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Table Of Contents

Source code for

"""Detection dataset from RecordIO files."""
from __future__ import absolute_import
from __future__ import division
import numpy as np
from mxnet import gluon

def _transform_label(label, height=None, width=None):
    label = np.array(label).ravel()
    header_len = int(label[0])  # label header
    label_width = int(label[1])  # the label width for each object, >= 5
    if label_width < 5:
        raise ValueError(
            "Label info for each object should >= 5, given {}".format(label_width))
    min_len = header_len + 5
    if len(label) < min_len:
        raise ValueError(
            "Expected label length >= {}, got {}".format(min_len, len(label)))
    if (len(label) - header_len) % label_width:
        raise ValueError(
            "Broken label of size {}, cannot reshape into (N, {}) "
            "if header length {} is excluded".format(len(label), label_width, header_len))
    gcv_label = label[header_len:].reshape(-1, label_width)
    # swap columns, gluon-cv requires [xmin-ymin-xmax-ymax-id-extra0-extra1-xxx]
    ids = gcv_label[:, 0].copy()
    gcv_label[:, :4] = gcv_label[:, 1:5]
    gcv_label[:, 4] = ids
    # restore to absolute coordinates
    if height is not None:
        gcv_label[:, (0, 2)] *= width
    if width is not None:
        gcv_label[:, (1, 3)] *= height
    return gcv_label

[docs]class RecordFileDetection( """Detection dataset loaded from record file. The supported record file is using the same format used by :py:meth:`mxnet.image.ImageDetIter` and :py:meth:``. Checkout :ref:`lst_record_dataset` for tutorial of how to prepare this file. .. note:: We suggest you to use ``RecordFileDetection`` only if you are familiar with the record files. Parameters ---------- filename : str Path of the record file. It require both *.rec and *.idx file in the same directory, where raw image and labels are stored in *.rec file for better IO performance, *.idx file is used to provide random access to the binary file. coord_normalized : boolean Indicate whether bounding box coordinates have been normalized to (0, 1) in labels. If so, we will rescale back to absolute coordinates by multiplying width or height. Examples -------- >>> record_dataset = RecordFileDetection('train.rec') >>> img, label = record_dataset[0] >>> print(img.shape, label.shape) (512, 512, 3) (1, 5) """ def __init__(self, filename, coord_normalized=True): super(RecordFileDetection, self).__init__(filename) self._coord_normalized = coord_normalized def __getitem__(self, idx): img, label = super(RecordFileDetection, self).__getitem__(idx) h, w, _ = img.shape if self._coord_normalized: label = _transform_label(label, h, w) else: label = _transform_label(label) return img, label