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SIFTBOW.py
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SIFTBOW.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Nov 14 23:06:36 2015
@author: Brian
"""
import cv2
import numpy as np
import os
image_paths = []
path = "C:\Python27\Project2\TestingSet"
#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("C:\Python27\Project2\TestingSet\\"+p)
for j in training_paths1:
training_paths.append("C:\Python27\Project2\TestingSet\\"+p+"\\"+j)
names_path.append(p)
sift = cv2.SIFT()
print names_path
descriptors_unclustered = []
dictionarySize = 5
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]=='Chair':
train_labels.append(1)
if names_path[i]=='Doge':
train_labels.append(2)
if names_path[i]=='Football':
train_labels.append(3)
if names_path[i]=='Watch':
train_labels.append(4)
if names_path[i]=='Wrench':
train_labels.append(5)
i = i+1
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((5,5))
def classify(pth):
feature = feature_extract(pth)
p = svm.predict(feature)
confusion[train_labels[count]-1,p-1] = confusion[train_labels[count]-1,p-1] +1
for p in training_paths:
classify(p)
count+=1
def normalizeRows(M):
row_sums = M.sum(axis=1)
return M / row_sums
confusion = normalizeRows(confusion)
confusion = confusion.transpose()
print confusion