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
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
fnames = all_files
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"
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)
if (idx % 1500 == 0 or idx == 36707):
if (idx > 34500):
all_features = extractSift(all_files)
#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, all_files, all_word_histgrams,
dataset_path + 'img_features' + str((idx - 1) / 1500 + 1) +
'.txt')
all_files = []
all_files_labels = {}
all_features = {}
#print "---------------------"
HISTOGRAMS_FILE = 'visual_words_for_test_data'
CODEBOOK_FILE = 'codebook_b.file'
IMAGE_INDEX_FILE = 'image_indexe_test'
def parse_arguments():
parser = argparse.ArgumentParser(description='generate visual words histogram for test image')
parser.add_argument('-c', help='path to the codebook file', required=False, default=CODEBOOK_FILE)
parser.add_argument('input_image', help='path to input image', nargs='+')
args = parser.parse_args()
return args
#print "## extract Sift features"
all_files = []
all_features = {}
args = parse_arguments()
codebook_file = args.c
fnames = args.input_image
all_features = extractSift(fnames)
with open(codebook_file, 'rb') as f:
codebook = load(f)
word_histgram = computeHistograms(codebook, all_features[fnames[0]])
nclusters = codebook.shape[0]
result = [x for x in word_histgram]
print result
