# /// 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 hf://buckets/user/ocr-input:/input:ro \\ -v hf://buckets/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 hf://buckets/user/ocr-input:/input:ro \\") print(" -v hf://buckets/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()