yolov5数据读取部分

数据读取部分

通过train.py我们可以发现，数据读取的主要是该函数

# utils->datasets.py
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):
    # 如果采用矩形训练 那就不能打乱
    if rect and shuffle:
        LOGGER.warning('WARNING: --rect is incompatible with DataLoader shuffle, setting shuffle=False')
        shuffle = False
    # torch_distributed_zero_first只有主线程去读取数据，其他线程等待主线程读取数据
    with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
        # 可以看到 数据读取部分的函数 为该函数LoadImagesAndLabels
        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)
	# 如果图片的数量少于batch_size了，那么久按照图片的数量当作batch_size
    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

LoadImagesAndLabels

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=''):
        # 初始化一些类变量
        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
            # 如果p是列表 则直接遍历 否则的话 把p转换为列表再遍历
			
            for p in path if isinstance(path, list) else [path]:
                # 如果p是列表 则直接遍历 否则的话 把p转换为列表再遍历, 这里的path是数据集目录  ../coco128/images/train2017
                p = Path(p)  # os-agnostic
                if p.is_dir():  # dir
                    # 如果是目录 那么把该目录下的文件都放到f里面
                    #  glob 该方法返回所有匹配的文件路径列表（list） recursive=True 对p里面的结果进行返回 **指的是所有目录和子目录里面的文件 *.*进行文件过滤
                    f += glob.glob(str(p / '**' / '*.*'), recursive=True)
                    # f = list(p.rglob('*.*'))  # pathlib
                elif p.is_file():  # file
                    with open(p) 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')
            # 把f中的文件进行过滤，仅仅保留格式为图片的文件
            """
            	x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS =>
            	for x in f:
            		if x.split('.')[-1].lower() in IMG_FORMATS:
            			x.replace('/', os.sep)
            			
            """
            self.im_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.im_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
        # img2label_paths函数的作用就是把 coco128/images/train2017/1.jpg -> coco128/labels/train2017/1.txt
        self.label_files = img2label_paths(self.im_files)  # labels
        # with_suffi给文件加后缀 "1.txt".with_suffix('cache') ->1.txt.cache
        cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')
        # 如果缓存存在，那么就读取缓存字典，否则就创建缓存
        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.im_files)  # same hash
        except Exception:
            # 如果缓存不存在，那么使用self.cache_labels函数去创建缓存，该函数会返回一个字典{'hash':xxx,'results':xxx, 'msgs':xxx,'version':xxx，'文件'：xxx}
            # 重点是results保存了对数据处理的结果
            cache, exists = self.cache_labels(cache_path, prefix), False  # cache

        # Display cache
        # nf是文件找到并且完好的个数，n是总数， 其他则是文件损坏或者丢失的个数
        nf, nm, ne, nc, n = cache.pop('results')  # found, missing, empty, corrupt, total
        # 当缓存存在的时候，读取缓存数据
        if exists and LOCAL_RANK in (-1, 0):
            d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupt"
            tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=BAR_FORMAT)  # display cache results
            if cache['msgs']:
                LOGGER.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
        # .values返回字典所有的值  现在字典只剩下 x[im_file]，当时在存x[im_file]使用zip打包存值，现在就可以解压缩取值
        #  x[im_file]: 保存了lb(row *（cls,x,y,w,h)) img的shape segments（是否有多边形）
        labels, shapes, self.segments = zip(*cache.values())
        self.labels = list(labels)
        self.shapes = np.array(shapes, dtype=np.float64)
        # 把文件和标签进行更新，只保留那些完好的文件
        self.im_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)):
            # 如果include_class 不为空， 那么去除掉不在include_class里面的标签
          
            if include_class:
                # label row*(cls,x,y,w,h)
                j = (label[:, 0:1] == include_class_array).any(1)
                self.labels[i] = label[j]
                if segment:
                    self.segments[i] = segment[j]
            # 如果是单类别训练，那就不需要判断，直接把有框的为一类即可
            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:  # 采用矩形训练
            # Sort by aspect ratio
            s = self.shapes  # wh
            ar = s[:, 1] / s[:, 0]  # aspect ratio
            irect = ar.argsort()
            self.im_files = [self.im_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 RAM/disk for faster training (WARNING: large datasets may exceed system resources)
        self.ims = [None] * n
        self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files]
         # 把图片进行缓存，加快训练速度，一般都不会这么做，内存足够大的当我没说
        if cache_images: 
            gb = 0  # Gigabytes of cached images
            self.im_hw0, self.im_hw = [None] * n, [None] * n
            fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image
            results = ThreadPool(NUM_THREADS).imap(fcn, range(n))
            pbar = tqdm(enumerate(results), total=n, bar_format=BAR_FORMAT)
            for i, x in pbar:
                if cache_images == 'disk':
                    gb += self.npy_files[i].stat().st_size
                else:  # 'ram'
                    self.ims[i], self.im_hw0[i], self.im_hw[i] = x  # im, hw_orig, hw_resized = load_image(self, i)
                    gb += self.ims[i].nbytes
                pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB {cache_images})'
            pbar.close()
	
    # 将标签缓存起来
    def cache_labels(self, path=Path('./labels.cache'), prefix=''):
        # Cache dataset labels, check images and read shapes
        """

            x[results]: 保存了nf, nm, ne, nc, len(self.im_files) self.im_files: 保存了img路径
            x[im_file]: 保存了lb(row *（cls,x,y,w,h)) img的shape segments（是否有多边形）
        """
        x = {}  # 缓存的字典
        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:
            # pool.imap_unordered(func, args): 对大量数据遍历多进程计算  返回一个迭代器
            # zip函数将可迭代对象进行打包， e.g a=[1,2,3] b=[2,4,5] list(zip(a,b))=[(1,2),(2.4).(3,6)]
            pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))),
                        desc=desc,
                        total=len(self.im_files),
                        bar_format=BAR_FORMAT)
            # pbar的返回值也就是verify_image_label函数的的返回值
            # verify_image_label 对传入的图片和标签进行读取，统计损坏和完好的个数，并且把图片的shape和标签的内容都给读取出来
            for im_file, lb, 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
                # 如果图片没问题，那就保存该图片相关信息
                if im_file:
                    x[im_file] = [lb, shape, segments]
                if msg:
                    msgs.append(msg)
                pbar.desc = f"{desc}{nf} found, {nm} missing, {ne} empty, {nc} corrupt"

        pbar.close()
        # 输出日志信息
        if msgs:
            LOGGER.info('\n'.join(msgs))
        if nf == 0:
            LOGGER.warning(f'{prefix}WARNING: No labels found in {path}. See {HELP_URL}')
       	
        # hashh和version是为了下一次读取的时候用来标识唯一值的作用
        x['hash'] = get_hash(self.label_files + self.im_files)
        x['results'] = nf, nm, ne, nc, len(self.im_files)
        x['msgs'] = msgs  # warnings
        x['version'] = self.cache_version  # cache version
        # 把缓存信息进行保存，为了下一次读取使用
        try:
            np.save(path, x)  # save cache for next time
            path.with_suffix('.cache.npy').rename(path)  # remove .npy suffix
            LOGGER.info(f'{prefix}New cache created: {path}')
        except Exception as e:
            LOGGER.warning(f'{prefix}WARNING: Cache directory {path.parent} is not writeable: {e}')  # not writeable
        return x
	
    # 返回图片个数
    def __len__(self):
        return len(self.im_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 = self.load_mosaic(index)
            shapes = None

            # MixUp augmentation
            if random.random() < hyp['mixup']:
                # 使用mixup数据增强，就是在这张图片里面，同时也会有另一张图片
                img, labels = mixup(img, labels, *self.load_mosaic(random.randint(0, self.n - 1)))

        else:
            # Load image
            img, (h0, w0), (h, w) = self.load_image(index)

            # Letterbox
            # 判断是否采用矩形训练，如果采用矩形训练那么则需要获取当前图片矩形的大小，否则则是正方形训练（通过自己设置大小）
            shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size  # final letterboxed shape
            # 将放缩后缺失的地方填充起来，  但是有点不理解为什么要用（114，114，114）而不用全零填充
            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()

            # 因为图片进行过了放缩，所以标注的文件也要进行放缩
            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:
                # 对图片进行透视变换
                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
            # 想要更深的了解，可以看这篇博文 https://zhuanlan.zhihu.com/p/107399127/ 
            img, labels = self.albumentations(img, labels)
            nl = len(labels)  # update after albumentations

            # HSV color-space 
            # 图像色彩增强
            augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])

            # Flip up-down
            if random.random() < hyp['flipud']:
                # 对图片进行上下翻转
                img = np.flipud(img)
                # 因为对图片进行的上下翻转，所以需要对标签里面的y进行翻转
                if nl:
                    labels[:, 2] = 1 - labels[:, 2]

            # Flip left-right
            if random.random() < hyp['fliplr']:
                # 对图片进行左右翻转
                img = np.fliplr(img)
                # 因为对图片进行的上下翻转，所以需要对标签里面的x进行翻转
                if nl:
                    labels[:, 1] = 1 - labels[:, 1]

            # Cutouts
            # labels = cutout(img, labels, p=0.5)
            # nl = len(labels)  # update after cutout
		# 要生成6个，是为了给一个图片的索引，img_index,cls,x,y,w,h 
        labels_out = torch.zeros((nl, 6))
        if nl:
            labels_out[:, 1:] = torch.from_numpy(labels)

        # Convert
        #  img = img[:, :, ::-1].transpose(2, 0, 1) 这是我从网上找到转换方法
        img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
        img = np.ascontiguousarray(img)  # 将内存不连续变成内存连续， 训练更快

        return torch.from_numpy(img), labels_out, self.im_files[index], shapes

    def load_image(self, i):
        # 读取图片，并将图片进行放缩成img_size大小
        # Loads 1 image from dataset index 'i', returns (im, original hw, resized hw)
        # 获取图片文件和图片缓存文件
        im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i],
        if im is None:  # not cached in RAM
            # 缓存，存在 直接加载
            if fn.exists():  # load npy
                im = np.load(fn)
            else:  # read image
                # 用的是opencv读取图片，是BGR格式 opencv h,w,c
                im = cv2.imread(f)  # BGR
                assert im is not None, f'Image Not Found {f}'
            # 图片本的宽高
            h0, w0 = im.shape[:2]  # orig hw
            # 把图片进行放缩
            r = self.img_size / max(h0, w0)  # ratio
            if r != 1:  # if sizes are not equal
                # 如果需要放缩，那么要等比例放缩保持图片不变形
                im = cv2.resize(im, (int(w0 * r), int(h0 * r)),
                                interpolation=cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA)
            return im, (h0, w0), im.shape[:2]  # im, hw_original, hw_resized
        else:
            return self.ims[i], self.im_hw0[i], self.im_hw[i]  # im, hw_original, hw_resized

    def cache_images_to_disk(self, i):
        # Saves an image as an *.npy file for faster loading
        f = self.npy_files[i]
        if not f.exists():
            np.save(f.as_posix(), cv2.imread(self.im_files[i]))

   
    @staticmethod
    def collate_fn(batch):
        """
        该函数的输入就是  batch*__getitem__的输出
        这个函数会在create_dataloader中生成dataloader时调用：
        整理函数  将image和label整合到一起
        :return torch.stack(img, 0): 如[16, 3, 640, 640] 整个batch的图片
        :return torch.cat(label, 0): 如[15, 6] [num_target, img_index+class_index+xywh(normalized)] 整个batch的label
        :return path: 整个batch所有图片的路径
        :return shapes: (h0, w0), ((h / h0, w / w0), pad)    for COCO mAP rescaling
        pytorch的DataLoader打包一个batch的数据集时要经过此函数进行打包 通过重写此函数实现标签与图片对应的划分，一个batch中哪些标签属于哪一张图片,形如
           [[0, 6, 0.5, 0.5, 0.26, 0.35],
            [0, 6, 0.5, 0.5, 0.26, 0.35],
            [1, 6, 0.5, 0.5, 0.26, 0.35],
            [2, 6, 0.5, 0.5, 0.26, 0.35],]
          前两行标签属于第一张图片, 第三行属于第二张。。。
        """
        im, label, path, shapes = zip(*batch)  # transposed
        for i, lb in enumerate(label):
            # 把这个标签归到给定的图片
            lb[:, 0] = i  # add target image index for build_targets()
        return torch.stack(im, 0), torch.cat(label, 0), path, shapes

    @staticmethod
    def collate_fn4(batch):
		"""
		与collate_fn类似
		"""
        img, label, path, shapes = zip(*batch)  # transposed
        n = len(shapes) // 4
        im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]

        ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]])
        wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]])
        s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]])  # 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=2.0, mode='bilinear',
                                   align_corners=False)[0].type(img[i].type())
                lb = label[i]
            else:
                im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2)
                lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
            im4.append(im)
            label4.append(lb)

        for i, lb in enumerate(label4):
            lb[:, 0] = i  # add target image index for build_targets()

        return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4

verify_image_label

def verify_image_label(args):
    """
     return
        nm :当前图片的label有是否缺失
        nf: 当前图片的label是否存在
        ne：当前图片的label是否为空
        nc: 当前图片的label是否损坏
        msg:返回图片label是否损坏的消息
    """
    # 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
        # 对图片进行验证，图片像素要大于10*10
        im = Image.open(im_file)
        im.verify()  # PIL verify
        # 图片的尺寸
        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}'
        # 如果是jpg的格式，要判断图片是否损坏
        if im.format.lower() in ('jpg', 'jpeg'):
            with open(im_file, 'rb') as f:
                f.seek(-2, 2)
                if f.read() != b'\xff\xd9':  # corrupt JPEG
                    ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100)
                    msg = f'{prefix}WARNING: {im_file}: corrupt JPEG restored and saved'

        # verify labels
        # 对图片的标签进行验证
        #### 做旋转检测的话，对下面进行修改？网络的输出维度80*80*86？损失函数加上一个角度损失（当作回归还是分类？）？ 这里不影响阅读只是个人的一个思考。
        if os.path.isfile(lb_file):
            nf = 1  # label found
            with open(lb_file) as f:
                # 就是一个 [row*[cls, x, y, w, h]]
                lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
                # 如果有某一行超过了6那就说明是多边形预测,对于segment框没了解。
                if any(len(x) > 6 for x in lb):  # is segment
                    classes = np.array([x[0] for x in lb], dtype=np.float32)
                    segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb]  # (cls, xy1...)
                    lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
                #  [row*[cls, x, y, w, h]]
                lb = np.array(lb, dtype=np.float32)
            nl = len(lb)  # 保存一共有多少行
            if nl:
                assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected'
                assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}'
                assert (lb[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}'
                # 去除重复的行
                _, i = np.unique(lb, axis=0, return_index=True)
                if len(i) < nl:  # duplicate row check
                    lb = lb[i]  # remove duplicates
                    if segments:
                        segments = segments[i]
                    msg = f'{prefix}WARNING: {im_file}: {nl - len(i)} duplicate labels removed'
            else:
                ne = 1  # label empty
                lb = np.zeros((0, 5), dtype=np.float32)
        else:
            nm = 1  # label missing
            lb = np.zeros((0, 5), dtype=np.float32)
        return im_file, lb, 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]

马赛克数据增强

可以配合这张图来理解代码，我这里只是考虑小图超过大图的情况，对于小图没超过大图，可以结合自己画图加深理解

     def load_mosaic(self, index):
        # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic
        # 使用的是4张图片的马赛克增强
        labels4, segments4 = [], []
        # 图片的大小，即四张图片拼在一起的大小
        s = self.img_size
        # random.uniform（x, y）返回一个随机数[x, y]
        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) = self.load_image(index)

            # place img in img4
            if i == 0:  # top left 右下角坐标的固定
                img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
                # x1a,y1a为左上角的坐标， x2a,y2a为右下角的坐标在大图的坐标
                x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
                # x1b, y1b 左上角坐标，x2b, y2b右下角坐标，在小图的坐标
                x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)	
            # 理解好了i=0的情况，下面的都是类似的操作，只是说固定的坐标不一样，反正就是一点，中心点的坐标是固定的
            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)
			
            # 把小图放到大图里面的，小图和大图的坐标都已经经过计算裁剪了
            img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
            # 如果小图宽度不够  或者高度不够 则需要padding
            padw = x1a - x1b
            padh = y1a - y1b

            # Labels 因为图的位置不一样了，训练的框也需要进行改变
            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]
            # 把该张图片的所有标签存起来，放到labels4里面去
            labels4.append(labels)
            segments4.extend(segments)

        # Concat/clip labels
        # 对标签进行一些处理，有的可能已经被截取了，存在的需要合并
        # labels4 （ 4*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'])
         # 随机透视变换 通过透视变换矩阵对mosaic整合后的图片进行随机旋转、缩放、平移、裁剪，透视变换，最后将大图进行resize= img_size，详细的可以去看看opencv相关的教程
        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
	# load_mosaic9和load_mosaic思想上大体都差不多
    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)
        hp, wp = -1, -1  # height, width previous
        for i, index in enumerate(indices):
            # Load image
            img, _, (h, w) = self.load_image(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

参考文献

https://blog.csdn.net/weixin_55073640/article/details/122853743
https://blog.csdn.net/YoGuohcx/article/details/121926120
https://zhuanlan.zhihu.com/p/361830892