TRex parameters

analysis_paused(bool)

default value: false

Halts the analysis.

analysis_range(pair<int, int>)

default value: [-1,-1]

Sets start and end of the analysed frames.

app_check_for_updates(app_update_check_t)

default value: none

possible values:

none: No status has been set yet and the program will ask the user.
manually: Manually check for updates, do not automatically check for them online.
automatically: Automatically check for updates periodically (once per week).

If enabled, the application will regularly check for updates online (https://api.github.com/repos/mooch443/trex/releases).

app_last_update_check(uint64)

default value: 0

Time-point of when the application has last checked for an update.

app_last_update_version(string): default value: “”

app_name(string)

default value: “TRex”

Name of the application.

auto_apply(bool)

default value: false

If set to true, the application will automatically apply the network with existing weights once the analysis is done. It will then automatically correct and reanalyse the video.

auto_categorize(bool)

default value: false

If set to true, the program will try to load <video>_categories.npz from the output_dir. If successful, then categories will be computed according to the current categories_ settings. Combine this with the auto_quit parameter to automatically save and quit afterwards. If weights cannot be loaded, the app crashes.

See also

output_dir(), auto_quit(),

auto_minmax_size(bool)

default value: false

Program will try to find minimum / maximum size of the individuals automatically for the current cm_per_pixel setting. Can only be passed as an argument upon startup. The calculation is based on the median blob size in the video and assumes a relatively low level of noise.

See also

cm_per_pixel(),

auto_no_memory_stats(bool)

default value: true

If set to true, no memory statistics will be saved on auto_quit.

auto_no_results(bool)

default value: false

If set to true, the auto_quit option will NOT save a .results file along with the NPZ (or CSV) files. This saves time and space, but also means that the tracked portion cannot be loaded via -load afterwards. Useful, if you only want to analyse the resulting data and never look at the tracked video again.

auto_no_tracking_data(bool)

default value: false

If set to true, the auto_quit option will NOT save any output_graphs tracking data - just the posture data (if enabled) and the results file (if not disabled). This saves time and space if that is a need.

See also

output_graphs(),

auto_number_individuals(bool)

default value: false

Program will automatically try to find the number of individuals (with sizes given in blob_size_ranges) and set track_max_individuals to that value.

auto_quit(bool)

default value: false

If set to true, the application will automatically save all results and export CSV files and quit, after the analysis is complete.

auto_tags(bool)

default value: false

If set to true, the application will automatically apply available tag information once the results file has been loaded. It will then automatically correct potential tracking mistakes based on this information.

auto_tags_on_startup(bool)

default value: false

Used internally by the software.

auto_train(bool)

default value: false

If set to true (and recognition_enable is also set to true), the application will automatically train the recognition network with the best track segment and apply it to the video.

See also

recognition_enable(),

auto_train_dont_apply(bool)

default value: false

If set to true, setting auto_train will only train and not apply the trained network.

See also

auto_train(),

auto_train_on_startup(bool)

default value: false

This is a parameter that is used by the system to determine whether auto_train was set on startup, and thus also whether a failure of auto_train should result in a crash (return code != 0).

See also

auto_train(), auto_train(),

blob_size_ranges(BlobSizeRange)

default value: [[0.1,3]]

Blobs below the lower bound are recognized as noise instead of individuals. Blobs bigger than the upper bound are considered to potentially contain more than one individual. You can look these values up by pressing D in TRex to get to the raw view (see https://trex.run/docs/gui.html for details). The unit is #pixels * (cm/px)^2. cm_per_pixel is used for this conversion.

See also

cm_per_pixel(),

blob_split_algorithm(blob_split_algorithm_t)

default value: threshold

possible values:

threshold: Adaptively increase the threshold of closeby objects, until separation.
fill: Use the previously known positions of objects to place a seed within the overlapped objects and perform a watershed run.

The default splitting algorithm used to split objects that are too close together.

blob_split_global_shrink_limit(float)

default value: 0.2

The minimum percentage of the minimum in blob_size_ranges, that a blob is allowed to be reduced to during splitting. If this value is set too low, the program might start recognizing parts of individual as other individual too quickly.

See also

blob_size_ranges(),

blob_split_max_shrink(float)

default value: 0.2

The minimum percentage of the starting blob size (after thresholding), that a blob is allowed to be reduced to during splitting. If this value is set too low, the program might start recognizing parts of individual as other individual too quickly.

blobs_per_thread(float)

default value: 150

Number of blobs for which properties will be calculated per thread.

build(string)

default value: “”

Current build version

build_architecture(string)

default value: “arm64”

The architecture this executable was built for.

build_cxx_options(string)

default value: ” -fvisibility-inlines-hidden -fvisibility=hidden -Werror=return-stack-address -Wno-c++98-compat-pedantic -Wall -Wextra -pedantic -DNDEBUG -std=c++2a -O3 -Wno-nullability-extension”

The mode the application was built in.

build_is_debug(string)

default value: “release”

If built in debug mode, this will show ‘debug’.

build_type(string)

default value: “$<$<CONFIG:Debug>:Release>$<$<CONFIG:Release>:Debug>”

The mode the application was built in.

calculate_posture(bool)

default value: true

Enables or disables posture calculation. Can only be set before the video is analysed (e.g. in a settings file or as a startup parameter).

cam_circle_mask(bool)

default value: false

If set to true, a circle with a diameter of the width of the video image will mask the video. Anything outside that circle will be disregarded as background.

cam_matrix(array<float>): default value: [2945.0896,0,617.255432,0,2942.825195,682.473633,0,0,1]

cam_scale(float)

default value: 1

Scales the image down or up by the given factor.

cam_undistort(bool)

default value: false

If set to true, the recorded video image will be undistorted using cam_undistort_vector (1x5) and cam_matrix (3x3).

cam_undistort_vector(array<float>): default value: [-0.257663,-0.192336,0.002455,0.003988,0.35924]

categories_min_sample_images(uint)

default value: 50

Minimum number of images for a sample to be considered relevant. This will default to 50, or ten percent of track_segment_max_length, if that parameter is set. If track_segment_max_length is set, the value of this parameter will be ignored. If set to zero or one, then all samples are valid.

categories_ordered(array<string>)

default value: []

Ordered list of names of categories that are used in categorization (classification of types of individuals).

cm_per_pixel(float)

default value: 0

The ratio of meta_real_width / video_width that is used to convert pixels to centimeters. Will be automatically calculated based on a meta-parameter saved inside the video file (meta_real_width) and does not need to be set manually.

See also

meta_real_width(),

cmd_line(string)

default value: “”

An approximation of the command-line arguments passed to the program.

correct_illegal_lines(bool)

default value: false

In older versions of the software, blobs can be constructed in ‘illegal’ ways, meaning the lines might be overlapping. If the software is printing warnings about it, this should probably be enabled (makes it slower).

debug(bool)

default value: false

Enables some verbose debug print-outs.

debug_recognition_output_all_methods(bool)

default value: false

If set to true, a complete training will attempt to output all images for each identity with all available normalization methods.

enable_absolute_difference(bool)

default value: true

If set to true, the threshold values will be applied to abs(image - background). Otherwise max(0, image - background).

error_terminate(bool): default value: false

event_min_peak_offset(float): default value: 0.15

exec(path)

default value: “”

This can be set to the path of an additional settings file that is executed after the normal settings file.

ffmpeg_path(path)

default value: “”

Path to an ffmpeg executable file. This is used for converting videos after recording them (from the GUI). It is not a critical component of the software, but mostly for convenience.

filename(path)

default value: “”

Opened filename (without .pv).

fishdata_dir(path)

default value: “data”

Subfolder (below output_dir) where the exported NPZ or CSV files will be saved (see output_graphs).

See also

output_dir(), output_graphs(),

frame_rate(int)

default value: 0

Specifies the frame rate of the video. It is used e.g. for playback speed and certain parts of the matching algorithm. Will be set by the .settings of a video (or by the video itself).

gpu_accepted_uniqueness(float)

default value: 0

If changed (from 0), the ratio given here will be the acceptable uniqueness for the video - which will stop accumulation if reached.

gpu_accumulation_enable_final_step(bool)

default value: true

If enabled, the network will be trained on all the validation + training data accumulated, as a last step of the accumulation protocol cascade. This is intentional overfitting.

gpu_accumulation_max_segments(uint)

default value: 15

If there are more than global segments to be trained on, they will be filtered according to their quality until said limit is reached.

gpu_enable_accumulation(bool)

default value: true

Enables or disables the idtrackerai-esque accumulation protocol cascade. It is usually a good thing to enable this (especially in more complicated videos), but can be disabled as a fallback (e.g. if computation time is a major constraint).

gpu_learning_rate(float)

default value: 0.0001

Learning rate for training a recognition network.

gpu_max_cache(float)

default value: 2

Size of the image cache (transferring to GPU) in GigaBytes when applying the network.

gpu_max_epochs(uchar)

default value: 150

Maximum number of epochs for training a recognition network (0 means infinite).

gpu_max_sample_gb(float)

default value: 2

Maximum size of per-individual sample images in GigaBytes. If the collected images are too many, they will be sub-sampled in regular intervals.

gpu_min_elements(uint)

default value: 25000

Minimum number of images being collected, before sending them to the GPU.

gpu_min_iterations(uchar)

default value: 100

Minimum number of iterations per epoch for training a recognition network.

gpu_verbosity(gpu_verbosity_t)

default value: full

possible values:

silent: No output during training.
full: An animated bar with detailed information about the training progress.
oneline: One line per epoch.

Determines the nature of the output on the command-line during training. This does not change any behaviour in the graphical interface.

grid_points(array<vec>)

default value: []

Whenever there is an identification network loaded and this array contains more than one point [[x0,y0],[x1,y1],...], then the network will only be applied to blobs within circles around these points. The size of these circles is half of the average distance between the points.

grid_points_scaling(float)

default value: 0.8

Scaling applied to the average distance between the points in order to shrink or increase the size of the circles for recognition (see grid_points).

See also

grid_points(),

gui_auto_scale(bool)

default value: false

If set to true, the tracker will always try to zoom in on the whole group. This is useful for some individuals in a huge video (because if they are too tiny, you cant see them and their posture anymore).

gui_auto_scale_focus_one(bool)

default value: true

If set to true (and gui_auto_scale set to true, too), the tracker will zoom in on the selected individual, if one is selected.

See also

gui_auto_scale(),

gui_background_color(color)

default value: [0,0,0,150]

Values < 255 will make the background more transparent in standard view. This might be useful with very bright backgrounds.

gui_connectivity_matrix(map<int, array<float>>)

default value: {}

Internally used to store the connectivity matrix.

gui_connectivity_matrix_file(path)

default value: “”

Path to connectivity table. Expected structure is a csv table with columns [frame | #(track_max_individuals^2) values] and frames in y-direction.

gui_draw_only_filtered_out(bool)

default value: false

Only show filtered out blob texts.

gui_equalize_blob_histograms(bool)

default value: true

Equalize histograms of blobs wihtin videos (makes them more visible).

gui_faded_brightness(uchar)

default value: 255

The alpha value of tracking-related elements when timeline is hidden (0-255).

gui_fish_color(string): default value: “identity”

gui_focus_group(array<Idx_t>)

default value: []

Focus on this group of individuals.

gui_foi_name(string)

default value: “correcting”

If not empty, the gui will display the given FOI type in the timeline and allow to navigate between them via M/N.

gui_foi_types(array<string>)

default value: []

A list of all the foi types registered.

gui_frame(frame)

default value: 0

The currently visible frame.

gui_happy_mode(bool)

default value: false

If calculate_posture is enabled, enabling this option likely improves your experience with TRex.

See also

calculate_posture(),

gui_highlight_categories(bool)

default value: false

If enabled, categories (if applied in the video) will be highlighted in the tracking view.

gui_interface_scale(float)

default value: 1.25

Scales the whole interface. A value greater than 1 will make it smaller.

gui_max_path_time(float)

default value: 3

Length (in time) of the trails shown in GUI.

gui_mode(mode_t)

default value: tracking

The currently used display mode for the GUI.

gui_outline_thickness(uchar)

default value: 1

The thickness of outline / midlines in the GUI.

gui_playback_speed(float)

default value: 1

Playback speed when pressing SPACE.

gui_recording_format(gui_recording_format_t)

default value: mp4

possible values:

avi: AVI / video format (codec MJPG is used)
mp4: MP4 / video format (codec H264 is used)
jpg: individual images in JPEG format
png: individual images in PNG format

Sets the format for recording mode (when R is pressed in the GUI). Supported formats are ‘avi’, ‘jpg’ and ‘png’. JPEGs have 75%% compression, AVI is using MJPEG compression.

gui_run(bool)

default value: false

When set to true, the GUI starts playing back the video and stops once it reaches the end, or is set to false.

gui_show_blobs(bool)

default value: true

Showing or hiding individual raw blobs in tracking view (are always shown in RAW mode).

gui_show_boundary_crossings(bool)

default value: true

If set to true (and the number of individuals is set to a number > 0), the tracker will show whenever an individual enters the recognition boundary. Indicated by an expanding cyan circle around it.

gui_show_cliques(bool)

default value: false

Show/hide cliques of potentially difficult tracking situations.

gui_show_dataset(bool)

default value: false

Show/hide detailed dataset information on-screen.

gui_show_detailed_probabilities(bool)

default value: false

Show/hide detailed probability stats when an individual is selected.

gui_show_export_options(bool)

default value: false

Show/hide the export options widget.

gui_show_fish(pair<blob, frame>)

default value: [4294967295,-1]

Show debug output for {blob_id, fish_id}.

gui_show_graph(bool)

default value: false

Show/hide the data time-series graph.

gui_show_heatmap(bool)

default value: false

Showing a heatmap per identity, normalized by maximum samples per grid-cell.

gui_show_histograms(bool)

default value: false

Equivalent to the checkbox visible in GUI on the bottom-left.

gui_show_inactive_individuals(bool)

default value: true

Show/hide individuals that have not been seen for longer than track_max_reassign_time.

See also

track_max_reassign_time(),

gui_show_match_modes(bool)

default value: false

Shows the match mode used for every tracked object. Green is ‘approximate’, yellow is ‘hungarian’, and red is ‘created/loaded’.

gui_show_memory_stats(bool)

default value: false

Showing or hiding memory statistics.

gui_show_midline(bool)

default value: true

Showing or hiding individual midlines in tracking view.

gui_show_midline_histogram(bool)

default value: false

Displays a histogram for midline lengths.

gui_show_number_individuals(bool)

default value: false

Show/hide the #individuals time-series graph.

gui_show_only_unassigned(bool)

default value: false

Showing only unassigned objects.

gui_show_outline(bool)

default value: true

Showing or hiding individual outlines in tracking view.

gui_show_paths(bool)

default value: true

Equivalent to the checkbox visible in GUI on the bottom-left.

gui_show_pixel_grid(bool)

default value: false

Shows the proximity grid generated for all blobs, which is used for history splitting.

gui_show_posture(bool)

default value: true

Show/hide the posture window on the top-right.

gui_show_probabilities(bool)

default value: false

Show/hide probability visualisation when an individual is selected.

gui_show_recognition_bounds(bool)

default value: true

Shows what is contained within tht recognition boundary as a cyan background. (See recognition_border for details.)

See also

recognition_border(),

gui_show_recognition_summary(bool)

default value: false

Show/hide confusion matrix (if network is loaded).

gui_show_selections(bool)

default value: true

Show/hide circles around selected individual.

gui_show_shadows(bool)

default value: true

Showing or hiding individual shadows in tracking view.

gui_show_texts(bool)

default value: true

Showing or hiding individual identity (and related) texts in tracking view.

gui_show_uniqueness(bool)

default value: false

Show/hide uniqueness overview after training.

gui_show_visualfield(bool)

default value: false

Show/hide the visual field rays.

gui_show_visualfield_ts(bool)

default value: false

Show/hide the visual field time series.

gui_single_identity_color(color)

default value: [0,0,0,0]

If set to something else than transparent, all individuals will be displayed with this color.

gui_timeline_alpha(uchar)

default value: 200

Determines the Alpha value for the timeline / consecutive segments display.

gui_transparent_background(bool)

default value: false

If enabled, fonts might look weird but you can record movies (and images) with transparent background (if gui_background_color.alpha is < 255).

gui_zoom_limit(size): default value: [300,300]

heatmap_dynamic(bool)

default value: false

If enabled the heatmap will only show frames before the frame currently displayed in the graphical user interface.

heatmap_frames(uint)

default value: 0

If heatmap_dynamic is enabled, this variable determines the range of frames that are considered. If set to 0, all frames up to the current frame are considered. Otherwise, this number determines the number of frames previous to the current frame that are considered.

See also

heatmap_dynamic(),

heatmap_ids(array<uint>)

default value: []

Add ID numbers to this array to exclusively display heatmap values for those individuals.

heatmap_normalization(heatmap_normalization_t)

default value: cell

possible values:

none: No normalization at all. Values will only be averaged per cell.
value: Normalization based in value-space. The average of each cell will be divided by the maximum value encountered.
cell: The cell sum will be divided by the maximum cell value encountered.
variance: Displays the variation within each cell.

Normalization used for the heatmaps. If value is selected, then the maximum of all values encountered will be used to normalize the average of each cell. If cell is selected, the sum of each cell will be divided by the maximum cell value encountered.

heatmap_resolution(uint)

default value: 64

Square resolution of individual heatmaps displayed with gui_show_heatmap. Will generate a square grid, each cell with dimensions (video_width / N, video_height / N), and sort all positions of each identity into it.

See also

gui_show_heatmap(),

heatmap_smooth(double)

default value: 0.05

Value between 0 and 1, think of as times video-width, indicating the maximum upscaled size of the heatmaps shown in the tracker. Makes them prettier, but maybe much slower.

heatmap_source(string)

default value: “”

If empty, the source will simply be an individuals identity. Otherwise, information from export data sources will be used.

heatmap_value_range(range<double>)

default value: [-1,-1]

Give a custom value range that is used to normalize heatmap cell values.

history_matching_log(path)

default value: “”

If this is set to a valid html file path, a detailed matching history log will be written to the given file for each frame.

httpd_accepted_ip(string)

default value: “”

Set this to an IP address that you want to accept exclusively.

httpd_port(int)

default value: 8080

This is where the webserver tries to establish a socket. If it fails, this will be set to the port that was chosen.

huge_timestamp_ends_segment(bool): default value: true

huge_timestamp_seconds(double)

default value: 0.2

Defaults to 0.5s (500ms), can be set to any value that should be recognized as being huge.

image_invert(bool)

default value: false

Inverts the image greyscale values before thresholding.

individual_names(map<uint, string>)

default value: {}

A map of {individual-id: "individual-name", ...} that names individuals in the GUI and exported data.

individual_prefix(string)

default value: “fish”

The prefix that is added to all the files containing certain IDs. So individual 0 will turn into ‘[prefix]0’ for all the npz files and within the program.

individuals_per_thread(float)

default value: 1

Number of individuals for which positions will be estimated per thread.

limit(float)

default value: 0.09

Limit for tailbeat event detection.

log_file(path)

default value: “”

Set this to a path you want to save the log file to.

manual_identities(set<Idx_t>): default value: []

manual_matches(map<frame, map<Idx_t, blob>>)

default value: {}

A map of manually defined matches (also updated by GUI menu for assigning manual identities). {{frame: {fish0: blob2, fish1: blob0}}, ...}

manual_splits(map<frame, set<blob>>)

default value: {}

This map contains {frame: [blobid1,blobid2,...]} where frame and blobid are integers. When this is read during tracking for a frame, the tracker will attempt to force-split the given blob ids.

manually_approved(map<int, int>)

default value: {}

A list of ranges of manually approved frames that may be used for generating training datasets, e.g. {232:233,5555:5560} where each of the numbers is a frame number. Meaning that frames 232-233 and 5555-5560 are manually set to be manually checked for any identity switches, and individual identities can be assumed to be consistent throughout these frames.

match_mode(matching_mode_t)

default value: automatic

possible values:

tree: Maximizes the probability sum by assigning (or potentially not assigning) individuals to objects in the frame. This returns the correct solution, but might take long for high quantities of individuals.
approximate: Simply assigns the highest probability edges (blob to individual) to all individuals - first come, first serve. Parameters have to be set very strictly (especially speed) in order to have as few objects to choose from as possible and limit the error.
hungarian: The hungarian algorithm (as implemented in O(n^3) by Mattias Andrée https://github.com/maandree/hungarian-algorithm-n3).
benchmark: Runs all algorithms and pits them against each other, outputting statistics every few frames.
automatic: Uses automatic selection based on density.

Changes the default algorithm to be used for matching blobs in one frame with blobs in the next frame. The accurate algorithm performs best, but also scales less well for more individuals than the approximate one. However, if it is too slow (temporarily) in a few frames, the program falls back to using the approximate one that doesnt slow down.

matching_probability_threshold(float)

default value: 0.1

The probability below which a possible connection between blob and identity is considered too low. The probability depends largely upon settings like track_max_speed.

See also

track_max_speed(),

meta_mass_mg(float)

default value: 200

Used for exporting event-energy levels.

meta_real_width(float)

default value: 0

Used to calculate the cm_per_pixel conversion factor, relevant for e.g. converting the speed of individuals from px/s to cm/s (to compare to track_max_speed which is given in cm/s). By default set to 30 if no other values are available (e.g. via command-line). This variable should reflect actual width (in cm) of what is seen in the video image. For example, if the video shows a tank that is 50cm in X-direction and 30cm in Y-direction, and the image is cropped exactly to the size of the tank, then this variable should be set to 50.

See also

cm_per_pixel(), track_max_speed(),

meta_source_path(path)

default value: “”

Path of the original video file for conversions (saved as debug info).

midline_invert(bool)

default value: false

If enabled, all midlines will be inverted (tail/head swapped).

midline_resolution(uint)

default value: 25

Number of midline points that are saved. Higher number increases detail.

midline_samples(uint64)

default value: 0

The maximum number of samples taken for generating a median midline length. Setting this to 0 removes the limit all together. A limit may be set for very long videos, or videos with lots of individuals, for memory reasons.

midline_start_with_head(bool)

default value: false

If enabled, the midline is going to be estimated starting at the head instead of the tail.

midline_stiff_percentage(float)

default value: 0.15

Percentage of the midline that can be assumed to be stiff. If the head position seems poorly approximated (straighened out too much), then decrease this value.

midline_walk_offset(float)

default value: 0.025

This percentage of the number of outline points is the amount of points that the midline-algorithm is allowed to move left and right upon each step. Higher numbers will make midlines more straight, especially when extremities are present (that need to be skipped over), but higher numbers will also potentially decrease accuracy for less detailed objects.

nowindow(bool)

default value: false

If set to true, no GUI will be created on startup (e.g. when starting from SSH).

outline_approximate(uchar)

default value: 3

If this is a number > 0, the outline detected from the image will be passed through an elliptical fourier transform with number of coefficients. When the given number is sufficiently low, the outline will be smoothed significantly (and more so for lower numbers of coefficients).

outline_curvature_range_ratio(float)

default value: 0.03

Determines the ratio between number of outline points and distance used to calculate its curvature. Program will look at index +- ratio * size() and calculate the distance between these points (see posture window red/green color).

outline_resample(float)

default value: 0.5

Spacing between outline points in pixels, after resampling (normalizing) the outline. A lower value here can drastically increase the number of outline points generated (and decrease speed).

outline_smooth_samples(uchar)

default value: 4

Use N samples for smoothing the outline. More samples will generate a smoother (less detailed) outline.

outline_smooth_step(uchar)

default value: 1

Jump over N outline points when smoothing (reducing accuracy).

outline_use_dft(bool)

default value: true

If enabled, the program tries to reduce outline noise by convolution of the curvature array with a low pass filter.

output_annotations(map<string, string>)

default value: {“ACCELERATION”:”cm/s2”,”ACCELERATION_SMOOTH”:”cm/s2”,”BORDER_DISTANCE”:”cm”,”NEIGHBOR_DISTANCE”:”cm”,”ORIENTATION”:”rad”,”SPEED”:”cm/s”,”SPEED_OLD”:”cm/s”,”SPEED_SMOOTH”:”cm/s”,”VX”:”cm/s”,”VY”:”cm/s”,”X”:”cm”,”Y”:”cm”,”global”:”px”}

Units (as a string) of output functions to be annotated in various places like graphs.

output_centered(bool)

default value: false

If set to true, the origin of all X and Y coordinates is going to be set to the center of the video.

output_csv_decimals(uchar)

default value: 2

Maximum number of decimal places that is written into CSV files (a text-based format for storing data). A value of 0 results in integer values.

output_default_options(map<string, array<string>>)

default value: {“event_acceleration”:[“/10”],”ACCELERATION”:[“/15”,”CENTROID”],”L_V”:[“/10”],”v_direction”:[“/10”],”DOT_V”:[“/10”],”ANGULAR_V”:[“/10”,”CENTROID”],”ANGULAR_A”:[“/1000”,”CENTROID”],”NEIGHBOR_VECTOR_T”:[“/1”],”SPEED”:[“/10”],”NEIGHBOR_DISTANCE”:[“/10”],”X”:[“/100”],”Y”:[“/100”],”tailbeat_threshold”:[“pm”],”tailbeat_peak”:[“pm”],”threshold_reached”:[“POINTS”],”midline_length”:[“/15”],”amplitude”:[“/100”],”outline_size”:[“/100”],”global”:[“/10”]}

Default scaling and smoothing options for output functions, which are applied to functions in output_graphs during export.

See also

output_graphs(),

output_dir(path)

default value: “/Users/tristan/Videos”

Default output-/input-directory. Change this in order to omit paths in front of filenames for open and save.

output_format(output_format_t)

default value: npz

possible values:

csv: A standard data format, comma-separated columns for each data stream. Use output_csv_decimals to adjust the maximum precision for exported data.
npz: NPZ is basically a collection of binary arrays, readable by NumPy and other plugins (there are plugins available for Matlab and R).

When pressing the S(ave) button or using auto_quit, this setting allows to switch between CSV and NPZ output. NPZ files are recommended and will be used by default - some functionality (such as visual fields, posture data, etc.) will remain in NPZ format due to technical constraints.

See also

auto_quit(),

output_frame_window(int)

default value: 100

If an individual is selected during CSV output, use these number of frames around it (or -1 for all frames).

output_graphs(array<pair<string, array<string>>>)

default value: [[“X”,[“RAW”,”WCENTROID”]],[“Y”,[“RAW”,”WCENTROID”]],[“X”,[“RAW”,”HEAD”]],[“Y”,[“RAW”,”HEAD”]],[“VX”,[“RAW”,”HEAD”]],[“VY”,[“RAW”,”HEAD”]],[“AX”,[“RAW”,”HEAD”]],[“AY”,[“RAW”,”HEAD”]],[“ANGLE”,[“RAW”]],[“ANGULAR_V”,[“RAW”]],[“ANGULAR_A”,[“RAW”]],[“MIDLINE_OFFSET”,[“RAW”]],[“normalized_midline”,[“RAW”]],[“midline_length”,[“RAW”]],[“midline_x”,[“RAW”]],[“midline_y”,[“RAW”]],[“segment_length”,[“RAW”]],[“SPEED”,[“RAW”,”WCENTROID”]],[“SPEED”,[“RAW”,”PCENTROID”]],[“SPEED”,[“RAW”,”HEAD”]],[“BORDER_DISTANCE”,[“PCENTROID”]],[“time”,[]],[“timestamp”,[]],[“frame”,[]],[“missing”,[]],[“num_pixels”,[]],[“ACCELERATION”,[“RAW”,”PCENTROID”]],[“ACCELERATION”,[“RAW”,”WCENTROID”]]]

The functions that will be exported when saving to CSV, or shown in the graph. [['X',[option], ...]]

output_heatmaps(bool)

default value: false

When set to true, heatmaps are going to be saved to a separate file, or set of files ‘_p*’ - with all the settings in heatmap_* applied.

output_image_per_tracklet(bool)

default value: false

If set to true, the program will output one median image per tracklet (time-series segment) and save it alongside the npz/csv files.

output_interpolate_positions(bool)

default value: false

If turned on this function will linearly interpolate X/Y, and SPEED values, for all frames in which an individual is missing.

output_invalid_value(output_invalid_t)

default value: inf

possible values:

inf: Infinity (e.g. np.inf)
nan: NaN (e.g. np.nan)

Determines, what is exported in cases where the individual was not found (or a certain value could not be calculated). For example, if an individual is found but posture could not successfully be generated, then all posture-based values (e.g. midline_length) default to the value specified here. By default (and for historic reasons), any invalid value is marked by ‘inf’.

output_min_frames(uint16)

default value: 1

Filters all individual with less than N frames when exporting. Individuals with fewer than N frames will also be hidden in the GUI unless gui_show_inactive_individuals is enabled (default).

See also

gui_show_inactive_individuals(),

output_normalize_midline_data(bool)

default value: false

If enabled: save a normalized version of the midline data saved whenever output_posture_data is set to true. Normalized means that the position of the midline points is normalized across frames (or the distance between head and point n in the midline array).

See also

output_posture_data(),

output_posture_data(bool)

default value: false

Save posture data npz file along with the usual NPZ/CSV files containing positions and such. If set to true, a file called ‘output_dir/fish_data_dir/<filename>_posture_fishXXX.npz’ will be created for each individual XXX.

See also

output_dir(),

output_prefix(string)

default value: “”

A prefix that is prepended to all output files (csv/npz).

output_recognition_data(bool)

default value: false

Save recognition / probability data npz file along with the usual NPZ/CSV files containing positions and such. If set to true, a file called ‘output_dir/fish_data_dir/<filename>_recognition_fishXXX.npz’ will be created for each individual XXX.

See also

output_dir(),

output_statistics(bool)

default value: false

Save an NPZ file containing an array with shape Nx16 and contents [adding_seconds, combined_posture_seconds, number_fish, loading_seconds, posture_seconds, match_number_fish, match_number_blob, match_number_edges, match_stack_objects, match_max_edges_per_blob, match_max_edges_per_fish, match_mean_edges_per_blob, match_mean_edges_per_fish, match_improvements_made, match_leafs_visited, method_used] and an 1D-array containing all frame numbers. If set to true, a file called ‘output_dir/fish_data_dir/<filename>_statistics.npz’ will be created. This will not output anything interesting, if the data was loaded instead of analysed.

See also

output_dir(),

panic_button(int)

default value: 0

42

peak_mode(peak_mode_t)

default value: pointy

possible values:

pointy: The head is broader than the tail.
broad: The tail is broader than the head.

This determines whether the tail of an individual should be expected to be pointy or broad.

posture_closing_size(uchar)

default value: 2

The kernel size for erosion / dilation of the posture algorithm. Only has an effect with posture_closing_steps > 0.

See also

posture_closing_steps(),

posture_closing_steps(uchar)

default value: 0

When enabled (> 0), posture will be processed using a combination of erode / dilate in order to close holes in the shape and get rid of extremities. An increased number of steps will shrink the shape, but will also be more time intensive.

posture_direction_smoothing(uint16)

default value: 0

Enables or disables smoothing of the posture orientation based on previous frames (not good for fast turns).

posture_head_percentage(float)

default value: 0.1

The percentage of the midline-length that the head is moved away from the front of the body.

postures_per_thread(float)

default value: 1

Number of individuals for which postures will be estimated per thread.

python_path(path)

default value: “/Users/tristan/miniforge3/envs/trex2/bin/python3.10”

Path to the python home folder. If left empty, the user is required to make sure that all necessary libraries are in-scope the PATH environment variable.

recognition_border(recognition_border_t)

default value: none

possible values:

none: No border at all. All points are inside the recognition boundary. (default)
heatmap: Looks at a subset of frames from the video, trying to find out where individuals go and masking all the places they do not.
outline: Similar to heatmap, but tries to build a convex border around the around (without holes in it).
shapes: Any array of convex shapes. Set coordinates by changing recognition_shapes.
grid: The points defined in grid_points are turned into N different circles inside the arena (with points in grid_points being the circle centers), which define in/out if inside/outside any of the circles.
circle: The video-file provides a binary mask (e.g. when cam_circle_mask was set to true during recording), which is then used to determine in/out.

This defines the type of border that is used in all automatic recognition routines. Depending on the type set here, you might need to set other parameters as well (e.g. recognition_shapes). In general, this defines whether an image of an individual is usable for automatic recognition. If it is inside the defined border, then it will be passed on to the recognition network - if not, then it wont.

See also

recognition_shapes(),

recognition_border_shrink_percent(float)

default value: 0.3

The amount by which the recognition border is shrunk after generating it (roughly and depends on the method).

recognition_border_size_rescale(float)

default value: 0.5

The amount that blob sizes for calculating the heatmap are allowed to go below or above values specified in blob_size_ranges (e.g. 0.5 means that the sizes can range between blob_size_ranges.min * (1 - 0.5) and blob_size_ranges.max * (1 + 0.5)).

See also

blob_size_ranges(),

recognition_coeff(uint16)

default value: 50

If recognition_border is ‘outline’, this is the number of coefficients to use when smoothing the recognition_border.

recognition_enable(bool)

default value: true

This enables internal training. Requires Python3 and Keras to be available.

recognition_image_scale(float)

default value: 1

Scaling applied to the images before passing them to the network.

recognition_image_size(size)

default value: [80,80]

Size of each image generated for network training.

recognition_normalization(recognition_normalization_t)

default value: posture

possible values:

none: No normalization. Images will only be cropped out and used as-is.
moments: Images will be cropped out and aligned as in idtracker.ai using the main axis calculated using image moments.
posture: Images will be cropped out and rotated so that the head will be fixed in one position and only the tail moves.
legacy: Images will be aligned parallel to the x axis.

This enables or disable normalizing the images before training. If set to none, the images will be sent to the GPU raw - they will only be cropped out. Otherwise they will be normalized based on head orientation (posture) or the main axis calculated using image moments.

recognition_save_progress_images(bool)

default value: false

If set to true, an image will be saved for all training epochs, documenting the uniqueness in each step.

recognition_save_training_images(bool)

default value: false

If set to true, the program will save the images used for a successful training of the recognition network to the output path.

recognition_segment_add_factor(float)

default value: 1.5

This factor will be multiplied with the probability that would be pure chance, during the decision whether a segment is to be added or not. The default value of 1.5 suggests that the minimum probability for each identity has to be 1.5 times chance (e.g. 0.5 in the case of two individuals).

recognition_shapes(array<array<vec>>)

default value: []

If recognition_border is set to ‘shapes’, then the identification network will only be applied to blobs within the convex shapes specified here.

See also

recognition_border(),

recognition_smooth_amount(uint16)

default value: 200

If recognition_border is ‘outline’, this is the amount that the recognition_border is smoothed (similar to outline_smooth_samples), where larger numbers will smooth more.

settings_file(path)

default value: “”

Name of the settings file. By default, this will be set to filename.settings in the same folder as filename.

See also

filename(), filename(),

smooth_window(uint)

default value: 2

Smoothing window used for exported data with the #smooth tag.

speed_extrapolation(float)

default value: 3

Used for matching when estimating the next position of an individual. Smaller values are appropriate for lower frame rates. The higher this value is, the more previous frames will have significant weight in estimating the next position (with an exponential decay).

tags_dont_track(bool)

default value: true

If true, disables the tracking of tags as objects in TRex. This means that tags are not displayed like other objects and are instead only used as additional ‘information’ to correct tracks. However, if you enabled tags_saved_only in TGrabs, setting this parameter to true will make your TRex look quite empty.

tags_image_size(size)

default value: [32,32]

The image size that tag images are normalized to.

tags_path(path)

default value: “”

If this path is set, the program will try to find tags and save them at the specified location.

terminate(bool)

default value: false

If set to true, the application terminates.

terminate_training(bool)

default value: false

Setting this to true aborts the training in progress.

threshold_ratio_range(range<float>)

default value: [0.5,1]

If track_threshold_2 is not equal to zero, this ratio will be multiplied by the number of pixels present before the second threshold. If the resulting size falls within the given range, the blob is deemed okay.

See also

track_threshold_2(),

track_consistent_categories(bool)

default value: false

Utilise categories (if present) when tracking. This may break trajectories in places with imperfect categorization, but only applies once categories have been applied.

track_do_history_split(bool)

default value: true

If disabled, blobs will not be split automatically in order to separate overlapping individuals. This usually happens based on their history.

track_end_segment_for_speed(bool)

default value: true

Sometimes individuals might be assigned to blobs that are far away from the previous position. This could indicate wrong assignments, but not necessarily. If this variable is set to true, consecutive frame segments will end whenever high speeds are reached, just to be on the safe side. For scenarios with lots of individuals (and no recognition) this might spam yellow bars in the timeline and may be disabled.

track_ignore(array<array<vec>>)

default value: []

If this is not empty, objects within the given rectangles or polygons (>= 3 points) [[x0,y0],[x1,y1](, ...)], ...] will be ignored during tracking.

track_include(array<array<vec>>)

default value: []

If this is not empty, objects within the given rectangles or polygons (>= 3 points) [[x0,y0],[x1,y1](, ...)], ...] will be the only objects being tracked. (overwrites track_ignore)

See also

track_ignore(),

track_intensity_range(range<int>)

default value: [-1,-1]

When set to valid values, objects will be filtered to have an average pixel intensity within the given range.

track_max_individuals(uint)

default value: 0

The maximal number of individual that are assigned at the same time (infinite if set to zero). If the given number is below the actual number of individual, then only a (random) subset of individual are assigned and a warning is shown.

track_max_reassign_time(float)

default value: 0.5

Distance in time (seconds) where the matcher will stop trying to reassign an individual based on previous position. After this time runs out, depending on the settings, the tracker will try to find it based on other criteria, or generate a new individual.

track_max_speed(float)

default value: 10

The maximum speed an individual can have (=> the maximum distance an individual can travel within one second) in cm/s. Uses and is influenced by meta_real_width and cm_per_pixel as follows: speed(px/s) * cm_per_pixel(cm/px) -> cm/s.

See also

meta_real_width(), cm_per_pixel(),

track_only_categories(array<string>)

default value: []

If this is a non-empty list, only objects that have previously been assigned one of the correct categories will be tracked. Note that this also excludes noise particles or very short segments with no tracking.

track_posture_threshold(int)

default value: 15

Same as track_threshold, but for posture estimation.

See also

track_threshold(),

track_segment_max_length(float)

default value: 0

If set to something bigger than zero, this represents the maximum number of seconds that a consecutive segment can be.

track_speed_decay(float)

default value: 0.7

The amount the expected speed is reduced over time when an individual is lost. When individuals collide, depending on the expected behavior for the given species, one should choose different values for this variable. If the individuals usually stop when they collide, this should be set to 1. If the individuals are expected to move over one another, the value should be set to 0.7 > value > 0.

track_threshold(int)

default value: 15

Constant used in background subtraction. Pixels with grey values above this threshold will be interpreted as potential individuals, while pixels below this threshold will be ignored.

track_threshold_2(int)

default value: 0

If not zero, a second threshold will be applied to all objects after they have been deemed do be theoretically large enough. Then they are compared to #before_pixels * threshold_ratio_range to see how much they have been shrunk).

See also

threshold_ratio_range(),

track_time_probability_enabled(bool): default value: true

track_trusted_probability(float)

default value: 0.5

If the probability, that is used to assign an individual to an object, is smaller than this value, the current segment will be ended (thus this will also not be a consecutive segment anymore for this individual).

tracklet_export_difference_images(bool)

default value: true

If set to true, then all exported tracklet images are difference images. If set to false, all exported tracklet images are normal-color images.

tracklet_max_images(uint16)

default value: 0

Maximum number of images that are being output per tracklet given that output_image_per_tracklet is true. If the number is 0, then every image will be exported that has been recognized as an individual.

See also

output_image_per_tracklet(),

tracklet_normalize_orientation(bool)

default value: true

If enabled, all exported tracklet images are normalized according to the calculated posture orientation, so that all heads are looking to the left and only the body moves.

use_differences(bool)

default value: false

This should be set to false unless when using really old files.

version(string)

default value: “v1.1.8_3-108-g1468dfb”

Current application version.

video_info(string)

default value: “”

Information on the current video as provided by PV.

video_length(uint64)

default value: 0

The length of the video in frames

video_size(size)

default value: [-1,-1]

The dimensions of the currently loaded video.

visual_field_eye_offset(float)

default value: 0.15

A percentage telling the program how much the eye positions are offset from the start of the midline.

visual_field_eye_separation(float)

default value: 60

Degrees of separation between the eye and looking straight ahead. Results in the eye looking towards head.angle +- .

visual_field_history_smoothing(uchar)

default value: 0

The maximum number of previous values (and look-back in frames) to take into account when smoothing visual field orientations. If greater than 0, visual fields will use smoothed previous eye positions to determine the optimal current eye position. This is usually only necessary when postures are somewhat noisy to a degree that makes visual fields unreliable.

visual_identification_version(visual_identification_version_t)

default value: current

possible values:

current: This always points to the current version.
v118_3: The order of Max-Pooling layers was changed, along with some other minor changes.
v110: Changed activation order, added BatchNormalization. No Flattening to maintain spatial context.
v100: The original layout.

Newer versions of TRex sometimes change the network layout for (e.g.) visual identification, which will make them incompatible with older trained models. This parameter allows you to change the expected version back, to ensure backwards compatibility.

web_quality(int)

default value: 75

JPEG quality of images transferred over the web interface.

web_time_threshold(float)

default value: 0.05

Maximum refresh rate in seconds for the web interface.