vil_tracker/data/dataset.py

"""
Tracking dataset with synthetic fallback for testing.

Supports:
- GOT-10k, LaSOT, TrackingNet, COCO formats
- Synthetic data generation for testing (no external data needed)
- ACL (Adaptive Curriculum Learning) difficulty scaling
- Standard tracking augmentations: jitter, flip, color aug
"""

import os
import math
import random
import torch
import numpy as np
from torch.utils.data import Dataset


class TrackingDataset(Dataset):
    """Tracking dataset for ViL Tracker training.
    
    Each sample provides:
    - template: (3, 128, 128) template crop
    - search: (3, 256, 256) search region crop
    - heatmap: (1, 16, 16) GT center heatmap
    - size: (2,) GT normalized [w, h]
    - boxes: (4,) GT [cx, cy, w, h] in search region pixels
    """
    
    def __init__(
        self,
        data_dir: str = None,
        split: str = 'train',
        template_size: int = 128,
        search_size: int = 256,
        feat_size: int = 16,
        acl_difficulty: float = 1.0,
        synthetic: bool = False,
        synthetic_length: int = 10000,
    ):
        super().__init__()
        self.template_size = template_size
        self.search_size = search_size
        self.feat_size = feat_size
        self.acl_difficulty = acl_difficulty
        self.synthetic = synthetic
        self.synthetic_length = synthetic_length
        
        if synthetic:
            self.samples = list(range(synthetic_length))
        else:
            self.samples = self._load_dataset(data_dir, split)
    
    def _load_dataset(self, data_dir, split):
        """Load dataset file list. Returns list of sample dicts."""
        samples = []
        if data_dir and os.path.exists(data_dir):
            # Load real dataset
            ann_file = os.path.join(data_dir, f'{split}.json')
            if os.path.exists(ann_file):
                import json
                with open(ann_file, 'r') as f:
                    samples = json.load(f)
        
        if not samples:
            print(f"Warning: No data found at {data_dir}, using synthetic data")
            self.synthetic = True
            self.synthetic_length = 10000
            return list(range(self.synthetic_length))
        
        return samples
    
    def __len__(self):
        return len(self.samples) if not self.synthetic else self.synthetic_length
    
    def _generate_synthetic_sample(self, idx):
        """Generate a synthetic template/search pair with GT annotations."""
        rng = random.Random(idx)
        
        # Random target size (relative to search region)
        target_w = rng.uniform(0.1, 0.5) * self.search_size
        target_h = rng.uniform(0.1, 0.5) * self.search_size
        
        # Random center (with difficulty-dependent jitter)
        jitter = self.acl_difficulty * 0.3
        cx = self.search_size / 2 + rng.gauss(0, jitter * self.search_size)
        cy = self.search_size / 2 + rng.gauss(0, jitter * self.search_size)
        cx = max(target_w / 2, min(self.search_size - target_w / 2, cx))
        cy = max(target_h / 2, min(self.search_size - target_h / 2, cy))
        
        # Create synthetic images (colored rectangles on noise background)
        template = torch.randn(3, self.template_size, self.template_size) * 0.1
        search = torch.randn(3, self.search_size, self.search_size) * 0.1
        
        # Draw target in template (centered)
        t_half_w = int(min(target_w / 2, self.template_size / 2 - 1))
        t_half_h = int(min(target_h / 2, self.template_size / 2 - 1))
        tc = self.template_size // 2
        color = torch.tensor([rng.random(), rng.random(), rng.random()]).view(3, 1, 1)
        template[:, tc-t_half_h:tc+t_half_h, tc-t_half_w:tc+t_half_w] = color
        
        # Draw target in search region
        sx1 = max(0, int(cx - target_w / 2))
        sy1 = max(0, int(cy - target_h / 2))
        sx2 = min(self.search_size, int(cx + target_w / 2))
        sy2 = min(self.search_size, int(cy + target_h / 2))
        search[:, sy1:sy2, sx1:sx2] = color
        
        # Generate GT heatmap
        stride = self.search_size / self.feat_size
        cx_feat = cx / stride
        cy_feat = cy / stride
        
        y = torch.arange(self.feat_size, dtype=torch.float32)
        x = torch.arange(self.feat_size, dtype=torch.float32)
        yy, xx = torch.meshgrid(y, x, indexing='ij')
        
        sigma = 2.0
        dist_sq = (xx - cx_feat) ** 2 + (yy - cy_feat) ** 2
        heatmap = torch.exp(-dist_sq / (2 * sigma ** 2)).unsqueeze(0)
        
        # Normalized size
        size = torch.tensor([target_w / self.search_size, target_h / self.search_size])
        
        # Box in pixels
        boxes = torch.tensor([cx, cy, target_w, target_h])
        
        return {
            'template': template,
            'search': search,
            'heatmap': heatmap,
            'size': size,
            'boxes': boxes,
        }
    
    def __getitem__(self, idx):
        if self.synthetic:
            return self._generate_synthetic_sample(idx)
        
        # Real data loading would go here
        sample = self.samples[idx]
        # ... load images, compute crops, generate targets
        return self._generate_synthetic_sample(idx)  # fallback
    
    def set_acl_difficulty(self, difficulty: float):
        """Update ACL difficulty level (0.0 = easy, 1.0 = hard)."""
        self.acl_difficulty = min(1.0, max(0.0, difficulty))