# Ultralytics YOLO 🚀, AGPL-3.0 license

from ultralytics.engine.predictor import BasePredictor
from ultralytics.engine.results import Results
from ultralytics.utils import ops

import numpy as np
import torch


class DetectionPredictor(BasePredictor):
    """
    A class extending the BasePredictor class for prediction based on a detection model.

    Example:
        ```python
        from ultralytics.utils import ASSETS
        from ultralytics.models.yolo.detect import DetectionPredictor

        args = dict(model='yolov8n.pt', source=ASSETS)
        predictor = DetectionPredictor(overrides=args)
        predictor.predict_cli()
        ```
    """

    def preprocess(self, im):
        """
        Prepares input image before inference.

        Args:
            im (torch.Tensor | List(np.ndarray)): BCHW for tensor, [(HWC) x B] for list.
        """
        not_tensor = not isinstance(im, torch.Tensor)
        if not_tensor:
            im = np.stack(self.pre_transform(im))
            im_rgb = im.copy()
            # for i in range(im.shape[3] // 3):
            #     start_idx = i * 3
            #     channel_idx = list(range(start_idx, start_idx + 3))
            #     im_rgb[..., channel_idx] = im[..., channel_idx][:, :, ::-1]  # BGR to RGB
            im = im_rgb.transpose((0, 3, 1, 2))  # BHWC to BCHW
            im = np.ascontiguousarray(im)  # contiguous
            im = torch.from_numpy(im)

        im = im.to(self.device)
        im = im.half() if self.model.fp16 else im.float()  # uint8 to fp16/32
        if not_tensor:
            im /= 255  # 0 - 255 to 0.0 - 1.0
        return im

    def postprocess(self, preds, img, orig_imgs):
        """Post-processes predictions and returns a list of Results objects."""
        preds = ops.non_max_suppression(
            preds,
            self.args.conf,
            self.args.iou,
            agnostic=self.args.agnostic_nms,
            max_det=self.args.max_det,
            classes=self.args.classes,
        )

        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

        results = []
        for i, pred in enumerate(preds):
            orig_img = orig_imgs[i]
            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape)
            # pred = self.fliplr(pred, orig_img.shape[1])
            img_path = self.batch[0][i]
            results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred))
        return results
    
    def fliplr(self, pred, w):
        """Reverses the order of the bounding boxes and segments horizontally."""
        bboxes = pred.clone()
        if True: # "xyxy"
            x1 = bboxes[:, 0].clone()
            x2 = bboxes[:, 2].clone()
            bboxes[:, 0] = w - x2
            bboxes[:, 2] = w - x1
        else:
            bboxes[:, 0] = w - bboxes[:, 0]
        return bboxes