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KerasCodeExamples.txt

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Instead of square images, many OCR models work with rectangular images. This will become clearer in a moment when we will visualize a few samples from the dataset. While aspect-unaware resizing square images does not introduce a significant amount of distortion this is not the case for rectangular images. But resizing ...
Aspect ratio is preserved.
Content of the images is not affected.
def distortion_free_resize(image, img_size):
w, h = img_size
image = tf.image.resize(image, size=(h, w), preserve_aspect_ratio=True)
# Check tha amount of padding needed to be done.
pad_height = h - tf.shape(image)[0]
pad_width = w - tf.shape(image)[1]
# Only necessary if you want to do same amount of padding on both sides.
if pad_height % 2 != 0:
height = pad_height // 2
pad_height_top = height + 1
pad_height_bottom = height
else:
pad_height_top = pad_height_bottom = pad_height // 2
if pad_width % 2 != 0:
width = pad_width // 2
pad_width_left = width + 1
pad_width_right = width
else:
pad_width_left = pad_width_right = pad_width // 2
image = tf.pad(
image,
paddings=[
[pad_height_top, pad_height_bottom],
[pad_width_left, pad_width_right],
[0, 0],
],
)
image = tf.transpose(image, perm=[1, 0, 2])
image = tf.image.flip_left_right(image)
return image
If we just go with the plain resizing then the images would look like so:
Notice how this resizing would have introduced unnecessary stretching.
Putting the utilities together
batch_size = 64
padding_token = 99
image_width = 128
image_height = 32
def preprocess_image(image_path, img_size=(image_width, image_height)):
image = tf.io.read_file(image_path)
image = tf.image.decode_png(image, 1)
image = distortion_free_resize(image, img_size)
image = tf.cast(image, tf.float32) / 255.0
return image
def vectorize_label(label):
label = char_to_num(tf.strings.unicode_split(label, input_encoding=\"UTF-8\"))
length = tf.shape(label)[0]
pad_amount = max_len - length
label = tf.pad(label, paddings=[[0, pad_amount]], constant_values=padding_token)
return label
def process_images_labels(image_path, label):
image = preprocess_image(image_path)
label = vectorize_label(label)
return {\"image\": image, \"label\": label}
def prepare_dataset(image_paths, labels):
dataset = tf.data.Dataset.from_tensor_slices((image_paths, labels)).map(
process_images_labels, num_parallel_calls=AUTOTUNE
)
return dataset.batch(batch_size).cache().prefetch(AUTOTUNE)
Prepare tf.data.Dataset objects
train_ds = prepare_dataset(train_img_paths, train_labels_cleaned)
validation_ds = prepare_dataset(validation_img_paths, validation_labels_cleaned)
test_ds = prepare_dataset(test_img_paths, test_labels_cleaned)
Visualize a few samples
for data in train_ds.take(1):
images, labels = data[\"image\"], data[\"label\"]
_, ax = plt.subplots(4, 4, figsize=(15, 8))
for i in range(16):
img = images[i]
img = tf.image.flip_left_right(img)
img = tf.transpose(img, perm=[1, 0, 2])
img = (img * 255.0).numpy().clip(0, 255).astype(np.uint8)
img = img[:, :, 0]
# Gather indices where label!= padding_token.
label = labels[i]
indices = tf.gather(label, tf.where(tf.math.not_equal(label, padding_token)))
# Convert to string.