def denseExtraction(im, im_special, technique, type, dirImage):
finalLines = []
count = 0
for diametro in [12, 19, 31, 50, 80, 128]:
radius = diametro / 2.0
os.system(SURF + " -i " + dirImage + "/" + im_special +
" -p1 tmp/dense/points-region-" + str(radius) + "-" + type +
".txt -o " + directory + technique + "/" + type + "/" +
im_special[:-4] + "-" + str(radius) +
".key -e 1> tmp/info.txt 2> tmp/errors.txt")
lines = open(
directory + technique + "/" + type + "/" + im[:-4] + "-" +
str(radius) + ".key", "rb").readlines()
finalLines += lines[2:]
count += int(lines[1])
common_functions.organizeFileSurfToDescriptor(directory + technique +
"/" + type + "/" +
im[:-4] + "-" +
str(radius) + ".key")
out = open(directory + technique + "/" + type + "/" + im[:-3] + "key",
"wb")
out.write("129\n" + str(count) + "\n")
for line in finalLines:
out.write(line)
out.close()
def denseExtraction(im, im_special, technique, type, dirImage):
finalLines = []
count = 0
for diametro in [12, 19, 31, 50, 80, 128]:
radius = diametro/2.0
os.system(SURF + " -i " + dirImage + "/" + im_special + " -p1 tmp/dense/points-region-" + str(radius) + "-" + type + ".txt -o " + directory + technique + "/" + type + "/" + im_special[:-4] + "-" + str(radius) + ".key -e 1> tmp/info.txt 2> tmp/errors.txt")
lines = open(directory + technique + "/" + type + "/" + im[:-4] + "-" + str(radius) + ".key","rb").readlines()
finalLines += lines[2:]
count += int(lines[1])
common_functions.organizeFileSurfToDescriptor(directory + technique + "/" + type + "/" + im[:-4] + "-" + str(radius) + ".key")
out = open(directory + technique + "/" + type + "/" + im[:-3] + "key","wb")
out.write("129\n" + str(count) + "\n")
for line in finalLines:
out.write(line)
out.close()
for technique in techniques:
if os.path.exists(directory + technique + "/" + type + "/" +
im[:-3] + "key"):
continue
fAux = open(
directory + technique + "/" + type + "/" + im[:-3] + "key",
"wb")
if technique == "sparse":
sparseExtraction(
im_special, technique, type,
"datasets/" + type + "-images-by-lesions/" +
common_functions.specialName(lesion_en))
common_functions.organizeFileSurfToDescriptor(directory +
technique +
"/" + type +
"/" +
im[:-3] +
"key")
common_functions.filterPoints(type, technique, im)
else:
denseExtraction(
im, im_special, technique, type,
"datasets/" + type + "-images-by-lesions/" +
common_functions.specialName(lesion_en))
common_functions.organizeFileSurfToDescriptor(directory +
technique +
"/" + type +
"/" +
im[:-3] +
"key")
print lesion_en
listImages = os.listdir("datasets/" + type + "-images-by-lesions/" + lesion_en)
start = timeit.default_timer()
for im in listImages:
sys.stdout.write(". ")
sys.stdout.flush()
im_special = common_functions.specialName(im)
for technique in techniques:
if os.path.exists(directory + technique + "/" + type + "/" + im[:-3] + "key"): continue
fAux = open(directory + technique + "/" + type + "/" + im[:-3] + "key","wb")
if technique == "sparse":
sparseExtraction(im_special, technique, type, "datasets/" + type + "-images-by-lesions/" + common_functions.specialName(lesion_en))
common_functions.organizeFileSurfToDescriptor(directory + technique + "/" + type + "/" + im[:-3] + "key")
common_functions.filterPoints(type, technique, im)
else:
denseExtraction(im, im_special, technique, type, "datasets/" + type + "-images-by-lesions/" + common_functions.specialName(lesion_en))
common_functions.organizeFileSurfToDescriptor(directory + technique + "/" + type + "/" + im[:-3] + "key")
stop = timeit.default_timer()
sys.stdout.write(common_functions.convertTime(stop - start) + "\n")
################################################
################################################
# Describe additional images
# (with marked regions but not labeled as normal or disease)
# Only when DR1 is defined as the training dataset
