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from transformers.pipelines import PIPELINE_REGISTRY
from transformers import Pipeline, AutoModelForImageClassification
import torch
from PIL import Image
import cv2
from pytorch_grad_cam import GradCAM
from pytorch_grad_cam.utils.model_targets import ClassifierOutputTarget
from pytorch_grad_cam.utils.image import show_cam_on_image
from facenet_pytorch import MTCNN
import torch.nn.functional as F
class DeepFakePipeline(Pipeline):
def __init__(self,**kwargs):
Pipeline.__init__(self,**kwargs)
def _sanitize_parameters(self, **kwargs):
return {}, {}, {}
def preprocess(self, inputs):
return inputs
def _forward(self,input):
return input
def postprocess(self,confidences,face_with_mask):
out = {"confidences":confidences,
"face_with_mask": face_with_mask}
return out
def predict(self,input_image:str):
DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'
mtcnn = MTCNN(
select_largest=False,
post_process=False,
device=DEVICE)
mtcnn.to(DEVICE)
model = self.model.model
model.to(DEVICE)
input_image = Image.open(input_image)
face = mtcnn(input_image)
if face is None:
raise Exception('No face detected')
face = face.unsqueeze(0) # add the batch dimension
face = F.interpolate(face, size=(256, 256), mode='bilinear', align_corners=False)
# convert the face into a numpy array to be able to plot it
prev_face = face.squeeze(0).permute(1, 2, 0).cpu().detach().int().numpy()
prev_face = prev_face.astype('uint8')
face = face.to(DEVICE)
face = face.to(torch.float32)
face = face / 255.0
face_image_to_plot = face.squeeze(0).permute(1, 2, 0).cpu().detach().int().numpy()
target_layers=[model.block8.branch1[-1]]
cam = GradCAM(model=model, target_layers=target_layers)
targets = [ClassifierOutputTarget(0)]
grayscale_cam = cam(input_tensor=face, targets=targets,eigen_smooth=True)
grayscale_cam = grayscale_cam[0, :]
visualization = show_cam_on_image(face_image_to_plot, grayscale_cam, use_rgb=True)
face_with_mask = cv2.addWeighted(prev_face, 1, visualization, 0.5, 0)
with torch.no_grad():
output = torch.sigmoid(model(face).squeeze(0))
prediction = "real" if output.item() < 0.5 else "fake"
real_prediction = 1 - output.item()
fake_prediction = output.item()
confidences = {
'real': real_prediction,
'fake': fake_prediction
}
return self.postprocess(confidences, face_with_mask) |