twobob · October 2, 2025 22:35 · Oct 2, 2025
diff --git a/make_vid_from_wav.py b/make_vid_from_wav.py
@@ -0,0 +1,203 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.cm as cm
+import wave
+import contextlib
+import cv2
+import os
+import shutil
+from moviepy.editor import AudioFileClip, VideoFileClip
+
+# --- Configuration ---
+# Input WAV and output MP4 paths
+WAV_PATH = "garbage.wav"
+OUTPUT_MP4 = "garbage_VISUAL.mp4"
+
+# Visualization parameters
+FPS = 30
+FIG_SIZE = (10, 10)
+DPI = 120
+NUM_LOOPS = 4 # Number of concentric visualizer loops
+
+# Aesthetics
+BG_COLOR = '#0B0014'
+COLORMAP = cm.magma # Colormap for dynamic colors (e.g., magma, plasma, inferno)
+PARTICLE_COLOR = '#FFFFFF'
+TITLE_TEXT = 'GARBAGE'
+TITLE_COLOR = (1.0, 1.0, 1.0, 0.08)
+
+# --- Setup Directories ---
+FRAMES_DIR = "temp_visualization_frames"
+if os.path.exists(FRAMES_DIR):
+    shutil.rmtree(FRAMES_DIR)
+os.makedirs(FRAMES_DIR, exist_ok=True)
+
+# --- Audio Processing ---
+try:
+    with contextlib.closing(wave.open(WAV_PATH, 'r')) as wf:
+        num_channels = wf.getnchannels()
+        sampwidth = wf.getsampwidth()
+        framerate = wf.getframerate()
+        num_frames = wf.getnframes()
+        audio_data = wf.readframes(num_frames)
+
+        dtype_map = {1: np.int8, 2: np.int16, 4: np.int32}
+        if sampwidth not in dtype_map:
+            raise ValueError("Unsupported sample width")
+        audio_np = np.frombuffer(audio_data, dtype=dtype_map[sampwidth])
+
+    if num_channels == 2:
+        audio_np = audio_np.reshape(-1, 2).mean(axis=1)
+
+    print("Audio file loaded successfully.")
+    print(f"Duration: {num_frames / float(framerate):.2f}s, Framerate: {framerate}Hz")
+
+except FileNotFoundError:
+    print(f"Error: The file '{WAV_PATH}' was not found.")
+    exit()
+except Exception as e:
+    print(f"An error occurred while reading the audio file: {e}")
+    exit()
+
+# --- Visualization Frame Generation ---
+samples_per_frame = framerate // FPS
+num_frames_to_generate = int(num_frames / samples_per_frame)
+max_amplitude = np.max(np.abs(audio_np)) if len(audio_np) > 0 else 1.0
+
+print(f"Generating {num_frames_to_generate} frames...")
+
+for i in range(num_frames_to_generate):
+    start = i * samples_per_frame
+    end = start + samples_per_frame
+    frame_audio = audio_np[start:end]
+
+    if frame_audio.size == 0:
+        continue
+
+    # --- Create the Radial Visualization ---
+    fig = plt.figure(figsize=FIG_SIZE, facecolor=BG_COLOR)
+    ax = plt.subplot(111, polar=True, facecolor=BG_COLOR)
+
+    fig.text(0.5, 0.5, TITLE_TEXT, fontsize=120, color=TITLE_COLOR, 
+             ha='center', va='center', weight='bold')
+
+    # Calculate RMS for intensity and get the dynamic color for this frame
+    rms = np.sqrt(np.mean(np.square(frame_audio.astype(np.float64))))
+    normalized_rms = rms / max_amplitude if max_amplitude > 0 else 0
+    if not np.isfinite(normalized_rms):
+        normalized_rms = 0
+
+    # The color is determined by the loudness of the frame
+    dynamic_color = COLORMAP(normalized_rms)
+    # A slightly brighter color for the main line
+    glow_color = COLORMAP(min(normalized_rms * 1.2, 1.0))
+
+    num_samples = len(frame_audio)
+    theta = np.linspace(0, 2 * np.pi, num_samples)
+
+    # --- Add Particle Effects ---
+    num_particles = int(150 * normalized_rms)
+    particle_thetas = np.random.uniform(0, 2 * np.pi, num_particles)
+    particle_radii = np.random.uniform(0, 1.5, num_particles) * (1 + normalized_rms)
+    particle_sizes = (np.random.rand(num_particles) * 30) * (0.5 + normalized_rms)
+    ax.scatter(particle_thetas, particle_radii, s=particle_sizes, color=PARTICLE_COLOR, alpha=0.4 * normalized_rms, zorder=2)
+
+    # --- Plot Multiple Concentric Loops ---
+    for j in range(NUM_LOOPS):
+        loop_base_radius = 0.4 * (j + 1)
+
+        # Modulate radius based on audio waveform
+        radius_modulation = (np.abs(frame_audio) / max_amplitude) * 0.2 * (1 + normalized_rms) if max_amplitude > 0 else np.zeros_like(frame_audio)
+        radii = loop_base_radius + radius_modulation
+
+        # Ensure smooth wrap-around
+        loop_theta = np.append(theta, theta[0])
+        loop_radii = np.append(radii, radii[0])
+
+        # Plot glow effect - increased alpha for more intensity
+        ax.plot(loop_theta, loop_radii, color=glow_color, linewidth=5 + j*2, alpha=0.7 * (0.3 + normalized_rms), zorder=3)
+        # Plot main line - increased linewidth and always visible
+        ax.plot(loop_theta, loop_radii, color=dynamic_color, linewidth=2.5, alpha=0.9, zorder=4)
+
+    # --- Final Touches on the Plot ---
+    ax.set_ylim(0, 2.5) # Increased limit for multiple loops
+    ax.grid(False)
+    ax.set_xticks([])
+    ax.set_yticks([])
+    ax.spines['polar'].set_visible(False)
+
+    plt.tight_layout()
+    frame_path = os.path.join(FRAMES_DIR, f"frame_{i:05d}.png")
+    plt.savefig(frame_path, dpi=DPI, facecolor=BG_COLOR)
+    plt.close(fig)
+
+    if (i + 1) % 10 == 0:
+        print(f"   ... {i+1}/{num_frames_to_generate} frames rendered")
+
+
+# --- Video Compilation ---
+print("\nAll frames generated. Compiling video...")
+
+first_frame_path = os.path.join(FRAMES_DIR, "frame_00000.png")
+if not os.path.exists(first_frame_path):
+    print("Error: No frames were generated. Cannot create video.")
+    exit()
+
+frame_example = cv2.imread(first_frame_path)
+h, w, _ = frame_example.shape
+temp_video_path = os.path.join(FRAMES_DIR, "visual.avi")
+
+# Use MJPEG codec which is more compatible
+fourcc = cv2.VideoWriter_fourcc(*'MJPG')
+video = cv2.VideoWriter(temp_video_path, fourcc, FPS, (w, h))
+
+for i in range(num_frames_to_generate):
+    frame_path = os.path.join(FRAMES_DIR, f"frame_{i:05d}.png")
+    if os.path.exists(frame_path):
+        frame = cv2.imread(frame_path)
+        video.write(frame)
+video.release()
+
+# --- Combine Video and Audio using ffmpeg-python ---
+print("Combining video and audio...")
+
+try:
+    import ffmpeg
+
+    # Use ffmpeg-python to combine video and audio
+    video_stream = ffmpeg.input(temp_video_path)
+    audio_stream = ffmpeg.input(WAV_PATH)
+
+    output = ffmpeg.output(
+        video_stream, 
+        audio_stream, 
+        OUTPUT_MP4,
+        vcodec='libx264',
+        acodec='aac',
+        shortest=None,
+        **{'b:v': '5000k'}  # Video bitrate for quality
+    )
+
+    # Overwrite output file if it exists
+    output = ffmpeg.overwrite_output(output)
+
+    # Run the ffmpeg command
+    ffmpeg.run(output, capture_stdout=True, capture_stderr=True)
+
+    print(f"\nSuccessfully created video: {OUTPUT_MP4}")
+
+except ImportError:
+    print("\nError: ffmpeg-python library not found.")
+    print("Please install it with: pip install ffmpeg-python")
+    print("You can find the silent video at:", temp_video_path)
+
+except Exception as e:
+    print(f"\nAn error occurred during the final video compilation: {e}")
+    import traceback
+    traceback.print_exc()
+    print("You can find the silent video at:", temp_video_path)
+
+finally:
+    print("Cleaning up temporary files...")
+    if os.path.exists(FRAMES_DIR):
+        shutil.rmtree(FRAMES_DIR)