def anconvert(d, l, inpath):
# print inpath
path = '/home/peth/Databases/rPascal/features/caffe/queries/'
finput = inpath + '.npy'
check = True
isfileaf = glob.glob("/home/peth/Databases/rPascal/features/nDCG/*.txt")
isfilebf = glob.glob('/home/peth/Databases/rPascal/features/caffe/queries/*.npy')
if len(isfileaf) == len(isfilebf):
check = False
return tosort(inpath, l)
if check and len(isfileaf) < len(isfilebf):
for fname in sorted(os.listdir(path)):
if finput == fname:
file = open("/home/peth/Databases/rPascal/features/nDCG/" + inpath + ".txt", 'w')
for key in d:
# print key
if inpath == key:
value = d[key]
for element in value:
dist = str(dist_cal(key, element))
ref = str(element).rstrip(".jpg")
file.write(ref)
file.write(" ")
file.write(dist)
file.write("\n")
file.close()
print 'All reference and distance of ' + str(inpath) + " is written"
return tosort(inpath, l)
def convert(d):
print "Processing all images in dataset... "
dict = OrderedDict()
dict2 = OrderedDict()
for key in d:
value = d[key]
min_dist = dist_cal(key, value[0])
min_y = str(value[0])
for element in value:
dist = dist_cal(key, element)
if dist <= min_dist:
query = str(key)
min_dist = dist
min_y = str(element).rstrip(".jpg")
print "Processing query: " + (query) + " <=> Most similar image is: " + (min_y) + " <=> Distance is: " + str(min_dist)
dict[query] = min_y
dict2[query] = str(min_dist)
# print dict
print "Dataset processing finished... "
savepair(dict)
savedist(dict2)
print "Dataset successfully created. "
