feat: add development and maintenance utilities

pyav_udp_test.py:
- Simple UDP packet receiver for testing data flow
- Frame shape validation and debugging

reprocess_metadata.py:
- Batch metadata regeneration for existing images
- Gradient generation from stored image files
- JSONL metadata format with duplicate detection

pyav_udp_test.py

@@ -0,0 +1,16 @@
+import socket
+import numpy as np
+
+"udp://10.81.2.120:5000"
+#10.81.2.183
+sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+sock.bind(("0.0.0.0", 5000))
+
+COLUMN_HEIGHT, COLUMN_WIDTH, CHANNELS = 2456, 1, 3  # Example dimensions, adjust as needed
+
+while True:
+    data, addr = sock.recvfrom(65536)  # buffer size is 65536 bytes
+    #print(f"Received {len(data)} bytes from {addr}")
+    frame = np.frombuffer(data, dtype=np.uint8).reshape((COLUMN_HEIGHT, COLUMN_WIDTH, CHANNELS))
+    print(frame.shape)

reprocess_metadata.py

@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+"""
+Reprocess existing images to add gradient metadata.
+This script reads all existing images in the output folder and generates
+gradient backgrounds for them, appending to metadata.jsonl
+"""
+
+import numpy as np
+import imageio.v3 as iio
+import os
+import re
+import json
+from tqdm import tqdm
+
+OUTPUT_DIR = "output"
+
+def img_to_gradient(input_image):
+    """Generate CSS gradient string from image"""
+    string = "linear-gradient(in oklab"
+    for i in np.array_split(input_image[:,0,:], 11):
+        r, g, b = np.mean(i, axis=0).astype(np.uint8)
+        string += f",rgb({r},{g},{b})"
+    string += ")"
+    return string
+
+def extract_length(filename):
+    """Extract length from filename like 'live-timestamp-start-end-length.ext'"""
+    match = re.match(r'live-\d+-\d+-\d+-(\d+)\.', filename)
+    if match:
+        return int(match.group(1))
+    return None
+
+def main():
+    # Get all existing image files
+    image_files = []
+    if os.path.exists(OUTPUT_DIR):
+        image_files = [f for f in os.listdir(OUTPUT_DIR) 
+                      if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.gif', '.avif'))]
+    
+    if not image_files:
+        print("No images found in output directory")
+        return
+    
+    print(f"Found {len(image_files)} images to process")
+    
+    # Load existing metadata to avoid duplicates
+    existing_metadata = set()
+    metadata_path = os.path.join(OUTPUT_DIR, 'metadata.jsonl')
+    if os.path.exists(metadata_path):
+        with open(metadata_path, 'r') as f:
+            for line in f:
+                if line.strip():
+                    data = json.loads(line)
+                    existing_metadata.add(data['filename'])
+        print(f"Found {len(existing_metadata)} existing metadata entries")
+    
+    # Process each image
+    processed = 0
+    skipped = 0
+    
+    for filename in tqdm(image_files, desc="Processing images"):
+        # Skip if metadata already exists
+        if filename in existing_metadata:
+            skipped += 1
+            continue
+        
+        try:
+            # Read the image
+            filepath = os.path.join(OUTPUT_DIR, filename)
+            image = iio.imread(filepath)
+            
+            # Rotate back to vertical orientation (undo the rot90 from write)
+            image_vertical = image
+            
+            # Generate gradient
+            gradient_string = img_to_gradient(image_vertical)
+            
+            # Extract length from filename
+            length = extract_length(filename)
+            if length is None:
+                print(f"Warning: Could not extract length from {filename}")
+                length = image.shape[1]  # Use actual width as fallback
+            
+            # Append to metadata file
+            with open(metadata_path, 'a') as f:
+                metadata_entry = {
+                    "filename": filename,
+                    "length": length,
+                    "gradient": gradient_string
+                }
+                f.write(json.dumps(metadata_entry) + '\n')
+            
+            processed += 1
+            
+        except Exception as e:
+            print(f"Error processing {filename}: {e}")
+    
+    print(f"\nProcessing complete!")
+    print(f"Processed: {processed}")
+    print(f"Skipped (already had metadata): {skipped}")
+    print(f"Total metadata entries: {processed + len(existing_metadata)}")
+
+if __name__ == '__main__':
+    main()