def minutiae_whole_image(model_path,
sample_path,
imgs,
output_name='reconstruction/gen:0'):
imgs = glob.glob('/Data/Rolled/NISTSD14/Image2/*.bmp')
imgs.sort()
with tf.Graph().as_default():
with TowerContext('', is_training=False):
with tf.Session() as sess:
is_training = get_current_tower_context().is_training
load_model(model_path)
images_placeholder = tf.get_default_graph().get_tensor_by_name(
'QueueInput/input_deque:0')
# is_training
minutiae_cylinder_placeholder = tf.get_default_graph(
).get_tensor_by_name(output_name)
for n, file in enumerate(imgs):
img0 = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
img = img0 / 128.0 - 1
img = np.expand_dims(img, axis=2)
img = np.expand_dims(img, axis=0)
feed_dict = {images_placeholder: img}
minutiae_cylinder = sess.run(minutiae_cylinder_placeholder,
feed_dict=feed_dict)
minutiae_cylinder = np.squeeze(minutiae_cylinder, axis=0)
minutiae = prepare_data.get_minutiae_from_cylinder(
minutiae_cylinder, thr=0.25)
minutiae = prepare_data.refine_minutiae(minutiae,
dist_thr=10,
ori_dist=np.pi / 4)
prepare_data.show_minutiae(img0, minutiae)
print n
def run(self, img, minu_thr=0.2):
h, w = img.shape
weight = get_weights(128, 128, 12)
nrof_samples = len(range(0, h, opt.SHAPE // 2)) * len(range(0, w, opt.SHAPE // 2))
patches = np.zeros((nrof_samples, opt.SHAPE, opt.SHAPE, 1))
n = 0
x = []
y = []
for i in range(0, h - opt.SHAPE + 1, opt.SHAPE // 2):
for j in range(0, w - opt.SHAPE + 1, opt.SHAPE // 2):
patch = img[i:i + opt.SHAPE, j:j + opt.SHAPE, np.newaxis]
x.append(j)
y.append(i)
patches[n, :, :, :] = patch
n = n + 1
feed_dict = {self.images_placeholder: patches}
minutiae_cylinder_array = self.sess.run(self.minutiae_cylinder_placeholder, feed_dict=feed_dict)
minutiae_cylinder = np.zeros((h, w, 12))
minutiae_cylinder_array[:, -10:, :, :] = 0
minutiae_cylinder_array[:, :10, :, :] = 0
minutiae_cylinder_array[:, :, -10:, :] = 0
minutiae_cylinder_array[:, :, 10, :] = 0
for i in range(n):
minutiae_cylinder[y[i]:y[i] + opt.SHAPE, x[i]:x[i] + opt.SHAPE, :] = minutiae_cylinder[
y[i]:y[i] + opt.SHAPE,
x[i]:x[i] + opt.SHAPE, :] + \
minutiae_cylinder_array[i] * weight
minutiae = prepare_data.get_minutiae_from_cylinder(minutiae_cylinder, thr=minu_thr)
minutiae = prepare_data.refine_minutiae(minutiae, dist_thr=10, ori_dist=np.pi / 4)
minutiae = self.remove_crowded_minutiae(minutiae)
minutiae = np.asarray(minutiae)
return minutiae
def minutiae_extraction3(model_path, sample_path, imgs, output_name='reconstruction/gen:0', block=True):
imgs = glob.glob('/research/prip-kaicao/Data/Rolled/NIST4/Image/' + '*.bmp')
imgs.sort()
import os
if not os.path.isdir(sample_path):
os.makedirs(sample_path)
weight = get_weights(opt.SHAPE, opt.SHAPE, 12)
with tf.Graph().as_default():
with TowerContext('', is_training=False):
with tf.Session() as sess:
is_training = get_current_tower_context().is_training
load_model(model_path)
images_placeholder = tf.get_default_graph().get_tensor_by_name('sub:0')
minutiae_cylinder_placeholder = tf.get_default_graph().get_tensor_by_name(output_name)
for k, file in enumerate(imgs):
img = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
h, w = img.shape
x = []
y = []
nrof_samples = len(range(0, h, opt.SHAPE // 2)) * len(range(0, w, opt.SHAPE // 2))
patches = np.zeros((nrof_samples, opt.SHAPE, opt.SHAPE, 1))
n = 0
for i in range(0, h - opt.SHAPE + 1, opt.SHAPE // 2):
for j in range(0, w - opt.SHAPE + 1, opt.SHAPE // 2):
print j
patch = img[i:i + opt.SHAPE, j:j + opt.SHAPE, np.newaxis]
x.append(j)
y.append(i)
patches[n, :, :, :] = patch
n = n + 1
# print x[-1]
feed_dict = {images_placeholder: patches}
minutiae_cylinder_array = sess.run(minutiae_cylinder_placeholder, feed_dict=feed_dict)
minutiae_cylinder = np.zeros((h, w, 12))
minutiae_cylinder_array[:, -10:, :, :] = 0
minutiae_cylinder_array[:, :10, :, :] = 0
minutiae_cylinder_array[:, :, -10:, :] = 0
minutiae_cylinder_array[:, :, 10, :] = 0
for i in range(n):
minutiae_cylinder[y[i]:y[i] + opt.SHAPE, x[i]:x[i] + opt.SHAPE, :] = minutiae_cylinder[y[i]:y[i] +
opt.SHAPE, x[i]:x[i] + opt.SHAPE, :] + minutiae_cylinder_array[i] * weight
minutiae = prepare_data.get_minutiae_from_cylinder(minutiae_cylinder, thr=0.1)
minutiae = prepare_data.refine_minutiae(minutiae, dist_thr=10, ori_dist=np.pi / 4)
minutiae_sets = []
minutiae_sets.append(minutiae)
fname = sample_path + os.path.basename(file)[:-4] + 'nms' + '.jpeg'
prepare_data.show_minutiae_sets(img, minutiae_sets, ROI=None, fname=fname, block=block)
print(n)
def run(self, img,minu_thr=0.2):
#feed_dict = {self.images_placeholder: img}
#minutiae_cylinder = self.sess.run(self.minutiae_cylinder_placeholder, feed_dict=feed_dict)
h,w = img.shape
weight = get_weights(128, 128, 12)
nrof_samples = len(range(0, h, opt.SHAPE // 2)) * len(range(0, w, opt.SHAPE // 2))
patches = np.zeros((nrof_samples, opt.SHAPE, opt.SHAPE, 1))
n = 0
x = []
y = []
for i in range(0, h - opt.SHAPE + 1, opt.SHAPE // 2):
for j in range(0, w - opt.SHAPE + 1, opt.SHAPE // 2):
#print j
patch = img[i:i + opt.SHAPE, j:j + opt.SHAPE, np.newaxis]
x.append(j)
y.append(i)
patches[n, :, :, :] = patch
n = n + 1
# print x[-1]
feed_dict = {self.images_placeholder: patches}
minutiae_cylinder_array = self.sess.run(self.minutiae_cylinder_placeholder, feed_dict=feed_dict)
minutiae_cylinder = np.zeros((h, w, 12))
minutiae_cylinder_array[:, -10:, :, :] = 0
minutiae_cylinder_array[:, :10, :, :] = 0
minutiae_cylinder_array[:, :, -10:, :] = 0
minutiae_cylinder_array[:, :, 10, :] = 0
for i in range(n):
minutiae_cylinder[y[i]:y[i] + opt.SHAPE, x[i]:x[i] + opt.SHAPE, :] = minutiae_cylinder[
y[i]:y[i] + opt.SHAPE,
x[i]:x[i] + opt.SHAPE, :] + \
minutiae_cylinder_array[i] * weight
# print minutiae_cylinder
minutiae = prepare_data.get_minutiae_from_cylinder(minutiae_cylinder, thr=minu_thr)
# cv2.imwrite('test_0.jpeg', (minutiae_cylinder[:, :, 0:3]) * 255)
# cv2.imwrite('test_1.jpeg', (minutiae_cylinder[:, :, 3:6]) * 255)
# cv2.imwrite('test_2.jpeg', (minutiae_cylinder[:, :, 6:9]) * 255)
# cv2.imwrite('test_3.jpeg', (minutiae_cylinder[:, :, 9:12]) * 255)
# prepare_data.show_features(img, minutiae, fname=os.path.basename(file)[:-4] +'.jpeg')
minutiae = prepare_data.refine_minutiae(minutiae, dist_thr=10, ori_dist=np.pi / 4)
minutiae = self.remove_crowded_minutiae(minutiae)
return minutiae
def minutiae_whole_image(model_path,sample_path, imgs, output_name='reconstruction/gen:0'):
#imgs = glob.glob('/media/kaicao/Data/Data/Rolled/NISTSD4/Image_Aligned'+'/*.jpeg')
#imgs = glob.glob('/home/kaicao/Dropbox/Research/Data/Latent/NISTSD27/image/'+'*.bmp')
imgs = glob.glob('/future/Data/Rolled/NISTSD14/Image2/*.bmp')
#imgs = glob.glob('/research/prip-kaicao/Data/Latent/DB/NIST27/image/' + '*.bmp')
#imgs = glob.glob('/research/prip-kaicao/Data/Rolled/NIST4/Image/'+'*.bmp')
imgs.sort()
weight = get_weights(opt.SHAPE, opt.SHAPE, 12)
import os
#if not os.path.isdir(sample_path):
# os.makedirs(sample_path)
with tf.Graph().as_default():
with TowerContext('', is_training=False):
with tf.Session() as sess:
is_training= get_current_tower_context().is_training
load_model(model_path)
images_placeholder = tf.get_default_graph().get_tensor_by_name('QueueInput/input_deque:0')
#is_training
minutiae_cylinder_placeholder = tf.get_default_graph().get_tensor_by_name(output_name)
for n, file in enumerate(imgs):
img0 = cv2.imread(file,cv2.IMREAD_GRAYSCALE)
img = img0/128.0-1
img = np.expand_dims(img,axis=2)
img = np.expand_dims(img,axis=0)
feed_dict = {images_placeholder: img}
minutiae_cylinder= sess.run(minutiae_cylinder_placeholder, feed_dict=feed_dict)
minutiae_cylinder = np.squeeze(minutiae_cylinder,axis=0)
minutiae = prepare_data.get_minutiae_from_cylinder(minutiae_cylinder,thr=0.25)
#cv2.imwrite('test_0.jpeg', (minutiae_cylinder[:, :, 0:3]) * 255)
#cv2.imwrite('test_1.jpeg', (minutiae_cylinder[:, :, 3:6]) * 255)
#cv2.imwrite('test_2.jpeg', (minutiae_cylinder[:, :, 6:9]) * 255)
#cv2.imwrite('test_3.jpeg', (minutiae_cylinder[:, :, 9:12]) * 255)
#prepare_data.show_features(img, minutiae, fname=os.path.basename(file)[:-4] +'.jpeg')
minutiae = prepare_data.refine_minutiae(minutiae, dist_thr=10, ori_dist=np.pi / 4)
prepare_data.show_minutiae(img0, minutiae)
print n
def minutiae_extraction_latent(model_path, sample_path, imgs, output_name='reconstruction/gen:0', block=True):
imgs = glob.glob('/Data/Latent/DB/NIST27/image/' + '*.bmp')
minu_files = glob.glob('/Data/Latent/DB/ManualInformation/NIST27/ManMinu/*.txt')
minu_files.sort()
imgs.sort()
import os
if not os.path.isdir(sample_path):
os.makedirs(sample_path)
weight = get_weights(opt.SHAPE, opt.SHAPE, 12)
with tf.Graph().as_default():
with TowerContext('', is_training=False):
with tf.Session() as sess:
is_training = get_current_tower_context().is_training
load_model(model_path)
images_placeholder = tf.get_default_graph().get_tensor_by_name('QueueInput/input_deque:0') # sub:0
minutiae_cylinder_placeholder = tf.get_default_graph().get_tensor_by_name(output_name)
for k, file in enumerate(imgs):
print file
img = cv2.imread(file, cv2.IMREAD_GRAYSCALE)
u, texture = LP.FastCartoonTexture(img)
img = texture / 128.0 - 1
h, w = img.shape
x = []
y = []
nrof_samples = len(range(0, h, opt.SHAPE // 2)) * len(range(0, w, opt.SHAPE // 2))
patches = np.zeros((nrof_samples, opt.SHAPE, opt.SHAPE, 1))
n = 0
for i in range(0, h - opt.SHAPE + 1, opt.SHAPE // 2):
for j in range(0, w - opt.SHAPE + 1, opt.SHAPE // 2):
print j
patch = img[i:i + opt.SHAPE, j:j + opt.SHAPE, np.newaxis]
x.append(j)
y.append(i)
patches[n, :, :, :] = patch
n = n + 1
# print x[-1]
feed_dict = {images_placeholder: patches}
minutiae_cylinder_array = sess.run(minutiae_cylinder_placeholder, feed_dict=feed_dict)
minutiae_cylinder = np.zeros((h, w, 12))
minutiae_cylinder_array[:, -10:, :, :] = 0
minutiae_cylinder_array[:, :10, :, :] = 0
minutiae_cylinder_array[:, :, -10:, :] = 0
minutiae_cylinder_array[:, :, 10, :] = 0
for i in range(n):
minutiae_cylinder[y[i]:y[i] + opt.SHAPE, x[i]:x[i] + opt.SHAPE, :] = minutiae_cylinder[y[i]
:y[i] + opt.SHAPE, x[i]:x[i] + opt.SHAPE, :] + minutiae_cylinder_array[i] * weight
minutiae = prepare_data.get_minutiae_from_cylinder(minutiae_cylinder, thr=0.05)
minutiae = prepare_data.refine_minutiae(minutiae, dist_thr=10, ori_dist=np.pi / 4)
minutiae_sets = []
minutiae_sets.append(minutiae)
manu_minutiae = np.loadtxt(minu_files[k])
manu_minutiae[:, 2] = manu_minutiae[:, 2] / 180 * np.pi
minutiae_sets.append(manu_minutiae)
fname = sample_path + os.path.basename(file)[:-4] + '.jpeg'
prepare_data.show_minutiae_sets(img, minutiae_sets, ROI=None, fname=fname, block=block)
fname = sample_path + os.path.basename(file)[:-4] + '.txt'
np.savetxt(fname, minutiae_sets[0])
print(n)