oxford_pets_image_segmentation.ipynb

Kernel: Python 3

Image segmentation with a U-Net-like architecture

Author: fchollet
Date created: 2019/03/20
Last modified: 2020/04/20
Description: Image segmentation model trained from scratch on the Oxford Pets dataset.

Download the data

!!wget https://www.robots.ox.ac.uk/~vgg/data/pets/data/images.tar.gz
!!wget https://www.robots.ox.ac.uk/~vgg/data/pets/data/annotations.tar.gz
!
!curl -O https://thor.robots.ox.ac.uk/datasets/pets/images.tar.gz
!curl -O https://thor.robots.ox.ac.uk/datasets/pets/annotations.tar.gz
!
!tar -xf images.tar.gz
!tar -xf annotations.tar.gz

Prepare paths of input images and target segmentation masks

import os

input_dir = "images/"
target_dir = "annotations/trimaps/"
img_size = (160, 160)
num_classes = 3
batch_size = 32

input_img_paths = sorted(
    [
        os.path.join(input_dir, fname)
        for fname in os.listdir(input_dir)
        if fname.endswith(".jpg")
    ]
)
target_img_paths = sorted(
    [
        os.path.join(target_dir, fname)
        for fname in os.listdir(target_dir)
        if fname.endswith(".png") and not fname.startswith(".")
    ]
)

print("Number of samples:", len(input_img_paths))

for input_path, target_path in zip(input_img_paths[:10], target_img_paths[:10]):
    print(input_path, "|", target_path)

What does one input image and corresponding segmentation mask look like?

from IPython.display import Image, display
from keras.utils import load_img
from PIL import ImageOps

# Display input image #7
display(Image(filename=input_img_paths[9]))

# Display auto-contrast version of corresponding target (per-pixel categories)
img = ImageOps.autocontrast(load_img(target_img_paths[9]))
display(img)

Prepare dataset to load & vectorize batches of data

import keras
import numpy as np
from tensorflow import data as tf_data
from tensorflow import image as tf_image
from tensorflow import io as tf_io


def get_dataset(
    batch_size,
    img_size,
    input_img_paths,
    target_img_paths,
    max_dataset_len=None,
):
    """Returns a TF Dataset."""

    def load_img_masks(input_img_path, target_img_path):
        input_img = tf_io.read_file(input_img_path)
        input_img = tf_io.decode_png(input_img, channels=3)
        input_img = tf_image.resize(input_img, img_size)
        input_img = tf_image.convert_image_dtype(input_img, "float32")

        target_img = tf_io.read_file(target_img_path)
        target_img = tf_io.decode_png(target_img, channels=1)
        target_img = tf_image.resize(target_img, img_size, method="nearest")
        target_img = tf_image.convert_image_dtype(target_img, "uint8")

        # Ground truth labels are 1, 2, 3. Subtract one to make them 0, 1, 2:
        target_img -= 1
        return input_img, target_img

    # For faster debugging, limit the size of data
    if max_dataset_len:
        input_img_paths = input_img_paths[:max_dataset_len]
        target_img_paths = target_img_paths[:max_dataset_len]
    dataset = tf_data.Dataset.from_tensor_slices((input_img_paths, target_img_paths))
    dataset = dataset.map(load_img_masks, num_parallel_calls=tf_data.AUTOTUNE)
    return dataset.batch(batch_size)

Prepare U-Net Xception-style model

from keras import layers


def get_model(img_size, num_classes):
    inputs = keras.Input(shape=img_size + (3,))

    ### [First half of the network: downsampling inputs] ###

    # Entry block
    x = layers.Conv2D(32, 3, strides=2, padding="same")(inputs)
    x = layers.BatchNormalization()(x)
    x = layers.Activation("relu")(x)

    previous_block_activation = x  # Set aside residual

    # Blocks 1, 2, 3 are identical apart from the feature depth.
    for filters in [64, 128, 256]:
        x = layers.Activation("relu")(x)
        x = layers.SeparableConv2D(filters, 3, padding="same")(x)
        x = layers.BatchNormalization()(x)

        x = layers.Activation("relu")(x)
        x = layers.SeparableConv2D(filters, 3, padding="same")(x)
        x = layers.BatchNormalization()(x)

        x = layers.MaxPooling2D(3, strides=2, padding="same")(x)

        # Project residual
        residual = layers.Conv2D(filters, 1, strides=2, padding="same")(
            previous_block_activation
        )
        x = layers.add([x, residual])  # Add back residual
        previous_block_activation = x  # Set aside next residual

    ### [Second half of the network: upsampling inputs] ###

    for filters in [256, 128, 64, 32]:
        x = layers.Activation("relu")(x)
        x = layers.Conv2DTranspose(filters, 3, padding="same")(x)
        x = layers.BatchNormalization()(x)

        x = layers.Activation("relu")(x)
        x = layers.Conv2DTranspose(filters, 3, padding="same")(x)
        x = layers.BatchNormalization()(x)

        x = layers.UpSampling2D(2)(x)

        # Project residual
        residual = layers.UpSampling2D(2)(previous_block_activation)
        residual = layers.Conv2D(filters, 1, padding="same")(residual)
        x = layers.add([x, residual])  # Add back residual
        previous_block_activation = x  # Set aside next residual

    # Add a per-pixel classification layer
    outputs = layers.Conv2D(num_classes, 3, activation="softmax", padding="same")(x)

    # Define the model
    model = keras.Model(inputs, outputs)
    return model


# Build model
model = get_model(img_size, num_classes)
model.summary()

Set aside a validation split

import random

# Split our img paths into a training and a validation set
val_samples = 1000
random.Random(1337).shuffle(input_img_paths)
random.Random(1337).shuffle(target_img_paths)
train_input_img_paths = input_img_paths[:-val_samples]
train_target_img_paths = target_img_paths[:-val_samples]
val_input_img_paths = input_img_paths[-val_samples:]
val_target_img_paths = target_img_paths[-val_samples:]

# Instantiate dataset for each split
# Limit input files in `max_dataset_len` for faster epoch training time.
# Remove the `max_dataset_len` arg when running with full dataset.
train_dataset = get_dataset(
    batch_size,
    img_size,
    train_input_img_paths,
    train_target_img_paths,
    max_dataset_len=1000,
)
valid_dataset = get_dataset(
    batch_size, img_size, val_input_img_paths, val_target_img_paths
)

Train the model

# Configure the model for training.
# We use the "sparse" version of categorical_crossentropy
# because our target data is integers.
model.compile(
    optimizer=keras.optimizers.Adam(1e-4), loss="sparse_categorical_crossentropy"
)

callbacks = [
    keras.callbacks.ModelCheckpoint("oxford_segmentation.keras", save_best_only=True)
]

# Train the model, doing validation at the end of each epoch.
epochs = 50
model.fit(
    train_dataset,
    epochs=epochs,
    validation_data=valid_dataset,
    callbacks=callbacks,
    verbose=2,
)

Visualize predictions

# Generate predictions for all images in the validation set

val_dataset = get_dataset(
    batch_size, img_size, val_input_img_paths, val_target_img_paths
)
val_preds = model.predict(val_dataset)


def display_mask(i):
    """Quick utility to display a model's prediction."""
    mask = np.argmax(val_preds[i], axis=-1)
    mask = np.expand_dims(mask, axis=-1)
    img = ImageOps.autocontrast(keras.utils.array_to_img(mask))
    display(img)


# Display results for validation image #10
i = 10

# Display input image
display(Image(filename=val_input_img_paths[i]))

# Display ground-truth target mask
img = ImageOps.autocontrast(load_img(val_target_img_paths[i]))
display(img)

# Display mask predicted by our model
display_mask(i)  # Note that the model only sees inputs at 150x150.