def process_FVC_markup_from_minutiae(
pathname,
data_path='/home/kaicao/Research/AutomatedLatentRecognition/Data/minutiae_cylinder/',
num_channels=12):
mat_files = glob.glob(pathname + '*.mat')
mat_files.sort()
for i, mat_file in enumerate(mat_files):
fname = os.path.basename(mat_file)
print i, fname
x = loadmat(mat_file)
img = x['img']
h, w = img.shape
ROI = np.ones((h, w))
img, ROI, minutiae_cylinder = extract_minutiae_cylinder(
img, x['minutiae'], ROI)
img = LP.local_constrast_enhancement(img)
img = (img + 1) * 128
img[img > 255] = 255
img = np.uint8(img)
if num_channels < 3:
minutiae_cylinder = (minutiae_cylinder + 1.0) / 2.0
minutiae_cylinder[minutiae_cylinder > 1] = 1.
minutiae_cylinder[minutiae_cylinder < 0] = 0.
minutiae_cylinder = np.uint8(minutiae_cylinder * 255)
else:
minutiae_cylinder[minutiae_cylinder > 1] = 1
minutiae_cylinder = np.uint8(minutiae_cylinder * 255)
matrix = np.concatenate((np.expand_dims(
img, axis=2), np.expand_dims(ROI, axis=2), minutiae_cylinder), 2)
outfile = data_path + fname + '.npy'
np.save(outfile, matrix)
def compare_AEM_with_MMM(model_path, img_path='/Data/Latent/NISTSD27/image/',
output_path='/AutomatedLatentRecognition/Results/minutiae/NISTSD27_latents_Contrast/',
minu_path='/Data/Latent/NISTSD27/ManMinu', processing=None, thr=0.01):
minu_model = ImportGraph(model_path)
img_files = glob.glob(img_path + '*.bmp')
img_files.sort()
manu_files = glob.glob(minu_path + '*.txt')
manu_files.sort()
for i, img_file in enumerate(img_files):
img = misc.imread(img_file, mode='L')
if processing == 'contrast':
img = LP.local_constrast_enhancement(img)
elif processing == 'STFT':
img = LP.STFT(img)
elif processing == 'texture':
img = LP.FastCartoonTexture(img)
elif processing == 'texture_STFT':
img = LP.FastCartoonTexture(img)
img = LP.STFT(img)
mnt = minu_model.run_whole_image(img, minu_thr=thr)
img_name = os.path.basename(img_file)
fname = output_path + os.path.splitext(img_name)[0] + '_minu.jpeg'
minutiae_set = []
minutiae_set.append(mnt)
input_minu = np.loadtxt(manu_files[i])
input_minu[:, 2] = input_minu[:, 2] / 180.0 * np.pi
minutiae_set.append(input_minu)
print i
show.show_minutiae_sets(img, minutiae_set, mask=None, block=False, fname=fname)
print fname
def get_quality_map_intensity(img):
img = img.astype(np.float32)
nimg = preprocessing.local_constrast_enhancement(img)
gy = np.gradient(nimg, axis=0)
gx = np.gradient(nimg, axis=1)
mag = np.abs(gx) + np.abs(gy)
mag = cv2.GaussianBlur(mag, (21, 21), 7)
mag = mag[::8, ::8]
mask = mag > 0.3 * 127.5
mask = mask.astype(np.uint8)
kernel = np.ones((5, 5), np.uint8)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
mask = cv2.resize(mask, (0, 0), fx=8, fy=8)
return mask
def get_quality_map_intensity(img):
img = img.astype(np.float32)
nimg = preprocessing.local_constrast_enhancement(img)
gy = np.gradient(nimg, axis=0)
gx = np.gradient(nimg, axis=1)
mag = np.abs(gx) + np.abs(gy)
sigma = 5
#mag[mag>1] = 1
mag = cv2.GaussianBlur(mag, (21, 21), 7)
mask = mag > 0.25 * 127.5
mask = mask.astype(np.uint8)
kernel = np.ones((20, 20), np.uint8)
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel)
# TODO: remove small area
#plt.imshow(mask, cmap='gray')
#plt.show()
return mask
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 process_kais_markup(
pathname='/home/kaicao/Dropbox/Share/markup/data/selected_prints_templates_Kai/',
data_path='/home/kaicao/Research/AutomatedLatentRecognition/Data/minutiae_cylinder/',
num_channels=12):
subjects = glob.glob(pathname + '*')
for i, subject in enumerate(subjects):
print i
feature_file = subject + '/feature.mat'
subjectID = subject.split('/')[-1]
if os.path.isfile(feature_file):
print feature_file
x = loadmat(feature_file)
if 'minutiae_rolled' in x.keys():
img, ROI, minutiae_cylinder = extract_minutiae_cylinder(
x['img_rolled'], x['minutiae_rolled'],
x['ROI_rolled_final'])
img = np.uint8(img)
if num_channels < 3:
minutiae_cylinder = (minutiae_cylinder + 1.0) / 2.0
minutiae_cylinder[minutiae_cylinder > 1] = 1.
minutiae_cylinder[minutiae_cylinder < 0] = 0.
minutiae_cylinder = np.uint8(minutiae_cylinder * 255)
else:
minutiae_cylinder[minutiae_cylinder > 1] = 1
minutiae_cylinder = np.uint8(minutiae_cylinder * 255)
matrix = np.concatenate(
(np.expand_dims(img, axis=2), np.expand_dims(
ROI, axis=2), minutiae_cylinder), 2)
outfile = data_path + subjectID + '_rolled.npy'
np.save(outfile, matrix)
img, ROI, minutiae_cylinder = extract_minutiae_cylinder(
x['img_latent'], x['minutiae_latent'],
x['ROI_latent_final'])
if num_channels < 3:
minutiae_cylinder = (minutiae_cylinder + 1.0) / 2.0
minutiae_cylinder[minutiae_cylinder > 1] = 1.
minutiae_cylinder[minutiae_cylinder < 0] = 0.
minutiae_cylinder = np.uint8(minutiae_cylinder * 255)
else:
minutiae_cylinder[minutiae_cylinder > 1] = 1
minutiae_cylinder = np.uint8(minutiae_cylinder * 255)
matrix = np.concatenate(
(np.expand_dims(img, axis=2), np.expand_dims(
ROI, axis=2), minutiae_cylinder), 2)
outfile = data_path + subjectID + '_latent.npy'
np.save(outfile, matrix)
else:
img, ROI, minutiae_cylinder = extract_minutiae_cylinder(
x['img_rolled'], x['minutiae_rolled_final'],
x['ROI_rolled_final'])
img = LP.local_constrast_enhancement(img)
img = (img + 1) * 128
img[img > 255] = 255
img = np.uint8(img)
if num_channels < 3:
minutiae_cylinder = (minutiae_cylinder + 1.0) / 2.0
minutiae_cylinder[minutiae_cylinder > 1] = 1.
minutiae_cylinder[minutiae_cylinder < 0] = 0.
minutiae_cylinder = np.uint8(minutiae_cylinder * 255)
else:
minutiae_cylinder[minutiae_cylinder > 1] = 1
minutiae_cylinder = np.uint8(minutiae_cylinder * 255)
matrix = np.concatenate(
(np.expand_dims(img, axis=2), np.expand_dims(
ROI, axis=2), minutiae_cylinder), 2)
outfile = data_path + subjectID + '_rolled.npy'
np.save(outfile, matrix)
img, ROI, minutiae_cylinder = extract_minutiae_cylinder(
x['img_latent'], x['minutiae_latent_final'],
x['ROI_latent_final'])
if num_channels < 3:
minutiae_cylinder = (minutiae_cylinder + 1.0) / 2.0
minutiae_cylinder[minutiae_cylinder > 1] = 1.
minutiae_cylinder[minutiae_cylinder < 0] = 0.
minutiae_cylinder = np.uint8(minutiae_cylinder * 255)
else:
minutiae_cylinder[minutiae_cylinder > 1] = 1
minutiae_cylinder = np.uint8(minutiae_cylinder * 255)
matrix = np.concatenate(
(np.expand_dims(img, axis=2), np.expand_dims(
ROI, axis=2), minutiae_cylinder), 2)
outfile = data_path + subjectID + '_latent.npy'
np.save(outfile, matrix)
def extract_minutiae_cylinder(img_input,
minutiae_input,
ROI=None,
num_ori=12,
angle=None,
processing=None):
# for the latent or the low quality rolled print
minutiae = minutiae_input.copy()
img = img_input.copy()
if processing == 'STFT':
img = LP.STFT(img)
elif processing == 'contrast':
img = LP.local_constrast_enhancement(img)
elif processing == 'texture':
img = LP.FastCartoonTexture(img)
sigma = 5**2
if ROI is not None:
h, w = ROI.shape
for i in range(h):
for j in range(w):
if ROI[i, j] == 0:
img[i, j] = 255
h, w = ROI.shape
col_sum = np.sum(ROI, axis=0)
ind = [x for x in range(len(col_sum)) if col_sum[x] > 0]
min_x = np.max([np.min(ind) - 32, 0])
max_x = np.min([np.max(ind) + 32, w])
row_sum = np.sum(ROI, axis=1)
ind = [x for x in range(len(row_sum)) if row_sum[x] > 0]
min_y = np.max([np.min(ind) - 32, 0])
max_y = np.min([np.max(ind) + 32, h])
ROI = ROI[min_y:max_y, min_x:max_x]
img = img[min_y:max_y, min_x:max_x]
minutiae[:, 0] = minutiae[:, 0] - min_x
minutiae[:, 1] = minutiae[:, 1] - min_y
else:
h, w = img.shape[0:2]
ROI = np.ones((h, w))
# rotate the image and ROI, and also update minutiae points
h0, w0 = ROI.shape
if angle is not None:
h02 = (h0 + 1) / 2
w02 = (w0 + 1) / 2
img = rotate(img, angle)
ROI = rotate(ROI, angle)
h, w = ROI.shape
h2 = (h + 1) / 2
w2 = (w + 1) / 2
angle = -angle / 180.0 * math.pi
cosTheta = math.cos(angle)
sinTheta = math.sin(angle)
xx = (minutiae[:, 0] - w02) * cosTheta - (minutiae[:, 1] -
h02) * sinTheta + w2
yy = (minutiae[:, 0] - w02) * sinTheta + (minutiae[:, 1] -
h02) * cosTheta + h2
ori = minutiae[:, 2] - angle
#
minutiae[:, 0] = xx
minutiae[:, 1] = yy
minutiae[:, 2] = ori
show = 0
if show:
minu_num = minutiae.shape[0]
fig, ax = plt.subplots(1)
ax.set_aspect('equal')
R = 10
arrow_len = 15
ax.imshow(img, cmap='gray')
for i in range(0, minu_num):
xx = minutiae[i, 0]
yy = minutiae[i, 1]
circ = Circle((xx, yy), R, color='r', fill=False)
ax.add_patch(circ)
ori = -minutiae[i, 2]
dx = math.cos(ori) * arrow_len
dy = math.sin(ori) * arrow_len
ax.arrow(xx,
yy,
dx,
dy,
head_width=0.05,
head_length=0.1,
fc='r',
ec='r')
plt.show()
h, w = ROI.shape
minutiae_cylinder = np.zeros((h, w, num_ori), dtype=float)
cylinder_ori = np.asarray(range(num_ori)) * math.pi * 2 / num_ori
Y, X = np.mgrid[0:h, 0:w]
minu_num = minutiae.shape[0]
for i in range(0, minu_num):
xx = minutiae[i, 0]
yy = minutiae[i, 1]
if yy < 0 or xx < 0:
continue
print xx, yy
minu_num = minutiae.shape[0]
fig, ax = plt.subplots(1)
ax.set_aspect('equal')
R = 10
arrow_len = 15
ax.imshow(img, cmap='gray')
for i in range(0, minu_num):
xx = minutiae[i, 0]
yy = minutiae[i, 1]
circ = Circle((xx, yy), R, color='r', fill=False)
ax.add_patch(circ)
ori = -minutiae[i, 2]
dx = math.cos(ori) * arrow_len
dy = math.sin(ori) * arrow_len
ax.arrow(xx,
yy,
dx,
dy,
head_width=0.05,
head_length=0.1,
fc='r',
ec='r')
plt.show()
weight = np.exp(-((X - xx) * (X - xx) + (Y - yy) * (Y - yy)) / sigma)
ori = minutiae[i, 2]
if ori < 0:
ori += np.pi * 2
if ori > np.pi * 2:
ori -= np.pi * 2
for j in range(num_ori):
ori_diff = np.fabs(ori - cylinder_ori[j])
if ori_diff > np.pi * 2:
ori_diff = ori_diff - np.pi * 2
ori_diff = np.min([ori_diff, np.pi * 2 - ori_diff])
minutiae_cylinder[:, :,
j] += weight * np.exp(-ori_diff / np.pi * 6)
show = 0
if show:
fig, ax = plt.subplots(1)
ax.set_aspect('equal')
R = 10
arrow_len = 15
ax.imshow(img, cmap='gray')
for i in range(0, minu_num):
xx = minutiae[i, 0]
yy = minutiae[i, 1]
circ = Circle((xx, yy), R, color='r', fill=False)
ax.add_patch(circ)
ori = -minutiae[i, 2]
dx = math.cos(ori) * arrow_len
dy = math.sin(ori) * arrow_len
ax.arrow(xx,
yy,
dx,
dy,
head_width=0.05,
head_length=0.1,
fc='r',
ec='r')
plt.show()
return img, ROI, minutiae_cylinder
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_demo(self,img_file, output_path = None ):
block = True
img = io.imread(img_file)
plt.imshow(img, cmap='gray')
plt.show(block=block)
plt.close()
name = os.path.basename(img_file)
#AEC_img = io.imread(AEC_img_file)
#mask_CNN = io.imread(mask_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_mean = preprocessing.local_constrast_enhancement(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)
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)
start = timer()
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)
bin_img = binarization.binarization(enh_contrast_img,dir_map,mask=mask)
thin_img = skeletonize(bin_img)
plt.imshow(thin_img,cmap='gray')
plt.show(block=True)
dt = timer() - start
print(dt)
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 > 0.55
minutiae_sets = []
mnt = self.minu_model.run(texture_img, minu_thr=0.1)
mnt = self.remove_spurious_minutiae(mnt, mask2)
minutiae_sets.append(mnt)
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)
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)
fname = 'minutiae_enh_contrast_img.jpeg'
show.show_minutiae(img, mnt, mask=np.ones((h,w)), block=block, fname=fname)
#show.show_minutiae(img, mnt, mask=mask,block=block, fname=fname)
fname = 'minutiae_skeleton.jpeg'
show.show_minutiae(img, mnt, mask=mask, 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 None
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