iRfcb (version 0.8.0)

ifcb_save_classification: Classify an IFCB Sample and Save Results

Description

Extracts PNG images from an IFCB .roi file, classifies each image via the Gradio API predict_scores endpoint (returning all class scores), fetches per-class thresholds, and writes the results in the specified format.

Usage

ifcb_save_classification(
  roi_file,
  output_folder,
  format = c("h5", "mat", "csv"),
  gradio_url = "https://irfcb-classify.hf.space",
  model_name = "SMHI NIVA ResNet50 V5",
  verbose = TRUE,
  ...
)

Value

The path to the saved file (invisibly).

Arguments

roi_file

A character string specifying the path to the .roi file.

output_folder

A character string specifying the directory where the output file will be saved. The file is named automatically based on the sample name (e.g. D20220522T003051_IFCB134_class.h5, D20220522T003051_IFCB134_class_v1.mat, or D20220522T003051_IFCB134.csv).

format

A character string specifying the output format. One of "h5" (default), "mat", or "csv".

gradio_url

A character string specifying the base URL of the Gradio application. Default is "https://irfcb-classify.hf.space", which is an example Hugging Face Space with limited resources intended for testing and demonstration. For large-scale classification, deploy your own instance of the classification app (source code: https://github.com/EuropeanIFCBGroup/ifcb-inference-app) and pass its URL here.

model_name

A character string specifying the name of the CNN model to use for classification. Default is "SMHI NIVA ResNet50 V5". Use ifcb_classify_models() to list all available models.

verbose

A logical value indicating whether to print progress messages. Default is TRUE.

...

Additional arguments passed to ifcb_extract_pngs() (e.g. ROInumbers, gamma).

Details

Three output formats are supported:

"h5"

IFCB Dashboard class_scores v3 HDF5 format. Contains output_scores, class_labels, roi_numbers (Dashboard-required), plus classifier_name, class_name, class_name_auto, and thresholds. Requires the hdf5r package.

"mat"

IFCB Dashboard class_scores v1 MATLAB format. Contains class2useTB, TBscores, roinum, TBclass, TBclass_above_threshold, and classifierName. Requires Python with scipy and numpy.

"csv"

ClassiPyR-compatible CSV format with columns file_name, class_name (threshold-applied), class_name_auto (winning class without threshold), and score (winning class confidence). See https://github.com/EuropeanIFCBGroup/ClassiPyR for details.

See Also

ifcb_classify_images(), ifcb_classify_sample(), ifcb_classify_models()

Examples

if (FALSE) {
# Classify a sample and save as HDF5 (default)
ifcb_save_classification(
  "path/to/D20220522T003051_IFCB134.roi",
  output_folder = "output"
)

# Save as Dashboard v1 .mat format
ifcb_save_classification(
  "path/to/D20220522T003051_IFCB134.roi",
  output_folder = "output",
  format = "mat"
)

# Save as CSV
ifcb_save_classification(
  "path/to/D20220522T003051_IFCB134.roi",
  output_folder = "output",
  format = "csv"
)
}