import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_addons as tfa
import matplotlib.pyplot as plt
import numpy as np
import cv2
import os
import scipy.io
import shutil

"""
Explanation: Object detection with Vision Transformers
Author: Karan V. Dave<br>
Date created: 2022/03/27<br>
Last modified: 2022/03/27<br>
Description: A simple Keras implementation of object detection using Vision Transformers.
Introduction
The article
Vision Transformer (ViT)
architecture by Alexey Dosovitskiy et al.
demonstrates that a pure transformer applied directly to sequences of image
patches can perform well on object detection tasks.
In this Keras example, we implement an object detection ViT
and we train it on the
Caltech 101 dataset
to detect an airplane in the given image.
This example requires TensorFlow 2.4 or higher, and
TensorFlow Addons,
from which we import the AdamW optimizer.
TensorFlow Addons can be installed via the following command:
pip install -U tensorflow-addons
Imports and setup
End of explanation
"""


# 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/tindfiles/serve/e41f5188-0b32-41fa-801b-d1e840915e80/",
    extract=True,
    archive_format="zip",  # downloaded file format
    cache_dir="/",  # cache and extract in current directory
)

# Extracting tar files found inside main zip file
shutil.unpack_archive("/datasets/caltech-101/101_ObjectCategories.tar.gz", "/")
shutil.unpack_archive("/datasets/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 train set images
    if i < int(len(annot_paths) * 0.8):
        # resize image if it is for training dataset
        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) :]),
)

"""
Explanation: Prepare dataset
We use the Caltech 101 Dataset.
End of explanation
"""



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


"""
Explanation: Implement multilayer-perceptron (MLP)
We use the code from the Keras example
Image classification with Vision Transformer
as a reference.
End of explanation
"""



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

    #     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, images):
        batch_size = tf.shape(images)[0]
        patches = tf.image.extract_patches(
            images=images,
            sizes=[1, self.patch_size, self.patch_size, 1],
            strides=[1, self.patch_size, self.patch_size, 1],
            rates=[1, 1, 1, 1],
            padding="VALID",
        )
        # return patches
        return tf.reshape(patches, [batch_size, -1, patches.shape[-1]])


"""
Explanation: Implement the patch creation layer
End of explanation
"""


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)(tf.convert_to_tensor([x_train[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 = tf.reshape(patch, (patch_size, patch_size, 3))
    plt.imshow(patch_img.numpy().astype("uint8"))
    plt.axis("off")

"""
Explanation: Display patches for an input image
End of explanation
"""



class PatchEncoder(layers.Layer):
    def __init__(self, num_patches, projection_dim):
        super(PatchEncoder, self).__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 = tf.range(start=0, limit=self.num_patches, delta=1)
        encoded = self.projection(patch) + self.position_embedding(positions)
        return encoded


"""
Explanation: Implement the patch encoding layer
The PatchEncoder layer linearly transforms a patch by projecting it into a
vector of size projection_dim. It also adds a learnable position
embedding to the projected vector.
End of explanation
"""



def create_vit_object_detector(
    input_shape,
    patch_size,
    num_patches,
    projection_dim,
    num_heads,
    transformer_units,
    transformer_layers,
    mlp_head_units,
):
    inputs = layers.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)


"""
Explanation: Build the ViT model
The ViT model has multiple Transformer blocks.
The MultiHeadAttention layer is used for self-attention,
applied to the sequence of image patches. The encoded patches (skip connection)
and self-attention layer outputs are normalized and fed into a
multilayer perceptron (MLP).
The model outputs four dimensions representing
the bounding box coordinates of an object.
End of explanation
"""



def run_experiment(model, learning_rate, weight_decay, batch_size, num_epochs):

    optimizer = tfa.optimizers.AdamW(
        learning_rate=learning_rate, weight_decay=weight_decay
    )

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

    checkpoint_filepath = "logs/"
    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
)


"""
Explanation: Run the experiment
End of explanation
"""


import matplotlib.patches as patches

# Saves the model in current path
vit_object_detector.save("vit_object_detector.h5", save_format="h5")

# 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()

"""
Explanation: Evaluate the model
End of explanation
"""


