Merge pull request #6 from qlue-dev/feature/multi-agg

Feature/multi agg

.gitignore

@@ -7,3 +7,5 @@ dist
 build
 ssvad_metrics/VERSION
 .vscode
+.pytest_cache
+.benchmarks

Makefile

@@ -17,7 +17,7 @@ install-requirements:
 	pip3 install -r requirements.txt
 
 test:
-	pytests -s
+	pytest -s
 
 build:
 	python3 setup.py bdist_wheel

README.md

@@ -6,6 +6,39 @@ This project contains evaluation protocol (metrics) for benchmarking single-scen
 
 This is an unofficial implementation of Sec. 2.2 of [A Survey of Single-Scene Video Anomaly Detection](https://arxiv.org/pdf/2004.05993.pdf).
 
+## Installation
+
+This metrics is available via PyPI.
+
+```bash
+pip install py-ssvad-metrics
+```
+
+## Usage
+
+1. Prepare ground-truth JSON file and prediction JSON file. Examples are in the `tests` folder.
+1. For UCSD Pedestrian 1 and 2 datasets, CUHK Avenue dataset, and Street Scene dataset,
+we provided scripts for converting ground-truth annotation files from Street Scene dataset. Download link is provided in the paper [http://www.merl.com/demos/video-anomaly-detection].
+1. Example usage for single groundtruth and prediction file pair:
+
+    ```python
+    import ssvad_metrics
+    result = ssvad_metrics.metrics.evaluate(
+        "tests/gt_examples/Test001_gt.json",
+        "tests/pred_examples/Test001_pred.json")
+    ```
+
+1. Example usage for multiple groundtruth and prediction file pairs:
+
+    ```python
+    import ssvad_metrics
+    result = ssvad_metrics.metrics.accumulated_evaluate(
+        "tests/gt_examples",
+        "tests/pred_examples",
+        gt_name_suffix="_gt",
+        pred_name_suffix="_pred")
+    ```
+
 ## References
 
 1. B. Ramachandra, M. Jones and R. R. Vatsavai, "A Survey of Single-Scene Video Anomaly Detection," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2020.3040591.