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# Copyright 2022 Brainchip Holdings Ltd.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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"""
Preprocessing tools for ImageNet dataset.
"""
import numpy as np
import tensorflow as tf
from .imagenet_labels2names import imagenet_labels
[docs]@tf.function
def preprocess_image(image, image_size, training=False):
""" ImageNet data preprocessing.
Preprocessing includes cropping, and resizing for both training and
validation images. Training preprocessing introduces some random distortion
of the image to improve accuracy.
Args:
image (tf.Tensor): input image as a 3-D tensor
image_size (int): desired image size
training (bool, optional): True for training preprocessing, False for
validation and inference. Defaults to False.
"""
shape = tf.shape(image)
if training:
# For training: crop, flip and resize
bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(
shape,
tf.zeros([0, 0, 4], tf.float32), # force using whole image
use_image_if_no_bounding_boxes=True,
min_object_covered=0.1,
aspect_ratio_range=[0.75, 1.33],
area_range=[0.05, 1.0],
max_attempts=100)
image = tf.slice(image, bbox_begin, bbox_size)
image = tf.image.random_flip_left_right(image)
image = tf.image.resize(image, (image_size, image_size))
else:
# For validation/inference: aspect preserving resize and central crop
height = tf.cast(shape[0], tf.float32)
width = tf.cast(shape[1], tf.float32)
resize_min = np.round(image_size * 1.143).astype(np.float32)
scale_ratio = resize_min / tf.minimum(height, width)
# Convert back to int for TF ops
new_height = tf.cast(height * scale_ratio, tf.int32)
new_width = tf.cast(width * scale_ratio, tf.int32)
image = tf.image.resize(image, [new_height, new_width])
# Second: central crop to desired image_size
image = tf.image.resize_with_crop_or_pad(image, image_size, image_size)
return tf.cast(image, tf.float32)
[docs]def index_to_label(index):
""" Function to get an ImageNet label from an index.
Args:
index: between 0 and 999
Returns:
str: a string of coma separated labels
"""
return imagenet_labels[index]