---
title: "Reading and Writing Images"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Reading and Writing Images}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  crop = TRUE
)
knitr::knit_hooks$set(crop = knitr::hook_pdfcrop)
```

## Reading TIFF files

Check out the following video:

![](`r system.file("img", "Rlogo-banana.gif", package = "ijtiff")`)

As you can see, it's a colour video of a banana dancing in front of the R logo. Hence, it has colour channel (red, green and blue) and frame (a video is comprised of several _frames_) information inside. I have this video saved in a TIFF file.

```{r dancing-banana-path}
path_dancing_banana <- system.file("img", "Rlogo-banana.tif",
                                   package = "ijtiff")
print(path_dancing_banana)
```

To read it in, you just need `read_tif()` and the path to the image.

```{r read-dancing-banana}
pacman::p_load(ijtiff, magrittr)
img_dancing_banana <- read_tif(path_dancing_banana)
```

Let's take a peek inside of `img_dancing_banana`.

```{r peek}
print(img_dancing_banana)
```

You can see it's a `r length(dim(img_dancing_banana))`-dimensional array. The last two dimensions are `r dplyr::nth(dim(img_dancing_banana), -2)` and `r dplyr::nth(dim(img_dancing_banana), -1)`; this is because these are the channel and frame slots respectively: the image has `r dplyr::nth(dim(img_dancing_banana), -2)` channels (red, green and blue) and `r dplyr::nth(dim(img_dancing_banana), -1)` frames. The first two dimensions tell us that the images in the video are `r dim(img_dancing_banana)[1]` pixels tall and `r dim(img_dancing_banana)[2]` pixels wide. The image object is of class `ijtiff_img`. This guarantees that it is a 4-dimensional array with this structure. The attributes of the `ijtiff_img` give information on the various TIFF tags that were part of the TIFF image. You can read more about various TIFF tags at https://www.loc.gov/preservation/digital/formats/content/tiff_tags.shtml. To read just the tags and not the image, use the `read_tags()` function.

Let's visualize the constituent parts of that 8-frame, colour TIFF.

```{r red-blue-green-banana, echo=FALSE, message=FALSE, out.width='100%', dpi=300}
d <- dim(img_dancing_banana)
reds <- purrr::map(seq_len(d[4]), ~ img_dancing_banana[, , 1, .]) %>% 
  purrr::reduce(cbind)
greens <- purrr::map(seq_len(d[4]), ~ img_dancing_banana[, , 2, .]) %>% 
  purrr::reduce(cbind)
blues <- purrr::map(seq_len(d[4]), ~ img_dancing_banana[, , 3, .]) %>% 
  purrr::reduce(cbind)
to_display <- array(0, dim = c(3 * nrow(reds), ncol(reds), 3, 1))
to_display[seq_len(nrow(reds)), , 1, ] <- reds
to_display[seq_len(nrow(reds)) + nrow(reds), , 2, ] <- greens
to_display[seq_len(nrow(reds)) + 2 * nrow(reds), , 3, ] <- blues
display(to_display, axes = FALSE)
```

There you go: 8 frames in 3 colours.


### Reading only certain frames

It's possible to read only certain frames. This can be a massive time and memory saver when working with large images.

Suppose we only want frames 3, 5 and 7 from the image above.

```{r threefiveseven}
img_dancing_banana357 <- read_tif(path_dancing_banana, frames = c(3, 5, 7))
```

Let's visualize again.

```{r red-bblue-green-banana357, echo=FALSE, message=FALSE, out.width='100%', dpi=300}
d <- dim(img_dancing_banana357)
reds <- purrr::map(seq_len(d[4]), ~ img_dancing_banana357[, , 1, .]) %>% 
  purrr::reduce(cbind)
greens <- purrr::map(seq_len(d[4]), ~ img_dancing_banana357[, , 2, .]) %>% 
  purrr::reduce(cbind)
blues <- purrr::map(seq_len(d[4]), ~ img_dancing_banana357[, , 3, .]) %>% 
  purrr::reduce(cbind)
to_display <- array(0, dim = c(3 * nrow(reds), ncol(reds), 3, 1))
to_display[seq_len(nrow(reds)), , 1, ] <- reds
to_display[seq_len(nrow(reds)) + nrow(reds), , 2, ] <- greens
to_display[seq_len(nrow(reds)) + 2 * nrow(reds), , 3, ] <- blues
display(to_display, axes = FALSE)
```

Just in case you're wondering, it's not currently possible to read only certain channels. 

### More examples

If you read an image with only one frame, the frame slot (4) will still be there:

```{r one-frame, dpi=300, out.width='90%'}
path_rlogo <- system.file("img", "Rlogo.tif", package = "ijtiff")
img_rlogo <- read_tif(path_rlogo) 
dim(img_rlogo)  # 4 channels, 1 frame
class(img_rlogo)
display(img_rlogo, axes = FALSE)
```

You can also have an image with only 1 channel:

```{r one-channel, dpi=300, out.width='90%'}
path_rlogo_grey <- system.file("img", "Rlogo-grey.tif", package = "ijtiff")
img_rlogo_grey <- read_tif(path_rlogo_grey)
dim(img_rlogo_grey)  # 1 channel, 1 frame
display(img_rlogo_grey, axes = FALSE)
```



## Writing TIFF files

To write an image, you need an object in the style of an `ijtiff_img` object (see `help("ijtiff_img", package = "ijtiff")`). The basic idea is to have your image in a 4-dimensional array with the structure `img[y, x, channel, frame]`. Then, to write this image to the location `path`, you just type `write_tif(img, path)`. 

```{r write-tif}
path <- tempfile(pattern = "dancing-banana", fileext = ".tif")
print(path)
write_tif(img_dancing_banana, path)
```



## Reading text images

Note: if you don't know what text images are, see `vignette("text-images", package = "ijtiff")`.

You may have a text image that you want to read (but realistically, you might never).

```{r read-txt-img}
path_txt_img <- system.file("img", "Rlogo-grey.txt", package = "ijtiff")
txt_img <- read_txt_img(path_txt_img)
```


## Writing text images

Writing a text image works as you'd expect.

```{r, write-txt-img}
write_txt_img(txt_img, path = tempfile(pattern = "txtimg", fileext = ".txt"))
```
