OutDir = "mid-level/" + techniqueLow + "/DR2/" + techniqueMid + "/additional/" + lesion + "/" for label in ["+1","-1"]: # describe the normal images if label == "-1": lesion = "imagem-normal" lesion_en = en[lesion] if image == "": if type == "DR2" and (lesion == "hemorragia-superficial" or lesion == "hemorragia-profunda"): listImages = os.listdir("datasets/" + type + "-images-by-lesions/Red Lesions") else: listImages = os.listdir("datasets/" + type + "-images-by-lesions/" + lesion_en) else: listImages = [image] for im in listImages: im_special = common_functions.specialName(im) if os.path.exists(OutDir + im[:-3] + "hist"): continue # define the output file (histogram) OutFile = OutDir + im[:-3] + "hist" f = open(OutFile,"wb") # get the points of interest PoIsTemp = open(PoIsDir + im[:-3] + "key","rb").readlines() PoIs = [] for i in range(2,len(PoIsTemp),2): PoIs.append([ float(p) for p in PoIsTemp[i].split() ]) PoIs = numpy.asarray(PoIs) sys.stdout.write(". ") sys.stdout.flush()
else: print "\n\nTest images -", type for lesion in lesions: if type == "DR2" and (lesion == "hemorragia-superficial" or lesion == "hemorragia-profunda"): continue lesion_en = en[lesion] 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 +
print "################################################" for type in [train,test]: if type == train: print "Training images -", type else: print "\n\nTest images -", type for lesion in lesions: if type == "DR2" and (lesion == "hemorragia-superficial" or lesion == "hemorragia-profunda"): continue lesion_en = en[lesion] 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")