Code Update

server/processor.py

@@ -6,12 +6,10 @@ import numpy as np
 import librosa
 import librosa.display
 import matplotlib
-# Set backend to Agg (Anti-Grain Geometry) to render without a GUI (essential for servers)
 matplotlib.use('Agg') 
 import matplotlib.pyplot as plt
 from PIL import Image
 
-# --- Constants ---
 MAX_MB = 40
 SIG_SHIFT = b'B2I!'
 SIG_STEGO = b'B2S!'
@@ -25,7 +23,6 @@ class AudioImageProcessor:
         os.makedirs(upload_folder, exist_ok=True)
 
     def _get_bytes(self, path):
-        """Helper to safely read bytes"""
         if os.path.getsize(path) > (MAX_MB * 1024 * 1024):
             raise ValueError("File too large (Max 40MB)")
         with open(path, 'rb') as f:
@@ -36,9 +33,7 @@ class AudioImageProcessor:
         ext_bytes = ext.encode('utf-8')
         return struct.pack(HEADER_FMT, signature, file_size, len(ext_bytes)) + ext_bytes
 
-    # --- Feature 1: Spectrogram Art ---
     def generate_spectrogram(self, audio_path, min_pixels=0):
-        """Generates a visual spectrogram from audio."""
         try:
             import torch
             import torchaudio
@@ -48,13 +43,10 @@ class AudioImageProcessor:
 
         if has_torch and torch.cuda.is_available():
             try:
-                # GPU Accelerated Path
                 device = "cuda"
                 waveform, sr = torchaudio.load(audio_path)
                 waveform = waveform.to(device)
                 
-                # Create transformation
-                # Mimic librosa defaults roughly: n_fft=2048, hop_length=512
                 n_fft = 2048
                 win_length = n_fft
                 hop_length = 512
@@ -72,17 +64,13 @@ class AudioImageProcessor:
                 S = mel_spectrogram(waveform)
                 S_dB = torchaudio.transforms.AmplitudeToDB()(S)
                 
-                # Back to CPU for plotting
-                S_dB = S_dB.cpu().numpy()[0] # Take first channel
-                # Librosa display expects numpy
+                S_dB = S_dB.cpu().numpy()[0]
             except Exception as e:
-                # Fallback to CPU/Librosa if any error occurs
                 print(f"GPU processing failed, falling back to CPU: {e}")
                 return self._generate_spectrogram_cpu(audio_path, min_pixels)
         else:
             return self._generate_spectrogram_cpu(audio_path, min_pixels)
 
-        # Plotting (Common)
         return self._plot_spectrogram(S_dB, sr, min_pixels)
 
     def _generate_spectrogram_cpu(self, audio_path, min_pixels=0):
@@ -92,44 +80,31 @@ class AudioImageProcessor:
         return self._plot_spectrogram(S_dB, sr, min_pixels)
         
     def _plot_spectrogram(self, S_dB, sr, min_pixels=0):
-        # Calculate DPI dynamically to ensure we have enough pixels for steganography
         dpi = 300
         if min_pixels > 0:
-            # Figure is 12x6 inches. Area = 72 sq inches.
-            # Total Pixels = 72 * dpi^2
             required_dpi = math.ceil((min_pixels / 72) ** 0.5)
-            # Add a small buffer
             dpi = max(dpi, int(required_dpi * 1.05))
 
-        # Use exact dimensions without margins
         width_in = 12
         height_in = 6
         fig = plt.figure(figsize=(width_in, height_in))
         
-        # Add axes covering the entire figure [left, bottom, width, height]
         ax = plt.axes([0, 0, 1, 1], frameon=False)
         ax.set_axis_off()
         
-        # 'magma' is a nice default
         librosa.display.specshow(S_dB, sr=sr, fmax=8000, cmap='magma', ax=ax)
 
         output_path = os.path.join(self.upload_folder, f"art_{int(time.time())}.png")
         
-        # specific DPI, no bbox_inches='tight' (which shrinks the image)
         plt.savefig(output_path, dpi=dpi)
         plt.close()
         return output_path
 
-
-
-    # --- Feature 3: Steganography (Embed in Host) ---
     def encode_stego(self, data_path, host_path):
-        # 1. Prepare Data
         file_data = self._get_bytes(data_path)
         header = self._create_header(SIG_STEGO, len(file_data), data_path)
         payload_bits = np.unpackbits(np.frombuffer(header + file_data, dtype=np.uint8))
         
-        # 2. Prepare Host
         host = Image.open(host_path).convert('RGB')
         host_arr = np.array(host)
         flat_host = host_arr.flatten()
@@ -137,7 +112,6 @@ class AudioImageProcessor:
         if len(payload_bits) > len(flat_host):
             raise ValueError(f"Host image too small. Need {len(payload_bits)/3/1e6:.2f} MP.")
 
-        # 3. Embed (LSB)
         padded_bits = np.pad(payload_bits, (0, len(flat_host) - len(payload_bits)), 'constant')
         embedded_flat = (flat_host & 0xFE) + padded_bits
         
@@ -147,19 +121,16 @@ class AudioImageProcessor:
         embedded_img.save(output_path, "PNG")
         return output_path
 
-    # --- Feature 4: Universal Decoder ---
     def decode_image(self, image_path):
         img = Image.open(image_path).convert('RGB')
         flat_bytes = np.array(img).flatten()
         
-        # Strategy A: Check for Shift Signature (Raw Bytes)
         try:
             sig = struct.unpack('>4s', flat_bytes[:4])[0]
             if sig == SIG_SHIFT:
                 return self._extract(flat_bytes, image_path, is_bits=False)
         except: pass
 
-        # Strategy B: Check for Stego Signature (LSB)
         try:
             sample_bytes = np.packbits(flat_bytes[:300] & 1)
             sig = struct.unpack('>4s', sample_bytes[:4])[0]