Weather augmentations in Albumentations¶

This notebook demonstrates weather augmentations that are supported by Albumentations.

Import the required libraries¶

Python

import random


import cv2
from matplotlib import pyplot as plt

import albumentations as A

Define a function to visualize an image¶

Python

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

Load the image from the disk¶

Python

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

Visualize the original image¶

Python

visualize(image)

png

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.

Python

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'])

png

RandomSnow¶

Python

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'])

png

RandomSunFlare¶

Python

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'])

png

RandomShadow¶

Python

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'])

png

RandomFog¶

Python

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'])

png