# Ultralytics YOLO 🚀, AGPL-3.0 license import contextlib import hashlib import json import os import subprocess import time import zipfile from multiprocessing.pool import ThreadPool from pathlib import Path from tarfile import is_tarfile import cv2 import numpy as np from PIL import ExifTags, Image, ImageOps from tqdm import tqdm from ultralytics.nn.autobackend import check_class_names from ultralytics.yolo.utils import (DATASETS_DIR, LOGGER, NUM_THREADS, ROOT, SETTINGS_YAML, clean_url, colorstr, emojis, yaml_load) from ultralytics.yolo.utils.checks import check_file, check_font, is_ascii from ultralytics.yolo.utils.downloads import download, safe_download, unzip_file from ultralytics.yolo.utils.ops import segments2boxes HELP_URL = 'See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data' IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # image suffixes VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv', 'webm' # video suffixes 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.sha256(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, num_cls, nkpt, ndim = 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] == (5 + nkpt * ndim), f'labels require {(5 + nkpt * ndim)} columns each' assert (lb[:, 5::ndim] <= 1).all(), 'non-normalized or out of bounds coordinate labels' assert (lb[:, 6::ndim] <= 1).all(), 'non-normalized or out of bounds coordinate labels' else: assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected' assert (lb[:, 1:] <= 1).all(), \ f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}' assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}' # All labels max_cls = int(lb[:, 0].max()) # max label count assert max_cls <= num_cls, \ f'Label class {max_cls} exceeds dataset class count {num_cls}. ' \ f'Possible class labels are 0-{num_cls - 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 + nkpt * ndim)), dtype=np.float32) if keypoint else np.zeros( (0, 5), dtype=np.float32) else: nm = 1 # label missing lb = np.zeros((0, (5 + nkpt * ndim)), dtype=np.float32) if keypoint else np.zeros((0, 5), dtype=np.float32) if keypoint: keypoints = lb[:, 5:].reshape(-1, nkpt, ndim) if ndim == 2: kpt_mask = np.ones(keypoints.shape[:2], dtype=np.float32) kpt_mask = np.where(keypoints[..., 0] < 0, 0.0, kpt_mask) kpt_mask = np.where(keypoints[..., 1] < 0, 0.0, kpt_mask) keypoints = np.concatenate([keypoints, kpt_mask[..., None]], axis=-1) # (nl, nkpt, 3) 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 (list[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 (zipfile.is_zipfile(data) or is_tarfile(data)): new_dir = safe_download(data, dir=DATASETS_DIR, unzip=True, delete=False, curl=False) data = next((DATASETS_DIR / new_dir).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': if k not in data: raise SyntaxError( emojis(f"{dataset} '{k}:' key missing ❌.\n'train' and 'val' are required in all data YAMLs.")) if 'names' not in data and 'nc' not in data: raise SyntaxError(emojis(f"{dataset} key missing ❌.\n either 'names' or 'nc' are required in all data YAMLs.")) if 'names' in data and 'nc' in data and len(data['names']) != data['nc']: raise SyntaxError(emojis(f"{dataset} 'names' length {len(data['names'])} and 'nc: {data['nc']}' must match.")) if 'names' not in data: data['names'] = [f'class_{i}' for i in range(data['nc'])] else: data['nc'] = len(data['names']) data['names'] = check_class_names(data['names']) # Resolve paths path = Path(extract_dir or data.get('path') or Path(data.get('yaml_file', '')).parent) # dataset root 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): name = clean_url(dataset) # dataset name with URL auth stripped m = f"\nDataset '{name}' images not found ⚠️, missing paths %s" % [str(x) for x in val if not x.exists()] if s and autodownload: LOGGER.warning(m) else: m += f"\nNote dataset download directory is '{DATASETS_DIR}'. You can update this in '{SETTINGS_YAML}'" raise FileNotFoundError(m) t = time.time() if s.startswith('http') and s.endswith('.zip'): # URL safe_download(url=s, dir=DATASETS_DIR, delete=True) 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}\n') check_font('Arial.ttf' if is_ascii(data['names']) else 'Arial.Unicode.ttf') # download fonts return data # dictionary def check_cls_dataset(dataset: str, split=''): """ Checks a classification dataset such as Imagenet. This function accepts a `dataset` name and attempts to retrieve the corresponding dataset information. If the dataset is not found locally, it attempts to download the dataset from the internet and save it locally. Args: dataset (str): The name of the dataset. split (str, optional): The split of the dataset. Either 'val', 'test', or ''. Defaults to ''. Returns: dict: A dictionary containing the following keys: - 'train' (Path): The directory path containing the training set of the dataset. - 'val' (Path): The directory path containing the validation set of the dataset. - 'test' (Path): The directory path containing the test set of the dataset. - 'nc' (int): The number of classes in the dataset. - 'names' (dict): A dictionary of class names in the dataset. Raises: FileNotFoundError: If the specified dataset is not found and cannot be downloaded. """ dataset = Path(dataset) data_dir = (dataset if dataset.is_dir() else (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 str(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' val_set = data_dir / 'val' if (data_dir / 'val').exists() else None # data/test or data/val test_set = data_dir / 'test' if (data_dir / 'test').exists() else None # data/val or data/test if split == 'val' and not val_set: LOGGER.info("WARNING ⚠️ Dataset 'split=val' not found, using 'split=test' instead.") elif split == 'test' and not test_set: LOGGER.info("WARNING ⚠️ Dataset 'split=test' not found, using 'split=val' instead.") 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': val_set or test_set, 'test': test_set or val_set, 'nc': nc, 'names': names} class HUBDatasetStats(): """ A class for generating HUB dataset JSON and `-hub` dataset directory. Args: path (str): Path to data.yaml or data.zip (with data.yaml inside data.zip). Default is 'coco128.yaml'. task (str): Dataset task. Options are 'detect', 'segment', 'pose', 'classify'. Default is 'detect'. autodownload (bool): Attempt to download dataset if not found locally. Default is False. Usage from ultralytics.yolo.data.utils import HUBDatasetStats stats = HUBDatasetStats('/Users/glennjocher/Downloads/coco8.zip', task='detect') # detect dataset stats = HUBDatasetStats('/Users/glennjocher/Downloads/coco8-seg.zip', task='segment') # segment dataset stats = HUBDatasetStats('/Users/glennjocher/Downloads/coco8-pose.zip', task='pose') # pose dataset stats.get_json(save=False) stats.process_images() """ def __init__(self, path='coco128.yaml', task='detect', autodownload=False): """Initialize class.""" LOGGER.info(f'Starting HUB dataset checks for {path}....') zipped, data_dir, yaml_path = self._unzip(Path(path)) try: # data = yaml_load(check_yaml(yaml_path)) # data dict data = check_det_dataset(yaml_path, autodownload) # data dict if zipped: data['path'] = data_dir except Exception as e: raise Exception('error/HUB/dataset_stats/yaml_load') from e self.hub_dir = Path(str(data['path']) + '-hub') self.im_dir = self.hub_dir / 'images' self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images self.stats = {'nc': len(data['names']), 'names': list(data['names'].values())} # statistics dictionary self.data = data self.task = task # detect, segment, pose, classify @staticmethod def _find_yaml(dir): """Return data.yaml file.""" files = list(dir.glob('*.yaml')) or list(dir.rglob('*.yaml')) # try root level first and then recursive assert files, f'No *.yaml file found in {dir}' if len(files) > 1: files = [f for f in files if f.stem == dir.stem] # prefer *.yaml files that match dir name assert files, f'Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed' assert len(files) == 1, f'Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}' return files[0] def _unzip(self, path): """Unzip data.zip.""" if not str(path).endswith('.zip'): # path is data.yaml return False, None, path unzip_dir = unzip_file(path, path=path.parent) assert unzip_dir.is_dir(), f'Error unzipping {path}, {unzip_dir} not found. ' \ f'path/to/abc.zip MUST unzip to path/to/abc/' return True, str(unzip_dir), self._find_yaml(unzip_dir) # zipped, data_dir, yaml_path def _hub_ops(self, f): """Saves a compressed image for HUB previews.""" compress_one_image(f, self.im_dir / Path(f).name) # save to dataset-hub def get_json(self, save=False, verbose=False): """Return dataset JSON for Ultralytics HUB.""" from ultralytics.yolo.data import YOLODataset # ClassificationDataset def _round(labels): """Update labels to integer class and 4 decimal place floats.""" if self.task == 'detect': coordinates = labels['bboxes'] elif self.task == 'segment': coordinates = [x.flatten() for x in labels['segments']] elif self.task == 'pose': n = labels['keypoints'].shape[0] coordinates = np.concatenate((labels['bboxes'], labels['keypoints'].reshape(n, -1)), 1) else: raise ValueError('Undefined dataset task.') zipped = zip(labels['cls'], coordinates) return [[int(c), *(round(float(x), 4) for x in points)] for c, points in zipped] for split in 'train', 'val', 'test': if self.data.get(split) is None: self.stats[split] = None # i.e. no test set continue dataset = YOLODataset(img_path=self.data[split], data=self.data, use_segments=self.task == 'segment', use_keypoints=self.task == 'pose') x = np.array([ np.bincount(label['cls'].astype(int).flatten(), minlength=self.data['nc']) for label in tqdm(dataset.labels, total=len(dataset), desc='Statistics')]) # shape(128x80) self.stats[split] = { 'instance_stats': { 'total': int(x.sum()), 'per_class': x.sum(0).tolist()}, 'image_stats': { 'total': len(dataset), 'unlabelled': int(np.all(x == 0, 1).sum()), 'per_class': (x > 0).sum(0).tolist()}, 'labels': [{ Path(k).name: _round(v)} for k, v in zip(dataset.im_files, dataset.labels)]} # Save, print and return if save: stats_path = self.hub_dir / 'stats.json' LOGGER.info(f'Saving {stats_path.resolve()}...') with open(stats_path, 'w') as f: json.dump(self.stats, f) # save stats.json if verbose: LOGGER.info(json.dumps(self.stats, indent=2, sort_keys=False)) return self.stats def process_images(self): """Compress images for Ultralytics HUB.""" from ultralytics.yolo.data import YOLODataset # ClassificationDataset for split in 'train', 'val', 'test': if self.data.get(split) is None: continue dataset = YOLODataset(img_path=self.data[split], data=self.data) with ThreadPool(NUM_THREADS) as pool: for _ in tqdm(pool.imap(self._hub_ops, dataset.im_files), total=len(dataset), desc=f'{split} images'): pass LOGGER.info(f'Done. All images saved to {self.im_dir}') return self.im_dir def compress_one_image(f, f_new=None, max_dim=1920, quality=50): """ Compresses a single image file to reduced size while preserving its aspect ratio and quality using either the Python Imaging Library (PIL) or OpenCV library. If the input image is smaller than the maximum dimension, it will not be resized. Args: f (str): The path to the input image file. f_new (str, optional): The path to the output image file. If not specified, the input file will be overwritten. max_dim (int, optional): The maximum dimension (width or height) of the output image. Default is 1920 pixels. quality (int, optional): The image compression quality as a percentage. Default is 50%. Usage: from pathlib import Path from ultralytics.yolo.data.utils import compress_one_image for f in Path('/Users/glennjocher/Downloads/dataset').rglob('*.jpg'): compress_one_image(f) """ try: # use PIL im = Image.open(f) r = max_dim / max(im.height, im.width) # ratio if r < 1.0: # image too large im = im.resize((int(im.width * r), int(im.height * r))) im.save(f_new or f, 'JPEG', quality=quality, optimize=True) # save except Exception as e: # use OpenCV LOGGER.info(f'WARNING ⚠️ HUB ops PIL failure {f}: {e}') im = cv2.imread(f) im_height, im_width = im.shape[:2] r = max_dim / max(im_height, im_width) # ratio if r < 1.0: # image too large im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA) cv2.imwrite(str(f_new or f), im) def delete_dsstore(path): """ Deletes all ".DS_store" files under a specified directory. Args: path (str, optional): The directory path where the ".DS_store" files should be deleted. Usage: from ultralytics.yolo.data.utils import delete_dsstore delete_dsstore('/Users/glennjocher/Downloads/dataset') Note: ".DS_store" files are created by the Apple operating system and contain metadata about folders and files. They are hidden system files and can cause issues when transferring files between different operating systems. """ # Delete Apple .DS_store files files = list(Path(path).rglob('.DS_store')) LOGGER.info(f'Deleting *.DS_store files: {files}') for f in files: f.unlink() def zip_directory(dir, use_zipfile_library=True): """ Zips a directory and saves the archive to the specified output path. Args: dir (str): The path to the directory to be zipped. use_zipfile_library (bool): Whether to use zipfile library or shutil for zipping. Usage: from ultralytics.yolo.data.utils import zip_directory zip_directory('/Users/glennjocher/Downloads/playground') zip -r coco8-pose.zip coco8-pose """ delete_dsstore(dir) if use_zipfile_library: dir = Path(dir) with zipfile.ZipFile(dir.with_suffix('.zip'), 'w', zipfile.ZIP_DEFLATED) as zip_file: for file_path in dir.glob('**/*'): if file_path.is_file(): zip_file.write(file_path, file_path.relative_to(dir)) else: import shutil shutil.make_archive(dir, 'zip', dir)