# coding: utf-8

# ============================================================================================= #
#  Author:       Samuel Šuľan, Lucia Hradecká, Filip Lux                                        #
#  Copyright:    Lucia Hradecká     : lucia.d.hradecka@gmail.com                                #
#                Filip Lux          : lux.filip@gmail.com                                       #
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#  MIT License.                                                                                 #
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#  furnished to do so, subject to the following conditions:                                     #
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#  SOFTWARE.                                                                                    #
# ============================================================================================= #

import time

from src.core.composition import *
from src.augmentations import *


# size_sample = [(1, 256, 256, 256), (3, 256, 256, 256), (1, 256, 256, 256, 10), (3, 256, 256, 256, 10)]
size_sample = [(1, 128, 128, 128)]

# num_repeat = 100
num_repeat = 10

augmentations_to_check = [
    # RandomAffineTransform(angle_limit=[22.5, 22.5, 22.5], p=1),
    RandomAffineTransform(angle_limit=[22.5, 22.5, 22.5],
                          translation_limit=[10, 10, 10],
                          scaling_limit=[.2, .2, .2],
                          spacing=[1, 0.5, 2],
                          p=1),
    # RandomScale(scaling_limit=(0.75, 1), p=1),
    # RandomScale(scaling_limit=(1, 1.5), p=1),
    Scale(scales=0.75, p=1),
    Scale(scales=1.5, p=1),
    Flip(axes=[1, 2, 3], p=1),
    # GaussianBlur(sigma=0, p=1),
    # GaussianNoise(var_limit=(0.001, 0.1), mean=0, p=1),
    # HistogramEqualization(bins=256, p=1),
    # Normalize(mean=0, std=1, p=1),
    # NormalizeMeanStd(mean=0.1, std=1, p=1),
    # RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.1),
    # RandomFlip(axes_to_choose=None, p=1),
    # RandomGamma(gamma_limit=(0.8, 1.2), p=1),
    # RandomGaussianBlur(max_sigma=0.8, p=1),
    # RandomRotate90(axes=[1, 2, 3] , p=1),
    # Scale(scale_factor=1.5, p=1),
    # Scale(scale_factor=0.75, p=1),
]


def single_transform(iterations, size, augmentation):
    cumulative = 0
    maximum = 0
    for i in range(iterations):
        test = np.random.uniform(low=0, high=1, size=size)
        aug = Compose(transforms=[augmentation], p=1)
        data = {'image': test}
        second_time = time.time()
        aug_data = aug(**data)
        _ = aug_data['image'].shape
        time_spent = time.time() - second_time
        cumulative += time_spent
        if time_spent > maximum:
            maximum = time_spent
    return maximum, cumulative


def transformation_speed_benchmark(iterations):
    f = open(f'./runtime-{num_repeat}_iterations.txt', 'w')

    for i, augmentation in enumerate(augmentations_to_check):  # random_scale_transform

        # augmentation = augmentation_getter(augmentations_to_check, i, size)
        aug_name = augmentation.__class__.__name__
        print(aug_name)

        for size in size_sample:
            test_sample = np.random.uniform(low=0, high=1, size=size)
            test = test_sample.copy()

            aug = Compose(transforms=[augmentation], p=1)
            data = {'image': test}
            first_time = time.time()
            aug_data = aug(**data)
            first_result = time.time() - first_time
            maximum, cumulative = single_transform(iterations, size, augmentation)
            result_time = cumulative / iterations
            log_message = f'Runtime in seconds. ' \
                          f'FirstRun: {first_result:.3f}, Average: {result_time:.3f}, Maximum: {maximum:.3f}. ' \
                          f'(Transform: {aug_name}, Iterations: {iterations}, ImageSize: {size})\n'
            f.write(log_message)
            print(log_message)

    f.close()


if __name__ == '__main__':
    transformation_speed_benchmark(num_repeat)