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import subprocess
subprocess.run(["sh", "tddfa/build.sh"])
import gradio as gr
from gradio.components import Dropdown
import cv2 as cv
import torch
from torchvision import transforms
from DeePixBiS.Model import DeePixBiS
import yaml
import numpy as np
import pandas as pd
from skimage.io import imread, imsave
# from tddfa.TDDFA import TDDFA
from tddfa.utils.depth import depth
from tddfa.TDDFA import TDDFA
import torch.optim as optim
from DSDG.DUM.models.CDCNs_u import Conv2d_cd, CDCN_u
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
os.environ['OMP_NUM_THREADS'] = '4'
device = torch.device("cpu")
labels = ['Live', 'Spoof']
pix_threshhold = 0.45
dsdg_threshold = 0.003
examples = [
['examples/1_1_21_2_33_scene_fake.jpg', "DeePixBiS"],
['examples/frame150_real.jpg', "DeePixBiS"],
['examples/1_2.avi_125_real.jpg', "DeePixBiS"],
['examples/1_3.avi_25_fake.jpg', "DeePixBiS"]]
faceClassifier = cv.CascadeClassifier('./DeePixBiS/Classifiers/haarface.xml')
tfms = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
model = DeePixBiS(pretrained=False)
model.load_state_dict(torch.load('./DeePixBiS/DeePixBiS.pth'))
model.eval()
depth_config_path = 'tddfa/configs/mb05_120x120.yml' # 'tddfa/configs/mb1_120x120.yml
cfg = yaml.load(open(depth_config_path), Loader=yaml.SafeLoader)
tddfa = TDDFA(gpu_mode=False, **cfg)
model = CDCN_u(basic_conv=Conv2d_cd, theta=0.7)
model = model.to(device)
weights = torch.load('./DSDG/DUM/checkpoint/CDCN_U_P1_updated.pkl', map_location=device)
model.load_state_dict(weights)
optimizer = optim.Adam(model.parameters(), lr=0.001, weight_decay=0.00005)
model.eval()
class Normaliztion_valtest(object):
"""
same as mxnet, normalize into [-1, 1]
image = (image - 127.5)/128
"""
def __call__(self, image_x):
image_x = (image_x - 127.5) / 128 # [-1,1]
return image_x
def prepare_data(images, boxes, depths):
transform = transforms.Compose([Normaliztion_valtest()])
files_total = 1
image_x = np.zeros((files_total, 256, 256, 3))
depth_x = np.ones((files_total, 32, 32))
for i, (image, bbox, depth_img) in enumerate(
zip(images, boxes, depths)):
x, y, w, h = bbox
depth_img = cv.cvtColor(depth_img, cv.COLOR_RGB2GRAY)
image = image[y:y + h, x:x + w]
depth_img = depth_img[y:y + h, x:x + w]
image_x[i, :, :, :] = cv.resize(image, (256, 256))
# transform to binary mask --> threshold = 0
depth_x[i, :, :] = cv.resize(depth_img, (32, 32))
image_x = image_x.transpose((0, 3, 1, 2))
image_x = transform(image_x)
image_x = torch.from_numpy(image_x.astype(float)).float()
depth_x = torch.from_numpy(depth_x.astype(float)).float()
return image_x, depth_x
def find_largest_face(faces):
largest_face = None
largest_area = 0
for (x, y, w, h) in faces:
area = w * h
if area > largest_area:
largest_area = area
largest_face = (x, y, w, h)
return largest_face
def inference(img, model_name):
confidences = {}
grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
faces = faceClassifier.detectMultiScale(
grey, scaleFactor=1.1, minNeighbors=4)
face = find_largest_face(faces)
if face is not None:
x, y, w, h = face
faceRegion = img[y:y + h, x:x + w]
faceRegion = cv.cvtColor(faceRegion, cv.COLOR_BGR2RGB)
faceRegion = tfms(faceRegion)
faceRegion = faceRegion.unsqueeze(0)
if model_name == 'DeePixBiS':
mask, binary = model.forward(faceRegion)
res = torch.mean(mask).item()
cls = 'Real' if res >= pix_threshhold else 'Spoof'
res = 1 - res
else:
dense_flag = True
boxes = list(face)
boxes.append(1)
param_lst, roi_box_lst = tddfa(img, [boxes])
ver_lst = tddfa.recon_vers(param_lst, roi_box_lst, dense_flag=dense_flag)
depth_img = depth(img, ver_lst, tddfa.tri, with_bg_flag=False)
with torch.no_grad():
map_score_list = []
image_x, map_x = prepare_data([img], [list(face)], [depth_img])
# get the inputs
image_x = image_x.unsqueeze(0)
map_x = map_x.unsqueeze(0)
inputs = image_x.to(device)
test_maps = map_x.to(device)
optimizer.zero_grad()
map_score = 0.0
for frame_t in range(inputs.shape[1]):
mu, logvar, map_x, x_concat, x_Block1, x_Block2, x_Block3, x_input = model(inputs[:, frame_t, :, :, :])
score_norm = torch.sum(mu) / torch.sum(test_maps[:, frame_t, :, :])
map_score += score_norm
map_score = map_score / inputs.shape[1]
map_score_list.append(map_score)
res = map_score_list[0].item()
if res > 10:
res = 0.0
cls = 'Real' if res >= dsdg_threshold else 'Spoof'
res = res * 100
label = f'{cls} {res:.2f}'
confidences = {label: res}
color = color = (0, 255, 0) if cls == 'Real' else (255, 0, 0)
cv.rectangle(img, (x, y), (x + w, y + h), color, 2)
cv.putText(img, label, (x, y + h + 30),
cv.FONT_HERSHEY_COMPLEX, 1, color)
return img, confidences
if __name__ == '__main__':
demo = gr.Interface(
fn=inference,
inputs=[gr.Image(source='webcam', shape=None, type='numpy'),
Dropdown(["DeePixBiS", "DSDG"], value="DeePixBiS")],
outputs=["image", gr.Label(num_top_classes=2)],
examples=examples).queue(concurrency_count=2)
demo.launch(share=False)
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