Virtualdub 1.10.4 volume meter code#
Virtualdub 1.10.4 volume meter license#

In order to implement effective mitigation strategies, rigorous monitoring of penguin colonies is required. In the Southern Ocean, two key threats – sea ice change 4, 5 and an expanding krill fishery 6 – have the potential to act synergistically, with a longer ice-free season facilitating a longer harvest 7. The penguins (Family: Spheniscidae) are amongst the most threatened, facing environmental stressors such as invasive species, overfishing, pollution and climate change 1– 3. Seabird population changes are considered a reflection of changes within the marine environment, making seabird species key indicators of ecosystem health 1, 2. We encourage the use of this large open-source dataset, and the associated processing methodologies, for both ecological studies and continued machine learning and computer vision development. We provide example files for 14 Penguin Watch cameras, generated from 63,070 raw images annotated by 50,445 volunteers. We present two alternative pathways for producing counts: (1) via the Zooniverse citizen science project Penguin Watch and (2) via a computer vision algorithm ( Pengbot), and share a comparison of citizen science-, machine learning-, and expert- derived counts.

The latter provide useful summaries of colony spatial structure (which can indicate phenological stage) and can be used to detect movement – metrics which could be valuable for a number of different monitoring scenarios, including image capture during aerial surveys. Here we publish pipelines to process raw time-lapse imagery, resulting in count data (number of penguins per image) and ‘nearest neighbour distance’ measurements. Time-lapse cameras facilitate remote and high-resolution monitoring of wild animal and plant communities, but the image data produced require further processing to be useful. 1, right, ‘Step 2’ and ‘Step 3’) is written in R (v3.6.0) it can be accessed through GitHub at and a static version is archived on Zenodo 30.

Virtualdub 1.10.4 volume meter code#

The Pengbot model 11, associated code and instructions, and the dataset used to train the neural network can be found at the following address.

The Narwhal Plotting Script (output = Narwhal Plots – graphs displaying Narwhal summary statistics) is written in R (v3.6.0) it can be accessed through GitHub at and a static version is archived on Zenodo 29.

1, left, ‘Step 4’ output = Narwhal Files) is written in R (v3.6.0) it can be accessed through GitHub at and a static version is archived on Zenodo 28. 1, left, ‘Step 3’ output = Kraken Files) is written in R (v3.6.0) it can be accessed through GitHub at, and a static version is archived on Zenodo 27. A static version of this script is also archived on Figshare 14.Ĭode used to produce files within Dataset 2 15: The aggregation algorithm is written in Python (v 2.7) and can be found on GitHub at.

Raw Penguin Watch volunteer classifications (xy coordinate clicks) were clustered into ‘consensus clicks’ using agglomerative hierarchical clustering (Fig.

A static version (written using v3.4.1) is archived on Figshare 26. The code is publically available via GitHub at.

Raw Penguin Watch time-lapse photographs are renamed and resized using an R (currently v3.6.0) script.

Ĭode used to produce files within Dataset 1 12: Source code for: Pengbot Counting Script. Source code for: Narwhal Plotting Script. Source code for: Penguin Watch Image Processing Script (R) Figshare. Data from: Processing citizen science- and machine- annotated time-lapse imagery for biologically meaningful metrics. Source code for: Penguin Watch Aggregation Script (Python) Figshare. Data from: Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project, v4. The Creative Commons Public Domain Dedication waiver applies to the metadata files associated with this article.

Virtualdub 1.10.4 volume meter license#

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