def predict_image(model_file, codebook_file, fnames):
print str(fnames[0]) + ".sift"
try:
os.remove(str(fnames[0]) + ".sift")
except OSError:
pass
# extract Sift features for each individual image"
all_files = []
all_files_labels = {}
all_features = {}
print fnames
try:
all_features = extractSift(fnames)
for i in fnames:
all_files_labels[i] = 0 # label is unknown
# loading codebook from codebook_file
# default codebookfile: codebook.file
with open(codebook_file, 'rb') as f:
codebook = load(f)
# computing visual word histograms"
all_word_histgrams = {}
for imagefname in all_features:
word_histgram = computeHistograms(codebook,
all_features[imagefname])
all_word_histgrams[imagefname] = word_histgram
# write the histograms to file to pass it to the svm
nclusters = codebook.shape[0]
writeHistogramsToFile(nclusters, all_files_labels, fnames,
all_word_histgrams, HISTOGRAMS_FILE)
# test data with svm"
print libsvm.test(HISTOGRAMS_FILE, model_file)
except Exception as e:
pass #Incase of error with sift extraction, just try again on next stroke
def test_func(img_file_name):
print("---------------------")
print("## extract Sift features")
all_files = []
all_files_labels = {}
all_features = {}
'''args = parse_arguments()
model_file = args.m
codebook_file = args.c
fnames = args.input_images'''
model_file = 'traintrainingdata.svm.model'
codebook_file = 'traincodebook.file'
fnames = []
fnames.append(img_file_name)
all_features = extractSift(fnames)
for i in fnames:
all_files_labels[i] = 0 # label is unknown
print("---------------------")
print("## loading codebook from " + codebook_file)
with open(codebook_file, 'rb') as f:
codebook = load(f)
print("---------------------")
print("## computing visual word histograms")
all_word_histgrams = {}
for imagefname in all_features:
word_histgram = computeHistograms(codebook, all_features[imagefname])
all_word_histgrams[imagefname] = word_histgram
print("---------------------")
print("## write the histograms to file to pass it to the svm")
nclusters = codebook.shape[0]
writeHistogramsToFile(nclusters, all_files_labels, fnames,
all_word_histgrams, HISTOGRAMS_FILE)
print("---------------------")
print("## test data with svm")
result = libsvm.test(HISTOGRAMS_FILE, model_file)
print(result)
return result
for i in fnames:
all_files_labels[i] = 0 # label is unknown
print "---------------------"
print "## loading codebook from " + codebook_file
with open(codebook_file, 'rb') as f:
codebook = load(f)
print "---------------------"
print "## computing visual word histograms"
all_word_histgrams = {}
for imagefname in all_features:
word_histgram = computeHistograms(codebook, all_features[imagefname])
all_word_histgrams[imagefname] = word_histgram
print "---------------------"
print "## write the histograms to file to pass it to the svm"
print all_word_histgrams.keys()
nclusters = codebook.shape[0]
writeHistogramsToFile(nclusters,
all_files_labels,
fnames,
all_word_histgrams,
datasetpath + str(set) + '.' + HISTOGRAMS_FILE)
print "---------------------"
print "## test data with svm"
accuracy = libsvm.test(datasetpath + str(set) + '.' + HISTOGRAMS_FILE, model_file)
print accuracy
codebook_file = args.c
fnames = args.input_images
all_features = extractSift(fnames)
for i in fnames:
all_files_labels[i] = 0 # label is unknown
# print "---------------------"
# print "## loading codebook from " + codebook_file
with open(codebook_file, 'rb') as f:
codebook = load(f)
# print "---------------------"
# print "## computing visual word histograms"
all_word_histgrams = {}
for imagefname in all_features:
word_histgram = computeHistograms(codebook, all_features[imagefname])
all_word_histgrams[imagefname] = word_histgram
# print "---------------------"
# print "## write the histograms to file to pass it to the svm"
nclusters = codebook.shape[0]
writeHistogramsToFile(nclusters,
all_files_labels,
fnames,
all_word_histgrams,
HISTOGRAMS_FILE)
# print "---------------------"
# print "## test data with svm"
print(libsvm.test(HISTOGRAMS_FILE, model_file))
if HISTOGRAMS_FILE == "hinditestdata.svm":
dest1 = 'hindiwords/'
def copyFile(src, dest):
try:
shutil.copy(src, dest)
except shutil.Error as e:
parserrint('Error: %s' % e)
except IOError as e:
print('Error: %s' % e.strerror)
kanmisclass = []
engmisclass = []
count = 0
for index,i in enumerate(libsvm.test(HISTOGRAMS_FILE,model_file)):
#print 'File:',filenames[index]
count += 1
print count
if i == 0 :
copyFile(filenames[index],dest1)
os.remove(filenames[index])
else:
continue
for imagefname in all_features:
word_histgram = computeHistograms(codebook, all_features[imagefname])
all_word_histgrams[imagefname] = word_histgram
print "---------------------"
print "## write the histograms to file to pass it to the svm"
nclusters = codebook.shape[0]
writeHistogramsToFile(nclusters,
all_files_labels,
fnames,
all_word_histgrams,
HISTOGRAMS_FILE)
print "---------------------"
print "## test data with svm"
hold=libsvm.test(HISTOGRAMS_FILE, model_file)
vl=hold[0]
val.append(vl)
print hold
print farr
print val
cm=np.zeros(shape=(26,26))
# confusion matrix
ct=0
# dct={'a' : 0,'b':1,'c':2,'d':3,'e':4,'f':5,'g':6,'h':7,'i':8,'j':9,'k':10,'l':11,'m':12,'n':13,'o':14,'p':15,'q':16,'r':17,'s':18,'t':19,'u':20,'v':21,'w':22,'x':23,'y':24,'z':25}
dct=pickle.load(open("dict1.p","rb"))
for item in farr:
# print item[0]
c=ord(item[0])-97
col=ord(dct.keys()[dct.values().index(val[ct])])-97
for imagefname in all_features:
word_histgram = computeHistograms(codebook, all_features[imagefname])
all_word_histgrams[imagefname] = word_histgram
print "---------------------"
print "## write the histograms to file to pass it to the svm"
nclusters = codebook.shape[0]
writeHistogramsToFile(nclusters,
all_files_labels,
fnames,
all_word_histgrams,
HISTOGRAMS_FILE)
print "---------------------"
print "## test data with svm"
resultLabel = libsvm.test(HISTOGRAMS_FILE, model_file)
print ('THIS IS THE LABEL')
ingList = open(r"C:\Users\Hannah Chen\Documents\Visual Studio 2013\Projects\ColorBasics-WPF\bin\Debug\query.txt", "a")
#url = "http://www.recipepuppy.com/?i="
res = resultLabel
print res
l = ['0','1','2','3','4','5','6','7','8','9']
st_res = 0
for ch in res:
if ch not in l:
st_res+=ch
with open('nameList.txt', 'r') as f:
count = 0;
for item in f:
print "COUNT IS: "
print count
args = parse_arguments()
model_file = args.m
codebook_file = args.c
fnames = args.input_images
all_features = extractSift(fnames)
for i in fnames:
all_files_labels[i] = 0 # label is unknown
print "---------------------"
print "## loading codebook from " + codebook_file
with open(codebook_file, 'rb') as f:
codebook = load(f)
print "---------------------"
print "## computing visual word histograms"
all_word_histgrams = {}
for imagefname in all_features:
word_histgram = computeHistograms(codebook, all_features[imagefname])
all_word_histgrams[imagefname] = word_histgram
print "---------------------"
print "## write the histograms to file to pass it to the svm"
nclusters = codebook.shape[0]
writeHistogramsToFile(nclusters, all_files_labels, fnames, all_word_histgrams,
HISTOGRAMS_FILE)
print "---------------------"
print "## test data with svm"
print libsvm.test(HISTOGRAMS_FILE, model_file)
with open(codebook_file, 'rb') as f:
codebook = load(f)
print "---------------------"
print "## computing visual word histograms"
all_word_histgrams = {}
for imagefname in all_features:
word_histgram = computeHistograms(codebook, all_features[imagefname])
all_word_histgrams[imagefname] = word_histgram
print "---------------------"
print "## write the histograms to file to pass it to the svm"
nclusters = codebook.shape[0]
writeHistogramsToFile(nclusters,
all_files_labels,
fnames,
all_word_histgrams,
HISTOGRAMS_FILE)
print "---------------------"
print "## test data with svm"
results = libsvm.test(HISTOGRAMS_FILE, model_file)
print results
print "Length of filenames : ", len(fnames)
f = open("filenames.txt", "w")
f.write("\n".join(fnames)+"\n")
f.close()