def train_lbp():
train_dir = "/home/gast/ImageHashing/face_train/"
suffix = ".png"
size = 200
train = []
for fn in sorted(os.listdir(train_dir)):
if fn.endswith(suffix):
rgb = Image.load(train_dir + fn)
gray = Image.rgb2gray(rgb)
resized = Image.resize(gray,size)
img = Image.gray2real(resized)
lbps = local_binary_pattern(img,3,24)
#print(flatten(lbps))
#print(len(flatten(lbps)))
train.append(flatten(lbps))
#print(lbps)
pca = PCA(n_components=100)
#print(len(train))
#print(len(train[0]))
train_np = np.array(train)
#print(train_np.shape)
pca.fit(train_np)
return pca
def train_eigen():
# train_dir = "/home/gast/ImageHashing/face_train/"
train_dir = "/home/gast/ImageHashing/FEC/fec_images_autoadjust/"
suffix = ".png"
size = 200
train = []
for fn in sorted(os.listdir(train_dir)):
if fn.endswith(suffix):
rgb = Image.load(train_dir + fn)
gray = Image.rgb2gray(rgb)
resized = Image.resize(gray,size)
img = Image.gray2real(resized)
#lbps = list(flatten(Lbp.real_hash(img)))
img_flat = list(flatten(img))
# print(len(flatten(lbps)))
train.append(img_flat)
#print(lbps)
t = np.array(train)
#print("len of train"+str(t.shape))
pca = PCA(n_components=100)
pca.fit(train)
return pca
def train_lbp():
train_dir = "/home/gast/ImageHashing/face_train/"
suffix = ".png"
size = 200
train = []
for fn in sorted(os.listdir(train_dir)):
if fn.endswith(suffix):
rgb = Image.load(train_dir + fn)
gray = Image.rgb2gray(rgb)
resized = Image.resize(gray,size)
img = Image.gray2real(resized)
lbps = list(flatten(Lbp.real_hash(img)))
# print(len(flatten(lbps)))
train.append(lbps)
#print(lbps)
pca = PCA(n_components=500)
pca.fit(train)
return pca
def compute_hash(filename):
re = 64
if args.r != None:
re = args.r[0]
rgb = Image.load(filename)
gray = Image.rgb2gray(rgb)
gray_pp = gray
if(args.pp != None and args.pp[0] == 'gb'):
gray_pp = Image.gauss_blur(gray,2)
if(args.pp != None and args.pp[0] == 'tt'):
gray_pp = tt_pipeline(gray)
resized = Image.resize(gray_pp,re)
img = Image.gray2real(resized)
hash_result = []
if(args.cm != None and args.cm[0] == "ham"):
if(args.hm != None):
if args.hm[0] == "ah":
img = Image.resize(img,8)
hash_result = Average.bin_hash(img)
if args.hm[0] == "dh":
img = Image.resize2(img,9,8)
hash_result = Difference.bin_hash(img)
if args.hm[0] == "dct":
hash_result = Dct.bin_hash(img,8,8)
if args.hm[0] == "zh":
hash_result = Zernike.bin_hash(img,0,8)
if args.hm[0] == "pzh":
hash_result = PseudoZernike.bin_hash(img,0,11)
if args.hm[0] == "rash":
print("Radon Hash can only output reals")
if args.hm[0] == "wu":
input_wu = Image.resize(gray,384)
hash_result = Wu.bin_hash(input_wu)
else:
if(args.hm != None):
if args.hm[0] == "ah":
img = Image.resize(img,8)
hash_result = Average.real_hash(img)
if args.hm[0] == "dh":
img = Image.resize2(img,9,8)
hash_result = Difference.real_hash(img)
if args.hm[0] == "dct":
#img1 = exposure.equalize_hist(img)
hash_result = Dct.real_hash(img,8,8)
if args.hm[0] == "zh":
hash_result = Zernike.real_hash(img,0,12)
if args.hm[0] == "pzh":
hash_result = PseudoZernike.real_hash(img,0,11)
if args.hm[0] == "lbp":
hash_result = Lbp.real_hash(img)
if args.hm[0] == "lbp1":
# im = Image.resize2(gray,220,220)
im = Image.resize(gray,220)
im2 = Image.gray2real(im)
img_blur = Image.gauss_blur(im2,2)
hash_result = Lbp.real_hash(img_blur)
#hash_result = Lbp.bin_hash(img_blur)
#if args.hm[0] == "lbp_pca":
# pcad = pickle.load(open("lbp_pca_train.p", "rb" ))
# hash_result = Lbp.pca_hash(img,pcad)
#print(len(hash_result))
#if args.hm[0] == "eigen":
# model = pickle.load(open("eigen_model.p", "rb" ))
# hash_result = eigen.transform_eigen(img,model)
if args.hm[0] == "fft":
hash_result = Fft.fft_hash(img)
if args.hm[0] == "rash":
img = Image.resize(img,63)
hash_result = Radon.real_hash(img)
# default
if hash_result == []:
print("default")
hash_result = Dct.real_hash(img,8,8)
return hash_result