detect-objects.py

#!/usr/bin/env python3
# /// script
# requires-python = ">=3.10"
# dependencies = [
#     "transformers@git+https://github.com/huggingface/transformers.git@1fba72361e8e0e865d569f7cd15e5aa50b41ac9a",
#     "datasets",
#     "huggingface-hub",
#     "pillow",
#     "tqdm",
#    "torchvision",
# ]
# ///

"""
Detect objects in images using Meta's SAM3 (Segment Anything Model 3).

This script processes images from a HuggingFace dataset and detects objects
based on text prompts, outputting bounding boxes in HuggingFace object detection format.

Examples:
    # Detect photographs in historical newspapers
    uv run detect-objects.py \\
        davanstrien/newspapers-with-images-after-photography \\
        my-username/newspapers-detected \\
        --classes photograph

    # Detect multiple object types
    uv run detect-objects.py \\
        my-dataset \\
        my-output \\
        --classes "photograph,illustration,headline" \\
        --confidence-threshold 0.7

    # Test on small subset
    uv run detect-objects.py input output \\
        --classes photo \\
        --max-samples 10

    # Run on HF Jobs with L4 GPU
    hfjobs run --flavor l4x1 \\
        -e HF_TOKEN=$HF_TOKEN \\
        ghcr.io/astral-sh/uv:latest \\
        /bin/bash -c "uv run https://huggingface.co/datasets/uv-scripts/sam3/raw/main/detect-objects.py \\
            input-dataset output-dataset --classes 'photo,illustration'"

Performance:
    - L4 GPU: ~2-4 images/sec (depending on image size and batch size)
    - Memory: ~8-12 GB VRAM
    - Recommended batch size: 4-8 for L4, 8-16 for A10
"""

import argparse
import logging
import os
import sys
from typing import List, Dict, Any

import torch
from PIL import Image
from datasets import load_dataset, Dataset, Features, Sequence, Value, ClassLabel
from datasets import Image as ImageFeature
from huggingface_hub import HfApi, login
from tqdm.auto import tqdm
from transformers import Sam3Processor, Sam3Model

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s',
    datefmt='%H:%M:%S'
)
logger = logging.getLogger(__name__)

# GPU availability check
if not torch.cuda.is_available():
    logger.error("❌ CUDA is not available. This script requires a GPU.")
    logger.error("For local testing, ensure you have a CUDA-capable GPU.")
    logger.error("For cloud execution, use HF Jobs with --flavor l4x1 or similar.")
    sys.exit(1)


def parse_args():
    """Parse command line arguments."""
    parser = argparse.ArgumentParser(
        description="Detect objects in images using SAM3",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog=__doc__
    )

    # Required arguments
    parser.add_argument(
        "input_dataset",
        help="Input HuggingFace dataset ID (e.g., 'username/dataset')"
    )
    parser.add_argument(
        "output_dataset",
        help="Output HuggingFace dataset ID (e.g., 'username/output')"
    )

    # Object detection configuration
    parser.add_argument(
        "--classes",
        required=True,
        help="Comma-separated list of object classes to detect (e.g., 'photograph,illustration,diagram')"
    )
    parser.add_argument(
        "--confidence-threshold",
        type=float,
        default=0.5,
        help="Minimum confidence score for detections (default: 0.5)"
    )
    parser.add_argument(
        "--mask-threshold",
        type=float,
        default=0.5,
        help="Threshold for mask generation (default: 0.5)"
    )

    # Dataset configuration
    parser.add_argument(
        "--image-column",
        default="image",
        help="Name of the column containing images (default: 'image')"
    )
    parser.add_argument(
        "--split",
        default="train",
        help="Dataset split to process (default: 'train')"
    )
    parser.add_argument(
        "--max-samples",
        type=int,
        default=None,
        help="Maximum number of samples to process (for testing)"
    )
    parser.add_argument(
        "--shuffle",
        action="store_true",
        help="Shuffle dataset before processing"
    )

    # Processing configuration
    parser.add_argument(
        "--batch-size",
        type=int,
        default=4,
        help="Batch size for processing (default: 4)"
    )
    parser.add_argument(
        "--model",
        default="facebook/sam3",
        help="SAM3 model ID (default: 'facebook/sam3')"
    )
    parser.add_argument(
        "--dtype",
        default="bfloat16",
        choices=["float32", "float16", "bfloat16"],
        help="Model precision (default: 'bfloat16')"
    )

    # Output configuration
    parser.add_argument(
        "--private",
        action="store_true",
        help="Make output dataset private"
    )
    parser.add_argument(
        "--hf-token",
        default=None,
        help="HuggingFace token (default: uses HF_TOKEN env var or cached token)"
    )

    return parser.parse_args()


def load_and_validate_dataset(
    dataset_id: str,
    split: str,
    image_column: str,
    max_samples: int = None,
    shuffle: bool = False, 
    hf_token: str = None
) -> Dataset:
    """Load dataset and validate it has the required image column."""
    logger.info(f"📂 Loading dataset: {dataset_id} (split: {split})")

    try:
        dataset = load_dataset(dataset_id, split=split, token=hf_token)
    except Exception as e:
        logger.error(f"Failed to load dataset '{dataset_id}': {e}")
        sys.exit(1)

    # Validate image column exists
    if image_column not in dataset.column_names:
        logger.error(f"Column '{image_column}' not found in dataset")
        logger.error(f"Available columns: {dataset.column_names}")
        sys.exit(1)

    # Shuffle if requested
    if shuffle:
        logger.info("🔀 Shuffling dataset")
        dataset = dataset.shuffle()

    # Limit samples if requested
    if max_samples is not None:
        logger.info(f"🔢 Limiting to {max_samples} samples")
        dataset = dataset.select(range(min(max_samples, len(dataset))))

    logger.info(f"✅ Loaded {len(dataset)} samples")
    return dataset


def process_batch(
    batch: Dict[str, List[Any]],
    image_column: str,
    class_names: List[str],
    processor: Sam3Processor,
    model: Sam3Model,
    confidence_threshold: float,
    mask_threshold: float
) -> Dict[str, List[List[Dict[str, Any]]]]:
    """Process a batch of images and return detections."""
    images = batch[image_column]

    # Convert to PIL Images and ensure RGB
    pil_images = []
    for img in images:
        if isinstance(img, str):
            img = Image.open(img)
        if img.mode == "L":
            img = img.convert("RGB")
        elif img.mode != "RGB":
            img = img.convert("RGB")
        pil_images.append(img)

    # Store original sizes for post-processing
    original_sizes = [(img.height, img.width) for img in pil_images]

    # Process batch through model
    try:
        inputs = processor(
            images=pil_images,
            text=class_names,  # All class names as prompts
            return_tensors="pt"
        ).to(model.device)

        with torch.no_grad():
            outputs = model(**inputs)

        # Post-process outputs
        results = processor.post_process_instance_segmentation(
            outputs,
            threshold=confidence_threshold,
            mask_threshold=mask_threshold,
            target_sizes=original_sizes
        )

    except Exception as e:
        logger.warning(f"⚠️  Failed to process batch: {e}")
        # Return empty detections for all images in batch
        return {"objects": [[] for _ in range(len(pil_images))]}

    # Convert to HuggingFace object detection format
    batch_objects = []
    for result in results:
        boxes = result.get('boxes', torch.tensor([]))
        scores = result.get('scores', torch.tensor([]))
        labels = result.get('labels', torch.tensor([]))

        # Handle empty results
        if len(boxes) == 0:
            batch_objects.append([])
            continue

        # Build list of detections
        detections = []
        for box, score, label_idx in zip(
            boxes.cpu().numpy(),
            scores.cpu().numpy(),
            labels.cpu().numpy()
        ):
            x1, y1, x2, y2 = box
            width = x2 - x1
            height = y2 - y1

            detection = {
                "bbox": [float(x1), float(y1), float(width), float(height)],
                "category": int(label_idx),  # Index into class_names
                "score": float(score)
            }
            detections.append(detection)

        batch_objects.append(detections)

    return {"objects": batch_objects}


def main():
    args = parse_args()

    # Parse class names
    class_names = [name.strip() for name in args.classes.split(',')]
    if not class_names or any(not name for name in class_names):
        logger.error("❌ Invalid --classes argument. Provide comma-separated class names.")
        sys.exit(1)

    logger.info("🚀 SAM3 Object Detection")
    logger.info(f"   Input: {args.input_dataset}")
    logger.info(f"   Output: {args.output_dataset}")
    logger.info(f"   Classes: {class_names}")
    logger.info(f"   Confidence threshold: {args.confidence_threshold}")
    logger.info(f"   Batch size: {args.batch_size}")

    # Authentication
    if args.hf_token:
        login(token=args.hf_token)
    elif os.getenv("HF_TOKEN"):
        login(token=os.getenv("HF_TOKEN"))

    # Load dataset
    dataset = load_and_validate_dataset(
        args.input_dataset,
        args.split,
        args.image_column,
        args.max_samples,
        args.shuffle,
        args.hf_token
    )

    # Load model
    logger.info(f"🤖 Loading SAM3 model: {args.model}")
    try:
        processor = Sam3Processor.from_pretrained(args.model)
        model = Sam3Model.from_pretrained(
            args.model,
            torch_dtype=getattr(torch, args.dtype),
            device_map="auto"
        )
        logger.info(f"✅ Model loaded on {model.device}")
    except Exception as e:
        logger.error(f"❌ Failed to load model: {e}")
        logger.error("Ensure the model exists and you have access permissions")
        sys.exit(1)

    # Process dataset
    logger.info("🔍 Processing images...")
    processed_dataset = dataset.map(
        lambda batch: process_batch(
            batch,
            args.image_column,
            class_names,
            processor,
            model,
            args.confidence_threshold,
            args.mask_threshold
        ),
        batched=True,
        batch_size=args.batch_size,
        desc="Detecting objects"
    )

    # Create dynamic features with ClassLabel
    logger.info("📊 Creating output schema...")
    new_features = processed_dataset.features.copy()
    new_features["objects"] = Sequence({
        "bbox": Sequence(Value("float32"), length=4),
        "category": ClassLabel(names=class_names),
        "score": Value("float32")
    })

    # Cast to proper types
    processed_dataset = processed_dataset.cast(new_features)

    # Calculate statistics
    total_detections = sum(len(objs) for objs in processed_dataset["objects"])
    images_with_detections = sum(1 for objs in processed_dataset["objects"] if len(objs) > 0)

    logger.info("✅ Detection complete!")
    logger.info(f"   Total detections: {total_detections}")
    logger.info(f"   Images with detections: {images_with_detections}/{len(processed_dataset)}")
    logger.info(f"   Average detections per image: {total_detections/len(processed_dataset):.2f}")

    # Push to hub
    logger.info(f"📤 Pushing to HuggingFace Hub: {args.output_dataset}")
    try:
        processed_dataset.push_to_hub(
            args.output_dataset,
            private=args.private
        )
        logger.info(f"✅ Dataset available at: https://huggingface.co/datasets/{args.output_dataset}")
    except Exception as e:
        logger.error(f"❌ Failed to push to hub: {e}")
        logger.info("💾 Saving locally as backup...")
        processed_dataset.save_to_disk("./output_dataset")
        logger.info("✅ Saved to ./output_dataset")
        sys.exit(1)


if __name__ == "__main__":
    main()