//! MOTChallenge evaluation functions.

use super::{InformationFile, MOTAccumulator};
use crate::{Error, Result};
use std::path::Path;

/// MOT evaluation metrics.
#[derive(Debug, Clone, Default)]
pub struct MOTMetrics {
    /// Multi-Object Tracking Accuracy
    pub mota: f64,
    /// Multi-Object Tracking Precision
    pub motp: f64,
    /// Identification F1 Score
    pub idf1: f64,
    /// Precision
    pub precision: f64,
    /// Recall
    pub recall: f64,
    /// Number of false positives
    pub num_false_positives: usize,
    /// Number of false negatives (misses)
    pub num_misses: usize,
    /// Number of ID switches
    pub num_switches: usize,
    /// Number of unique ground truth IDs
    pub num_gt_ids: usize,
    /// Number of unique hypothesis IDs
    pub num_hyp_ids: usize,
    /// Number of mostly tracked objects (>80% tracked)
    pub mostly_tracked: usize,
    /// Number of mostly lost objects (<20% tracked)
    pub mostly_lost: usize,
    /// Number of fragmentations
    pub num_fragmentations: usize,
}

impl MOTMetrics {
    /// Create metrics from an accumulator.
    pub fn from_accumulator(acc: &MOTAccumulator) -> Self {
        let matches = acc.num_matches() + acc.num_switches();
        let fp = acc.num_false_positives();
        let misses = acc.num_misses();

        let precision = if matches + fp > 0 {
            matches as f64 / (matches + fp) as f64
        } else {
            0.0
        };

        let recall = if matches + misses > 0 {
            matches as f64 / (matches + misses) as f64
        } else {
            0.0
        };

        let idf1 = if precision + recall > 0.0 {
            2.0 * precision * recall / (precision + recall)
        } else {
            0.0
        };

        Self {
            mota: acc.mota(),
            motp: acc.motp(),
            idf1,
            precision,
            recall,
            num_false_positives: fp,
            num_misses: misses,
            num_switches: acc.num_switches(),
            num_gt_ids: acc.num_gt_ids(),
            num_hyp_ids: acc.num_hyp_ids(),
            mostly_tracked: 0,     // TODO: Implement
            mostly_lost: 0,        // TODO: Implement
            num_fragmentations: 0, // TODO: Implement
        }
    }
}

/// Evaluate MOT challenge results.
///
/// # Arguments
/// * `gt_path` - Path to ground truth file
/// * `predictions_path` - Path to predictions file
/// * `seqinfo_path` - Optional path to seqinfo.ini file
/// * `iou_threshold` - IoU threshold for matching (default: 0.5)
///
/// # Returns
/// MOTMetrics containing evaluation results.
pub fn eval_mot_challenge<P1: AsRef<Path>, P2: AsRef<Path>>(
    _gt_path: P1,
    _predictions_path: P2,
    seqinfo_path: Option<&Path>,
    iou_threshold: Option<f64>,
) -> Result<MOTMetrics> {
    let _iou_threshold = iou_threshold.unwrap_or(0.5);

    // Load sequence info if provided
    let _info = if let Some(path) = seqinfo_path {
        Some(InformationFile::new(path)?)
    } else {
        None
    };

    // TODO: Parse ground truth and predictions files
    // TODO: Run accumulator on each frame
    // TODO: Compute final metrics

    Err(Error::MetricsError(
        "eval_mot_challenge not yet fully implemented".to_string(),
    ))
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_metrics_from_accumulator() {
        let acc = MOTAccumulator::new();
        let metrics = MOTMetrics::from_accumulator(&acc);

        // Empty accumulator should have zero metrics
        assert_eq!(metrics.num_false_positives, 0);
        assert_eq!(metrics.num_misses, 0);
        assert_eq!(metrics.num_switches, 0);
    }
}
