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)