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
print "---------------------"
print "## extract Sift features"
all_files = []
all_files_labels = {}
all_features = {}
for cat_path in files.keys():
cat_files = [cat_path + '/' + name for name in files[cat_path][set][1]]
all_files.extend(cat_files)
datasetpath = DATASETPATH
model_file = datasetpath + str(set) + '.' + MODEL_FILE
codebook_file = datasetpath + str(set) + '.' + CODEBOOK_FILE
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
dataset_path = dataset_path[2:len(dataset_path) - 2]
codebook_file = dataset_path + 'codebook.file'
f = open(dataset_path + 'filename.txt', 'r')
idx = 0
for line in f.readlines():
imgfilename = line.split()[0]
label = line.split()[1]
all_files.append(imgfilename)
all_files_labels[imgfilename] = int(label)
idx = idx + 1
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
all_files = []
all_files_labels = {}
all_features = {}
args = parse_arguments()
model_file = args.m
codebook_file = args.c
farr=[]
val=[]
fol = args.input_images
fl=os.listdir(fol[0])
fl.sort()
for f in fl:
fnames=[fol[0]+'/'+f]
farr.append(f[0])
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
with open(SVM_MODEL_PATH, 'rb') as fid:
crf = cPickle.load(fid)
print "---------------------"
print "## extract Sift features"
all_files = []
all_files_labels = {}
all_features = {}
model_file = SVM_MODEL_PATH
codebook_file = CODEBOOK_FILE
files = learn.get_imgfiles(DATASETPATH)
all_features = extractSift(files)
for i in files:
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
