import numpy as np

from deeplodocus.utils.notification import Notification
from deeplodocus.flags.notif import DEEP_NOTIF_WARNING


def reformat(x, image_shape, n_obj=100):
    # Initialize label array
    label = np.empty((n_obj, 5), dtype=np.float32)
    label.fill(-1)

    # If the input array is empty, return the empty label array
    if not np.any(x):
        return label, None

    # Enumerate over the list of object labels
    for i, item in enumerate(x):
        # If the array does not have enough rows, print a warning and break
        if i >= n_obj:
            Notification(
                DEEP_NOTIF_WARNING,
                "label.reformat : Unable to fit all %i objects into label array : consider increasing n_obj" % len(x)
            )
            break
        # Put the box and category id into the array
        # NB: Coco format is x0, y0, w, h (where x0,y0 = upper left corner)
        # NB: we want x, y, w, h (where x, y = centre)
        box = np.array(item["bbox"])
        label[i, 0:2] = box[0:2] + box[2:4] / 2
        label[i, 2:4] = box[2:4]
        label[i, 4] = item["category_id"] - 1

    # Normalise box labels w.r.t. image height and width
    label[:i + 1, 0:4] /= np.tile(np.array(image_shape)[[1, 0]], 2).reshape(1, -1)
    return label, None


def scale(label, image_shape):
    # Where label box is not all -1, multiple by (w, h, w, h)
    label[~ np.all(label == -1, axis=1), 0:4] *= np.tile(np.array(image_shape), 2).reshape(1, -1)
    return label, None

063ce195ebf7d31d1666eeb42000ee9af6d242dc
<mask token>

<mask token>


def scale(label, image_shape):
    label[~np.all(label == -1, axis=1), 0:4] *= np.tile(np.array(
        image_shape), 2).reshape(1, -1)
    return label, None

<mask token>


def reformat(x, image_shape, n_obj=100):
    label = np.empty((n_obj, 5), dtype=np.float32)
    label.fill(-1)
    if not np.any(x):
        return label, None
    for i, item in enumerate(x):
        if i >= n_obj:
            Notification(DEEP_NOTIF_WARNING, 
                'label.reformat : Unable to fit all %i objects into label array : consider increasing n_obj'
                 % len(x))
            break
        box = np.array(item['bbox'])
        label[i, 0:2] = box[0:2] + box[2:4] / 2
        label[i, 2:4] = box[2:4]
        label[i, 4] = item['category_id'] - 1
    label[:i + 1, 0:4] /= np.tile(np.array(image_shape)[[1, 0]], 2).reshape(
        1, -1)
    return label, None


def scale(label, image_shape):
    label[~np.all(label == -1, axis=1), 0:4] *= np.tile(np.array(
        image_shape), 2).reshape(1, -1)
    return label, None

import numpy as np
from deeplodocus.utils.notification import Notification
from deeplodocus.flags.notif import DEEP_NOTIF_WARNING


def reformat(x, image_shape, n_obj=100):
    label = np.empty((n_obj, 5), dtype=np.float32)
    label.fill(-1)
    if not np.any(x):
        return label, None
    for i, item in enumerate(x):
        if i >= n_obj:
            Notification(DEEP_NOTIF_WARNING, 
                'label.reformat : Unable to fit all %i objects into label array : consider increasing n_obj'
                 % len(x))
            break
        box = np.array(item['bbox'])
        label[i, 0:2] = box[0:2] + box[2:4] / 2
        label[i, 2:4] = box[2:4]
        label[i, 4] = item['category_id'] - 1
    label[:i + 1, 0:4] /= np.tile(np.array(image_shape)[[1, 0]], 2).reshape(
        1, -1)
    return label, None


def scale(label, image_shape):
    label[~np.all(label == -1, axis=1), 0:4] *= np.tile(np.array(
        image_shape), 2).reshape(1, -1)
    return label, None

import numpy as np

from deeplodocus.utils.notification import Notification
from deeplodocus.flags.notif import DEEP_NOTIF_WARNING


def reformat(x, image_shape, n_obj=100):
    # Initialize label array
    label = np.empty((n_obj, 5), dtype=np.float32)
    label.fill(-1)

    # If the input array is empty, return the empty label array
    if not np.any(x):
        return label, None

    # Enumerate over the list of object labels
    for i, item in enumerate(x):
        # If the array does not have enough rows, print a warning and break
        if i >= n_obj:
            Notification(
                DEEP_NOTIF_WARNING,
                "label.reformat : Unable to fit all %i objects into label array : consider increasing n_obj" % len(x)
            )
            break
        # Put the box and category id into the array
        # NB: Coco format is x0, y0, w, h (where x0,y0 = upper left corner)
        # NB: we want x, y, w, h (where x, y = centre)
        box = np.array(item["bbox"])
        label[i, 0:2] = box[0:2] + box[2:4] / 2
        label[i, 2:4] = box[2:4]
        label[i, 4] = item["category_id"] - 1

    # Normalise box labels w.r.t. image height and width
    label[:i + 1, 0:4] /= np.tile(np.array(image_shape)[[1, 0]], 2).reshape(1, -1)
    return label, None


def scale(label, image_shape):
    # Where label box is not all -1, multiple by (w, h, w, h)
    label[~ np.all(label == -1, axis=1), 0:4] *= np.tile(np.array(image_shape), 2).reshape(1, -1)
    return label, None
