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# Ultralytics YOLO 🚀, AGPL-3.0 license |
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import glob |
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import math |
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import os |
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import random |
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from copy import deepcopy |
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from multiprocessing.pool import ThreadPool |
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from pathlib import Path |
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from typing import Optional |
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import cv2 |
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import numpy as np |
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import psutil |
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from torch.utils.data import Dataset |
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from tqdm import tqdm |
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from ..utils import DEFAULT_CFG, LOCAL_RANK, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT |
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from .utils import HELP_URL, IMG_FORMATS |
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class BaseDataset(Dataset): |
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""" |
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Base dataset class for loading and processing image data. |
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Args: |
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img_path (str): Path to the folder containing images. |
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imgsz (int, optional): Image size. Defaults to 640. |
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cache (bool, optional): Cache images to RAM or disk during training. Defaults to False. |
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augment (bool, optional): If True, data augmentation is applied. Defaults to True. |
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hyp (dict, optional): Hyperparameters to apply data augmentation. Defaults to None. |
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prefix (str, optional): Prefix to print in log messages. Defaults to ''. |
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rect (bool, optional): If True, rectangular training is used. Defaults to False. |
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batch_size (int, optional): Size of batches. Defaults to None. |
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stride (int, optional): Stride. Defaults to 32. |
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pad (float, optional): Padding. Defaults to 0.0. |
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single_cls (bool, optional): If True, single class training is used. Defaults to False. |
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classes (list): List of included classes. Default is None. |
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Attributes: |
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im_files (list): List of image file paths. |
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labels (list): List of label data dictionaries. |
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ni (int): Number of images in the dataset. |
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ims (list): List of loaded images. |
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npy_files (list): List of numpy file paths. |
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transforms (callable): Image transformation function. |
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""" |
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def __init__(self, |
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img_path, |
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imgsz=640, |
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cache=False, |
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augment=True, |
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hyp=DEFAULT_CFG, |
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prefix='', |
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rect=False, |
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batch_size=None, |
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stride=32, |
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pad=0.5, |
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single_cls=False, |
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classes=None): |
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super().__init__() |
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self.img_path = img_path |
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self.imgsz = imgsz |
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self.augment = augment |
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self.single_cls = single_cls |
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self.prefix = prefix |
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self.im_files = self.get_img_files(self.img_path) |
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self.labels = self.get_labels() |
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self.update_labels(include_class=classes) # single_cls and include_class |
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self.ni = len(self.labels) # number of images |
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self.rect = rect |
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self.batch_size = batch_size |
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self.stride = stride |
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self.pad = pad |
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if self.rect: |
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assert self.batch_size is not None |
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self.set_rectangle() |
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# Cache stuff |
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if cache == 'ram' and not self.check_cache_ram(): |
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cache = False |
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self.ims = [None] * self.ni |
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self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files] |
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if cache: |
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self.cache_images(cache) |
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# Transforms |
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self.transforms = self.build_transforms(hyp=hyp) |
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def get_img_files(self, img_path): |
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"""Read image files.""" |
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try: |
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f = [] # image files |
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for p in img_path if isinstance(img_path, list) else [img_path]: |
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p = Path(p) # os-agnostic |
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if p.is_dir(): # dir |
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f += glob.glob(str(p / '**' / '*.*'), recursive=True) |
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# F = list(p.rglob('*.*')) # pathlib |
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elif p.is_file(): # file |
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with open(p) as t: |
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t = t.read().strip().splitlines() |
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parent = str(p.parent) + os.sep |
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f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path |
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# F += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib) |
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else: |
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raise FileNotFoundError(f'{self.prefix}{p} does not exist') |
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im_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS) |
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# self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib |
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assert im_files, f'{self.prefix}No images found' |
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except Exception as e: |
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raise FileNotFoundError(f'{self.prefix}Error loading data from {img_path}\n{HELP_URL}') from e |
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return im_files |
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def update_labels(self, include_class: Optional[list]): |
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"""include_class, filter labels to include only these classes (optional).""" |
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include_class_array = np.array(include_class).reshape(1, -1) |
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for i in range(len(self.labels)): |
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if include_class is not None: |
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cls = self.labels[i]['cls'] |
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bboxes = self.labels[i]['bboxes'] |
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segments = self.labels[i]['segments'] |
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keypoints = self.labels[i]['keypoints'] |
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j = (cls == include_class_array).any(1) |
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self.labels[i]['cls'] = cls[j] |
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self.labels[i]['bboxes'] = bboxes[j] |
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if segments: |
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self.labels[i]['segments'] = [segments[si] for si, idx in enumerate(j) if idx] |
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if keypoints is not None: |
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self.labels[i]['keypoints'] = keypoints[j] |
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if self.single_cls: |
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self.labels[i]['cls'][:, 0] = 0 |
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def load_image(self, i): |
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"""Loads 1 image from dataset index 'i', returns (im, resized hw).""" |
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im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i] |
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if im is None: # not cached in RAM |
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if fn.exists(): # load npy |
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im = np.load(fn) |
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else: # read image |
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im = cv2.imread(f) # BGR |
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if im is None: |
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raise FileNotFoundError(f'Image Not Found {f}') |
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h0, w0 = im.shape[:2] # orig hw |
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r = self.imgsz / max(h0, w0) # ratio |
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if r != 1: # if sizes are not equal |
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interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA |
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im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp) |
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return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized |
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return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized |
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def cache_images(self, cache): |
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"""Cache images to memory or disk.""" |
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b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes |
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self.im_hw0, self.im_hw = [None] * self.ni, [None] * self.ni |
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fcn = self.cache_images_to_disk if cache == 'disk' else self.load_image |
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with ThreadPool(NUM_THREADS) as pool: |
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results = pool.imap(fcn, range(self.ni)) |
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pbar = tqdm(enumerate(results), total=self.ni, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0) |
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for i, x in pbar: |
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if cache == 'disk': |
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b += self.npy_files[i].stat().st_size |
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else: # 'ram' |
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self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i) |
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b += self.ims[i].nbytes |
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pbar.desc = f'{self.prefix}Caching images ({b / gb:.1f}GB {cache})' |
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pbar.close() |
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def cache_images_to_disk(self, i): |
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"""Saves an image as an *.npy file for faster loading.""" |
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f = self.npy_files[i] |
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if not f.exists(): |
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np.save(f.as_posix(), cv2.imread(self.im_files[i])) |
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def check_cache_ram(self, safety_margin=0.5): |
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"""Check image caching requirements vs available memory.""" |
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b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes |
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n = min(self.ni, 30) # extrapolate from 30 random images |
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for _ in range(n): |
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im = cv2.imread(random.choice(self.im_files)) # sample image |
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ratio = self.imgsz / max(im.shape[0], im.shape[1]) # max(h, w) # ratio |
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b += im.nbytes * ratio ** 2 |
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mem_required = b * self.ni / n * (1 + safety_margin) # GB required to cache dataset into RAM |
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mem = psutil.virtual_memory() |
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cache = mem_required < mem.available # to cache or not to cache, that is the question |
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if not cache: |
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LOGGER.info(f'{self.prefix}{mem_required / gb:.1f}GB RAM required to cache images ' |
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f'with {int(safety_margin * 100)}% safety margin but only ' |
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f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, ' |
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f"{'caching images ✅' if cache else 'not caching images ⚠️'}") |
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return cache |
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def set_rectangle(self): |
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"""Sets the shape of bounding boxes for YOLO detections as rectangles.""" |
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bi = np.floor(np.arange(self.ni) / self.batch_size).astype(int) # batch index |
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nb = bi[-1] + 1 # number of batches |
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s = np.array([x.pop('shape') for x in self.labels]) # hw |
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ar = s[:, 0] / s[:, 1] # aspect ratio |
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irect = ar.argsort() |
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self.im_files = [self.im_files[i] for i in irect] |
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self.labels = [self.labels[i] for i in irect] |
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ar = ar[irect] |
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# Set training image shapes |
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shapes = [[1, 1]] * nb |
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for i in range(nb): |
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ari = ar[bi == i] |
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mini, maxi = ari.min(), ari.max() |
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if maxi < 1: |
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shapes[i] = [maxi, 1] |
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elif mini > 1: |
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shapes[i] = [1, 1 / mini] |
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self.batch_shapes = np.ceil(np.array(shapes) * self.imgsz / self.stride + self.pad).astype(int) * self.stride |
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self.batch = bi # batch index of image |
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def __getitem__(self, index): |
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"""Returns transformed label information for given index.""" |
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return self.transforms(self.get_label_info(index)) |
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def get_label_info(self, index): |
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"""Get and return label information from the dataset.""" |
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label = deepcopy(self.labels[index]) # requires deepcopy() https://github.com/ultralytics/ultralytics/pull/1948 |
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label.pop('shape', None) # shape is for rect, remove it |
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label['img'], label['ori_shape'], label['resized_shape'] = self.load_image(index) |
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label['ratio_pad'] = (label['resized_shape'][0] / label['ori_shape'][0], |
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label['resized_shape'][1] / label['ori_shape'][1]) # for evaluation |
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if self.rect: |
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label['rect_shape'] = self.batch_shapes[self.batch[index]] |
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label = self.update_labels_info(label) |
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return label |
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def __len__(self): |
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"""Returns the length of the labels list for the dataset.""" |
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return len(self.labels) |
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def update_labels_info(self, label): |
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"""custom your label format here.""" |
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return label |
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def build_transforms(self, hyp=None): |
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"""Users can custom augmentations here |
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like: |
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if self.augment: |
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# Training transforms |
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return Compose([]) |
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else: |
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# Val transforms |
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return Compose([]) |
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""" |
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raise NotImplementedError |
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def get_labels(self): |
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"""Users can custom their own format here. |
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Make sure your output is a list with each element like below: |
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dict( |
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im_file=im_file, |
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shape=shape, # format: (height, width) |
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cls=cls, |
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bboxes=bboxes, # xywh |
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segments=segments, # xy |
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keypoints=keypoints, # xy |
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normalized=True, # or False |
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bbox_format="xyxy", # or xywh, ltwh |
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) |
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""" |
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raise NotImplementedError
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