I am working on a image classification model which classify images into 5 categories. I have 5000 training images data stored in a folder but all images are of different heigth and width . like this -
'631.jpg': {'width': 81, 'heigth': 25},
'8595.jpg': {'width': 1173, 'heigth': 769},
'284.jpg': {'width': 94, 'heigth': 75},
'5999.jpg': {'width': 4220, 'heigth': 1951}
Can anyone tell me about any technique to handle this kind of data?
tf.image.resize_with_crop_or_pad(image, desired_height, desired_width)
Images smaller than desired_height
and desired_width
will be padded, and those larger will be centrally cropped.
import tensorflow as tf
import matplotlib.pyplot as plt
_, ((first, *rest), _) = tf.keras.datasets.cifar10.load_data()
modified = tf.image.resize_with_crop_or_pad(first[None, ...]/255, 48, 48)
plt.imshow(tf.squeeze(modified))
plt.show()
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