03-annotateimg.Rmd

# Classifying Image Attributes with VIA

After completing image masking, the next step of image classification is done entirely in VGG Image Annotator (VIA).
This is an HTML software found online, and the demo can be accessed [here](https://www.robots.ox.ac.uk/~vgg/software/via/via_demo.html). It should look like this:

```{r knitr-via-preview, echo=FALSE}
knitr::include_graphics("./imgs/VIASoftware.png")
```

In our data pipeline, fully classified images become the training data for our machine learning model. Thus, it is important to have well-annotated images before proceeding onto the final step.


## Creating Project

If a project already exists in VIA, navigate to the project tab in the menu bar and click
load project to load in a preexisting .json file. Otherwise, remove the demo images
and upload your own images using **add files**. 

At any point during annotation, these files may be saved into a `.json` project using
**Project > Save** from the menu bar.

## Creating Attributes

The next step is to create 'attributes,' which are effectively characteristics for pixels
in the image. To create attributes for pixel classification, navigate to the bottom left-hand side
of the screen to name and assign characteristics for the attribute. 

More information about declaring attributes as well as answers to other questions you may have about
using VIA can be found on the user guide, available [here](https://www.robots.ox.ac.uk/~vgg/software/via/docs/user_guide.html).


## Classify Pixels

Classify pixels by dragging and clicking on the image to create boxes or shapes
using the desired **region shape**, and then assign them to attributes.
Repeat this process for all images in the folder. See below for an example using VIA's demo file.

```{r knitr-via-demo, echo=FALSE}
knitr::include_graphics("./imgs/viademo.png")
```

If you must stop midway, save your project by clicking on the **Settings** pane
in the menu bar and following the instructions. This will allow you to load in
your previously saved progress directly in the future, rather than starting from the
demo.

When finished, export your annotations as a `.csv` and `.json` file for use in the machine learning model. Here is an example CSV-file output, containing information about the file and
region attributes.

```{r csv-file-output-img, echo=FALSE}
knitr::include_graphics("./imgs/outputcsvimg.png")

```


## For Hubbard Brook Users

In our project, the classified images outputted from VIA are the *training data* for the machine learning model to classify snow, ice, and leaf cover. Our project used 12 different attributes,
including `ice_t` (transparent ice), `leaf_green`, `leaf_fall`, `open_water`, and
`snow_o` (opaque snow), among others. 

To load the attributes that we used in our project, clicking this [link](./imgs/hbwater_cameratrap_demo.json) will open a text file. Copy the contents
of this file and save it as a `.json` file. Then, select load project from the menu bar,
and upload the `.json` file. This loads a sample image and all 12 attribute classifications.

Ideally, for each image, obtain at least 20 labels.