https://github.com/devvyyxyz/images-and-audio-converter-with-python
https://github.com/devvyyxyz/images-and-audio-converter-with-python
audio bit converter image images python visual
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/devvyyxyz/images-and-audio-converter-with-python
- Owner: devvyyxyz
- License: mit
- Created: 2024-06-11T00:57:54.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-11T01:14:22.000Z (about 2 years ago)
- Last Synced: 2025-02-09T15:44:18.355Z (over 1 year ago)
- Topics: audio, bit, converter, image, images, python, visual
- Language: Python
- Homepage:
- Size: 9.56 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
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README
# Image to Audio Converter and Vice Versa
This Python script allows you to convert an image to an audio file and also convert an audio file back to an image. It uses the `Pillow` library for image processing, `pydub` for audio processing, `tqdm` for progress bars, and `scipy` for audio analysis.
# Requirements
Before running the script, ensure you have the following libraries installed:
- `Pillow`
- `numpy`
- `pydub`
- `tqdm`
- `scipy`
-
You can install these libraries using `pip`:
```bash
pip install pillow numpy pydub tqdm scipy
```
# Usage
1. Clone the repository or download the script.
2. Run the script using Python.
## Example
```bash
python script.py
```
When you run the script, you will be prompted to choose an option:
```vbnet
What would you like to do?
1: Convert image to audio
2: Convert audio to image
Enter your choice (1 or 2):
```
## Convert Image to Audio
1. Choose option `1`.
2. Enter the path to the input image.
3. Enter the path to save the output audio file.
```lua
Enter your choice (1 or 2): 1
Enter the path to the input image: input_image.jpg
Enter the path to save the output audio: output_audio.wav
```
## Convert Audio to Image
1. Choose option 2.
2. Enter the path to the input audio file.
3. Enter the path to save the output image.
```lua
Enter your choice (1 or 2): 2
Enter the path to the input audio: input_audio.wav
Enter the path to save the output image: output_image.jpg
```
# code
```py
from PIL import Image
import numpy as np
from pydub import AudioSegment
from pydub.generators import Sine
from tqdm import tqdm
from scipy.fft import fft
import wave
import contextlib
def image_to_audio(image_path, output_audio_path):
# Load the image
img = Image.open(image_path).convert('L') # Convert image to grayscale
img = img.resize((100, 100)) # Resize for simplicity
pixels = np.array(img)
# Create an empty audio segment
audio = AudioSegment.silent(duration=0)
# Map pixel brightness to frequency (pitch)
max_freq = 2000 # Maximum frequency (Hz)
min_freq = 200 # Minimum frequency (Hz)
duration_ms = 50 # Duration of each tone (ms)
# Initialize the progress bar
total_pixels = pixels.shape[0] * pixels.shape[1]
with tqdm(total=total_pixels, desc="Converting image to audio") as pbar:
for row in pixels:
for pixel in row:
# Normalize pixel value to range between min_freq and max_freq
frequency = min_freq + (max_freq - min_freq) * (pixel / 255.0)
# Generate a sine wave for the corresponding frequency
sine_wave = Sine(frequency).to_audio_segment(duration=duration_ms)
# Append the sine wave to the audio segment
audio += sine_wave
# Update the progress bar
pbar.update(1)
# Export the generated audio
audio.export(output_audio_path, format="wav")
def audio_to_image(audio_path, output_image_path):
# Load the audio
with contextlib.closing(wave.open(audio_path, 'r')) as f:
frames = f.getnframes()
rate = f.getframerate()
duration = frames / float(rate)
audio_data = np.frombuffer(f.readframes(frames), dtype=np.int16)
# Parameters
min_freq = 200 # Minimum frequency (Hz)
max_freq = 2000 # Maximum frequency (Hz)
duration_ms = 50 # Duration of each tone (ms)
# Calculate the number of samples per segment
segment_length = int(rate * (duration_ms / 1000.0))
# Calculate the number of segments
num_segments = int(duration * 1000 / duration_ms)
# Initialize an empty list to store pixel values
pixel_values = []
# Initialize the progress bar
with tqdm(total=num_segments, desc="Converting audio to image") as pbar:
for i in range(num_segments):
# Extract the segment of audio data
segment = audio_data[i * segment_length:(i + 1) * segment_length]
# Perform FFT to find the dominant frequency
freqs = np.fft.fftfreq(len(segment))
fft_values = np.abs(fft(segment))
dominant_freq = abs(freqs[np.argmax(fft_values)])
# Map the frequency to a pixel value
pixel_value = 255 * (dominant_freq - min_freq) / (max_freq - min_freq)
pixel_value = int(np.clip(pixel_value, 0, 255))
# Append the pixel value to the list
pixel_values.append(pixel_value)
# Update the progress bar
pbar.update(1)
# Convert the list of pixel values to a 2D array
pixels = np.array(pixel_values).reshape((100, 100))
# Create an image from the 2D array of pixels
img = Image.fromarray(pixels.astype(np.uint8))
img.save(output_image_path)
if __name__ == "__main__":
import sys
print("What would you like to do?")
print("1: Convert image to audio")
print("2: Convert audio to image")
choice = input("Enter your choice (1 or 2): ")
if choice == "1":
image_path = input("Enter the path to the input image: ")
output_audio_path = input("Enter the path to save the output audio: ")
image_to_audio(image_path, output_audio_path)
elif choice == "2":
audio_path = input("Enter the path to the input audio: ")
output_image_path = input("Enter the path to save the output image: ")
audio_to_image(audio_path, output_image_path)
else:
print("Invalid choice. Please enter 1 or 2.")
```
## License
This project is licensed under the MIT - see the [LICENSE](LICENSE) file for details.
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