ParticleDetection.utils.detection

Functions to run inference with a trained and exported network and save the results for further computations.

Author: Adrian Niemann (adrian.niemann@ovgu.de)

Date: 07.11.2022

_run_detection(model: ScriptModule, img: Path, threshold: float = 0.5) → DetectionResult

Runs detection on one image.

Runs the detection model with the given image and converts the returned ROI masks to bitmasks.

Parameters:

• model (torch.ScriptModule) –

  Model used for the detection process. It must return a tuple of:

  [0] -> ROI boxes

  [1] -> predicted classes

  [2] -> ROI masks

  [3] -> prediction scores (confidence)

  [4] -> image dimensions (height, width)

• img (Path) – Path to an image the detection shall be run on.

• threshold (float, optional) –

  Threshold for the minimum score of predicted instances.

  By default 0.5.

Returns:

DetectionResult – Has the following keys:

"pred_boxes", "pred_classes", "pred_masks", "scores", "input_size"

run_detection(model: ScriptModule, dataset_format: str, classes: dict | None = None, output_dir: Path = Path('./'), threshold: float = 0.5, frames: List[int] = [], cam1_name: str = 'gp1', cam2_name: str = 'gp2') → None

Runs inference on a given set of images and saves the output to a *.csv.

This function runs a rod detection on images and generates rod enpoints from the generated masks, if the network predicted these. Finally, these endpoints are saved to a single rods_df.csv file in the specified output folder.

Parameters:

• model (ScriptModule) –

  Model used for the detection process. It must return a tuple of:

  [0] -> ROI boxes

  [1] -> predicted classes

  [2] -> ROI masks

  [3] -> prediction scores (confidence)

  [4] -> image dimensions (height, width)

• dataset_format (str) –

  String that can be formatted to specify the file locations of images, that shall be used for inference. For this the string must contain a frame and a cam_id field that can be formatted.

  Example:

  "my/dataset/path/{cam_id:s}/experiment_{frame:05d}.png"

• classes (dict, optional) –

  Dictionary of classes detectable by the model with

  {key} -> Index of class in the model

  {value} -> Name of the class

  By default None.

• output_dir (Path, optional) –

  Path to the intended output directory. It’s parent directory must exist prior to running this function.

  By default Path("./").

• threshold (float, optional) –

  Threshold for the minimum score of predicted instances.

  By default 0.5.

• frames (List[int], optional) –

  A list of frames, that shall be used for rod detection.

  By default [].

• cam1_name (str, optional) –

  The name/ID of the first camera in the experiment. This name will be used for image discovery (see dataset_format) and naming of the output *.csv file’s columns.

  By default "gp1".

• cam2_name (str, optional) –

  The name/ID of the second camera in the experiment. This name will be used for image discovery (see dataset_format) and naming of the output *.csv file columns.

  By default "gp2".