Object detection with Vision Transformers

import os

os.environ["KERAS_BACKEND"] = "jax"  # @param ["tensorflow", "jax", "torch"]


import numpy as np
import keras
from keras import layers
from keras import ops
import matplotlib.pyplot as plt
import numpy as np
import cv2
import os
import scipy.io
import shutil

# Path to images and annotations
path_images = "./101_ObjectCategories/airplanes/"
path_annot = "./Annotations/Airplanes_Side_2/"

path_to_downloaded_file = keras.utils.get_file(
    fname="caltech_101_zipped",
    origin="https://data.caltech.edu/records/mzrjq-6wc02/files/caltech-101.zip",
    extract=True,
    archive_format="zip",  # downloaded file format
    cache_dir="/",  # cache and extract in current directory
)
download_base_dir = os.path.dirname(path_to_downloaded_file)

# Extracting tar files found inside main zip file
shutil.unpack_archive(
    os.path.join(download_base_dir, "caltech-101", "101_ObjectCategories.tar.gz"), "."
)
shutil.unpack_archive(
    os.path.join(download_base_dir, "caltech-101", "Annotations.tar"), "."
)

# list of paths to images and annotations
image_paths = [
    f for f in os.listdir(path_images) if os.path.isfile(os.path.join(path_images, f))
]
annot_paths = [
    f for f in os.listdir(path_annot) if os.path.isfile(os.path.join(path_annot, f))
]

image_paths.sort()
annot_paths.sort()

image_size = 224  # resize input images to this size

images, targets = [], []

# loop over the annotations and images, preprocess them and store in lists
for i in range(0, len(annot_paths)):
    # Access bounding box coordinates
    annot = scipy.io.loadmat(path_annot + annot_paths[i])["box_coord"][0]

    top_left_x, top_left_y = annot[2], annot[0]
    bottom_right_x, bottom_right_y = annot[3], annot[1]

    image = keras.utils.load_img(
        path_images + image_paths[i],
    )
    (w, h) = image.size[:2]

    # resize images
    image = image.resize((image_size, image_size))

    # convert image to array and append to list
    images.append(keras.utils.img_to_array(image))

    # apply relative scaling to bounding boxes as per given image and append to list
    targets.append(
        (
            float(top_left_x) / w,
            float(top_left_y) / h,
            float(bottom_right_x) / w,
            float(bottom_right_y) / h,
        )
    )

# Convert the list to numpy array, split to train and test dataset
(x_train), (y_train) = (
    np.asarray(images[: int(len(images) * 0.8)]),
    np.asarray(targets[: int(len(targets) * 0.8)]),
)
(x_test), (y_test) = (
    np.asarray(images[int(len(images) * 0.8) :]),
    np.asarray(targets[int(len(targets) * 0.8) :]),
)

def mlp(x, hidden_units, dropout_rate):
    for units in hidden_units:
        x = layers.Dense(units, activation=keras.activations.gelu)(x)
        x = layers.Dropout(dropout_rate)(x)
    return x

class Patches(layers.Layer):
    def __init__(self, patch_size):
        super().__init__()
        self.patch_size = patch_size

    def call(self, images):
        input_shape = ops.shape(images)
        batch_size = input_shape[0]
        height = input_shape[1]
        width = input_shape[2]
        channels = input_shape[3]
        num_patches_h = height // self.patch_size
        num_patches_w = width // self.patch_size
        patches = keras.ops.image.extract_patches(images, size=self.patch_size)
        patches = ops.reshape(
            patches,
            (
                batch_size,
                num_patches_h * num_patches_w,
                self.patch_size * self.patch_size * channels,
            ),
        )
        return patches

    def get_config(self):
        config = super().get_config()
        config.update({"patch_size": self.patch_size})
        return config

patch_size = 32  # Size of the patches to be extracted from the input images

plt.figure(figsize=(4, 4))
plt.imshow(x_train[0].astype("uint8"))
plt.axis("off")

patches = Patches(patch_size)(np.expand_dims(x_train[0], axis=0))
print(f"Image size: {image_size} X {image_size}")
print(f"Patch size: {patch_size} X {patch_size}")
print(f"{patches.shape[1]} patches per image \n{patches.shape[-1]} elements per patch")


n = int(np.sqrt(patches.shape[1]))
plt.figure(figsize=(4, 4))
for i, patch in enumerate(patches[0]):
    ax = plt.subplot(n, n, i + 1)
    patch_img = ops.reshape(patch, (patch_size, patch_size, 3))
    plt.imshow(ops.convert_to_numpy(patch_img).astype("uint8"))
    plt.axis("off")

class PatchEncoder(layers.Layer):
    def __init__(self, num_patches, projection_dim):
        super().__init__()
        self.num_patches = num_patches
        self.projection = layers.Dense(units=projection_dim)
        self.position_embedding = layers.Embedding(
            input_dim=num_patches, output_dim=projection_dim
        )

    # Override function to avoid error while saving model
    def get_config(self):
        config = super().get_config().copy()
        config.update(
            {
                "input_shape": input_shape,
                "patch_size": patch_size,
                "num_patches": num_patches,
                "projection_dim": projection_dim,
                "num_heads": num_heads,
                "transformer_units": transformer_units,
                "transformer_layers": transformer_layers,
                "mlp_head_units": mlp_head_units,
            }
        )
        return config

    def call(self, patch):
        positions = ops.expand_dims(
            ops.arange(start=0, stop=self.num_patches, step=1), axis=0
        )
        projected_patches = self.projection(patch)
        encoded = projected_patches + self.position_embedding(positions)
        return encoded

def create_vit_object_detector(
    input_shape,
    patch_size,
    num_patches,
    projection_dim,
    num_heads,
    transformer_units,
    transformer_layers,
    mlp_head_units,
):
    inputs = keras.Input(shape=input_shape)
    # Create patches
    patches = Patches(patch_size)(inputs)
    # Encode patches
    encoded_patches = PatchEncoder(num_patches, projection_dim)(patches)

    # Create multiple layers of the Transformer block.
    for _ in range(transformer_layers):
        # Layer normalization 1.
        x1 = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)
        # Create a multi-head attention layer.
        attention_output = layers.MultiHeadAttention(
            num_heads=num_heads, key_dim=projection_dim, dropout=0.1
        )(x1, x1)
        # Skip connection 1.
        x2 = layers.Add()([attention_output, encoded_patches])
        # Layer normalization 2.
        x3 = layers.LayerNormalization(epsilon=1e-6)(x2)
        # MLP
        x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1)
        # Skip connection 2.
        encoded_patches = layers.Add()([x3, x2])

    # Create a [batch_size, projection_dim] tensor.
    representation = layers.LayerNormalization(epsilon=1e-6)(encoded_patches)
    representation = layers.Flatten()(representation)
    representation = layers.Dropout(0.3)(representation)
    # Add MLP.
    features = mlp(representation, hidden_units=mlp_head_units, dropout_rate=0.3)

    bounding_box = layers.Dense(4)(
        features
    )  # Final four neurons that output bounding box

    # return Keras model.
    return keras.Model(inputs=inputs, outputs=bounding_box)

def run_experiment(model, learning_rate, weight_decay, batch_size, num_epochs):
    optimizer = keras.optimizers.AdamW(
        learning_rate=learning_rate, weight_decay=weight_decay
    )

    # Compile model.
    model.compile(optimizer=optimizer, loss=keras.losses.MeanSquaredError())

    checkpoint_filepath = "vit_object_detector.weights.h5"
    checkpoint_callback = keras.callbacks.ModelCheckpoint(
        checkpoint_filepath,
        monitor="val_loss",
        save_best_only=True,
        save_weights_only=True,
    )

    history = model.fit(
        x=x_train,
        y=y_train,
        batch_size=batch_size,
        epochs=num_epochs,
        validation_split=0.1,
        callbacks=[
            checkpoint_callback,
            keras.callbacks.EarlyStopping(monitor="val_loss", patience=10),
        ],
    )

    return history


input_shape = (image_size, image_size, 3)  # input image shape
learning_rate = 0.001
weight_decay = 0.0001
batch_size = 32
num_epochs = 100
num_patches = (image_size // patch_size) ** 2
projection_dim = 64
num_heads = 4
# Size of the transformer layers
transformer_units = [
    projection_dim * 2,
    projection_dim,
]
transformer_layers = 4
mlp_head_units = [2048, 1024, 512, 64, 32]  # Size of the dense layers


history = []
num_patches = (image_size // patch_size) ** 2

vit_object_detector = create_vit_object_detector(
    input_shape,
    patch_size,
    num_patches,
    projection_dim,
    num_heads,
    transformer_units,
    transformer_layers,
    mlp_head_units,
)

# Train model
history = run_experiment(
    vit_object_detector, learning_rate, weight_decay, batch_size, num_epochs
)


def plot_history(item):
    plt.plot(history.history[item], label=item)
    plt.plot(history.history["val_" + item], label="val_" + item)
    plt.xlabel("Epochs")
    plt.ylabel(item)
    plt.title("Train and Validation {} Over Epochs".format(item), fontsize=14)
    plt.legend()
    plt.grid()
    plt.show()


plot_history("loss")

import matplotlib.patches as patches

# Saves the model in current path
vit_object_detector.save("vit_object_detector.keras")


# To calculate IoU (intersection over union, given two bounding boxes)
def bounding_box_intersection_over_union(box_predicted, box_truth):
    # get (x, y) coordinates of intersection of bounding boxes
    top_x_intersect = max(box_predicted[0], box_truth[0])
    top_y_intersect = max(box_predicted[1], box_truth[1])
    bottom_x_intersect = min(box_predicted[2], box_truth[2])
    bottom_y_intersect = min(box_predicted[3], box_truth[3])

    # calculate area of the intersection bb (bounding box)
    intersection_area = max(0, bottom_x_intersect - top_x_intersect + 1) * max(
        0, bottom_y_intersect - top_y_intersect + 1
    )

    # calculate area of the prediction bb and ground-truth bb
    box_predicted_area = (box_predicted[2] - box_predicted[0] + 1) * (
        box_predicted[3] - box_predicted[1] + 1
    )
    box_truth_area = (box_truth[2] - box_truth[0] + 1) * (
        box_truth[3] - box_truth[1] + 1
    )

    # calculate intersection over union by taking intersection
    # area and dividing it by the sum of predicted bb and ground truth
    # bb areas subtracted by  the interesection area

    # return ioU
    return intersection_area / float(
        box_predicted_area + box_truth_area - intersection_area
    )


i, mean_iou = 0, 0

# Compare results for 10 images in the test set
for input_image in x_test[:10]:
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 15))
    im = input_image

    # Display the image
    ax1.imshow(im.astype("uint8"))
    ax2.imshow(im.astype("uint8"))

    input_image = cv2.resize(
        input_image, (image_size, image_size), interpolation=cv2.INTER_AREA
    )
    input_image = np.expand_dims(input_image, axis=0)
    preds = vit_object_detector.predict(input_image)[0]

    (h, w) = (im).shape[0:2]

    top_left_x, top_left_y = int(preds[0] * w), int(preds[1] * h)

    bottom_right_x, bottom_right_y = int(preds[2] * w), int(preds[3] * h)

    box_predicted = [top_left_x, top_left_y, bottom_right_x, bottom_right_y]
    # Create the bounding box
    rect = patches.Rectangle(
        (top_left_x, top_left_y),
        bottom_right_x - top_left_x,
        bottom_right_y - top_left_y,
        facecolor="none",
        edgecolor="red",
        linewidth=1,
    )
    # Add the bounding box to the image
    ax1.add_patch(rect)
    ax1.set_xlabel(
        "Predicted: "
        + str(top_left_x)
        + ", "
        + str(top_left_y)
        + ", "
        + str(bottom_right_x)
        + ", "
        + str(bottom_right_y)
    )

    top_left_x, top_left_y = int(y_test[i][0] * w), int(y_test[i][1] * h)

    bottom_right_x, bottom_right_y = int(y_test[i][2] * w), int(y_test[i][3] * h)

    box_truth = top_left_x, top_left_y, bottom_right_x, bottom_right_y

    mean_iou += bounding_box_intersection_over_union(box_predicted, box_truth)
    # Create the bounding box
    rect = patches.Rectangle(
        (top_left_x, top_left_y),
        bottom_right_x - top_left_x,
        bottom_right_y - top_left_y,
        facecolor="none",
        edgecolor="red",
        linewidth=1,
    )
    # Add the bounding box to the image
    ax2.add_patch(rect)
    ax2.set_xlabel(
        "Target: "
        + str(top_left_x)
        + ", "
        + str(top_left_y)
        + ", "
        + str(bottom_right_x)
        + ", "
        + str(bottom_right_y)
        + "\n"
        + "IoU"
        + str(bounding_box_intersection_over_union(box_predicted, box_truth))
    )
    i = i + 1

print("mean_iou: " + str(mean_iou / len(x_test[:10])))
plt.show()