29_聚类提取特征猫狗大战.py

import numpy as np
import mahotas as mh
from mahotas.features import surf
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import *
from sklearn.cluster import MiniBatchKMeans
import os
import glob

all_instance_filenames = []
all_instance_targets = []

for f in glob.glob('data/PetImages/*/*.jpg'):
    target = 1 if 'Dog' in os.path.split(f)[0] else 0
    all_instance_filenames.append(f)
    all_instance_targets.append(target)

surf_features = []
for f in all_instance_filenames:
    image = mh.imread(f, as_grey=True)
    surf_features.append(surf.surf(image)[:, 5:])

train_len = int(len(all_instance_filenames) * .90)
X_train_surf_features = np.concatenate(surf_features[:train_len])
X_test_surf_feautres = np.concatenate(surf_features[train_len:])
y_train = all_instance_targets[:train_len]
y_test = all_instance_targets[train_len:]

n_clusters = 300
estimator = MiniBatchKMeans(n_clusters=n_clusters)
estimator.fit_transform(X_train_surf_features)

X_train = []
for instance in surf_features[:train_len]:
    clusters = estimator.predict(instance)
    features = np.bincount(clusters)
    if len(features) < n_clusters:
        features = np.append(features, np.zeros((1, n_clusters-len(features))))
    X_train.append(features)

X_test = []
for instance in surf_features[train_len:]:
    clusters = estimator.predict(instance)
    features = np.bincount(clusters)
    if len(features) < n_clusters:
        features = np.append(features, np.zeros((1, n_clusters-len(features))))
    X_test.append(features)

clf = LogisticRegression(C=0.01, penalty='l2')
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)
print(classification_report(y_test, predictions))