utils/dataloaders.py

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Dataloaders and dataset utils
"""

import contextlib
import glob
import hashlib
import json
import math
import os
import random
import shutil
import time
from itertools import repeat
from multiprocessing.pool import Pool, ThreadPool
from pathlib import Path
from threading import Thread
from urllib.parse import urlparse

import numpy as np
import psutil
import torch
import torch.nn.functional as F
import torchvision
import yaml
from PIL import ExifTags, Image, ImageOps
from torch.utils.data import DataLoader, Dataset, dataloader, distributed
from tqdm import tqdm

from utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste,
                                 letterbox, mixup, random_perspective)
from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, check_dataset, check_requirements,
                           check_yaml, clean_str, cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy,
                           xywh2xyxy, xywhn2xyxy, xyxy2xywhn)
from utils.torch_utils import torch_distributed_zero_first

# Parameters
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

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


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 exif_transpose(image):
    """
    Transpose a PIL image accordingly if it has an EXIF Orientation tag.
    Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose()

    :param image: The image to transpose.
    :return: An image.
    """
    exif = image.getexif()
    orientation = exif.get(0x0112, 1)  # default 1
    if orientation > 1:
        method = {
            2: Image.FLIP_LEFT_RIGHT,
            3: Image.ROTATE_180,
            4: Image.FLIP_TOP_BOTTOM,
            5: Image.TRANSPOSE,
            6: Image.ROTATE_270,
            7: Image.TRANSVERSE,
            8: Image.ROTATE_90}.get(orientation)
        if method is not None:
            image = image.transpose(method)
            del exif[0x0112]
            image.info["exif"] = exif.tobytes()
    return image


def seed_worker(worker_id):
    # Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader
    worker_seed = torch.initial_seed() % 2 ** 32
    np.random.seed(worker_seed)
    random.seed(worker_seed)



class LoadImages:
    # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`
    def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
        if isinstance(path, str) and Path(path).suffix == ".txt":  # *.txt file with img/vid/dir on each line
            path = Path(path).read_text().rsplit()
        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
        # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0)  # disable https://github.com/ultralytics/yolov5/issues/8493

    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

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]

# Ancillary functions --------------------------------------------------------------------------------------------------
def flatten_recursive(path=DATASETS_DIR / 'coco128'):
    # Flatten a recursive directory by bringing all files to top level
    new_path = Path(f'{str(path)}_flat')
    if os.path.exists(new_path):
        shutil.rmtree(new_path)  # delete output folder
    os.makedirs(new_path)  # make new output folder
    for file in tqdm(glob.glob(f'{str(Path(path))}/**/*.*', recursive=True)):
        shutil.copyfile(file, new_path / Path(file).name)


def extract_boxes(path=DATASETS_DIR / 'coco128'):  # from utils.dataloaders import *; extract_boxes()
    # Convert detection dataset into classification dataset, with one directory per class
    path = Path(path)  # images dir
    shutil.rmtree(path / 'classification') if (path / 'classification').is_dir() else None  # remove existing
    files = list(path.rglob('*.*'))
    n = len(files)  # number of files
    for im_file in tqdm(files, total=n):
        if im_file.suffix[1:] in IMG_FORMATS:
            # image
            im = cv2.imread(str(im_file))[..., ::-1]  # BGR to RGB
            h, w = im.shape[:2]

            # labels
            lb_file = Path(img2label_paths([str(im_file)])[0])
            if Path(lb_file).exists():
                with open(lb_file) as f:
                    lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32)  # labels

                for j, x in enumerate(lb):
                    c = int(x[0])  # class
                    f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg'  # new filename
                    if not f.parent.is_dir():
                        f.parent.mkdir(parents=True)

                    b = x[1:] * [w, h, w, h]  # box
                    # b[2:] = b[2:].max()  # rectangle to square
                    b[2:] = b[2:] * 1.2 + 3  # pad
                    b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int)

                    b[[0, 2]] = np.clip(b[[0, 2]], 0, w)  # clip boxes outside of image
                    b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
                    assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}'


def autosplit(path=DATASETS_DIR / 'coco128/images', weights=(0.9, 0.1, 0.0), annotated_only=False):
    """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
    Usage: from utils.dataloaders import *; autosplit()
    Arguments
        path:            Path to images directory
        weights:         Train, val, test weights (list, tuple)
        annotated_only:  Only use images with an annotated txt file
    """
    path = Path(path)  # images dir
    files = sorted(x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS)  # image files only
    n = len(files)  # number of files
    random.seed(0)  # for reproducibility
    indices = random.choices([0, 1, 2], weights=weights, k=n)  # assign each image to a split

    txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt']  # 3 txt files
    for x in txt:
        if (path.parent / x).exists():
            (path.parent / x).unlink()  # remove existing

    print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only)
    for i, img in tqdm(zip(indices, files), total=n):
        if not annotated_only or Path(img2label_paths([str(img)])[0]).exists():  # check label
            with open(path.parent / txt[i], 'a') as f:
                f.write(f'./{img.relative_to(path.parent).as_posix()}' + '\n')  # add image to txt file


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
                    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):  # 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:
                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, 5), dtype=np.float32)
        else:
            nm = 1  # label missing
            lb = np.zeros((0, 5), dtype=np.float32)
        return im_file, lb, shape, segments, 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, nm, nf, ne, nc, msg]