import numpy as np

from .detection.iou import DetectionIoUEvaluator


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count
        return self


class QuadMetric:
    def __init__(self, is_output_polygon=False):
        self.is_output_polygon = is_output_polygon
        self.evaluator = DetectionIoUEvaluator(is_output_polygon=is_output_polygon)

    def measure(self, batch, output, box_thresh=0.6):
        """
        batch: (image, polygons, ignore_tags
        batch: a dict produced by dataloaders.
            image: tensor of shape (N, C, H, W).
            polygons: tensor of shape (N, K, 4, 2), the polygons of objective regions.
            ignore_tags: tensor of shape (N, K), indicates whether a region is ignorable or not.
            shape: the original shape of images.
            filename: the original filenames of images.
        output: (polygons, ...)
        """
        results = []
        gt_polyons_batch = batch["text_polys"]
        ignore_tags_batch = batch["ignore_tags"]
        pred_polygons_batch = np.array(output[0])
        pred_scores_batch = np.array(output[1])
        for polygons, pred_polygons, pred_scores, ignore_tags in zip(
            gt_polyons_batch, pred_polygons_batch, pred_scores_batch, ignore_tags_batch
        ):
            gt = [
                dict(points=np.int64(polygons[i]), ignore=ignore_tags[i])
                for i in range(len(polygons))
            ]
            if self.is_output_polygon:
                pred = [
                    dict(points=pred_polygons[i]) for i in range(len(pred_polygons))
                ]
            else:
                pred = []
                # print(pred_polygons.shape)
                for i in range(pred_polygons.shape[0]):
                    if pred_scores[i] >= box_thresh:
                        # print(pred_polygons[i,:,:].tolist())
                        pred.append(
                            dict(points=pred_polygons[i, :, :].astype(np.int32))
                        )
                # pred = [dict(points=pred_polygons[i,:,:].tolist()) if pred_scores[i] >= box_thresh for i in range(pred_polygons.shape[0])]
            results.append(self.evaluator.evaluate_image(gt, pred))
        return results

    def validate_measure(self, batch, output, box_thresh=0.6):
        return self.measure(batch, output, box_thresh)

    def evaluate_measure(self, batch, output):
        return (
            self.measure(batch, output),
            np.linspace(0, batch["image"].shape[0]).tolist(),
        )

    def gather_measure(self, raw_metrics):
        raw_metrics = [
            image_metrics
            for batch_metrics in raw_metrics
            for image_metrics in batch_metrics
        ]

        result = self.evaluator.combine_results(raw_metrics)

        precision = AverageMeter()
        recall = AverageMeter()
        fmeasure = AverageMeter()

        precision.update(result["precision"], n=len(raw_metrics))
        recall.update(result["recall"], n=len(raw_metrics))
        fmeasure_score = (
            2 * precision.val * recall.val / (precision.val + recall.val + 1e-8)
        )
        fmeasure.update(fmeasure_score)

        return {"precision": precision, "recall": recall, "fmeasure": fmeasure}