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