Source code for akida_models.detection.preprocess_data

#!/usr/bin/env python
# ******************************************************************************
# Copyright 2024 Brainchip Holdings Ltd.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# ******************************************************************************
Apply transformations on images and labels

__all__ = ["preprocess_dataset"]

import numpy as np
import tensorflow as tf

from .data_augmentation import (augment_sample,

def _to_tensor(image, objects):
    image = tf.convert_to_tensor(image, dtype=tf.float32)
    objects['bbox'] = tf.convert_to_tensor(objects['bbox'], dtype=tf.float32)
    objects['label'] = tf.convert_to_tensor(objects['label'], dtype=tf.int32)

    return image, objects

def _to_numpy(image, objects):
    image = image.numpy()
    objects['bbox'] = objects['bbox'].numpy()
    objects['label'] = objects['label'].numpy()

    return image, objects

def _to_absolute_coord(boxes, h, w):
    return boxes * np.array([h, w, h, w])

def _to_relative_coord(boxes, h, w):
    return boxes / np.array([h, w, h, w])

def preprocess(image, objects, aug_pipe, labels, input_shape, training):
    Preprocesses an image and its associated objects.

        image (np.ndarray): the input image as a NumPy array.
        objects (dict): dictionary containing information about objects in the image,
            including labels and bounding boxes.
        aug_pipe (iaa.Augmenter): the augmentation pipeline.
        labels (list): list of labels of interest.
        input_shape (tuple): the desired input shape for the image.
        training (bool): enable training in the augmentation pipeline.

        np.ndarray, dict: processed image and objects.
    image, objects = _to_numpy(image, objects)

    h, w, _ = image.shape
    # Transform bbox coordinates to absolute so they are compatible with imgaug
    objects['bbox'] = _to_absolute_coord(objects['bbox'], h, w)
    flip, scale, offx, offy = init_random_vars(h, w)

    if training:
        augmented_image, augmented_objects = augment_sample(image, objects, aug_pipe, labels,
                                                            flip, scale, offx, offy)
        # Due to some transformation, some bboxes get deleted which throws an error in the next
        # steps. A quick fix is to not augment these images.
        if len(augmented_objects['bbox']) != 0:
            image = augmented_image
            objects = augmented_objects

    image = resize_image(image, input_shape)
    objects = fix_obj_position_and_size(
        objects, h, w, input_shape, scale, offx, offy, training, flip)

    # Transform bbox coordinates back to relative
    objects['bbox'] = _to_relative_coord(objects['bbox'], input_shape[0], input_shape[1])
    image, objects = _to_tensor(image, objects)

    return image, objects

[docs]def preprocess_dataset(dataset, input_shape, grid_size, labels, batch_size, aug_pipe, create_targets_fn, training=True, buffer_size=1000, *args, **kwargs): """ Preprocesses the input dataset by applying the necessary image and label transformations. Args: dataset ( The input dataset. input_shape (tuple): The desired input shape for the image. grid_size (tuple): The grid size used for YOLO target generation. labels (list[str]): List of class labels. batch_size (int): Batch size for the preprocessed dataset. aug_pipe (iaa.Augmenter): The augmentation pipeline. create_targets_fn (callable): Function for creating target labels. It should accept the following parameters: objects, grid_size, num_classes and others arguments such as anchors. training (bool, optional): Flag indicating whether the dataset is for training or not. Defaults to True. buffer_size (int, optional): Buffer size for shuffling. Default to 1000. Returns: dataset ( The preprocessed dataset. """ def preprocess_sample(sample): def _apply_transformation(image, bbox, label): objects = {'bbox': bbox, 'label': label} image, objects = preprocess(image, objects, aug_pipe, labels, input_shape, training) return image, objects['bbox'], objects['label'] image, bbox, label = tf.py_function(_apply_transformation, inp=[sample['image'], sample['objects']['bbox'], sample['objects']['label']], Tout=[tf.float32, tf.float32, tf.int32]) # Set the shape of the image to avoid unknown shape errors image.set_shape(input_shape) objects = {'bbox': bbox, 'label': label} targets = create_targets_fn(objects, grid_size, len(labels), *args, **kwargs) return image, targets if training: dataset = (dataset.shuffle(buffer_size) .map(preprocess_sample, .batch(batch_size=batch_size, drop_remainder=True) .repeat() .prefetch( else: dataset = (, .batch(batch_size=batch_size, drop_remainder=True)) return dataset