captcha_ocr.py

"""
Title: OCR model for reading Captchas
Author: [A_K_Nain](https://twitter.com/A_K_Nain)
Date created: 2020/06/14
Last modified: 2024/03/13
Description: How to implement an OCR model using CNNs, RNNs and CTC loss.
Accelerator: GPU
Converted to Keras 3 by: [Sitam Meur](https://github.com/sitamgithub-MSIT)
"""

"""
## Introduction

This example demonstrates a simple OCR model built with the Functional API. Apart from
combining CNN and RNN, it also illustrates how you can instantiate a new layer
and use it as an "Endpoint layer" for implementing CTC loss. For a detailed
guide to layer subclassing, please check out
[this page](https://keras.io/guides/making_new_layers_and_models_via_subclassing/)
in the developer guides.
"""

"""
## Setup
"""

import os

os.environ["KERAS_BACKEND"] = "tensorflow"

import numpy as np
import matplotlib.pyplot as plt

from pathlib import Path

import tensorflow as tf
import keras
from keras import ops
from keras import layers

"""
## Load the data: [Captcha Images](https://www.kaggle.com/fournierp/captcha-version-2-images)
Let's download the data.
"""


"""shell
curl -LO https://github.com/AakashKumarNain/CaptchaCracker/raw/master/captcha_images_v2.zip
unzip -qq captcha_images_v2.zip
"""


"""
The dataset contains 1040 captcha files as `png` images. The label for each sample is a string,
the name of the file (minus the file extension).
We will map each character in the string to an integer for training the model. Similary,
we will need to map the predictions of the model back to strings. For this purpose
we will maintain two dictionaries, mapping characters to integers, and integers to characters,
respectively.
"""


# Path to the data directory
data_dir = Path("./captcha_images_v2/")

# Get list of all the images
images = sorted(list(map(str, list(data_dir.glob("*.png")))))
labels = [img.split(os.path.sep)[-1].split(".png")[0] for img in images]
characters = set(char for label in labels for char in label)
characters = sorted(list(characters))

print("Number of images found: ", len(images))
print("Number of labels found: ", len(labels))
print("Number of unique characters: ", len(characters))
print("Characters present: ", characters)

# Batch size for training and validation
batch_size = 16

# Desired image dimensions
img_width = 200
img_height = 50

# Factor by which the image is going to be downsampled
# by the convolutional blocks. We will be using two
# convolution blocks and each block will have
# a pooling layer which downsample the features by a factor of 2.
# Hence total downsampling factor would be 4.
downsample_factor = 4

# Maximum length of any captcha in the dataset
max_length = max([len(label) for label in labels])


"""
## Preprocessing
"""


# Mapping characters to integers
char_to_num = layers.StringLookup(vocabulary=list(characters), mask_token=None)

# Mapping integers back to original characters
num_to_char = layers.StringLookup(
    vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
)


def split_data(images, labels, train_size=0.9, shuffle=True):
    # 1. Get the total size of the dataset
    size = len(images)
    # 2. Make an indices array and shuffle it, if required
    indices = ops.arange(size)
    if shuffle:
        indices = keras.random.shuffle(indices)
    # 3. Get the size of training samples
    train_samples = int(size * train_size)
    # 4. Split data into training and validation sets
    x_train, y_train = images[indices[:train_samples]], labels[indices[:train_samples]]
    x_valid, y_valid = images[indices[train_samples:]], labels[indices[train_samples:]]
    return x_train, x_valid, y_train, y_valid


# Splitting data into training and validation sets
x_train, x_valid, y_train, y_valid = split_data(np.array(images), np.array(labels))


def encode_single_sample(img_path, label):
    # 1. Read image
    img = tf.io.read_file(img_path)
    # 2. Decode and convert to grayscale
    img = tf.io.decode_png(img, channels=1)
    # 3. Convert to float32 in [0, 1] range
    img = tf.image.convert_image_dtype(img, tf.float32)
    # 4. Resize to the desired size
    img = ops.image.resize(img, [img_height, img_width])
    # 5. Transpose the image because we want the time
    # dimension to correspond to the width of the image.
    img = ops.transpose(img, axes=[1, 0, 2])
    # 6. Map the characters in label to numbers
    label = char_to_num(tf.strings.unicode_split(label, input_encoding="UTF-8"))
    # 7. Return a dict as our model is expecting two inputs
    return {"image": img, "label": label}


"""
## Create `Dataset` objects
"""


train_dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
train_dataset = (
    train_dataset.map(encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE)
    .batch(batch_size)
    .prefetch(buffer_size=tf.data.AUTOTUNE)
)

validation_dataset = tf.data.Dataset.from_tensor_slices((x_valid, y_valid))
validation_dataset = (
    validation_dataset.map(encode_single_sample, num_parallel_calls=tf.data.AUTOTUNE)
    .batch(batch_size)
    .prefetch(buffer_size=tf.data.AUTOTUNE)
)

"""
## Visualize the data
"""


_, ax = plt.subplots(4, 4, figsize=(10, 5))
for batch in train_dataset.take(1):
    images = batch["image"]
    labels = batch["label"]
    for i in range(16):
        img = (images[i] * 255).numpy().astype("uint8")
        label = tf.strings.reduce_join(num_to_char(labels[i])).numpy().decode("utf-8")
        ax[i // 4, i % 4].imshow(img[:, :, 0].T, cmap="gray")
        ax[i // 4, i % 4].set_title(label)
        ax[i // 4, i % 4].axis("off")
plt.show()

"""
## Model
"""


def ctc_batch_cost(y_true, y_pred, input_length, label_length):
    label_length = ops.cast(ops.squeeze(label_length, axis=-1), dtype="int32")
    input_length = ops.cast(ops.squeeze(input_length, axis=-1), dtype="int32")
    sparse_labels = ops.cast(
        ctc_label_dense_to_sparse(y_true, label_length), dtype="int32"
    )

    y_pred = ops.log(ops.transpose(y_pred, axes=[1, 0, 2]) + keras.backend.epsilon())

    return ops.expand_dims(
        tf.compat.v1.nn.ctc_loss(
            inputs=y_pred, labels=sparse_labels, sequence_length=input_length
        ),
        1,
    )


def ctc_label_dense_to_sparse(labels, label_lengths):
    label_shape = ops.shape(labels)
    num_batches_tns = ops.stack([label_shape[0]])
    max_num_labels_tns = ops.stack([label_shape[1]])

    def range_less_than(old_input, current_input):
        return ops.expand_dims(ops.arange(ops.shape(old_input)[1]), 0) < tf.fill(
            max_num_labels_tns, current_input
        )

    init = ops.cast(tf.fill([1, label_shape[1]], 0), dtype="bool")
    dense_mask = tf.compat.v1.scan(
        range_less_than, label_lengths, initializer=init, parallel_iterations=1
    )
    dense_mask = dense_mask[:, 0, :]

    label_array = ops.reshape(
        ops.tile(ops.arange(0, label_shape[1]), num_batches_tns), label_shape
    )
    label_ind = tf.compat.v1.boolean_mask(label_array, dense_mask)

    batch_array = ops.transpose(
        ops.reshape(
            ops.tile(ops.arange(0, label_shape[0]), max_num_labels_tns),
            tf.reverse(label_shape, [0]),
        )
    )
    batch_ind = tf.compat.v1.boolean_mask(batch_array, dense_mask)
    indices = ops.transpose(
        ops.reshape(ops.concatenate([batch_ind, label_ind], axis=0), [2, -1])
    )

    vals_sparse = tf.compat.v1.gather_nd(labels, indices)

    return tf.SparseTensor(
        ops.cast(indices, dtype="int64"),
        vals_sparse,
        ops.cast(label_shape, dtype="int64"),
    )


class CTCLayer(layers.Layer):
    def __init__(self, name=None):
        super().__init__(name=name)
        self.loss_fn = ctc_batch_cost

    def call(self, y_true, y_pred):
        # Compute the training-time loss value and add it
        # to the layer using `self.add_loss()`.
        batch_len = ops.cast(ops.shape(y_true)[0], dtype="int64")
        input_length = ops.cast(ops.shape(y_pred)[1], dtype="int64")
        label_length = ops.cast(ops.shape(y_true)[1], dtype="int64")

        input_length = input_length * ops.ones(shape=(batch_len, 1), dtype="int64")
        label_length = label_length * ops.ones(shape=(batch_len, 1), dtype="int64")

        loss = self.loss_fn(y_true, y_pred, input_length, label_length)
        self.add_loss(loss)

        # At test time, just return the computed predictions
        return y_pred


def build_model():
    # Inputs to the model
    input_img = layers.Input(
        shape=(img_width, img_height, 1), name="image", dtype="float32"
    )
    labels = layers.Input(name="label", shape=(None,), dtype="float32")

    # First conv block
    x = layers.Conv2D(
        32,
        (3, 3),
        activation="relu",
        kernel_initializer="he_normal",
        padding="same",
        name="Conv1",
    )(input_img)
    x = layers.MaxPooling2D((2, 2), name="pool1")(x)

    # Second conv block
    x = layers.Conv2D(
        64,
        (3, 3),
        activation="relu",
        kernel_initializer="he_normal",
        padding="same",
        name="Conv2",
    )(x)
    x = layers.MaxPooling2D((2, 2), name="pool2")(x)

    # We have used two max pool with pool size and strides 2.
    # Hence, downsampled feature maps are 4x smaller. The number of
    # filters in the last layer is 64. Reshape accordingly before
    # passing the output to the RNN part of the model
    new_shape = ((img_width // 4), (img_height // 4) * 64)
    x = layers.Reshape(target_shape=new_shape, name="reshape")(x)
    x = layers.Dense(64, activation="relu", name="dense1")(x)
    x = layers.Dropout(0.2)(x)

    # RNNs
    x = layers.Bidirectional(layers.LSTM(128, return_sequences=True, dropout=0.25))(x)
    x = layers.Bidirectional(layers.LSTM(64, return_sequences=True, dropout=0.25))(x)

    # Output layer
    x = layers.Dense(
        len(char_to_num.get_vocabulary()) + 1, activation="softmax", name="dense2"
    )(x)

    # Add CTC layer for calculating CTC loss at each step
    output = CTCLayer(name="ctc_loss")(labels, x)

    # Define the model
    model = keras.models.Model(
        inputs=[input_img, labels], outputs=output, name="ocr_model_v1"
    )
    # Optimizer
    opt = keras.optimizers.Adam()
    # Compile the model and return
    model.compile(optimizer=opt)
    return model


# Get the model
model = build_model()
model.summary()

"""
## Training
"""


# TODO restore epoch count.
epochs = 100
early_stopping_patience = 10
# Add early stopping
early_stopping = keras.callbacks.EarlyStopping(
    monitor="val_loss", patience=early_stopping_patience, restore_best_weights=True
)

# Train the model
history = model.fit(
    train_dataset,
    validation_data=validation_dataset,
    epochs=epochs,
    callbacks=[early_stopping],
)


"""
## Inference

You can use the trained model hosted on [Hugging Face Hub](https://huggingface.co/keras-io/ocr-for-captcha)
and try the demo on [Hugging Face Spaces](https://huggingface.co/spaces/keras-io/ocr-for-captcha).
"""


def ctc_decode(y_pred, input_length, greedy=True, beam_width=100, top_paths=1):
    input_shape = ops.shape(y_pred)
    num_samples, num_steps = input_shape[0], input_shape[1]
    y_pred = ops.log(ops.transpose(y_pred, axes=[1, 0, 2]) + keras.backend.epsilon())
    input_length = ops.cast(input_length, dtype="int32")

    if greedy:
        (decoded, log_prob) = tf.nn.ctc_greedy_decoder(
            inputs=y_pred, sequence_length=input_length
        )
    else:
        (decoded, log_prob) = tf.compat.v1.nn.ctc_beam_search_decoder(
            inputs=y_pred,
            sequence_length=input_length,
            beam_width=beam_width,
            top_paths=top_paths,
        )
    decoded_dense = []
    for st in decoded:
        st = tf.SparseTensor(st.indices, st.values, (num_samples, num_steps))
        decoded_dense.append(tf.sparse.to_dense(sp_input=st, default_value=-1))
    return (decoded_dense, log_prob)


# Get the prediction model by extracting layers till the output layer
prediction_model = keras.models.Model(
    model.input[0], model.get_layer(name="dense2").output
)
prediction_model.summary()


# A utility function to decode the output of the network
def decode_batch_predictions(pred):
    input_len = np.ones(pred.shape[0]) * pred.shape[1]
    # Use greedy search. For complex tasks, you can use beam search
    results = ctc_decode(pred, input_length=input_len, greedy=True)[0][0][
        :, :max_length
    ]
    # Iterate over the results and get back the text
    output_text = []
    for res in results:
        res = tf.strings.reduce_join(num_to_char(res)).numpy().decode("utf-8")
        output_text.append(res)
    return output_text


#  Let's check results on some validation samples
for batch in validation_dataset.take(1):
    batch_images = batch["image"]
    batch_labels = batch["label"]

    preds = prediction_model.predict(batch_images)
    pred_texts = decode_batch_predictions(preds)

    orig_texts = []
    for label in batch_labels:
        label = tf.strings.reduce_join(num_to_char(label)).numpy().decode("utf-8")
        orig_texts.append(label)

    _, ax = plt.subplots(4, 4, figsize=(15, 5))
    for i in range(len(pred_texts)):
        img = (batch_images[i, :, :, 0] * 255).numpy().astype(np.uint8)
        img = img.T
        title = f"Prediction: {pred_texts[i]}"
        ax[i // 4, i % 4].imshow(img, cmap="gray")
        ax[i // 4, i % 4].set_title(title)
        ax[i // 4, i % 4].axis("off")
plt.show()