def gera_GABOR_GLCM_LPB_features(sujeito):
lstImagens = input.load_image('./Publication_Dataset/' + sujeito +
'/TIFFs/8bitTIFFs/')
volumeFeatures = []
for imagem in lstImagens:
frame_denoise = input.apply_filter(imagem, 'anisotropic')
bdValue, new = flatten.flat_image(frame_denoise)
crop = cropping.croppy_mona(new, bdValue)
image_features = []
image_features += features_extraction.apply_gabor(crop).tolist()
crop2 = crop.astype(int)
image_features += features_extraction.apply_glcm(crop2)
image_features += features_extraction.apply_lbp(crop).tolist()
volumeFeatures += image_features
# features_extraction.apply_sift(crop)
# lstSIFTFeatures.append(features_extraction.apply_sift(crop))
print('gabor', len(volumeFeatures))
# dictionary = features_extraction.apply_BOW(lstSIFTFeatures)
fileObject = open('./gabor_glcm_lbp_repLine/' + sujeito, 'wb')
if (fileObject != None):
print('salvando...')
pickle.dump(volumeFeatures, fileObject)
fileObject.close
def testarEnquadramento():
lstImagens = input.load_image(
'./Publication_Dataset/AMD6/TIFFs/8bitTIFFs/')
for imagem in lstImagens:
# frame_gauss = input.apply_filter(imagem,'gauss')
frame_dif = input.apply_filter(imagem, 'anisotropic')
bdValue, new = flatten.flat_image(frame_dif)
crop = cropping.croppy_mona(new, bdValue)
def extractFeatures(sujeito):
lstImagens = input.load_image('./base_interpol/' + sujeito)
lstGeoFeatures = []
for imagem in lstImagens:
imagem = np.asarray(imagem)
frame_denoise = input.apply_filter(imagem, 'anisotropic')
bdValue, new = flatten.flat_image(frame_denoise)
crop = cropping.croppy_mona(new, bdValue)
lstGeoFeatures.append(geo.run((crop), [crop.shape[0], crop.shape[1]]))
print('geo', len(lstGeoFeatures), len(lstGeoFeatures[0]))
# dictionary = features_extraction.apply_BOW(lstSIFTFeatures)
fileObject = open('./geo_features/' + sujeito, 'wb')
if (fileObject != None):
print('salvando...')
pickle.dump(lstGeoFeatures, fileObject)
fileObject.close
def geraGABORFeatures(sujeito):
lstImagens = input.load_image('./Publication_Dataset/' + sujeito +
'/TIFFs/8bitTIFFs/')
lstGABORFeatures = []
for imagem in lstImagens:
frame_denoise = input.apply_filter(imagem, 'anisotropic')
bdValue, new = flatten.flat_image(frame_denoise)
crop = cropping.croppy_mona(new, bdValue)
lstGABORFeatures.append(features_extraction.apply_gabor(crop).tolist())
# features_extraction.apply_sift(crop)
# lstSIFTFeatures.append(features_extraction.apply_sift(crop))
print('gabor', len(lstGABORFeatures), len(lstGABORFeatures[0]))
# dictionary = features_extraction.apply_BOW(lstSIFTFeatures)
fileObject = open('./gabor_features/' + sujeito, 'wb')
if (fileObject != None):
print('salvando...')
pickle.dump(lstGABORFeatures, fileObject)
fileObject.close
print('tam:', len(volumeInLine))
featuresGeo.append(volumeInLine)
vetLabelsGeo.append(getClass(vol))
print('Gerando arff file for glcm', len(vetLabelsGeo), len(featuresGeo))
arffGenerator.createArffFile('./geo_features/GEODATASET',
featuresGeo, vetLabelsGeo, 'DME,NORMAL',
len(featuresGeo[0]))
# getBaseFeatures()
# extractFeatures("DME7")
# loadFeatures()
# import math
lstImagens = input.load_image('./base_interpol/DME7')
imagem = lstImagens[2]
# print lstImagens[3][79][1]
# print lstImagens[3][76][0]
# map(lambda x: x if x<255 else 0, imagem)
imagem = np.asarray(imagem)
frame_denoise = input.apply_filter(imagem, 'anisotropic')
bdValue, new = flatten.flat_image(frame_denoise)
crop = cropping.croppy_mona(new, bdValue)
plt.figure()
plt.subplot(121)
plt.imshow(crop, 'gray')
plt.show()