from pathlib import Path

import hydra
import torch

from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
from ultralytics.yolo.utils import ROOT, ops
from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box

from ..detect.predict import DetectionPredictor


class SegmentationPredictor(DetectionPredictor):

    def postprocess(self, preds, img, orig_img):
        masks = []
        if len(preds) == 2:  # eval
            p, proto, = preds
        else:  # len(3) train
            p, proto, _ = preds
        # TODO: filter by classes
        p = ops.non_max_suppression(p,
                                    self.args.conf_thres,
                                    self.args.iou_thres,
                                    agnostic=self.args.agnostic_nms,
                                    max_det=self.args.max_det,
                                    nm=32)
        for i, pred in enumerate(p):
            shape = orig_img[i].shape if self.webcam else orig_img.shape
            if not len(pred):
                continue
            if self.args.retina_masks:
                pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
                masks.append(ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], shape[:2]))  # HWC
            else:
                masks.append(ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True))  # HWC
                pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()

        return (p, masks)

    def write_results(self, idx, preds, batch):
        p, im, im0 = batch
        log_string = ""
        if len(im.shape) == 3:
            im = im[None]  # expand for batch dim
        self.seen += 1
        if self.webcam:  # batch_size >= 1
            log_string += f'{idx}: '
            frame = self.dataset.count
        else:
            frame = getattr(self.dataset, 'frame', 0)

        self.data_path = p
        self.txt_path = str(self.save_dir / 'labels' / p.stem) + ('' if self.dataset.mode == 'image' else f'_{frame}')
        log_string += '%gx%g ' % im.shape[2:]  # print string
        self.annotator = self.get_annotator(im0)

        preds, masks = preds
        det = preds[idx]
        if len(det) == 0:
            return log_string
        # Segments
        mask = masks[idx]
        if self.args.save_txt:
            segments = [
                ops.scale_segments(im0.shape if self.arg.retina_masks else im.shape[2:], x, im0.shape, normalize=True)
                for x in reversed(ops.masks2segments(mask))]

        # Print results
        for c in det[:, 5].unique():
            n = (det[:, 5] == c).sum()  # detections per class
            log_string += f"{n} {self.model.names[int(c)]}{'s' * (n > 1)}, "  # add to string

        # Mask plotting
        self.annotator.masks(
            mask,
            colors=[colors(x, True) for x in det[:, 5]],
            im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(self.device).permute(2, 0, 1).flip(0).contiguous() /
            255 if self.args.retina_masks else im[idx])

        # Write results
        for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
            if self.args.save_txt:  # Write to file
                seg = segments[j].reshape(-1)  # (n,2) to (n*2)
                line = (cls, *seg, conf) if self.args.save_conf else (cls, *seg)  # label format
                with open(f'{self.txt_path}.txt', 'a') as f:
                    f.write(('%g ' * len(line)).rstrip() % line + '\n')

            if self.save_img or self.args.save_crop or self.args.view_img:
                c = int(cls)  # integer class
                label = None if self.args.hide_labels else (
                    self.model.names[c] if self.args.hide_conf else f'{self.model.names[c]} {conf:.2f}')
                self.annotator.box_label(xyxy, label, color=colors(c, True))
                # annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
            if self.args.save_crop:
                imc = im0.copy()
                save_one_box(xyxy, imc, file=self.save_dir / 'crops' / self.model.names[c] / f'{p.stem}.jpg', BGR=True)

        return log_string


@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
def predict(cfg):
    cfg.model = cfg.model or "n.pt"
    sz = cfg.img_size
    if type(sz) != int:  # recieved listConfig
        cfg.img_size = [sz[0], sz[0]] if len(cfg.img_size) == 1 else [sz[0], sz[1]]  # expand
    else:
        cfg.img_size = [sz, sz]
    predictor = SegmentationPredictor(cfg)
    predictor()


if __name__ == "__main__":
    predict()