---
title: "Extracting Data from Camera Trapping Images"
author: Juergen Niedballa (<niedballa@izw-berlin.de>)
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    toc: yes
    fig_width: 6
    fig_height: 5
vignette: >
  %\VignetteIndexEntry{3. Extracting data from camera trap images}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

```{r results = "hide"}
library(camtrapR)
```

# Tabulating species and individual records: the recordTable functions

There are 2 function to tabulate species records after identification of species from images: 

* `recordTable` tabulates records of all images after species-level identification and 
* `recordTableIndividual` tabulates individuals of one species. 

Nevertheless, the underlying idea is the same. For each image, the date and time it was taken are read from the image's Exif metadata using ExifTool. Species or individual ID are read from the directory structure or image metadata (see vignette "Species and Individual Identification"). 


## `recordTable`: tabulating species records

`recordTable` is typically run after identifying species from images. It reads species IDs from the directory structure the images are placed in or from image metadata tags. 

First we define the directory containing our renamed, identified images 

```{r}
# find the directory with sample images contained in the package
wd_images_ID <- system.file("pictures/sample_images", package = "camtrapR", lib.loc = .libPaths())
```

and see how many JPG images we have (this is not necessary, but informative here).

```{r}
length(list.files(wd_images_ID, pattern = "JPG", recursive = TRUE))
```

Now we can run `recordTable`. Here is a minimal example:

```{r}
rec.db.species0 <- recordTable(inDir  = wd_images_ID,
                               IDfrom = "directory")

head(rec.db.species0)
```

Argument `IDfrom` tells the function to look for species directories within the station directories and to take species IDs from these. `IDfrom` must be set to "metadata" if metadata tagging was used for species identification.

By default, the function returns all records. Nevertheless, we get a data frame containing `r nrow(rec.db.species0)` records, less than the number of images in the image directory. This is because a number of images were taken at the same time (measured with 1 minute precision by these cameras) and the function removes duplicate records.

It may suffice to illustrate this with the Pig-tailed macaque images from stationB:

```{r}
list.files(file.path(wd_images_ID, "StationB", "MNE"))
```

There are, amongst others, 3 images taken at 17:48:00 on the 28th of April 2009. Of these, only 1 will be returned.


Here is what the columns of the record table contain:

| column  | content  |
|-------|-------------------------------------------------------------|
| `Station`   |   the station the image is from |
| `Species`   |   species name |
| `DateTimeOriginal` |  Date and time of record in R-readable format  |
| `Date` |  record date  |
| `Time` |  record time of day |
| `delta.time.secs`  |  time difference between record and last (independent) record of same species at same station / camera* (in seconds) |
| `delta.time.mins`  |  time difference between record and last (independent) record of same species at same station / camera* (in minutes)  |
| `delta.time.hours` |  time difference between record and last (independent) record of same species at same station / camera* (in hours)   |
| `delta.time.days`  |  time difference between record and last (independent) record of same species at same station / camera* (in days)   |
| `Directory` |  directory the image is in |
| `FileName`   |  image file name |

*see below: Independence between cameras within stations

### Temporal independence between records

Imagine a species that loves to hang out in front of your cameras. You will end up with hundreds of shots of the same species, maybe even the same animal. Therefore, images can be filtered using an adjustable criterion for temporal independence between subsequent records of the same species in an attempt to remove non-independent records. This is achieved via argument `minDeltaTime`. It is the minimum time difference (in minutes) between two records of the same species at the same station which are to be considered independent. The default is 0, causing the function to return all records. Setting it to a higher number, e.g. 60 (i.e., 1 hour), is commonly done to thin the number of records. Note that you will not lose records of different species, even if they fall within the specified time interval from a record.

The argument `deltaTimeComparedTo` further controls how independence between records is assessed. Setting it to "lastRecord" returns only records taken `minDeltaTime` minutes after the last record. Setting it to "lastIndependentRecord" returns only records taken `minDeltaTime` minutes after the last *independent* record.

Let's now apply an 1-hour independence criterion and define a time zone.


```{r}
rec.db.species60 <- recordTable(inDir               = wd_images_ID,
                                IDfrom              = "directory",
                                minDeltaTime        = 60,
                                deltaTimeComparedTo = "lastRecord",
                                timeZone            = "Asia/Kuala_Lumpur")

nrow(rec.db.species60)
```

Now `r nrow(rec.db.species60)` records were returned instead of `r nrow(rec.db.species0)`. The missing records were taken less than 1 hour after the prior record of the same species at the same station and therefore omitted.


### Time zones and daylight saving time

Note the warning about the missing time zone in the minimal example above. By default, camtrapR will assume UTC time zone. This should work well in most situations (even though the time zone may not be correct, strictly speaking), because UTC does not use Daylight saving time (DST, aka 'summer time'), and camera traps normally don't use time zones (hence, camera traps will normally not respect DST of the area you work in).

Now, if you work in an area that uses DST, and your camera does not know about DST, there will be a systematic 1-hour offset of all records taken during DST. In addition, records may fall into the non-existing when clocks are advanced in spring.

So, the question whether or not your cameras record the time zone becomes very important. Here is some recommendations on how to use the argument.

If your cameras save the time zone, set argument `timeZone` (both if your area has DST or not). 
If your cameras don't save the time zone, and your time zone does not have DST, argument `timeZone`
If your cameras don't save the time zone, and your time zone has DST, use the default UTC.

In other words, it is strongly advised to set argument `timeZone` to your study area's time zone (one of `OlsonNames()`), *unless* your area has DST, but your cameras don't record it. 


### Independence between cameras within stations

The issue of temporal independence between records becomes slightly more complex if more than one camera was operated at stations. That information can and should be included in the output of `recordTable` (and `recordTableIndividual`). Users can then decide whether temporal independence is to be assessed within stations or within cameras at each station. In the first case, argument `camerasIndependent` must be set to FALSE. 2 images taken at different 2 cameras at the same station within `minDeltaTime` minutes will be reported as 1 record in the record table (suitable if cameras were places in pairs). In the second case `camerasIndependent` must be set to TRUE and 2 images taken at different 2 cameras at the same station within `minDeltaTime` minutes will be reported as 2 record in the record table (suitable e.g., if cameras were located at some distance to one another and faced different trails).

The `cameraID` argument controls where camtrapR will look for camera IDs: in the file names (after renaming using `imageRename`, e.g. "renamedImages/StationA/StationA__**Camera1**__2015-12-31__23-59-59(1).JPG") or in the directory structure (e.g. renamedImages/StationA/**Camera1**/StationA__Camera1__2015-12-31__23-59-59(1).JPG"). If missing, it will be assumed there was only 1 camera per station.
  
  
### Ignoring species

Argument `exclude` can be used to ignore certain species. This is useful for omitting images in directories like "team" or "unidentified". Here is an example:

```{r}
# see what species  we recorded
table(rec.db.species60$Species)

# remove "NO_ID" by setting argument exclude = "NO_ID"
rec.db.species60.exclude <- recordTable(inDir               = wd_images_ID,
                                        IDfrom              = "directory",
                                        minDeltaTime        = 60,
                                        deltaTimeComparedTo = "lastIndependentRecord",
                                        timeZone            = "Asia/Kuala_Lumpur",
                                        exclude             = "NO_ID")

# note that "NO_ID" is gone now
table(rec.db.species60.exclude$Species)

```



### Extracting image metadata

`recordTable` and `recordTableIndividual` can both extract additional metadata from images (apart from date and time). For example, some camera models record ambient temperature or moon phase, which may be of interest. Metadata tags are stored in the images at the time they are taken and can be accessed and extracted if their tag names are known. Some tag names are standardised (e.g. "DateTimeOriginal") while others are manufacturer-specific. Therefore, function `exifTagNames` returns all Exif metadata it finds in a sample image. Users can then choose which to include in `recordTable` and `recordTableIndividual`.  


```{r}
wd_images_ID <- system.file("pictures/sample_images", package = "camtrapR")
exifTagNames(inDir = wd_images_ID, returnMetadata = FALSE)
```

If one has no idea what kind of data are behind these tag names, `returnMetadata` can be set to TRUE to return the actual metadata instead of the  tag names only. 

```{r}
exifTagNames(inDir = wd_images_ID, returnMetadata = TRUE)
```

Note that when `returnMetadata = TRUE` the tag names contain spaces whereas they don't when `returnMetadata = FALSE`. When supplying tagnames to the `recordTable` functions, use the tag names without spaces (as returned when `returnMetadata = FALSE`).

Please also note that `exifTagNames` by default returns the metadata tag group along with the tag names (since version 0.99.6), which is helpful to unambiguously identify and extract specific metadata tags as shown in the next step.

Of the tags shown here, "DateTimeOriginal" contains the date and time that camtrapR reads out. Apart from that, there is little information of ecological interest in the example data. However, for demonstration purposes, let's extract information about the camera model and make:

```{r}
rec.db.species.metadata1 <- recordTable(inDir                  = wd_images_ID,
                                        IDfrom                 = "directory",
                                        timeZone               = "Asia/Kuala_Lumpur",
                                        additionalMetadataTags = c("EXIF:Model", "EXIF:Make"))

head(rec.db.species.metadata1)
```

There are 2 additional columns containing the information from the metadata we requested.


## `recordTableIndividual`: tabulating individuals of a species

Individual identification of species is a prerequiste for running (spatial) capture-recapture models. `recordTableIndividual` and the subsequent `spatialDetectionHistory` function prepare data for these models.




```{r}
# find the directory with tagged sample images contained in the package
wd_images_individual_ID <- system.file("pictures/sample_images_tagged/LeopardCat", package = "camtrapR")
 # missing space in species = "LeopardCat" is because of CRAN package policies

 rec.db.pbe <- recordTableIndividual(inDir                  = wd_images_individual_ID,
                                     IDfrom                 = "metadata",
                                     minDeltaTime           = 60,
                                     deltaTimeComparedTo    = "lastIndependentRecord",
                                     hasStationFolders      = FALSE,         # images are not in station directories
                                     metadataIDTag          = "individual",  # the name of the metadata tag containing individual IDs
                                     timeZone               = "Asia/Kuala_Lumpur"
 )

```


### Extracting custom image metadata

In addition to the metadata that were saved when the image was taken (see above), custom metadata tags assigned in image management software can also be extracted. These may contain species ID tags if species were identified using metadata tags (instead of moving images into species directories), but also individual IDs, animal counts, sex of animals recorded, etc..



```{r}
head(rec.db.pbe)
```


# Camera Operation 

The camera operation matrix is a day-by-station matrix that states how many cameras were active at a station on a given day. Rows represent stations and columns days, beginning with the day the first camera was set up and ending the day the last camera was retrieved.

```{r}
 # first load the camera trap station table
data(camtraps)
 
camop_problem <- cameraOperation(CTtable      = camtraps,
                                 stationCol   = "Station",
                                 setupCol     = "Setup_date",
                                 retrievalCol = "Retrieval_date",
                                 writecsv     = FALSE,
                                 hasProblems  = TRUE,
                                 dateFormat   = "%d/%m/%Y"
)

# as a reminder, these are the dates in our station information table
camtraps[,-which(colnames(camtraps) %in% c("utm_y", "utm_x"))]
# now let's have a look at the first few columns of the camera operation matrix
camop_problem[, 1:5]
# and the last few
camop_problem[, (ncol(camop_problem)-6):ncol(camop_problem)]
```

If stations were not set up, values are NA. If they were set up but malfunctioning, it is 0. Operational stations get value 1.


Here is a little function for plotting the camera operation matrix. Imagine there is a typo in one of your date fields and the setup or retrieval year is wrong. You will easily be able to spot it this way.

```{r}
camopPlot <- function(camOp){
  
  which.tmp <- grep(as.Date(colnames(camOp)), pattern = "01$")
  label.tmp <- format(as.Date(colnames(camOp))[which.tmp], "%Y-%m")
  at.tmp <- which.tmp / ncol(camOp)
  
  image(t(as.matrix(camOp)), xaxt = "n", yaxt = "n", col = c("red", "grey70"))
  axis(1, at = at.tmp, labels = label.tmp)
  axis(2, at = seq(from = 0, to = 1, length.out = nrow(camOp)), labels = rownames(camOp), las = 1)
  abline(v = at.tmp, col = rgb(0,0,0, 0.2))
  box()
}
```
And this is what we get

```{r}
camopPlot(camOp = camop_problem)
```

## Saving and loading camera operation matrices

The camera operation matrix can easily be saved as a csv file (by setting argument `writecsv = TRUE`, `check.names = FALSE` and defining `outdir`). In order to load the csv into R again, it is necessary to tell R to use the station IDs (the first column) as row names:

```{r eval = FALSE}
camOp <- read.csv(file = ..., row.names = 1, check.names = FALSE)
```

`check.names = FALSE` ensures that column names (the dates) are read back into R as they are (e.g. "2015-12-01"). Otherwise one may end up with unreadable column names (at least for camtrapR) such as "X2015.12.01".

# Input for subsequent analyses

It is very easy to prepare input for occupancy and spatial capture-recapture (SCR) analyses in camtrapR. All one needs it a record table and the camera operation matrix. Making input for SCR analyses further requires the camera trap station table.

## Occupancy analyses

Occupancy models use detection/non-detection matrices in which for every station and every occasion "1"" signifies a detection of a given species and "0"" signifies non-detecion.

Here is how to obtain a detection/non-detection matrix using function `detectionHistory`. Because the function builds on prior functions (`recordTable` and `cameraOperation`) we also show these function here to provide the context.


```{r}

# create camera operation matrix
camop_no_problem <- cameraOperation(CTtable      = camtraps,
                                    stationCol   = "Station",
                                    setupCol     = "Setup_date",
                                    retrievalCol = "Retrieval_date",
                                    hasProblems  = FALSE,
                                    dateFormat   = "%d/%m/%Y"
)

# define image directory
wd_images_ID <- system.file("pictures/sample_images", package = "camtrapR")

# make record table
recordTableSample <- recordTable(inDir               = wd_images_ID,
                                 IDfrom              = "directory",
                                 minDeltaTime        = 60,
                                 deltaTimeComparedTo = "lastIndependentRecord",
                                 timeZone            = "Asia/Kuala_Lumpur"
)

# make detection history (without trapping effort)
DetHist1 <- detectionHistory(recordTable         = recordTableSample,
                            camOp                = camop_no_problem,
                            stationCol           = "Station",
                            speciesCol           = "Species",
                            recordDateTimeCol    = "DateTimeOriginal",
                            species              = "VTA",
                            occasionLength       = 7,
                            day1                 = "station",
                            includeEffort        = FALSE
)

DetHist1

```

Note the warning about the missing time zone (as in the functions `recordTable` and `recordTable`). Normally, it should be fine, but to be on the safe side, better set it to your study area's time zone.

If trapping effort is thought to influence detection probability, it can be returned by setting `includeEffort = TRUE`. This way the number of active trapping days per occasion and station is returned. 


```{r}

# make detection history (with trapping effort)
DetHist2 <- detectionHistory(recordTable          = recordTableSample,
                             camOp                = camop_no_problem,
                             stationCol           = "Station",
                             speciesCol           = "Species",
                             recordDateTimeCol    = "DateTimeOriginal",
                             species              = "VTA",
                             timeZone             = "Asia/Kuala_Lumpur",
                             occasionLength       = 7,
                             day1                 = "station",
                             includeEffort        = TRUE,
                             scaleEffort          = FALSE
)

DetHist2[[1]]  # detection history
DetHist2[[2]]  # effort (in days per occasion)
```

To help with convergence of models, the effort matrix can be scaled to mean = 0 and sd = 1 by setting `scaleEffort = TRUE`. If `writecsv = TRUE`, the scaling parameters will also be saved in a separate csv file.

```{r}

DetHist3 <- detectionHistory(recordTable          = recordTableSample,
                             camOp                = camop_no_problem,
                             stationCol           = "Station",
                             speciesCol           = "Species",
                             recordDateTimeCol    = "DateTimeOriginal",
                             species              = "VTA",
                             timeZone             = "Asia/Kuala_Lumpur",
                             occasionLength       = 7,
                             day1                 = "station",
                             includeEffort        = TRUE,
                             scaleEffort          = TRUE
)

DetHist3[[2]]  # effort (scaled)
DetHist3[[3]]  # scaling parameters for back-transformation

# backtransform scaled effort like this if needed
(DetHist3[[2]] * DetHist3[[3]]$effort.scaled.scale) + DetHist3[[3]]$effort.scaled.center
```

### Handling of incomplete occasions

The following table shows the behaviour of the detectionHistory function for different occasion-level camera operation values (table head) under different combinations of the arguments `includeEffort` and `minActiveDaysPerOccasion` in the function `detectionHistory`.

| includeEffort  | minActiveDaysPerOccasion  | camera operation all 1 | at least one 1  | all 0 | 0 and NA |  all NA |
|---|---|---|---|---|---|---|---| 
| TRUE    |   not defined |  0/1  | 0/1     |  NA  |  NA |   NA | 
| TRUE    |   defined     |  0/1  | 0/1/NA* |  NA  |  NA |   NA | 
| FALSE   |   not defined |  0/1  | NA      |  NA  |  NA |   NA | 
| FALSE   |   defined     |  0/1  | 0/1/NA* |  NA  |  NA |   NA | 
*: NA if there were less active days in an occasion than `minActiveDaysPerOccasion`

The same applies to generation of input for spatial capture-recapture analyses using `spatialDetectionHistory` as described below.

### Saving and loading detection histories

The detection history and effort matrices can easily be saved as csv files (by setting argument `writecsv = TRUE` and defining `outdir`). In order to load the csv into R again, it is necessary to tell R to use the station IDs as row names:

```{r eval = FALSE}
detHist <- read.csv(file = ..., row.names = 1)
effort  <- read.csv(file = ..., row.names = 1)
```

## Spatial Capture-Recapture analyses

Input for spatial capture-recapture analyses can be generated in the form of `capthist`-objects as defined in the `secr` package with the function `spatialDetectionHistory`. Output can be in the form of counts (number of individual detections per occasion, argument, argument `output = "count"`) or binary (was an individual detected during an occasion, argument `output = "binary"`). note that the detector type will change accordingly: "proximity" if `output = "binary"` and "count" if `output = "count"`.



```{r}

data(recordTableIndividualSample)
data(camtraps)

# create camera operation matrix (with problems/malfunction)
camop_problem <- cameraOperation(CTtable      = camtraps,
                                 stationCol   = "Station",
                                 setupCol     = "Setup_date",
                                 retrievalCol = "Retrieval_date",
                                 writecsv     = FALSE,
                                 hasProblems  = TRUE,
                                 dateFormat   = "%d/%m/%Y"
)

sdh <- spatialDetectionHistory(recordTableIndividual = recordTableIndividualSample, 
                               species               = "LeopardCat",  
                               output                = "binary",
                               camOp                 = camop_problem, 
                               CTtable               = camtraps,
                               stationCol            = "Station", 
                               speciesCol            = "Species",
                               Xcol                  = "utm_x",
                               Ycol                  = "utm_y",
                               individualCol         = "Individual",
                               recordDateTimeCol     = "DateTimeOriginal",
                               recordDateTimeFormat  = "%Y-%m-%d %H:%M:%S",
                               occasionLength        = 10, 
                               day1                  = "survey",
                               includeEffort         = TRUE,
                               timeZone              = "Asia/Kuala_Lumpur"
  )
  
# missing space in species = "LeopardCat" was introduced by recordTableIndividual 
# (because of CRAN package policies. You can have spaces in your directory names)

  summary(sdh)
  plot(sdh, tracks = TRUE)

```
