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Merge pull request #2 from Bebra777228/formatter/main
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Automatic code formatting
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@Bebra777228
Bebra777228 authored Oct 17, 2024
2 parents 4ca179c + 36a6239 commit 05610ac
Showing 1 changed file with 102 additions and 42 deletions.
144 changes: 102 additions & 42 deletions txt2spec.py
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# Setup logging
logging.basicConfig(level=logging.INFO)


# Function for creating a spectrogram image with text
def text_to_spectrogram_image(text, base_width=512, height=256, max_font_size=80, margin=10, letter_spacing=5):
def text_to_spectrogram_image(
text, base_width=512, height=256, max_font_size=80, margin=10, letter_spacing=5
):
try:
font = ImageFont.truetype(DEFAULT_FONT_PATH, max_font_size)
except IOError:
logging.warning(f"Font not found at {DEFAULT_FONT_PATH}. Using default font.")
font = ImageFont.load_default()

draw = ImageDraw.Draw(Image.new('L', (1, 1)))

text_width = sum(draw.textbbox((0, 0), char, font=font)[2] - draw.textbbox((0, 0), char, font=font)[0] + letter_spacing for char in text) - letter_spacing
text_height = draw.textbbox((0, 0), text[0], font=font)[3] - draw.textbbox((0, 0), text[0], font=font)[1]
draw = ImageDraw.Draw(Image.new("L", (1, 1)))

text_width = (
sum(
draw.textbbox((0, 0), char, font=font)[2]
- draw.textbbox((0, 0), char, font=font)[0]
+ letter_spacing
for char in text
)
- letter_spacing
)
text_height = (
draw.textbbox((0, 0), text[0], font=font)[3]
- draw.textbbox((0, 0), text[0], font=font)[1]
)

# Adjust width and height based on text size
width = max(base_width, text_width + margin * 2)
height = max(height, text_height + margin * 2)

image = Image.new('L', (width, height), 'black')
image = Image.new("L", (width, height), "black")
draw = ImageDraw.Draw(image)

text_x = (width - text_width) // 2
text_y = (height - text_height) // 2

for char in text:
draw.text((text_x, text_y), char, font=font, fill='white')
draw.text((text_x, text_y), char, font=font, fill="white")
char_bbox = draw.textbbox((0, 0), char, font=font)
text_x += char_bbox[2] - char_bbox[0] + letter_spacing

image = np.array(image)
image = np.where(image > 0, 255, image)
return image


# Converting an image to audio
def spectrogram_image_to_audio(image, sr=DEFAULT_SAMPLE_RATE):
flipped_image = np.flipud(image)
S = flipped_image.astype(np.float32) / 255.0 * 100.0
y = librosa.griffinlim(S)
return y


# Function for creating an audio file and spectrogram from text
def create_audio_with_spectrogram(text, base_width, height, max_font_size, margin, letter_spacing):
spec_image = text_to_spectrogram_image(text, base_width, height, max_font_size, margin, letter_spacing)
def create_audio_with_spectrogram(
text, base_width, height, max_font_size, margin, letter_spacing
):
spec_image = text_to_spectrogram_image(
text, base_width, height, max_font_size, margin, letter_spacing
)
y = spectrogram_image_to_audio(spec_image)
with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as temp_audio:

with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
audio_path = temp_audio.name
sf.write(audio_path, y, DEFAULT_SAMPLE_RATE)

# Create spectrogram from audio
S = librosa.feature.melspectrogram(y=y, sr=DEFAULT_SAMPLE_RATE)
S_dB = librosa.power_to_db(S, ref=np.max)
plt.figure(figsize=(10, 4))
librosa.display.specshow(S_dB, sr=DEFAULT_SAMPLE_RATE, x_axis='time', y_axis='mel')
plt.axis('off')
librosa.display.specshow(S_dB, sr=DEFAULT_SAMPLE_RATE, x_axis="time", y_axis="mel")
plt.axis("off")
plt.tight_layout(pad=0)
with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as temp_spectrogram:

with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_spectrogram:
spectrogram_path = temp_spectrogram.name
plt.savefig(spectrogram_path, bbox_inches='tight', pad_inches=0, transparent=True)
plt.savefig(
spectrogram_path, bbox_inches="tight", pad_inches=0, transparent=True
)
plt.close()

return audio_path, spectrogram_path


# Function for displaying the spectrogram of an audio file
def display_audio_spectrogram(audio_path):
y, sr = librosa.load(audio_path, sr=None)
S = librosa.feature.melspectrogram(y=y, sr=sr)
S_dB = librosa.power_to_db(S, ref=np.max)

plt.figure(figsize=(10, 4))
librosa.display.specshow(S_dB, sr=sr, x_axis='time', y_axis='mel')
plt.axis('off')
librosa.display.specshow(S_dB, sr=sr, x_axis="time", y_axis="mel")
plt.axis("off")
plt.tight_layout(pad=0)

with tempfile.NamedTemporaryFile(delete=False, suffix='.png') as temp_spectrogram:
with tempfile.NamedTemporaryFile(delete=False, suffix=".png") as temp_spectrogram:
spectrogram_path = temp_spectrogram.name
plt.savefig(spectrogram_path, bbox_inches='tight', pad_inches=0, transparent=True)
plt.savefig(
spectrogram_path, bbox_inches="tight", pad_inches=0, transparent=True
)
plt.close()
return spectrogram_path


# Converting a downloaded image to an audio spectrogram
def image_to_spectrogram_audio(image_path, sr=DEFAULT_SAMPLE_RATE):
image = Image.open(image_path).convert('L')
image = Image.open(image_path).convert("L")
image = np.array(image)
y = spectrogram_image_to_audio(image, sr)
with tempfile.NamedTemporaryFile(delete=False, suffix='.wav') as temp_audio:

with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
img2audio_path = temp_audio.name
sf.write(img2audio_path, y, sr)
return img2audio_path


# Gradio interface
def gradio_interface_fn(text, base_width, height, max_font_size, margin, letter_spacing):
audio_path, spectrogram_path = create_audio_with_spectrogram(text, base_width, height, max_font_size, margin, letter_spacing)
def gradio_interface_fn(
text, base_width, height, max_font_size, margin, letter_spacing
):
audio_path, spectrogram_path = create_audio_with_spectrogram(
text, base_width, height, max_font_size, margin, letter_spacing
)
return audio_path, spectrogram_path


def gradio_image_to_audio_fn(upload_image):
return image_to_spectrogram_audio(upload_image)


def gradio_decode_fn(upload_audio):
return display_audio_spectrogram(upload_audio)

with gr.Blocks(title='Audio Steganography', css="footer{display:none !important}", theme=gr.themes.Soft(primary_hue="green", secondary_hue="green", spacing_size="sm", radius_size="lg")) as txt2spec:

with gr.Blocks(
title="Audio Steganography",
css="footer{display:none !important}",
theme=gr.themes.Soft(
primary_hue="green", secondary_hue="green", spacing_size="sm", radius_size="lg"
),
) as txt2spec:
with gr.Tab("Text to Spectrogram"):
with gr.Group():
text = gr.Textbox(lines=2, placeholder="Enter your text:", label="Text")
with gr.Row(variant='panel'):
with gr.Row(variant="panel"):
base_width = gr.Slider(value=512, label="Image Width", visible=False)
height = gr.Slider(value=256, label="Image Height", visible=False)
max_font_size = gr.Slider(minimum=10, maximum=130, step=5, value=80, label="Font size")
margin = gr.Slider(minimum=0, maximum=50, step=1, value=10, label="Indent")
letter_spacing = gr.Slider(minimum=0, maximum=50, step=1, value=5, label="Letter spacing")
generate_button = gr.Button("Generate", variant='primary', size="lg")

with gr.Column(variant='panel'):
max_font_size = gr.Slider(
minimum=10, maximum=130, step=5, value=80, label="Font size"
)
margin = gr.Slider(
minimum=0, maximum=50, step=1, value=10, label="Indent"
)
letter_spacing = gr.Slider(
minimum=0, maximum=50, step=1, value=5, label="Letter spacing"
)
generate_button = gr.Button("Generate", variant="primary", size="lg")

with gr.Column(variant="panel"):
with gr.Group():
output_audio = gr.Audio(type="filepath", label="Generated audio")
output_spectrogram = gr.Image(type="filepath", label="Spectrogram")

generate_button.click(gradio_interface_fn, inputs=[text, base_width, height, max_font_size, margin, letter_spacing], outputs=[output_audio, output_spectrogram])
generate_button.click(
gradio_interface_fn,
inputs=[text, base_width, height, max_font_size, margin, letter_spacing],
outputs=[output_audio, output_spectrogram],
)

with gr.Tab("Image to Spectrogram"):
with gr.Group():
with gr.Column():
upload_image = gr.Image(type="filepath", label="Upload image")
convert_button = gr.Button("Convert to audio", variant='primary', size="lg")
convert_button = gr.Button(
"Convert to audio", variant="primary", size="lg"
)

with gr.Column(variant='panel'):
with gr.Column(variant="panel"):
output_audio_from_image = gr.Audio(type="filepath", label="Generated audio")

convert_button.click(gradio_image_to_audio_fn, inputs=[upload_image], outputs=[output_audio_from_image])
convert_button.click(
gradio_image_to_audio_fn,
inputs=[upload_image],
outputs=[output_audio_from_image],
)

with gr.Tab("Audio Spectrogram"):
with gr.Group():
with gr.Column():
upload_audio = gr.Audio(type="filepath", label="Upload audio", scale=3)
decode_button = gr.Button("Show spectrogram", variant='primary', size="lg")
decode_button = gr.Button(
"Show spectrogram", variant="primary", size="lg"
)

with gr.Column(variant='panel'):
with gr.Column(variant="panel"):
decoded_image = gr.Image(type="filepath", label="Audio Spectrogram")

decode_button.click(gradio_decode_fn, inputs=[upload_audio], outputs=[decoded_image])
decode_button.click(
gradio_decode_fn, inputs=[upload_audio], outputs=[decoded_image]
)

txt2spec.launch(share=True)

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