"""
Evaluate detection results from inference_k2.py output.
No GPU needed — pure CPU computation.
"""
import json
import re
import argparse
from pathlib import Path
from collections import defaultdict

GRID_SIZE = 1000


def extract_bboxes_from_response(response_text):
    """Extract bboxes from model response, handles both GT and model output formats."""
    try:
        response_clean = response_text.strip()
        if response_clean.startswith('"') and response_clean.endswith('"'):
            try:
                response_clean = json.loads(response_clean)
            except Exception:
                pass

        if isinstance(response_clean, str):
            if not (response_clean.startswith('{') or response_clean.startswith('[')):
                match = re.search(r'[\{\[].*[\}\]]', response_clean, re.DOTALL)
                if match:
                    response_clean = match.group()
            data = json.loads(response_clean)
        else:
            data = response_clean

        pattern = r'<x(\d+)><y(\d+)><x(\d+)><y(\d+)>'

        # Format 1: [{"bbox": [...]}] — model output
        if isinstance(data, list):
            all_bbox_strs = []
            for item in data:
                all_bbox_strs.extend(item.get("bbox", []))
        # Format 2: {"bboxes": [...]} — GT
        elif isinstance(data, dict):
            all_bbox_strs = data.get("bboxes", [])
        else:
            return []

        bboxes = []
        for bbox_str in all_bbox_strs:
            match = re.search(pattern, bbox_str)
            if match:
                x1, y1, x2, y2 = map(int, match.groups())
                bboxes.append([x1, y1, x2, y2])
        return bboxes
    except Exception:
        return []


def grid_to_pixel(boxes, img_w, img_h):
    """Convert 1000-grid coords to pixel coords."""
    return [[
        x1 * img_w / GRID_SIZE,
        y1 * img_h / GRID_SIZE,
        x2 * img_w / GRID_SIZE,
        y2 * img_h / GRID_SIZE,
    ] for x1, y1, x2, y2 in boxes]


def compute_iou(box_a, box_b):
    """Compute IoU between two boxes [x1, y1, x2, y2]."""
    x1 = max(box_a[0], box_b[0])
    y1 = max(box_a[1], box_b[1])
    x2 = min(box_a[2], box_b[2])
    y2 = min(box_a[3], box_b[3])
    inter_w = max(0, x2 - x1)
    inter_h = max(0, y2 - y1)
    inter = inter_w * inter_h
    if inter == 0:
        return 0.0
    area_a = (box_a[2] - box_a[0]) * (box_a[3] - box_a[1])
    area_b = (box_b[2] - box_b[0]) * (box_b[3] - box_b[1])
    return inter / (area_a + area_b - inter + 1e-9)


def match_boxes(gt_boxes, pred_boxes, iou_thr=0.5):
    """Greedy matching of pred to GT boxes. Returns tp, fp, fn counts and per-match details."""
    gt_matched = [False] * len(gt_boxes)
    tp = 0
    matches = []

    for pred_idx, pred_box in enumerate(pred_boxes):
        best_iou = 0.0
        best_gt_idx = -1
        for gt_idx, gt_box in enumerate(gt_boxes):
            if gt_matched[gt_idx]:
                continue
            iou = compute_iou(pred_box, gt_box)
            if iou > best_iou:
                best_iou = iou
                best_gt_idx = gt_idx
        if best_gt_idx >= 0 and best_iou >= iou_thr:
            gt_matched[best_gt_idx] = True
            tp += 1
            matches.append({'pred_idx': pred_idx, 'gt_idx': best_gt_idx, 'iou': best_iou, 'matched': True})
        else:
            matches.append({'pred_idx': pred_idx, 'gt_idx': -1, 'iou': best_iou, 'matched': False})

    fp = len(pred_boxes) - tp
    fn = len(gt_boxes) - tp
    return tp, fp, fn, matches


def eval_results(input_path, output_dir, iou_thr=0.5):
    """Main evaluation: re-extract bboxes, compute metrics, save outputs."""
    with open(input_path, 'r', encoding='utf-8') as f:
        raw_data = json.load(f)

    output_dir = Path(output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)

    # Re-extract bboxes (fix empty pred_bboxes from old extraction)
    per_sample = []
    for item in raw_data:
        pred_boxes = extract_bboxes_from_response(item['model_response'])
        gt_boxes = extract_bboxes_from_response(item['ground_truth'])
        per_sample.append({
            'index': item['index'],
            'image': item['image'],
            'pred_boxes_grid': pred_boxes,
            'gt_boxes_grid': gt_boxes,
        })

    # Compute metrics per sample
    total_gt = 0
    total_pred = 0
    total_tp = 0
    sample_metrics = []

    for s in per_sample:
        gt = s['gt_boxes_grid']
        pred = s['pred_boxes_grid']
        tp, fp, fn, matches = match_boxes(gt, pred, iou_thr)

        precision = tp / max(1, tp + fp)
        recall = tp / max(1, len(gt)) if len(gt) > 0 else 0.0
        f1 = 2 * precision * recall / (precision + recall + 1e-9)

        total_gt += len(gt)
        total_pred += len(pred)
        total_tp += tp

        sample_metrics.append({
            'index': s['index'],
            'image': s['image'],
            'num_gt': len(gt),
            'num_pred': len(pred),
            'tp': tp,
            'fp': fp,
            'fn': fn,
            'precision': round(precision, 4),
            'recall': round(recall, 4),
            'f1': round(f1, 4),
        })

    # Overall metrics
    overall_precision = total_tp / max(1, total_tp + (total_pred - total_tp))
    overall_recall = total_tp / max(1, total_gt)
    overall_f1 = 2 * overall_precision * overall_recall / (overall_precision + overall_recall + 1e-9)

    # Save corrected full results
    corrected = []
    for item, s in zip(raw_data, per_sample):
        corrected.append({
            **item,
            'pred_bboxes': s['pred_boxes_grid'],
            'gt_bboxes': s['gt_boxes_grid'],
            'num_pred': len(s['pred_boxes_grid']),
            'num_gt': len(s['gt_boxes_grid']),
        })

    corrected_path = output_dir / Path(input_path).name
    with open(corrected_path, 'w', encoding='utf-8') as f:
        json.dump(corrected, f, ensure_ascii=False, indent=2)

    # Save simplified
    simplified = [{'image': r['image'], 'gt_bboxes': r['gt_bboxes'], 'pred_bboxes': r['pred_bboxes']}
                  for r in corrected]
    simplified_path = str(corrected_path).replace('.json', '_simplified.json')
    with open(simplified_path, 'w', encoding='utf-8') as f:
        json.dump(simplified, f, ensure_ascii=False, indent=2)

    # Save per-sample metrics
    metrics_path = output_dir / Path(input_path).name.replace('.json', '_metrics.json')
    metrics_summary = {
        'input': str(input_path),
        'iou_threshold': iou_thr,
        'num_samples': len(sample_metrics),
        'total_gt': total_gt,
        'total_pred': total_pred,
        'total_tp': total_tp,
        'total_fp': total_pred - total_tp,
        'total_fn': total_gt - total_tp,
        'precision': round(overall_precision, 4),
        'recall': round(overall_recall, 4),
        'f1': round(overall_f1, 4),
        'per_sample': sample_metrics,
    }
    with open(metrics_path, 'w', encoding='utf-8') as f:
        json.dump(metrics_summary, f, ensure_ascii=False, indent=2)

    # Print summary
    print(f"\n{'='*60}")
    print(f"  Evaluation @ IoU={iou_thr}")
    print(f"{'='*60}")
    print(f"  Samples     : {len(sample_metrics)}")
    print(f"  GT boxes    : {total_gt}")
    print(f"  Pred boxes  : {total_pred}")
    print(f"  TP / FP / FN: {total_tp} / {total_pred - total_tp} / {total_gt - total_tp}")
    print(f"  Precision   : {overall_precision:.4f}")
    print(f"  Recall      : {overall_recall:.4f}")
    print(f"  F1          : {overall_f1:.4f}")
    print(f"{'='*60}")
    print(f"\nPer-sample details:")
    for sm in sample_metrics:
        print(f"  [{sm['index']:3d}] GT={sm['num_gt']} Pred={sm['num_pred']} "
              f"P={sm['precision']:.3f} R={sm['recall']:.3f} F1={sm['f1']:.3f}  "
              f"{Path(sm['image']).name}")

    print(f"\nCorrected results : {corrected_path}")
    print(f"Simplified results: {simplified_path}")
    print(f"Metrics summary   : {metrics_path}")


def main():
    parser = argparse.ArgumentParser(description="Evaluate detection results")
    parser.add_argument('--input', '-i', required=True, help='Full results JSON from inference_k2.py')
    parser.add_argument('--output-dir', '-o', default='.', help='Output directory')
    parser.add_argument('--iou', type=float, default=0.5, help='IoU threshold')
    args = parser.parse_args()
    eval_results(args.input, args.output_dir, args.iou)


if __name__ == '__main__':
    main()