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import tensorflow as tf
import numpy as np
import cv2
import os
import gradio as gr
from keras.models import load_model
from pytube import YouTube
import pickle
def load_video_from_url(youtube_url):
visible = True
try:
# DOWNLOAD THE VIDEO USING THE GIVEN URL
yt = YouTube(youtube_url)
yt_stream = yt.streams.filter(file_extension='mp4').first()
title = yt_stream.title
src = yt_stream.download()
capture = cv2.VideoCapture(src)
# SAMPLE FRAMES FROM VIDEO FILE
sampled_frames = sample_frames_from_video_file(capture)
# PICK EXAMPLE FRAME FROM THE MIDDLE OF THE SAMPLED FRAMES
example_frames = [
sampled_frames[len(sampled_frames) // 4],
sampled_frames[len(sampled_frames) // 2],
sampled_frames[3 * len(sampled_frames) // 4],
]
# DELETE VIDEO FILE
if os.path.exists(src):
os.remove(src)
# CONVERT SAMPLED FRAMES TO TENSOR
frames_tensor = tf.expand_dims(tf.convert_to_tensor(sampled_frames, dtype=tf.float32), axis=0)
# SAVE TENSOR TO FILE
pickle.dump(frames_tensor, open("frames_tf.pkl", "wb"))
except Exception as e:
title = "Error while loading video: " + str(e)
visible = False
example_frames = [np.zeros((256, 256, 3)) for _ in range(3)]
# Define visible prediction components to show upon video loaded
predVideoBtn = gr.Button(value="Classify Video", visible=visible)
predOutput = gr.Label(
label="DETECTED LABEL (AND CONFIDENCE LEVEL)",
num_top_classes=2,
visible=visible
)
return title, example_frames, predVideoBtn, predOutput
def detect_deepfake():
# LOAD FRAMES
frames_tf = pickle.load(open("frames_tf.pkl", "rb"))
# DELETE FRAMES FILE
if os.path.exists("frames_tf.pkl"):
os.remove("frames_tf.pkl")
# LOAD THE RNN MODEL FROM DISK
loaded_model = load_model("MesonetRNN.keras")
# loaded_model.summary()
# GET PREDICTION
out = loaded_model.predict(frames_tf)
real_confidence = out[0][0]
fake_confidence = 1 - real_confidence
confidence_dict = {"FAKE": fake_confidence, "REAL": real_confidence}
# MAKE FLAG BUTTON VISIBLE
flagBtn = gr.Button(value="Flag Output", visible=True)
# RETURN THE OUTPUT LABEL AND EXAMPLE FRAMES
return confidence_dict, flagBtn
def sample_frames_from_video_file(capture, sample_count=10, frames_per_sample=10, frame_step=10,
output_size=(256, 256)):
# Read each video frame by frame
result = []
video_length = capture.get(cv2.CAP_PROP_FRAME_COUNT)
need_length = 1 + (frames_per_sample - 1) * frame_step
max_start = video_length - need_length
sample_starts = []
for sample in range(sample_count):
sample_start = int(max_start * sample / sample_count)
sample_starts.append(sample_start)
# print(sample_start)
for start in sample_starts:
capture.set(cv2.CAP_PROP_POS_FRAMES, start)
# ret is a boolean indicating whether read was successful, frame is the image itself
ret, frame = capture.read()
result.append(format_frames(frame, output_size))
for _ in range(frames_per_sample - 1):
for _ in range(frame_step):
ret, frame = capture.read()
if ret:
frame = format_frames(frame, output_size)
result.append(frame)
else:
result.append(np.zeros_like(result[0]))
capture.release()
return np.array(result)
def format_frames(frame, output_size):
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = tf.image.convert_image_dtype(frame, tf.float32)
frame = tf.image.resize_with_pad(frame, *output_size)
return frame
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