https://github.com/jaypmorgan/cloud-removal

Remove cloud shadows from ground-based observations
https://github.com/jaypmorgan/cloud-removal

astrophysics computer-vision deep-learning heliophysics machine-learning

Last synced: about 1 month ago
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Remove cloud shadows from ground-based observations

• Host: GitHub
• URL: https://github.com/jaypmorgan/cloud-removal
• Owner: jaypmorgan
• License: gpl-3.0
• Created: 2023-02-24T14:11:37.000Z (almost 2 years ago)
• Default Branch: main
• Last Pushed: 2023-12-11T11:43:09.000Z (about 1 year ago)
• Last Synced: 2023-12-11T13:11:03.861Z (about 1 year ago)
• Topics: astrophysics, computer-vision, deep-learning, heliophysics, machine-learning
• Language: Python
• Homepage:
• Size: 521 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        


# Removal of Cloud Shadows from Ground-based Solar Imagary with Deep Learning

Deep Neural Networks for the removal of cloud contamination in

ground-based observations. These codes were presented in the article

'Removing cloud shadows from ground-based solar imagery, Chaoui et

al.'

## Install

You can pip install directly from this github repo:

```bash

pip install git+https://github.com/jaypmorgan/cloud-removal.git

```

or if you've cloned the repo to a local directory:

```bash

cd cloudremoval

pip install ./

```

## Usage

Using the existing synthetic clouds dataset:

```python

import dfp

from cloudremoval.dataset import SyntheticClouds, CloudsTransform

# download the data

dataset = SyntheticClouds(download=True)

# get only a single wavelength from the data

caii = dataset.filter(lambda row: dfp.has_props(row, {"type": "Ca II"}))

# split into train and test

train, test = caii.split(lambda row: dfp.has_props(row, {"subset": "train"}))

# get the first instance:

item = train[0]

inp1 = item.input

item.target

item.mask

# Add a transform

train.transform = CloudsTransform(hflip_p=0.5, vflip_p=0.5)

item = train[0]

inp2 = item.input

```

To create a model, or load one using existing model weights:

```python

from cloudremoval.model import CloudRemover

# create a new model from scratch (i.e. random model weights)

model = CloudRemover()

# create a model using the existing weights

model = CloudRemover(pretrained=True)

# create a model using a different wavelength

model = CloudRemover(wavelength="H-alpha", pretrained=True)

# test making of predictions

dataset = SyntheticClouds(download=True, transform=CloudsTransform())

model = CloudRemover(pretrained=True)

out = model(dataset[0].input[None,...])*dataset[0].mask[None,...]

import matplotlib.pyplot as plt

plt.imshow(out[0,0].detach().cpu().numpy(), cmap="Greys_r")

```