Python怎么实现图像分割

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方法一

import randomimport numpy as npfrom PIL import Image, ImageOps, ImageFilterfrom skimage.filters import gaussianimport torchimport mathimport numbersimport randomclass RandomVerticalFlip(object):    def __call__(self, img):        if random.random() < 0.5:            return img.transpose(Image.FLIP_TOP_BOTTOM)        return imgclass DeNormalize(object):    def __init__(self, mean, std):        self.mean = mean        self.std = std    def __call__(self, tensor):        for t, m, s in zip(tensor, self.mean, self.std):            t.mul_(s).add_(m)        return tensorclass MaskToTensor(object):    def __call__(self, img):        return torch.from_numpy(np.array(img, dtype=np.int32)).long()class FreeScale(object):    def __init__(self, size, interpolation=Image.BILINEAR):        self.size = tuple(reversed(size))  # size: (h, w)        self.interpolation = interpolation    def __call__(self, img):        return img.resize(self.size, self.interpolation)class FlipChannels(object):    def __call__(self, img):        img = np.array(img)[:, :, ::-1]        return Image.fromarray(img.astype(np.uint8))class RandomGaussianBlur(object):    def __call__(self, img):        sigma = 0.15 + random.random() * 1.15        blurred_img = gaussian(np.array(img), sigma=sigma, multichannel=True)        blurred_img *= 255        return Image.fromarray(blurred_img.astype(np.uint8))# 组合class Compose(object):    def __init__(self, transforms):        self.transforms = transforms    def __call__(self, img, mask):        assert img.size == mask.size        for t in self.transforms:            img, mask = t(img, mask)        return img, mask# 随机裁剪class RandomCrop(object):    def __init__(self, size, padding=0):        if isinstance(size, numbers.Number):            self.size = (int(size), int(size))        else:            self.size = size        self.padding = padding    def __call__(self, img, mask):        if self.padding > 0:            img = ImageOps.expand(img, border=self.padding, fill=0)            mask = ImageOps.expand(mask, border=self.padding, fill=0)        assert img.size == mask.size        w, h = img.size        th, tw = self.size        if w == tw and h == th:            return img, mask        if w < tw or h < th:            return img.resize((tw, th), Image.BILINEAR), mask.resize((tw, th), Image.NEAREST)        x1 = random.randint(0, w - tw)        y1 = random.randint(0, h - th)        return img.crop((x1, y1, x1 + tw, y1 + th)), mask.crop((x1, y1, x1 + tw, y1 + th))#  中心裁剪class CenterCrop(object):    def __init__(self, size):        if isinstance(size, numbers.Number):            self.size = (int(size), int(size))        else:            self.size = size    def __call__(self, img, mask):        assert img.size == mask.size        w, h = img.size        th, tw = self.size        x1 = int(round((w - tw) / 2.))        y1 = int(round((h - th) / 2.))        return img.crop((x1, y1, x1 + tw, y1 + th)), mask.crop((x1, y1, x1 + tw, y1 + th))class RandomHorizontallyFlip(object):    def __call__(self, img, mask):        if random.random() < 0.5:            return img.transpose(Image.FLIP_LEFT_RIGHT), mask.transpose(Image.FLIP_LEFT_RIGHT)        return img, maskclass Scale(object):    def __init__(self, size):        self.size = size    def __call__(self, img, mask):        assert img.size == mask.size        w, h = img.size        if (w >= h and w == self.size) or (h >= w and h == self.size):            return img, mask        if w > h:            ow = self.size            oh = int(self.size * h / w)            return img.resize((ow, oh), Image.BILINEAR), mask.resize((ow, oh), Image.NEAREST)        else:            oh = self.size            ow = int(self.size * w / h)            return img.resize((ow, oh), Image.BILINEAR), mask.resize((ow, oh), Image.NEAREST)class RandomSizedCrop(object):    def __init__(self, size):        self.size = size    def __call__(self, img, mask):        assert img.size == mask.size        for attempt in range(10):            area = img.size[0] * img.size[1]            target_area = random.uniform(0.45, 1.0) * area            aspect_ratio = random.uniform(0.5, 2)            w = int(round(math.sqrt(target_area * aspect_ratio)))            h = int(round(math.sqrt(target_area / aspect_ratio)))            if random.random() < 0.5:                w, h = h, w            if w <= img.size[0] and h <= img.size[1]:                x1 = random.randint(0, img.size[0] - w)                y1 = random.randint(0, img.size[1] - h)                img = img.crop((x1, y1, x1 + w, y1 + h))                mask = mask.crop((x1, y1, x1 + w, y1 + h))                assert (img.size == (w, h))                return img.resize((self.size, self.size), Image.BILINEAR), mask.resize((self.size, self.size),                                                                                       Image.NEAREST)        # Fallback        scale = Scale(self.size)        crop = CenterCrop(self.size)        return crop(*scale(img, mask))class RandomRotate(object):    def __init__(self, degree):        self.degree = degree    def __call__(self, img, mask):        rotate_degree = random.random() * 2 * self.degree - self.degree        return img.rotate(rotate_degree, Image.BILINEAR), mask.rotate(rotate_degree, Image.NEAREST)class RandomSized(object):    def __init__(self, size):        self.size = size        self.scale = Scale(self.size)        self.crop = RandomCrop(self.size)    def __call__(self, img, mask):        assert img.size == mask.size        w = int(random.uniform(0.5, 2) * img.size[0])        h = int(random.uniform(0.5, 2) * img.size[1])        img, mask = img.resize((w, h), Image.BILINEAR), mask.resize((w, h), Image.NEAREST)        return self.crop(*self.scale(img, mask))class SlidingCropOld(object):    def __init__(self, crop_size, stride_rate, ignore_label):        self.crop_size = crop_size        self.stride_rate = stride_rate        self.ignore_label = ignore_label    def _pad(self, img, mask):        h, w = img.shape[: 2]        pad_h = max(self.crop_size - h, 0)        pad_w = max(self.crop_size - w, 0)        img = np.pad(img, ((0, pad_h), (0, pad_w), (0, 0)), 'constant')        mask = np.pad(mask, ((0, pad_h), (0, pad_w)), 'constant', constant_values=self.ignore_label)        return img, mask    def __call__(self, img, mask):        assert img.size == mask.size        w, h = img.size        long_size = max(h, w)        img = np.array(img)        mask = np.array(mask)        if long_size > self.crop_size:            stride = int(math.ceil(self.crop_size * self.stride_rate))            h_step_num = int(math.ceil((h - self.crop_size) / float(stride))) + 1            w_step_num = int(math.ceil((w - self.crop_size) / float(stride))) + 1            img_sublist, mask_sublist = [], []            for yy in range(h_step_num):                for xx in range(w_step_num):                    sy, sx = yy * stride, xx * stride                    ey, ex = sy + self.crop_size, sx + self.crop_size                    img_sub = img[sy: ey, sx: ex, :]                    mask_sub = mask[sy: ey, sx: ex]                    img_sub, mask_sub = self._pad(img_sub, mask_sub)                    img_sublist.append(Image.fromarray(img_sub.astype(np.uint8)).convert('RGB'))                    mask_sublist.append(Image.fromarray(mask_sub.astype(np.uint8)).convert('P'))            return img_sublist, mask_sublist        else:            img, mask = self._pad(img, mask)            img = Image.fromarray(img.astype(np.uint8)).convert('RGB')            mask = Image.fromarray(mask.astype(np.uint8)).convert('P')            return img, maskclass SlidingCrop(object):    def __init__(self, crop_size, stride_rate, ignore_label):        self.crop_size = crop_size        self.stride_rate = stride_rate        self.ignore_label = ignore_label    def _pad(self, img, mask):        h, w = img.shape[: 2]        pad_h = max(self.crop_size - h, 0)        pad_w = max(self.crop_size - w, 0)        img = np.pad(img, ((0, pad_h), (0, pad_w), (0, 0)), 'constant')        mask = np.pad(mask, ((0, pad_h), (0, pad_w)), 'constant', constant_values=self.ignore_label)        return img, mask, h, w    def __call__(self, img, mask):        assert img.size == mask.size        w, h = img.size        long_size = max(h, w)        img = np.array(img)        mask = np.array(mask)        if long_size > self.crop_size:            stride = int(math.ceil(self.crop_size * self.stride_rate))            h_step_num = int(math.ceil((h - self.crop_size) / float(stride))) + 1            w_step_num = int(math.ceil((w - self.crop_size) / float(stride))) + 1            img_slices, mask_slices, slices_info = [], [], []            for yy in range(h_step_num):                for xx in range(w_step_num):                    sy, sx = yy * stride, xx * stride                    ey, ex = sy + self.crop_size, sx + self.crop_size                    img_sub = img[sy: ey, sx: ex, :]                    mask_sub = mask[sy: ey, sx: ex]                    img_sub, mask_sub, sub_h, sub_w = self._pad(img_sub, mask_sub)                    img_slices.append(Image.fromarray(img_sub.astype(np.uint8)).convert('RGB'))                    mask_slices.append(Image.fromarray(mask_sub.astype(np.uint8)).convert('P'))                    slices_info.append([sy, ey, sx, ex, sub_h, sub_w])            return img_slices, mask_slices, slices_info        else:            img, mask, sub_h, sub_w = self._pad(img, mask)            img = Image.fromarray(img.astype(np.uint8)).convert('RGB')            mask = Image.fromarray(mask.astype(np.uint8)).convert('P')            return [img], [mask], [[0, sub_h, 0, sub_w, sub_h, sub_w]]

方法二

import numpy as npimport randomimport torchfrom torchvision import transforms as Tfrom torchvision.transforms import functional as Fdef pad_if_smaller(img, size, fill=0):    # 如果图像最小边长小于给定size，则用数值fill进行padding    min_size = min(img.size)    if min_size < size:        ow, oh = img.size        padh = size - oh if oh < size else 0        padw = size - ow if ow < size else 0        img = F.pad(img, (0, 0, padw, padh), fill=fill)    return imgclass Compose(object):    def __init__(self, transforms):        self.transforms = transforms    def __call__(self, image, target):        for t in self.transforms:            image, target = t(image, target)        return image, targetclass RandomResize(object):    def __init__(self, min_size, max_size=None):        self.min_size = min_size        if max_size is None:            max_size = min_size        self.max_size = max_size    def __call__(self, image, target):        size = random.randint(self.min_size, self.max_size)        # 这里size传入的是int类型，所以是将图像的最小边长缩放到size大小        image = F.resize(image, size)        # 这里的interpolation注意下，在torchvision(0.9.0)以后才有InterpolationMode.NEAREST        # 如果是之前的版本需要使用PIL.Image.NEAREST        target = F.resize(target, size, interpolation=T.InterpolationMode.NEAREST)        return image, targetclass RandomHorizontalFlip(object):    def __init__(self, flip_prob):        self.flip_prob = flip_prob    def __call__(self, image, target):        if random.random() < self.flip_prob:            image = F.hflip(image)            target = F.hflip(target)        return image, targetclass RandomCrop(object):    def __init__(self, size):        self.size = size    def __call__(self, image, target):        image = pad_if_smaller(image, self.size)        target = pad_if_smaller(target, self.size, fill=255)        crop_params = T.RandomCrop.get_params(image, (self.size, self.size))        image = F.crop(image, *crop_params)        target = F.crop(target, *crop_params)        return image, targetclass CenterCrop(object):    def __init__(self, size):        self.size = size    def __call__(self, image, target):        image = F.center_crop(image, self.size)        target = F.center_crop(target, self.size)        return image, targetclass ToTensor(object):    def __call__(self, image, target):        image = F.to_tensor(image)        target = torch.as_tensor(np.array(target), dtype=torch.int64)        return image, targetclass Normalize(object):    def __init__(self, mean, std):        self.mean = mean        self.std = std    def __call__(self, image, target):        image = F.normalize(image, mean=self.mean, std=self.std)        return image, target

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