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prepare_test.py
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prepare_test.py
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import os
from datetime import timedelta
from moviepy import editor
from PIL import Image
import numpy as np
import speech_recognition as sr
def split_audio_and_frames(video_path, output_audio_path, output_frames_path, fps):
video_clip = editor.VideoFileClip(video_path)
audio_clip = video_clip.audio
audio_clip.write_audiofile(output_audio_path)
# text = audio_to_text(output_audio_path)
# with open(output_text_path, 'w') as text_file:
# text_file.write(text)
frames = list(video_clip.iter_frames(fps=fps, dtype='uint8'))
os.makedirs(output_frames_path, exist_ok=True)
frame_timestamps = []
for i, frame in enumerate(frames):
timestamp = video_clip.duration * (i / len(frames))
image = Image.fromarray(np.array(frame))
image_path = os.path.join(output_frames_path, f"frame_{i:04d}.png")
image.save(image_path)
# frame_timestamps.append((timestamp, image))
video_clip.close()
audio_clip.close()
return frame_timestamps
def audio_to_text(audio_path):
recognizer = sr.Recognizer()
with sr.AudioFile(audio_path) as audio_file:
audio_data = recognizer.record(audio_file)
text = recognizer.recognize_google(audio_data)
return text
if __name__ == "__main__":
input_video_path = '/home/lenovo/Downloads/testing_2.mp4'
output_audio_path = '/home/lenovo/mnt/hackathon/Detection-of-Sensitive-Data-Exposure-in-Images/output/output_audio.wav'
output_frames_path = '/home/lenovo/mnt/hackathon/Detection-of-Sensitive-Data-Exposure-in-Images/output/images'
output_text_path = '/home/lenovo/mnt/hackathon/Detection-of-Sensitive-Data-Exposure-in-Images/output/output_text.txt'
fps = 5
timestamps_and_images = split_audio_and_frames(input_video_path, output_audio_path, output_frames_path, fps)
for timestamp, image in timestamps_and_images:
print(f"Timestamp: {str(timedelta(seconds=timestamp))}, Image: {image}")