vignettes/camtrapr1.Rmd
camtrapr1.RmdcamtrapR provides functions for organising and managing camera trap images and for the preparation of occupancy and (spatial) capture-recapture analyses. The package handles JPG images and supports video files.
The five vignettes demonstrate the camtrapR workflow using the sample data set that is included in the package. Sample data were generated in a camera trapping survey in Sabah, Malaysian Borneo (A. Mohamed et al. J. Mammal., 94, 82-89; 2013).
camtrapR is compatible with other software for camera trap data management. If you prefer to manage images and identify species using other software, you can usually do so. For example, Camelot and TRAPPER can export csv files that can be used as input for camtrapR. To maximize compatibility, camtrapR uses a simple data structure and standard data types (data frames and matrices) to store information about the camera trap survey and the detected species and individuals in separate data frames.
You can install the newest stable version from CRAN via:
install.packages("camtrapR")Alternatively, you can install the development version with the latest features and bugfixes from GitHub (requires package remotes):
library(remotes)
install_github("jniedballa/camtrapR")Afterwards, load camtrapR via:
The latest vignettes, function reference and changelog can be found on the camtrapR GitHub pages or the camtrapR page on CRAN. Source code can be found on GitHub.
The camtrapR workflow consists of a series of functions that build upon one another in a logical sequence. Below is a list of functions grouped by work steps. Detailed information about each function can be found in the sections below, as well as in the other vignettes and the function help files.
createStationFoldersfixDateTimeOriginal (only if necessary)timeShiftImages (only if necessary)imageRenameaddCopyrightTagcheckSpeciesNamescreateSpeciesFolderscheckSpeciesIdentificationappendSpeciesNamesexifTagNamesrecordTabledetectionMapsactivityHistogramactivityDensityactivityRadialactivityOverlapsurveyReportgetSpeciesImagesrecordTableIndividualcameraOperationdetectionHistory (for occupancy analyses)spatialDetectionHistory (for spatial capture-recapture
analyses)communityModel (for multi-species occupancy
models)camtrapR was designed for camera trapping surveys in which an array of camera trap stations (each consisting of one or more camera traps per station) is operated in a study area. The vignettes will provide a guideline for how to use camtrapR in different sampling situations.
Since version 2.0.0, the functions recordTable and
recordTableIndividual support video files. In addition
exifTagNames can read and display video metadata via
ExifTool.
Video support allows you to process video files and identify species and individuals using the same workflow as for images. With minimal effort, you can extract and tabulate these information using the familiar functions within camtrapR.
Since this is a new feature, please be sure to read the vignette sections on video and the help files of the relevant functions and check the example code. Also, please test the workflow before working on larger amounts of data. Unfortunately, I could not include a sample video in the package due to file size constraints, but I hope you will get the idea. And please, let me know about your experiences working with videos in camtrapR and possible problems. I would really appreciate the feedback to inmprove and extend video support.
The main problem in working with videos is the lack of a standardized date/time tag in the metadata. Only if such a tag is present, camtrapR can successfully extract the video date/time. Not all cameras save date and time in a suitable format in the metadata, so before conducting a study using videos, please make sure that your cameras record a suitable (i.e., persistent) date/time tag in the metadata. You can check if there is a suitable tag available in the video metadata:
exifTagNames to identify a usable video date/time
tagrecordTable
When using exifTagNames, make sure the date/time tag you
choose is identical to the date/time shown in the data field of the
video. In the example videos I tests, tags such as
“QuickTime:CreateDate” worked well. The tags “File Creation Date/Time”,
“File Modification Date/Time” and “File Access Date/Time” are not
suitable, since they are updated when the video files are moved or
copied.
This also means that if there is no suitable date/time tag in the video metadata, camtrapR will not be able to return information for these videos and will omit them. It really depends on your camera model. So if you consider using video for your surveys, please first check (with that specific camera model) if there is a suitable date/time tag in the video metadata. A suitable date/time tag is one that doesn’t change when the video file is moved, copied or otherwise modified. If a camera fails to record such a tag, camtrapR can’t return proper date and time, making the videos useless.
In digiKam, it is possible to assign tags to videos. Just like images, videos can be tagged with species or individual IDs, group size, sex, age group or whatever information you are interested in. BUT, in contrast to JPG images, digiKam cannot write these tags directly into the video metadata. Instead, video tags are stored in the digiKam database on your hard disk. To access tags assigned to videos, camtrapR has to read them directly from the digiKam database. This means that in order for camtrapR to read video tags, the videos need to be included in the digiKam database. Moving or copying the videos to a different location will strip the tags you assigned.
Users need to provide the filename of the digiKam database and the
directory it is in (digiKam > Settings > configure digiKam >
Database; the directory is stated there). In that menu, also ensure you
use a SQLite database (the default). When the digiKam database
information are provided to camtrapR, all the tags you assigned to
videos in digiKam will be extracted and tabulated just like image tags.
See the help files of recordTable and
recordTableIndividual for details.
To facilitate understanding here is a glossary on how we use specific terms. It is standard camera trapping nomenclature.
camtrapR relies on the free and open-source software ExifTool written by Phil Harvey to extract metadata from images. ExifTool is freely available here and runs on Windows, Linux and MacOS. In order to use it, your system must be able to find the software. On MacOS, Linux and Unix it needs installation and should afterwards be found by the system without problems. How to set up Exiftool for Windows is detailed below. General installation instruction can be found here.
On Windows, ExifTool does not need installation. In order for Windows to be able to find ExifTool, follow the instructions found here. Be sure to download the stand-alone executable. The download file is a .zip which you need to unzip, rename the contained “exiftool(-k).exe” file to “exiftool.exe” and place it in, e.g., C:/Windows. Windows will be able to find ExifTool if exiftool.exe is placed in a directory that is contained in the Windows PATH variable (e.g. C:/Windows).
# Check which directories are in PATH (output not shown here)
Sys.getenv("PATH")
# Check if the system can find Exiftool (if output is empty "", system can't find Exiftool)
Sys.which("exiftool")## exiftool
## "C:\\PR2A0A~1\\exiftool.exe"If exiftool.exe cannot be placed in a PATH directory (e.g. due to
limited user privileges), users can place exiftool.exe in any directory
and then add that directory to PATH temporarily for use in the running R
session using the function exiftoolPath. This is necessary
if the directory containing exiftool.exe is not in PATH by default.
# this is a dummy example assuming the directory structure: C:/Path/To/Exiftool/exiftool.exe
exiftool_dir <- "C:/Path/To/Exiftool"
exiftoolPath(exiftoolDir = exiftool_dir)## Warning in exiftoolPath(exiftoolDir = exiftool_dir): The 'exiftoolPath'
## function is deprecated. Please use 'addToPath' instead.Afterwards, Windows (via R) should be able to find ExifTool during the active session.
ExifTool is the performance bottleneck of the package functions. It can extract metadata at a rate of about 160 images per second (approx. 10,000 images per minute, measured on a laptop computer running Windows 7, Intel Core i5-2410M / 2.3 GHz, 6 GB RAM). Performance tends to be lower when ExifTool is first called and increases afterwards; and it decreases in situations with numerous camera trap stations with few images each (because ExifTool is called separately on every station directory and the associated overhead of loading ExifTool each time).
Before looking at how images are handled, let’s first familiarise ourselves with how information about camera trap stations are stored. Camera trap station information are tabulated in a simple data frame with 1 row per station (if there was 1 camera per station) or 1 row per camera (if there was more than 1 camera per station). Information to be stored in this table are: station (and camera) IDs, geographic coordinates, setup and retrieval dates and possible dates when camera were not operational (because of malfunction, animal interference, empty batteries, full memory cards etc.). The table can be generated standard spreadsheet software and loaded into R.
# here is a sample camera trap station table
data("camtraps")
camtraps## Station utm_y utm_x Setup_date Retrieval_date Problem1_from Problem1_to
## 1 StationA 604000 526000 02/04/2009 14/05/2009
## 2 StationB 606000 523000 03/04/2009 16/05/2009
## 3 StationC 607050 525000 04/04/2009 17/05/2009 12/05/2009 17/05/2009Note that the sample data contain only one camera per station. Thus, there is no need to differentiate between station and camera IDs. If there was more than one camera per station, an additional column for camera IDs would be needed. Consequently, there would be one row for each camera and thus several rows per station.
Station and camera IDs can be any combination of characters and
numbers. Please don’t use underscores though, and more specifically,
don’t use double underscores ("__"), as these are used in a number of
functions to separate bits of information (e.g. in file names created by
imageRename or row names in
cameraOperation).
Column names in camtrapR are not prescribed, allowing users the freedom to name their data as they wish, with one exception detailed below. In the sample table, ‘Station’ contains station IDs, ‘utm_y’ and ‘utm_x’ contain the station’s geographic coordinates. ‘Setup_date’ and ‘Retrieval_date’ are the dates the stations were set up and retrieved from the field. All of these column names can be user-defined.
‘Problem1_from’ and ‘Problem1_to’ are optional columns to specify periods in which cameras/stations were not operational. If cameras or stations malfunctioned repeatedly, additional pairs of columns such as ‘Problem2_from’ and ‘Problem2_to’ can be added. These column names must follow the given pattern.
All dates in this table (setup, retrieval and problem columns) should
be of data type "character" and can be formatted in any way that
function as.Date can interpret. Since version 1.2, camtrapR
also supports the more flexible and convenient naming conventions of
package lubridate, which makes it easier to specify dates
correctly.
camtrapR by default assumes the standard format "YYYY-MM-DD", e.g. "2015-12-31", and we recommend to use that format (it is also best for sorting by date). In any case, the date format must be consistent between the various date columns.
Two functions (cameraOperation,
surveyReport) utilize these date information and therefore
contain arguments to specify the date format (dateFormat
and CTDateFormat, respectively). These arguments are used
to interpret the character string in the table (e.g. "2015-12-31") as a
date.
If these arguments contain the character “%” (characteristic of the
format argument to strptime), they are passed to function
as.Date as argument format. If they don’t
contain character “%”, they are interpreted as orders
arguments to the function parse_date_time in the
lubridate package.
Details on how to set it correctly can be found in the Details
section of the help files of functions as.Date and
strptime or parse_date_time.
Here is some information about date format in base R.
Here is some information about date format in lubridate.
Here are a few examples for December 1st 2015 ("generic" is the
abstract representation of your date string (Y = year, M = month, D =
day), "example" is a formatted example and "dateFormat argument" is what
you’d specify in dateFormat and CTDateFormat
in either the base R or lubridate naming convention):
| generic | example | dateFormat argument (base) | dateFormat argument (lubridate) |
|---|---|---|---|
| "YYYY-MM-DD" | "2015-12-01" | "%Y-%m-%d" | "ymd" |
| "YYYY/MM/DD" | "2015/12/01" | "%Y/%m/%d" | "ymd" |
| "YYYY/MM/DD" | "2015/12/1" | "%Y/%m/%d" | "ymd" |
| "MM/DD/YY" | "12/01/15" | "%m/%d/%y" | "mdy" |
| "YYYYMMDD" | "20151201" | "%Y%m%d" | "ymd" |
| "DD.MM.YY" | "1.12.15" | "%d.%m.%y" | "dmy" |
| "DD-MMM-YY" | "01-Dec-15" | "%d-%b-%y" | "dmy" |
In the sample table (camtraps), we use another date
format for demonstration purposes.
Data type of the ID and date columns in the table must be
character or factor. Please don’t use data
type Date. You can turn data type Date into
character using as.character.
After collecting images from camera traps, the images are saved into a directory structure like this:
rawImages/stationA
rawImages/stationB
If there was more than 1 camera per station, the station directories must contain camera subdirectories, e.g.
rawImages/stationA/camera1
rawImages/stationA/camera2
If you have more than 1 camera per station but don’t separate the
images from different cameras at this stage, you will not be able to do
so at a later point. Later in the workflow, you can decide whether you
would like to keep them separate or merge them (in the function
imageRename).
Generally, you should not work on your raw data and instead keep them
as a backup. If you rename you images with imageRename, the
whole camtrapR workflow will take place in a copy of the images to
prevent data loss.
Another important point is not to save any other data besides images in image directories. It may interfere with the operation of the package and ExifTool.
The directories for the raw images can be created automatically using
the function createStationFolders and the camera trap
station table. As mentioned above, images can either be stored in
station directories (if there was 1 camera per station) or in
station/camera directories (if there was >1 camera per station). The
behaviour is controlled by the function argument cameras.
It specifies the camera ID column in the camera trap information table.
If it is defined, camera subdirectories will be created within the
station directories.
Here is an example in which station directories without camera subdirectories are created.
# create a temporary dummy directory for tests
# (normally, you'd set up an empty directory in a proper place, e.g. .../myStudy/rawImages)
wd_createStationDir <- file.path(normalizePath(tempdir(), winslash = "/"), "createStationFoldersTest")
# create station directories in wd_createStationDir
StationFolderCreate1 <- createStationFolders (inDir = wd_createStationDir,
stations = as.character(camtraps$Station),
createinDir = TRUE)## created 3 directories
StationFolderCreate1## station
## 1 StationA
## 2 StationB
## 3 StationC
## directory
## 1 C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/createStationFoldersTest/StationA
## 2 C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/createStationFoldersTest/StationB
## 3 C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/createStationFoldersTest/StationC
## created exists
## 1 TRUE TRUE
## 2 TRUE TRUE
## 3 TRUE TRUEIMPORTANT: Please note that in this vignette station directories are set up in a temporary directory. That does not make any sense in real life and is done here solely to demonstrate how the functions works. So please don’t do this at home! Instead, always work in permanent directories!
Once the directories are created, you can copy over your images from the memory cards.
There are situations in which the date and time of your images may be incorrect. Imagine you forgot to set the system time in one of your cameras or reset the time accidentally. Or think of a bug in the camera software that causes years to be wrong as happened in the cameras of a major manufacturer at the turn of the year 2015/2016. Another situation in which images times may need a little shift is when users wish to synchronise the record times between camera pairs.
In any case, systematic offsets of date and time recorded by cameras
can be corrected easily. The function timeShiftImages does
just that by utilising the date/time shift module of ExifTool. All you
need is a table containing the time offset in a certain format, i.e.
data(timeShiftTable)
timeShiftTable## Station camera timeshift sign
## 1 StationA NA 1:0:0 0:0:0 -
## 2 StationB NA 0:0:0 12:0:0 +There is a station column with station IDs,
camera is NA because there was 1 camera per station only in
our example, timeshift is the amount by which to shift the
time of all images at that station, and sign specifies the
direction in which to shift time. Setting sign to
+ sets image time ahead by the amount specified in
timeshift, - sets image times back.
timeshift format is "year:month:day hour:minute:second",
i.e. "1:0:0 0:0:0" is a shift of exactly 1 year and "0:0:0 12:10:01" 12
hours and 10 minutes and 1 second. In the above table, time stampes of
images taken at StationA are shifted back by 1 year, and images taken at
StationB are shifted ahead by 12 hours.
The function timeShiftImages should be run directly
after saving the raw images on the hard disk.
# copy sample images to another location (so we don't mess around in the package directory)
wd_images_raw <- system.file("pictures/raw_images", package = "camtrapR")
file.copy(from = wd_images_raw, to = normalizePath(tempdir(), winslash = "/") , recursive = TRUE)## [1] TRUE
wd_images_raw_copy <- file.path(normalizePath(tempdir(), winslash = "/"), "raw_images")
timeshift_run <- timeShiftImages(inDir = wd_images_raw_copy,
timeShiftTable = timeShiftTable,
stationCol = "Station",
hasCameraFolders = FALSE,
timeShiftColumn = "timeshift",
timeShiftSignColumn = "sign"
)## C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/raw_images/StationA## C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/raw_images/StationB
timeshift_run## directory
## 1 C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/raw_images/StationA
## 2 C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/raw_images/StationB
## n_directories n_images
## 1 1 directories scanned 3 image files updated
## 2 1 directories scanned 3 image files updatedExifTool has now updated the time tag of all images in the
directories specified in the timeShiftTable. The original
images are preserved as XYZ.JPG_original files (XYZ being the original
file name). By setting argument undo = TRUE, the orginals
are restored and the modified images are deleted. The function will
check if the number of JPG_original files and JPG files in folders is
identical. If not, the function will not work. It does not check for
consistency of file names.
timeshift_undo <- timeShiftImages(inDir = wd_images_raw_copy,
timeShiftTable = timeShiftTable,
stationCol = "Station",
hasCameraFolders = FALSE,
timeShiftColumn = "timeshift",
timeShiftSignColumn = "sign",
undo = TRUE
)
timeshift_undo## directory
## 1 C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/raw_images/StationA
## 2 C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/raw_images/StationB
## n_images_undo
## 1 3
## 2 3Some Reconyx Hyperfire cameras (e.g. Hyperfire HC500) don’t store
date and time in the standard Exif format. Consequently, camtrapR cannot
read the DateTimeOriginal tag from these images. This can be fixed
easily by using the camtrapR function fixDateTimeOriginal
or by following the instructions given by Mathias Tobler in the
CameraBase documentation (page 7):
http://www.atrium-biodiversity.org/tools/camerabase/files/CameraBaseDoc1.7.pdf
Raw images are copied from the raw image directories into a new location and renamed at the same time. The renamed file names follow this pattern (depending on whether there was one or more cameras per station):
StationID__Date__Time(X).JPG
StationID__CameraID__Date__Time(X).JPG
Station ID and camera ID are derived from the raw image directory
structure created earlier with createStationFolders Date
and time are taken from image Exif metadata (read using ExifTool). (X)
is a numeric identifier that is assigned to all images taken at the same
station (and camera, if applicable) within one minute. This is to avoid
identical image names if images were taken in the same second (or in the
same minute, if cameras record time in hours and minutes only).
Again, please be aware that temporary directories are used in the
example. Set outDir to a permanent directory. In addition,
images are not actually copied (argument
copyImages = FALSE), but copying and renaming is mererly
simulated in out example. So, in real world data, set
copyImages = TRUE.
# raw image location
wd_images_raw <- system.file("pictures/raw_images", package = "camtrapR")
# destination for renamed images to be copied to
wd_images_raw_renamed <- file.path(normalizePath(tempdir(), winslash = "/"), "raw_images_renamed")
renaming.table2 <- imageRename(inDir = wd_images_raw,
outDir = wd_images_raw_renamed,
hasCameraFolders = FALSE,
copyImages = FALSE
)## StationA: 3 images |========== | 50%## StationB: 3 images |====================| 100%
# here is the information for a few images
head(renaming.table2)## Directory
## 1 C:/Users/niedballa/AppData/Local/Programs/R/R-4.3.2/library/camtrapR/pictures/raw_images/StationA
## 2 C:/Users/niedballa/AppData/Local/Programs/R/R-4.3.2/library/camtrapR/pictures/raw_images/StationA
## 3 C:/Users/niedballa/AppData/Local/Programs/R/R-4.3.2/library/camtrapR/pictures/raw_images/StationA
## 4 C:/Users/niedballa/AppData/Local/Programs/R/R-4.3.2/library/camtrapR/pictures/raw_images/StationB
## 5 C:/Users/niedballa/AppData/Local/Programs/R/R-4.3.2/library/camtrapR/pictures/raw_images/StationB
## 6 C:/Users/niedballa/AppData/Local/Programs/R/R-4.3.2/library/camtrapR/pictures/raw_images/StationB
## FileName Station Camera DateTimeOriginal DateReadable
## 1 IMG0001.JPG StationA NA 2009-04-10 05:07:00 TRUE
## 2 IMG0002.JPG StationA NA 2009-04-21 00:40:00 TRUE
## 3 IMG0003.JPG StationA NA 2009-04-22 20:19:00 TRUE
## 4 IMG0001.JPG StationB NA 2009-04-05 00:11:00 TRUE
## 5 IMG0002.JPG StationB NA 2009-04-05 23:13:00 TRUE
## 6 IMG0003.JPG StationB NA 2009-04-07 00:23:00 TRUE
## outDir
## 1 C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/raw_images_renamed/StationA
## 2 C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/raw_images_renamed/StationA
## 3 C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/raw_images_renamed/StationA
## 4 C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/raw_images_renamed/StationB
## 5 C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/raw_images_renamed/StationB
## 6 C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/raw_images_renamed/StationB
## filename_new fileExistsAlready CopyStatus
## 1 StationA__2009-04-10__05-07-00(1).JPG FALSE FALSE
## 2 StationA__2009-04-21__00-40-00(1).JPG FALSE FALSE
## 3 StationA__2009-04-22__20-19-00(1).JPG FALSE FALSE
## 4 StationB__2009-04-05__00-11-00(1).JPG FALSE FALSE
## 5 StationB__2009-04-05__23-13-00(1).JPG FALSE FALSE
## 6 StationB__2009-04-07__00-23-00(1).JPG FALSE FALSESo, the first image, "IMG0001.JPG" was renamed to "StationA__2009-04-10__05-07-00(1).JPG". Here is what the columns mean:
| column | content |
|---|---|
Directory |
the raw image directory |
FileName |
raw image file name |
Station |
the station the image was taken at |
Camera |
the camera ID (here NA because there were no camera subdirectories ) |
DateTimeOriginal |
Date and time in R-readable format |
DateReadable |
whether the date could be read from the metadata or not (TRUE = yes) |
outDir |
the directory the renamed image was copied to |
filename_new |
the new file name |
CopyStatus |
whether the images was copied successfully (TRUE = yes) |
At this point you created a copy of your raw images and renamed them with station ID, date and time.
The function addCopyrightTag can write a copyright tag
into image Exif metadata. While this can be useful when sharing or
publishing data, it is not required in the camtrapR workflow. The
function will recursively find all images in inDir and its
subdirectories and writes a user-defined text into the Copyright field
of the Exif metadata.
In doing so, Exiftool normally keeps the original images as
.JPG_original files. Note that this behaviour will instantly double the
number of images in inDir and the disk space required. If this is not
desired, users can set the function argument
keepJPG_original = FALSE (default is TRUE) in
order to replace the original files without creating *.JPG_original
files. I recommend to first try this on a copy of a subdataset and to
then check the function output. If there are warnings like "Warning:
[minor] Maker notes could not be parsed" image makernotes may be
unreadable afterwards (they are proprietary, undocumented formats). On
the other hand, you may never need these for analysing camera trapping
data.
# copy sample images to temporary directory (so we don't mess around in the package directory)
wd_images_ID <- system.file("pictures/sample_images_species_dir", package = "camtrapR")
file.copy(from = wd_images_ID, to = normalizePath(tempdir(), winslash = "/"), recursive = TRUE)## [1] TRUE
wd_images_ID_copy <- file.path(normalizePath(tempdir(), winslash = "/"), "sample_images_species_dir")
# define an example copyright tag
copyrightTagToAdd <- "Your Name (Your Organisation)"
# write the tag to images
addCopyrightTag(inDir = wd_images_ID_copy,
copyrightTag = copyrightTagToAdd,
askFirst = FALSE) # askFirst = FALSE prevents function from asking user to confirm action (which doesn't work in vignettes)## 14 directories scanned 68 image files updatedWhen we check the outcome with function exifTagNames, we find field “Copyright” in line 27.
exiftagnames_extracted <- exifTagNames(wd_images_ID_copy)## Metadata of:
## C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/sample_images_species_dir/StationA/PBE/StationA__2009-04-21__00-40-00(1).JPG
knitr::kable(exiftagnames_extracted, row.names = TRUE)| tag_group | tag_name | value | |
|---|---|---|---|
| 1 | ExifTool | ExifToolVersion | 12.49 |
| 2 | ExifTool | Warning | Invalid EXIF text encoding for UserComment |
| 3 | File | FileName | StationA__2009-04-21__00-40-00(1).JPG |
| 4 | File | Directory | C:/Users/niedballa/AppData/Local/Temp/Rtmpw9ymYJ/sample_images_species_dir/StationA/PBE |
| 5 | File | FileSize | 28 kB |
| 6 | File | FileModifyDate | 2024:02:01 12:20:58+08:00 |
| 7 | File | FileAccessDate | 2024:02:01 12:20:58+08:00 |
| 8 | File | FileCreateDate | 2024:02:01 12:20:57+08:00 |
| 9 | File | FilePermissions | -rw-rw-rw- |
| 10 | File | FileType | JPEG |
| 11 | File | FileTypeExtension | jpg |
| 12 | File | MIMEType | image/jpeg |
| 13 | File | ExifByteOrder | Little-endian (Intel, II) |
| 14 | File | ImageWidth | 100 |
| 15 | File | ImageHeight | 75 |
| 16 | File | EncodingProcess | Baseline DCT, Huffman coding |
| 17 | File | BitsPerSample | 8 |
| 18 | File | ColorComponents | 3 |
| 19 | File | YCbCrSubSampling | YCbCr4:2:2 (2 1) |
| 20 | EXIF | Make | G4 |
| 21 | EXIF | Model | CUDDEBACK |
| 22 | EXIF | XResolution | 72 |
| 23 | EXIF | YResolution | 72 |
| 24 | EXIF | ResolutionUnit | inches |
| 25 | EXIF | ModifyDate | 2009:04:21 00:40:00 |
| 26 | EXIF | YCbCrPositioning | Co-sited |
| 27 | EXIF | Copyright | Your Name (Your Organisation) |
| 28 | EXIF | ExifVersion | 0200 |
| 29 | EXIF | DateTimeOriginal | 2009:04:21 00:40:00 |
| 30 | EXIF | CreateDate | 2009:04:21 00:40:00 |
| 31 | EXIF | ComponentsConfiguration | Y, Cb, Cr, - |
| 32 | EXIF | ShutterSpeedValue | 1 |
| 33 | EXIF | UserComment | 1 ATTEMPT G1H |
| 34 | EXIF | FlashpixVersion | 0100 |
| 35 | EXIF | ColorSpace | sRGB |
| 36 | EXIF | ExifImageWidth | 100 |
| 37 | EXIF | ExifImageHeight | 75 |
| 38 | EXIF | Compression | JPEG (old-style) |
| 39 | EXIF | ThumbnailOffset | 450 |
| 40 | EXIF | ThumbnailLength | 25353 |
| 41 | EXIF | ThumbnailImage | (Binary data 25353 bytes, use -b option to extract) |
| 42 | Composite | ImageSize | 100x75 |
| 43 | Composite | Megapixels | 0.007 |
| 44 | Composite | ShutterSpeed | 1 |
The copyright tag will henceforth be visible in digiKam and other image management software.
Now that the raw images are organised, the next step is species identification, covered in the next vignette.