Create app.py

app.py

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+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from moviepy.editor import VideoFileClip, AudioFileClip
+import librosa
+import librosa.display
+import soundfile as sf
+import gradio as gr
+import tempfile
+
+# Function for displaying progress
+def display_progress(percent, message, progress=gr.Progress()):
+ progress(percent, desc=message)
+
+# Function for extracting audio from video
+def extract_audio(video_path, progress):
+ display_progress(0.1, "Extracting audio from video", progress)
+ video = VideoFileClip(video_path)
+ audio_path = "extracted_audio.wav"
+ video.audio.write_audiofile(audio_path)
+ display_progress(0.2, "Audio extracted", progress)
+ return audio_path
+
+# Function for dividing video into frames
+def extract_frames(video_path, progress):
+ display_progress(0.3, "Extract frames from video", progress)
+ video = cv2.VideoCapture(video_path)
+ frames = []
+ success, frame = video.read()
+ while success:
+ frames.append(frame)
+ success, frame = video.read()
+ video.release()
+ display_progress(0.4, "Frames extracted", progress)
+ return frames
+
+# Convert frame to spectrogram
+def frame_to_spectrogram(frame, sr=22050):
+ gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+ S = np.flipud(gray_frame.astype(np.float32) / 255.0 * 100.0)
+ y = librosa.griffinlim(S)
+ return y
+
+# Saving audio
+def save_audio(y, sr=22050):
+ audio_path = 'output_frame_audio.wav'
+ sf.write(audio_path, y, sr)
+ return audio_path
+
+# Saving frame spectrogram
+def save_spectrogram_image(S, frame_number, temp_dir):
+ plt.figure(figsize=(10, 4))
+ librosa.display.specshow(S)
+ plt.tight_layout()
+ image_path = os.path.join(temp_dir, f'spectrogram_frame_{frame_number}.png')
+ plt.savefig(image_path)
+ plt.close()
+ return image_path
+
+# Processing all video frames
+def process_video_frames(frames, sr=22050, temp_dir=None, progress=gr.Progress()):
+ processed_frames = []
+ total_frames = len(frames)
+ for i, frame in enumerate(frames):
+ y = frame_to_spectrogram(frame, sr)
+ S = librosa.feature.melspectrogram(y=y, sr=sr)
+ image_path = save_spectrogram_image(S, i, temp_dir)
+ processed_frame = cv2.imread(image_path)
+ processed_frames.append(processed_frame)
+ display_progress(0.5 + int((i + 1) / total_frames * 0.7), f"Frame processing {i + 1}/{total_frames}", progress)
+ display_progress(0.8, "All frames processed", progress)
+ return processed_frames
+
+# Saving video from frames
+def save_video_from_frames(frames, output_path, fps=30):
+ height, width, layers = frames[0].shape
+ video = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
+ for frame in frames:
+ video.write(frame)
+ video.release()
+
+# Adding audio back to video
+def add_audio_to_video(video_path, audio_path, output_path, progress):
+ display_progress(0.9, "Adding audio back to video", progress)
+ video = VideoFileClip(video_path)
+ audio = AudioFileClip(audio_path)
+ final_video = video.set_audio(audio)
+ final_video.write_videofile(output_path, codec='libx264', audio_codec='aac')
+ display_progress(1, "Video's ready", progress)
+
+# Gradio interface
+def process_video(video_path, progress=gr.Progress()):
+ audio_path = extract_audio(video_path, progress)
+ frames = extract_frames(video_path, progress)
+
+ # Creating a temporary folder for saving frames
+ with tempfile.TemporaryDirectory() as temp_dir:
+ processed_frames = process_video_frames(frames, temp_dir=temp_dir, progress=progress)
+ temp_video_path = os.path.join(temp_dir, 'processed_video.mp4')
+ save_video_from_frames(processed_frames, temp_video_path)
+ output_video_path = 'output_video_with_audio.mp4'
+ add_audio_to_video(temp_video_path, audio_path, output_video_path, progress)
+ return output_video_path
+
+with gr.Blocks(title='Video from Spectrogram', theme=gr.themes.Soft(primary_hue="green", secondary_hue="green", spacing_size="sm", radius_size="lg")) as iface:
+ 
+ with gr.Group():
+ with gr.Row(variant='panel'):
+ with gr.Column():
+ gr.HTML("<center><h2><a href='https://t.me/pol1trees'>Telegram Channel</a></h2></center>")
+ with gr.Column():
+ gr.HTML("<center><h2><a href='https://t.me/+GMTP7hZqY0E4OGRi'>Telegram Chat</a></h2></center>")
+ with gr.Column():
+ gr.HTML("<center><h2><a href='https://www.youtube.com/channel/UCHb3fZEVxUisnqLqCrEM8ZA'>YouTube</a></h2></center>")
+ with gr.Column():
+ gr.HTML("<center><h2><a href='https://github.com/Bebra777228/Audio-Steganography'>GitHub</a></h2></center>")
+
+ with gr.Column(variant='panel'):
+ video_input = gr.Video(label="Upload video")
+ with gr.Column(variant='panel'):
+ generate_button = gr.Button("Generate")
+ with gr.Column(variant='panel'):
+ video_output = gr.Video(label="VideoSpectrogram")
+
+ def gradio_video_process_fn(video_input, progress=gr.Progress()):
+ return process_video(video_input, progress)
+
+ generate_button.click(
+ gradio_video_process_fn,
+ inputs=[video_input],
+ outputs=[video_output]
+ )
+
+iface.launch(share=True)