846 lines
38 KiB
Python
846 lines
38 KiB
Python
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import glob
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import logging
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import math
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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
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from pathlib import Path
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from threading import Thread
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import cv2
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import numpy as np
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import torch
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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.general import xyxy2xywh, xywh2xyxy, clean_str
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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'] # acceptable image suffixes
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vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes
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logger = logging.getLogger(__name__)
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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(files):
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# Returns a single hash value of a list of files
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return sum(os.path.getsize(f) for f in files if os.path.isfile(f))
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def img2label_paths(img_paths):
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# Define label paths as a function of image paths
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sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep # /images/, /labels/ substrings
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return [x.replace(sa, sb, 1).replace('.' + x.split('.')[-1], '.txt') for x in img_paths]
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# return [x.replace(".jpg",".txt") for x in img_paths]
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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 create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
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rank=-1, world_size=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 = LoadFaceImagesAndLabels(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=opt.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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)
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batch_size = min(batch_size, len(dataset))
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nw = min([os.cpu_count() // world_size, 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=LoadFaceImagesAndLabels.collate_fn4 if quad else LoadFaceImagesAndLabels.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 LoadFaceImagesAndLabels(Dataset): # for training/testing
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def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
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cache_images=False, single_cls=False, stride=32, pad=0.0, rank=-1):
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self.img_size = img_size
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self.augment = augment
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self.hyp = hyp
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self.image_weights = image_weights
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self.rect = False if image_weights else rect
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self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training)
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self.mosaic_border = [-img_size // 2, -img_size // 2]
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self.stride = stride
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try:
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f = [] # image files
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for p in path if isinstance(path, list) else [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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elif p.is_file(): # file
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with open(p, 'r') 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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else:
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raise Exception('%s does not exist' % p)
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self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats])
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assert self.img_files, 'No images found'
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except Exception as e:
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raise Exception('Error loading data from %s: %s\nSee %s' % (path, e, help_url))
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# Check cache
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self.label_files = img2label_paths(self.img_files) # labels
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cache_path = Path(self.label_files[0]).parent.with_suffix('.cache') # cached labels
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if cache_path.is_file():
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cache = torch.load(cache_path) # load
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if cache['hash'] != get_hash(self.label_files + self.img_files) or 'results' not in cache: # changed
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cache = self.cache_labels(cache_path) # re-cache
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else:
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cache = self.cache_labels(cache_path) # cache
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# Display cache
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[nf, nm, ne, nc, n] = cache.pop('results') # found, missing, empty, corrupted, total
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desc = f"Scanning '{cache_path}' for images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
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tqdm(None, desc=desc, total=n, initial=n)
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assert nf > 0 or not augment, f'No labels found in {cache_path}. Can not train without labels. See {help_url}'
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# Read cache
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cache.pop('hash') # remove hash
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labels, shapes = zip(*cache.values())
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self.labels = list(labels)
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self.shapes = np.array(shapes, dtype=np.float64)
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self.img_files = list(cache.keys()) # update
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self.label_files = img2label_paths(cache.keys()) # update
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if single_cls:
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for x in self.labels:
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x[:, 0] = 0
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n = len(shapes) # number of images
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bi = np.floor(np.arange(n) / batch_size).astype(np.int_) # batch index
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nb = bi[-1] + 1 # number of batches
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self.batch = bi # batch index of image
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self.n = n
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self.indices = range(n)
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# Rectangular Training
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if self.rect:
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# Sort by aspect ratio
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s = self.shapes # wh
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ar = s[:, 1] / s[:, 0] # aspect ratio
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irect = ar.argsort()
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self.img_files = [self.img_files[i] for i in irect]
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self.label_files = [self.label_files[i] for i in irect]
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self.labels = [self.labels[i] for i in irect]
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self.shapes = s[irect] # wh
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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) * img_size / stride + pad).astype(np.int_) * stride
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# Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
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self.imgs = [None] * n
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if cache_images:
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gb = 0 # Gigabytes of cached images
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self.img_hw0, self.img_hw = [None] * n, [None] * n
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results = ThreadPool(8).imap(lambda x: load_image(*x), zip(repeat(self), range(n))) # 8 threads
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pbar = tqdm(enumerate(results), total=n)
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for i, x in pbar:
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self.imgs[i], self.img_hw0[i], self.img_hw[i] = x # img, hw_original, hw_resized = load_image(self, i)
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gb += self.imgs[i].nbytes
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pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9)
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def cache_labels(self, path=Path('./labels.cache')):
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# Cache dataset labels, check images and read shapes
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x = {} # dict
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nm, nf, ne, nc = 0, 0, 0, 0 # number missing, found, empty, duplicate
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pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files))
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for i, (im_file, lb_file) in enumerate(pbar):
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try:
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# verify images
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im = Image.open(im_file)
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im.verify() # PIL verify
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shape = exif_size(im) # image size
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assert (shape[0] > 9) & (shape[1] > 9), 'image size <10 pixels'
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# verify labels
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if os.path.isfile(lb_file):
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nf += 1 # label found
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with open(lb_file, 'r') as f:
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l = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels
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if len(l):
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assert l.shape[1] == 13, 'labels require 13 columns each'
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assert (l >= -1).all(), 'negative labels'
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assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
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assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels'
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else:
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ne += 1 # label empty
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l = np.zeros((0, 13), dtype=np.float32)
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else:
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nm += 1 # label missing
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l = np.zeros((0, 13), dtype=np.float32)
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x[im_file] = [l, shape]
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except Exception as e:
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nc += 1
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print('WARNING: Ignoring corrupted image and/or label %s: %s' % (im_file, e))
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pbar.desc = f"Scanning '{path.parent / path.stem}' for images and labels... " \
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f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted"
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if nf == 0:
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print(f'WARNING: No labels found in {path}. See {help_url}')
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x['hash'] = get_hash(self.label_files + self.img_files)
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x['results'] = [nf, nm, ne, nc, i + 1]
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torch.save(x, path) # save for next time
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logging.info(f"New cache created: {path}")
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return x
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def __len__(self):
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return len(self.img_files)
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# def __iter__(self):
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# self.count = -1
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# print('ran dataset iter')
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# #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
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# return self
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def __getitem__(self, index):
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index = self.indices[index] # linear, shuffled, or image_weights
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hyp = self.hyp
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mosaic = self.mosaic and random.random() < hyp['mosaic']
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if mosaic:
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# Load mosaic
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img, labels = load_mosaic_face(self, index)
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shapes = None
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# MixUp https://arxiv.org/pdf/1710.09412.pdf
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if random.random() < hyp['mixup']:
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img2, labels2 = load_mosaic_face(self, random.randint(0, self.n - 1))
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r = np.random.beta(8.0, 8.0) # mixup ratio, alpha=beta=8.0
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img = (img * r + img2 * (1 - r)).astype(np.uint8)
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labels = np.concatenate((labels, labels2), 0)
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else:
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# Load image
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img, (h0, w0), (h, w) = load_image(self, index)
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# Letterbox
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shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape
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img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
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shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling
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# Load labels
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labels = []
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x = self.labels[index]
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if x.size > 0:
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# Normalized xywh to pixel xyxy format
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labels = x.copy()
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labels[:, 1] = ratio[0] * w * (x[:, 1] - x[:, 3] / 2) + pad[0] # pad width
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labels[:, 2] = ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + pad[1] # pad height
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labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0]
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labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1]
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#labels[:, 5] = ratio[0] * w * x[:, 5] + pad[0] # pad width
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labels[:, 5] = np.array(x[:, 5] > 0, dtype=np.int32) * (ratio[0] * w * x[:, 5] + pad[0]) + (
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np.array(x[:, 5] > 0, dtype=np.int32) - 1)
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labels[:, 6] = np.array(x[:, 6] > 0, dtype=np.int32) * (ratio[1] * h * x[:, 6] + pad[1]) + (
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np.array(x[:, 6] > 0, dtype=np.int32) - 1)
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labels[:, 7] = np.array(x[:, 7] > 0, dtype=np.int32) * (ratio[0] * w * x[:, 7] + pad[0]) + (
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np.array(x[:, 7] > 0, dtype=np.int32) - 1)
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labels[:, 8] = np.array(x[:, 8] > 0, dtype=np.int32) * (ratio[1] * h * x[:, 8] + pad[1]) + (
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np.array(x[:, 8] > 0, dtype=np.int32) - 1)
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labels[:, 9] = np.array(x[:, 5] > 0, dtype=np.int32) * (ratio[0] * w * x[:, 9] + pad[0]) + (
|
||
|
|
np.array(x[:, 9] > 0, dtype=np.int32) - 1)
|
||
|
|
labels[:, 10] = np.array(x[:, 5] > 0, dtype=np.int32) * (ratio[1] * h * x[:, 10] + pad[1]) + (
|
||
|
|
np.array(x[:, 10] > 0, dtype=np.int32) - 1)
|
||
|
|
labels[:, 11] = np.array(x[:, 11] > 0, dtype=np.int32) * (ratio[0] * w * x[:, 11] + pad[0]) + (
|
||
|
|
np.array(x[:, 11] > 0, dtype=np.int32) - 1)
|
||
|
|
labels[:, 12] = np.array(x[:, 12] > 0, dtype=np.int32) * (ratio[1] * h * x[:, 12] + pad[1]) + (
|
||
|
|
np.array(x[:, 12] > 0, dtype=np.int32) - 1)
|
||
|
|
# labels[:, 13] = np.array(x[:, 13] > 0, dtype=np.int32) * (ratio[0] * w * x[:, 13] + pad[0]) + (
|
||
|
|
# np.array(x[:, 13] > 0, dtype=np.int32) - 1)
|
||
|
|
# labels[:, 14] = np.array(x[:, 14] > 0, dtype=np.int32) * (ratio[1] * h * x[:, 14] + pad[1]) + (
|
||
|
|
# np.array(x[:, 14] > 0, dtype=np.int32) - 1)
|
||
|
|
|
||
|
|
if self.augment:
|
||
|
|
# Augment imagespace
|
||
|
|
if not mosaic:
|
||
|
|
img, labels = random_perspective(img, labels,
|
||
|
|
degrees=hyp['degrees'],
|
||
|
|
translate=hyp['translate'],
|
||
|
|
scale=hyp['scale'],
|
||
|
|
shear=hyp['shear'],
|
||
|
|
perspective=hyp['perspective'])
|
||
|
|
|
||
|
|
# Augment colorspace
|
||
|
|
augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
|
||
|
|
|
||
|
|
# Apply cutouts
|
||
|
|
# if random.random() < 0.9:
|
||
|
|
# labels = cutout(img, labels)
|
||
|
|
|
||
|
|
nL = len(labels) # number of labels
|
||
|
|
if nL:
|
||
|
|
labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) # convert xyxy to xywh
|
||
|
|
labels[:, [2, 4]] /= img.shape[0] # normalized height 0-1
|
||
|
|
labels[:, [1, 3]] /= img.shape[1] # normalized width 0-1
|
||
|
|
|
||
|
|
labels[:, [5, 7, 9, 11]] /= img.shape[1] # normalized landmark x 0-1
|
||
|
|
labels[:, [5, 7, 9, 11]] = np.where(labels[:, [5, 7, 9, 11]] < 0, -1, labels[:, [5, 7, 9, 11]])
|
||
|
|
labels[:, [6, 8, 10, 12]] /= img.shape[0] # normalized landmark y 0-1
|
||
|
|
labels[:, [6, 8, 10, 12]] = np.where(labels[:, [6, 8, 10, 12]] < 0, -1, labels[:, [6, 8, 10, 12]])
|
||
|
|
|
||
|
|
if self.augment:
|
||
|
|
# flip up-down
|
||
|
|
if random.random() < hyp['flipud']:
|
||
|
|
img = np.flipud(img)
|
||
|
|
if nL:
|
||
|
|
labels[:, 2] = 1 - labels[:, 2]
|
||
|
|
|
||
|
|
labels[:, 6] = np.where(labels[:,6] < 0, -1, 1 - labels[:, 6])
|
||
|
|
labels[:, 8] = np.where(labels[:, 8] < 0, -1, 1 - labels[:, 8])
|
||
|
|
labels[:, 10] = np.where(labels[:, 10] < 0, -1, 1 - labels[:, 10])
|
||
|
|
labels[:, 12] = np.where(labels[:, 12] < 0, -1, 1 - labels[:, 12])
|
||
|
|
# labels[:, 14] = np.where(labels[:, 14] < 0, -1, 1 - labels[:, 14])
|
||
|
|
|
||
|
|
# flip left-right
|
||
|
|
if random.random() < hyp['fliplr']:
|
||
|
|
img = np.fliplr(img)
|
||
|
|
if nL:
|
||
|
|
labels[:, 1] = 1 - labels[:, 1]
|
||
|
|
|
||
|
|
labels[:, 5] = np.where(labels[:, 5] < 0, -1, 1 - labels[:, 5])
|
||
|
|
labels[:, 7] = np.where(labels[:, 7] < 0, -1, 1 - labels[:, 7])
|
||
|
|
labels[:, 9] = np.where(labels[:, 9] < 0, -1, 1 - labels[:, 9])
|
||
|
|
labels[:, 11] = np.where(labels[:, 11] < 0, -1, 1 - labels[:, 11])
|
||
|
|
# labels[:, 13] = np.where(labels[:, 13] < 0, -1, 1 - labels[:, 13])
|
||
|
|
|
||
|
|
#左右镜像的时候，关键点应该交换位置，不然的话顺序就错了
|
||
|
|
left_top = np.copy(labels[:, [5, 6]])
|
||
|
|
left_bottom = np.copy(labels[:, [9, 10]])
|
||
|
|
labels[:, [5, 6]] = labels[:, [7, 8]]
|
||
|
|
labels[:, [7, 8]] = left_top
|
||
|
|
labels[:, [9, 10]] = labels[:, [11, 12]]
|
||
|
|
labels[:, [11, 12]] = left_bottom
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
# eye_left = np.copy(labels[:, [5, 6]])
|
||
|
|
# mouth_left = np.copy(labels[:, [11, 12]])
|
||
|
|
# labels[:, [5, 6]] = labels[:, [7, 8]]
|
||
|
|
# labels[:, [7, 8]] = eye_left
|
||
|
|
# labels[:, [11, 12]] = labels[:, [13, 14]]
|
||
|
|
# labels[:, [13, 14]] = mouth_left
|
||
|
|
|
||
|
|
labels_out = torch.zeros((nL, 14))
|
||
|
|
if nL:
|
||
|
|
labels_out[:, 1:] = torch.from_numpy(labels)
|
||
|
|
#showlabels(img, labels[:, 1:5], labels[:, 5:13])
|
||
|
|
|
||
|
|
# Convert
|
||
|
|
img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416
|
||
|
|
img = np.ascontiguousarray(img)
|
||
|
|
#print(index, ' --- labels_out: ', labels_out)
|
||
|
|
#if nL:
|
||
|
|
#print( ' : landmarks : ', torch.max(labels_out[:, 5:13]), ' --- ', torch.min(labels_out[:, 5:13]))
|
||
|
|
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
|
||
|
|
|
||
|
|
|
||
|
|
def showlabels(img, boxs, landmarks):
|
||
|
|
for box in boxs:
|
||
|
|
x,y,w,h = box[0] * img.shape[1], box[1] * img.shape[0], box[2] * img.shape[1], box[3] * img.shape[0]
|
||
|
|
#cv2.rectangle(image, (x,y), (x+w,y+h), (0,255,0), 2)
|
||
|
|
cv2.rectangle(img, (int(x - w/2), int(y - h/2)), (int(x + w/2), int(y + h/2)), (0, 255, 0), 2)
|
||
|
|
|
||
|
|
for landmark in landmarks:
|
||
|
|
#cv2.circle(img,(60,60),30,(0,0,255))
|
||
|
|
for i in range(4):
|
||
|
|
cv2.circle(img, (int(landmark[2*i] * img.shape[1]), int(landmark[2*i+1]*img.shape[0])), 3 ,(0,0,255), -1)
|
||
|
|
cv2.imshow('test', img)
|
||
|
|
cv2.waitKey(0)
|
||
|
|
|
||
|
|
|
||
|
|
def load_mosaic_face(self, index):
|
||
|
|
# loads images in a mosaic
|
||
|
|
labels4 = []
|
||
|
|
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] + [self.indices[random.randint(0, self.n - 1)] for _ in range(3)] # 3 additional image 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
|
||
|
|
x = self.labels[index]
|
||
|
|
labels = x.copy()
|
||
|
|
if x.size > 0: # Normalized xywh to pixel xyxy format
|
||
|
|
#box, x1,y1,x2,y2
|
||
|
|
labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padw
|
||
|
|
labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + padh
|
||
|
|
labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padw
|
||
|
|
labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + padh
|
||
|
|
#10 landmarks
|
||
|
|
|
||
|
|
labels[:, 5] = np.array(x[:, 5] > 0, dtype=np.int32) * (w * x[:, 5] + padw) + (np.array(x[:, 5] > 0, dtype=np.int32) - 1)
|
||
|
|
labels[:, 6] = np.array(x[:, 6] > 0, dtype=np.int32) * (h * x[:, 6] + padh) + (np.array(x[:, 6] > 0, dtype=np.int32) - 1)
|
||
|
|
labels[:, 7] = np.array(x[:, 7] > 0, dtype=np.int32) * (w * x[:, 7] + padw) + (np.array(x[:, 7] > 0, dtype=np.int32) - 1)
|
||
|
|
labels[:, 8] = np.array(x[:, 8] > 0, dtype=np.int32) * (h * x[:, 8] + padh) + (np.array(x[:, 8] > 0, dtype=np.int32) - 1)
|
||
|
|
labels[:, 9] = np.array(x[:, 9] > 0, dtype=np.int32) * (w * x[:, 9] + padw) + (np.array(x[:, 9] > 0, dtype=np.int32) - 1)
|
||
|
|
labels[:, 10] = np.array(x[:, 10] > 0, dtype=np.int32) * (h * x[:, 10] + padh) + (np.array(x[:, 10] > 0, dtype=np.int32) - 1)
|
||
|
|
labels[:, 11] = np.array(x[:, 11] > 0, dtype=np.int32) * (w * x[:, 11] + padw) + (np.array(x[:, 11] > 0, dtype=np.int32) - 1)
|
||
|
|
labels[:, 12] = np.array(x[:, 12] > 0, dtype=np.int32) * (h * x[:, 12] + padh) + (np.array(x[:, 12] > 0, dtype=np.int32) - 1)
|
||
|
|
# labels[:, 13] = np.array(x[:, 13] > 0, dtype=np.int32) * (w * x[:, 13] + padw) + (np.array(x[:, 13] > 0, dtype=np.int32) - 1)
|
||
|
|
# labels[:, 14] = np.array(x[:, 14] > 0, dtype=np.int32) * (h * x[:, 14] + padh) + (np.array(x[:, 14] > 0, dtype=np.int32) - 1)
|
||
|
|
labels4.append(labels)
|
||
|
|
|
||
|
|
# Concat/clip labels
|
||
|
|
if len(labels4):
|
||
|
|
labels4 = np.concatenate(labels4, 0)
|
||
|
|
np.clip(labels4[:, 1:5], 0, 2 * s, out=labels4[:, 1:5]) # use with random_perspective
|
||
|
|
# img4, labels4 = replicate(img4, labels4) # replicate
|
||
|
|
|
||
|
|
#landmarks
|
||
|
|
labels4[:, 5:] = np.where(labels4[:, 5:] < 0, -1, labels4[:, 5:])
|
||
|
|
labels4[:, 5:] = np.where(labels4[:, 5:] > 2 * s, -1, labels4[:, 5:])
|
||
|
|
|
||
|
|
labels4[:, 5] = np.where(labels4[:, 6] == -1, -1, labels4[:, 5])
|
||
|
|
labels4[:, 6] = np.where(labels4[:, 5] == -1, -1, labels4[:, 6])
|
||
|
|
|
||
|
|
labels4[:, 7] = np.where(labels4[:, 8] == -1, -1, labels4[:, 7])
|
||
|
|
labels4[:, 8] = np.where(labels4[:, 7] == -1, -1, labels4[:, 8])
|
||
|
|
|
||
|
|
labels4[:, 9] = np.where(labels4[:, 10] == -1, -1, labels4[:, 9])
|
||
|
|
labels4[:, 10] = np.where(labels4[:, 9] == -1, -1, labels4[:, 10])
|
||
|
|
|
||
|
|
labels4[:, 11] = np.where(labels4[:, 12] == -1, -1, labels4[:, 11])
|
||
|
|
labels4[:, 12] = np.where(labels4[:, 11] == -1, -1, labels4[:, 12])
|
||
|
|
|
||
|
|
# labels4[:, 13] = np.where(labels4[:, 14] == -1, -1, labels4[:, 13])
|
||
|
|
# labels4[:, 14] = np.where(labels4[:, 13] == -1, -1, labels4[:, 14])
|
||
|
|
|
||
|
|
# Augment
|
||
|
|
img4, labels4 = random_perspective(img4, labels4,
|
||
|
|
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
|
||
|
|
|
||
|
|
|
||
|
|
# Ancillary functions --------------------------------------------------------------------------------------------------
|
||
|
|
def load_image(self, index):
|
||
|
|
# loads 1 image from dataset, returns img, original hw, resized hw
|
||
|
|
img = self.imgs[index]
|
||
|
|
if img is None: # not cached
|
||
|
|
path = self.img_files[index]
|
||
|
|
img = cv2.imread(path) # BGR
|
||
|
|
assert img is not None, 'Image Not Found ' + path
|
||
|
|
h0, w0 = img.shape[:2] # orig hw
|
||
|
|
r = self.img_size / max(h0, w0) # resize image to img_size
|
||
|
|
if r != 1: # always resize down, only resize up if training with augmentation
|
||
|
|
interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR
|
||
|
|
img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp)
|
||
|
|
return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized
|
||
|
|
else:
|
||
|
|
return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized
|
||
|
|
|
||
|
|
|
||
|
|
def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
|
||
|
|
r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains
|
||
|
|
hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
|
||
|
|
dtype = img.dtype # uint8
|
||
|
|
|
||
|
|
x = np.arange(0, 256, dtype=np.int16)
|
||
|
|
lut_hue = ((x * r[0]) % 180).astype(dtype)
|
||
|
|
lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
|
||
|
|
lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
|
||
|
|
|
||
|
|
img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype)
|
||
|
|
cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed
|
||
|
|
|
||
|
|
# Histogram equalization
|
||
|
|
# if random.random() < 0.2:
|
||
|
|
# for i in range(3):
|
||
|
|
# img[:, :, i] = cv2.equalizeHist(img[:, :, i])
|
||
|
|
|
||
|
|
def replicate(img, labels):
|
||
|
|
# Replicate labels
|
||
|
|
h, w = img.shape[:2]
|
||
|
|
boxes = labels[:, 1:].astype(int)
|
||
|
|
x1, y1, x2, y2 = boxes.T
|
||
|
|
s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels)
|
||
|
|
for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices
|
||
|
|
x1b, y1b, x2b, y2b = boxes[i]
|
||
|
|
bh, bw = y2b - y1b, x2b - x1b
|
||
|
|
yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y
|
||
|
|
x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
|
||
|
|
img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
|
||
|
|
labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
|
||
|
|
|
||
|
|
return img, labels
|
||
|
|
|
||
|
|
|
||
|
|
def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True):
|
||
|
|
# Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
|
||
|
|
shape = img.shape[:2] # current shape [height, width]
|
||
|
|
if isinstance(new_shape, int):
|
||
|
|
new_shape = (new_shape, new_shape)
|
||
|
|
|
||
|
|
# Scale ratio (new / old)
|
||
|
|
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
|
||
|
|
if not scaleup: # only scale down, do not scale up (for better test mAP)
|
||
|
|
r = min(r, 1.0)
|
||
|
|
|
||
|
|
# Compute padding
|
||
|
|
ratio = r, r # width, height ratios
|
||
|
|
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
|
||
|
|
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
|
||
|
|
if auto: # minimum rectangle
|
||
|
|
dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding
|
||
|
|
elif scaleFill: # stretch
|
||
|
|
dw, dh = 0.0, 0.0
|
||
|
|
new_unpad = (new_shape[1], new_shape[0])
|
||
|
|
ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
|
||
|
|
|
||
|
|
dw /= 2 # divide padding into 2 sides
|
||
|
|
dh /= 2
|
||
|
|
|
||
|
|
if shape[::-1] != new_unpad: # resize
|
||
|
|
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
|
||
|
|
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
|
||
|
|
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
|
||
|
|
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
|
||
|
|
return img, ratio, (dw, dh)
|
||
|
|
|
||
|
|
|
||
|
|
def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)):
|
||
|
|
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
|
||
|
|
# targets = [cls, xyxy]
|
||
|
|
|
||
|
|
height = img.shape[0] + border[0] * 2 # shape(h,w,c)
|
||
|
|
width = img.shape[1] + border[1] * 2
|
||
|
|
|
||
|
|
# Center
|
||
|
|
C = np.eye(3)
|
||
|
|
C[0, 2] = -img.shape[1] / 2 # x translation (pixels)
|
||
|
|
C[1, 2] = -img.shape[0] / 2 # y translation (pixels)
|
||
|
|
|
||
|
|
# Perspective
|
||
|
|
P = np.eye(3)
|
||
|
|
P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
|
||
|
|
P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
|
||
|
|
|
||
|
|
# Rotation and Scale
|
||
|
|
R = np.eye(3)
|
||
|
|
a = random.uniform(-degrees, degrees)
|
||
|
|
# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
|
||
|
|
s = random.uniform(1 - scale, 1 + scale)
|
||
|
|
# s = 2 ** random.uniform(-scale, scale)
|
||
|
|
R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
|
||
|
|
|
||
|
|
# Shear
|
||
|
|
S = np.eye(3)
|
||
|
|
S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
|
||
|
|
S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
|
||
|
|
|
||
|
|
# Translation
|
||
|
|
T = np.eye(3)
|
||
|
|
T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels)
|
||
|
|
T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels)
|
||
|
|
|
||
|
|
# Combined rotation matrix
|
||
|
|
M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
|
||
|
|
if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
|
||
|
|
if perspective:
|
||
|
|
img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114))
|
||
|
|
else: # affine
|
||
|
|
img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
|
||
|
|
|
||
|
|
# Visualize
|
||
|
|
# import matplotlib.pyplot as plt
|
||
|
|
# ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
|
||
|
|
# ax[0].imshow(img[:, :, ::-1]) # base
|
||
|
|
# ax[1].imshow(img2[:, :, ::-1]) # warped
|
||
|
|
|
||
|
|
# Transform label coordinates
|
||
|
|
n = len(targets)
|
||
|
|
if n:
|
||
|
|
# warp points
|
||
|
|
#xy = np.ones((n * 4, 3))
|
||
|
|
xy = np.ones((n * 8, 3))
|
||
|
|
xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2, 5, 6, 7, 8, 9, 10, 11, 12]].reshape(n * 8, 2) # x1y1, x2y2, x1y2, x2y1
|
||
|
|
xy = xy @ M.T # transform
|
||
|
|
if perspective:
|
||
|
|
xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 16) # rescale
|
||
|
|
else: # affine
|
||
|
|
xy = xy[:, :2].reshape(n, 16)
|
||
|
|
|
||
|
|
# create new boxes
|
||
|
|
x = xy[:, [0, 2, 4, 6]]
|
||
|
|
y = xy[:, [1, 3, 5, 7]]
|
||
|
|
|
||
|
|
landmarks = xy[:, [8, 9, 10, 11, 12, 13, 14,15]]
|
||
|
|
mask = np.array(targets[:, 5:] > 0, dtype=np.int32)
|
||
|
|
landmarks = landmarks * mask
|
||
|
|
landmarks = landmarks + mask - 1
|
||
|
|
|
||
|
|
landmarks = np.where(landmarks < 0, -1, landmarks)
|
||
|
|
landmarks[:, [0, 2, 4, 6]] = np.where(landmarks[:, [0, 2, 4, 6]] > width, -1, landmarks[:, [0, 2, 4, 6]])
|
||
|
|
landmarks[:, [1, 3, 5, 7]] = np.where(landmarks[:, [1, 3, 5, 7]] > height, -1,landmarks[:, [1, 3, 5, 7]])
|
||
|
|
|
||
|
|
landmarks[:, 0] = np.where(landmarks[:, 1] == -1, -1, landmarks[:, 0])
|
||
|
|
landmarks[:, 1] = np.where(landmarks[:, 0] == -1, -1, landmarks[:, 1])
|
||
|
|
|
||
|
|
landmarks[:, 2] = np.where(landmarks[:, 3] == -1, -1, landmarks[:, 2])
|
||
|
|
landmarks[:, 3] = np.where(landmarks[:, 2] == -1, -1, landmarks[:, 3])
|
||
|
|
|
||
|
|
landmarks[:, 4] = np.where(landmarks[:, 5] == -1, -1, landmarks[:, 4])
|
||
|
|
landmarks[:, 5] = np.where(landmarks[:, 4] == -1, -1, landmarks[:, 5])
|
||
|
|
|
||
|
|
landmarks[:, 6] = np.where(landmarks[:, 7] == -1, -1, landmarks[:, 6])
|
||
|
|
landmarks[:, 7] = np.where(landmarks[:, 6] == -1, -1, landmarks[:, 7])
|
||
|
|
|
||
|
|
# landmarks[:, 8] = np.where(landmarks[:, 9] == -1, -1, landmarks[:, 8])
|
||
|
|
# landmarks[:, 9] = np.where(landmarks[:, 8] == -1, -1, landmarks[:, 9])
|
||
|
|
|
||
|
|
targets[:,5:] = landmarks
|
||
|
|
|
||
|
|
xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
|
||
|
|
|
||
|
|
# # apply angle-based reduction of bounding boxes
|
||
|
|
# radians = a * math.pi / 180
|
||
|
|
# reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
|
||
|
|
# x = (xy[:, 2] + xy[:, 0]) / 2
|
||
|
|
# y = (xy[:, 3] + xy[:, 1]) / 2
|
||
|
|
# w = (xy[:, 2] - xy[:, 0]) * reduction
|
||
|
|
# h = (xy[:, 3] - xy[:, 1]) * reduction
|
||
|
|
# xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T
|
||
|
|
|
||
|
|
# clip boxes
|
||
|
|
xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
|
||
|
|
xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)
|
||
|
|
|
||
|
|
# filter candidates
|
||
|
|
i = box_candidates(box1=targets[:, 1:5].T * s, box2=xy.T)
|
||
|
|
targets = targets[i]
|
||
|
|
targets[:, 1:5] = xy[i]
|
||
|
|
|
||
|
|
return img, targets
|
||
|
|
|
||
|
|
|
||
|
|
def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1): # box1(4,n), box2(4,n)
|
||
|
|
# Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
|
||
|
|
w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
|
||
|
|
w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
|
||
|
|
ar = np.maximum(w2 / (h2 + 1e-16), h2 / (w2 + 1e-16)) # aspect ratio
|
||
|
|
return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + 1e-16) > area_thr) & (ar < ar_thr) # candidates
|
||
|
|
|
||
|
|
|
||
|
|
def cutout(image, labels):
|
||
|
|
# Applies image cutout augmentation https://arxiv.org/abs/1708.04552
|
||
|
|
h, w = image.shape[:2]
|
||
|
|
|
||
|
|
def bbox_ioa(box1, box2):
|
||
|
|
# Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2
|
||
|
|
box2 = box2.transpose()
|
||
|
|
|
||
|
|
# Get the coordinates of bounding boxes
|
||
|
|
b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
|
||
|
|
b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
|
||
|
|
|
||
|
|
# Intersection area
|
||
|
|
inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
|
||
|
|
(np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
|
||
|
|
|
||
|
|
# box2 area
|
||
|
|
box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16
|
||
|
|
|
||
|
|
# Intersection over box2 area
|
||
|
|
return inter_area / box2_area
|
||
|
|
|
||
|
|
# create random masks
|
||
|
|
scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction
|
||
|
|
for s in scales:
|
||
|
|
mask_h = random.randint(1, int(h * s))
|
||
|
|
mask_w = random.randint(1, int(w * s))
|
||
|
|
|
||
|
|
# box
|
||
|
|
xmin = max(0, random.randint(0, w) - mask_w // 2)
|
||
|
|
ymin = max(0, random.randint(0, h) - mask_h // 2)
|
||
|
|
xmax = min(w, xmin + mask_w)
|
||
|
|
ymax = min(h, ymin + mask_h)
|
||
|
|
|
||
|
|
# apply random color mask
|
||
|
|
image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
|
||
|
|
|
||
|
|
# return unobscured labels
|
||
|
|
if len(labels) and s > 0.03:
|
||
|
|
box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
|
||
|
|
ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
|
||
|
|
labels = labels[ioa < 0.60] # remove >60% obscured labels
|
||
|
|
|
||
|
|
return labels
|
||
|
|
|
||
|
|
|
||
|
|
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='../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='../coco128/'): # from utils.datasets import *; extract_boxes('../coco128')
|
||
|
|
# 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='../coco128', weights=(0.9, 0.1, 0.0)): # from utils.datasets import *; autosplit('../coco128')
|
||
|
|
""" Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
|
||
|
|
# Arguments
|
||
|
|
path: Path to images directory
|
||
|
|
weights: Train, val, test weights (list)
|
||
|
|
"""
|
||
|
|
path = Path(path) # images dir
|
||
|
|
files = list(path.rglob('*.*'))
|
||
|
|
n = len(files) # number of files
|
||
|
|
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 / x).unlink() for x in txt if (path / x).exists()] # remove existing
|
||
|
|
for i, img in tqdm(zip(indices, files), total=n):
|
||
|
|
if img.suffix[1:] in img_formats:
|
||
|
|
with open(path / txt[i], 'a') as f:
|
||
|
|
f.write(str(img) + '\n') # add image to txt file
|