Python Path.db_root_dir方法代码示例

# 需要导入模块: from mypath import Path [as 别名]
# 或者: from mypath.Path import db_root_dir [as 别名]
def calculate_weigths_labels(dataset, dataloader, num_classes):
    # Create an instance from the data loader
    z = np.zeros((num_classes,))
    # Initialize tqdm
    tqdm_batch = tqdm(dataloader)
    print('Calculating classes weights')
    for sample in tqdm_batch:
        y = sample['label']
        y = y.detach().cpu().numpy()
        mask = (y >= 0) & (y < num_classes)
        labels = y[mask].astype(np.uint8)
        count_l = np.bincount(labels, minlength=num_classes)
        z += count_l
    tqdm_batch.close()
    total_frequency = np.sum(z)
    class_weights = []
    for frequency in z:
        class_weight = 1 / (np.log(1.02 + (frequency / total_frequency)))
        class_weights.append(class_weight)
    ret = np.array(class_weights)
    classes_weights_path = os.path.join(Path.db_root_dir(dataset), dataset+'_classes_weights.npy')
    np.save(classes_weights_path, ret)

    return ret 

# 需要导入模块: from mypath import Path [as 别名]
# 或者: from mypath.Path import db_root_dir [as 别名]
def __init__(self,
                 args,
                 base_dir=Path.db_root_dir('coco'),
                 split='train',
                 year='2017'):
        super().__init__()
        ann_file = os.path.join(base_dir, 'annotations/instances_{}{}.json'.format(split, year))
        ids_file = os.path.join(base_dir, 'annotations/{}_ids_{}.pth'.format(split, year))
        self.img_dir = os.path.join(base_dir, 'images/{}{}'.format(split, year))
        self.split = split
        self.coco = COCO(ann_file)
        self.coco_mask = mask
        if os.path.exists(ids_file):
            self.ids = torch.load(ids_file)
        else:
            ids = list(self.coco.imgs.keys())
            self.ids = self._preprocess(ids, ids_file)
        self.args = args 

# 需要导入模块: from mypath import Path [as 别名]
# 或者: from mypath.Path import db_root_dir [as 别名]
def __init__(self, args, root=Path.db_root_dir('cityscapes'), split="train"):

        self.root = root
        self.split = split
        self.args = args
        self.files = {}

        self.images_base = os.path.join(self.root, 'leftImg8bit', self.split)
        self.annotations_base = os.path.join(self.root, 'gtFine_trainvaltest', 'gtFine', self.split)

        self.files[split] = self.recursive_glob(rootdir=self.images_base, suffix='.png')

        self.void_classes = [0, 1, 2, 3, 4, 5, 6, 9, 10, 14, 15, 16, 18, 29, 30, -1]
        self.valid_classes = [7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33]
        self.class_names = ['unlabelled', 'road', 'sidewalk', 'building', 'wall', 'fence', \
                            'pole', 'traffic_light', 'traffic_sign', 'vegetation', 'terrain', \
                            'sky', 'person', 'rider', 'car', 'truck', 'bus', 'train', \
                            'motorcycle', 'bicycle']

        self.ignore_index = 255
        self.class_map = dict(zip(self.valid_classes, range(self.NUM_CLASSES)))

        if not self.files[split]:
            raise Exception("No files for split=[%s] found in %s" % (split, self.images_base))

        print("Found %d %s images" % (len(self.files[split]), split)) 

# 需要导入模块: from mypath import Path [as 别名]
# 或者: from mypath.Path import db_root_dir [as 别名]
def __init__(self, root=Path.db_root_dir('cityscapes'), split="train", transform=None):

        self.root = root
        self.split = split
        self.transform = transform
        self.files = {}
        self.n_classes = 19

        self.images_base = os.path.join(self.root, 'leftImg8bit', self.split)
        self.annotations_base = os.path.join(self.root, 'gtFine_trainvaltest', 'gtFine', self.split)

        self.files[split] = recursive_glob(rootdir=self.images_base, suffix='.png')

        self.void_classes = [0, 1, 2, 3, 4, 5, 6, 9, 10, 14, 15, 16, 18, 29, 30, -1]
        self.valid_classes = [7, 8, 11, 12, 13, 17, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 31, 32, 33]
        self.class_names = ['unlabelled', 'road', 'sidewalk', 'building', 'wall', 'fence', \
                            'pole', 'traffic_light', 'traffic_sign', 'vegetation', 'terrain', \
                            'sky', 'person', 'rider', 'car', 'truck', 'bus', 'train', \
                            'motorcycle', 'bicycle']

        self.ignore_index = 255
        self.class_map = dict(zip(self.valid_classes, range(self.n_classes)))

        if not self.files[split]:
            raise Exception("No files for split=[%s] found in %s" % (split, self.images_base))

        print("Found %d %s images" % (len(self.files[split]), split)) 

# 需要导入模块: from mypath import Path [as 别名]
# 或者: from mypath.Path import db_root_dir [as 别名]
def __init__(self,
                 base_dir=Path.db_root_dir('fundus'),
                 dataset='refuge',
                 split='train',
                 testid=None,
                 transform=None
                 ):
        """
        :param base_dir: path to VOC dataset directory
        :param split: train/val
        :param transform: transform to apply
        """
        # super().__init__()
        self._base_dir = base_dir
        self.image_list = []
        self.split = split

        self.image_pool = []
        self.label_pool = []
        self.img_name_pool = []
        SEED = 1212
        random.seed(SEED)

        self._image_dir = os.path.join(self._base_dir, dataset, split, 'image')
        print(self._image_dir)
        imagelist = glob(self._image_dir + "/*.png")
        for image_path in imagelist:
            gt_path = image_path.replace('image', 'mask')
            self.image_list.append({'image': image_path, 'label': gt_path, 'id': testid})

        self.transform = transform
        self._read_img_into_memory()
        # Display stats
        print('Number of images in {}: {:d}'.format(split, len(self.image_list))) 

# 需要导入模块: from mypath import Path [as 别名]
# 或者: from mypath.Path import db_root_dir [as 别名]
def __init__(self,
                 args,
                 base_dir=Path.db_root_dir('sbd'),
                 split='train',
                 ):
        """
        :param base_dir: path to VOC dataset directory
        :param split: train/val
        :param transform: transform to apply
        """
        super().__init__()
        self._base_dir = base_dir
        self._dataset_dir = os.path.join(self._base_dir, 'dataset')
        self._image_dir = os.path.join(self._dataset_dir, 'img')
        self._cat_dir = os.path.join(self._dataset_dir, 'cls')


        if isinstance(split, str):
            self.split = [split]
        else:
            split.sort()
            self.split = split

        self.args = args

        # Get list of all images from the split and check that the files exist
        self.im_ids = []
        self.images = []
        self.categories = []
        for splt in self.split:
            with open(os.path.join(self._dataset_dir, splt + '.txt'), "r") as f:
                lines = f.read().splitlines()

            for line in lines:
                _image = os.path.join(self._image_dir, line + ".jpg")
                _categ= os.path.join(self._cat_dir, line + ".mat")
                assert os.path.isfile(_image)
                assert os.path.isfile(_categ)
                self.im_ids.append(line)
                self.images.append(_image)
                self.categories.append(_categ)

        assert (len(self.images) == len(self.categories))

        # Display stats
        print('Number of images: {:d}'.format(len(self.images))) 

# 需要导入模块: from mypath import Path [as 别名]
# 或者: from mypath.Path import db_root_dir [as 别名]
def __init__(self,
                 args,
                 base_dir=Path.db_root_dir('pascal'),
                 split='train',
                 ):
        """
        :param base_dir: path to VOC dataset directory
        :param split: train/val
        :param transform: transform to apply
        """
        super().__init__()
        self._base_dir = base_dir
        self._image_dir = os.path.join(self._base_dir, 'JPEGImages')
        self._cat_dir = os.path.join(self._base_dir, 'SegmentationClass')

        if isinstance(split, str):
            self.split = [split]
        else:
            split.sort()
            self.split = split

        self.args = args

        _splits_dir = os.path.join(self._base_dir, 'ImageSets', 'Segmentation')

        self.im_ids = []
        self.images = []
        self.categories = []

        for splt in self.split:
            with open(os.path.join(os.path.join(_splits_dir, splt + '.txt')), "r") as f:
                lines = f.read().splitlines()

            for ii, line in enumerate(lines):
                _image = os.path.join(self._image_dir, line + ".jpg")
                _cat = os.path.join(self._cat_dir, line + ".png")
                assert os.path.isfile(_image)
                assert os.path.isfile(_cat)
                self.im_ids.append(line)
                self.images.append(_image)
                self.categories.append(_cat)

        assert (len(self.images) == len(self.categories))

        # Display stats
        print('Number of images in {}: {:d}'.format(split, len(self.images))) 

# 需要导入模块: from mypath import Path [as 别名]
# 或者: from mypath.Path import db_root_dir [as 别名]
def __init__(self,
                 base_dir=Path.db_root_dir('sbd'),
                 split='train',
                 transform=None
                 ):
        """
        :param base_dir: path to VOC dataset directory
        :param split: train/val
        :param transform: transform to apply
        """
        super().__init__()
        self._base_dir = base_dir
        self._dataset_dir = os.path.join(self._base_dir, 'dataset')
        self._image_dir = os.path.join(self._dataset_dir, 'img')
        self._cat_dir = os.path.join(self._dataset_dir, 'cls')


        if isinstance(split, str):
            self.split = [split]
        else:
            split.sort()
            self.split = split

        self.transform = transform



        # Get list of all images from the split and check that the files exist
        self.im_ids = []
        self.images = []
        self.categories = []
        for splt in self.split:
            with open(os.path.join(self._dataset_dir, splt + '.txt'), "r") as f:
                lines = f.read().splitlines()

            for line in lines:
                _image = os.path.join(self._image_dir, line + ".jpg")
                _categ= os.path.join(self._cat_dir, line + ".mat")
                assert os.path.isfile(_image)
                assert os.path.isfile(_categ)
                self.im_ids.append(line)
                self.images.append(_image)
                self.categories.append(_categ)

        assert (len(self.images) == len(self.categories))



        # Display stats
        print('Number of images: {:d}'.format(len(self.images))) 

# 需要导入模块: from mypath import Path [as 别名]
# 或者: from mypath.Path import db_root_dir [as 别名]
def __init__(self,
                 base_dir=Path.db_root_dir('pascal'),
                 split='train',
                 transform=None
                 ):
        """
        :param base_dir: path to VOC dataset directory
        :param split: train/val
        :param transform: transform to apply
        """
        super().__init__()
        self._base_dir = base_dir
        self._image_dir = os.path.join(self._base_dir, 'JPEGImages')
        self._cat_dir = os.path.join(self._base_dir, 'SegmentationClass')

        if isinstance(split, str):
            self.split = [split]
        else:
            split.sort()
            self.split = split

        self.transform = transform

        _splits_dir = os.path.join(self._base_dir, 'ImageSets', 'Segmentation')

        self.im_ids = []
        self.images = []
        self.categories = []

        for splt in self.split:
            with open(os.path.join(os.path.join(_splits_dir, splt + '.txt')), "r") as f:
                lines = f.read().splitlines()

            for ii, line in enumerate(lines):
                _image = os.path.join(self._image_dir, line + ".jpg")
                _cat = os.path.join(self._cat_dir, line + ".png")
                assert os.path.isfile(_image)
                assert os.path.isfile(_cat)
                self.im_ids.append(line)
                self.images.append(_image)
                self.categories.append(_cat)

        assert (len(self.images) == len(self.categories))

        # Display stats
        print('Number of images in {}: {:d}'.format(split, len(self.images)))