# pkg
from .datasets_wrapper import Dataset
from ..utils_detection.yolo_util import letterbox,preproc

# code
# from utils.utils_tracker.datasets_wrapper import Dataset
# from utils.utils_detection.yolo_util import letterbox,preproc


import cv2
import numpy as np
from pycocotools.coco import COCO
import os
import torch

class ValTransform:
    def __init__(self,dt_name='yolov7',stride=32):
        self.test_size = 0
        
        self.dt_name = dt_name
        self.stride = stride
        
    # assume input is cv2 img for now
    def __call__(self, img, res, input_size):
        img = img[:, :, ::-1]
        
        if 'yolov7' in self.dt_name:
            img = letterbox(img, input_size, stride=self.stride)[0]
        elif 'yolov5' in self.dt_name:
            img = letterbox(img, input_size, stride=self.stride)[0]
        elif 'yolox' in self.dt_name:
            img,ratio = preproc(img,input_size=input_size)
            
        # Convert
        img = np.ascontiguousarray(img)
        img = img.astype(np.float32)
        if not 'yolox' in self.dt_name:
            img = img/255.0  # 0 - 255 to 0.0 - 1.0
            
        img = img.transpose(2, 0, 1)  
        img = np.expand_dims(img,axis=0)
        
        return img, np.zeros((1, 5)) #, img_info

class MOTDataset(Dataset):
    """
    COCO dataset class.
    """

    def __init__(
        self,
        data_dir=None,
        json_file="train_half.json",
        name="train",
        img_size=(640,640),
        transform=None,
    ):
        """
        COCO dataset initialization. Annotation data are read into memory by COCO API.
        Args:
            data_dir (str): dataset root directory
            json_file (str): COCO json file name
            name (str): COCO data name (e.g. 'train2017' or 'val2017')
            img_size (int): target image size after pre-processing
            transform: data augmentation strategy
        """
        super().__init__(img_size)
        self.data_dir = data_dir
        self.json_file = json_file

        self.coco = COCO(os.path.join(self.data_dir, "annotations", self.json_file))
        self.ids = self.coco.getImgIds()
        self.class_ids = sorted(self.coco.getCatIds())
        cats = self.coco.loadCats(self.coco.getCatIds())
        self._classes = tuple([c["name"] for c in cats])
        self.annotations = self._load_coco_annotations()
        self.img_size = img_size
        self.transform = transform
        self.name = name

    def __len__(self):
        return len(self.ids)

    def _load_coco_annotations(self):
        return [self.load_anno_from_ids(_ids) for _ids in self.ids]

    def load_anno_from_ids(self, id_):
        im_ann = self.coco.loadImgs(id_)[0]
        width = im_ann["width"]
        height = im_ann["height"]
        frame_id = im_ann["frame_id"]
        video_id = im_ann["video_id"]
        anno_ids = self.coco.getAnnIds(imgIds=[int(id_)], iscrowd=False)
        annotations = self.coco.loadAnns(anno_ids)
        objs = []
        for obj in annotations:
            x1 = obj["bbox"][0]
            y1 = obj["bbox"][1]
            x2 = x1 + obj["bbox"][2]
            y2 = y1 + obj["bbox"][3]
            if obj["area"] > 0 and x2 >= x1 and y2 >= y1:
                obj["clean_bbox"] = [x1, y1, x2, y2]
                objs.append(obj)

        num_objs = len(objs)


        res = np.zeros((num_objs, 6))

        for ix, obj in enumerate(objs):
            cls = self.class_ids.index(obj["category_id"])
            res[ix, 0:4] = obj["clean_bbox"]
            res[ix, 4] = cls
            res[ix, 5] = obj["track_id"]
        
        #print("imann:",im_ann["file_name"])
        # datasets/mix_det/mot/train/MOT17-02-FRCNN/img1/000001.jpg
        file_name = im_ann["file_name"] if "file_name" in im_ann else "{:012}".format(id_) + ".jpg"
        #file_name = os.path.join(*im_ann["file_name"].split("/")[-3:])
        img_info = (height, width, frame_id, video_id, file_name)

        del im_ann, annotations

        return (res, img_info, file_name)

    def load_anno(self, index):
        return self.annotations[index][0]

    def pull_item(self, index):
        id_ = self.ids[index]

        res, img_info, file_name = self.annotations[index]
        # load image and preprocess
        img_file = os.path.join(
           self.data_dir,self.name , file_name
        )

        img = cv2.imread(img_file)

        try:
            assert img is not None
        except Exception as e:
            print(e)
            print(img_file)

        return img, res.copy(), img_info, np.array([id_])

    @Dataset.resize_getitem
    def __getitem__(self, index):
        """
        One image / label pair for the given index is picked up and pre-processed.

        Args:
            index (int): data index

        Returns:
            img (numpy.ndarray): pre-processed image
            padded_labels (torch.Tensor): pre-processed label data.
                The shape is :math:`[max_labels, 5]`.
                each label consists of [class, xc, yc, w, h]:
                    class (float): class index.
                    xc, yc (float) : center of bbox whose values range from 0 to 1.
                    w, h (float) : size of bbox whose values range from 0 to 1.
            info_img : tuple of h, w, nh, nw, dx, dy.
                h, w (int): original shape of the image
                nh, nw (int): shape of the resized image without padding
                dx, dy (int): pad size
            img_id (int): same as the input index. Used for evaluation.
        """
        img, target, img_info, img_id = self.pull_item(index)
        
        if self.transform is not None:
            img, target = self.transform(img, target,input_size=self.img_size)
        return img, target, img_info, img_id


def get_eval_loader(batch_size, data_dir,json_file,img_size=(640,640),name='train',testdev=False,transform=None):
    valdataset = MOTDataset(
        data_dir=data_dir,
        json_file=json_file,
        name=name,
        transform= transform,
        img_size=img_size
    )
    
    sampler = torch.utils.data.SequentialSampler(valdataset)
    dataloader_kwargs = {
        "num_workers": 0,
        "pin_memory": True,
        "sampler": sampler,
    }
    dataloader_kwargs["batch_size"] = batch_size
    val_loader = torch.utils.data.DataLoader(valdataset, **dataloader_kwargs)

    return val_loader