from models import TRTModule  # isort:skip
import argparse
from pathlib import Path

import cv2
import torch

from config import CLASSES_OBB, COLORS_OBB
from models.torch_utils import obb_postprocess
from models.utils import blob, letterbox, path_to_list


def main(args: argparse.Namespace) -> None:
    device = torch.device(args.device)
    Engine = TRTModule(args.engine, device)
    H, W = Engine.inp_info[0].shape[-2:]

    images = path_to_list(args.imgs)
    save_path = Path(args.out_dir)

    if not args.show and not save_path.exists():
        save_path.mkdir(parents=True, exist_ok=True)

    for image in images:
        save_image = save_path / image.name
        bgr = cv2.imread(str(image))
        draw = bgr.copy()
        bgr, ratio, dwdh = letterbox(bgr, (W, H))
        rgb = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
        tensor = blob(rgb, return_seg=False)
        dwdh = torch.asarray(dwdh, dtype=torch.float32, device=device)
        tensor = torch.asarray(tensor, device=device)
        # inference
        data = Engine(tensor)

        points, scores, labels = obb_postprocess(data, args.conf_thres,
                                                 args.iou_thres)
        if points.numel() == 0:
            # if no points
            print(f'{image}: no object!')
            continue
        points -= dwdh
        points /= ratio

        for (point, score, label) in zip(points, scores, labels):
            point = point.round().int().cpu().numpy()
            label = int(label)
            score = float(score)
            cls = CLASSES_OBB[label]
            color = COLORS_OBB[cls]
            cv2.polylines(draw, [point], True, color, 2)
            cv2.putText(draw,
                        f'{cls}:{score:.3f}', (point[0, 0], point[0, 1] - 2),
                        cv2.FONT_HERSHEY_SIMPLEX,
                        0.75, [225, 255, 255],
                        thickness=2)

        if args.show:
            cv2.imshow('result', draw)
            cv2.waitKey(0)
        else:
            cv2.imwrite(str(save_image), draw)


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument('--engine', type=str, help='Engine file')
    parser.add_argument('--imgs', type=str, help='Images file')
    parser.add_argument('--show',
                        action='store_true',
                        help='Show the detection results')
    parser.add_argument('--out-dir',
                        type=str,
                        default='./output',
                        help='Path to output file')
    parser.add_argument('--conf-thres',
                        type=float,
                        default=0.25,
                        help='Confidence threshold')
    parser.add_argument('--iou-thres',
                        type=float,
                        default=0.65,
                        help='Confidence threshold')
    parser.add_argument('--device',
                        type=str,
                        default='cuda:0',
                        help='TensorRT infer device')
    args = parser.parse_args()
    return args


if __name__ == '__main__':
    args = parse_args()
    main(args)