hans

hans

【机器学习】【Python】提取直方图特征做图片预分类(决策树,随机森林,Adaboost)


github 地址: https://github.com/HansRen1024/Image-Pre-Classification

想法是在卷积神经网络分类图片之前先进行一次预分类,二分类就好,判断当前图片是否包含我要分类的物品。

因为只要你丢一张图片进卷积神经网络,它总归会输出一个结果,可能这个结果置信度不高,但是在某些情况这个置信度也会超过所设阈值。

特征提取有很多算法,直方图太寒酸了,并不适用。

对于少量数据集,直方图还能训练出一个还不错的模型。但是一上完整数据集就歇菜。

我提供的是一种思路,不是解决方案,我也在摸索当中。

---------【2018.01.24】更新 --------------------

同样的训练集(7W+)和测试集(1W)

未微调参数,256 维灰度直方图,adaboost,忘记记录在测试集上准确率了。

未微调参数,768 维颜色直方图,adaboost,准确率 0.8516;

参数同上,根据 0.99 方差百分比 PCA 降到 221 维,adaboost,准确率 0.6483,说明直方图特征独立性很强啊。后来尝试 mle 算法自动降维,发现只降了一维,好吧。以后尝试用 LDA。

经过微调参数,768 维颜色直方图,adaboost,准确率暂时 0.90,还没调完。参数好多,真的好慢啊!

参数待定,256 维 lbp 直方图,adaboost,下回更新。

参数待定,1024 维颜色直方图 & lbp 直方图,adaboost,下会更新。

暂时没做 0 均值,归一化,正则化等预处理。

没做上面预处理,每一维度方差还蛮大的,不管了。

通过相关系数法,查看了下排名前 221 维度的特征,通过这种方式降维效果如何待验证。


train 和 test 的 list 文档格式和 caffe 转 lmdb 用的文档格式一样:

路径 + 空格 + 类别索引

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 17 13:09:08 2018

@author: hans
"""

import cv2
import os
import numpy as np
from sklearn.externals import joblib
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from skimage import transform
from sklearn import tree
import datetime



def gray(img_path):
    img = cv2.imread(img_path, 0)
    img=transform.resize(img, (227, 227))
    img = img*255
    img = img.astype(np.uint8)
    feature = cv2.calcHist([img],[0],None,[256],[0,256]).reshape(1,-1)
    return feature

def rgb(img_path):
    img = cv2.imread(img_path)
    img=transform.resize(img, (227, 227,3))
    b = img[:,:,0]*255
    g = img[:,:,1]*255
    r = img[:,:,2]*255
    b = b.astype(np.uint8)
    g = g.astype(np.uint8)
    r = r.astype(np.uint8)
    feature_b = cv2.calcHist([b],[0],None,[256],[0,256]).reshape(1,-1)
    feature_g = cv2.calcHist([g],[0],None,[256],[0,256]).reshape(1,-1)
    feature_r = cv2.calcHist([r],[0],None,[256],[0,256]).reshape(1,-1)
    feature = np.hstack((feature_b,feature_g,feature_r))
    return feature
    
def hist_feature(list_txt):
    root_path = 'image/'
    with open(list_txt, 'r') as f:
        line = f.readline()
        img_path = os.path.join(root_path,line.split(' ')[0])
        if mode == 0:
            feature = gray(img_path)
        elif mode == 1:
            feature = rgb(img_path)
        label = np.array([int(line.split(' ')[1].split('\n')[0])])
        line = f.readline()
        num = 2
        while line:
            img_path = os.path.join(root_path,line.split(' ')[0])
            if not os.path.isfile(img_path):
                line = f.readline()
                continue
            print("%d dealing with %s ..." %(num, line.split(' ')[0]))
            if mode == 0:
                hist_cv = gray(img_path)
            elif mode == 1:
                hist_cv = rgb(img_path)
            feature = np.vstack((feature,hist_cv))
            label = np.hstack((label,np.array([int(line.split(' ')[1].split('\n')[0])])))
            num+=1
            line = f.readline()
    joblib.dump(feature, list_txt.split('.')[0]+filename,compress=5)
    joblib.dump(label, list_txt.split('.')[0]+'_label.pkl', compress=5)
    return feature, label

def save_feature():
    t1 = datetime.datetime.now()
    X_train, y_train = hist_feature(train_list)
    t2 = datetime.datetime.now()
    X_test, y_test = hist_feature(test_list)
    t3 = datetime.datetime.now()
    print("\ntime of extracting train features: %0.2f"%(t2-t1).total_seconds())
    print("time of extracting test features: %0.2f"%(t3-t2).total_seconds())

def decision_tree():
    dt = tree.DecisionTreeClassifier(criterion='gini',max_depth=None, min_samples_split=2, min_samples_leaf=1,random_state=80)
    return fit(dt, 'dt')

def random_forest():
    # criterion: 分支的标准(gini/entropy), n_estimators: 树的数量, bootstrap: 是否随机有放回, n_jobs: 可并行运行的数量
    rf = RandomForestClassifier(n_estimators=25,criterion='entropy',bootstrap=True,n_jobs=4,random_state=80) # 随机森林
    return fit(rf, 'rf')

def adaboost():
    ada = AdaBoostClassifier(tree.DecisionTreeClassifier(criterion='gini',max_depth=11, min_samples_split=400, \
                                                         min_samples_leaf=30,max_features=30,random_state=10), \
                             algorithm="SAMME", n_estimators=100, learning_rate=0.001,random_state=10)
    return fit(ada, 'ada')

def fit(clf, s):
    t3 = datetime.datetime.now()
    X_train = joblib.load(train_list.split('.')[0]+filename)
    y_train = joblib.load(train_list.split('.')[0]+'_label.pkl')
    clf = clf.fit(X_train, y_train)
    joblib.dump(clf, s+'_model.pkl')
    t4 = datetime.datetime.now()
    print("--------------------------------\ntime of training model: %0.2f"%(t4-t3).total_seconds())
    scores = cross_val_score(clf, X_train, y_train,scoring='accuracy' ,cv=3)
    print("Train Cross Avg. Score: %0.4f (+/- %0.4f)" % (scores.mean(), scores.std() * 2))
    return clf
    
def testScore(clf):
    t4 = datetime.datetime.now()
    X_test = joblib.load(test_list.split('.')[0]+filename)
    y_test = joblib.load(test_list.split('.')[0]+'_label.pkl')
    clf_score = clf.score(X_test, y_test)
    print ("--------------------------------\nTest score: %.4f" %clf_score)
    clf_pred = clf.predict(X_test)
    print clf_pred[:10]
    t5 = datetime.datetime.now()
    print("time of testing model: %0.2f"%(t5-t4).total_seconds())
    scores = cross_val_score(clf, X_test, y_test,scoring='accuracy' ,cv=10)
    print("Test Cross Avg. Score: %0.4f (+/- %0.4f)" % (scores.mean(), scores.std() * 2))
    
mode=1
if mode==0:
    filename = '_feature_gray.pkl'
elif mode==1:
    filename = '_feature_rgb.pkl'

train_list = "train_all.txt"
test_list = "test_all.txt"

#train_list = "train.txt"
#test_list = "test.txt"

if __name__ == '__main__':
    
#    save_feature()
    
#    dt = decision_tree()
#    rf = random_forest()
    ada = adaboost()
#    gbdt = gradientboost()
    
#    dt = joblib.load('dt_model.pkl')
#    rf = joblib.load('rf_model.pkl')
    ada = joblib.load('ada_model.pkl')
    testScore(ada)
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