| import cv2 as cv |
| import torch |
| import torch.nn as nn |
| from torchvision import transforms |
| import numpy as np |
| from DeePixBis.Model import DeePixBiS |
| from DeePixBis.Loss import PixWiseBCELoss |
| from DeePixBis.Metrics import predict, test_accuracy, test_loss |
|
|
| model = DeePixBiS(pretrained=False) |
| model.load_state_dict(torch.load('./DeePixBiS.pth')) |
| model.eval() |
|
|
| 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)) |
| ]) |
|
|
| faceClassifier = cv.CascadeClassifier('Classifiers/haarface.xml') |
|
|
| camera = cv.VideoCapture(0) |
|
|
| while cv.waitKey(1) & 0xFF != ord('q'): |
| _, img = camera.read() |
| grey = cv.cvtColor(img, cv.COLOR_BGR2GRAY) |
| faces = faceClassifier.detectMultiScale(grey, scaleFactor=1.1, minNeighbors=4) |
|
|
| for x, y, w, h in faces: |
| faceRegion = img[y:y + h, x:x + w] |
| faceRegion = cv.cvtColor(faceRegion, cv.COLOR_BGR2RGB) |
| |
|
|
| faceRegion = tfms(faceRegion) |
| faceRegion = faceRegion.unsqueeze(0) |
|
|
| mask, binary = model.forward(faceRegion) |
| res = torch.mean(mask).item() |
| |
|
|
| if res < 0.7: |
| label = f'Spoof {res:.2f}' |
| color = (0, 0, 255) |
| else: |
| label = f'Real {res:.2f}' |
| color = (0, 255, 0) |
| print(f'{label}: {res}') |
| 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) |
|
|
| cv.imshow('Deep Pixel-wise Binary Supervision Anti-Spoofing', img) |
|
|
