# Ultralytics YOLO 🚀, GPL-3.0 license

import contextlib
import hashlib
import os
import subprocess
import time
from pathlib import Path
from tarfile import is_tarfile
from zipfile import is_zipfile

import cv2
import numpy as np
import torch
from PIL import ExifTags, Image, ImageOps

from ultralytics.yolo.utils import DATASETS_DIR, LOGGER, ROOT, colorstr, emojis, yaml_load
from ultralytics.yolo.utils.checks import check_file, check_font, is_ascii
from ultralytics.yolo.utils.downloads import download
from ultralytics.yolo.utils.files import unzip_file
from ultralytics.yolo.utils.ops import segments2boxes

HELP_URL = "See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data"
IMG_FORMATS = "bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp", "pfm"  # include image suffixes
VID_FORMATS = "asf", "avi", "gif", "m4v", "mkv", "mov", "mp4", "mpeg", "mpg", "ts", "wmv"  # include video suffixes
LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1))  # https://pytorch.org/docs/stable/elastic/run.html
RANK = int(os.getenv('RANK', -1))
PIN_MEMORY = str(os.getenv("PIN_MEMORY", True)).lower() == "true"  # global pin_memory for dataloaders
IMAGENET_MEAN = 0.485, 0.456, 0.406  # RGB mean
IMAGENET_STD = 0.229, 0.224, 0.225  # RGB standard deviation

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


def img2label_paths(img_paths):
    # Define label paths as a function of image paths
    sa, sb = f"{os.sep}images{os.sep}", f"{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


def exif_size(img):
    # Returns exif-corrected PIL size
    s = img.size  # (width, height)
    with contextlib.suppress(Exception):
        rotation = dict(img._getexif().items())[orientation]
        if rotation in [6, 8]:  # rotation 270 or 90
            s = (s[1], s[0])
    return s


def verify_image_label(args):
    # Verify one image-label pair
    im_file, lb_file, prefix, keypoint = args
    # number (missing, found, empty, corrupt), message, segments, keypoints
    nm, nf, ne, nc, msg, segments, keypoints = 0, 0, 0, 0, "", [], None
    try:
        # verify images
        im = Image.open(im_file)
        im.verify()  # PIL verify
        shape = exif_size(im)  # image size
        shape = (shape[1], shape[0])  # hw
        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
                    ImageOps.exif_transpose(Image.open(im_file)).save(im_file, "JPEG", subsampling=0, quality=100)
                    msg = f"{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved"

        # verify labels
        if os.path.isfile(lb_file):
            nf = 1  # label found
            with open(lb_file) as f:
                lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
                if any(len(x) > 6 for x in lb) and (not keypoint):  # is segment
                    classes = np.array([x[0] for x in lb], dtype=np.float32)
                    segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb]  # (cls, xy1...)
                    lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
                lb = np.array(lb, dtype=np.float32)
            nl = len(lb)
            if nl:
                if keypoint:
                    assert lb.shape[1] == 56, "labels require 56 columns each"
                    assert (lb[:, 5::3] <= 1).all(), "non-normalized or out of bounds coordinate labels"
                    assert (lb[:, 6::3] <= 1).all(), "non-normalized or out of bounds coordinate labels"
                    kpts = np.zeros((lb.shape[0], 39))
                    for i in range(len(lb)):
                        kpt = np.delete(lb[i, 5:], np.arange(2, lb.shape[1] - 5,
                                                             3))  # remove the occlusion parameter from the GT
                        kpts[i] = np.hstack((lb[i, :5], kpt))
                    lb = kpts
                    assert lb.shape[1] == 39, "labels require 39 columns each after removing occlusion parameter"
                else:
                    assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected"
                    assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}"
                    assert (lb[:, 1:] <=
                            1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}"
                _, i = np.unique(lb, axis=0, return_index=True)
                if len(i) < nl:  # duplicate row check
                    lb = lb[i]  # remove duplicates
                    if segments:
                        segments = [segments[x] for x in i]
                    msg = f"{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed"
            else:
                ne = 1  # label empty
                lb = np.zeros((0, 39), dtype=np.float32) if keypoint else np.zeros((0, 5), dtype=np.float32)
        else:
            nm = 1  # label missing
            lb = np.zeros((0, 39), dtype=np.float32) if keypoint else np.zeros((0, 5), dtype=np.float32)
        if keypoint:
            keypoints = lb[:, 5:].reshape(-1, 17, 2)
        lb = lb[:, :5]
        return im_file, lb, shape, segments, keypoints, nm, nf, ne, nc, msg
    except Exception as e:
        nc = 1
        msg = f"{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}"
        return [None, None, None, None, None, nm, nf, ne, nc, msg]


def polygon2mask(imgsz, polygons, color=1, downsample_ratio=1):
    """
    Args:
        imgsz (tuple): The image size.
        polygons (np.ndarray): [N, M], N is the number of polygons, M is the number of points(Be divided by 2).
        color (int): color
        downsample_ratio (int): downsample ratio
    """
    mask = np.zeros(imgsz, dtype=np.uint8)
    polygons = np.asarray(polygons)
    polygons = polygons.astype(np.int32)
    shape = polygons.shape
    polygons = polygons.reshape(shape[0], -1, 2)
    cv2.fillPoly(mask, polygons, color=color)
    nh, nw = (imgsz[0] // downsample_ratio, imgsz[1] // downsample_ratio)
    # NOTE: fillPoly firstly then resize is trying the keep the same way
    # of loss calculation when mask-ratio=1.
    mask = cv2.resize(mask, (nw, nh))
    return mask


def polygons2masks(imgsz, polygons, color, downsample_ratio=1):
    """
    Args:
        imgsz (tuple): The image size.
        polygons (list[np.ndarray]): each polygon is [N, M], N is number of polygons, M is number of points (M % 2 = 0)
        color (int): color
        downsample_ratio (int): downsample ratio
    """
    masks = []
    for si in range(len(polygons)):
        mask = polygon2mask(imgsz, [polygons[si].reshape(-1)], color, downsample_ratio)
        masks.append(mask)
    return np.array(masks)


def polygons2masks_overlap(imgsz, segments, downsample_ratio=1):
    """Return a (640, 640) overlap mask."""
    masks = np.zeros((imgsz[0] // downsample_ratio, imgsz[1] // downsample_ratio),
                     dtype=np.int32 if len(segments) > 255 else np.uint8)
    areas = []
    ms = []
    for si in range(len(segments)):
        mask = polygon2mask(imgsz, [segments[si].reshape(-1)], downsample_ratio=downsample_ratio, color=1)
        ms.append(mask)
        areas.append(mask.sum())
    areas = np.asarray(areas)
    index = np.argsort(-areas)
    ms = np.array(ms)[index]
    for i in range(len(segments)):
        mask = ms[i] * (i + 1)
        masks = masks + mask
        masks = np.clip(masks, a_min=0, a_max=i + 1)
    return masks, index


def check_det_dataset(dataset, autodownload=True):
    # Download, check and/or unzip dataset if not found locally
    data = check_file(dataset)

    # Download (optional)
    extract_dir = ''
    if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):
        download(data, dir=f'{DATASETS_DIR}/{Path(data).stem}', unzip=True, delete=False, curl=False, threads=1)
        data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml'))
        extract_dir, autodownload = data.parent, False

    # Read yaml (optional)
    if isinstance(data, (str, Path)):
        data = yaml_load(data, append_filename=True)  # dictionary

    # Checks
    for k in 'train', 'val', 'names':
        if k not in data:
            raise SyntaxError(
                emojis(f"{dataset} '{k}:' key missing ❌.\n"
                       f"'train', 'val' and 'names' are required in data.yaml files."))
    if isinstance(data['names'], (list, tuple)):  # old array format
        data['names'] = dict(enumerate(data['names']))  # convert to dict
    data['nc'] = len(data['names'])

    # Resolve paths
    path = Path(extract_dir or data.get('path') or '')  # optional 'path' default to '.'
    if not path.is_absolute():
        path = (DATASETS_DIR / path).resolve()
        data['path'] = path  # download scripts
    for k in 'train', 'val', 'test':
        if data.get(k):  # prepend path
            if isinstance(data[k], str):
                x = (path / data[k]).resolve()
                if not x.exists() and data[k].startswith('../'):
                    x = (path / data[k][3:]).resolve()
                data[k] = str(x)
            else:
                data[k] = [str((path / x).resolve()) for x in data[k]]

    # Parse yaml
    train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download'))
    if val:
        val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val path
        if not all(x.exists() for x in val):
            msg = f"\nDataset '{dataset}' not found ⚠️, missing paths %s" % [str(x) for x in val if not x.exists()]
            if s and autodownload:
                LOGGER.warning(msg)
            else:
                raise FileNotFoundError(msg)
            t = time.time()
            if s.startswith('http') and s.endswith('.zip'):  # URL
                f = Path(s).name  # filename
                LOGGER.info(f'Downloading {s} to {f}...')
                torch.hub.download_url_to_file(s, f)
                Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True)  # create root
                unzip_file(f, path=DATASETS_DIR)  # unzip
                Path(f).unlink()  # remove zip
                r = None  # success
            elif s.startswith('bash '):  # bash script
                LOGGER.info(f'Running {s} ...')
                r = os.system(s)
            else:  # python script
                r = exec(s, {'yaml': data})  # return None
            dt = f'({round(time.time() - t, 1)}s)'
            s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f"failure {dt} ❌"
            LOGGER.info(f"Dataset download {s}")
    check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf', progress=True)  # download fonts

    return data  # dictionary


def check_cls_dataset(dataset: str):
    """
    Check a classification dataset such as Imagenet.

    Copy code
    This function takes a `dataset` name as input and returns a dictionary containing information about the dataset.
    If the dataset is not found, it attempts to download the dataset from the internet and save it to the local file system.

    Args:
        dataset (str): Name of the dataset.

    Returns:
        data (dict): A dictionary containing the following keys and values:
            'train': Path object for the directory containing the training set of the dataset
            'val': Path object for the directory containing the validation set of the dataset
            'nc': Number of classes in the dataset
            'names': List of class names in the dataset
    """
    data_dir = (DATASETS_DIR / dataset).resolve()
    if not data_dir.is_dir():
        LOGGER.info(f'\nDataset not found ⚠️, missing path {data_dir}, attempting download...')
        t = time.time()
        if dataset == 'imagenet':
            subprocess.run(f"bash {ROOT / 'yolo/data/scripts/get_imagenet.sh'}", shell=True, check=True)
        else:
            url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{dataset}.zip'
            download(url, dir=data_dir.parent)
        s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
        LOGGER.info(s)
    train_set = data_dir / "train"
    test_set = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val'  # data/test or data/val
    nc = len([x for x in (data_dir / 'train').glob('*') if x.is_dir()])  # number of classes
    names = [x.name for x in (data_dir / 'train').iterdir() if x.is_dir()]  # class names list
    names = dict(enumerate(sorted(names)))
    return {"train": train_set, "val": test_set, "nc": nc, "names": names}
