - 我的训练阵列，标签，没有底片，这完全由标签只有一个类。

The problem was because of a very silly mistake - my training array, labels, had no negatives, it consisted entirely of labels for only one class.

这篇关于在绘制轴的曲线，但得到没有任何结果，而试图基于HOG特征进行分类图像的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！