1034 lines
44 KiB
Python
1034 lines
44 KiB
Python
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Dataloaders and dataset utils
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"""
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import glob
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import hashlib
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import json
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import logging
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import os
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import random
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import shutil
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import time
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from itertools import repeat
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from multiprocessing.pool import ThreadPool, Pool
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from pathlib import Path
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from threading import Thread
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from zipfile import ZipFile
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import cv2
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import numpy as np
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import torch
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import torch.nn.functional as F
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import yaml
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from PIL import Image, ExifTags
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from torch.utils.data import Dataset
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from tqdm import tqdm
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from utils.augmentations import Albumentations, augment_hsv, copy_paste, letterbox, mixup, random_perspective
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from utils.general import check_dataset, check_requirements, check_yaml, clean_str, segments2boxes, \
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xywh2xyxy, xywhn2xyxy, xyxy2xywhn, xyn2xy
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from utils.torch_utils import torch_distributed_zero_first
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# Parameters
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HELP_URL = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
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IMG_FORMATS = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo'] # acceptable image suffixes
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VID_FORMATS = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes
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NUM_THREADS = min(8, os.cpu_count()) # number of multiprocessing threads
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# Get orientation exif tag
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for orientation in ExifTags.TAGS.keys():
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if ExifTags.TAGS[orientation] == 'Orientation':
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break
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def get_hash(paths):
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# Returns a single hash value of a list of paths (files or dirs)
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size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes
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h = hashlib.md5(str(size).encode()) # hash sizes
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h.update(''.join(paths).encode()) # hash paths
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return h.hexdigest() # return hash
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def exif_size(img):
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# Returns exif-corrected PIL size
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s = img.size # (width, height)
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try:
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rotation = dict(img._getexif().items())[orientation]
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if rotation == 6: # rotation 270
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s = (s[1], s[0])
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elif rotation == 8: # rotation 90
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s = (s[1], s[0])
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except:
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pass
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return s
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def exif_transpose(image):
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"""
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Transpose a PIL image accordingly if it has an EXIF Orientation tag.
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From https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py
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:param image: The image to transpose.
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:return: An image.
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"""
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exif = image.getexif()
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orientation = exif.get(0x0112, 1) # default 1
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if orientation > 1:
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method = {2: Image.FLIP_LEFT_RIGHT,
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3: Image.ROTATE_180,
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4: Image.FLIP_TOP_BOTTOM,
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5: Image.TRANSPOSE,
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6: Image.ROTATE_270,
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7: Image.TRANSVERSE,
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8: Image.ROTATE_90,
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}.get(orientation)
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if method is not None:
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image = image.transpose(method)
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del exif[0x0112]
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image.info["exif"] = exif.tobytes()
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return image
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def create_dataloader(path, imgsz, batch_size, stride, single_cls=False, hyp=None, augment=False, cache=False, pad=0.0,
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rect=False, rank=-1, workers=8, image_weights=False, quad=False, prefix=''):
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# Make sure only the first process in DDP process the dataset first, and the following others can use the cache
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with torch_distributed_zero_first(rank):
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dataset = LoadImagesAndLabels(path, imgsz, batch_size,
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augment=augment, # augment images
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hyp=hyp, # augmentation hyperparameters
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rect=rect, # rectangular training
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cache_images=cache,
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single_cls=single_cls,
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stride=int(stride),
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pad=pad,
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image_weights=image_weights,
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prefix=prefix)
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batch_size = min(batch_size, len(dataset))
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nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, workers]) # number of workers
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sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None
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loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader
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# Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader()
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dataloader = loader(dataset,
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batch_size=batch_size,
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num_workers=nw,
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sampler=sampler,
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pin_memory=True,
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collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn)
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return dataloader, dataset
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class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
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""" Dataloader that reuses workers
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Uses same syntax as vanilla DataLoader
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
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self.iterator = super().__iter__()
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def __len__(self):
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return len(self.batch_sampler.sampler)
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def __iter__(self):
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for i in range(len(self)):
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yield next(self.iterator)
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class _RepeatSampler(object):
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""" Sampler that repeats forever
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Args:
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sampler (Sampler)
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"""
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def __init__(self, sampler):
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self.sampler = sampler
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def __iter__(self):
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while True:
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yield from iter(self.sampler)
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class LoadImages:
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# YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`
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def __init__(self, path, img_size=640, stride=32, auto=True):
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p = str(Path(path).resolve()) # os-agnostic absolute path
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if '*' in p:
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files = sorted(glob.glob(p, recursive=True)) # glob
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elif os.path.isdir(p):
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files = sorted(glob.glob(os.path.join(p, '*.*'))) # dir
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elif os.path.isfile(p):
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files = [p] # files
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else:
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raise Exception(f'ERROR: {p} does not exist')
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images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]
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videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS]
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ni, nv = len(images), len(videos)
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self.img_size = img_size
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self.stride = stride
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self.files = images + videos
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self.nf = ni + nv # number of files
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self.video_flag = [False] * ni + [True] * nv
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self.mode = 'image'
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self.auto = auto
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if any(videos):
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self.new_video(videos[0]) # new video
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else:
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self.cap = None
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assert self.nf > 0, f'No images or videos found in {p}. ' \
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f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}'
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def __iter__(self):
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self.count = 0
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return self
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def __next__(self):
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if self.count == self.nf:
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raise StopIteration
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path = self.files[self.count]
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if self.video_flag[self.count]:
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# Read video
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self.mode = 'video'
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ret_val, img0 = self.cap.read()
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if not ret_val:
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self.count += 1
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self.cap.release()
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if self.count == self.nf: # last video
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raise StopIteration
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else:
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path = self.files[self.count]
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self.new_video(path)
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ret_val, img0 = self.cap.read()
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self.frame += 1
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print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: ', end='')
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else:
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# Read image
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self.count += 1
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img0 = cv2.imread(path) # BGR
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assert img0 is not None, 'Image Not Found ' + path
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print(f'image {self.count}/{self.nf} {path}: ', end='')
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# Padded resize
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img = letterbox(img0, self.img_size, stride=self.stride, auto=self.auto)[0]
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# Convert
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img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
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img = np.ascontiguousarray(img)
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return path, img, img0, self.cap
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def new_video(self, path):
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self.frame = 0
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self.cap = cv2.VideoCapture(path)
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self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
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def __len__(self):
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return self.nf # number of files
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class LoadWebcam: # for inference
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# YOLOv5 local webcam dataloader, i.e. `python detect.py --source 0`
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def __init__(self, pipe='0', img_size=640, stride=32):
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self.img_size = img_size
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self.stride = stride
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self.pipe = eval(pipe) if pipe.isnumeric() else pipe
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self.cap = cv2.VideoCapture(self.pipe) # video capture object
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self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size
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def __iter__(self):
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self.count = -1
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return self
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def __next__(self):
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self.count += 1
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if cv2.waitKey(1) == ord('q'): # q to quit
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self.cap.release()
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cv2.destroyAllWindows()
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raise StopIteration
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# Read frame
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ret_val, img0 = self.cap.read()
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img0 = cv2.flip(img0, 1) # flip left-right
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# Print
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assert ret_val, f'Camera Error {self.pipe}'
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img_path = 'webcam.jpg'
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print(f'webcam {self.count}: ', end='')
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# Padded resize
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img = letterbox(img0, self.img_size, stride=self.stride)[0]
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# Convert
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img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
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img = np.ascontiguousarray(img)
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return img_path, img, img0, None
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def __len__(self):
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return 0
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class LoadStreams:
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# YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams`
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def __init__(self, sources='streams.txt', img_size=640, stride=32, auto=True):
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self.mode = 'stream'
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self.img_size = img_size
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self.stride = stride
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if os.path.isfile(sources):
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with open(sources, 'r') as f:
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sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())]
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else:
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sources = [sources]
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n = len(sources)
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self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n
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self.sources = [clean_str(x) for x in sources] # clean source names for later
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self.auto = auto
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for i, s in enumerate(sources): # index, source
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# Start thread to read frames from video stream
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print(f'{i + 1}/{n}: {s}... ', end='')
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if 'youtube.com/' in s or 'youtu.be/' in s: # if source is YouTube video
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check_requirements(('pafy', 'youtube_dl'))
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import pafy
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s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL
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s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam
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cap = cv2.VideoCapture(s)
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assert cap.isOpened(), f'Failed to open {s}'
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w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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self.fps[i] = max(cap.get(cv2.CAP_PROP_FPS) % 100, 0) or 30.0 # 30 FPS fallback
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self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf') # infinite stream fallback
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_, self.imgs[i] = cap.read() # guarantee first frame
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self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)
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print(f" success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)")
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self.threads[i].start()
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print('') # newline
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# check for common shapes
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s = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0].shape for x in self.imgs])
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self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal
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if not self.rect:
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print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')
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def update(self, i, cap, stream):
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# Read stream `i` frames in daemon thread
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n, f, read = 0, self.frames[i], 1 # frame number, frame array, inference every 'read' frame
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while cap.isOpened() and n < f:
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n += 1
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# _, self.imgs[index] = cap.read()
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cap.grab()
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if n % read == 0:
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success, im = cap.retrieve()
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if success:
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self.imgs[i] = im
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else:
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print('WARNING: Video stream unresponsive, please check your IP camera connection.')
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self.imgs[i] *= 0
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cap.open(stream) # re-open stream if signal was lost
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time.sleep(1 / self.fps[i]) # wait time
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def __iter__(self):
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self.count = -1
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return self
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def __next__(self):
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self.count += 1
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if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'): # q to quit
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|
|
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
|
||
|
|
|
||
|
|
|
||
|
|
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]
|
||
|
|
|
||
|
|
|
||
|
|
class LoadImagesAndLabels(Dataset):
|
||
|
|
# YOLOv5 train_loader/val_loader, loads images and labels for training and validation
|
||
|
|
cache_version = 0.6 # 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}\nSee {HELP_URL}')
|
||
|
|
|
||
|
|
# 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. See {HELP_URL}'
|
||
|
|
|
||
|
|
# 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
|
||
|
|
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)
|
||
|
|
|
||
|
|
# Update labels
|
||
|
|
include_class = [] # filter labels to include only these classes (optional)
|
||
|
|
include_class_array = np.array(include_class).reshape(1, -1)
|
||
|
|
for i, (label, segment) in enumerate(zip(self.labels, self.segments)):
|
||
|
|
if include_class:
|
||
|
|
j = (label[:, 0:1] == include_class_array).any(1)
|
||
|
|
self.labels[i] = label[j]
|
||
|
|
if segment:
|
||
|
|
self.segments[i] = segment[j]
|
||
|
|
if single_cls: # single-class training, merge all classes into 0
|
||
|
|
self.labels[i][:, 0] = 0
|
||
|
|
if segment:
|
||
|
|
self.segments[i][:, 0] = 0
|
||
|
|
|
||
|
|
# 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}. See {HELP_URL}')
|
||
|
|
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
|
||
|
|
|
||
|
|
|
||
|
|
# 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):
|
||
|
|
# YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image 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 load_mosaic9(self, index):
|
||
|
|
# YOLOv5 9-mosaic loader. Loads 1 image + 8 random images into a 9-image mosaic
|
||
|
|
labels9, segments9 = [], []
|
||
|
|
s = self.img_size
|
||
|
|
indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices
|
||
|
|
random.shuffle(indices)
|
||
|
|
for i, index in enumerate(indices):
|
||
|
|
# Load image
|
||
|
|
img, _, (h, w) = load_image(self, index)
|
||
|
|
|
||
|
|
# place img in img9
|
||
|
|
if i == 0: # center
|
||
|
|
img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
|
||
|
|
h0, w0 = h, w
|
||
|
|
c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates
|
||
|
|
elif i == 1: # top
|
||
|
|
c = s, s - h, s + w, s
|
||
|
|
elif i == 2: # top right
|
||
|
|
c = s + wp, s - h, s + wp + w, s
|
||
|
|
elif i == 3: # right
|
||
|
|
c = s + w0, s, s + w0 + w, s + h
|
||
|
|
elif i == 4: # bottom right
|
||
|
|
c = s + w0, s + hp, s + w0 + w, s + hp + h
|
||
|
|
elif i == 5: # bottom
|
||
|
|
c = s + w0 - w, s + h0, s + w0, s + h0 + h
|
||
|
|
elif i == 6: # bottom left
|
||
|
|
c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
|
||
|
|
elif i == 7: # left
|
||
|
|
c = s - w, s + h0 - h, s, s + h0
|
||
|
|
elif i == 8: # top left
|
||
|
|
c = s - w, s + h0 - hp - h, s, s + h0 - hp
|
||
|
|
|
||
|
|
padx, pady = c[:2]
|
||
|
|
x1, y1, x2, y2 = [max(x, 0) for x in c] # allocate coords
|
||
|
|
|
||
|
|
# Labels
|
||
|
|
labels, segments = self.labels[index].copy(), self.segments[index].copy()
|
||
|
|
if labels.size:
|
||
|
|
labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format
|
||
|
|
segments = [xyn2xy(x, w, h, padx, pady) for x in segments]
|
||
|
|
labels9.append(labels)
|
||
|
|
segments9.extend(segments)
|
||
|
|
|
||
|
|
# Image
|
||
|
|
img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax]
|
||
|
|
hp, wp = h, w # height, width previous
|
||
|
|
|
||
|
|
# Offset
|
||
|
|
yc, xc = [int(random.uniform(0, s)) for _ in self.mosaic_border] # mosaic center x, y
|
||
|
|
img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]
|
||
|
|
|
||
|
|
# Concat/clip labels
|
||
|
|
labels9 = np.concatenate(labels9, 0)
|
||
|
|
labels9[:, [1, 3]] -= xc
|
||
|
|
labels9[:, [2, 4]] -= yc
|
||
|
|
c = np.array([xc, yc]) # centers
|
||
|
|
segments9 = [x - c for x in segments9]
|
||
|
|
|
||
|
|
for x in (labels9[:, 1:], *segments9):
|
||
|
|
np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()
|
||
|
|
# img9, labels9 = replicate(img9, labels9) # replicate
|
||
|
|
|
||
|
|
# Augment
|
||
|
|
img9, labels9 = random_perspective(img9, labels9, segments9,
|
||
|
|
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 img9, labels9
|
||
|
|
|
||
|
|
|
||
|
|
def create_folder(path='./new'):
|
||
|
|
# Create folder
|
||
|
|
if os.path.exists(path):
|
||
|
|
shutil.rmtree(path) # delete output folder
|
||
|
|
os.makedirs(path) # make new output folder
|
||
|
|
|
||
|
|
|
||
|
|
def flatten_recursive(path='../datasets/coco128'):
|
||
|
|
# Flatten a recursive directory by bringing all files to top level
|
||
|
|
new_path = Path(path + '_flat')
|
||
|
|
create_folder(new_path)
|
||
|
|
for file in tqdm(glob.glob(str(Path(path)) + '/**/*.*', recursive=True)):
|
||
|
|
shutil.copyfile(file, new_path / Path(file).name)
|
||
|
|
|
||
|
|
|
||
|
|
def extract_boxes(path='../datasets/coco128'): # from utils.datasets import *; extract_boxes()
|
||
|
|
# Convert detection dataset into classification dataset, with one directory per class
|
||
|
|
path = Path(path) # images dir
|
||
|
|
shutil.rmtree(path / 'classifier') if (path / 'classifier').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, 'r') 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(np.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/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.datasets 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 = sum([list(path.rglob(f"*.{img_ext}")) for img_ext 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
|
||
|
|
[(path.parent / x).unlink(missing_ok=True) for x in txt] # 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('./' + 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
|
||
|
|
Image.open(im_file).save(im_file, format='JPEG', subsampling=0, quality=100) # re-save image
|
||
|
|
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, '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)
|
||
|
|
nl = len(l)
|
||
|
|
if nl:
|
||
|
|
assert l.shape[1] == 5, f'labels require 5 columns, {l.shape[1]} columns detected'
|
||
|
|
assert (l >= 0).all(), f'negative label values {l[l < 0]}'
|
||
|
|
assert (l[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {l[:, 1:][l[:, 1:] > 1]}'
|
||
|
|
l = np.unique(l, axis=0) # remove duplicate rows
|
||
|
|
if len(l) < nl:
|
||
|
|
segments = np.unique(segments, axis=0)
|
||
|
|
msg = f'{prefix}WARNING: {im_file}: {nl - len(l)} duplicate labels removed'
|
||
|
|
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: {im_file}: ignoring corrupt image/label: {e}'
|
||
|
|
return [None, None, None, None, nm, nf, ne, nc, msg]
|
||
|
|
|
||
|
|
|
||
|
|
def dataset_stats(path='coco128.yaml', autodownload=False, verbose=False, profile=False, hub=False):
|
||
|
|
""" Return dataset statistics dictionary with images and instances counts per split per class
|
||
|
|
To run in parent directory: export PYTHONPATH="$PWD/yolov5"
|
||
|
|
Usage1: from utils.datasets import *; dataset_stats('coco128.yaml', autodownload=True)
|
||
|
|
Usage2: from utils.datasets import *; dataset_stats('../datasets/coco128_with_yaml.zip')
|
||
|
|
Arguments
|
||
|
|
path: Path to data.yaml or data.zip (with data.yaml inside data.zip)
|
||
|
|
autodownload: Attempt to download dataset if not found locally
|
||
|
|
verbose: Print stats dictionary
|
||
|
|
"""
|
||
|
|
|
||
|
|
def round_labels(labels):
|
||
|
|
# Update labels to integer class and 6 decimal place floats
|
||
|
|
return [[int(c), *[round(x, 4) for x in points]] for c, *points in labels]
|
||
|
|
|
||
|
|
def unzip(path):
|
||
|
|
# Unzip data.zip TODO: CONSTRAINT: path/to/abc.zip MUST unzip to 'path/to/abc/'
|
||
|
|
if str(path).endswith('.zip'): # path is data.zip
|
||
|
|
assert Path(path).is_file(), f'Error unzipping {path}, file not found'
|
||
|
|
ZipFile(path).extractall(path=path.parent) # unzip
|
||
|
|
dir = path.with_suffix('') # dataset directory == zip name
|
||
|
|
return True, str(dir), next(dir.rglob('*.yaml')) # zipped, data_dir, yaml_path
|
||
|
|
else: # path is data.yaml
|
||
|
|
return False, None, path
|
||
|
|
|
||
|
|
def hub_ops(f, max_dim=1920):
|
||
|
|
# HUB ops for 1 image 'f': resize and save at reduced quality in /dataset-hub for web/app viewing
|
||
|
|
f_new = im_dir / Path(f).name # dataset-hub image filename
|
||
|
|
try: # use PIL
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||
|
|
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)))
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||
|
|
im.save(f_new, quality=75) # save
|
||
|
|
except Exception as e: # use OpenCV
|
||
|
|
print(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_LINEAR)
|
||
|
|
cv2.imwrite(str(f_new), im)
|
||
|
|
|
||
|
|
zipped, data_dir, yaml_path = unzip(Path(path))
|
||
|
|
with open(check_yaml(yaml_path), errors='ignore') as f:
|
||
|
|
data = yaml.safe_load(f) # data dict
|
||
|
|
if zipped:
|
||
|
|
data['path'] = data_dir # TODO: should this be dir.resolve()?
|
||
|
|
check_dataset(data, autodownload) # download dataset if missing
|
||
|
|
hub_dir = Path(data['path'] + ('-hub' if hub else ''))
|
||
|
|
stats = {'nc': data['nc'], 'names': data['names']} # statistics dictionary
|
||
|
|
for split in 'train', 'val', 'test':
|
||
|
|
if data.get(split) is None:
|
||
|
|
stats[split] = None # i.e. no test set
|
||
|
|
continue
|
||
|
|
x = []
|
||
|
|
dataset = LoadImagesAndLabels(data[split]) # load dataset
|
||
|
|
for label in tqdm(dataset.labels, total=dataset.n, desc='Statistics'):
|
||
|
|
x.append(np.bincount(label[:, 0].astype(int), minlength=data['nc']))
|
||
|
|
x = np.array(x) # shape(128x80)
|
||
|
|
stats[split] = {'instance_stats': {'total': int(x.sum()), 'per_class': x.sum(0).tolist()},
|
||
|
|
'image_stats': {'total': dataset.n, 'unlabelled': int(np.all(x == 0, 1).sum()),
|
||
|
|
'per_class': (x > 0).sum(0).tolist()},
|
||
|
|
'labels': [{str(Path(k).name): round_labels(v.tolist())} for k, v in
|
||
|
|
zip(dataset.img_files, dataset.labels)]}
|
||
|
|
|
||
|
|
if hub:
|
||
|
|
im_dir = hub_dir / 'images'
|
||
|
|
im_dir.mkdir(parents=True, exist_ok=True)
|
||
|
|
for _ in tqdm(ThreadPool(NUM_THREADS).imap(hub_ops, dataset.img_files), total=dataset.n, desc='HUB Ops'):
|
||
|
|
pass
|
||
|
|
|
||
|
|
# Profile
|
||
|
|
stats_path = hub_dir / 'stats.json'
|
||
|
|
if profile:
|
||
|
|
for _ in range(1):
|
||
|
|
file = stats_path.with_suffix('.npy')
|
||
|
|
t1 = time.time()
|
||
|
|
np.save(file, stats)
|
||
|
|
t2 = time.time()
|
||
|
|
x = np.load(file, allow_pickle=True)
|
||
|
|
print(f'stats.npy times: {time.time() - t2:.3f}s read, {t2 - t1:.3f}s write')
|
||
|
|
|
||
|
|
file = stats_path.with_suffix('.json')
|
||
|
|
t1 = time.time()
|
||
|
|
with open(file, 'w') as f:
|
||
|
|
json.dump(stats, f) # save stats *.json
|
||
|
|
t2 = time.time()
|
||
|
|
with open(file, 'r') as f:
|
||
|
|
x = json.load(f) # load hyps dict
|
||
|
|
print(f'stats.json times: {time.time() - t2:.3f}s read, {t2 - t1:.3f}s write')
|
||
|
|
|
||
|
|
# Save, print and return
|
||
|
|
if hub:
|
||
|
|
print(f'Saving {stats_path.resolve()}...')
|
||
|
|
with open(stats_path, 'w') as f:
|
||
|
|
json.dump(stats, f) # save stats.json
|
||
|
|
if verbose:
|
||
|
|
print(json.dumps(stats, indent=2, sort_keys=False))
|
||
|
|
return stats
|