Section 1: Background

This document was produced at Bedford Institute of Oceanography (BIO) to accompany the vprr package, a processing and visualization package for data obtained from the Video Plankton Recorder (VPR). The package will format data and perform a series of calculations to assist in data analysis, including joining CTD and ROI data and calculating concentrations of plankton through the water column. The VPR is an In Situ plankton imaging instrument originally designed at Woods Hole Oceanographic Institute (WHOI) and subsequently produced by SeaScan, Inc. This document refers to processing of data from the 2008 version of the Digital AutoVPR produced by SeaScan (for more information on the instrument please see SeaScan manual provided with your specific model). The instrument package consists of a CPU, CTD (SBE49), and camera system with different optical settings (i.e. magnifications). It captures underwater images and records their corresponding salinity, temperature, and depth. The data processing tools in vprr should be adaptable to more recent VPR versions from SeaScan.

The VPR outputs two raw files (.dat and .idx) for a given time period in a deployment. These files are processed together in a software provided with the VPR (i.e., Autodeck), which decompresses the images, scans them for “regions of interest” (ROIs), and outputs ROI image files and a corresponding CTD data file (.dat). The ROI file names are numeric consisting of 10 digits. The first 8 digits correspond to the number of milliseconds elapsed in the day before data were collected. The last two digits correspond to the ROI identifier (01-99) at the time the image was captured. The ROIs and corresponding CTD data are linked by their 8 digit time stamp. After the ROIs have been extracted from the raw files they may be sorted manually or by an automated procedure. At BIO, the images were sorted using automated classification using a software called “Visual Plankton” (VP) in Matlab, which was developed specifically for the VPR. A version of VP modified by personnel at BIO, primarily to update and utilize the Support Vector Machine (SVM) image classification component, can be found here (https://gccode.ssc-spc.gc.ca/dfo-mar-odis/visual-plankton/matlab). For permission to view the linked project on GC code, contact DataServicesDonnees@dfo-mpo.gc.ca.

Visual Plankton outputs two text files for each category of classification (“aid” and “aidmeas” files). Each “aid” (i.e., autoid) file contains file paths to individual ROIs that have been classified to the category of interest. The corresponding “aidmeas” file contains morphological data for the ROIs (e.g., long axis length, perimeter, etc.). These aid, aidmeas, and CTD files are the data inputs for processing using vprr. The primary functions of the vprr package are to manually correct misclassifications made by VP, join CTD and ROI datasets, and compute bin averaged plankton concentrations over the VPR deployment. Examples for visualization of final data products are provided in this vignette. Note that the functionality of vprr is dependent on the format and directory structure of the data output from VP, but is not dependent on the use of VP itself.

Figure 1. VPR data processing flow chart. Blue boxes represent software, green and yellow boxes represent data sets, where yellow is visual data and green is text format data. This document outlines the processes within ’Processing and Visualization (R).

Section 2: Summary of vprr data processing steps

# set VPR processing environment

# WORKING DIRECTORY
wd <- "C:/VPR_PROJECT/COR2019002/SCRIPTS"
setwd(wd)

# MISSION
cruise <- 'COR2019002'

year <- 2019

# CSV FILE WITH STATION NAMES AND CORRESPONDING DAY/HOUR INFO
station_names_file <- paste0("station_names_", cruise, ".csv")
# example: 'C:/VPR_PROJECT/vp_info/station_names_COR2019002.csv'
# note columns should be labeled : station, day, hour

# DIRECTORY FOR CTD DATA (output from AutoDeck)
castdir <- paste0('D:/', cruise, "/", cruise, "_autodeck/")
# example: 'D:/COR2019002/COR2019002_autodeck/'


# AUTOID FOLDER FOR MEASUREMENT DATA (aidmeas & aid files)
drive <- 'C:/'
auto_id_folder <- paste0(drive, "cruise_", cruise, "/", autoid)
# example: 'E:/COR2019002/autoid' #!!NO BACKSLASH AT END OF STRING

# PATH TO AUTOID CATEGORY FOLDERS
auto_id_path <- list.files(paste0(auto_id_folder, "/"), full.names = T) 

# CREATE STANDARD DIRECTORY FOR SAVED DATA FILES PER MISSION
savedir <- paste0(cruise, '_data_files')
dir.create(savedir, showWarnings = FALSE)

# CREATE STANDARD DIRECTORY FOR SAVED DATA PRODUCTS PER MISSION AND STATION
stdir <- paste('data_product/', cruise, sep = "")
dir.create(stdir, showWarnings = FALSE, recursive = TRUE)

# DEPTH BIN SIZE FOR AVERAGING
binSize <- 3

#### SAVE ####
# SAVE ALL FILE PATHS AND SETTINGS AS PROJECT ENVIRONMENT

save.image(file = paste0(cruise,'_env.RData'))

load('COR2019002_env.RData') # where COR2019002 is mission name

Section 3: Image Copying

In this step, ROIs are copied to folders that are organized based on the day and hour of data collection and classification category assigned from VP. The images are organized by Autodeck into day and hour, but reorganizing them based on classification allows easier human interaction with the data, allowing visual inspection of classifications by VP. Moreover, this directory structure is used by the next step of processing (i.e., Manual re-classification). To implement this step use the function vprr::vpr_autoid_copy(). For more information on input variables, please see documentation for vpr_autoid_copy (?vpr_autoid_copy).

# create variables
# ---------------------
basepath <- "C:\\data\\cruise_COR2019002\\autoid\\" 
# note this is the same as the auto_id_folder environment variable except the file separator is different, because this script will run source code in command line which does not recognize '/' as a file separator

day <- "123"
hour <- "01" # note leading zero 
classifier_type <- "svm" 
classifier_name <- "myclassifier"

# run file organizer
# ---------------------
vpr_autoid_copy(basepath, day, hour, classifer_type, classifier_name)

Section 4: Manual Re-Classification

Manual Re-classification of some categories after classification by VP may be required to achieve identification accuracy standards. In this step, ROIs are displayed on the screen one at a time for manual verification. If VP has misclassified an image or if it falls into a new user defined category (described below), the image can be reclassified. At the end of this process, text files which match the formats produced by VP for aid and aidmeas files are generated with the new classifications.

The Manual Re-classification process also allows for creation of new categories. This is especially useful for classification of rare categories that were not defined prior to classification in VP. For example, if VP produces a classification group “copepods” containing Paraeuchaeta spp. and Metridia spp, the user can add “Paraeuchaeta” and “Metridia” categories in the Manual re-classification step using the function vprr::vpr_category_create (see example below). After completing manual re-classification for a day-hour set, new files are created for new categories. The new files are identical in format to original aid and aidmeas files produced by VP.

#### CLASSIFICATION CHECK
# -------------------------------------

# Once classified images are sorted by taxa
# verify classification accuracy by manually 
# looking through classified images

#### USER INPUT REQUIRED ####     

load('COR2019002_env.RData')

day <- '235' 
hr <- '19' # keep leading zeros, must be two characters


category_of_interest <-
  c(
    'krill',
    'Calanus',
    'chaetognaths',
    'ctenophores',
    'Other',
    'larval_fish',
    'marine_snow',
    'small_copepod',
    'other_copepods',
    'larval_crab',
    'amphipod',
    'Metridia',
    'Paraeuchaeta',
    'cnidarians'
    
  )


# add new category (optional)

vpr_category_create(taxa = taxa_of_interest, auto_id_folder)
# ensures there is proper folder structure for all categories of interest

# reclassify images
vpr_manual_classification(day = day, hour= hr, basepath = auto_id_folder,gr = FALSE, 
          taxa_of_interest = category_of_interest, scale = 'x300',
          opticalSetting = 'S3')

#### REORGANIZE ROI AND ROIMEAS DATA
# -----------------------------------------

day_hour_files <-  paste0('d', day, '.h', hr)

misclassified <- list.files(day_hour_files, pattern = 'misclassified_', full.names = TRUE)

reclassify <- list.files(day_hour_files, pattern = 'reclassify_', full.names = TRUE)

# MOVE ROIS THAT WERE MISCLASSIFIED INTO CORRECT FILES
vpr_autoid_create(reclassify, misclassified, auto_id_folder)

#ALERT ALERT ALERT ALERT ALERT ALERT ALERT ALERT ALERT ALERT ALERT ALERT ALERT ALERT ALERT #
#==========================================================================================#
#                            ONCE ALL AID FILES ARE COMPLETE                               #
#==========================================================================================#
#                                     REQUIRED!!!!                                         #
#       manual organization of new aid and aidmeas files into base path directory          #
#==========================================================================================#


#### CHECK FILES
# --------------------------------


# aid check step
# removes empty aid files, and checks for errors in writing

vpr_autoid_check(basepath, cruise) #OUTPUT: text log 'CRUISE_aid_file_check.txt’ in working directory

Section 5: Data Processing

This is the main chunk of coding required to generate data products from the VPR. This step does not require Image Copying or Manual re-classification steps. The following is a walk-through of processing data from a DFO field mission (i.e. mission IML2018051) in the southern GSL in 2018.

First, all libraries should be loaded and the processing environment, described in Section 2.4 should be loaded.

##### PROCESSING  --------------------------------------------------------------------------------------------------------------------
library(vprr)

#### FILE PATHS & SETTINGS --------------------------------------------------------------------------------------------------------------------
# loads processing environment specific to user

load('IML2018051_env.RData')

This section allows a user to process all stations of a particular mission in a loop. This can be modified or removed based on personal preference

##### STATION LOOP ----------------------------------------------------------------------------------------------------------------------------

all_stations <- read.csv(station_names_file, stringsAsFactors = FALSE) 
all_stations_of_interest <- unique(all_stations$station) 


for (j in 1:length(all_stations_of_interest)){
  
  station_of_interest <- all_stations_of_interest[j] 
  
  cat('Station', station_of_interest, 'processing... \n')
  cat('\n')

Optical settings and image volume variables should be set. If they are consistent throughout the mission, they could also be added to the processing environment.

  #==========================================#
  #   Set optical settings  & Image Volume   #
  #   !Should be updated with each mission!   #
  #==========================================#
  if(cruise == "IML2018051") {
    
    #VPR OPTICAL SETTING (S0, S1, S2 OR S3)
    opticalSetting <- "S2"
    imageVolume <- 108155 #mm^3
  }

CTD casts are loaded in using vprr::vpr_ctd_files to find files and vprr::vpr_ctd_read to read in files. During CTD data read in, a standardized seawater density variable sigmaT is derived using the function oce::swSigmaT, and depth (in meters) is derived using the function oce::swDepth.

#get day and hour info from station names list
dayhour <- vpr_dayhour(station_of_interest, file = station_names_file)


##### PULL CTD CASTS ----------------------------------------------------------------------------------------------------------------------------
# get file path for ctd data

# list ctd files for desired day.hours
ctd_files <- vpr_ctd_files(castdir, cruise, dayhour) 

##### READ CTD DATA ----------------------------------------------------------------------------------------------------------------------------

ctd_dat_combine <- vpr_ctd_read(ctd_files, station_of_interest)

cat('CTD data read complete! \n')
cat('\n')

VPR data files are then found, within the VP directory structure.

##### FIND VPR DATA FILES ----------------------------------------------------------------------------------------------------------------------

# Path to aid for each taxa                               
aid_path <- paste0(auto_id_path, '/aid/')              
# Path to mea for each taxa                               
aidmea_path <- paste0(auto_id_path, '/aidmea/')        

# AUTO ID FILES
aid_file_list <- list()
aidmea_file_list <- list()
for (i in 1:length(dayhour)) {
  aid_file_list[[i]] <-
    list.files(aid_path, pattern = dayhour[[i]], full.names = TRUE)
  # SIZE DATA FILES
  aidmea_file_list[[i]] <-
    list.files(aidmea_path, pattern = dayhour[[i]], full.names = TRUE)
}

aid_file_list_all <- unlist(aid_file_list)
aidmea_file_list_all <- unlist(aidmea_file_list)

remove(aid_file_list, aidmea_file_list, aid_path, aidmea_path)

ROI and measurement data files are then read using vprr::vpr_autoid_read.

##### READ ROI AND MEASUREMENT DATA ------------------------------------------------------------------------------------------------------------


# ROIs
roi_dat_combine <-
  vpr_autoid_read(
    file_list_aid = aid_file_list_all,
    file_list_aidmeas = aidmea_file_list_all,
    export = 'aid',
    station_of_interest = station_of_interest,
    opticalSetting = opticalSetting
  )

# MEASUREMENTS
roimeas_dat_combine <-
  vpr_autoid_read(
    file_list_aid = aid_file_list_all,
    file_list_aidmeas = aidmea_file_list_all,
    export = 'aidmeas',
    station_of_interest = station_of_interest,
    opticalSetting = opticalSetting
  )

cat('ROI and measurement data read in complete! \n')
cat('\n')

Next, CTD and aid data are merged to create a data frame describing both water properties and classified images. The function used is vprr::vpr_ctdroi_merge.

 ##### MERGE CTD AND ROI DATA ---------------------------------------------------------------------------------------------------------------------
 ctd_roi_merge <- vpr_ctdroi_merge(ctd_dat_combine, roi_dat_combine)
 
 cat('CTD and ROI data combined! \n')
 cat('\n')

Before final export of data products, the following variables are added to the data frame: time in hours avg_hr is calculated, and a time stamp (ymdhms) with POSIXct signature in Y-M-D h:m:s format is added using the function vpr_ctd_ymd.

##### CALCULATED VARS ----------------------------------------------------------------------------------------------------------------------------

# add avg hr and sigma T data and depth
data <- ctd_roi_merge %>%
  dplyr::mutate(., avg_hr = time_ms / 3.6e+06) 

  data <- vpr_ctd_ymd(data, year)

cat('Initial processing complete! \n')
cat('\n')

# clean environment
remove(ctd_roi_merge)

Average plankton concentration and water properties (e.g., temperature, salinity, density, etc.) are then computed within a user defined depth bin. The bin-averaging step standardizes plankton concentrations when the VPR does not sample the water column evenly (due to characteristics of the deployment or variability in the sampling rate, which is not constant in older versions of the VPR) and reduces noise in the data. The bin-averaging is done by creating an oce CTD object using vprr::vpr_oce_create, then binning data using vprr::bin_cast. Concentrations are calculated for each category.

##### BIN DATA AND DERIVE CONCENTRATION ----------------------------------------------------------------------------------------------------------

ctd_roi_oce <- vpr_oce_create(data)

# bin and calculate concentration for all taxa (combined)
vpr_depth_bin <- bin_cast(ctd_roi_oce = ctd_roi_oce, binSize =  binSize, imageVolume = imageVolume)

# get list of valid taxa
taxas_list <- unique(roimeas_dat_combine$taxa)

# bin and calculate concentrations for each category
taxa_conc_n <- vpr_roi_concentration(data, taxas_list, station_of_interest, binSize, imageVolume)  

cat('Station', station_of_interest, 'processing complete! \n')
cat('\n')

# bin size data

size_df_f <- vpr_ctdroisize_merge(data, measdata = roimeas_dat_combine, taxa_of_interest = category_of_interest)

size_df_b <- vpr_size_bin(size_df_f, bin_mea = 3)

Finally, data are saved as RData and csv files for export and plotting. Data are also saved as an oce object in order to preserve both data and metadata in an efficient format.

##### SAVE DATA ---------------------------------------------------------------------------------------------------------------------------------
# Save oce object
oce_dat <- vpr_save(taxa_conc_n)
save(file = paste0(savedir, '/oceData_', station_of_interest,'.RData'), oce_dat) # oce data and metadata object


# Save RData files
save(file = paste0(savedir, '/ctdData_', station_of_interest,'.RData'), ctd_dat_combine) #CTD data
save(file = paste0(savedir, '/stationData_', station_of_interest,'.RData'), data) # VPR and CTD data
save(file = paste0(savedir, '/meas_dat_', station_of_interest,'.RData'), roimeas_dat_combine) #measurement data
save(file = paste0(savedir, '/bin_dat_', station_of_interest,'.RData'), vpr_depth_bin) # binned data with cumulative concentrations
save(file = paste0(savedir, '/bin_size_dat_', station_of_interest,'.RData'), size_df_b) # binned data inclouded measurements

cat('CTD, ROI-VPR merge, ROI measurement saved as RData! \n')
cat('\n')

# Write csv files
# write.csv(file = paste0(stdir, '/vpr_data_unbinned', station_of_interest, '.csv'), data, row.names = F) # VPR and CTD data
# write.csv(file = paste0(stdir, '/vpr_meas', station_of_interest, '.csv'), roimeas_dat_combine) # measurement data
write.csv(file = paste0(stdir, '/vpr_data_binned', station_of_interest, '.csv'), taxa_conc_n) # VPR and CTD data with concentrations by taxa

cat('ROI measurments, ROI-CTD merge-unbinned, and ROI-CTD merge-binned written to csv! \n')
cat('\n')

} #end of station loop

Section 6: Plotting

Although not primarily a plotting package, vprr can produce contour plots, profile plots and temperature-salinity plots from VPR tow-yo data sets. The following are a few example plots. The first step to plotting is properly loading in the processed VPR data objects developed in processing. The environment, described in Section 2.4 should also be loaded. The individual data files are found by distinct names (e.g., “stationData”). Note that the directory structure may be different depending on the savedir where data files were saved during processing.

Note that the following plotting examples are best used for tow-yo pattern VPR deployments.

##### FILE PATH & SETTINGS -----------------------------------------------------------------------------------------------------------------------

library(vprr)

# loads all file paths and environment vars specific to User
load('COR2019002_env.RData')

#find all data files
fn_all_st <- list.files(paste0(cruise, "_data_files/"), pattern = "stationData", full.names = T)
fn_all_meas <- list.files(paste0(cruise, "_data_files/"), pattern = "meas", full.names = T)
fn_all_conc <- list.files(paste0("data_product/", cruise, "/"), pattern = "data_binned", full.names = T)
fn_all_bin <- list.files(paste0(cruise,"_data_files/"), pattern = 'bin_dat', full.names = T)

Once files are loaded, plots for all stations in a mission can be generated using a loop, in order to efficiently generate comparable plots. The example below uses a loop to run through a list of stations described by a csv file. This loop also isolates two specific classification categories to plot (Calanus and krill).

####START STATION LOOP ---------------------------------------------------------------------------------------------------------------------------

setwd(wd)

all_stations <- read.csv(station_names_file, stringsAsFactors = FALSE)
all_stations_of_interest <- unique(all_stations$station)

taxa_to_plot <- c("Calanus", "krill")


for (j in 1:length(all_stations_of_interest)){
  
  setwd(wd)
  station <- all_stations_of_interest[j]
  
  cat('station', station ,'starting to plot.... \n')
  cat('\n')

Data files are loaded for the specific station of interest. This loads in all relevant RData files as well as the concentration data saved as a csv file.

#load station roi and ctd data
fn_st <- grep(fn_all_st, pattern = station, value = TRUE, ignore.case = TRUE)
fn_meas <- grep(fn_all_meas, pattern = station, value = TRUE, ignore.case = TRUE)
fn_conc <- grep(fn_all_conc, pattern = station, value = TRUE, ignore.case = TRUE)
fn_bin <- grep(fn_all_bin, pattern = station, value = TRUE, ignore.case = TRUE)

load(fn_st)
load(fn_meas)
load(fn_conc)
load(fn_bin)


# load concentration data
taxa_conc_n <- read.csv(fn_conc, stringsAsFactors = F)

station_name <- paste('Station ', station)

The final section of set up indicates the directory in which plots will be saved and provides generic plot size arguments which will control how large the saved .png files are.

# directory for plots
stdir <- paste0('figures/', cruise, '/station', station)
dir.create(stdir, showWarnings = FALSE, recursive = TRUE)
setwd(stdir)

width = 1200
height = 1000

The following example presents a plot of the concentrations of a taxon as scaled bubbles along the tow path, overlain on contours of an environmental variable. The main function used is vprr::vpr_plot_contour which uses a standard VPR data frame (taxa_conc_n - produced from Processing) and plots the background contours. Interpolation methods can be adjusted based on data or preference. The VPR tow path can be added on top of contours, with concentration data displayed as scaled bubbles. This method can be repeated with various water parameters (e.g., temperature, salinity etc.) used to calculate the contours, by changing the var argument in vprr::vpr_plot_contour.

# Density (sigmaT)
png('conPlot_taxa_dens.png', width = width, height = height)
p <- vpr_plot_contour(taxa_conc_n[taxa_conc_n$taxa %in% c(taxa_to_plot),], var = 'density', dup = 'strip', method = 'oce', bw = 0.5)
p <- p + geom_line(data = data, aes(x = avg_hr - min(avg_hr), y = pressure), col = 'snow4', inherit.aes = FALSE) +
  geom_point(data = taxa_conc_n[taxa_conc_n$taxa %in% c(taxa_to_plot),], aes(x = avg_hr, y = min_pressure, size = conc_m3), alpha = 0.5)+
  ggtitle(station_name ) +
  labs(size = expression("Concentration /m" ^3), fill = 'Density')+
  scale_size_continuous(range = c(0, 10)) +
  facet_wrap(~taxa, ncol = 1, scales = 'free') +
  theme(legend.key.size = unit(0.8, 'cm'),
        axis.title = element_text(size = 20),
        strip.text = element_text(size = 20),
        plot.title = element_text(size = 32), 
        axis.ticks = element_line(size = 1, lineend = 'square'),
        axis.text = element_text(size = 30),
        legend.text = element_text(size = 20),
        legend.title = element_text(size = 25)
  )

print(p)
dev.off()

Vertical profiles of plankton concentration and water properties compressed over the sampling duration can be generated using vprr::vpr_plot_profile. This type of plot indicates the overall pattern in vertical distribution over the VPR deployment.

  png('profilePlots_RK.png', width = 1000, height = 500)
  p <- vpr_plot_profile(taxa_conc_n, taxa_to_plot)
  print(p)
  dev.off()

Temperature-salinity (TS) plots can be generated to visualize how plankton concentration varies across different water masses. In the example below, a TS plot is produced in ggplot (with labeled isopycnals), and concentration bubbles for each selected classification group are overlaid on the plot. The basic TS bubble plot can be easily manipulated using ggplot2 grammar, for example the plots can be faceted by classification group or axis labels and sizing can be adjusted.

####TS BUBBLE PLOT ----------------------------------------------------------------------------------------------------------------------------
  
  # plot by taxa
  taxa_conc <- taxa_conc_n[taxa_conc_n$conc_m3 > 0,]
  png('TS_conc_taxa.png', width = 1000, height = 500)
  p <- vpr_plot_TS(taxa_conc[taxa_conc$taxa %in% c(taxa_to_plot),], var = 'conc_m3') +
    facet_wrap(~taxa, nrow = 1) +
    theme(strip.text = element_text(size = 18),
          axis.title = element_text(size = 20),
          panel.spacing = unit(2, 'lines'))
  print(p)
  dev.off()
  
  
  cat('station', station, 'complete! \n')
  cat('\n')
  
} # end station loop

Section 7: Disclaimer

The functions in vprr were created for a specific project and have not been tested on a broad range of field mission data. It is possible that deviations in data format and directory structure from that described herein may result in errors when using vprr. The vprr package was developed for the purpose of processing data collected during tow-yo VPR deployments and image classification using VP. The purpose of this document is to provide a template for processing and visualizing VPR data that can be adapted by other users for their own objectives.

vpr_img_copy(auto_id_folder, taxas.of.interest, day, hour)

# perform manual removal of images not matching classification group

img_check_folder <- file.path(auto_id_folder, taxas.of.interest, fsep = '\')

vpr_img_check(img_check_folder)

Appendix 1: Directory Structure

Visual Plankton (MatLab image classification software) requires a very specific directory structure in order to function. Since this processing is meant to directly follow this image classification, the VP directory structure is used for consistency. This allows a smooth transition between the MatLab classifications and the completion of processing in R. The directory structure required is described below

• C:/

  • data

    • cruise_name

      • autoid

        • taxa
          • aid
          • aidmea
          • image folders (Note: these folders can be moved as long as file paths within processing are updated appropriately)

      • clpar

      • feature

        • vprtow#

      • idsize

      • rois

        • vprtow#
          • day
            • hour

      • tefeature

        • vprtow#
          • day
            • hour

      • trrois

        • vprtow#
          • day
            • hour

Appendix 2: Glossary

Working Directory - File path on your computer that defines the default location of any files you read into R, or save out of R

CPU - Central processing unit (computer processor)

SeaScan - Oceanographic instrument manufacturing company

ROI - Region of interest, images identified by autodeck within VPR frames based on settings defined in autoDeck program

TRROIS - Training set of images used to train machine learning algorithm in Visual Plankton

VP - Visual Plankton program run in MatLab

CTD - Conductivity, Temperature and depth sensor instrument

VPR - Video Plankton Recorder, oceanographic instrument used to image small volumes of water for the purpose of capturing images of plankton

Auto Deck - software which pulls plankton images from Video Plankton Recorder frames based on specific settings

AutoID files - Includes both Aid and AidMeas files as part of Visual Plankton’s automatic classifications

Aid files - Visual Plankton file output text file, listing file path information for ROI’s of a specific classification group

AidMeas files (AutoID measurements) - Visual Plankton output text file, listing measurement data for ROI’s of a specific classification group. Unit is pixels and columns are ‘Perimeter’, ‘Area’, ‘width1’, ‘width2’, ‘width3’, ‘short_axis_length’, ‘long_axis_length’

Day - Julian calendar day on which VPR data was collected (three digits)

Hour - Two digit hour (24 hour clock) describing time at which VPR data was collected

vprtow# - A numeric code which is unique to each VPR deployment

station - A named geographic location, where the VPR was deployed

Classification category (Taxa) - A defined group under which VPR images can be classified, often represents a taxonomic group (e.g. Krill), but can also be defined by image type (e.g. ‘bad_image_blurry’), or other (e.g. ‘marine_snow’), should be one continuous string (no spaces)

BIO - Bedford Institute of Oceanography, a research institute in Halifax NS, Canada

Tow-yo - A VPR deployment method where the VPR is towed behind a vessel while being raised and lowered through the water column in order to sample over both depth and distance

Optical Setting - A VPR setting controlling image magnification and field of view, which can be S0, S1, S2 or S3, where S0 has the greatest magnification and smallest image volume, and S3 has the least magnification and largest image volume

Image volume - The measured volume of water captured within a VPR image. Calculated based on optical setting and VPR standards

Auto ID - The automatic classification given to an image from Visual Plankton machine learning algorithm