图像检索：BoW图像检索原理与实战

#!/usr/local/bin/python2.7
#python findFeatures.py -t dataset/train/

import argparse as ap
import cv2
import numpy as np
import os
from sklearn.externals import joblib
from scipy.cluster.vq import *

from sklearn import preprocessing
from rootsift import RootSIFT
import math

# Get the path of the training set
parser = ap.ArgumentParser()
parser.add_argument("-t", "--trainingSet", help="Path to Training Set", required="True")
args = vars(parser.parse_args())

# Get the training classes names and store them in a list
train_path = args["trainingSet"]
#train_path = "dataset/train/"

training_names = os.listdir(train_path)

numWords = 1000

# Get all the path to the images and save them in a list
# image_paths and the corresponding label in image_paths
image_paths = []
for training_name in training_names:
    image_path = os.path.join(train_path, training_name)
    image_paths += [image_path]

# Create feature extraction and keypoint detector objects
fea_det = cv2.FeatureDetector_create("SIFT")
des_ext = cv2.DescriptorExtractor_create("SIFT")

# List where all the descriptors are stored
des_list = []

for i, image_path in enumerate(image_paths):
    im = cv2.imread(image_path)
    print "Extract SIFT of %s image, %d of %d images" %(training_names[i], i, len(image_paths))
    kpts = fea_det.detect(im)
    kpts, des = des_ext.compute(im, kpts)
    # rootsift
    #rs = RootSIFT()
    #des = rs.compute(kpts, des)
    des_list.append((image_path, des))

# Stack all the descriptors vertically in a numpy array
#downsampling = 1
#descriptors = des_list[0][1][::downsampling,:]
#for image_path, descriptor in des_list[1:]:
#    descriptors = np.vstack((descriptors, descriptor[::downsampling,:]))

# Stack all the descriptors vertically in a numpy array
descriptors = des_list[0][1]
for image_path, descriptor in des_list[1:]:
    descriptors = np.vstack((descriptors, descriptor))

# Perform k-means clustering
print "Start k-means: %d words, %d key points" %(numWords, descriptors.shape[0])
voc, variance = kmeans(descriptors, numWords, 1)

# Calculate the histogram of features
im_features = np.zeros((len(image_paths), numWords), "float32")
for i in xrange(len(image_paths)):
    words, distance = vq(des_list[i][1],voc)
    for w in words:
        im_features[i][w] += 1

# Perform Tf-Idf vectorization
nbr_occurences = np.sum( (im_features > 0) * 1, axis = 0)
idf = np.array(np.log((1.0*len(image_paths)+1) / (1.0*nbr_occurences + 1)), 'float32')

# Perform L2 normalization
im_features = im_features*idf
im_features = preprocessing.normalize(im_features, norm='l2')

joblib.dump((im_features, image_paths, idf, numWords, voc), "bof.pkl", compress=3)

#!/usr/local/bin/python2.7
#python search.py -i dataset/train/ukbench00000.jpg

import argparse as ap
import cv2
import imutils
import numpy as np
import os
from sklearn.externals import joblib
from scipy.cluster.vq import *

from sklearn import preprocessing
import numpy as np

from pylab import *
from PIL import Image
from rootsift import RootSIFT

# Get the path of the training set
parser = ap.ArgumentParser()
parser.add_argument("-i", "--image", help="Path to query image", required="True")
args = vars(parser.parse_args())

# Get query image path
image_path = args["image"]

# Load the classifier, class names, scaler, number of clusters and vocabulary
im_features, image_paths, idf, numWords, voc = joblib.load("bof.pkl")

# Create feature extraction and keypoint detector objects
fea_det = cv2.FeatureDetector_create("SIFT")
des_ext = cv2.DescriptorExtractor_create("SIFT")

# List where all the descriptors are stored
des_list = []

im = cv2.imread(image_path)
kpts = fea_det.detect(im)
kpts, des = des_ext.compute(im, kpts)

# rootsift
#rs = RootSIFT()
#des = rs.compute(kpts, des)

des_list.append((image_path, des))

# Stack all the descriptors vertically in a numpy array
descriptors = des_list[0][1]

#
test_features = np.zeros((1, numWords), "float32")
words, distance = vq(descriptors,voc)
for w in words:
    test_features[0][w] += 1

# Perform Tf-Idf vectorization and L2 normalization
test_features = test_features*idf
test_features = preprocessing.normalize(test_features, norm='l2')

score = np.dot(test_features, im_features.T)
rank_ID = np.argsort(-score)

# Visualize the results
figure()
gray()
subplot(5,4,1)
imshow(im[:,:,::-1])
axis('off')
for i, ID in enumerate(rank_ID[0][0:16]):
	img = Image.open(image_paths[ID])
	gray()
	subplot(5,4,i+5)
	imshow(img)
	axis('off')

show()