import numpy as np
from typing import Callable, Sequence, List, Dict


__all__ = ["matching_batch"]


def matching_batch(
    iou_fn: Callable[[np.ndarray, np.ndarray], np.ndarray], 
    iou_thresholds: Sequence[float], pred_boxes: Sequence[np.ndarray],
    pred_classes: Sequence[np.ndarray], pred_scores: Sequence[np.ndarray],
    gt_boxes: Sequence[np.ndarray], gt_classes: Sequence[np.ndarray],
    gt_ignore: Sequence[Sequence[bool]], max_detections: int = 100,
    ) -> List[Dict[int, Dict[str, np.ndarray]]]:
    """
    Match boxes of a batch to corresponding ground truth for each category
    independently

    Args:
        iou_fn: compute overlap for each pair
        iou_thresholds: defined which IoU thresholds should be evaluated
        pred_boxes: predicted boxes from single batch; List[[D, dim * 2]],
            D number of predictions
        pred_classes: predicted classes from a single batch; List[[D]],
            D number of predictions
        pred_scores: predicted score for each bounding box; List[[D]],
            D number of predictions
        gt_boxes: ground truth boxes; List[[G, dim * 2]], G number of ground
            truth
        gt_classes: ground truth classes; List[[G]], G number of ground truth
        gt_ignore: specified if which ground truth boxes are not counted as
            true positives
            (detections which match theses boxes are not counted as false
            positives either); List[[G]], G number of ground truth
        max_detections: maximum number of detections which should be evaluated

    Returns:
        List[Dict[int, Dict[str, np.ndarray]]]
            matched detections [dtMatches] and ground truth [gtMatches]
            boxes [str, np.ndarray] for each category (stored in dict keys)
            for each image (list)
    """
    results = []
    # iterate over images/batches
    for pboxes, pclasses, pscores, gboxes, gclasses, gignore in zip(
        pred_boxes, pred_classes, pred_scores, gt_boxes, gt_classes, gt_ignore):
        img_classes = np.union1d(pclasses, gclasses)
        result = {}  # dict contains results for each class in one image
        for c in img_classes:
            pred_mask = pclasses == c # mask predictions with current class
            gt_mask = gclasses == c # mask ground trtuh with current class

            if not np.any(gt_mask): # no ground truth
                result[c] = _matching_no_gt(
                    iou_thresholds=iou_thresholds,
                    pred_scores=pscores[pred_mask],
                    max_detections=max_detections)
            elif not np.any(pred_mask): # no predictions
                result[c] = _matching_no_pred(
                    iou_thresholds=iou_thresholds,
                    gt_ignore=gignore[gt_mask],
                )
            else: # at least one prediction and one ground truth
                result[c] = _matching_single_image_single_class(
                    iou_fn=iou_fn,
                    pred_boxes=pboxes[pred_mask],
                    pred_scores=pscores[pred_mask],
                    gt_boxes=gboxes[gt_mask],
                    gt_ignore=gignore[gt_mask],
                    max_detections=max_detections,
                    iou_thresholds=iou_thresholds,
                )
        results.append(result)
    return results


def _matching_no_gt(
        iou_thresholds: Sequence[float],
        pred_scores: np.ndarray,
        max_detections: int,
        ):
    """
    Matching result with not ground truth in image

    Args:
        iou_thresholds: defined which IoU thresholds should be evaluated
        dt_scores: predicted scores
        max_detections: maximum number of allowed detections per image.
            This functions uses this parameter to stay consistent with
            the actual matching function which needs this limit.

    Returns:
        dict: computed matching
            `dtMatches`: matched detections [T, D], where T = number of
                thresholds, D = number of detections
            `gtMatches`: matched ground truth boxes [T, G], where T = number
                of thresholds, G = number of ground truth
            `dtScores`: prediction scores [D] detection scores
            `gtIgnore`: ground truth boxes which should be ignored
                [G] indicate whether ground truth should be ignored
            `dtIgnore`: detections which should be ignored [T, D],
                indicate which detections should be ignored
    """
    dt_ind = np.argsort(-pred_scores, kind='mergesort')
    dt_ind = dt_ind[:max_detections]
    dt_scores = pred_scores[dt_ind]

    num_preds = len(dt_scores)

    gt_match = np.array([[]] * len(iou_thresholds))
    dt_match = np.zeros((len(iou_thresholds), num_preds))
    dt_ignore = np.zeros((len(iou_thresholds), num_preds))

    return {
        'dtMatches': dt_match,  # [T, D], where T = number of thresholds, D = number of detections
        'gtMatches': gt_match,  # [T, G], where T = number of thresholds, G = number of ground truth
        'dtScores': dt_scores,  # [D] detection scores
        'gtIgnore': np.array([]).reshape(-1),  # [G] indicate whether ground truth should be ignored
        'dtIgnore': dt_ignore,  # [T, D], indicate which detections should be ignored
    }


def _matching_no_pred(
        iou_thresholds: Sequence[float],
        gt_ignore: np.ndarray,
        ):
    """
    Matching result with no predictions

    Args:
        iou_thresholds: defined which IoU thresholds should be evaluated
        gt_ignore: specified if which ground truth boxes are not counted as
            true positives (detections which match theses boxes are not
            counted as false positives either); [G], G number of ground truth

    Returns:
        dict: computed matching
            `dtMatches`: matched detections [T, D], where T = number of
                thresholds, D = number of detections
            `gtMatches`: matched ground truth boxes [T, G], where T = number
                of thresholds, G = number of ground truth
            `dtScores`: prediction scores [D] detection scores
            `gtIgnore`: ground truth boxes which should be ignored
                [G] indicate whether ground truth should be ignored
            `dtIgnore`: detections which should be ignored [T, D],
                indicate which detections should be ignored
    """
    dt_scores = np.array([])
    dt_match = np.array([[]] * len(iou_thresholds))
    dt_ignore = np.array([[]] * len(iou_thresholds))

    gt_match = np.zeros((len(iou_thresholds), len(gt_ignore)))

    return {
        'dtMatches': dt_match,  # [T, D], where T = number of thresholds, D = number of detections
        'gtMatches': gt_match,  # [T, G], where T = number of thresholds, G = number of ground truth
        'dtScores': dt_scores,  # [D] detection scores
        'gtIgnore': gt_ignore.reshape(-1),  # [G] indicate whether ground truth should be ignored
        'dtIgnore': dt_ignore,  # [T, D], indicate which detections should be ignored
    }


def _matching_single_image_single_class(
        iou_fn: Callable[[np.ndarray, np.ndarray], np.ndarray],
        pred_boxes: np.ndarray,
        pred_scores: np.ndarray,
        gt_boxes: np.ndarray,
        gt_ignore: np.ndarray,
        max_detections: int,
        iou_thresholds: Sequence[float],
        ) -> Dict[str, np.ndarray]:
    """
    Adapted from https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py

    Args:
        iou_fn: compute overlap for each pair
        iou_thresholds: defined which IoU thresholds should be evaluated
        pred_boxes: predicted boxes from single batch; [D, dim * 2], D number
            of predictions
        pred_scores: predicted score for each bounding box; [D], D number of
            predictions
        gt_boxes: ground truth boxes; [G, dim * 2], G number of ground truth
        gt_ignore: specified if which ground truth boxes are not counted as
            true positives (detections which match theses boxes are not
            counted as false positives either); [G], G number of ground truth
        max_detections: maximum number of detections which should be evaluated

    Returns:
        dict: computed matching
            `dtMatches`: matched detections [T, D], where T = number of
                thresholds, D = number of detections
            `gtMatches`: matched ground truth boxes [T, G], where T = number
                of thresholds, G = number of ground truth
            `dtScores`: prediction scores [D] detection scores
            `gtIgnore`: ground truth boxes which should be ignored
                [G] indicate whether ground truth should be ignored
            `dtIgnore`: detections which should be ignored [T, D],
                indicate which detections should be ignored
    """
    # filter for max_detections highest scoring predictions to speed up computation
    dt_ind = np.argsort(-pred_scores, kind='mergesort')
    dt_ind = dt_ind[:max_detections]

    pred_boxes = pred_boxes[dt_ind]
    pred_scores = pred_scores[dt_ind]

    # sort ignored ground truth to last positions
    gt_ind = np.argsort(gt_ignore, kind='mergesort')
    gt_boxes = gt_boxes[gt_ind]
    gt_ignore = gt_ignore[gt_ind]

    # ious between sorted(!) predictions and ground truth
    ious = iou_fn(pred_boxes, gt_boxes)

    num_preds, num_gts = ious.shape[0], ious.shape[1]
    gt_match = np.zeros((len(iou_thresholds), num_gts))
    dt_match = np.zeros((len(iou_thresholds), num_preds))
    dt_ignore = np.zeros((len(iou_thresholds), num_preds))

    for tind, t in enumerate(iou_thresholds):
        for dind, _d in enumerate(pred_boxes):  # iterate detections starting from highest scoring one
            # information about best match so far (m=-1 -> unmatched)
            iou = min([t, 1-1e-10])
            m = -1

            for gind, _g in enumerate(gt_boxes):  # iterate ground truth
                # if this gt already matched, continue
                if gt_match[tind, gind] > 0:
                    continue

                # if dt matched to reg gt, and on ignore gt, stop
                if m > -1 and gt_ignore[m] == 0 and gt_ignore[gind] == 1:
                    break

                # continue to next gt unless better match made
                if ious[dind, gind] < iou:
                    continue

                # if match successful and best so far, store appropriately
                iou = ious[dind, gind]
                m = gind

            # if match made, store id of match for both dt and gt
            if m == -1:
                continue
            else:
                dt_ignore[tind, dind] = int(gt_ignore[m])
                dt_match[tind, dind] = 1
                gt_match[tind, m] = 1

    # store results for given image and category
    return {
            'dtMatches': dt_match,  # [T, D], where T = number of thresholds, D = number of detections
            'gtMatches': gt_match,  # [T, G], where T = number of thresholds, G = number of ground truth
            'dtScores': pred_scores,  # [D] detection scores
            'gtIgnore': gt_ignore.reshape(-1),  # [G] indicate whether ground truth should be ignored
            'dtIgnore': dt_ignore,  # [T, D], indicate which detections should be ignored
        }