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 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)
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(self,
img_file,
output_path=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)
mnt = self.minu_model.run_whole_image(contrast_img, minu_thr=0.1)
stop = timeit.default_timer()
minu_time = stop - start
print('time for minutiae : %f' % (stop - start))
name = os.path.basename(img_file)
show.show_minutiae(img, mnt, block=True)
return None
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
def feature_extraction_single_latent(raw_img_file,
AEC_img_file,
mask_file,
patch_types=None,
des_models=None):
raw_img = io.imread(raw_img_file)
AEC_img = io.imread(AEC_img_file)
mask = io.imread(mask_file)
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)
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)
bin_img2 = 1 - bin_img
thin_img = skeletonize(bin_img2)
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(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
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)
