yolov5_flask项目

# YOLOv5 ���� by Ultralytics, GPL-3.0 license """ Dataloaders and dataset utils """ import glob import hashlib import json import math import os import random import shutil import time from itertools import repeat from multiprocessing.pool import Pool, ThreadPool from pathlib import Path from threading import Thread from urllib.parse import urlparse from zipfile import ZipFile import numpy as np import torch import torch.nn.functional as F import yaml from PIL import ExifTags, Image, ImageOps from torch.utils.data import DataLoader, Dataset, dataloader, distributed from tqdm import tqdm from utils.augmentations import Albumentations, augment_hsv, copy_paste, letterbox, mixup, random_perspective from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, check_dataset, check_requirements, check_yaml, clean_str, cv2, segments2boxes, xyn2xy, xywh2xyxy, xywhn2xyxy, xyxy2xywhn) from utils.torch_utils import torch_distributed_zero_first # Parameters HELP_URL = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp' # include image suffixes VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes BAR_FORMAT = '{l_bar}{bar:10}{r_bar}{bar:-10b}' # tqdm bar format # Get orientation exif tag for orientation in ExifTags.TAGS.keys(): if ExifTags.TAGS[orientation] == 'Orientation': break def get_hash(paths): # Returns a single hash value of a list of paths (files or dirs) size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes h = hashlib.md5(str(size).encode()) # hash sizes h.update(''.join(paths).encode()) # hash paths return h.hexdigest() # return hash def exif_size(img): # Returns exif-corrected PIL size s = img.size # (width, height) try: rotation = dict(img._getexif().items())[orientation] if rotation == 6: # rotation 270 s = (s[1], s[0]) elif rotation == 8: # rotation 90 s = (s[1], s[0]) except Exception: pass return s def exif_transpose(image): """ Transpose a PIL image accordingly if it has an EXIF Orientation tag. Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose() :param image: The image to transpose. :return: An image. """ exif = image.getexif() orientation = exif.get(0x0112, 1) # default 1 if orientation > 1: method = {2: Image.FLIP_LEFT_RIGHT, 3: Image.ROTATE_180, 4: Image.FLIP_TOP_BOTTOM, 5: Image.TRANSPOSE, 6: Image.ROTATE_270, 7: Image.TRANSVERSE, 8: Image.ROTATE_90, }.get(orientation) if method is not None: image = image.transpose(method) del exif[0x0112] image.info["exif"] = exif.tobytes() return image def create_dataloader(path, imgsz, batch_size, stride, single_cls=False, hyp=None, augment=False, cache=False, pad=0.0, rect=False, rank=-1, workers=8, image_weights=False, quad=False, prefix='', shuffle=False): if rect and shuffle: LOGGER.warning('WARNING: --rect is incompatible with DataLoader shuffle, setting shuffle=False') shuffle = False with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP dataset = LoadImagesAndLabels(path, imgsz, batch_size, augment=augment, # augmentation hyp=hyp, # hyperparameters rect=rect, # rectangular batches cache_images=cache, single_cls=single_cls, stride=int(stride), pad=pad, image_weights=image_weights, prefix=prefix) batch_size = min(batch_size, len(dataset)) nd = torch.cuda.device_count() # number of CUDA devices nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates return loader(dataset, batch_size=batch_size, shuffle=shuffle and sampler is None, num_workers=nw, sampler=sampler, pin_memory=True, collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn), dataset class InfiniteDataLoader(dataloader.DataLoader): """ Dataloader that reuses workers Uses same syntax as vanilla DataLoader """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler)) self.iterator = super().__iter__() def __len__(self): return len(self.batch_sampler.sampler) def __iter__(self): for i in range(len(self)): yield next(self.iterator) class _RepeatSampler: """ Sampler that repeats forever Args: sampler (Sampler) """ def __init__(self, sampler): self.sampler = sampler def __iter__(self): while True: yield from iter(self.sampler) class LoadImages: # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4` def __init__(self, path, img_size=640, stride=32, auto=True): self.files = [path] # p = str(Path(path).resolve()) # os-agnostic absolute path # if '*' in p: # files = sorted(glob.glob(p, recursive=True)) # glob # elif os.path.isdir(p): # files = sorted(glob.glob(os.path.join(p, '*.*'))) # dir # elif os.path.isfile(p): # files = [p] # files # else: # raise Exception(f'ERROR: {p} does not exist') # images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS] # videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS] # ni, nv = len(images), len(videos) self.img_size = img_size self.stride = stride # self.files = images + videos # self.nf = ni + nv # number of files self.nf = len(self.files) # self.video_flag = [False] * ni + [True] * nv # self.mode = 'image' self.auto = auto # if any(videos): # self.new_video(videos[0]) # new video # else: self.cap = None # assert self.nf > 0, f'No images or videos found in {p}. ' \ # f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}' def __iter__(self): self.count = 0 return self def __next__(self): if self.count == self.nf: raise StopIteration path = self.files[self.count] # if self.video_flag[self.count]: # # Read video # self.mode = 'video' # ret_val, img0 = self.cap.read() # while not ret_val: # self.count += 1 # self.cap.release() # if self.count == self.nf: # last video # raise StopIteration # else: # path = self.files[self.count] # self.new_video(path) #