import argparse
import glob
import os
from enum import Enum
from typing import List, Optional, Union

import norfair
import numpy as np
import torch
import torchvision.ops.boxes as bops
from norfair import Detection

DISTANCE_THRESHOLD_BBOX: float = 3.33
DISTANCE_THRESHOLD_CENTROID: int = 30
MAX_DISTANCE: int = 10000


class ModelsPath(Enum):
    YoloV7 = "models/yolov7.pt"


class Style(Enum):
    Boxes = "bbox"
    Centroid = "centroid"


class YOLO:
    def __init__(self, model_path: str, device: Optional[str] = None):
        if device is not None and "cuda" in device and not torch.cuda.is_available():
            raise Exception("Selected device='cuda', but cuda is not available to Pytorch.")
        # automatically set device if its None
        elif device is None:
            device = "cuda:0" if torch.cuda.is_available() else "cpu"

        if not os.path.exists(model_path):
            os.system(
                f"wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/{os.path.basename(model_path)} -O {model_path}"
            )

        # load model
        try:
            self.model = torch.hub.load("WongKinYiu/yolov7", "custom", model_path)
        except:
            raise Exception("Failed to load model from {}".format(model_path))

    def __call__(
        self,
        img: Union[str, np.ndarray],
        conf_threshold: float = 0.25,
        iou_threshold: float = 0.45,
        image_size: int = 720,
        classes: Optional[List[int]] = None,
    ) -> torch.tensor:

        self.model.conf = conf_threshold
        self.model.iou = iou_threshold
        if classes is not None:
            self.model.classes = classes
        detections = self.model(img, size=image_size)
        return detections


def euclidean_distance(detection, tracked_object):
    return np.linalg.norm(detection.points - tracked_object.estimate)


def center(points):
    return [np.mean(np.array(points), axis=0)]


def iou_pytorch(detection, tracked_object):
    # Slower but simplier version of iou

    detection_points = np.concatenate([detection.points[0], detection.points[1]])
    tracked_object_points = np.concatenate([tracked_object.estimate[0], tracked_object.estimate[1]])

    box_a = torch.tensor([detection_points], dtype=torch.float)
    box_b = torch.tensor([tracked_object_points], dtype=torch.float)
    iou = bops.box_iou(box_a, box_b)

    # Since 0 <= IoU <= 1, we define 1/IoU as a distance.
    # Distance values will be in [1, inf)
    return np.float(1 / iou if iou else MAX_DISTANCE)


def iou(detection, tracked_object):
    # Detection points will be box A
    # Tracked objects point will be box B.

    box_a = np.concatenate([detection.points[0], detection.points[1]])
    box_b = np.concatenate([tracked_object.estimate[0], tracked_object.estimate[1]])

    x_a = max(box_a[0], box_b[0])
    y_a = max(box_a[1], box_b[1])
    x_b = min(box_a[2], box_b[2])
    y_b = min(box_a[3], box_b[3])

    # Compute the area of intersection rectangle
    inter_area = max(0, x_b - x_a + 1) * max(0, y_b - y_a + 1)

    # Compute the area of both the prediction and tracker
    # rectangles
    box_a_area = (box_a[2] - box_a[0] + 1) * (box_a[3] - box_a[1] + 1)
    box_b_area = (box_b[2] - box_b[0] + 1) * (box_b[3] - box_b[1] + 1)

    # Compute the intersection over union by taking the intersection
    # area and dividing it by the sum of prediction + tracker
    # areas - the interesection area
    iou = inter_area / float(box_a_area + box_b_area - inter_area)

    # Since 0 <= IoU <= 1, we define 1/IoU as a distance.
    # Distance values will be in [1, inf)
    return 1 / iou if iou else (MAX_DISTANCE)


def yolo_detections_to_norfair_detections(
    yolo_detections: torch.tensor, track_points: str = "centroid"  # bbox or centroid
) -> List[Detection]:
    """convert detections_as_xywh to norfair detections"""
    norfair_detections: List[Detection] = []

    if track_points == "centroid":
        detections_as_xywh = yolo_detections.xywh[0]
        for detection_as_xywh in detections_as_xywh:
            centroid = np.array([detection_as_xywh[0].item(), detection_as_xywh[1].item()])
            scores = np.array([detection_as_xywh[4].item()])
            norfair_detections.append(Detection(points=centroid, scores=scores))
    elif track_points == "bbox":
        detections_as_xyxy = yolo_detections.xyxy[0]
        for detection_as_xyxy in detections_as_xyxy:
            bbox = np.array(
                [
                    [detection_as_xyxy[0].item(), detection_as_xyxy[1].item()],
                    [detection_as_xyxy[2].item(), detection_as_xyxy[3].item()],
                ]
            )
            scores = np.array([detection_as_xyxy[4].item(), detection_as_xyxy[4].item()])
            norfair_detections.append(Detection(points=bbox, scores=scores))

    return norfair_detections


def clean_videos(path: str):
    # Remove past videos
    files = glob.glob(f"{path}/*")
    for file in files:
        if file.endswith(".mp4"):
            os.remove(file)


def draw(paths_drawer, track_points, frame, detections, tracked_objects):
    if track_points == "centroid":
        norfair.draw_points(frame, detections)
        norfair.draw_tracked_objects(frame, tracked_objects)
    elif track_points == "bbox":
        norfair.draw_boxes(frame, detections)
        norfair.draw_tracked_boxes(frame, tracked_objects)

    if paths_drawer is not None:
        frame = paths_drawer.draw(frame, tracked_objects)

    return frame