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base_config.yaml
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base_config.yaml
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# base config, do not modify
# instead, create a new config and only specify settings from here you wish to
# change there
# base config optimised for processing wound repair image sequences containing
# airway epithelial cells, as seen in https://doi.org/10.3390/jpm12050809
# conversion command settings
conversion:
# convert specified dataset format to EPIC supported format
format: incucyte # choose from 'incucyte'
# output directory, null for auto-created directory
output_dir: null
# detection command settings
detection:
# number of images to detect objects in, null for all
num_frames: null
# sliding window width in pixels
window_width: 224
# sliding window height in pixels
window_height: 224
# percentage, with respect to the window area, by which sliding windows
# horizontally and vertically overlap
window_overlap: 25
# IoU threshold to use for performing non maximum suppression (nms) on
# detected bounding boxes, null for no nms
nms_threshold: 0.1
# use window size equal to image size (ignores window_width and
# window_height)
full_window: no
# perform object detection for all images, even those with
# existing MOTChallenge CSV detection text-files
always_detect: yes
# assume directories are in MOTChallenge format
motchallenge: no
# generate analysis report after detecting objects
analyse: yes
# name of object detector to use
# ID or file path of trained object detection model checkpoint
checkpoint_file: ./misc/checkpoints/swin_transformer/airway_epithelial_cell_wound_repair.pth
# ID or file path of trained object detection model configuration file
config_file: ./misc/checkpoints/swin_transformer/airway_epithelial_cell_wound_repair.py
# device to use for performing object detection
# 'cpu' for GPU or 'cuda' for GPU
device: cpu
# image batch size for inference (higher number uses more memory)
batch_size: 1
# the maximum number of inference jobs that can be fed to the model
# simultaneously (higher number uses more memory)
max_num_sim_jobs: 1
# load images in greyscale
greyscale_images: no
# tracking command settings
tracking:
# number of frames to track objects in, null for all
num_frames: null
# perform object tracking for all image sequences, even those with
# existing MOTChallenge CSV tracking text-files
always_track: yes
# assume directories are in MOTChallenge format
motchallenge: no
# minimum confidence score detected objects must have to be tracked
dets_min_score: 0.75
# generate analysis report after tracking objects
analyse: yes
# load images in greyscale
greyscale_images: yes
# name of object tracker to use
tracker_name: epic_tracker
# epic_tracker settings
# see: https://doi.org/10.3390/jpm12050809 (Supplementary Methods in
# Supplementary Materials)
epic_tracker:
# maximum of number of times to traverse image sequence using tracking
# algorithm
max_tracker_runs: 100
# apply track refinement algorithm during tracking
track_refinement: yes
# appearance and motion features to use for tracking
feats:
# enabled -> enable for primary & secondary runs, respectively
# thresh -> threshold of the feature score, -1 for no threshold
# weight -> weight of the feature, between 0 and 1
temp_dist: # temporal distance feature
enabled: [no, yes]
thresh: [-1, 2] # greater than 1
weight: [1, 1]
iou: # intersection over union feature
enabled: [yes, yes]
thresh: [-1, -1] # between 0 and 1
weight: [0.5, 0.5]
euclid_dist: # euclidean distance feature
enabled: [yes, yes]
thresh: [30, 35] # greater than 0
weight: [1, 1]
# cells behind the detected leading edges will have
# euclid_dist thresholds of thresh * non_le_pen for each
# respective run
# null to use same as thresh
non_le_pen: [null, 0.3]
mot_vects: # motion vector feature
enabled: [yes, yes]
thresh: [-1, -1] # between 0 and 1
weight: [1, 1]
struct_sim: # structural similarity index measure feature
enabled: [yes, yes]
thresh: [-1, -1] # between 0 and 1
weight: [0.5, 0.5]
gray_hist: # greyscale histogram feature
enabled: [yes, yes]
thresh: [-1, -1] # between 0 and 1
weight: [1, 1]
bin_width: 128 # histogram size for histogram calculation
boundary: # boundary feature
enabled: [no, yes]
thresh: [-1, 15] # greater than 0
weight: [1, 0]
# the number of proceeding frames to use for association during primary
# and secondary runs, respectively
glob_temp_dist: [1, 2]
# detection association euclidean distance settings
glob_euclid_dist:
# the maximum euclidean distance betweens detections in different
# frames that can be linked during primary and secondary runs,
#respectively
# null to use euclid_dist feature thresh values
thresh: [null, null]
# the maximum number of neighboring detections in different frames
# to a given detection that can be candidate detections for linking
num_nns: 30
# optimise tracking for wound repair image sequences
wound_repair: yes
# parameters for leading edge detection in wound repair images
leading_edge_params:
# starting position of the sliding in widow in strides from the top
# and bottom of the first frame of a wound repair image
start_position: 24
# analysis command settings
analysis:
# perform data analysis for all image sequences, even those with
# existing analysis report files
always_analyse: yes
# jupyter notebook file to use for generating reports
# treats value as the full path to a notebook
# if notebook cannot be found will treat value as the filename to a
# notebook expected to be in EPIC's home directory (valid values are
# report_auto_tracks.ipynb and report_auto_and_man_tracks.ipynb)
report: ./misc/reports/cell_wound_repair_report.ipynb
# miscellaneous settings
misc:
# detection visualisation settings
vis_dets:
# BGR colour of visualised detections
colour: [255, 0, 0]
# thickness of visualised detections
thickness: 2
# track visualisation settings
vis_tracks:
# terminated tracks remain visualised on proceeding frames
persist: no
# BGR colour of tracks
# null for random colour
colour: null
# use different colours for tracks if track colour setting is null
diff_cols: yes
# BGR colour of track segments linked during secondary runs
# null to use same colour as corresponding track
linking_colour: null
# thickness of tracks
thickness: 2
# copy input config file to data root directory when EPIC is ran
archive_config_file: yes
# number of workers to utilize for parallel processing
# -1 = CPU core count
num_workers: 1
# enable progress bar
progress_bar: yes