# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

from __future__ import annotations

from multiprocessing.pool import ThreadPool
from pathlib import Path
from typing import Any

import numpy as np
import torch
import torch.nn.functional as F

from ultralytics.models.yolo.detect import DetectionValidator
from ultralytics.utils import LOGGER, NUM_THREADS, ops
from ultralytics.utils.checks import check_requirements
from ultralytics.utils.metrics import SegmentMetrics, mask_iou


class SegmentationValidator(DetectionValidator):
    """
    A class extending the DetectionValidator class for validation based on a segmentation model.

    This validator handles the evaluation of segmentation models, processing both bounding box and mask predictions
    to compute metrics such as mAP for both detection and segmentation tasks.

    Attributes:
        plot_masks (list): List to store masks for plotting.
        process (callable): Function to process masks based on save_json and save_txt flags.
        args (namespace): Arguments for the validator.
        metrics (SegmentMetrics): Metrics calculator for segmentation tasks.
        stats (dict): Dictionary to store statistics during validation.

    Examples:
        >>> from ultralytics.models.yolo.segment import SegmentationValidator
        >>> args = dict(model="yolo11n-seg.pt", data="coco8-seg.yaml")
        >>> validator = SegmentationValidator(args=args)
        >>> validator()
    """

    def __init__(self, dataloader=None, save_dir=None, args=None, _callbacks=None) -> None:
        """
        Initialize SegmentationValidator and set task to 'segment', metrics to SegmentMetrics.

        Args:
            dataloader (torch.utils.data.DataLoader, optional): Dataloader to use for validation.
            save_dir (Path, optional): Directory to save results.
            args (namespace, optional): Arguments for the validator.
            _callbacks (list, optional): List of callback functions.
        """
        super().__init__(dataloader, save_dir, args, _callbacks)
        self.process = None
        self.args.task = "segment"
        self.metrics = SegmentMetrics()

    def preprocess(self, batch: dict[str, Any]) -> dict[str, Any]:
        """
        Preprocess batch of images for YOLO segmentation validation.

        Args:
            batch (dict[str, Any]): Batch containing images and annotations.

        Returns:
            (dict[str, Any]): Preprocessed batch.
        """
        batch = super().preprocess(batch)
        batch["masks"] = batch["masks"].float()
        return batch

    def init_metrics(self, model: torch.nn.Module) -> None:
        """
        Initialize metrics and select mask processing function based on save_json flag.

        Args:
            model (torch.nn.Module): Model to validate.
        """
        super().init_metrics(model)
        if self.args.save_json:
            check_requirements("faster-coco-eval>=1.6.7")
        # More accurate vs faster
        self.process = ops.process_mask_native if self.args.save_json or self.args.save_txt else ops.process_mask

    def get_desc(self) -> str:
        """Return a formatted description of evaluation metrics."""
        return ("%22s" + "%11s" * 10) % (
            "Class",
            "Images",
            "Instances",
            "Box(P",
            "R",
            "mAP50",
            "mAP50-95)",
            "Mask(P",
            "R",
            "mAP50",
            "mAP50-95)",
        )

    def postprocess(self, preds: list[torch.Tensor]) -> list[dict[str, torch.Tensor]]:
        """
        Post-process YOLO predictions and return output detections with proto.

        Args:
            preds (list[torch.Tensor]): Raw predictions from the model.

        Returns:
            list[dict[str, torch.Tensor]]: Processed detection predictions with masks.
        """
        proto = preds[1][-1] if len(preds[1]) == 3 else preds[1]  # second output is len 3 if pt, but only 1 if exported
        preds = super().postprocess(preds[0])
        imgsz = [4 * x for x in proto.shape[2:]]  # get image size from proto
        for i, pred in enumerate(preds):
            coefficient = pred.pop("extra")
            pred["masks"] = (
                self.process(proto[i], coefficient, pred["bboxes"], shape=imgsz)
                if coefficient.shape[0]
                else torch.zeros(
                    (0, *(imgsz if self.process is ops.process_mask_native else proto.shape[2:])),
                    dtype=torch.uint8,
                    device=pred["bboxes"].device,
                )
            )
        return preds

    def _prepare_batch(self, si: int, batch: dict[str, Any]) -> dict[str, Any]:
        """
        Prepare a batch for training or inference by processing images and targets.

        Args:
            si (int): Batch index.
            batch (dict[str, Any]): Batch data containing images and annotations.

        Returns:
            (dict[str, Any]): Prepared batch with processed annotations.
        """
        prepared_batch = super()._prepare_batch(si, batch)
        nl = prepared_batch["cls"].shape[0]
        if self.args.overlap_mask:
            masks = batch["masks"][si]
            index = torch.arange(1, nl + 1, device=masks.device).view(nl, 1, 1)
            masks = (masks == index).float()
        else:
            masks = batch["masks"][batch["batch_idx"] == si]
        if nl:
            mask_size = [s if self.process is ops.process_mask_native else s // 4 for s in prepared_batch["imgsz"]]
            if masks.shape[1:] != mask_size:
                masks = F.interpolate(masks[None], mask_size, mode="bilinear", align_corners=False)[0]
                masks = masks.gt_(0.5)
        prepared_batch["masks"] = masks
        return prepared_batch

    def _process_batch(self, preds: dict[str, torch.Tensor], batch: dict[str, Any]) -> dict[str, np.ndarray]:
        """
        Compute correct prediction matrix for a batch based on bounding boxes and optional masks.

        Args:
            preds (dict[str, torch.Tensor]): Dictionary containing predictions with keys like 'cls' and 'masks'.
            batch (dict[str, Any]): Dictionary containing batch data with keys like 'cls' and 'masks'.

        Returns:
            (dict[str, np.ndarray]): A dictionary containing correct prediction matrices including 'tp_m' for mask IoU.

        Notes:
            - If `masks` is True, the function computes IoU between predicted and ground truth masks.
            - If `overlap` is True and `masks` is True, overlapping masks are taken into account when computing IoU.

        Examples:
            >>> preds = {"cls": torch.tensor([1, 0]), "masks": torch.rand(2, 640, 640), "bboxes": torch.rand(2, 4)}
            >>> batch = {"cls": torch.tensor([1, 0]), "masks": torch.rand(2, 640, 640), "bboxes": torch.rand(2, 4)}
            >>> correct_preds = validator._process_batch(preds, batch)
        """
        tp = super()._process_batch(preds, batch)
        gt_cls = batch["cls"]
        if gt_cls.shape[0] == 0 or preds["cls"].shape[0] == 0:
            tp_m = np.zeros((preds["cls"].shape[0], self.niou), dtype=bool)
        else:
            iou = mask_iou(batch["masks"].flatten(1), preds["masks"].flatten(1).float())  # float, uint8
            tp_m = self.match_predictions(preds["cls"], gt_cls, iou).cpu().numpy()
        tp.update({"tp_m": tp_m})  # update tp with mask IoU
        return tp

    def plot_predictions(self, batch: dict[str, Any], preds: list[dict[str, torch.Tensor]], ni: int) -> None:
        """
        Plot batch predictions with masks and bounding boxes.

        Args:
            batch (dict[str, Any]): Batch containing images and annotations.
            preds (list[dict[str, torch.Tensor]]): List of predictions from the model.
            ni (int): Batch index.
        """
        for p in preds:
            masks = p["masks"]
            if masks.shape[0] > self.args.max_det:
                LOGGER.warning(f"Limiting validation plots to 'max_det={self.args.max_det}' items.")
            p["masks"] = torch.as_tensor(masks[: self.args.max_det], dtype=torch.uint8).cpu()
        super().plot_predictions(batch, preds, ni, max_det=self.args.max_det)  # plot bboxes

    def save_one_txt(self, predn: torch.Tensor, save_conf: bool, shape: tuple[int, int], file: Path) -> None:
        """
        Save YOLO detections to a txt file in normalized coordinates in a specific format.

        Args:
            predn (torch.Tensor): Predictions in the format (x1, y1, x2, y2, conf, class).
            save_conf (bool): Whether to save confidence scores.
            shape (tuple[int, int]): Shape of the original image.
            file (Path): File path to save the detections.
        """
        from ultralytics.engine.results import Results

        Results(
            np.zeros((shape[0], shape[1]), dtype=np.uint8),
            path=None,
            names=self.names,
            boxes=torch.cat([predn["bboxes"], predn["conf"].unsqueeze(-1), predn["cls"].unsqueeze(-1)], dim=1),
            masks=torch.as_tensor(predn["masks"], dtype=torch.uint8),
        ).save_txt(file, save_conf=save_conf)

    def pred_to_json(self, predn: dict[str, torch.Tensor], pbatch: dict[str, Any]) -> None:
        """
        Save one JSON result for COCO evaluation.

        Args:
            predn (dict[str, torch.Tensor]): Predictions containing bboxes, masks, confidence scores, and classes.
            pbatch (dict[str, Any]): Batch dictionary containing 'imgsz', 'ori_shape', 'ratio_pad', and 'im_file'.
        """
        from faster_coco_eval.core.mask import encode

        def single_encode(x):
            """Encode predicted masks as RLE and append results to jdict."""
            rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0]
            rle["counts"] = rle["counts"].decode("utf-8")
            return rle

        pred_masks = np.transpose(predn["masks"], (2, 0, 1))
        with ThreadPool(NUM_THREADS) as pool:
            rles = pool.map(single_encode, pred_masks)
        super().pred_to_json(predn, pbatch)
        for i, r in enumerate(rles):
            self.jdict[-len(rles) + i]["segmentation"] = r  # segmentation

    def scale_preds(self, predn: dict[str, torch.Tensor], pbatch: dict[str, Any]) -> dict[str, torch.Tensor]:
        """Scales predictions to the original image size."""
        return {
            **super().scale_preds(predn, pbatch),
            "masks": ops.scale_image(
                torch.as_tensor(predn["masks"], dtype=torch.uint8).permute(1, 2, 0).contiguous().cpu().numpy(),
                pbatch["ori_shape"],
                ratio_pad=pbatch["ratio_pad"],
            ),
        }

    def eval_json(self, stats: dict[str, Any]) -> dict[str, Any]:
        """Return COCO-style instance segmentation evaluation metrics."""
        pred_json = self.save_dir / "predictions.json"  # predictions
        anno_json = (
            self.data["path"]
            / "annotations"
            / ("instances_val2017.json" if self.is_coco else f"lvis_v1_{self.args.split}.json")
        )  # annotations
        return super().coco_evaluate(stats, pred_json, anno_json, ["bbox", "segm"], suffix=["Box", "Mask"])
