import glob
import os
import time
import hashlib
import random
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from utils import torch_distributed_zero_first, xywhn2xyxy, xyxy2xywhn, xyn2xy, segments2boxes, clean_str
from augmentations import Albumentations, copy_paste, mixup, letterbox, random_perspective, augment_hsv, copy_paste
from threading import Thread
from pathlib import Path
from tqdm import tqdm
import logging
from multiprocessing.pool import ThreadPool, Pool
from itertools import repeat
from PIL import Image, ExifTags


IMG_FORMATS = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo']  # acceptable image suffixes
VID_FORMATS = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv']  # acceptable video suffixes
NUM_THREADS = min(8, os.cpu_count())  # number of multiprocessing threads


# Get orientation exif tag
for orientation in ExifTags.TAGS.keys():
    if ExifTags.TAGS[orientation] == 'Orientation':
        break


def exif_size(img):
    # Returns exif-corrected PIL size
    s = img.size  # (width, height)
    try:
        rotation = dict(img._getexif().items())[orientation]
        if rotation == 6:  # rotation 270
            s = (s[1], s[0])
        elif rotation == 8:  # rotation 90
            s = (s[1], s[0])
    except:
        pass

    return s

    
def create_dataloader(path, imgsz, batch_size, stride, single_cls=False, hyp=None, augment=False, cache=False, pad=0.0,
                      rect=False, rank=-1, workers=8, image_weights=False, quad=False, prefix=''):
    # Make sure only the first process in DDP process the dataset first, and the following others can use the cache
    with torch_distributed_zero_first(rank):
        dataset = LoadImagesAndLabels(path, imgsz, batch_size,
                                      augment=augment,  # augment images
                                      hyp=hyp,  # augmentation hyperparameters
                                      rect=rect,  # rectangular training
                                      cache_images=cache,
                                      single_cls=single_cls,
                                      stride=int(stride),
                                      pad=pad,
                                      image_weights=image_weights,
                                      prefix=prefix)

    batch_size = min(batch_size, len(dataset))
    nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, workers])  # number of workers
    sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None
    loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader
    # Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader()
    dataloader = loader(dataset,
                        batch_size=batch_size,
                        num_workers=nw,
                        sampler=sampler,
                        pin_memory=True,
                        collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn)
    return dataloader, dataset


class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
    """ Dataloader that reuses workers
    Uses same syntax as vanilla DataLoader
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
        self.iterator = super().__iter__()

    def __len__(self):
        return len(self.batch_sampler.sampler)

    def __iter__(self):
        for i in range(len(self)):
            yield next(self.iterator)


class _RepeatSampler(object):
    """ Sampler that repeats forever
    Args:
        sampler (Sampler)
    """

    def __init__(self, sampler):
        self.sampler = sampler

    def __iter__(self):
        while True:
            yield from iter(self.sampler)


def img2label_paths(img_paths):
    # Define label paths as a function of image paths
    sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep  # /images/, /labels/ substrings
    return [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths]


def get_hash(paths):
    # Returns a single hash value of a list of paths (files or dirs)
    size = sum(os.path.getsize(p) for p in paths if os.path.exists(p))  # sizes
    h = hashlib.md5(str(size).encode())  # hash sizes
    h.update(''.join(paths).encode())  # hash paths
    return h.hexdigest()  # return hash


class LoadImagesAndLabels(Dataset):  # for training/testing
    cache_version = 0.5  # dataset labels *.cache version

    def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
                 cache_images=False, single_cls=False, stride=32, pad=0.0, prefix=''):
        self.img_size = img_size
        self.augment = augment
        self.hyp = hyp
        self.image_weights = image_weights
        self.rect = False if image_weights else rect
        self.mosaic = self.augment and not self.rect  # load 4 images at a time into a mosaic (only during training)
        self.mosaic_border = [-img_size // 2, -img_size // 2]
        self.stride = stride
        self.path = path
        self.albumentations = Albumentations() if augment else None

        try:
            f = []  # image files
            for p in path if isinstance(path, list) else [path]:
                p = Path(p)  # os-agnostic
                if p.is_dir():  # dir
                    f += glob.glob(str(p / '**' / '*.*'), recursive=True)
                    # f = list(p.rglob('**/*.*'))  # pathlib
                elif p.is_file():  # file
                    with open(p, 'r') as t:
                        t = t.read().strip().splitlines()
                        parent = str(p.parent) + os.sep
                        f += [x.replace('./', parent) if x.startswith('./') else x for x in t]  # local to global path
                        # f += [p.parent / x.lstrip(os.sep) for x in t]  # local to global path (pathlib)
                else:
                    raise Exception(f'{prefix}{p} does not exist')
            self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS])
            # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS])  # pathlib
            assert self.img_files, f'{prefix}No images found'
        except Exception as e:
            raise Exception(f'{prefix}Error loading data from {path}: {e}\n')

        # Check cache
        self.label_files = img2label_paths(self.img_files)  # labels
        cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')
        try:
            cache, exists = np.load(cache_path, allow_pickle=True).item(), True  # load dict
            assert cache['version'] == self.cache_version  # same version
            assert cache['hash'] == get_hash(self.label_files + self.img_files)  # same hash
        except:
            cache, exists = self.cache_labels(cache_path, prefix), False  # cache

        # Display cache
        nf, nm, ne, nc, n = cache.pop('results')  # found, missing, empty, corrupted, total
        if exists:
            d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
            tqdm(None, desc=prefix + d, total=n, initial=n)  # display cache results
            if cache['msgs']:
                logging.info('\n'.join(cache['msgs']))  # display warnings
        assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels.'

        # Read cache
        [cache.pop(k) for k in ('hash', 'version', 'msgs')]  # remove items
        labels, shapes, self.segments = zip(*cache.values())
        self.labels = list(labels)
        self.shapes = np.array(shapes, dtype=np.float64)
        self.img_files = list(cache.keys())  # update
        self.label_files = img2label_paths(cache.keys())  # update
        if single_cls:
            for x in self.labels:
                x[:, 0] = 0

        n = len(shapes)  # number of images
        bi = np.floor(np.arange(n) / batch_size).astype(np.int)  # batch index
        nb = bi[-1] + 1  # number of batches
        self.batch = bi  # batch index of image
        self.n = n
        self.indices = range(n)

        # Rectangular Training
        if self.rect:
            # Sort by aspect ratio
            s = self.shapes  # wh
            ar = s[:, 1] / s[:, 0]  # aspect ratio
            irect = ar.argsort()
            self.img_files = [self.img_files[i] for i in irect]
            self.label_files = [self.label_files[i] for i in irect]
            self.labels = [self.labels[i] for i in irect]
            self.shapes = s[irect]  # wh
            ar = ar[irect]

            # Set training image shapes
            shapes = [[1, 1]] * nb
            for i in range(nb):
                ari = ar[bi == i]
                mini, maxi = ari.min(), ari.max()
                if maxi < 1:
                    shapes[i] = [maxi, 1]
                elif mini > 1:
                    shapes[i] = [1, 1 / mini]

            self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride

        # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
        self.imgs, self.img_npy = [None] * n, [None] * n
        if cache_images:
            if cache_images == 'disk':
                self.im_cache_dir = Path(Path(self.img_files[0]).parent.as_posix() + '_npy')
                self.img_npy = [self.im_cache_dir / Path(f).with_suffix('.npy').name for f in self.img_files]
                self.im_cache_dir.mkdir(parents=True, exist_ok=True)
            gb = 0  # Gigabytes of cached images
            self.img_hw0, self.img_hw = [None] * n, [None] * n
            results = ThreadPool(NUM_THREADS).imap(lambda x: load_image(*x), zip(repeat(self), range(n)))
            pbar = tqdm(enumerate(results), total=n)
            for i, x in pbar:
                if cache_images == 'disk':
                    if not self.img_npy[i].exists():
                        np.save(self.img_npy[i].as_posix(), x[0])
                    gb += self.img_npy[i].stat().st_size
                else:
                    self.imgs[i], self.img_hw0[i], self.img_hw[i] = x  # im, hw_orig, hw_resized = load_image(self, i)
                    gb += self.imgs[i].nbytes
                pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB {cache_images})'
            pbar.close()

    def cache_labels(self, path=Path('./labels.cache'), prefix=''):
        # Cache dataset labels, check images and read shapes
        x = {}  # dict
        nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number missing, found, empty, corrupt, messages
        desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels..."
        with Pool(NUM_THREADS) as pool:
            pbar = tqdm(pool.imap(verify_image_label, zip(self.img_files, self.label_files, repeat(prefix))),
                        desc=desc, total=len(self.img_files))
            for im_file, l, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
                nm += nm_f
                nf += nf_f
                ne += ne_f
                nc += nc_f
                if im_file:
                    x[im_file] = [l, shape, segments]
                if msg:
                    msgs.append(msg)
                pbar.desc = f"{desc}{nf} found, {nm} missing, {ne} empty, {nc} corrupted"

        pbar.close()
        if msgs:
            logging.info('\n'.join(msgs))
        if nf == 0:
            logging.info(f'{prefix}WARNING: No labels found in {path}.')
        x['hash'] = get_hash(self.label_files + self.img_files)
        x['results'] = nf, nm, ne, nc, len(self.img_files)
        x['msgs'] = msgs  # warnings
        x['version'] = self.cache_version  # cache version
        try:
            np.save(path, x)  # save cache for next time
            path.with_suffix('.cache.npy').rename(path)  # remove .npy suffix
            logging.info(f'{prefix}New cache created: {path}')
        except Exception as e:
            logging.info(f'{prefix}WARNING: Cache directory {path.parent} is not writeable: {e}')  # path not writeable
        return x

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

    # def __iter__(self):
    #     self.count = -1
    #     print('ran dataset iter')
    #     #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
    #     return self

    def __getitem__(self, index):
        index = self.indices[index]  # linear, shuffled, or image_weights

        hyp = self.hyp
        mosaic = self.mosaic and random.random() < hyp['mosaic']
        if mosaic:
            # Load mosaic
            img, labels = load_mosaic(self, index)
            shapes = None

            # MixUp augmentation
            if random.random() < hyp['mixup']:
                img, labels = mixup(img, labels, *load_mosaic(self, random.randint(0, self.n - 1)))

        else:
            # Load image
            img, (h0, w0), (h, w) = load_image(self, index)

            # Letterbox
            shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size  # final letterboxed shape
            img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
            shapes = (h0, w0), ((h / h0, w / w0), pad)  # for COCO mAP rescaling

            labels = self.labels[index].copy()
            if labels.size:  # normalized xywh to pixel xyxy format
                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])

            if self.augment:
                img, labels = random_perspective(img, labels,
                                                 degrees=hyp['degrees'],
                                                 translate=hyp['translate'],
                                                 scale=hyp['scale'],
                                                 shear=hyp['shear'],
                                                 perspective=hyp['perspective'])

        nl = len(labels)  # number of labels
        if nl:
            labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1E-3)

        if self.augment:
            # Albumentations
            img, labels = self.albumentations(img, labels)
            nl = len(labels)  # update after albumentations

            # HSV color-space
            augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])

            # Flip up-down
            if random.random() < hyp['flipud']:
                img = np.flipud(img)
                if nl:
                    labels[:, 2] = 1 - labels[:, 2]

            # Flip left-right
            if random.random() < hyp['fliplr']:
                img = np.fliplr(img)
                if nl:
                    labels[:, 1] = 1 - labels[:, 1]

            # Cutouts
            # labels = cutout(img, labels, p=0.5)

        labels_out = torch.zeros((nl, 6))
        if nl:
            labels_out[:, 1:] = torch.from_numpy(labels)

        # Convert
        img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
        img = np.ascontiguousarray(img)

        return torch.from_numpy(img), labels_out, self.img_files[index], shapes

    @staticmethod
    def collate_fn(batch):
        img, label, path, shapes = zip(*batch)  # transposed
        for i, l in enumerate(label):
            l[:, 0] = i  # add target image index for build_targets()
        return torch.stack(img, 0), torch.cat(label, 0), path, shapes

    @staticmethod
    def collate_fn4(batch):
        img, label, path, shapes = zip(*batch)  # transposed
        n = len(shapes) // 4
        img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]

        ho = torch.tensor([[0., 0, 0, 1, 0, 0]])
        wo = torch.tensor([[0., 0, 1, 0, 0, 0]])
        s = torch.tensor([[1, 1, .5, .5, .5, .5]])  # scale
        for i in range(n):  # zidane torch.zeros(16,3,720,1280)  # BCHW
            i *= 4
            if random.random() < 0.5:
                im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2., mode='bilinear', align_corners=False)[
                    0].type(img[i].type())
                l = label[i]
            else:
                im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2)
                l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
            img4.append(im)
            label4.append(l)

        for i, l in enumerate(label4):
            l[:, 0] = i  # add target image index for build_targets()

        return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4



class LoadImages:
    # ObjectBox image/video dataloader, i.e. `python detect.py`
    def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
        files = []
        for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:
            p = str(Path(p).resolve())
            if '*' in p:
                files.extend(sorted(glob.glob(p, recursive=True)))  # glob
            elif os.path.isdir(p):
                files.extend(sorted(glob.glob(os.path.join(p, '*.*'))))  # dir
            elif os.path.isfile(p):
                files.append(p)  # files
            else:
                raise FileNotFoundError(f'{p} does not exist')

        images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]
        videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS]
        ni, nv = len(images), len(videos)

        self.img_size = img_size
        self.stride = stride
        self.files = images + videos
        self.nf = ni + nv  # number of files
        self.video_flag = [False] * ni + [True] * nv
        self.mode = 'image'
        self.auto = auto
        self.transforms = transforms  # optional
        self.vid_stride = vid_stride  # video frame-rate stride
        if any(videos):
            self._new_video(videos[0])  # new video
        else:
            self.cap = None
        assert self.nf > 0, f'No images or videos found in {p}. ' \
                            f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}'

    def __iter__(self):
        self.count = 0
        return self

    def __next__(self):
        if self.count == self.nf:
            raise StopIteration
        path = self.files[self.count]

        if self.video_flag[self.count]:
            # Read video
            self.mode = 'video'
            for _ in range(self.vid_stride):
                self.cap.grab()
            ret_val, im0 = self.cap.retrieve()
            while not ret_val:
                self.count += 1
                self.cap.release()
                if self.count == self.nf:  # last video
                    raise StopIteration
                path = self.files[self.count]
                self._new_video(path)
                ret_val, im0 = self.cap.read()

            self.frame += 1
            # im0 = self._cv2_rotate(im0)  # for use if cv2 autorotation is False
            s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: '

        else:
            # Read image
            self.count += 1
            im0 = cv2.imread(path)  # BGR
            assert im0 is not None, f'Image Not Found {path}'
            s = f'image {self.count}/{self.nf} {path}: '

        if self.transforms:
            im = self.transforms(im0)  # transforms
        else:
            im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0]  # padded resize
            im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
            im = np.ascontiguousarray(im)  # contiguous

        return path, im, im0, self.cap, s

    def _new_video(self, path):
        # Create a new video capture object
        self.frame = 0
        self.cap = cv2.VideoCapture(path)
        self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
        self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META))  # rotation degrees

    def _cv2_rotate(self, im):
        # Rotate a cv2 video manually
        if self.orientation == 0:
            return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE)
        elif self.orientation == 180:
            return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE)
        elif self.orientation == 90:
            return cv2.rotate(im, cv2.ROTATE_180)
        return im

    def __len__(self):
        return self.nf  # number of files


class LoadStreams:  # multiple IP or RTSP cameras
    def __init__(self, sources='streams.txt', img_size=640, stride=32, auto=True):
        self.mode = 'stream'
        self.img_size = img_size
        self.stride = stride

        if os.path.isfile(sources):
            with open(sources, 'r') as f:
                sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())]
        else:
            sources = [sources]

        n = len(sources)
        self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n
        self.sources = [clean_str(x) for x in sources]  # clean source names for later
        self.auto = auto
        for i, s in enumerate(sources):  # index, source
            # Start thread to read frames from video stream
            print(f'{i + 1}/{n}: {s}... ', end='')
            if 'youtube.com/' in s or 'youtu.be/' in s:  # if source is YouTube video
                import pafy
                s = pafy.new(s).getbest(preftype="mp4").url  # YouTube URL
            s = eval(s) if s.isnumeric() else s  # i.e. s = '0' local webcam
            cap = cv2.VideoCapture(s)
            assert cap.isOpened(), f'Failed to open {s}'
            w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
            h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
            self.fps[i] = max(cap.get(cv2.CAP_PROP_FPS) % 100, 0) or 30.0  # 30 FPS fallback
            self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf')  # infinite stream fallback

            _, self.imgs[i] = cap.read()  # guarantee first frame
            self.threads[i] = Thread(target=self.update, args=([i, cap]), daemon=True)
            print(f" success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)")
            self.threads[i].start()
        print('')  # newline

        # check for common shapes
        s = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0].shape for x in self.imgs])
        self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal
        if not self.rect:
            print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')

    def update(self, i, cap):
        # Read stream `i` frames in daemon thread
        n, f, read = 0, self.frames[i], 1  # frame number, frame array, inference every 'read' frame
        while cap.isOpened() and n < f:
            n += 1
            # _, self.imgs[index] = cap.read()
            cap.grab()
            if n % read == 0:
                success, im = cap.retrieve()
                self.imgs[i] = im if success else self.imgs[i] * 0
            time.sleep(1 / self.fps[i])  # wait time

    def __iter__(self):
        self.count = -1
        return self

    def __next__(self):
        self.count += 1
        if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'):  # q to quit
            cv2.destroyAllWindows()
            raise StopIteration

        # Letterbox
        img0 = self.imgs.copy()
        img = [letterbox(x, self.img_size, stride=self.stride, auto=self.rect and self.auto)[0] for x in img0]

        # Stack
        img = np.stack(img, 0)

        # Convert
        img = img[..., ::-1].transpose((0, 3, 1, 2))  # BGR to RGB, BHWC to BCHW
        img = np.ascontiguousarray(img)

        return self.sources, img, img0, None

    def __len__(self):
        return len(self.sources)  # 1E12 frames = 32 streams at 30 FPS for 30 years


class LoadScreenshots:
    def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):
        # source = [screen_number left top width height] (pixels)
        import mss

        source, *params = source.split()
        self.screen, left, top, width, height = 0, None, None, None, None  # default to full screen 0
        if len(params) == 1:
            self.screen = int(params[0])
        elif len(params) == 4:
            left, top, width, height = (int(x) for x in params)
        elif len(params) == 5:
            self.screen, left, top, width, height = (int(x) for x in params)
        self.img_size = img_size
        self.stride = stride
        self.transforms = transforms
        self.auto = auto
        self.mode = 'stream'
        self.frame = 0
        self.sct = mss.mss()

        # Parse monitor shape
        monitor = self.sct.monitors[self.screen]
        self.top = monitor["top"] if top is None else (monitor["top"] + top)
        self.left = monitor["left"] if left is None else (monitor["left"] + left)
        self.width = width or monitor["width"]
        self.height = height or monitor["height"]
        self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height}

    def __iter__(self):
        return self

    def __next__(self):
        # mss screen capture: get raw pixels from the screen as np array
        im0 = np.array(self.sct.grab(self.monitor))[:, :, :3]  # [:, :, :3] BGRA to BGR
        s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: "

        if self.transforms:
            im = self.transforms(im0)  # transforms
        else:
            im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0]  # padded resize
            im = im.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
            im = np.ascontiguousarray(im)  # contiguous
        self.frame += 1
        return str(self.screen), im, im0, None, s  # screen, img, original img, im0s, s


# Ancillary functions --------------------------------------------------------------------------------------------------
def load_image(self, i):
    # loads 1 image from dataset index 'i', returns im, original hw, resized hw
    im = self.imgs[i]
    if im is None:  # not cached in ram
        npy = self.img_npy[i]
        if npy and npy.exists():  # load npy
            im = np.load(npy)
        else:  # read image
            path = self.img_files[i]
            im = cv2.imread(path)  # BGR
            assert im is not None, 'Image Not Found ' + path
        h0, w0 = im.shape[:2]  # orig hw
        r = self.img_size / max(h0, w0)  # ratio
        if r != 1:  # if sizes are not equal
            im = cv2.resize(im, (int(w0 * r), int(h0 * r)),
                            interpolation=cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR)
        return im, (h0, w0), im.shape[:2]  # im, hw_original, hw_resized
    else:
        return self.imgs[i], self.img_hw0[i], self.img_hw[i]  # im, hw_original, hw_resized


def load_mosaic(self, index):
    # loads images in a 4-mosaic

    labels4, segments4 = [], []
    s = self.img_size
    yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border]  # mosaic center x, y
    indices = [index] + random.choices(self.indices, k=3)  # 3 additional image indices
    random.shuffle(indices)
    for i, index in enumerate(indices):
        # Load image
        img, _, (h, w) = load_image(self, index)

        # place img in img4
        if i == 0:  # top left
            img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
            x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
            x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)
        elif i == 1:  # top right
            x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
            x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
        elif i == 2:  # bottom left
            x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
            x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
        elif i == 3:  # bottom right
            x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
            x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)

        img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
        padw = x1a - x1b
        padh = y1a - y1b

        # Labels
        labels, segments = self.labels[index].copy(), self.segments[index].copy()
        if labels.size:
            labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh)  # normalized xywh to pixel xyxy format
            segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
        labels4.append(labels)
        segments4.extend(segments)

    # Concat/clip labels
    labels4 = np.concatenate(labels4, 0)
    for x in (labels4[:, 1:], *segments4):
        np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
    # img4, labels4 = replicate(img4, labels4)  # replicate

    # Augment
    img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste'])
    img4, labels4 = random_perspective(img4, labels4, segments4,
                                       degrees=self.hyp['degrees'],
                                       translate=self.hyp['translate'],
                                       scale=self.hyp['scale'],
                                       shear=self.hyp['shear'],
                                       perspective=self.hyp['perspective'],
                                       border=self.mosaic_border)  # border to remove

    return img4, labels4


def verify_image_label(args):
    # Verify one image-label pair
    im_file, lb_file, prefix = args
    nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, '', []  # number (missing, found, empty, corrupt), message, segments
    try:
        # verify images
        im = Image.open(im_file)
        im.verify()  # PIL verify
        shape = exif_size(im)  # image size
        assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
        assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}'
        if im.format.lower() in ('jpg', 'jpeg'):
            with open(im_file, 'rb') as f:
                f.seek(-2, 2)
                if f.read() != b'\xff\xd9':  # corrupt JPEG
                    Image.open(im_file).save(im_file, format='JPEG', subsampling=0, quality=100)  # re-save image
                    msg = f'{prefix}WARNING: corrupt JPEG restored and saved {im_file}'

        # verify labels
        if os.path.isfile(lb_file):
            nf = 1  # label found
            with open(lb_file, 'r') as f:
                l = [x.split() for x in f.read().strip().splitlines() if len(x)]
                if any([len(x) > 8 for x in l]):  # is segment
                    classes = np.array([x[0] for x in l], dtype=np.float32)
                    segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l]  # (cls, xy1...)
                    l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
                l = np.array(l, dtype=np.float32)
            if len(l):
                assert l.shape[1] == 5, 'labels require 5 columns each'
                assert (l >= 0).all(), 'negative labels'
                assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
                assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels'
            else:
                ne = 1  # label empty
                l = np.zeros((0, 5), dtype=np.float32)
        else:
            nm = 1  # label missing
            l = np.zeros((0, 5), dtype=np.float32)
        return im_file, l, shape, segments, nm, nf, ne, nc, msg
    except Exception as e:
        nc = 1
        msg = f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}'
        return [None, None, None, None, nm, nf, ne, nc, msg]

