#!/usr/bin/env python3 """ NSFW Classification Job - Process human crops from SAM3D bboxes with EraX YOLO Requires: SAM 3D Body outputs for human bboxes Outputs: Per-human NSFW detections with bboxes and confidence scores """ import argparse import os from pathlib import Path import warnings warnings.filterwarnings('ignore') import logging import sys logging.basicConfig( level=logging.INFO, format='[%(asctime)s] %(levelname)s: %(message)s', datefmt='%Y-%m-%d %H:%M:%S', stream=sys.stdout, force=True ) logger = logging.getLogger(__name__) import numpy as np import torch from datasets import load_dataset, Dataset as HFDataset, Features, Value from PIL import Image import json from huggingface_hub import snapshot_download from ultralytics import YOLO def make_square_bbox_with_padding(bbox, img_width, img_height, padding=0.1): """Convert bbox to square with padding""" x1, y1, x2, y2 = bbox w = x2 - x1 h = y2 - y1 # Make square size = max(w, h) cx = (x1 + x2) / 2 cy = (y1 + y2) / 2 # Add padding size = size * (1 + padding) # Get square bbox x1_sq = max(0, cx - size / 2) y1_sq = max(0, cy - size / 2) x2_sq = min(img_width, cx + size / 2) y2_sq = min(img_height, cy + size / 2) return [x1_sq, y1_sq, x2_sq, y2_sq] def process_batch(batch, sam3d_dataset): """Process batch of images - join with SAM3D results to get human bboxes""" images = batch['image'] image_paths = batch.get('image_path', [f'img_{i:06d}' for i in range(len(images))]) results_list = [] # Collect all crops for batch inference crops = [] crop_info = [] # (image_idx, human_idx, original_bbox) for idx, image_pil in enumerate(images): image_id = Path(image_paths[idx]).stem if image_paths[idx] else f'img_{idx:06d}' # Find corresponding SAM3D data sam3d_row = sam3d_dataset.filter(lambda x: x['image_id'] == image_id).take(1) sam3d_row = list(sam3d_row) if not sam3d_row or not sam3d_row[0]['sam3d_data']: results_list.append({ 'image_id': image_id, 'human_detections': None }) continue humans_data = json.loads(sam3d_row[0]['sam3d_data']) image_rgb = np.array(image_pil.convert('RGB')) img_width, img_height = image_pil.size # Collect crops for each human for human_idx, human in enumerate(humans_data): bbox = human.get('bbox') if bbox is None: continue # Make square bbox with padding square_bbox = make_square_bbox_with_padding(bbox, img_width, img_height, padding=0.15) x1, y1, x2, y2 = map(int, square_bbox) # Crop and resize to standard size for YOLO crop = image_rgb[y1:y2, x1:x2] if crop.size > 0: crops.append(crop) crop_info.append((idx, human_idx, square_bbox, bbox)) # Batch NSFW inference on all crops human_results = {} # {image_idx: {human_idx: detections}} if crops: try: yolo_results = model(crops, conf=0.2, iou=0.3, verbose=False) for crop_idx, result in enumerate(yolo_results): img_idx, human_idx, square_bbox, orig_bbox = crop_info[crop_idx] x1_sq, y1_sq, x2_sq, y2_sq = square_bbox detections = [] if result.boxes: for box in result.boxes: class_id = int(box.cls.item()) confidence = box.conf.item() class_names = ['anus', 'make_love', 'nipple', 'penis', 'vagina'] class_name = class_names[class_id] if class_id < len(class_names) else f'class_{class_id}' # Convert crop coordinates to image coordinates dx1, dy1, dx2, dy2 = box.xyxy[0].tolist() abs_x1 = x1_sq + dx1 abs_y1 = y1_sq + dy1 abs_x2 = x1_sq + dx2 abs_y2 = y1_sq + dy2 detections.append({ 'class': class_name, 'confidence': confidence, 'bbox': [abs_x1, abs_y1, abs_x2, abs_y2] }) if not detections: detections = [{'class': 'safe', 'confidence': 1.0, 'bbox': orig_bbox}] if img_idx not in human_results: human_results[img_idx] = {} human_results[img_idx][human_idx] = detections except Exception as e: logger.error(f"NSFW batch failed: {e}") # Organize results by image for idx, image_path in enumerate(image_paths): image_id = Path(image_path).stem if image_path else f'img_{idx:06d}' if idx in human_results: # Convert dict to list ordered by human_idx max_human_idx = max(human_results[idx].keys()) detections_list = [] for h_idx in range(max_human_idx + 1): detections_list.append(human_results[idx].get(h_idx, [{'class': 'safe', 'confidence': 1.0}])) results_list.append({ 'image_id': image_id, 'human_detections': json.dumps(detections_list) }) else: results_list.append({ 'image_id': image_id, 'human_detections': None }) return { 'image_id': [r['image_id'] for r in results_list], 'nsfw_detections': [r['human_detections'] for r in results_list] } def main(): global model logger.info("="*60) logger.info("NSFW Classification with EraX YOLO (Per-Human)") logger.info("="*60) ap = argparse.ArgumentParser() ap.add_argument('--input-dataset', type=str, required=True, help='Original images') ap.add_argument('--sam3d-dataset', type=str, required=True, help='SAM3D outputs with bboxes') ap.add_argument('--output-dataset', type=str, required=True) ap.add_argument('--split', type=str, default='train') ap.add_argument('--batch-size', type=int, default=4) ap.add_argument('--shard-index', type=int, default=0) ap.add_argument('--num-shards', type=int, default=1) args = ap.parse_args() logger.info(f"Arguments: {vars(args)}") device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') logger.info(f"Using device: {device}") # Download and load NSFW model logger.info("Downloading EraX-NSFW model...") snapshot_download(repo_id="erax-ai/EraX-NSFW-V1.0", local_dir="./", force_download=False) logger.info("Loading YOLO model...") model = YOLO('erax_nsfw_yolo11m.pt') logger.info("✓ Model loaded") # Load SAM3D results logger.info(f"Loading SAM3D results from {args.sam3d_dataset}...") sam3d_ds = load_dataset(args.sam3d_dataset, split=args.split, streaming=True) # Load images dataset logger.info(f"Loading images from {args.input_dataset}...") ds = load_dataset(args.input_dataset, split=args.split, streaming=True) if args.num_shards > 1: ds = ds.shard(num_shards=args.num_shards, index=args.shard_index) sam3d_ds = sam3d_ds.shard(num_shards=args.num_shards, index=args.shard_index) logger.info(f"Using shard {args.shard_index+1}/{args.num_shards}") # Process with batching logger.info(f"Processing with batch_size={args.batch_size}") from functools import partial process_fn = partial(process_batch, sam3d_dataset=sam3d_ds) processed_ds = ds.map( process_fn, batched=True, batch_size=args.batch_size, remove_columns=ds.column_names ) # Collect results results = [] for batch_idx, item in enumerate(processed_ds): results.append(item) if (batch_idx + 1) % 100 == 0: logger.info(f"Processed {batch_idx + 1} images") logger.info(f"✓ Processed {len(results)} images") # Create output dataset features = Features({ 'image_id': Value('string'), 'nsfw_detections': Value('string') }) output_ds = HFDataset.from_dict({ 'image_id': [r['image_id'] for r in results], 'nsfw_detections': [r['nsfw_detections'] for r in results] }, features=features) # Upload logger.info(f"Uploading to {args.output_dataset}...") output_ds.push_to_hub( args.output_dataset, split=args.split, token=os.environ.get('HF_TOKEN'), private=True ) logger.info("✓ Upload complete") if __name__ == '__main__': main()