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import gradio as gr
import cv2
from mtcnn.mtcnn import MTCNN
import tensorflow as tf
import tensorflow_addons
import numpy as np
import os
import zipfile
local_zip = "FINAL-EFFICIENTNETV2-B0.zip"
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('FINAL-EFFICIENTNETV2-B0')
zip_ref.close()
local_zip = "FINAL-EFFICIENTNETV2-S.zip"
zip_ref = zipfile.ZipFile(local_zip, 'r')
zip_ref.extractall('FINAL-EFFICIENTNETV2-S')
zip_ref.close()
model_b0 = tf.keras.models.load_model("FINAL-EFFICIENTNETV2-B0")
model_s = tf.keras.models.load_model("FINAL-EFFICIENTNETV2-S")
detector = MTCNN()
def deepfakespredict(select_model, input_img ):
tf.keras.backend.clear_session()
if select_model == "EfficientNetV2-B0":
model = model_b0
elif select_model == "EfficientNetV2-B0":
model = model_s
text =""
face = detector.detect_faces(input_img)
if len(face) > 0:
x, y, width, height = face[0]['box']
x2, y2 = x + width, y + height
cv2.rectangle(input_img, (x, y), (x2, y2), (0, 255, 0), 2)
face_image = input_img[y:y2, x:x2]
face_image2 = cv2.cvtColor(face_image, cv2.COLOR_BGR2RGB)
face_image3 = cv2.resize(face_image2, (224, 224))
face_image4 = face_image3/255
pred = model.predict(np.expand_dims(face_image4, axis=0))[0]
if pred[1] >= 0.6:
text = "The image is fake."
elif pred[0] >= 0.6:
text = "The image is real."
else:
text = "The image might be real or fake."
else:
text = "Face is not detected in the image."
return text, input_img, {labels[i]: float(pred[i]) for i in range(2)}
title="EfficientNetV2 Deepfakes Image Detector"
description="This is a demo implementation of EfficientNetV2 Deepfakes Image Detector. To use it, simply upload your image, or click one of the examples to load them."
examples = [
[
['deepfakes-test-images/Fake-1.jpg'],
['deepfakes-test-images/Fake-2.jpg'],
['deepfakes-test-images/Fake-3.jpg'],
['deepfakes-test-images/Fake-4.jpg'],
['deepfakes-test-images/Fake-5.jpg']
],
[
['deepfakes-test-images/Real-1.jpg'],
['deepfakes-test-images/Real-2.jpg'],
['deepfakes-test-images/Real-3.jpg'],
['deepfakes-test-images/Real-4.jpg'],
['deepfakes-test-images/Real-5.jpg'],
]
]
gr.Interface(deepfakespredict,
inputs = [gr.inputs.Radio(["EfficientNetV2-B0", "EfficientNetV2-S"], label = "Select model:"), "image"],
outputs=["text", gr.outputs.Image(type="pil", label="Detected face"), gr.outputs.Label(num_top_classes=None, type="auto", label="Confidence")],
title=title,
description=description,
examples = examples
).launch() |