def feature_extraction_single_rolled(self, img_file):
block_size = 16
if not os.path.exists(img_file):
return None
img = io.imread(img_file)
h, w = img.shape
mask = get_maps.get_quality_map_intensity(img)
#if np.max(mask) == 0:
# print img_file
#return None
start = timeit.default_timer()
mnt = self.minu_model.run_whole_image(img, minu_thr=0.2)
stop = timeit.default_timer()
minu_time = stop - start
# show.show_minutiae(img, mnt, mask=mask, block=True, fname=None)
# show.show_minutiae(img,mnt)
# start = timeit.default_timer()
des = descriptor.minutiae_descriptor_extraction(
img,
mnt,
self.patch_types,
self.des_models,
self.patchIndexV,
batch_size=256,
patch_size=self.patch_size)
# stop = timeit.default_timer()
# des_time = stop - start
print minu_time #, des_time
#texture_img = preprocessing.FastCartoonTexture(img, sigma=2.5, show=False)
dir_map, _ = get_maps.get_maps_STFT(img,
patch_size=64,
block_size=block_size,
preprocess=True)
#stop = timeit.default_timer()
blkH = h // block_size
blkW = w // block_size
# dir_map = np.zeros((blkH,blkW))
# print stop - start
#blkH, blkW = dir_map.shape
minu_template = template.MinuTemplate(h=h,
w=w,
blkH=blkH,
blkW=blkW,
minutiae=mnt,
des=des,
oimg=dir_map,
mask=mask)
rolled_template = template.Template()
rolled_template.add_minu_template(minu_template)
return rolled_template
def feature_extraction_longitudinal(self, img_file):
block_size = 16
img = io.imread(img_file)
#print img.shape
img = preprocessing.adjust_image_size(img, block_size)
h, w = img.shape
texture_img = preprocessing.FastCartoonTexture(img,
sigma=2.5,
show=False)
contrast_img_guassian = preprocessing.local_constrast_enhancement_gaussian(
img)
mask = get_maps.get_quality_map_intensity(img)
#show.show_mask(mask, img, fname=None, block=True)
quality_map, dir_map, fre_map = get_maps.get_quality_map_dict(
texture_img,
self.dict_all,
self.dict_ori,
self.dict_spacing,
block_size=16,
process=False)
enh_constrast_img = filtering.gabor_filtering_pixel(
contrast_img_guassian,
dir_map + math.pi / 2,
fre_map,
mask=np.ones((h, w), np.int),
block_size=16,
angle_inc=3)
mnt = self.minu_model.run(img, minu_thr=0.2)
#show.show_minutiae(img,mnt)
des = descriptor.minutiae_descriptor_extraction(img,
mnt,
self.patch_types,
self.des_models,
self.patchIndexV,
batch_size=128)
blkH, blkW = dir_map.shape
minu_template = template.MinuTemplate(h=h,
w=w,
blkH=blkH,
blkW=blkW,
minutiae=mnt,
des=des,
oimg=dir_map,
mask=mask)
rolled_template = template.Template()
rolled_template.add_minu_template(minu_template)
return rolled_template, texture_img, enh_constrast_img
def feature_extraction_single_rolled(self, img_file):
block_size = 16
img = io.imread(img_file)
h, w = img.shape
mask = get_maps.get_quality_map_intensity(img)
mnt = self.minu_model.run(img, minu_thr=0.2)
# show.show_minutiae(img,mnt)
des = descriptor.minutiae_descriptor_extraction(img,
mnt,
self.patch_types,
self.des_models,
self.patchIndexV,
batch_size=128)
#texture_img = preprocessing.FastCartoonTexture(img, sigma=2.5, show=False)
start = timeit.default_timer()
dir_map, _ = get_maps.get_maps_STFT(img,
patch_size=64,
block_size=block_size,
preprocess=True)
stop = timeit.default_timer()
print stop - start
blkH, blkW = dir_map.shape
minu_template = template.MinuTemplate(h=h,
w=w,
blkH=blkH,
blkW=blkW,
minutiae=mnt,
des=des,
oimg=dir_map,
mask=mask)
rolled_template = template.Template()
rolled_template.add_minu_template(minu_template)
return rolled_template
def feature_extraction_single_rolled_enhancement(self, img_file):
block_size = 16
img = io.imread(img_file)
# print img.shape
img = preprocessing.adjust_image_size(img, block_size)
h, w = img.shape
#texture_img = preprocessing.FastCartoonTexture(img, sigma=2.5, show=False)
contrast_img_guassian = preprocessing.local_constrast_enhancement_gaussian(
img)
mask = get_maps.get_quality_map_intensity(img)
#show.show_mask(mask, img, fname=None, block=True)
start = timeit.default_timer()
quality_map, dir_map, fre_map = get_maps.get_quality_map_dict(
contrast_img_guassian,
self.dict_all,
self.dict_ori,
self.dict_spacing,
block_size=16,
process=False)
stop = timeit.default_timer()
OF_time = stop - start
print 'of estimate time: %f' % (OF_time)
start = timeit.default_timer()
enh_constrast_img = filtering.gabor_filtering_pixel2(
contrast_img_guassian,
dir_map + math.pi / 2,
fre_map,
mask=mask,
gabor_filters=self.gabor_filters)
stop = timeit.default_timer()
filtering_time = stop - start
print 'filtering time: %f' % (filtering_time)
mnt = self.minu_model.run_whole_image(img, minu_thr=0.2)
# show.show_minutiae(img,mnt)
des = descriptor.minutiae_descriptor_extraction(img,
mnt,
self.patch_types,
self.des_models,
self.patchIndexV,
batch_size=128)
blkH, blkW = dir_map.shape
minu_template = template.MinuTemplate(h=h,
w=w,
blkH=blkH,
blkW=blkW,
minutiae=mnt,
des=des,
oimg=dir_map,
mask=mask)
rolled_template = template.Template()
rolled_template.add_minu_template(minu_template)
# texture templates
stride = 32
x = np.arange(24, w - 24, stride)
y = np.arange(24, h - 24, stride)
virtual_minutiae = []
distFromBg = scipy.ndimage.morphology.distance_transform_edt(mask)
for y_i in y:
for x_i in x:
if (distFromBg[y_i][x_i] <= 16):
continue
ofY = int(y_i / 16)
ofX = int(x_i / 16)
ori = -dir_map[ofY][ofX]
# print("ori = " + str(ori))
virtual_minutiae.append([x_i, y_i,
ori]) #, distFromBg[y_i,x_i]
virtual_minutiae = np.asarray(virtual_minutiae)
if len(virtual_minutiae) > 3:
virtual_des = descriptor.minutiae_descriptor_extraction(
img,
virtual_minutiae,
self.patch_types,
self.des_models,
self.patchIndexV,
batch_size=128)
#show.show_minutiae(img,virtual_minutiae)
texture_template = template.TextureTemplate(
h=h,
w=w,
minutiae=virtual_minutiae,
des=virtual_des,
mask=mask)
rolled_template.add_texture_template(texture_template)
return rolled_template, enh_constrast_img
def feature_extraction_single_latent(raw_img_file,
AEC_img_file,
mask_file,
patch_types=None,
des_models=None):
###
# input:
# raw_img, original latent image
# AEC_img, enhanced latent image by Autoencoder
# mask: ROI
# main idea:
# 1) Use AEC_img to estimate ridge flow and ridge spacing
# 2) use AEC_image and raw_img to extract two different minutiae set
###
raw_img = io.imread(raw_img_file)
AEC_img = io.imread(AEC_img_file)
mask = io.imread(mask_file)
#mask = mask_dilation(mask, block_size=16)
texture_img = preprocessing.FastCartoonTexture(raw_img,
sigma=2.5,
show=False)
dir_map, fre_map, rec_img = get_maps.get_maps_STFT(AEC_img,
patch_size=64,
block_size=16,
preprocess=True)
descriptor_img = filtering.gabor_filtering_pixel(texture_img,
dir_map + math.pi / 2,
fre_map,
mask=mask,
block_size=16,
angle_inc=3)
bin_img = binarization.binarization(texture_img,
dir_map,
block_size=16,
mask=mask)
enhanced_img = filtering.gabor_filtering_block(bin_img,
dir_map + math.pi / 2,
fre_map,
patch_size=64,
block_size=16)
enhanced_img = filtering.gabor_filtering_block(enhanced_img,
dir_map + math.pi / 2,
fre_map,
patch_size=64,
block_size=16)
# plt.subplot(131), plt.imshow(raw_img, cmap='gray')
# plt.title('Input image'), plt.xticks([]), plt.yticks([])
# plt.subplot(132), plt.imshow(descriptor_img, cmap='gray')
# plt.title('Feature image'), plt.xticks([]), plt.yticks([])
#
# plt.subplot(133), plt.imshow(enhanced_img, cmap='gray')
# plt.title('Feature image'), plt.xticks([]), plt.yticks([])
# plt.show(block=True)
# plt.close()
enhanced_AEC_img = filtering.gabor_filtering_block(AEC_img,
dir_map + math.pi / 2,
fre_map,
patch_size=64,
block_size=16)
bin_img = binarization.binarization(enhanced_AEC_img,
dir_map,
block_size=16,
mask=mask)
# plt.imshow(AEC_img,cmap='gray')
# plt.show()
# plt.close()
bin_img2 = 1 - bin_img
thin_img = skeletonize(bin_img2)
# thin_img2 = thin_img.astype(np.uint8)
# thin_img2[thin_img2 > 0] = 255
mnt, thin_img2 = crossnumber.extract_minutiae(1 - thin_img,
mask=mask,
R=10)
crossnumber.show_minutiae(thin_img, mnt)
patchSize = 160
oriNum = 64
patchIndexV = descriptor.get_patch_index(patchSize,
patchSize,
oriNum,
isMinu=1)
if len(descriptor_img.shape) == 2:
h, w = descriptor_img.shape
ret = np.empty((h, w, 3), dtype=np.float)
ret[:, :, :] = descriptor_img[:, :, np.newaxis]
descriptor_img = ret
if len(enhanced_AEC_img.shape) == 2:
h, w = enhanced_AEC_img.shape
ret = np.empty((h, w, 3), dtype=np.float)
ret[:, :, :] = enhanced_AEC_img[:, :, np.newaxis]
enhanced_AEC_img = ret
des = descriptor.minutiae_descriptor_extraction(enhanced_AEC_img,
mnt,
patch_types,
des_models,
patchIndexV,
batch_size=128)
h, w = mask.shape
blkH, blkW = dir_map.shape
minu_template = template.MinuTemplate(h=h,
w=w,
blkH=blkH,
blkW=blkW,
minutiae=mnt,
des=des,
oimg=dir_map,
mask=mask)
latent_template = template.Template()
latent_template.add_minu_template(minu_template)
print des
def feature_extraction_single_latent_evaluation(self,img_file, mask_file, AEC_img_file,output_path = None ):
img = io.imread(img_file)
name = os.path.basename(img_file)
AEC_img = io.imread(AEC_img_file)
mask = io.imread(mask_file)
h,w = mask.shape
#mask = mask_dilation(mask, block_size=16)
latent_template = template.Template()
block = False
minu_thr = 0.3
contrast_img = preprocessing.local_constrast_enhancement(img)
# Two ways for orientation field estimation
# Use the AEC_img and STFT on texture image
dir_map_sets = []
texture_img = preprocessing.FastCartoonTexture(img, sigma=2.5, show=False)
dir_map, fre_map = get_maps.get_maps_STFT(texture_img, patch_size=64, block_size=16, preprocess=True)
dir_map_sets.append(dir_map)
blkH, blkW = dir_map.shape
dir_map, fre_map = get_maps.get_maps_STFT(AEC_img, patch_size=64, block_size=16, preprocess=True)
dir_map_sets.append(dir_map)
#dir_map, fre_map = get_maps.get_maps_STFT(contrast_img, patch_size=64, block_size=16, preprocess=True)
#dir_map_sets.append(dir_map)
# based on the OF, we can use texture image and AEC image for frequency field estimation
fre_map_sets = []
quality_map, fre_map = get_maps.get_quality_map_ori_dict(AEC_img, self.dict, self.spacing, dir_map=dir_map_sets[0],
block_size=16)
fre_map_sets.append(fre_map)
quality_map, fre_map = get_maps.get_quality_map_ori_dict(contrast_img, self.dict, self.spacing,
dir_map=dir_map_sets[1],
block_size=16)
fre_map_sets.append(fre_map)
descriptor_imgs = [texture_img]
descriptor_imgs.append(contrast_img)
enh_texture_img = filtering.gabor_filtering_pixel(texture_img, dir_map + math.pi / 2, fre_map_sets[0], mask=mask,
block_size=16, angle_inc=3)
descriptor_imgs.append(enh_texture_img)
enh_contrast_img = filtering.gabor_filtering_pixel(contrast_img, dir_map + math.pi / 2, fre_map_sets[1], mask=mask,
block_size=16, angle_inc=3)
descriptor_imgs.append(enh_contrast_img)
minutiae_sets = []
mnt = self.minu_model.run(texture_img, minu_thr=0.1)
mnt = self.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=fname)
mnt = self.minu_model.run(contrast_img, minu_thr=0.1)
mnt = self.remove_spurious_minutiae(mnt, mask)
minutiae_sets.append(mnt)
fname = output_path + os.path.splitext(name)[0] + '_contrast_img.jpeg'
show.show_minutiae(contrast_img, mnt, block=block, fname=fname)
mnt = self.minu_model.run(enh_texture_img, minu_thr=minu_thr)
mnt = self.remove_spurious_minutiae(mnt, mask)
minutiae_sets.append(mnt)
fname = output_path + os.path.splitext(name)[0] + '_enh_texture_img.jpeg'
show.show_minutiae(enh_texture_img, mnt, block=block, fname=fname)
mnt = self.minu_model.run(enh_contrast_img, minu_thr=minu_thr)
mnt = self.remove_spurious_minutiae(mnt, mask)
minutiae_sets.append(mnt)
fname = output_path + os.path.splitext(name)[0] + '_enh_contrast_img.jpeg'
show.show_minutiae(enh_contrast_img, mnt, block=block, fname=fname)
mnt = self.minu_model.run(AEC_img, minu_thr=minu_thr)
mnt = self.remove_spurious_minutiae(mnt, mask)
minutiae_sets.append(mnt)
fname = output_path + os.path.splitext(name)[0] + '_AEC_img.jpeg'
show.show_minutiae(AEC_img, mnt, block=block, fname=fname)
for mnt in minutiae_sets:
for des_img in descriptor_imgs:
des = descriptor.minutiae_descriptor_extraction(des_img, mnt, self.patch_types, self.des_models,
self.patchIndexV,batch_size=128)
minu_template = template.MinuTemplate(h=h, w=w, blkH=blkH, blkW=blkW, minutiae=mnt,
des=des, oimg=dir_map_sets[1], mask=mask)
latent_template.add_minu_template(minu_template)
return latent_template
def feature_extraction_single_latent(self,img_file, output_path = None, show_processes=False ):
#block = True
img = io.imread(img_file)
name = os.path.basename(img_file)
mask_CNN,_ = self.ROI_model.run(img)
h,w = mask_CNN.shape
#mask = mask_dilation(mask, block_size=16)
latent_template = template.Template()
minu_thr = 0.3
# template set 1: no ROI and enhancement are required
# texture image is used for coase segmentation
descriptor_imgs = []
texture_img = preprocessing.FastCartoonTexture(img, sigma=2.5, show=False)
descriptor_imgs.append(texture_img)
contrast_img_guassian = preprocessing.local_constrast_enhancement_gaussian(img)
quality_map, _, _ = get_maps.get_quality_map_dict(texture_img, self.dict_all, self.dict_ori,
self.dict_spacing, block_size=16, process=False)
quality_map_pixel = cv2.resize(quality_map, (0, 0), fx=16, fy=16)
#plt.imshow(quality_map_pixel,cmap='gray')
#plt.show()
mask_coarse = quality_map_pixel > 0.3
mask_coarse = mask_coarse.astype(np.int)
mask = mask_coarse * mask_CNN
# show.show_mask(mask_CNN, img, fname='mask_RCNN.jpeg',block=block)
# show.show_mask(mask_coarse,img,fname = 'mask_coarse.jpeg',block=block)
# show.show_mask(mask, img, fname='mask.jpeg',block=block)
#show.show_mask(mask, AEC_img, fname='mask_AEC.jpeg',block=block)
# plt.imshow(AEC_img,cmap = 'gray')
# plt.show(block=block)
# plt.close()
#show.show_mask(mask_CNN, img, fname='mask_RCNN.jpeg',block=block)
# AEC_img[mask == 0] = 128
# plt.imshow(AEC_img, cmap='gray')
# plt.show(block=block)
# plt.close()
AEC_img = self.enhancement_model.run(texture_img)
quality_map, dir_map, fre_map = get_maps.get_quality_map_dict(AEC_img, self.dict_all, self.dict_ori,self.dict_spacing, block_size=16, process=False)
blkH, blkW = dir_map.shape
if show_processes:
show.show_orientation_field(img, dir_map,mask = mask,fname='OF.jpeg')
# mnt = self.minu_model.run(contrast_img_mean, minu_thr=0.1)
# mnt = self.remove_spurious_minutiae(mnt, mask)
# minutiae_sets.append(mnt)
#
# fname = output_path + os.path.splitext(name)[0] + '_contrast_img_mean.jpeg'
# show.show_minutiae(contrast_img_mean, mnt, block=block, fname=fname)
enh_contrast_img = filtering.gabor_filtering_pixel(contrast_img_guassian, dir_map + math.pi / 2, fre_map,
mask=mask,
block_size=16, angle_inc=3)
enh_texture_img = filtering.gabor_filtering_pixel(texture_img, dir_map + math.pi / 2, fre_map,
mask=mask,
block_size=16, angle_inc=3)
if show_processes:
show.show_image(texture_img, mask=mask, block=True, fname='cropped_texture_image.jpeg')
show.show_image(AEC_img, mask=mask, block=True, fname='cropped_AEC_image.jpeg')
show.show_image(enh_contrast_img, mask=mask, block=True, fname='cropped_enh_image.jpeg')
#np.ones((h, w), np.int)
descriptor_imgs.append(enh_contrast_img)
quality_map2, _ , _ = get_maps.get_quality_map_dict(enh_contrast_img, self.dict_all,self.dict_ori,self.dict_spacing, block_size=16,
process=False)
quality_map_pixel2 = cv2.resize(quality_map2, (0, 0), fx=16, fy=16)
mask2 = quality_map_pixel2 > 0.50
#mask = mask*mask2
minutiae_sets = []
mnt = self.minu_model.run(contrast_img_guassian, minu_thr=0.05)
mnt = self.remove_spurious_minutiae(mnt, mask)
minutiae_sets.append(mnt)
if show_processes:
fname = 'minutiae_texture_img.jpeg'
show.show_minutiae(texture_img, mnt, mask=mask,block=block, fname=fname)
mnt = self.minu_model.run(AEC_img, minu_thr=0.3)
mnt = self.remove_spurious_minutiae(mnt, mask2)
minutiae_sets.append(mnt)
if show_processes:
fname = 'minutiae_AEC_img.jpeg'
show.show_minutiae(AEC_img, mnt, mask=mask, block=block, fname=fname)
mnt = self.minu_model.run(enh_contrast_img, minu_thr=0.3)
mnt = self.remove_spurious_minutiae(mnt, mask2)
minutiae_sets.append(mnt)
if show_processes:
fname = 'minutiae_enh_contrast_img.jpeg'
show.show_minutiae(enh_contrast_img, mnt, mask=mask,block=block, fname=fname)
mnt = self.minu_model.run(enh_texture_img, minu_thr=0.3)
mnt = self.remove_spurious_minutiae(mnt, mask2)
minutiae_sets.append(mnt)
# minutiae template 1
des = descriptor.minutiae_descriptor_extraction(texture_img, minutiae_sets[0], self.patch_types, self.des_models,
self.patchIndexV, batch_size=128)
minu_template = template.MinuTemplate(h=h, w=w, blkH=blkH, blkW=blkW, minutiae=minutiae_sets[0],
des=des, oimg=dir_map, mask=mask)
latent_template.add_minu_template(minu_template)
# minutiae template 2
des = descriptor.minutiae_descriptor_extraction(texture_img, minutiae_sets[1], self.patch_types,
self.des_models,
self.patchIndexV, batch_size=128)
minu_template = template.MinuTemplate(h=h, w=w, blkH=blkH, blkW=blkW, minutiae=minutiae_sets[1],
des=des, oimg=dir_map, mask=mask)
latent_template.add_minu_template(minu_template)
# minutiae template 3
des = descriptor.minutiae_descriptor_extraction(enh_texture_img, minutiae_sets[2], self.patch_types,
self.des_models,
self.patchIndexV, batch_size=128)
minu_template = template.MinuTemplate(h=h, w=w, blkH=blkH, blkW=blkW, minutiae=minutiae_sets[2],
des=des, oimg=dir_map, mask=mask)
latent_template.add_minu_template(minu_template)
# minutiae template 4
des = descriptor.minutiae_descriptor_extraction(enh_texture_img, minutiae_sets[3], self.patch_types,
self.des_models,
self.patchIndexV, batch_size=128)
minu_template = template.MinuTemplate(h=h, w=w, blkH=blkH, blkW=blkW, minutiae=minutiae_sets[3],
des=des, oimg=dir_map, mask=mask)
latent_template.add_minu_template(minu_template)
return latent_template
def feature_extraction_single_latent_evaluation_AEM18T(self,img_file, mask_file, AEC_img_file,output_path = None ):
img = io.imread(img_file)
name = os.path.basename(img_file)
AEC_img = io.imread(AEC_img_file)
mask_CNN = io.imread(mask_file)
h,w = mask_CNN.shape
#mask = mask_dilation(mask, block_size=16)
latent_template = template.Template()
block = False
minu_thr = 0.3
# template set 1: no ROI and enhancement are required
# texture image is used for coase segmentation
descriptor_imgs = []
texture_img = preprocessing.FastCartoonTexture(img, sigma=2.5, show=False)
descriptor_imgs.append(texture_img)
contrast_img_mean = preprocessing.local_constrast_enhancement(img)
contrast_img_guassian = preprocessing.local_constrast_enhancement_gaussian(img)
quality_map, dir_map, fre_map = get_maps.get_quality_map_dict(texture_img, self.dict_all, self.dict_ori,
self.dict_spacing, block_size=16, process=False)
quality_map_pixel = cv2.resize(quality_map, (0, 0), fx=16, fy=16)
mask_coarse = quality_map_pixel > 0.3
mask_coarse = mask_coarse.astype(np.int)
quality_map, dir_map, fre_map = get_maps.get_quality_map_dict(AEC_img, self.dict_all, self.dict_ori,self.dict_spacing, block_size=16, process=False)
minutiae_sets = []
mnt = self.minu_model.run(texture_img, minu_thr=0.1)
mnt = self.remove_spurious_minutiae(mnt, mask_coarse)
minutiae_sets.append(mnt)
fname = output_path + os.path.splitext(name)[0] + '_texture_img.jpeg'
show.show_minutiae(texture_img, mnt, block=block, fname=fname)
mnt = self.minu_model.run(contrast_img_mean, minu_thr=0.1)
mnt = self.remove_spurious_minutiae(mnt, mask_coarse)
minutiae_sets.append(mnt)
fname = output_path + os.path.splitext(name)[0] + '_contrast_img_mean.jpeg'
show.show_minutiae(contrast_img_mean, mnt, block=block, fname=fname)
mnt = self.minu_model.run(contrast_img_guassian, minu_thr=0.1)
mnt = self.remove_spurious_minutiae(mnt, mask_coarse)
minutiae_sets.append(mnt)
fname = output_path + os.path.splitext(name)[0] + '_contrast_img_guassian.jpeg'
show.show_minutiae(contrast_img_guassian, mnt, block=block, fname=fname)
#show.show_orientation_field(AEC_img,dir_map)
enh_texture_img = filtering.gabor_filtering_pixel(texture_img, dir_map + math.pi / 2, fre_map,
mask=np.ones((h, w), np.int),
block_size=16, angle_inc=3)
descriptor_imgs.append(enh_texture_img)
enh_constrast_img = filtering.gabor_filtering_pixel(contrast_img_guassian, dir_map + math.pi / 2, fre_map,
mask=np.ones((h,w),np.int),
block_size=16, angle_inc=3)
descriptor_imgs.append(enh_constrast_img)
quality_map2, _ , _ = get_maps.get_quality_map_dict(enh_texture_img, self.dict_all,self.dict_ori,self.dict_spacing, block_size=16,
process=False)
quality_map_pixel2 = cv2.resize(quality_map2, (0, 0), fx=16, fy=16)
mask = quality_map_pixel2 > 0.55
mask = mask.astype(np.int)
mask = mask_coarse * mask
mask = mask * mask_CNN
mnt = self.minu_model.run(AEC_img, minu_thr=0.3)
mnt = self.remove_spurious_minutiae(mnt, mask)
minutiae_sets.append(mnt)
fname = output_path + os.path.splitext(name)[0] + '_AEC_img.jpeg'
show.show_minutiae(AEC_img, mnt, block=block, fname=fname)
mnt = self.minu_model.run(enh_texture_img, minu_thr=0.3)
mnt = self.remove_spurious_minutiae(mnt, mask)
minutiae_sets.append(mnt)
fname = output_path + os.path.splitext(name)[0] + '_enh_texture_img.jpeg'
show.show_minutiae(enh_texture_img, mnt, block=block, fname=fname)
mnt = self.minu_model.run(enh_constrast_img, minu_thr=0.3)
mnt = self.remove_spurious_minutiae(mnt, mask)
minutiae_sets.append(mnt)
fname = output_path + os.path.splitext(name)[0] + '_enh_constrast_img.jpeg'
show.show_minutiae(enh_constrast_img, mnt, block=block, fname=fname)
blkH, blkW = dir_map.shape
for mnt in minutiae_sets:
for des_img in descriptor_imgs:
des = descriptor.minutiae_descriptor_extraction(des_img, mnt, self.patch_types, self.des_models,
self.patchIndexV, batch_size=128)
minu_template = template.MinuTemplate(h=h, w=w, blkH=blkH, blkW=blkW, minutiae=mnt,
des=des, oimg=dir_map, mask=mask)
latent_template.add_minu_template(minu_template)
return latent_template
def feature_extraction_single_latent(self,
img_file,
output_dir=None,
ppi=500,
show_processes=False,
show_minutiae=False,
minu_file=None):
block = False
block_size = 16
img0 = io.imread(img_file, mode='L') # / 255.0
img = img0.copy()
if ppi != 500:
img = cv2.resize(img, (0, 0), fx=500.0 / ppi, fy=500.0 / ppi)
img = preprocessing.adjust_image_size(img, block_size)
name = os.path.basename(img_file)
start = timer()
h, w = img.shape
if h > 1000 and w > 1000:
return None, None
# cropping using two dictionary based approach
if minu_file is not None:
manu_minu = np.loadtxt(minu_file)
# # # remove low quality minutiae points
input_minu = np.array(manu_minu)
input_minu[:, 2] = input_minu[:, 2] / 180.0 * np.pi
else:
input_minu = []
descriptor_imgs = []
texture_img = preprocessing.FastCartoonTexture(img,
sigma=2.5,
show=False)
STFT_texture_img = preprocessing.STFT(texture_img)
contrast_img_guassian = preprocessing.local_constrast_enhancement_gaussian(
img)
STFT_img = preprocessing.STFT(img)
constrast_STFT_img = preprocessing.STFT(contrast_img_guassian)
# step 1: enhance the latent based on our autoencoder
AEC_img = self.enhancement_model.run_whole_image(STFT_texture_img)
quality_map_AEC, dir_map_AEC, fre_map_AEC = get_maps.get_quality_map_dict(
AEC_img, self.dict_all, self.dict_ori, self.dict_spacing, R=500.0)
blkmask_AEC = quality_map_AEC > 0.45
blkmask_AEC = binary_closing(blkmask_AEC, np.ones(
(3, 3))).astype(np.int)
blkmask_AEC = binary_opening(blkmask_AEC, np.ones(
(3, 3))).astype(np.int)
blkmask_SSIM = get_maps.SSIM(STFT_texture_img, AEC_img, thr=0.2)
blkmask = blkmask_SSIM * blkmask_AEC
blkH, blkW = blkmask.shape
mask = cv2.resize(blkmask.astype(float),
(block_size * blkW, block_size * blkH),
interpolation=cv2.INTER_LINEAR)
mask[mask > 0] = 1
minutiae_sets = []
mnt_STFT = self.minu_model[0].run_whole_image(STFT_img, minu_thr=0.05)
minutiae_sets.append(mnt_STFT)
if show_minutiae:
fname = output_dir + os.path.splitext(name)[0] + '_STFT_img.jpeg'
show.show_minutiae_sets(STFT_img, [input_minu, mnt_STFT],
mask=None,
block=block,
fname=fname)
mnt_STFT = self.minu_model[0].run_whole_image(constrast_STFT_img,
minu_thr=0.1)
minutiae_sets.append(mnt_STFT)
mnt_AEC = self.minu_model[1].run_whole_image(AEC_img, minu_thr=0.25)
mnt_AEC = self.remove_spurious_minutiae(mnt_AEC, mask)
minutiae_sets.append(mnt_AEC)
if show_minutiae:
fname = output_dir + os.path.splitext(name)[0] + '_AEC_img.jpeg'
show.show_minutiae_sets(AEC_img, [input_minu, mnt_AEC],
mask=mask,
block=block,
fname=fname)
enh_contrast_img = filtering.gabor_filtering_pixel2(
contrast_img_guassian,
dir_map_AEC + math.pi / 2,
fre_map_AEC,
mask=np.ones((h, w)),
block_size=16,
angle_inc=3)
mnt_contrast = self.minu_model[1].run_whole_image(enh_contrast_img,
minu_thr=0.25)
mnt_contrast = self.remove_spurious_minutiae(mnt_contrast, mask)
minutiae_sets.append(mnt_contrast)
enh_texture_img = filtering.gabor_filtering_pixel2(
texture_img,
dir_map_AEC + math.pi / 2,
fre_map_AEC,
mask=np.ones((h, w)),
block_size=16,
angle_inc=3)
mnt_texture = self.minu_model[1].run_whole_image(enh_texture_img,
minu_thr=0.25)
mnt_texture = self.remove_spurious_minutiae(mnt_texture, mask)
minutiae_sets.append(mnt_texture)
h, w = img.shape
latent_template = template.Template()
# template set 1: no ROI and enhancement are required
# texture image is used for coase segmentation
descriptor_imgs = []
descriptor_imgs.append(STFT_img)
descriptor_imgs.append(texture_img)
descriptor_imgs.append(enh_texture_img)
descriptor_imgs.append(enh_contrast_img)
mnt2 = self.get_common_minutiae(minutiae_sets, thr=2)
mnt3 = self.get_common_minutiae(minutiae_sets, thr=3)
minutiae_sets.append(mnt3)
minutiae_sets.append(mnt2)
if show_minutiae:
fname = output_dir + os.path.splitext(name)[0] + '_common_2.jpeg'
show.show_minutiae_sets(img, [input_minu, mnt2],
mask=mask,
block=block,
fname=fname)
end = timer()
print('Time for minutiae extraction: %f' % (end - start))
start = timer()
for mnt in minutiae_sets:
for des_img in descriptor_imgs:
des = descriptor.minutiae_descriptor_extraction(
des_img,
mnt,
self.patch_types,
self.des_models,
self.patchIndexV,
batch_size=128)
minu_template = template.MinuTemplate(h=h,
w=w,
blkH=blkH,
blkW=blkW,
minutiae=mnt,
des=des,
oimg=dir_map_AEC,
mask=mask)
latent_template.add_minu_template(minu_template)
end = timer()
print('Time for minutiae descriptor generation: %f' % (end - start))
start = timer()
# texture templates
stride = 16
x = np.arange(24, w - 24, stride)
y = np.arange(24, h - 24, stride)
virtual_minutiae = []
distFromBg = scipy.ndimage.morphology.distance_transform_edt(mask)
for y_i in y:
for x_i in x:
if (distFromBg[y_i][x_i] <= 16):
continue
ofY = int(y_i / 16)
ofX = int(x_i / 16)
ori = -dir_map_AEC[ofY][ofX]
virtual_minutiae.append([x_i, y_i, ori])
virtual_minutiae.append([x_i, y_i, math.pi + ori])
virtual_minutiae = np.asarray(virtual_minutiae)
texture_template = []
if len(virtual_minutiae) > 3:
virtual_des = descriptor.minutiae_descriptor_extraction(
enh_contrast_img,
virtual_minutiae,
self.patch_types,
self.des_models,
self.patchIndexV,
batch_size=128,
patch_size=96)
texture_template = template.TextureTemplate(
h=h,
w=w,
minutiae=virtual_minutiae,
des=virtual_des,
mask=None)
latent_template.add_texture_template(texture_template)
end = timer()
print('Time for texture template generation: %f' % (end - start))
return latent_template, texture_template
def feature_extraction_single_rolled(self,
img_file,
output_dir=None,
ppi=500):
block_size = 16
if not os.path.exists(img_file):
return None
img = io.imread(img_file, s_grey=True)
if ppi != 500:
img = cv2.resize(img, (0, 0), fx=500.0 / ppi, fy=500.0 / ppi)
img = preprocessing.adjust_image_size(img, block_size)
if len(img.shape) > 2:
img = rgb2gray(img)
h, w = img.shape
start = timeit.default_timer()
mask = get_maps.get_quality_map_intensity(img)
stop = timeit.default_timer()
print('time for cropping : %f' % (stop - start))
start = timeit.default_timer()
contrast_img = preprocessing.local_constrast_enhancement(img)
texture_img = preprocessing.FastCartoonTexture(img,
sigma=2.5,
show=False)
mnt = self.minu_model.run_whole_image(texture_img, minu_thr=0.15)
stop = timeit.default_timer()
print('time for minutiae : %f' % (stop - start))
start = timeit.default_timer()
des = descriptor.minutiae_descriptor_extraction(
img,
mnt,
self.patch_types,
self.des_models,
self.patchIndexV,
batch_size=256,
patch_size=self.patch_size)
stop = timeit.default_timer()
print('time for descriptor : %f' % (stop - start))
dir_map, _ = get_maps.get_maps_STFT(img,
patch_size=64,
block_size=block_size,
preprocess=True)
blkH = h // block_size
blkW = w // block_size
minu_template = template.MinuTemplate(h=h,
w=w,
blkH=blkH,
blkW=blkW,
minutiae=mnt,
des=des,
oimg=dir_map,
mask=mask)
rolled_template = template.Template()
rolled_template.add_minu_template(minu_template)
start = timeit.default_timer()
# texture templates
stride = 16
x = np.arange(24, w - 24, stride)
y = np.arange(24, h - 24, stride)
virtual_minutiae = []
distFromBg = scipy.ndimage.morphology.distance_transform_edt(mask)
for y_i in y:
for x_i in x:
if (distFromBg[y_i][x_i] <= 24):
continue
ofY = int(y_i / 16)
ofX = int(x_i / 16)
ori = -dir_map[ofY][ofX]
virtual_minutiae.append([x_i, y_i, ori])
virtual_minutiae = np.asarray(virtual_minutiae)
if len(virtual_minutiae) > 1000:
virtual_minutiae = virtual_minutiae[:1000]
print len(virtual_minutiae)
if len(virtual_minutiae) > 3:
virtual_des = descriptor.minutiae_descriptor_extraction(
contrast_img,
virtual_minutiae,
self.patch_types,
self.des_models,
self.patchIndexV,
batch_size=128)
texture_template = template.TextureTemplate(
h=h,
w=w,
minutiae=virtual_minutiae,
des=virtual_des,
mask=mask)
rolled_template.add_texture_template(texture_template)
stop = timeit.default_timer()
print('time for texture : %f' % (stop - start))
return rolled_template