def __init__(self,
patch_types=None,
des_model_dirs=None,
minu_model_dir=None):
self.des_models = None
self.patch_types = patch_types
self.minu_model = None
self.minu_model_dir = minu_model_dir
self.des_model_dirs = des_model_dirs
if self.minu_model_dir is not None:
self.minu_model = (minutiae_AEC.ImportGraph(minu_model_dir))
self.dict, self.spacing, self.dict_all, self.dict_ori, self.dict_spacing = get_maps.construct_dictionary(
ori_num=24)
patchSize = 160
oriNum = 64
self.patchIndexV = descriptor.get_patch_index(patchSize,
patchSize,
oriNum,
isMinu=1)
if self.des_model_dirs is not None:
self.des_models = []
for model_dir in self.des_model_dirs:
self.des_models.append(descriptor.ImportGraph(model_dir))
self.patch_size = 96
# self.minu_models = []
# for model_dir in self.des_model_dirs:
# self.minu_models.append(descriptor.ImportGraph(model_dir))
self.gabor_filters = filtering.get_gabor_filters()
def __init__(self,
patch_types=None,
des_model_dirs=None,
minu_model_dir=None):
self.des_models = None
self.patch_types = patch_types
self.minu_model = None
self.minu_model_dir = minu_model_dir
self.des_model_dirs = des_model_dirs
if self.minu_model_dir is not None:
self.minu_model = (minutiae_AEC.ImportGraph(minu_model_dir))
self.dict, self.spacing, self.dict_all, self.dict_ori, self.dict_spacing = get_maps.construct_dictionary(
ori_num=24)
patchSize = 160
oriNum = 64
if des_model_dirs is not None and len(des_model_dirs) > 0:
self.patchIndexV = descriptor.get_patch_index(patchSize,
patchSize,
oriNum,
isMinu=1)
if self.des_model_dirs is not None:
self.des_models = []
for model_dir in self.des_model_dirs:
self.des_models.append(
descriptor.ImportGraph(model_dir,
input_name="inputs:0",
output_name='embedding:0'))
self.patch_size = 96
def __init__(self,patch_types=None,des_model_dirs=None,minu_model_dir=None, enhancement_model_dir = None, ROI_model_dir=None):
self.des_models = None
self.patch_types = patch_types
self.minu_model = None
self.minu_model_dir = minu_model_dir
self.des_model_dirs = des_model_dirs
self.enhancement_model_dir = enhancement_model_dir
self.ROI_model_dir = ROI_model_dir
self.dict, self.spacing, self.dict_all, self.dict_ori, self.dict_spacing = get_maps.construct_dictionary(ori_num=60)
if self.minu_model_dir is not None:
self.minu_model = (minutiae_AEC.ImportGraph(minu_model_dir))
patchSize = 160
oriNum = 64
self.patchIndexV = descriptor.get_patch_index(patchSize, patchSize, oriNum, isMinu=1)
if self.des_model_dirs is not None:
self.des_models = []
for model_dir in self.des_model_dirs:
self.des_models.append(descriptor.ImportGraph(des_model_dirs))
if self.enhancement_model_dir is not None:
self.enhancement_model = enhancement_AEC.ImportGraph(enhancement_model_dir)
if self.ROI_model_dir is not None:
self.ROI_model = (RCNN.ImportGraph(ROI_model_dir))
def __init__(self,
patch_types=None,
des_model_dirs=None,
minu_model_dir=None):
self.des_models = None
self.patch_types = patch_types
self.minu_model = None
self.minu_model_dir = minu_model_dir
self.des_model_dirs = des_model_dirs
print("Loading models, this may take some time...")
if self.minu_model_dir is not None:
print("Loading minutiae model: " + minu_model_dir)
self.minu_model = (minutiae_AEC.ImportGraph(minu_model_dir))
self.dict, self.spacing, self.dict_all, self.dict_ori, self.dict_spacing = get_maps.construct_dictionary(
ori_num=24)
patchSize = 160
oriNum = 64
if des_model_dirs is not None and len(des_model_dirs) > 0:
self.patchIndexV = descriptor.get_patch_index(patchSize,
patchSize,
oriNum,
isMinu=1)
if self.des_model_dirs is not None:
self.des_models = []
for i, model_dir in enumerate(des_model_dirs):
print("Loading descriptor model (" + str(i + 1) + " of " +
str(len(des_model_dirs)) + "): " + model_dir)
self.des_models.append(
descriptor.ImportGraph(model_dir,
input_name="inputs:0",
output_name='embedding:0'))
self.patch_size = 96
def __init__(self,
patch_types=None,
des_model_dirs=None,
minu_model_dirs=None,
enhancement_model_dir=None,
ROI_model_dir=None,
coarsenet_dir=None,
FineNet_dir=None):
self.des_models = None
self.patch_types = patch_types
self.minu_model = None
self.minu_model_dirs = minu_model_dirs
self.des_model_dirs = des_model_dirs
self.enhancement_model_dir = enhancement_model_dir
self.ROI_model_dir = ROI_model_dir
self.dict, self.spacing, self.dict_all, self.dict_ori, self.dict_spacing = get_maps.construct_dictionary(
ori_num=60)
print("Loading models, this may take some time...")
if self.minu_model_dirs is not None:
self.minu_model = []
for i, minu_model_dir in enumerate(minu_model_dirs):
print("Loading minutiae model (" + str(i + 1) + " of " +
str(len(minu_model_dirs)) + "): " + minu_model_dir)
self.minu_model.append(
minutiae_AEC.ImportGraph(minu_model_dir))
self.coarsenet_dir = coarsenet_dir
patchSize = 160
oriNum = 64
self.patchIndexV = descriptor.get_patch_index(patchSize,
patchSize,
oriNum,
isMinu=1)
if self.des_model_dirs is not None:
self.des_models = []
for i, model_dir in enumerate(des_model_dirs):
print("Loading descriptor model (" + str(i + 1) + " of " +
str(len(des_model_dirs)) + "): " + model_dir)
self.des_models.append(
descriptor.ImportGraph(model_dir,
input_name="inputs:0",
output_name='embedding:0'))
if self.enhancement_model_dir is not None:
print("Loading enhancement model: " + self.enhancement_model_dir)
self.enhancement_model = enhancement_AEC.ImportGraph(
enhancement_model_dir)
print("Finished loading models.")
def demo_minutiae_extraction(img_path,minu_model_dir):
img_files = glob.glob(img_path+'*.bmp')
img_files.sort()
minu_model = (minutiae_AEC.ImportGraph(minu_model_dir))
block = True
for i, img_file in enumerate(img_files):
if i<11:
continue
img = io.imread(img_file)
name = os.path.basename(img_file)
h, w = img.shape
mask = np.ones((h,w),dtype=np.uint8)
minu_thr = 0.3
texture_img = preprocessing.FastCartoonTexture(img)
contrast_img = preprocessing.local_constrast_enhancement_gaussian(img)
dir_map, fre_map = get_maps.get_maps_STFT(contrast_img, patch_size=64, block_size=16, preprocess=True)
dict, spacing,_ = get_maps.construct_dictionary(ori_num=60)
quality_map, fre_map = get_maps.get_quality_map_ori_dict(contrast_img, dict, spacing,
dir_map=dir_map,
block_size=16)
enh_texture_img = filtering.gabor_filtering_pixel(contrast_img, dir_map + math.pi / 2, fre_map,
mask=mask,
block_size=16, angle_inc=3)
mnt = minu_model.run(contrast_img, minu_thr=0.1)
#mnt = minu_model.remove_spurious_minutiae(mnt, mask)
#minutiae_sets.append(mnt)
#fname = output_path + os.path.splitext(name)[0] + '_texture_img.jpeg'
show.show_minutiae(contrast_img, mnt, block=block, fname=None)
mnt = minu_model.run(texture_img, minu_thr=0.1)
# mnt = minu_model.remove_spurious_minutiae(mnt, mask)
# minutiae_sets.append(mnt)
# fname = output_path + os.path.splitext(name)[0] + '_texture_img.jpeg'
show.show_minutiae(texture_img, mnt, block=block, fname=None)
print(i)
ax0.imshow(image, cmap='gray')
ax0.set_title('original image')
ax0.axis('off')
ax0.set_adjustable('box-forced')
ax1.imshow(dilated, vmin=image.min(), vmax=image.max(), cmap='gray')
ax1.set_title('dilated')
ax1.axis('off')
ax1.set_adjustable('box-forced')
ax2.imshow(image - dilated, cmap='gray')
ax2.set_title('image - dilated')
ax2.axis('off')
ax2.set_adjustable('box-forced')
fig.tight_layout()
plt.show()
if __name__ == '__main__':
# construct ridge structure dictionary for quality estimation or ridge spacing estimation
dict = get_maps.construct_dictionary()
imgfiles = glob.glob('/home/kaicao/Dropbox/Research/Data/Latent/NISTSD27/image/*.bmp')
imgfiles.sort()
for imgfile in imgfiles:
img = cv2.imread(imgfile, cv2.IMREAD_GRAYSCALE)
img = np.asarray(img, np.float32)
texture = FastCartoonTexture(img)
dir_map, _, _ = get_maps.get_maps_STFT(texture, patch_size=64, block_size=16, preprocess=False)