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 modify_minutiae_cylinder(input_file,
output_file,
angle=None,
processing=None):
cylinder = np.load(input_file)
img = cylinder[:, :, 0]
if processing == 'STFT':
img = LP.STFT(img)
cylinder[:, :, 0] = img
np.save(output_file, cylinder)
def generate_training_npy(datadir, output_path):
finger_paths = glob.glob(datadir + '/MI*')
imgs = []
for finger_path in finger_paths:
img_files = glob.glob(finger_path + '/high*.bmp')
for img_file in img_files:
img = cv2.imread(img_file, cv2.IMREAD_GRAYSCALE)
img_STFT = preprocessing.STFT(img)
matrix = np.concatenate((np.expand_dims(
img_STFT, axis=2), np.expand_dims(img, axis=2)), 2)
output_file = output_path + img_file.split('/')[-1][:-3] + 'npy'
matrix = np.uint8(matrix)
np.save(output_file, 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(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
