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Weather augmentations in Albumentations¶

This notebook demonstrates weather augmentations that are supported by Albumentations.

Import the required libraries¶

import random

import cv2
from matplotlib import pyplot as plt

import albumentations as A

Define a function to visualize an image¶

def visualize(image):
    plt.figure(figsize=(20, 10))
    plt.axis('off')
    plt.imshow(image)

Load the image from the disk¶

image = cv2.imread('images/weather_example.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

Visualize the original image¶

visualize(image)

RandomRain¶

We fix the random seed for visualization purposes, so the augmentation will always produce the same result. In a real computer vision pipeline, you shouldn't fix the random seed before applying a transform to the image because, in that case, the pipeline will always output the same image. The purpose of image augmentation is to use different transformations each time.

transform = A.Compose(
    [A.RandomRain(brightness_coefficient=0.9, drop_width=1, blur_value=5, p=1)],
)
random.seed(7)
transformed = transform(image=image)
visualize(transformed['image'])

RandomSnow¶

transform = A.Compose(
    [A.RandomSnow(brightness_coeff=2.5, snow_point_lower=0.3, snow_point_upper=0.5, p=1)],
)
random.seed(7)
transformed = transform(image=image)
visualize(transformed['image'])

RandomSunFlare¶

transform = A.Compose(
    [A.RandomSunFlare(flare_roi=(0, 0, 1, 0.5), angle_lower=0.5, p=1)],
)
random.seed(7)
transformed = transform(image=image)
visualize(transformed['image'])

RandomShadow¶

transform = A.Compose(
    [A.RandomShadow(num_shadows_lower=1, num_shadows_upper=1, shadow_dimension=5, shadow_roi=(0, 0.5, 1, 1), p=1)],
)
random.seed(7)
transformed = transform(image=image)
visualize(transformed['image'])

RandomFog¶

transform = A.Compose(
    [A.RandomFog(fog_coef_lower=0.7, fog_coef_upper=0.8, alpha_coef=0.1, p=1)],
)
random.seed(7)
transformed = transform(image=image)
visualize(transformed['image'])

RandomShadow¶

transform = A.Compose(
    [A.RandomShadow(num_shadows_lower=1, num_shadows_upper=1, shadow_dimension=5, shadow_roi=(0, 0.5, 1, 1), p=1)],
)
random.seed(7)
transformed = transform(image=image)
visualize(transformed['image'])

RandomFog¶

transform = A.Compose(
    [A.RandomFog(fog_coef_lower=0.7, fog_coef_upper=0.8, alpha_coef=0.1, p=1)],
)
random.seed(7)
transformed = transform(image=image)
visualize(transformed['image'])