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import gradio as gr
import torch
import torch.nn.functional as F
from facenet_pytorch import MTCNN, InceptionResnetV1
import os
import numpy as np
from PIL import Image
import zipfile
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
with zipfile.ZipFile("examples.zip","r") as zip_ref:
zip_ref.extractall(".")
DEVICE = 'cuda:0' if torch.cuda.is_available() else 'cpu'
mtcnn = MTCNN(
select_largest=False,
post_process=False,
device=DEVICE
).to(DEVICE).eval()
model = InceptionResnetV1(
pretrained="vggface2",
classify=True,
num_classes=1,
device=DEVICE
)
checkpoint = torch.load("resnetinceptionv1_epoch_32.pth", map_location=torch.device('cpu'))
model.load_state_dict(checkpoint['model_state_dict'])
model.to(DEVICE)
model.eval()
EXAMPLES_FOLDER = 'examples'
examples_names = os.listdir(EXAMPLES_FOLDER)
examples = []
for example_name in examples_names:
example_path = os.path.join(EXAMPLES_FOLDER, example_name)
label = example_name.split('_')[0]
example = {
'path': example_path,
'label': label
}
examples.append(example)
np.random.shuffle(examples) # shuffle
def predict(input_image:Image.Image, true_label:str):
"""Predict the label of the 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]]
use_cuda = True if torch.cuda.is_available() else False
cam = GradCAM(model=model, target_layers=target_layers, use_cuda=use_cuda)
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 confidences, true_label, face_with_mask
interface = gr.Interface(
fn=predict,
inputs=[
gr.inputs.Image(label="Input Image", type="pil"),
"text"
],
outputs=[
gr.outputs.Label(label="Class"),
"text",
gr.outputs.Image(label="Face with Explainability")
],
examples=[[examples[i]["path"], examples[i]["label"]] for i in range(10)]
).launch() |