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Repository: papermsucode/advhat
Branch: master
Commit: b34289475e24
Files: 23
Total size: 244.8 KB
Directory structure:
gitextract_3cazs35n/
├── Attack/
│ ├── README.md
│ ├── attack.py
│ ├── cos_mx.py
│ ├── cos_tf.py
│ ├── face_preparation.py
│ ├── stn.py
│ └── utils.py
├── Demo/
│ ├── 1000_from_CASIA/
│ │ ├── centroids.npy
│ │ └── centroids_names.txt
│ ├── README.md
│ ├── align/
│ │ ├── .gitignore
│ │ ├── __init__.py
│ │ ├── det1.npy
│ │ ├── det2.npy
│ │ ├── det3.npy
│ │ └── detect_face.py
│ ├── alignment.py
│ ├── demo.py
│ └── dumping.py
├── LICENSE
├── README.md
└── Utils/
├── MXtoTF.ipynb
└── README.md
================================================
FILE CONTENTS
================================================
================================================
FILE: Attack/README.md
================================================
## Preparing an attack
1. First, make a full-face photo of attacked person, a full-face photo in a hat, and a
full-face photo in a hat with an example sticker on the hat. To be sure that you use a
sticker with the correct size follow instructions:
1. Download an example.png.
2. Open downloaded image with standard Windows print util.
3. Choose the regime with 4 photos per page (9 x 13 sm).
4. Uncheck the box "Fit picture to frame".
5. Print page with an example sticker.
6. Cut out the sticker and put in on the hat.
2. Use the next command to prepare photos:
`python3 face_preparation.py PATH_TO_THE_IMAGE`
3. You need to find parameters for the sticker position initialization. Use the next
command to find these parameters:
`python3 face_preparation.py PATH_TO_THE_IMAGE_WITH_HAT_ONLY --mask`
It will show sticker placement with default parameters. Change parameters until the
image looks like a prepared image with the sticker. You can see the parameters using `--help`
flag.
4. Download TensorFlow ArcFace model
[here](https://drive.google.com/file/d/1fb70KgMRSmaEUF5cJ67BCD_DmTPCR5uJ/view?usp=sharing).
5. Launch an attack preparation:
`python3 attack.py PATH_TO_THE_PREPARED_IMAGED_WITH_HAT PATH_TO_THE_TF_MODEL --anchor_face
PATH_TO_THE_PREPARED_IMAGE_WITHOUT_HAT (sticker position parameters in the same format from
the third step)`
6. Print the obtained sticker, put it on the hat as before, and make a new photo with the sticker.
7. Use "face_preparation.py" again to prepare a new photo and "cos_tf.py" to calculate a new similarity.
`python3 cos_tf.py PATH_TO_THE_PREPARED_IMAGE_WITHOUT_HAT PATH_TO_THE_PREPARED_IMAGE_WITH_HAT_ONLY` - baseline similarity
`python3 cos_tf.py PATH_TO_THE_PREPARED_IMAGE_WITHOUT_HAT PATH_TO_THE_PREPARED_IMAGE_WITH_THE_NEW_STICKER` - final similarity
### Notes
Note that our printer has good color rendering, that is why NPS-loss does not make influence in our experiments.
You may need to add NPS-loss for your printer.
================================================
FILE: Attack/attack.py
================================================
import argparse
import sys
import os
import tensorflow as tf
import numpy as np
import skimage.io as io
from skimage.transform import rescale
from tqdm import tqdm
from stn import spatial_transformer_network as stn
from utils import TVloss, projector
from sklearn.linear_model import LinearRegression as LR
from time import time
import datetime
import matplotlib.pyplot as plt
# Prepare image to network input format
def prep(im):
if len(im.shape)==3:
return np.transpose(im,[2,0,1]).reshape((1,3,112,112))*2-1
elif len(im.shape)==4:
return np.transpose(im,[0,3,1,2]).reshape((im.shape[0],3,112,112))*2-1
def main(args):
print(args)
now = str(datetime.datetime.now())
sess = tf.Session()
# Off-plane sticker projection
logo = tf.placeholder(tf.float32,shape=[None,400,900,3],name='logo_input')
param = tf.placeholder(tf.float32,shape=[None,1],name='param_input')
ph = tf.placeholder(tf.float32,shape=[None,1],name='ph_input')
result = projector(param,ph,logo)
# Union of the sticker and face image
mask_input = tf.placeholder(tf.float32,shape=[None,900,900,3],name='mask_input')
face_input = tf.placeholder(tf.float32,shape=[None,600,600,3],name='face_input')
theta = tf.placeholder(tf.float32,shape=[None,6],name='theta_input')
prepared = stn(result,theta)
# Transformation to ArcFace template
theta2 = tf.placeholder(tf.float32,shape=[None,6],name='theta2_input')
united = prepared[:,300:,150:750]*mask_input[:,300:,150:750]+\
face_input*(1-mask_input[:,300:,150:750])
final_crop = tf.clip_by_value(stn(united,theta2,(112,112)),0.,1.)
# TV loss and gradients
w_tv = tf.placeholder(tf.float32,name='w_tv_input')
tv_loss = TVloss(logo,w_tv)
grads_tv = tf.gradients(tv_loss,logo)
grads_input = tf.placeholder(tf.float32,shape=[None,112,112,3],name='grads_input')
grads1 = tf.gradients(final_crop,logo,grad_ys=grads_input)
# Varios images generator
class Imgen(object):
def __init__(self):
self.fdict = {ph:[[args.ph]],\
logo:np.ones((1,400,900,3)),\
param:[[args.param]],\
theta:1./args.scale*np.array([[1.,0.,-args.x/450.,0.,1.,-args.y/450.]]),\
theta2:[[1.,0.,0.,0.,1.,0.]],\
w_tv:args.w_tv}
mask = sess.run(prepared,feed_dict=self.fdict)
self.fdict[mask_input] = mask
def gen_fixed(self,im,advhat):
self.fdict[face_input] = np.expand_dims(im,0)
self.fdict[logo] = np.expand_dims(advhat,0)
return self.fdict, sess.run(final_crop,feed_dict=self.fdict)
def gen_random(self,im,advhat,batch=args.batch_size):
alpha1 = np.random.uniform(-1.,1.,size=(batch,1))/180.*np.pi
scale1 = np.random.uniform(args.scale-0.02,args.scale+0.02,size=(batch,1))
y1 = np.random.uniform(args.y-600./112.,args.y+600./112.,size=(batch,1))
x1 = np.random.uniform(args.x-600./112.,args.x+600./112.,size=(batch,1))
alpha2 = np.random.uniform(-1.,1.,size=(batch,1))/180.*np.pi
scale2 = np.random.uniform(1./1.04,1.04,size=(batch,1))
y2 = np.random.uniform(-1.,1.,size=(batch,1))/66.
angle = np.random.uniform(args.ph-2.,args.ph+2.,size=(batch,1))
parab = np.random.uniform(args.param-0.0002,args.param+0.0002,size=(batch,1))
fdict = {ph:angle,param:parab,w_tv:args.w_tv,\
theta:1./scale1*np.hstack([np.cos(alpha1),np.sin(alpha1),-x1/450.,\
-np.sin(alpha1),np.cos(alpha1),-y1/450.]),\
theta2:scale2*np.hstack([np.cos(alpha2),np.sin(alpha2),np.zeros((batch,1)),\
-np.sin(alpha2),np.cos(alpha2),y2]),\
logo:np.ones((batch,400,900,3)),\
face_input:np.tile(np.expand_dims(im,0),[batch,1,1,1])}
mask = sess.run(prepared,feed_dict=fdict)
fdict[mask_input] = mask
fdict[logo] = np.tile(np.expand_dims(advhat,0),[batch,1,1,1])
return fdict, sess.run(final_crop,feed_dict=fdict)
gener = Imgen()
# Initialization of the sticker
init_logo = np.ones((400,900,3))*127./255.
if args.init_face!=None:
init_face = io.imread(args.init_face)/255.
init_loss = tv_loss+tf.reduce_sum(tf.abs(init_face-united[0]))
init_grads = tf.gradients(init_loss,logo)
init_logo = np.ones((400,900,3))*127./255.
fdict, _ = gener.gen_fixed(init_face,init_logo)
moments = np.zeros((400,900,3))
print('Initialization from face, step 1/2')
for i in tqdm(range(500)):
fdict[logo] = np.expand_dims(init_logo,0)
grads = moments*0.9+sess.run(init_grads,feed_dict=fdict)[0][0]
moments = moments*0.9 + grads*0.1
init_logo = np.clip(init_logo-1./51.*np.sign(grads),0.,1.)
print('Initialization from face, step 2/2')
for i in tqdm(range(500)):
fdict[logo] = np.expand_dims(init_logo,0)
grads = moments*0.9+sess.run(init_grads,feed_dict=fdict)[0][0]
moments = moments*0.9 + grads*0.1
init_logo = np.clip(init_logo-1./255.*np.sign(grads),0.,1.)
io.imsave(now+'_init_logo.png',init_logo)
elif args.init_logo!=None:
init_logo[:] = io.imread(args.init_logo)/255.
# Embedding model
with tf.gfile.GFile(args.model, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def,
input_map=None,
return_elements=None,
name="")
image_input = tf.get_default_graph().get_tensor_by_name('image_input:0')
keep_prob = tf.get_default_graph().get_tensor_by_name('keep_prob:0')
is_train = tf.get_default_graph().get_tensor_by_name('training_mode:0')
embedding = tf.get_default_graph().get_tensor_by_name('embedding:0')
orig_emb = tf.placeholder(tf.float32,shape=[None,512],name='orig_emb_input')
cos_loss = tf.reduce_sum(tf.multiply(embedding,orig_emb),axis=1)
grads2 = tf.gradients(cos_loss,image_input)
fdict2 = {keep_prob:1.0,is_train:False}
# Anchor embedding calculation
if args.anchor_face!=None:
anch_im = rescale(io.imread(args.anchor_face)/255.,112./600.,order=5)
fdict2[image_input] = prep(anch_im)
fdict2[orig_emb] = sess.run(embedding,feed_dict=fdict2)
elif args.anchor_emb!=None:
fdict2[orig_emb] = np.load(args.anchor_emb)[-1:]
else:
anch_im = rescale(io.imread(args.image)/255.,112./600.,order=5)
fdict2[image_input] = prep(anch_im)
fdict2[orig_emb] = sess.run(embedding,feed_dict=fdict2)
# Attack constants
im0 = io.imread(args.image)/255.
regr = LR(n_jobs=4)
regr_len = 100
regr_coef = -1.
moments = np.zeros((400,900,3))
moment_val = 0.9
step_val = 1./51.
stage = 1
step = 0
lr_thresh = 100
ls = []
t = time()
while True:
# Projecting sticker to the face and feeding it to the embedding model
fdict,ims = gener.gen_random(im0,init_logo)
fdict2[image_input] = prep(ims)
grad_tmp = sess.run(grads2,feed_dict=fdict2)
fdict_val, im_val = gener.gen_fixed(im0,init_logo)
fdict2[image_input] = prep(im_val)
ls.append(sess.run(cos_loss,feed_dict=fdict2)[0])
# Gradients to the original sticker image
fdict[grads_input] = np.transpose(grad_tmp[0],[0,2,3,1])
grads_on_logo = np.mean(sess.run(grads1,feed_dict=fdict)[0],0)
grads_on_logo += sess.run(grads_tv,feed_dict=fdict)[0][0]
moments = moments*moment_val + grads_on_logo*(1.-moment_val)
init_logo -= step_val*np.sign(moments)
init_logo = np.clip(init_logo,0.,1.)
# Logging
step += 1
if step%20==0:
print('Stage:',stage,'Step:',step,'Av. time:',round((time()-t)/step,2),'Loss:',round(ls[-1],2),'Coef:',regr_coef)
# Switching to the second stage
if step>lr_thresh:
regr.fit(np.expand_dims(np.arange(100),1),np.hstack(ls[-100:]))
regr_coef = regr.coef_[0]
if regr_coef>=0:
if stage==1:
stage = 2
moment_val = 0.995
step_val = 1./255.
step = 0
regr_coef = -1.
lr_thresh = 200
t = time()
else:
break
plt.plot(range(len(ls)),ls)
plt.savefig(now+'_cosine.png')
io.imsave(now+'_advhat.png',init_logo)
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('image', type=str, help='Path to the image for attack.')
parser.add_argument('model', type=str, help='Path to the model for attack.')
parser.add_argument('--init_face', type=str, default=None, help='Path to the face for sticker inititalization.')
parser.add_argument('--init_logo', type=str, default=None, help='Path to the image for inititalization.')
parser.add_argument('--anchor_face', type=str, default=None, help='Path to the anchor face.')
parser.add_argument('--anchor_emb', type=str, default=None, help='Path to the anchor emb (the last will be used)')
parser.add_argument('--w_tv', type=float, default=1e-4, help='Weight of the TV loss')
parser.add_argument('--ph', type=float, default=17., help='Angle of the off-plane rotation')
parser.add_argument('--param', type=float, default=0.0013, help='Parabola rate for the off-plane parabolic transformation')
parser.add_argument('--scale', type=float, default=0.465, help='Scaling parameter for the sticker')
parser.add_argument('--x', type=float, default=0., help='Translation of the sticker along x-axis')
parser.add_argument('--y', type=float, default=-15., help='Translation of the sticker along y-axis')
parser.add_argument('--batch_size', type=int, default=20, help='Batch size for attack')
return parser.parse_args(argv)
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))
================================================
FILE: Attack/cos_mx.py
================================================
import argparse
import sys
import mxnet as mx
import mxnet.ndarray as nd
import numpy as np
import skimage.io as io
from skimage.transform import rescale
from numpy import linalg as LA
# Prepare image to network input format
def prep(im):
if len(im.shape)==3:
return np.transpose(im,[2,0,1]).reshape((1,3,112,112))
elif len(im.shape)==4:
return np.transpose(im,[0,3,1,2]).reshape((im.shape[0],3,112,112))
def main(args):
print(args)
# Embedding model
sym, arg_params, aux_params = mx.model.load_checkpoint(args.model, 0)
sym = sym.get_internals()['fc1_output']
model = mx.mod.Module(symbol=sym, context=mx.gpu(0), label_names = None)
model.bind(data_shapes=[('data', (1, 3, 112, 112))])
model.set_params(arg_params, aux_params)
# Embedding calculation
im1 = (prep(rescale(io.imread(args.face1)/255.,112./600.,order=5))*255.).astype(np.uint8)
im2 = (prep(rescale(io.imread(args.face2)/255.,112./600.,order=5))*255.).astype(np.uint8)
batch = mx.io.DataBatch(data=[nd.array(im1)])
model.forward(batch, is_train=False)
emb1 = model.get_outputs()[0].asnumpy()[0]
batch = mx.io.DataBatch(data=[nd.array(im2)])
model.forward(batch, is_train=False)
emb2 = model.get_outputs()[0].asnumpy()[0]
# Normalization
emb1 /= LA.norm(emb1)
emb2 /= LA.norm(emb2)
cos_sim = np.sum(emb1 * emb2)
# Result
print('Cos_sim(face1, face2) =', cos_sim)
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('face1', type=str, help='Path to the preprocessed face1.')
parser.add_argument('face2', type=str, help='Path to the preprocessed face2.')
parser.add_argument('model', type=str, help='Path to the model.')
return parser.parse_args(argv)
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))
================================================
FILE: Attack/cos_tf.py
================================================
import argparse
import sys
import tensorflow as tf
import numpy as np
import skimage.io as io
from skimage.transform import rescale
# Prepare image to network input format
def prep(im):
if len(im.shape)==3:
return np.transpose(im,[2,0,1]).reshape((1,3,112,112))*2-1
elif len(im.shape)==4:
return np.transpose(im,[0,3,1,2]).reshape((im.shape[0],3,112,112))*2-1
def main(args):
print(args)
sess = tf.Session()
# Embedding model
with tf.gfile.GFile(args.model, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def,
input_map=None,
return_elements=None,
name="")
image_input = tf.get_default_graph().get_tensor_by_name('image_input:0')
keep_prob = tf.get_default_graph().get_tensor_by_name('keep_prob:0')
is_train = tf.get_default_graph().get_tensor_by_name('training_mode:0')
embedding = tf.get_default_graph().get_tensor_by_name('embedding:0')
tfdict = {keep_prob:1.0, is_train:False}
# Embedding calculation
im1 = prep(rescale(io.imread(args.face1)/255.,112./600.,order=5))
im2 = prep(rescale(io.imread(args.face2)/255.,112./600.,order=5))
tfdict[image_input] = im1
emb1 = sess.run(embedding,feed_dict=tfdict)
tfdict[image_input] = im2
emb2 = sess.run(embedding,feed_dict=tfdict)
# Result
cos_sim = np.sum(emb1 * emb2)
print('Cos_sim(face1, face2) =', cos_sim)
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('face1', type=str, help='Path to the preprocessed face1.')
parser.add_argument('face2', type=str, help='Path to the preprocessed face2.')
parser.add_argument('model', type=str, help='Path to the model.')
return parser.parse_args(argv)
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))
================================================
FILE: Attack/face_preparation.py
================================================
import argparse
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '../Demo/'))
import tensorflow as tf
import numpy as np
import cv2
import skimage.io as io
from skimage import transform as trans
from align import detect_face
from stn import spatial_transformer_network as stn
from utils import projector
# Align face as ArcFace template
def preprocess(img, landmark):
image_size = [600,600]
src = 600./112.*np.array([
[38.2946, 51.6963],
[73.5318, 51.5014],
[56.0252, 71.7366],
[41.5493, 92.3655],
[70.7299, 92.2041] ], dtype=np.float32)
dst = landmark.astype(np.float32)
tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2,:]
warped = cv2.warpAffine(img,M,(image_size[1],image_size[0]), borderValue = 0.0)
return warped
def main(args):
sess = tf.Session()
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
threshold = [ 0.6, 0.7, 0.7 ]
factor = 0.709
img = io.imread(args.image)
_minsize = min(min(img.shape[0]//5, img.shape[1]//5),80)
bounding_boxes, points = detect_face.detect_face(img, _minsize, pnet, rnet, onet, threshold, factor)
assert bounding_boxes.size>0
points = points[:, 0]
landmark = points.reshape((2,5)).T
warped = preprocess(img, landmark)
io.imsave(args.image[:-4]+'_aligned.png',warped)
if args.mask:
logo_mask = np.ones((1,400,900,3),dtype=np.float32)
logo = tf.placeholder(tf.float32,shape=[1,400,900,3])
param = tf.placeholder(tf.float32,shape=[1,1])
ph = tf.placeholder(tf.float32,shape=[1,1])
result = projector(param,ph,logo)
face_input = tf.placeholder(tf.float32,shape=[1,600,600,3])
theta = tf.placeholder(tf.float32,shape=[1,6])
prepared = stn(result,theta)
united = prepared[:,300:,150:750]+face_input*(1-prepared[:,300:,150:750])
img_with_mask = sess.run(united,feed_dict={ph:[[args.ph]],logo:logo_mask,param:[[args.param]],\
face_input:np.expand_dims(warped/255.,0),\
theta:1./args.scale*np.array([[1.,0.,-args.x/450.,0.,1.,-args.y/450.]])})[0]
io.imsave(args.image[:-4]+'_mask.png',img_with_mask)
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('image', type=str, help='Path to the image.')
parser.add_argument('--mask', action='store_true', help='Use when search the sticker parameters')
parser.add_argument('--ph', type=float, default=17., help='Angle of the off-plane rotation')
parser.add_argument('--param', type=float, default=0.0013, help='Parabola rate for the off-plane parabolic transformation')
parser.add_argument('--scale', type=float, default=0.465, help='Scaling parameter for the sticker')
parser.add_argument('--x', type=float, default=0., help='Translation of the sticker along x-axis')
parser.add_argument('--y', type=float, default=-15., help='Translation of the sticker along y-axis')
return parser.parse_args(argv)
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))
================================================
FILE: Attack/stn.py
================================================
import tensorflow as tf
def spatial_transformer_network(input_fmap, theta, out_dims=None, **kwargs):
"""
Spatial Transformer Network layer implementation as described in [1].
The layer is composed of 3 elements:
- localization_net: takes the original image as input and outputs
the parameters of the affine transformation that should be applied
to the input image.
- affine_grid_generator: generates a grid of (x,y) coordinates that
correspond to a set of points where the input should be sampled
to produce the transformed output.
- bilinear_sampler: takes as input the original image and the grid
and produces the output image using bilinear interpolation.
Input
-----
- input_fmap: output of the previous layer. Can be input if spatial
transformer layer is at the beginning of architecture. Should be
a tensor of shape (B, H, W, C).
- theta: affine transform tensor of shape (B, 6). Permits cropping,
translation and isotropic scaling. Initialize to identity matrix.
It is the output of the localization network.
Returns
-------
- out_fmap: transformed input feature map. Tensor of size (B, H, W, C).
Notes
-----
[1]: 'Spatial Transformer Networks', Jaderberg et. al,
(https://arxiv.org/abs/1506.02025)
"""
# grab input dimensions
B = tf.shape(input_fmap)[0]
H = tf.shape(input_fmap)[1]
W = tf.shape(input_fmap)[2]
# reshape theta to (B, 2, 3)
theta = tf.reshape(theta, [B, 2, 3])
# generate grids of same size or upsample/downsample if specified
if out_dims:
out_H = out_dims[0]
out_W = out_dims[1]
batch_grids = affine_grid_generator(out_H, out_W, theta)
else:
batch_grids = affine_grid_generator(H, W, theta)
x_s = batch_grids[:, 0, :, :]
y_s = batch_grids[:, 1, :, :]
# sample input with grid to get output
out_fmap = bilinear_sampler(input_fmap, x_s, y_s)
return out_fmap
def get_pixel_value(img, x, y):
"""
Utility function to get pixel value for coordinate
vectors x and y from a 4D tensor image.
Input
-----
- img: tensor of shape (B, H, W, C)
- x: flattened tensor of shape (B*H*W,)
- y: flattened tensor of shape (B*H*W,)
Returns
-------
- output: tensor of shape (B, H, W, C)
"""
shape = tf.shape(x)
batch_size = shape[0]
height = shape[1]
width = shape[2]
batch_idx = tf.range(0, batch_size)
batch_idx = tf.reshape(batch_idx, (batch_size, 1, 1))
b = tf.tile(batch_idx, (1, height, width))
indices = tf.stack([b, y, x], 3)
return tf.gather_nd(img, indices)
def affine_grid_generator(height, width, theta):
"""
This function returns a sampling grid, which when
used with the bilinear sampler on the input feature
map, will create an output feature map that is an
affine transformation [1] of the input feature map.
Input
-----
- height: desired height of grid/output. Used
to downsample or upsample.
- width: desired width of grid/output. Used
to downsample or upsample.
- theta: affine transform matrices of shape (num_batch, 2, 3).
For each image in the batch, we have 6 theta parameters of
the form (2x3) that define the affine transformation T.
Returns
-------
- normalized grid (-1, 1) of shape (num_batch, 2, H, W).
The 2nd dimension has 2 components: (x, y) which are the
sampling points of the original image for each point in the
target image.
Note
----
[1]: the affine transformation allows cropping, translation,
and isotropic scaling.
"""
num_batch = tf.shape(theta)[0]
# create normalized 2D grid
x = tf.linspace(-1.0, 1.0, width)
y = tf.linspace(-1.0, 1.0, height)
x_t, y_t = tf.meshgrid(x, y)
# flatten
x_t_flat = tf.reshape(x_t, [-1])
y_t_flat = tf.reshape(y_t, [-1])
# reshape to [x_t, y_t , 1] - (homogeneous form)
ones = tf.ones_like(x_t_flat)
sampling_grid = tf.stack([x_t_flat, y_t_flat, ones])
# repeat grid num_batch times
sampling_grid = tf.expand_dims(sampling_grid, axis=0)
sampling_grid = tf.tile(sampling_grid, tf.stack([num_batch, 1, 1]))
# cast to float32 (required for matmul)
theta = tf.cast(theta, 'float32')
sampling_grid = tf.cast(sampling_grid, 'float32')
# transform the sampling grid - batch multiply
batch_grids = tf.matmul(theta, sampling_grid)
# batch grid has shape (num_batch, 2, H*W)
# reshape to (num_batch, H, W, 2)
batch_grids = tf.reshape(batch_grids, [num_batch, 2, height, width])
return batch_grids
def bilinear_sampler(img, x, y):
"""
Performs bilinear sampling of the input images according to the
normalized coordinates provided by the sampling grid. Note that
the sampling is done identically for each channel of the input.
To test if the function works properly, output image should be
identical to input image when theta is initialized to identity
transform.
Input
-----
- img: batch of images in (B, H, W, C) layout.
- grid: x, y which is the output of affine_grid_generator.
Returns
-------
- out: interpolated images according to grids. Same size as grid.
"""
H = tf.shape(img)[1]
W = tf.shape(img)[2]
max_y = tf.cast(H - 1, 'int32')
max_x = tf.cast(W - 1, 'int32')
zero = tf.zeros([], dtype='int32')
# rescale x and y to [0, W-1/H-1]
x = tf.cast(x, 'float32')
y = tf.cast(y, 'float32')
x = 0.5 * ((x + 1.0) * tf.cast(max_x-1, 'float32'))
y = 0.5 * ((y + 1.0) * tf.cast(max_y-1, 'float32'))
# grab 4 nearest corner points for each (x_i, y_i)
x0 = tf.cast(tf.floor(x), 'int32')
x1 = x0 + 1
y0 = tf.cast(tf.floor(y), 'int32')
y1 = y0 + 1
# clip to range [0, H-1/W-1] to not violate img boundaries
x0 = tf.clip_by_value(x0, zero, max_x)
x1 = tf.clip_by_value(x1, zero, max_x)
y0 = tf.clip_by_value(y0, zero, max_y)
y1 = tf.clip_by_value(y1, zero, max_y)
# get pixel value at corner coords
Ia = get_pixel_value(img, x0, y0)
Ib = get_pixel_value(img, x0, y1)
Ic = get_pixel_value(img, x1, y0)
Id = get_pixel_value(img, x1, y1)
# recast as float for delta calculation
x0 = tf.cast(x0, 'float32')
x1 = tf.cast(x1, 'float32')
y0 = tf.cast(y0, 'float32')
y1 = tf.cast(y1, 'float32')
# calculate deltas
wa = (x1-x) * (y1-y)
wb = (x1-x) * (y-y0)
wc = (x-x0) * (y1-y)
wd = (x-x0) * (y-y0)
# add dimension for addition
wa = tf.expand_dims(wa, axis=3)
wb = tf.expand_dims(wb, axis=3)
wc = tf.expand_dims(wc, axis=3)
wd = tf.expand_dims(wd, axis=3)
# compute output
out = tf.add_n([wa*Ia, wb*Ib, wc*Ic, wd*Id])
return out
================================================
FILE: Attack/utils.py
================================================
import numpy as np
import tensorflow as tf
def tf_integral(x,a):
return 0.5*(x*tf.sqrt(x**2+a)+a*tf.log(tf.abs(x+tf.sqrt(x**2+a))))
def tf_pre_parabol(x,par):
x = x-450.
prev = 2.*par*(tf_integral(tf.abs(x),0.25/(par**2))-tf_integral(0,0.25/(par**2)))
return prev+450.
def projector(param,ph,logo):
'''Apply off-plane transformations to the sticker images
param: parabola rate of the off-plane parabolic tranformation, rank 2 tensor with shape [N, 1]
ph:angle of the off-plane rotation, rank 2 tensor with shape [N, 1]
logo: rank 4 tensor with format NHWC and shape [N, 400, 900, 3]
return: rank 4 tensor with format NHWC and shape [N, 900, 900, 3]
'''
right_cumsum = tf.transpose(tf.pad(tf.cumsum(logo[:,:,450:],axis=2),tf.constant([[0,0],[0,0],[1,0],[0,0]])),[0,2,1,3])
left_cumsum = tf.transpose(tf.pad(tf.cumsum(logo[:,:,:450][:,:,::-1],axis=2),tf.constant([[0,0],[0,0],[1,0],[0,0]])),[0,2,1,3])
anchors = tf.expand_dims(tf.cast(tf.round(tf.clip_by_value(\
tf_pre_parabol(tf.expand_dims(tf.constant(np.arange(450,901,dtype=np.float32)),0),\
param)-450.,0,450.)),tf.int32),2)
anch_inds = tf.tile(tf.expand_dims(tf.expand_dims(tf.range(tf.shape(param)[0]),1),2),[1,451,1])
new_anchors = tf.concat([anch_inds,anchors],2)
anchors_div = tf.expand_dims(tf.cast(tf.clip_by_value(anchors[:,1:]-anchors[:,:-1],1,900),tf.float32),3)
right_anchors_cumsum = tf.gather_nd(right_cumsum,new_anchors)
right_anchors_diffs = right_anchors_cumsum[:,1:]-right_anchors_cumsum[:,:-1]
right = right_anchors_diffs/anchors_div
left_anchors_cumsum = tf.gather_nd(left_cumsum,new_anchors)
left_anchors_diffs = left_anchors_cumsum[:,1:]-left_anchors_cumsum[:,:-1]
left = left_anchors_diffs/anchors_div
tmp_result = tf.transpose(tf.concat([left[:,::-1],right],axis=1),[0,2,1,3])
cumsum = tf.pad(tf.cumsum(tmp_result,axis=1),tf.constant([[0,0],[1,0],[0,0],[0,0]]))
angle = tf.expand_dims(np.pi/180.*ph,2)
z = param*tf.constant((np.arange(900,dtype=np.float32)-449.5)**2)
z_tile = tf.tile(tf.expand_dims(z,1),tf.constant([1,901,1]))
y_coord = tf.constant(np.arange(-250,651,dtype=np.float32))
y_tile = tf.tile(tf.expand_dims(tf.expand_dims(y_coord,1),0),[tf.shape(param)[0],1,900])
y_prev = (y_tile+z_tile*tf.sin(-angle))/tf.cos(angle)
y_round = tf.cast(tf.round(tf.clip_by_value(y_prev,0,400.)),tf.int32)
y_div = tf.clip_by_value(y_round[:,1:]-y_round[:,:-1],1,900)
x_coord = tf.constant(np.arange(900,dtype=np.int32))
x_tile = tf.tile(tf.expand_dims(tf.expand_dims(x_coord,0),0),[tf.shape(param)[0],901,1])
b_coord = tf.tile(tf.expand_dims(tf.expand_dims(tf.range(tf.shape(param)[0]),1),2),[1,901,900])
indices = tf.stack([b_coord,y_round,x_tile],axis=3)
chosen_cumsum = tf.gather_nd(cumsum,indices)
chosen_cumsum_diffs = chosen_cumsum[:,1:]-chosen_cumsum[:,:-1]
final_results = tf.clip_by_value(chosen_cumsum_diffs/tf.expand_dims(tf.cast(y_div,tf.float32),3),0.,1.)
return final_results
def TVloss(logo,w_tv):
'''Calculate TV loss of the sticker image with predefined weight.
logo: rank 4 tensor with format NHWC
w_tv: weight of the TV loss
return: scalar value of the TV loss
'''
vert_diff = logo[:,1:]-logo[:,:-1]
hor_diff = logo[:,:,1:]-logo[:,:,:-1]
vert_diff_sq = tf.square(vert_diff)
hor_diff_sq = tf.square(hor_diff)
vert_pad = tf.pad(vert_diff_sq,tf.constant([[0,0],[1,0],[0,0],[0,0]]))
hor_pad = tf.pad(hor_diff_sq,tf.constant([[0,0],[0,0],[1,0],[0,0]]))
tv_sum = vert_pad+hor_pad
tv = tf.sqrt(tv_sum+1e-5)
tv_final_sum = tf.reduce_sum(tv)
tv_loss = w_tv*tv_final_sum
return tv_loss
================================================
FILE: Demo/1000_from_CASIA/centroids_names.txt
================================================
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================================================
FILE: Demo/README.md
================================================
## Demo launch
ArcFace@ms1m-refine-v2 transformed to TensorFlow is available [here](https://drive.google.com/file/d/1fb70KgMRSmaEUF5cJ67BCD_DmTPCR5uJ/view?usp=sharing).
The command for demo launch:
`python3 demo.py PATH_TO_THE_DOWNLOADED_MODEL PATH_TO_THE_DIRECTORY_WITH_CLASS_CENTROIDS`
Centroids for the first 1000 classes of CASIA are in the "1000_from_CASIA" directory.
## Preparation of your own centroids
### Alignment
The dataset of your images has to be arranged in the following way:
├── Person 1
│ ├── Person_1_image_1.png
│ ├── Person_1_image_2.png
│ ├── Person_1_image_3.png
│ └── Person_1_image_4.png
├── Person 2
│ ├── Person_2_image_1.png
│ ├── Person_2_image_2.png
│ ├── Person_2_image_3.png
│ ├── Person_2_image_4.png
│ └── Person_2_image_5.png
├── Person 3
│ ├── Person_3_image_1.png
│ ├── Person_3_image_2.png
...
The command for images alignment:
`python3 alignment.py PATH_TO_DIRECTIRY_WITH_IMAGES PATH_FOR_THE_ALIGNED_IMAGES`
### Centroids calculation
Using directory with aligned images from the previous step, you can obtain centroids with the next command:
`python3 dumping.py PATH_TO_DIRECTORY_WITH_ALIGNED_IMAGES PATH_FOR_THE_CENTROIDS PATH_TO_THE_DOWNLOADED_MODEL`
================================================
FILE: Demo/align/.gitignore
================================================
*.pyc
================================================
FILE: Demo/align/__init__.py
================================================
================================================
FILE: Demo/align/detect_face.py
================================================
""" Tensorflow implementation of the face detection / alignment algorithm found at
https://github.com/kpzhang93/MTCNN_face_detection_alignment
"""
# MIT License
#
# Copyright (c) 2016 David Sandberg
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from six import string_types, iteritems
import numpy as np
import tensorflow as tf
#from math import floor
import cv2
import os
def layer(op):
'''Decorator for composable network layers.'''
def layer_decorated(self, *args, **kwargs):
# Automatically set a name if not provided.
name = kwargs.setdefault('name', self.get_unique_name(op.__name__))
# Figure out the layer inputs.
if len(self.terminals) == 0:
raise RuntimeError('No input variables found for layer %s.' % name)
elif len(self.terminals) == 1:
layer_input = self.terminals[0]
else:
layer_input = list(self.terminals)
# Perform the operation and get the output.
layer_output = op(self, layer_input, *args, **kwargs)
# Add to layer LUT.
self.layers[name] = layer_output
# This output is now the input for the next layer.
self.feed(layer_output)
# Return self for chained calls.
return self
return layer_decorated
class Network(object):
def __init__(self, inputs, trainable=True):
# The input nodes for this network
self.inputs = inputs
# The current list of terminal nodes
self.terminals = []
# Mapping from layer names to layers
self.layers = dict(inputs)
# If true, the resulting variables are set as trainable
self.trainable = trainable
self.setup()
def setup(self):
'''Construct the network. '''
raise NotImplementedError('Must be implemented by the subclass.')
def load(self, data_path, session, ignore_missing=False):
'''Load network weights.
data_path: The path to the numpy-serialized network weights
session: The current TensorFlow session
ignore_missing: If true, serialized weights for missing layers are ignored.
'''
data_dict = np.load(data_path, encoding='latin1', allow_pickle=True).item() #pylint: disable=no-member
for op_name in data_dict:
with tf.variable_scope(op_name, reuse=True):
for param_name, data in iteritems(data_dict[op_name]):
try:
var = tf.get_variable(param_name)
session.run(var.assign(data))
except ValueError:
if not ignore_missing:
raise
def feed(self, *args):
'''Set the input(s) for the next operation by replacing the terminal nodes.
The arguments can be either layer names or the actual layers.
'''
assert len(args) != 0
self.terminals = []
for fed_layer in args:
if isinstance(fed_layer, string_types):
try:
fed_layer = self.layers[fed_layer]
except KeyError:
raise KeyError('Unknown layer name fed: %s' % fed_layer)
self.terminals.append(fed_layer)
return self
def get_output(self):
'''Returns the current network output.'''
return self.terminals[-1]
def get_unique_name(self, prefix):
'''Returns an index-suffixed unique name for the given prefix.
This is used for auto-generating layer names based on the type-prefix.
'''
ident = sum(t.startswith(prefix) for t, _ in self.layers.items()) + 1
return '%s_%d' % (prefix, ident)
def make_var(self, name, shape):
'''Creates a new TensorFlow variable.'''
return tf.get_variable(name, shape, trainable=self.trainable)
def validate_padding(self, padding):
'''Verifies that the padding is one of the supported ones.'''
assert padding in ('SAME', 'VALID')
@layer
def conv(self,
inp,
k_h,
k_w,
c_o,
s_h,
s_w,
name,
relu=True,
padding='SAME',
group=1,
biased=True):
# Verify that the padding is acceptable
self.validate_padding(padding)
# Get the number of channels in the input
c_i = int(inp.get_shape()[-1])
# Verify that the grouping parameter is valid
assert c_i % group == 0
assert c_o % group == 0
# Convolution for a given input and kernel
convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
with tf.variable_scope(name) as scope:
kernel = self.make_var('weights', shape=[k_h, k_w, c_i // group, c_o])
# This is the common-case. Convolve the input without any further complications.
output = convolve(inp, kernel)
# Add the biases
if biased:
biases = self.make_var('biases', [c_o])
output = tf.nn.bias_add(output, biases)
if relu:
# ReLU non-linearity
output = tf.nn.relu(output, name=scope.name)
return output
@layer
def prelu(self, inp, name):
with tf.variable_scope(name):
i = int(inp.get_shape()[-1])
alpha = self.make_var('alpha', shape=(i,))
output = tf.nn.relu(inp) + tf.multiply(alpha, -tf.nn.relu(-inp))
return output
@layer
def max_pool(self, inp, k_h, k_w, s_h, s_w, name, padding='SAME'):
self.validate_padding(padding)
return tf.nn.max_pool(inp,
ksize=[1, k_h, k_w, 1],
strides=[1, s_h, s_w, 1],
padding=padding,
name=name)
@layer
def fc(self, inp, num_out, name, relu=True):
with tf.variable_scope(name):
input_shape = inp.get_shape()
if input_shape.ndims == 4:
# The input is spatial. Vectorize it first.
dim = 1
for d in input_shape[1:].as_list():
dim *= int(d)
feed_in = tf.reshape(inp, [-1, dim])
else:
feed_in, dim = (inp, input_shape[-1].value)
weights = self.make_var('weights', shape=[dim, num_out])
biases = self.make_var('biases', [num_out])
op = tf.nn.relu_layer if relu else tf.nn.xw_plus_b
fc = op(feed_in, weights, biases, name=name)
return fc
"""
Multi dimensional softmax,
refer to https://github.com/tensorflow/tensorflow/issues/210
compute softmax along the dimension of target
the native softmax only supports batch_size x dimension
"""
@layer
def softmax(self, target, axis, name=None):
max_axis = tf.reduce_max(target, axis, keep_dims=True)
target_exp = tf.exp(target-max_axis)
normalize = tf.reduce_sum(target_exp, axis, keep_dims=True)
softmax = tf.div(target_exp, normalize, name)
return softmax
class PNet(Network):
def setup(self):
(self.feed('data') #pylint: disable=no-value-for-parameter, no-member
.conv(3, 3, 10, 1, 1, padding='VALID', relu=False, name='conv1')
.prelu(name='PReLU1')
.max_pool(2, 2, 2, 2, name='pool1')
.conv(3, 3, 16, 1, 1, padding='VALID', relu=False, name='conv2')
.prelu(name='PReLU2')
.conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv3')
.prelu(name='PReLU3')
.conv(1, 1, 2, 1, 1, relu=False, name='conv4-1')
.softmax(3,name='prob1'))
(self.feed('PReLU3') #pylint: disable=no-value-for-parameter
.conv(1, 1, 4, 1, 1, relu=False, name='conv4-2'))
class RNet(Network):
def setup(self):
(self.feed('data') #pylint: disable=no-value-for-parameter, no-member
.conv(3, 3, 28, 1, 1, padding='VALID', relu=False, name='conv1')
.prelu(name='prelu1')
.max_pool(3, 3, 2, 2, name='pool1')
.conv(3, 3, 48, 1, 1, padding='VALID', relu=False, name='conv2')
.prelu(name='prelu2')
.max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
.conv(2, 2, 64, 1, 1, padding='VALID', relu=False, name='conv3')
.prelu(name='prelu3')
.fc(128, relu=False, name='conv4')
.prelu(name='prelu4')
.fc(2, relu=False, name='conv5-1')
.softmax(1,name='prob1'))
(self.feed('prelu4') #pylint: disable=no-value-for-parameter
.fc(4, relu=False, name='conv5-2'))
class ONet(Network):
def setup(self):
(self.feed('data') #pylint: disable=no-value-for-parameter, no-member
.conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv1')
.prelu(name='prelu1')
.max_pool(3, 3, 2, 2, name='pool1')
.conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv2')
.prelu(name='prelu2')
.max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
.conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv3')
.prelu(name='prelu3')
.max_pool(2, 2, 2, 2, name='pool3')
.conv(2, 2, 128, 1, 1, padding='VALID', relu=False, name='conv4')
.prelu(name='prelu4')
.fc(256, relu=False, name='conv5')
.prelu(name='prelu5')
.fc(2, relu=False, name='conv6-1')
.softmax(1, name='prob1'))
(self.feed('prelu5') #pylint: disable=no-value-for-parameter
.fc(4, relu=False, name='conv6-2'))
(self.feed('prelu5') #pylint: disable=no-value-for-parameter
.fc(10, relu=False, name='conv6-3'))
def create_mtcnn(sess, model_path):
if not model_path:
model_path,_ = os.path.split(os.path.realpath(__file__))
with tf.variable_scope('pnet'):
data = tf.placeholder(tf.float32, (None,None,None,3), 'input')
pnet = PNet({'data':data})
pnet.load(os.path.join(model_path, 'det1.npy'), sess)
with tf.variable_scope('rnet'):
data = tf.placeholder(tf.float32, (None,24,24,3), 'input')
rnet = RNet({'data':data})
rnet.load(os.path.join(model_path, 'det2.npy'), sess)
with tf.variable_scope('onet'):
data = tf.placeholder(tf.float32, (None,48,48,3), 'input')
onet = ONet({'data':data})
onet.load(os.path.join(model_path, 'det3.npy'), sess)
pnet_fun = lambda img : sess.run(('pnet/conv4-2/BiasAdd:0', 'pnet/prob1:0'), feed_dict={'pnet/input:0':img})
rnet_fun = lambda img : sess.run(('rnet/conv5-2/conv5-2:0', 'rnet/prob1:0'), feed_dict={'rnet/input:0':img})
onet_fun = lambda img : sess.run(('onet/conv6-2/conv6-2:0', 'onet/conv6-3/conv6-3:0', 'onet/prob1:0'), feed_dict={'onet/input:0':img})
return pnet_fun, rnet_fun, onet_fun
def detect_face(img, minsize, pnet, rnet, onet, threshold, factor):
# im: input image
# minsize: minimum of faces' size
# pnet, rnet, onet: caffemodel
# threshold: threshold=[th1 th2 th3], th1-3 are three steps's threshold
# fastresize: resize img from last scale (using in high-resolution images) if fastresize==true
factor_count=0
total_boxes=np.empty((0,9))
points=[]
h=img.shape[0]
w=img.shape[1]
minl=np.amin([h, w])
m=12.0/minsize
minl=minl*m
# creat scale pyramid
scales=[]
while minl>=12:
scales += [m*np.power(factor, factor_count)]
minl = minl*factor
factor_count += 1
# first stage
for j in range(len(scales)):
scale=scales[j]
hs=int(np.ceil(h*scale))
ws=int(np.ceil(w*scale))
im_data = imresample(img, (hs, ws))
im_data = (im_data-127.5)*0.0078125
img_x = np.expand_dims(im_data, 0)
img_y = np.transpose(img_x, (0,2,1,3))
out = pnet(img_y)
out0 = np.transpose(out[0], (0,2,1,3))
out1 = np.transpose(out[1], (0,2,1,3))
boxes, _ = generateBoundingBox(out1[0,:,:,1].copy(), out0[0,:,:,:].copy(), scale, threshold[0])
# inter-scale nms
pick = nms(boxes.copy(), 0.5, 'Union')
if boxes.size>0 and pick.size>0:
boxes = boxes[pick,:]
total_boxes = np.append(total_boxes, boxes, axis=0)
numbox = total_boxes.shape[0]
if numbox>0:
pick = nms(total_boxes.copy(), 0.7, 'Union')
total_boxes = total_boxes[pick,:]
regw = total_boxes[:,2]-total_boxes[:,0]
regh = total_boxes[:,3]-total_boxes[:,1]
qq1 = total_boxes[:,0]+total_boxes[:,5]*regw
qq2 = total_boxes[:,1]+total_boxes[:,6]*regh
qq3 = total_boxes[:,2]+total_boxes[:,7]*regw
qq4 = total_boxes[:,3]+total_boxes[:,8]*regh
total_boxes = np.transpose(np.vstack([qq1, qq2, qq3, qq4, total_boxes[:,4]]))
total_boxes = rerec(total_boxes.copy())
total_boxes[:,0:4] = np.fix(total_boxes[:,0:4]).astype(np.int32)
dy,edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)
numbox = total_boxes.shape[0]
if numbox>0:
# second stage
tempimg = np.zeros((24,24,3,numbox))
for k in range(0,numbox):
tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))
tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]
if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:
tempimg[:,:,:,k] = imresample(tmp, (24, 24))
else:
return np.empty()
tempimg = (tempimg-127.5)*0.0078125
tempimg1 = np.transpose(tempimg, (3,1,0,2))
out = rnet(tempimg1)
out0 = np.transpose(out[0])
out1 = np.transpose(out[1])
score = out1[1,:]
ipass = np.where(score>threshold[1])
total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])
mv = out0[:,ipass[0]]
if total_boxes.shape[0]>0:
pick = nms(total_boxes, 0.7, 'Union')
total_boxes = total_boxes[pick,:]
total_boxes = bbreg(total_boxes.copy(), np.transpose(mv[:,pick]))
total_boxes = rerec(total_boxes.copy())
numbox = total_boxes.shape[0]
if numbox>0:
# third stage
total_boxes = np.fix(total_boxes).astype(np.int32)
dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)
tempimg = np.zeros((48,48,3,numbox))
for k in range(0,numbox):
tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))
tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]
if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:
tempimg[:,:,:,k] = imresample(tmp, (48, 48))
else:
return np.empty()
tempimg = (tempimg-127.5)*0.0078125
tempimg1 = np.transpose(tempimg, (3,1,0,2))
out = onet(tempimg1)
out0 = np.transpose(out[0])
out1 = np.transpose(out[1])
out2 = np.transpose(out[2])
score = out2[1,:]
points = out1
ipass = np.where(score>threshold[2])
points = points[:,ipass[0]]
total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])
mv = out0[:,ipass[0]]
w = total_boxes[:,2]-total_boxes[:,0]+1
h = total_boxes[:,3]-total_boxes[:,1]+1
points[0:5,:] = np.tile(w,(5, 1))*points[0:5,:] + np.tile(total_boxes[:,0],(5, 1))-1
points[5:10,:] = np.tile(h,(5, 1))*points[5:10,:] + np.tile(total_boxes[:,1],(5, 1))-1
if total_boxes.shape[0]>0:
total_boxes = bbreg(total_boxes.copy(), np.transpose(mv))
pick = nms(total_boxes.copy(), 0.7, 'Min')
total_boxes = total_boxes[pick,:]
points = points[:,pick]
return total_boxes, points
def detect_face_force(img, bbox, pnet, rnet, onet):
total_boxes = np.zeros( (1,5), dtype=np.float32)
total_boxes[0,0:4] = bbox
threshold = [0.0,0.0,0.0]
h=img.shape[0]
w=img.shape[1]
numbox = total_boxes.shape[0]
if numbox>0:
dy,edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)
# second stage
tempimg = np.zeros((24,24,3,numbox))
for k in range(0,numbox):
tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))
tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]
if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:
tempimg[:,:,:,k] = imresample(tmp, (24, 24))
else:
return np.empty()
tempimg = (tempimg-127.5)*0.0078125
tempimg1 = np.transpose(tempimg, (3,1,0,2))
out = rnet(tempimg1)
out0 = np.transpose(out[0])
out1 = np.transpose(out[1])
score = out1[1,:]
ipass = np.where(score>threshold[1])
total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])
mv = out0[:,ipass[0]]
if total_boxes.shape[0]>0:
pick = nms(total_boxes, 0.7, 'Union')
total_boxes = total_boxes[pick,:]
total_boxes = bbreg(total_boxes.copy(), np.transpose(mv[:,pick]))
total_boxes = rerec(total_boxes.copy())
numbox = total_boxes.shape[0]
if numbox>0:
# third stage
total_boxes = np.fix(total_boxes).astype(np.int32)
dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)
tempimg = np.zeros((48,48,3,numbox))
for k in range(0,numbox):
tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))
tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]
if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:
tempimg[:,:,:,k] = imresample(tmp, (48, 48))
else:
return np.empty()
tempimg = (tempimg-127.5)*0.0078125
tempimg1 = np.transpose(tempimg, (3,1,0,2))
out = onet(tempimg1)
out0 = np.transpose(out[0])
out1 = np.transpose(out[1])
out2 = np.transpose(out[2])
score = out2[1,:]
points = out1
ipass = np.where(score>threshold[2])
points = points[:,ipass[0]]
total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])
mv = out0[:,ipass[0]]
w = total_boxes[:,2]-total_boxes[:,0]+1
h = total_boxes[:,3]-total_boxes[:,1]+1
points[0:5,:] = np.tile(w,(5, 1))*points[0:5,:] + np.tile(total_boxes[:,0],(5, 1))-1
points[5:10,:] = np.tile(h,(5, 1))*points[5:10,:] + np.tile(total_boxes[:,1],(5, 1))-1
if total_boxes.shape[0]>0:
total_boxes = bbreg(total_boxes.copy(), np.transpose(mv))
pick = nms(total_boxes.copy(), 0.7, 'Min')
total_boxes = total_boxes[pick,:]
points = points[:,pick]
return total_boxes, points
def bulk_detect_face(images, detection_window_size_ratio, pnet, rnet, onet, threshold, factor):
# im: input image
# minsize: minimum of faces' size
# pnet, rnet, onet: caffemodel
# threshold: threshold=[th1 th2 th3], th1-3 are three steps's threshold [0-1]
all_scales = [None] * len(images)
images_with_boxes = [None] * len(images)
for i in range(len(images)):
images_with_boxes[i] = {'total_boxes': np.empty((0, 9))}
# create scale pyramid
for index, img in enumerate(images):
all_scales[index] = []
h = img.shape[0]
w = img.shape[1]
minsize = int(detection_window_size_ratio * np.minimum(w, h))
factor_count = 0
minl = np.amin([h, w])
if minsize <= 12:
minsize = 12
m = 12.0 / minsize
minl = minl * m
while minl >= 12:
all_scales[index].append(m * np.power(factor, factor_count))
minl = minl * factor
factor_count += 1
# # # # # # # # # # # # #
# first stage - fast proposal network (pnet) to obtain face candidates
# # # # # # # # # # # # #
images_obj_per_resolution = {}
# TODO: use some type of rounding to number module 8 to increase probability that pyramid images will have the same resolution across input images
for index, scales in enumerate(all_scales):
h = images[index].shape[0]
w = images[index].shape[1]
for scale in scales:
hs = int(np.ceil(h * scale))
ws = int(np.ceil(w * scale))
if (ws, hs) not in images_obj_per_resolution:
images_obj_per_resolution[(ws, hs)] = []
im_data = imresample(images[index], (hs, ws))
im_data = (im_data - 127.5) * 0.0078125
img_y = np.transpose(im_data, (1, 0, 2)) # caffe uses different dimensions ordering
images_obj_per_resolution[(ws, hs)].append({'scale': scale, 'image': img_y, 'index': index})
for resolution in images_obj_per_resolution:
images_per_resolution = [i['image'] for i in images_obj_per_resolution[resolution]]
outs = pnet(images_per_resolution)
for index in range(len(outs[0])):
scale = images_obj_per_resolution[resolution][index]['scale']
image_index = images_obj_per_resolution[resolution][index]['index']
out0 = np.transpose(outs[0][index], (1, 0, 2))
out1 = np.transpose(outs[1][index], (1, 0, 2))
boxes, _ = generateBoundingBox(out1[:, :, 1].copy(), out0[:, :, :].copy(), scale, threshold[0])
# inter-scale nms
pick = nms(boxes.copy(), 0.5, 'Union')
if boxes.size > 0 and pick.size > 0:
boxes = boxes[pick, :]
images_with_boxes[image_index]['total_boxes'] = np.append(images_with_boxes[image_index]['total_boxes'],
boxes,
axis=0)
for index, image_obj in enumerate(images_with_boxes):
numbox = image_obj['total_boxes'].shape[0]
if numbox > 0:
h = images[index].shape[0]
w = images[index].shape[1]
pick = nms(image_obj['total_boxes'].copy(), 0.7, 'Union')
image_obj['total_boxes'] = image_obj['total_boxes'][pick, :]
regw = image_obj['total_boxes'][:, 2] - image_obj['total_boxes'][:, 0]
regh = image_obj['total_boxes'][:, 3] - image_obj['total_boxes'][:, 1]
qq1 = image_obj['total_boxes'][:, 0] + image_obj['total_boxes'][:, 5] * regw
qq2 = image_obj['total_boxes'][:, 1] + image_obj['total_boxes'][:, 6] * regh
qq3 = image_obj['total_boxes'][:, 2] + image_obj['total_boxes'][:, 7] * regw
qq4 = image_obj['total_boxes'][:, 3] + image_obj['total_boxes'][:, 8] * regh
image_obj['total_boxes'] = np.transpose(np.vstack([qq1, qq2, qq3, qq4, image_obj['total_boxes'][:, 4]]))
image_obj['total_boxes'] = rerec(image_obj['total_boxes'].copy())
image_obj['total_boxes'][:, 0:4] = np.fix(image_obj['total_boxes'][:, 0:4]).astype(np.int32)
dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(image_obj['total_boxes'].copy(), w, h)
numbox = image_obj['total_boxes'].shape[0]
tempimg = np.zeros((24, 24, 3, numbox))
if numbox > 0:
for k in range(0, numbox):
tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3))
tmp[dy[k] - 1:edy[k], dx[k] - 1:edx[k], :] = images[index][y[k] - 1:ey[k], x[k] - 1:ex[k], :]
if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
tempimg[:, :, :, k] = imresample(tmp, (24, 24))
else:
return np.empty()
tempimg = (tempimg - 127.5) * 0.0078125
image_obj['rnet_input'] = np.transpose(tempimg, (3, 1, 0, 2))
# # # # # # # # # # # # #
# second stage - refinement of face candidates with rnet
# # # # # # # # # # # # #
bulk_rnet_input = np.empty((0, 24, 24, 3))
for index, image_obj in enumerate(images_with_boxes):
if 'rnet_input' in image_obj:
bulk_rnet_input = np.append(bulk_rnet_input, image_obj['rnet_input'], axis=0)
out = rnet(bulk_rnet_input)
out0 = np.transpose(out[0])
out1 = np.transpose(out[1])
score = out1[1, :]
i = 0
for index, image_obj in enumerate(images_with_boxes):
if 'rnet_input' not in image_obj:
continue
rnet_input_count = image_obj['rnet_input'].shape[0]
score_per_image = score[i:i + rnet_input_count]
out0_per_image = out0[:, i:i + rnet_input_count]
ipass = np.where(score_per_image > threshold[1])
image_obj['total_boxes'] = np.hstack([image_obj['total_boxes'][ipass[0], 0:4].copy(),
np.expand_dims(score_per_image[ipass].copy(), 1)])
mv = out0_per_image[:, ipass[0]]
if image_obj['total_boxes'].shape[0] > 0:
h = images[index].shape[0]
w = images[index].shape[1]
pick = nms(image_obj['total_boxes'], 0.7, 'Union')
image_obj['total_boxes'] = image_obj['total_boxes'][pick, :]
image_obj['total_boxes'] = bbreg(image_obj['total_boxes'].copy(), np.transpose(mv[:, pick]))
image_obj['total_boxes'] = rerec(image_obj['total_boxes'].copy())
numbox = image_obj['total_boxes'].shape[0]
if numbox > 0:
tempimg = np.zeros((48, 48, 3, numbox))
image_obj['total_boxes'] = np.fix(image_obj['total_boxes']).astype(np.int32)
dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(image_obj['total_boxes'].copy(), w, h)
for k in range(0, numbox):
tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3))
tmp[dy[k] - 1:edy[k], dx[k] - 1:edx[k], :] = images[index][y[k] - 1:ey[k], x[k] - 1:ex[k], :]
if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
tempimg[:, :, :, k] = imresample(tmp, (48, 48))
else:
return np.empty()
tempimg = (tempimg - 127.5) * 0.0078125
image_obj['onet_input'] = np.transpose(tempimg, (3, 1, 0, 2))
i += rnet_input_count
# # # # # # # # # # # # #
# third stage - further refinement and facial landmarks positions with onet
# # # # # # # # # # # # #
bulk_onet_input = np.empty((0, 48, 48, 3))
for index, image_obj in enumerate(images_with_boxes):
if 'onet_input' in image_obj:
bulk_onet_input = np.append(bulk_onet_input, image_obj['onet_input'], axis=0)
out = onet(bulk_onet_input)
out0 = np.transpose(out[0])
out1 = np.transpose(out[1])
out2 = np.transpose(out[2])
score = out2[1, :]
points = out1
i = 0
ret = []
for index, image_obj in enumerate(images_with_boxes):
if 'onet_input' not in image_obj:
ret.append(None)
continue
onet_input_count = image_obj['onet_input'].shape[0]
out0_per_image = out0[:, i:i + onet_input_count]
score_per_image = score[i:i + onet_input_count]
points_per_image = points[:, i:i + onet_input_count]
ipass = np.where(score_per_image > threshold[2])
points_per_image = points_per_image[:, ipass[0]]
image_obj['total_boxes'] = np.hstack([image_obj['total_boxes'][ipass[0], 0:4].copy(),
np.expand_dims(score_per_image[ipass].copy(), 1)])
mv = out0_per_image[:, ipass[0]]
w = image_obj['total_boxes'][:, 2] - image_obj['total_boxes'][:, 0] + 1
h = image_obj['total_boxes'][:, 3] - image_obj['total_boxes'][:, 1] + 1
points_per_image[0:5, :] = np.tile(w, (5, 1)) * points_per_image[0:5, :] + np.tile(
image_obj['total_boxes'][:, 0], (5, 1)) - 1
points_per_image[5:10, :] = np.tile(h, (5, 1)) * points_per_image[5:10, :] + np.tile(
image_obj['total_boxes'][:, 1], (5, 1)) - 1
if image_obj['total_boxes'].shape[0] > 0:
image_obj['total_boxes'] = bbreg(image_obj['total_boxes'].copy(), np.transpose(mv))
pick = nms(image_obj['total_boxes'].copy(), 0.7, 'Min')
image_obj['total_boxes'] = image_obj['total_boxes'][pick, :]
points_per_image = points_per_image[:, pick]
ret.append((image_obj['total_boxes'], points_per_image))
else:
ret.append(None)
i += onet_input_count
return ret
# function [boundingbox] = bbreg(boundingbox,reg)
def bbreg(boundingbox,reg):
# calibrate bounding boxes
if reg.shape[1]==1:
reg = np.reshape(reg, (reg.shape[2], reg.shape[3]))
w = boundingbox[:,2]-boundingbox[:,0]+1
h = boundingbox[:,3]-boundingbox[:,1]+1
b1 = boundingbox[:,0]+reg[:,0]*w
b2 = boundingbox[:,1]+reg[:,1]*h
b3 = boundingbox[:,2]+reg[:,2]*w
b4 = boundingbox[:,3]+reg[:,3]*h
boundingbox[:,0:4] = np.transpose(np.vstack([b1, b2, b3, b4 ]))
return boundingbox
def generateBoundingBox(imap, reg, scale, t):
# use heatmap to generate bounding boxes
stride=2
cellsize=12
imap = np.transpose(imap)
dx1 = np.transpose(reg[:,:,0])
dy1 = np.transpose(reg[:,:,1])
dx2 = np.transpose(reg[:,:,2])
dy2 = np.transpose(reg[:,:,3])
y, x = np.where(imap >= t)
if y.shape[0]==1:
dx1 = np.flipud(dx1)
dy1 = np.flipud(dy1)
dx2 = np.flipud(dx2)
dy2 = np.flipud(dy2)
score = imap[(y,x)]
reg = np.transpose(np.vstack([ dx1[(y,x)], dy1[(y,x)], dx2[(y,x)], dy2[(y,x)] ]))
if reg.size==0:
reg = np.empty((0,3))
bb = np.transpose(np.vstack([y,x]))
q1 = np.fix((stride*bb+1)/scale)
q2 = np.fix((stride*bb+cellsize-1+1)/scale)
boundingbox = np.hstack([q1, q2, np.expand_dims(score,1), reg])
return boundingbox, reg
# function pick = nms(boxes,threshold,type)
def nms(boxes, threshold, method):
if boxes.size==0:
return np.empty((0,3))
x1 = boxes[:,0]
y1 = boxes[:,1]
x2 = boxes[:,2]
y2 = boxes[:,3]
s = boxes[:,4]
area = (x2-x1+1) * (y2-y1+1)
I = np.argsort(s)
pick = np.zeros_like(s, dtype=np.int16)
counter = 0
while I.size>0:
i = I[-1]
pick[counter] = i
counter += 1
idx = I[0:-1]
xx1 = np.maximum(x1[i], x1[idx])
yy1 = np.maximum(y1[i], y1[idx])
xx2 = np.minimum(x2[i], x2[idx])
yy2 = np.minimum(y2[i], y2[idx])
w = np.maximum(0.0, xx2-xx1+1)
h = np.maximum(0.0, yy2-yy1+1)
inter = w * h
if method == 'Min':
o = inter / np.minimum(area[i], area[idx])
else:
o = inter / (area[i] + area[idx] - inter)
I = I[np.where(o<=threshold)]
pick = pick[0:counter]
return pick
# function [dy edy dx edx y ey x ex tmpw tmph] = pad(total_boxes,w,h)
def pad(total_boxes, w, h):
# compute the padding coordinates (pad the bounding boxes to square)
tmpw = (total_boxes[:,2]-total_boxes[:,0]+1).astype(np.int32)
tmph = (total_boxes[:,3]-total_boxes[:,1]+1).astype(np.int32)
numbox = total_boxes.shape[0]
dx = np.ones((numbox), dtype=np.int32)
dy = np.ones((numbox), dtype=np.int32)
edx = tmpw.copy().astype(np.int32)
edy = tmph.copy().astype(np.int32)
x = total_boxes[:,0].copy().astype(np.int32)
y = total_boxes[:,1].copy().astype(np.int32)
ex = total_boxes[:,2].copy().astype(np.int32)
ey = total_boxes[:,3].copy().astype(np.int32)
tmp = np.where(ex>w)
edx.flat[tmp] = np.expand_dims(-ex[tmp]+w+tmpw[tmp],1)
ex[tmp] = w
tmp = np.where(ey>h)
edy.flat[tmp] = np.expand_dims(-ey[tmp]+h+tmph[tmp],1)
ey[tmp] = h
tmp = np.where(x<1)
dx.flat[tmp] = np.expand_dims(2-x[tmp],1)
x[tmp] = 1
tmp = np.where(y<1)
dy.flat[tmp] = np.expand_dims(2-y[tmp],1)
y[tmp] = 1
return dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph
# function [bboxA] = rerec(bboxA)
def rerec(bboxA):
# convert bboxA to square
h = bboxA[:,3]-bboxA[:,1]
w = bboxA[:,2]-bboxA[:,0]
l = np.maximum(w, h)
bboxA[:,0] = bboxA[:,0]+w*0.5-l*0.5
bboxA[:,1] = bboxA[:,1]+h*0.5-l*0.5
bboxA[:,2:4] = bboxA[:,0:2] + np.transpose(np.tile(l,(2,1)))
return bboxA
def imresample(img, sz):
im_data = cv2.resize(img, (sz[1], sz[0]), interpolation=cv2.INTER_AREA) #@UndefinedVariable
return im_data
# This method is kept for debugging purpose
# h=img.shape[0]
# w=img.shape[1]
# hs, ws = sz
# dx = float(w) / ws
# dy = float(h) / hs
# im_data = np.zeros((hs,ws,3))
# for a1 in range(0,hs):
# for a2 in range(0,ws):
# for a3 in range(0,3):
# im_data[a1,a2,a3] = img[int(floor(a1*dy)),int(floor(a2*dx)),a3]
# return im_data
================================================
FILE: Demo/alignment.py
================================================
import argparse
import numpy as np
import cv2
from skimage import transform as trans
import tensorflow as tf
import os
import skimage.io as io
import sys
from tqdm import tqdm
import align.detect_face as detect_face
# Transform grey image to RGB image
def to_rgb(img):
w, h = img.shape
ret = np.empty((w, h, 3), dtype=np.uint8)
ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img
return ret
# Align face as ArcFace template
def preprocess(img, landmark):
image_size = [112,112]
src = np.array([
[38.2946, 51.6963],
[73.5318, 51.5014],
[56.0252, 71.7366],
[41.5493, 92.3655],
[70.7299, 92.2041] ], dtype=np.float32)
dst = landmark.astype(np.float32)
tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2,:]
warped = cv2.warpAffine(img,M,(image_size[1],image_size[0]), borderValue = 0.0)
return warped
def main(args):
# MTCNN
with tf.Graph().as_default():
sess = tf.Session()
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
threshold = [ 0.6, 0.7, 0.7 ]
factor = 0.709
# Output dirs creation
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
images = []
for path in sorted(os.listdir(args.input_dir)):
if not os.path.exists(os.path.join(args.output_dir,path)):
os.mkdir(os.path.join(args.output_dir,path))
for name in sorted(os.listdir(os.path.join(args.input_dir,path))):
images.append(os.path.join(path,name))
# Alignment procedure
for path in tqdm(images):
img = io.imread(os.path.join(args.input_dir,path))
if img.ndim == 2:
img = to_rgb(img)
img = img[:,:,0:3]
_minsize = min(min(img.shape[0]//5, img.shape[1]//5),80)
bounding_boxes, points = detect_face.detect_face(img, _minsize, pnet, rnet, onet, threshold, factor)
if bounding_boxes.size>0:
bindex = -1
nrof_faces = bounding_boxes.shape[0]
if nrof_faces>0:
det = bounding_boxes[:,0:4]
img_size = np.asarray(img.shape)[0:2]
bindex = 0
if nrof_faces>1:
bounding_box_size = (det[:,2]-det[:,0])*(det[:,3]-det[:,1])
img_center = img_size / 2
offsets = np.vstack([ (det[:,0]+det[:,2])/2-img_center[1], (det[:,1]+det[:,3])/2-img_center[0] ])
offset_dist_squared = np.sum(np.power(offsets,2.0),0)
bindex = np.argmax(bounding_box_size-offset_dist_squared*2.0)
points = points[:, bindex]
landmark = points.reshape((2,5)).T
warped = preprocess(img, landmark)
io.imsave(os.path.join(args.output_dir,path), warped)
else:
print(path+' was skipped')
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('input_dir', type=str, help='Directory with unaligned images.')
parser.add_argument('output_dir', type=str, help='Directory for aligned face thumbnails.')
return parser.parse_args(argv)
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))
================================================
FILE: Demo/demo.py
================================================
import sys
import argparse
import numpy as np
import cv2
import tensorflow as tf
from align import detect_face
from skimage import transform as trans
from skimage.io import imsave
import os
import datetime
# Align face as ArcFace template
def preprocess(img, landmark):
image_size = [112,112]
src = np.array([
[38.2946, 51.6963],
[73.5318, 51.5014],
[56.0252, 71.7366],
[41.5493, 92.3655],
[70.7299, 92.2041] ], dtype=np.float32)
dst = landmark.astype(np.float32)
tform = trans.SimilarityTransform()
tform.estimate(dst, src)
M = tform.params[0:2,:]
warped = cv2.warpAffine(img,M,(image_size[1],image_size[0]), borderValue = 0.0)
return warped
def main(args):
# Models download
frozen_graph = args.model
with tf.gfile.GFile(frozen_graph, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
tf.import_graph_def(graph_def,
input_map=None,
return_elements=None,
name="")
image_input = graph.get_tensor_by_name('image_input:0')
keep_prob = graph.get_tensor_by_name('keep_prob:0')
is_train = graph.get_tensor_by_name('training_mode:0')
embedding = graph.get_tensor_by_name('embedding:0')
minsize = 100
threshold = [ 0.6, 0.7, 0.7 ]
factor = 0.709
sess = tf.Session(graph=graph)
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
# Centroids download
anchor = np.load(os.path.join(args.centroids,'centroids.npy'))
names = open(os.path.join(args.centroids,'centroids_names.txt')).read().split('\n')[:-1]
IDcolor = [255., 255., 255.]
IDcolor2 = [255., 0., 0.]
video_capture = cv2.VideoCapture(0)
video_capture.set(3, 1280)
video_capture.set(4, 1024)
while(True):
# Start of video sequence processing
ret, frame = video_capture.read()
frame = cv2.flip(frame[:,:,::-1], 1)
if not ret:
print('Cannot access the webcam')
break
key = cv2.waitKey(1)
if key == ord('q'):
break
if key == ord('s'):
imsave('Demo-'+str(datetime.datetime.now())+'.jpg',frame)
# Search and preparation of all faces on the frame
bounding_boxes, points = detect_face.detect_face(frame, minsize, pnet, rnet, onet, threshold, factor)
batch = np.zeros((bounding_boxes.shape[0],3,112,112),dtype=np.float32)
for i in range(bounding_boxes.shape[0]):
landmark = points[:,i].reshape((2,5)).T
warped = preprocess(frame, landmark = landmark)
warped = np.transpose(warped,[2,0,1]).reshape((1,3,112,112))
batch[i] = (warped-127.5)*0.0078125
# Recognition of all faces
if batch.shape[0]!=0:
embs = sess.run(embedding,feed_dict={image_input:batch,keep_prob:1.0,is_train:False})
for i in range(bounding_boxes.shape[0]):
probabilities = np.dot(anchor,embs[i])
val = np.max(probabilities)
pos = np.argmax(probabilities)
pt1 = (int(bounding_boxes[i][0]), int(bounding_boxes[i][1]))
pt2 = (int(bounding_boxes[i][2]), int(bounding_boxes[i][3]))
cv2.rectangle(frame, pt1, pt2, IDcolor)
cv2.putText(frame, 'Top-1 class: '+names[pos],
(int(bounding_boxes[i][0]), int(bounding_boxes[i][1])-5),
cv2.FONT_HERSHEY_SIMPLEX, 1., IDcolor, 3)
cv2.putText(frame, 'Sim. to top-1 class: '+str(round(val,4)),
(int(bounding_boxes[i][0]), int(bounding_boxes[i][3])+30),
cv2.FONT_HERSHEY_SIMPLEX, 1., IDcolor, 3)
cv2.imshow('Camera ("q" to quit, "s" to save frame)', frame[:,:,::-1])
video_capture.release()
cv2.destroyAllWindows()
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('model',type=str, help='Path to the model.')
parser.add_argument('centroids',type=str, help='Dir with centoids of classes for classifier.')
return parser.parse_args(argv)
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))
================================================
FILE: Demo/dumping.py
================================================
import skimage.io as io
import os
import numpy as np
from tqdm import tqdm
import sys
import argparse
def main(args):
# Output dirs creation
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
images = []
labels = []
label = 0
for path in sorted(os.listdir(args.input_dir)):
for name in sorted(os.listdir(os.path.join(args.input_dir,path))):
if args.mx:
images.append([[label],os.path.join(args.input_dir,path,name)])
else:
images.append(os.path.join(args.input_dir,path,name))
labels.append(label)
label += 1
if args.mx:
# MXnet model
import mxnet as mx
sym, arg_params, aux_params = mx.model.load_checkpoint(args.model, 0)
sym = sym.get_internals()['fc1_output']
model = mx.mod.Module(symbol=sym, context=mx.gpu(0), label_names = None)
model.bind(data_shapes=[('data', (1, 3, 112, 112))])
model.set_params(arg_params, aux_params)
iterator = mx.image.ImageIter(batch_size=args.batch,data_shape=(3,112,112),imglist=images,path_root='')
else:
# TensorFlow model
import tensorflow as tf
frozen_graph = args.model
with tf.gfile.GFile(frozen_graph, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
with tf.Graph().as_default() as graph:
tf.import_graph_def(graph_def,
input_map=None,
return_elements=None,
name="")
image_input = graph.get_tensor_by_name('image_input:0')
keep_prob = graph.get_tensor_by_name('keep_prob:0')
is_train = graph.get_tensor_by_name('training_mode:0')
embedding = graph.get_tensor_by_name('embedding:0')
sess = tf.Session(graph=graph)
inp_place = tf.placeholder(np.array(['1','2'],dtype='str').dtype)
pipeline = tf.data.Dataset.from_tensor_slices(inp_place)
def parse(filename):
image_string = tf.read_file(filename)
image = tf.image.decode_jpeg(image_string,dct_method="INTEGER_ACCURATE")
image = tf.cast(image,tf.float32)
image = (image - 127.5)*0.0078125
image = tf.transpose(image,perm=[2,0,1])
return image
pipeline = pipeline.map(parse,num_parallel_calls=4)
pipeline = pipeline.batch(args.batch)
pipiline = pipeline.prefetch(8)
iterator = pipeline.make_initializable_iterator()
next_element = iterator.get_next()
sess.run(iterator.initializer,feed_dict={inp_place:images})
# Embeddings evaluation
embs = np.zeros((len(images),512),dtype=np.float32)
for i in tqdm(range(int(np.ceil(len(images)/args.batch)))):
if args.mx:
db = mx.io.DataBatch(data=iterator.next().data)
model.forward(db, is_train=False)
emb = model.get_outputs()[0].asnumpy()
length = min(args.batch,len(images)-i*args.batch)
embs[i*args.batch:i*args.batch+length] = emb[:length]/np.expand_dims(np.sqrt(np.sum(emb[:length]**2,1)),1)
else:
db = sess.run(next_element)
embs[i*args.batch:min((i+1)*args.batch,len(images))] = sess.run(embedding,feed_dict=\
{image_input:db,keep_prob:1.0,is_train:False})
# Centroids preparation
anchor = np.zeros((label,512),dtype=np.float32)
labels = np.array(labels)
for i in range(label):
tmp = np.sum(embs[labels==i],axis=0)
anchor[i] = tmp/np.sqrt(np.sum(tmp**2))
np.save(os.path.join(args.output_dir,'centroids'),anchor)
names = open(os.path.join(args.output_dir,'centroids_names.txt'),'w')
for i in sorted(os.listdir(args.input_dir)):
names.write(i+'\n')
names.close()
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('input_dir', type=str, help='Directory with aligned images.')
parser.add_argument('output_dir', type=str, help='Directory to save embeddings.')
parser.add_argument('model',type=str, help='Path to the model.')
parser.add_argument('--mx',action='store_true', help='Flag to use the original mxnet model.')
parser.add_argument('--batch',type=int, help='Batch size.',default=30)
return parser.parse_args(argv)
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2019 Stepan Komkov, Aleksandr Petiushko
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: README.md
================================================
# AdvHat: Real-world adversarial attack on ArcFace Face ID system
By Stepan Komkov and Aleksandr Petiushko
This is the code repository for the AdvHat research article. The article is available [here](https://arxiv.org/abs/1908.08705). The video demo is available [here](https://youtu.be/a4iNg0wWBsQ). Code that is used for the article is available right here.
## Abstract
We propose a novel easily reproducible technique to attack the best public Face ID system ArcFace in different shooting conditions. To create an attack, we print the rectangular paper sticker on a common color printer and put it on the hat. The adversarial sticker is prepared with a novel algorithm for off-plane transformations of the image which imitates sticker location on the hat. Such an approach confuses the state-of-the-art public Face ID model LResNet100E-IR, ArcFace@ms1m-refine-v2 and is transferable to other Face ID models.
## The repository
The repository is organized as follows:
* In the Attack directory, you can find code and instructions on how to reproduce an attack for your images.
* In the Demo directory, you can find a demo script which can help you to verify the robustness of the prepared attack to the real-world shooting conditions.
## Built With
* [InsightFace's ArcFace](https://github.com/deepinsight/insightface) - The SOTA public FaceID model
* [Kevin Zakka's STN](https://github.com/kevinzakka/spatial-transformer-network) - Spatial Transformer realization
## Citation
```
@article{komkov2019advhat,
title={AdvHat: Real-world adversarial attack on ArcFace Face ID system},
author={Komkov, Stepan and Petiushko, Aleksandr},
journal={arXiv preprint arXiv:1908.08705},
year={2019}
}
```
## License
This project is licensed under the MIT License - see the [LICENSE.md](https://github.com/papermsucode/advhat/blob/master/LICENSE) file for details.
================================================
FILE: Utils/MXtoTF.ipynb
================================================
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import mxnet as mx\n",
"from mxnet import ndarray as nd\n",
"\n",
"from easydict import EasyDict as edict\n",
"import numpy as np\n",
"import os\n",
"from tqdm import tqdm\n",
"import skimage.io as io\n",
"\n",
"import tensorflow as tf\n",
"import tensorflow.contrib.slim as slim\n",
"gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.3)\n",
"sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### My versions of libraries"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1.4.1'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mx.__version__"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1.13.1'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Transformation of the MXNet model weights to NumPy"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"ctx = mx.gpu(0)\n",
"net = edict()\n",
"net.ctx = ctx\n",
"net.sym, net.arg_params, net.aux_params = mx.model.load_checkpoint('../../Matcher/models/model-r34-amf/model', 0)\n",
"all_layers = net.sym.get_internals()\n",
"net.sym = all_layers['fc1_output']\n",
"net.model = mx.mod.Module(symbol=net.sym, context=net.ctx, label_names = None)\n",
"net.model.bind(data_shapes=[('data', (2, 3, 112, 112))])\n",
"net.model.set_params(net.arg_params, net.aux_params)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"stage1_length = len(set([x.name.split('_')[1] for x in net.sym.get_internals() if 'stage1' in x.name]))\n",
"stage2_length = len(set([x.name.split('_')[1] for x in net.sym.get_internals() if 'stage2' in x.name]))\n",
"stage3_length = len(set([x.name.split('_')[1] for x in net.sym.get_internals() if 'stage3' in x.name]))\n",
"stage4_length = len(set([x.name.split('_')[1] for x in net.sym.get_internals() if 'stage4' in x.name]))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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gitextract_3cazs35n/
├── Attack/
│ ├── README.md
│ ├── attack.py
│ ├── cos_mx.py
│ ├── cos_tf.py
│ ├── face_preparation.py
│ ├── stn.py
│ └── utils.py
├── Demo/
│ ├── 1000_from_CASIA/
│ │ ├── centroids.npy
│ │ └── centroids_names.txt
│ ├── README.md
│ ├── align/
│ │ ├── .gitignore
│ │ ├── __init__.py
│ │ ├── det1.npy
│ │ ├── det2.npy
│ │ ├── det3.npy
│ │ └── detect_face.py
│ ├── alignment.py
│ ├── demo.py
│ └── dumping.py
├── LICENSE
├── README.md
└── Utils/
├── MXtoTF.ipynb
└── README.md
SYMBOL INDEX (60 symbols across 10 files)
FILE: Attack/attack.py
function prep (line 17) | def prep(im):
function main (line 23) | def main(args):
function parse_arguments (line 209) | def parse_arguments(argv):
FILE: Attack/cos_mx.py
function prep (line 11) | def prep(im):
function main (line 17) | def main(args):
function parse_arguments (line 46) | def parse_arguments(argv):
FILE: Attack/cos_tf.py
function prep (line 9) | def prep(im):
function main (line 15) | def main(args):
function parse_arguments (line 47) | def parse_arguments(argv):
FILE: Attack/face_preparation.py
function preprocess (line 15) | def preprocess(img, landmark):
function main (line 31) | def main(args):
function parse_arguments (line 67) | def parse_arguments(argv):
FILE: Attack/stn.py
function spatial_transformer_network (line 4) | def spatial_transformer_network(input_fmap, theta, out_dims=None, **kwar...
function get_pixel_value (line 66) | def get_pixel_value(img, x, y):
function affine_grid_generator (line 95) | def affine_grid_generator(height, width, theta):
function bilinear_sampler (line 159) | def bilinear_sampler(img, x, y):
FILE: Attack/utils.py
function tf_integral (line 5) | def tf_integral(x,a):
function tf_pre_parabol (line 7) | def tf_pre_parabol(x,par):
function projector (line 12) | def projector(param,ph,logo):
function TVloss (line 66) | def TVloss(logo,w_tv):
FILE: Demo/align/detect_face.py
function layer (line 37) | def layer(op):
class Network (line 61) | class Network(object):
method __init__ (line 63) | def __init__(self, inputs, trainable=True):
method setup (line 75) | def setup(self):
method load (line 79) | def load(self, data_path, session, ignore_missing=False):
method feed (line 97) | def feed(self, *args):
method get_output (line 112) | def get_output(self):
method get_unique_name (line 116) | def get_unique_name(self, prefix):
method make_var (line 123) | def make_var(self, name, shape):
method validate_padding (line 127) | def validate_padding(self, padding):
method conv (line 132) | def conv(self,
method prelu (line 167) | def prelu(self, inp, name):
method max_pool (line 175) | def max_pool(self, inp, k_h, k_w, s_h, s_w, name, padding='SAME'):
method fc (line 184) | def fc(self, inp, num_out, name, relu=True):
method softmax (line 209) | def softmax(self, target, axis, name=None):
class PNet (line 216) | class PNet(Network):
method setup (line 217) | def setup(self):
class RNet (line 232) | class RNet(Network):
method setup (line 233) | def setup(self):
class ONet (line 251) | class ONet(Network):
method setup (line 252) | def setup(self):
function create_mtcnn (line 276) | def create_mtcnn(sess, model_path):
function detect_face (line 298) | def detect_face(img, minsize, pnet, rnet, onet, threshold, factor):
function detect_face_force (line 419) | def detect_face_force(img, bbox, pnet, rnet, onet):
function bulk_detect_face (line 490) | def bulk_detect_face(images, detection_window_size_ratio, pnet, rnet, on...
function bbreg (line 713) | def bbreg(boundingbox,reg):
function generateBoundingBox (line 727) | def generateBoundingBox(imap, reg, scale, t):
function nms (line 754) | def nms(boxes, threshold, method):
function pad (line 787) | def pad(total_boxes, w, h):
function rerec (line 822) | def rerec(bboxA):
function imresample (line 832) | def imresample(img, sz):
FILE: Demo/alignment.py
function to_rgb (line 14) | def to_rgb(img):
function preprocess (line 21) | def preprocess(img, landmark):
function main (line 37) | def main(args):
function parse_arguments (line 86) | def parse_arguments(argv):
FILE: Demo/demo.py
function preprocess (line 13) | def preprocess(img, landmark):
function main (line 29) | def main(args):
function parse_arguments (line 116) | def parse_arguments(argv):
FILE: Demo/dumping.py
function main (line 8) | def main(args):
function parse_arguments (line 95) | def parse_arguments(argv):
Condensed preview — 23 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (276K chars).
[
{
"path": "Attack/README.md",
"chars": 2016,
"preview": "## Preparing an attack\n\n1. First, make a full-face photo of attacked person, a full-face photo in a hat, and a \nfull-fac"
},
{
"path": "Attack/attack.py",
"chars": 12054,
"preview": "import argparse\nimport sys\nimport os\nimport tensorflow as tf\nimport numpy as np\nimport skimage.io as io\nfrom skimage.tra"
},
{
"path": "Attack/cos_mx.py",
"chars": 1992,
"preview": "import argparse\nimport sys\nimport mxnet as mx\nimport mxnet.ndarray as nd\nimport numpy as np\nimport skimage.io as io\nfrom"
},
{
"path": "Attack/cos_tf.py",
"chars": 2120,
"preview": "import argparse\nimport sys\nimport tensorflow as tf\nimport numpy as np\nimport skimage.io as io\nfrom skimage.transform imp"
},
{
"path": "Attack/face_preparation.py",
"chars": 2990,
"preview": "import argparse\nimport os\nimport sys\nsys.path.append(os.path.join(os.path.dirname(__file__), '../Demo/'))\nimport tensorf"
},
{
"path": "Attack/stn.py",
"chars": 6879,
"preview": "import tensorflow as tf\n\n\ndef spatial_transformer_network(input_fmap, theta, out_dims=None, **kwargs):\n \"\"\"\n Spati"
},
{
"path": "Attack/utils.py",
"chars": 3569,
"preview": "import numpy as np\nimport tensorflow as tf\n\n\ndef tf_integral(x,a):\n\treturn 0.5*(x*tf.sqrt(x**2+a)+a*tf.log(tf.abs(x+tf.s"
},
{
"path": "Demo/1000_from_CASIA/centroids_names.txt",
"chars": 8000,
"preview": "0000045\n0000099\n0000100\n0000102\n0000103\n0000105\n0000107\n0000108\n0000114\n0000117\n0000119\n0000121\n0000133\n0000137\n0000141\n"
},
{
"path": "Demo/README.md",
"chars": 1262,
"preview": "## Demo launch\n\nArcFace@ms1m-refine-v2 transformed to TensorFlow is available [here](https://drive.google.com/file/d/1fb"
},
{
"path": "Demo/align/.gitignore",
"chars": 6,
"preview": "*.pyc\n"
},
{
"path": "Demo/align/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "Demo/align/detect_face.py",
"chars": 34597,
"preview": "\"\"\" Tensorflow implementation of the face detection / alignment algorithm found at\nhttps://github.com/kpzhang93/MTCNN_fa"
},
{
"path": "Demo/alignment.py",
"chars": 2916,
"preview": "import argparse\nimport numpy as np\nimport cv2\nfrom skimage import transform as trans\nimport tensorflow as tf\nimport os\ni"
},
{
"path": "Demo/demo.py",
"chars": 3976,
"preview": "import sys\r\nimport argparse\r\nimport numpy as np\r\nimport cv2\r\nimport tensorflow as tf\r\nfrom align import detect_face\r\nfro"
},
{
"path": "Demo/dumping.py",
"chars": 3976,
"preview": "import skimage.io as io\nimport os\nimport numpy as np\nfrom tqdm import tqdm\nimport sys\nimport argparse\n\ndef main(args):\n\t"
},
{
"path": "LICENSE",
"chars": 1091,
"preview": "MIT License\n\nCopyright (c) 2019 Stepan Komkov, Aleksandr Petiushko\n\nPermission is hereby granted, free of charge, to any"
},
{
"path": "README.md",
"chars": 1873,
"preview": "# AdvHat: Real-world adversarial attack on ArcFace Face ID system\n\nBy Stepan Komkov and Aleksandr Petiushko\n\nThis is the"
},
{
"path": "Utils/MXtoTF.ipynb",
"chars": 160951,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n "
},
{
"path": "Utils/README.md",
"chars": 401,
"preview": "An example notebook of the MX to TF transformation of the ArcFace models. Can be directly applicable for *LResNet100E-IR"
}
]
// ... and 4 more files (download for full content)
About this extraction
This page contains the full source code of the papermsucode/advhat GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 23 files (244.8 KB), approximately 88.9k tokens, and a symbol index with 60 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.