---
title: "Introduction to imgrec"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to imgrec}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, echo = FALSE}
LOCAL <- identical(Sys.getenv("LOCAL"), "true")
knitr::opts_chunk$set(purl = LOCAL)
```

## imgrec - Image Recognition with R

*imgrec* provides an interface for image recognition using the [Google Vision API](https://cloud.google.com/vision/). It includes functions to convert data for features such as object detection and optical character recognition to data frames. The package also includes functions for analyzing image annotations.

## How to Install

You can download and install the latest development version with the devtools package by running ``devtools::install_github('cschwem2er/imgrec')``.    

For Windows users installing from github requires proper setup of [Rtools](https://cran.r-project.org/bin/windows/Rtools/).

The package can also be installed from CRAN by running `install.packages('imgrec')`.

## How to Use

### Authentification

Before loading *imgrec* you first need to initiate your authentification credentials. You need an API key from a Google Project with access permission for the Google Vision API. For this, you can first create a project using the Google Cloud platform. The setup process is explained in the API [documentation](https://cloud.google.com/vision/docs/before-you-begin). You will probably need to enable billing, but depending on your feature selection up to 1000 requests per month are free (see [pricing](https://cloud.google.com/vision/pricing)). Next following the [instructions](https://cloud.google.com/docs/authentication/api-keys#creating_an_api_key) for creating an API key.  Finally, the API key needs to be set as environment variable before using the initialization function `gvision_init()`:


```{r, eval = FALSE}
Sys.setenv(gvision_key = "Your Google Vision API key")
```

```{r eval=LOCAL, warning=FALSE}
library(imgrec)
gvision_init()
```

In order to avoid calling `Sys.setenv`, you can permanently store the API key in your `.Renviron`. I recommend `usethis::edit_r_environ()` to find and edit your environment file.

### Image annotations

Google Vision accepts common file types such as JPG, PNG, or BMP.  Images can be passed to several `get_annotations`, either as url strings or file paths to local images. In the following example,  `get_annotations` is used to retrieve annotations for a poster of the Star Wars movie [The Force Awakens](https://en.wikipedia.org/wiki/Star_Wars:_The_Force_Awakens).

<img src='https://upload.wikimedia.org/wikipedia/en/a/a2/Star_Wars_The_Force_Awakens_Theatrical_Poster.jpg' width='250'>

```{r, eval = LOCAL}
sw_image <- 'https://upload.wikimedia.org/wikipedia/en/a/a2/Star_Wars_The_Force_Awakens_Theatrical_Poster.jpg'
results <- get_annotations(images = sw_image, # image character vector
                           features = 'all', # request all available features
                           max_res = 5, # maximum number of results per feature
                           mode = 'url') # determine image type
```

The function returns a response object from the Google Vision API. It also recognizes if a user passes a character vector with multiple images. In this case, request batches are created automatically to reduce the number of required calls to the API.

After retrieving annotations, raw data can be stored in an UTF-8 encoded [JSON](https://en.wikipedia.org/wiki/JSON) file:

```{r, eval = FALSE}
temp_file_path <- tempfile(fileext = '.json')
save_json(results, temp_file_path)
```

While some users might prefer to work with raw `.json` data, which includes every single detail returned by the API, the structure is quite complex and deeply nested. To simplify the data, `parse_annotations` converts most of the features to data frames. For each feature, the original identifier of each image is included as `img_id`.


```{r, eval = LOCAL}
img_data <- parse_annotations(results) # returns list of data frames
names(img_data) # all available features
```

Once the features are converted to data frames, other R packages can be used to analyze the data. For instance, the `labels` data frame contains annotations about image content:

```{r, eval = FALSE}
img_labels <- img_data$labels
head(img_labels)
```

```{r, results = 'asis', echo = FALSE, eval = LOCAL}
img_labels <- img_data$labels
knitr::kable(head(img_labels))
```



*imgrec* also extracts bounding polygons for logos, objects, faces and landmarks. We can for instance visualize all recognized logos of the Star Wars movie poster with  [magick](https://CRAN.R-project.org/package=magick) and [ggplot2](https://CRAN.R-project.org/package=ggplot2):


*[!!] There is currently a bug when using `magick` and `ggplot2` which leads to upside down annotations. A temporary work around is to subtract image height (y) values (see code below).*




```{r eval=LOCAL, fig.height=5.36, fig.width=3.66, message=FALSE, warning=FALSE}
library(magick)
library(ggplot2)

img <- image_read(sw_image)
image_ggplot(img) +
  geom_rect(
    data = img_data$logos,
    aes(
      xmin = poly_x_min,
      xmax = poly_x_max,
      ymin = 322 - poly_y_min,
      ymax = 322 - poly_y_max
    ),
    inherit.aes = FALSE,
    color = 'yellow',
    fill = NA,
    linetype = 'dashed',
    size = 2
  ) +
  geom_text(
    data = img_data$logos,
    aes(x = poly_x_max, y = 322 - poly_y_max, label = description),
    size = 4,
    color = "yellow",
    vjust = 1
  ) +
  theme(legend.position = "none")

```

Please note that for *object recognition* data, bounding polygons are relative to image dimensions. Therefore, you need to multiply them with image width (x) and height (y). These attributes are not returned by Google Vision, but can for instance be identified with ``magick::image_info()``:

```{r, eval=LOCAL}
img_info <- image_info(img) 
img_info
```

Additional functions for feature analysis are currently in development.

## Citation


Please cite imgrec if you use it for publications:


```
Carsten Schwemmer (2024). imgrec: Image Recognition. R package version 0.1.3.
https://CRAN.R-project.org/package=imgrec
```

A BibTeX entry for LaTeX users is:

```
@Manual{,
  title = {imgrec: Image Recognition},
  author = {Carsten Schwemmer},
  year = {2024},
  note = {R package version 0.1.4},
  url = {https://CRAN.R-project.org/package=imgrec},
}
```