sift+svm图像分类

注意：opencv版本为2.4.11，代码也没咋么修改，直接可以用

# -*- coding: utf-8 -*-

"""

Created on Sat Nov 14 23:06:36 2015
@author: Brian
https://github.com/briansrls/SIFTBOW/blob/master/SIFTBOW.py
"""

import cv2

import numpy as np

import os

image_paths = []

path = "E:\\huang\\My_project\\data"

#list of our class names

training_names = os.listdir(path)

training_paths = []

names_path = []

#get full list of all training images

for p in training_names:

    training_paths1 = os.listdir("E:\\huang\\My_project\\data\\"+p)

    for j in training_paths1:

        training_paths.append("E:\\huang\\My_project\\data\\"+p+"\\"+j)

        names_path.append(p)

sift = cv2.SIFT()

print names_path

descriptors_unclustered = []

dictionarySize = 60

BOW = cv2.BOWKMeansTrainer(dictionarySize)

for p in training_paths:

    image = cv2.imread(p)

    gray = cv2.cvtColor(image, cv2.CV_LOAD_IMAGE_GRAYSCALE)

    kp, dsc= sift.detectAndCompute(gray, None)

    BOW.add(dsc)

#dictionary created

dictionary = BOW.cluster()

FLANN_INDEX_KDTREE = 0

index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)

search_params = dict(checks=50)   # or pass empty dictionary

flann = cv2.FlannBasedMatcher(index_params,search_params)

sift2 = cv2.DescriptorExtractor_create("SIFT")

bowDiction = cv2.BOWImgDescriptorExtractor(sift2, cv2.BFMatcher(cv2.NORM_L2))

bowDiction.setVocabulary(dictionary)

print "bow dictionary", np.shape(dictionary)

#returns descriptor of image at pth

def feature_extract(pth):

    im = cv2.imread(pth, 1)

    gray = cv2.cvtColor(im, cv2.CV_LOAD_IMAGE_GRAYSCALE)

    return bowDiction.compute(gray, sift.detect(gray))

train_desc = []

train_labels = []

i = 0

for p in training_paths:

    train_desc.extend(feature_extract(p))

    if names_path[i]=='gou':

        train_labels.append(1)

    if names_path[i]=='zhu':

        train_labels.append(2)

    i = i+1

print(train_desc)

print "svm items", len(train_desc), len(train_desc[0])

count=0

svm = cv2.SVM()

svm.train(np.array(train_desc), np.array(train_labels))

i=0

j=0

confusion = np.zeros((2,2))

def classify(pth):

    feature = feature_extract(pth)

    p = svm.predict(feature)

    w = int(p)

    confusion[train_labels[count]-1, w-1] = confusion[train_labels[count]-1, w-1] + 1

    

    

for p in training_paths:

    #print(p)

    classify(p)

    count += 1

print(confusion)

def normalizeRows(M):

    row_sums = M.sum(axis=1)

    return M / row_sums

    

confusion = normalizeRows(confusion)

confusion = confusion.transpose()

    

print confusion