Add Falcon OCR bucket script for directory/volume I/O

Companion to falcon-ocr.py (dataset mode). Reads images/PDFs from a
directory, writes markdown files preserving structure. Designed for
HF Buckets mounted as volumes via -v flag.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

falcon-ocr-bucket.py

@@ -0,0 +1,278 @@
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "pillow",
+#     "pymupdf",
+#     "torch>=2.5",
+#     "torchvision",
+#     "falcon-perception[ocr]",
+# ]
+# ///
+
+"""
+OCR images and PDFs from a directory using Falcon OCR, writing markdown files.
+
+Designed to work with HF Buckets mounted as volumes via `hf jobs uv run -v ...`.
+Reads images/PDFs from INPUT_DIR, runs Falcon OCR via the optimized falcon-perception
+engine (CUDA graphs + paged inference), and writes one .md file per image (or per
+PDF page) to OUTPUT_DIR, preserving directory structure.
+
+Input:                          Output:
+  /input/page1.png        ->      /output/page1.md
+  /input/report.pdf       ->      /output/report/page_001.md
+  (3 pages)                       /output/report/page_002.md
+                                  /output/report/page_003.md
+  /input/sub/photo.jpg    ->      /output/sub/photo.md
+
+Examples:
+
+  # Local test
+  uv run falcon-ocr-bucket.py ./test-images ./test-output
+
+  # HF Jobs with bucket volumes
+  hf jobs uv run --flavor l4x1 \\
+      -s HF_TOKEN \\
+      -v bucket/user/ocr-input:/input:ro \\
+      -v bucket/user/ocr-output:/output \\
+      https://huggingface.co/datasets/uv-scripts/ocr/raw/main/falcon-ocr-bucket.py \\
+      /input /output
+
+Model: tiiuae/Falcon-OCR (0.3B, 80.3% olmOCR, Apache 2.0)
+Backend: falcon-perception (OCRInferenceEngine with CUDA graphs)
+"""
+
+import argparse
+import logging
+import sys
+import time
+from pathlib import Path
+
+import torch
+from PIL import Image
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
+logger = logging.getLogger(__name__)
+
+MODEL_ID = "tiiuae/Falcon-OCR"
+IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".tiff", ".tif", ".bmp", ".webp"}
+
+
+def check_cuda_availability():
+    if not torch.cuda.is_available():
+        logger.error("CUDA is not available. This script requires a GPU.")
+        sys.exit(1)
+    logger.info(f"CUDA available. GPU: {torch.cuda.get_device_name(0)}")
+
+
+def discover_files(input_dir: Path) -> list[Path]:
+    files = []
+    for path in sorted(input_dir.rglob("*")):
+        if not path.is_file():
+            continue
+        ext = path.suffix.lower()
+        if ext in IMAGE_EXTENSIONS or ext == ".pdf":
+            files.append(path)
+    return files
+
+
+def prepare_images(
+    files: list[Path], input_dir: Path, output_dir: Path, pdf_dpi: int
+) -> list[tuple[Image.Image, Path]]:
+    import fitz  # pymupdf
+
+    items: list[tuple[Image.Image, Path]] = []
+
+    for file_path in files:
+        rel = file_path.relative_to(input_dir)
+        ext = file_path.suffix.lower()
+
+        if ext == ".pdf":
+            pdf_output_dir = output_dir / rel.with_suffix("")
+            try:
+                doc = fitz.open(file_path)
+                num_pages = len(doc)
+                logger.info(f"PDF: {rel} ({num_pages} pages)")
+                for page_num in range(num_pages):
+                    page = doc[page_num]
+                    zoom = pdf_dpi / 72.0
+                    mat = fitz.Matrix(zoom, zoom)
+                    pix = page.get_pixmap(matrix=mat)
+                    img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
+                    md_path = pdf_output_dir / f"page_{page_num + 1:03d}.md"
+                    items.append((img, md_path))
+                doc.close()
+            except Exception as e:
+                logger.error(f"Failed to open PDF {rel}: {e}")
+        else:
+            try:
+                img = Image.open(file_path).convert("RGB")
+                md_path = output_dir / rel.with_suffix(".md")
+                items.append((img, md_path))
+            except Exception as e:
+                logger.error(f"Failed to open image {rel}: {e}")
+
+    return items
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="OCR images/PDFs from a directory using Falcon OCR, output markdown files.",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog=__doc__,
+    )
+    parser.add_argument("input_dir", help="Directory containing images and/or PDFs")
+    parser.add_argument("output_dir", help="Directory to write markdown output files")
+    parser.add_argument(
+        "--batch-size", type=int, default=8, help="Images per batch (default: 8)",
+    )
+    parser.add_argument(
+        "--pdf-dpi", type=int, default=300,
+        help="DPI for PDF page rendering (default: 300)",
+    )
+    parser.add_argument(
+        "--no-compile", action="store_true", help="Disable torch.compile",
+    )
+    parser.add_argument(
+        "--no-cudagraph", action="store_true", help="Disable CUDA graph capture",
+    )
+    parser.add_argument(
+        "--verbose", action="store_true", help="Print resolved package versions",
+    )
+
+    args = parser.parse_args()
+
+    check_cuda_availability()
+
+    input_dir = Path(args.input_dir)
+    output_dir = Path(args.output_dir)
+
+    if not input_dir.is_dir():
+        logger.error(f"Input directory does not exist: {input_dir}")
+        sys.exit(1)
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    start_time = time.time()
+
+    # Discover files
+    logger.info(f"Scanning {input_dir} for images and PDFs...")
+    files = discover_files(input_dir)
+    if not files:
+        logger.error(f"No image or PDF files found in {input_dir}")
+        sys.exit(1)
+
+    pdf_count = sum(1 for f in files if f.suffix.lower() == ".pdf")
+    img_count = len(files) - pdf_count
+    logger.info(f"Found {img_count} image(s) and {pdf_count} PDF(s)")
+
+    # Prepare images
+    logger.info("Preparing images (rendering PDFs)...")
+    items = prepare_images(files, input_dir, output_dir, args.pdf_dpi)
+    if not items:
+        logger.error("No processable images after preparation")
+        sys.exit(1)
+
+    logger.info(f"Total images to OCR: {len(items)}")
+
+    # Load model
+    logger.info(f"Loading {MODEL_ID} via falcon-perception engine...")
+    from falcon_perception import load_and_prepare_model
+    from falcon_perception.data import ImageProcessor
+    from falcon_perception.paged_ocr_inference import OCRInferenceEngine
+
+    do_compile = not args.no_compile
+    do_cudagraph = not args.no_cudagraph
+
+    model, tokenizer, model_args = load_and_prepare_model(
+        hf_model_id=MODEL_ID,
+        device="cuda",
+        dtype="bfloat16",
+        compile=do_compile,
+    )
+
+    image_processor = ImageProcessor(patch_size=16, merge_size=1)
+    engine = OCRInferenceEngine(
+        model, tokenizer, image_processor, capture_cudagraph=do_cudagraph
+    )
+    logger.info(f"Engine loaded. compile={do_compile}, cudagraph={do_cudagraph}")
+
+    # Process in batches
+    errors = 0
+    processed = 0
+    total = len(items)
+    batch_size = args.batch_size
+
+    for batch_start in range(0, total, batch_size):
+        batch_end = min(batch_start + batch_size, total)
+        batch = items[batch_start:batch_end]
+        batch_num = batch_start // batch_size + 1
+        total_batches = (total + batch_size - 1) // batch_size
+
+        logger.info(f"Batch {batch_num}/{total_batches} ({processed}/{total} done)")
+
+        try:
+            batch_images = [img for img, _ in batch]
+            texts = engine.generate_plain(images=batch_images, use_tqdm=False)
+
+            for (_, md_path), text in zip(batch, texts):
+                md_path.parent.mkdir(parents=True, exist_ok=True)
+                md_path.write_text(text.strip(), encoding="utf-8")
+                processed += 1
+
+        except Exception as e:
+            logger.error(f"Batch {batch_num} failed: {e}")
+            for _, md_path in batch:
+                md_path.parent.mkdir(parents=True, exist_ok=True)
+                md_path.write_text(f"[OCR ERROR: {e}]", encoding="utf-8")
+            errors += len(batch)
+            processed += len(batch)
+
+    elapsed = time.time() - start_time
+    elapsed_str = f"{elapsed / 60:.1f} min" if elapsed > 60 else f"{elapsed:.1f}s"
+
+    logger.info("=" * 50)
+    logger.info(f"Done! Processed {total} images in {elapsed_str}")
+    logger.info(f"  Output: {output_dir}")
+    logger.info(f"  Errors: {errors}")
+    if total > 0:
+        logger.info(f"  Speed: {total / elapsed:.2f} images/sec")
+
+    if args.verbose:
+        import importlib.metadata
+
+        logger.info("--- Package versions ---")
+        for pkg in ["falcon-perception", "torch", "pillow", "pymupdf"]:
+            try:
+                logger.info(f"  {pkg}=={importlib.metadata.version(pkg)}")
+            except importlib.metadata.PackageNotFoundError:
+                logger.info(f"  {pkg}: not installed")
+
+
+if __name__ == "__main__":
+    if len(sys.argv) == 1:
+        print("=" * 60)
+        print("Falcon OCR Bucket Script")
+        print("=" * 60)
+        print(f"\nModel: {MODEL_ID} (0.3B, Apache 2.0)")
+        print("OCR images/PDFs from a directory -> markdown files.")
+        print("Designed for HF Buckets mounted as volumes.")
+        print()
+        print("Usage:")
+        print("  uv run falcon-ocr-bucket.py INPUT_DIR OUTPUT_DIR")
+        print()
+        print("Examples:")
+        print("  uv run falcon-ocr-bucket.py ./images ./output")
+        print()
+        print("HF Jobs with bucket volumes:")
+        print("  hf jobs uv run --flavor l4x1 -s HF_TOKEN \\")
+        print("      -v bucket/user/ocr-input:/input:ro \\")
+        print("      -v bucket/user/ocr-output:/output \\")
+        print(
+            "      https://huggingface.co/datasets/uv-scripts/ocr/raw/main/falcon-ocr-bucket.py \\"
+        )
+        print("      /input /output")
+        print()
+        print("For full help: uv run falcon-ocr-bucket.py --help")
+        sys.exit(0)
+
+    main()