feat: 🔥 change fire cli to click cli and lower the dependencies from pyproject.toml

.github/workflows/ci.yml

@@ -93,7 +93,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest, windows-latest]
-        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12", "3.13"]
     steps:
       - name: Setup uv python package manager
         uses: astral-sh/setup-uv@5a095e7a2014a4212f075830d4f7277575a9d098 # v7.3.1

docs/cli.md

@@ -1,4 +1,14 @@
-# CLI Commands
+## CLI commands
+
+This page documents the SAHI CLI (the program exposed by `sahi/cli.py`).
+
+### Top-level commands
+
+- `predict` — run sliced/standard prediction pipeline and export results (images, COCO json, pickles)
+- `predict-fiftyone` — run prediction and open results in FiftyOne
+- `coco` — subgroup for COCO-format utilities (evaluate, analyse, convert, slice)
+- `version` — print SAHI package version
+- `env` — print environment and dependency versions
 
 SAHI provides a comprehensive command-line interface for object detection tasks. This guide covers all available commands with detailed examples and options.
 

pyproject.toml

@@ -13,7 +13,6 @@ dependencies = [
   "pybboxes==0.1.6",
   "pillow>=8.2.0",
   "pyyaml",
-  "fire",
   "terminaltables",
   "requests",
   "click",

sahi/__init__.py

@@ -6,7 +6,6 @@
 except importlib_metadata.PackageNotFoundError:
     __version__ = "development"
 
-
 from sahi.annotation import BoundingBox, Category, Mask
 from sahi.auto_model import AutoDetectionModel
 from sahi.models.base import DetectionModel

sahi/cli.py

@@ -1,4 +1,4 @@
-import fire
+import click
 
 from sahi import __version__ as sahi_version
 from sahi.predict import predict, predict_fiftyone
@@ -7,7 +7,15 @@
 from sahi.scripts.coco_error_analysis import analyse
 from sahi.scripts.coco_evaluation import evaluate
 from sahi.scripts.slice_coco import slicer
-from sahi.utils.import_utils import print_environment_info
+from sahi.utils.cli_helper import make_click_command
+from sahi.utils.package_utils import print_environment_info
+
+
+@click.group(help="SAHI command-line utilities: slicing-aided high-resolution inference and COCO tools")
+def cli():
+    """Top-level click group for SAHI CLI."""
+    pass
+
 
 coco_app = {
     "evaluate": evaluate,
@@ -18,19 +26,43 @@
     "yolov5": coco2yolo,
 }
 
+
+# Create wrapper functions with proper help text for commands that need it
+def version_command():
+    """Show SAHI version."""
+    click.echo(sahi_version)
+
+
+def env_command():
+    """Show environment information."""
+    print_environment_info()
+
+
 sahi_app = {
     "predict": predict,
     "predict-fiftyone": predict_fiftyone,
     "coco": coco_app,
-    "version": sahi_version,
-    "env": print_environment_info,
+    "version": version_command,
+    "env": env_command,
 }
 
 
 def app() -> None:
     """Cli app."""
-    fire.Fire(sahi_app)
+
+    for command_name, command_func in sahi_app.items():
+        if isinstance(command_func, dict):
+            # add subcommands (create a named Group with help text)
+            sub_cli = click.Group(command_name, help=f"{command_name} related commands")
+            for sub_command_name, sub_command_func in command_func.items():
+                sub_cli.add_command(make_click_command(sub_command_name, sub_command_func))
+            cli.add_command(sub_cli)
+        else:
+            cli.add_command(make_click_command(command_name, command_func))
+
+    cli()
 
 
 if __name__ == "__main__":
+    # build the application (register commands) and run
     app()

sahi/models/base.py

@@ -7,7 +7,7 @@
 from sahi.annotation import Category
 from sahi.logger import logger
 from sahi.prediction import ObjectPrediction
-from sahi.utils.import_utils import check_requirements
+from sahi.utils.package_utils import check_requirements
 from sahi.utils.torch_utils import empty_cuda_cache, select_device
 
 

sahi/models/huggingface.py

@@ -9,7 +9,7 @@
 from sahi.models.base import DetectionModel
 from sahi.prediction import ObjectPrediction
 from sahi.utils.compatibility import fix_full_shape_list, fix_shift_amount_list
-from sahi.utils.import_utils import ensure_package_minimum_version
+from sahi.utils.package_utils import check_package_minimum_version
 
 
 class HuggingfaceDetectionModel(DetectionModel):
@@ -33,7 +33,7 @@ def __init__(
         self._token = token
         existing_packages = getattr(self, "required_packages", None) or []
         self.required_packages = [*list(existing_packages), "torch", "transformers"]
-        ensure_package_minimum_version("transformers", "4.42.0")
+        check_package_minimum_version("transformers", "4.42.0", raise_error=True)
         super().__init__(
             model_path,
             model,

sahi/models/mmdet.py

@@ -9,7 +9,7 @@
 from sahi.prediction import ObjectPrediction
 from sahi.utils.compatibility import fix_full_shape_list, fix_shift_amount_list
 from sahi.utils.cv import get_bbox_from_bool_mask, get_coco_segmentation_from_bool_mask
-from sahi.utils.import_utils import check_requirements
+from sahi.utils.package_utils import check_requirements
 
 check_requirements(["torch", "mmdet", "mmcv", "mmengine"])
 

sahi/models/ultralytics.py

@@ -10,7 +10,7 @@
 from sahi.prediction import ObjectPrediction
 from sahi.utils.compatibility import fix_full_shape_list, fix_shift_amount_list
 from sahi.utils.cv import get_coco_segmentation_from_bool_mask
-from sahi.utils.import_utils import check_requirements
+from sahi.utils.package_utils import check_requirements
 
 
 class UltralyticsDetectionModel(DetectionModel):

sahi/models/yolov5.py

@@ -8,7 +8,7 @@
 from sahi.models.base import DetectionModel
 from sahi.prediction import ObjectPrediction
 from sahi.utils.compatibility import fix_full_shape_list, fix_shift_amount_list
-from sahi.utils.import_utils import check_package_minimum_version
+from sahi.utils.package_utils import check_package_minimum_version
 
 
 class Yolov5DetectionModel(DetectionModel):
@@ -77,7 +77,7 @@ def has_mask(self):
 
     @property
     def category_names(self):
-        if check_package_minimum_version("yolov5", "6.2.0"):
+        if check_package_minimum_version("yolov5", "6.2.0", raise_error=False):
             return list(self.model.names.values())
         else:
             return self.model.names

sahi/postprocess/combine.py

@@ -7,7 +7,6 @@
 from sahi.logger import logger
 from sahi.postprocess.utils import ObjectPredictionList, has_match, merge_object_prediction_pair
 from sahi.prediction import ObjectPrediction
-from sahi.utils.import_utils import check_requirements
 
 
 def batched_nms(predictions: torch.tensor, match_metric: str = "IOU", match_threshold: float = 0.5):
@@ -458,8 +457,6 @@ def __init__(
         self.class_agnostic = class_agnostic
         self.match_metric = match_metric
 
-        check_requirements(["torch"])
-
     def __call__(self, predictions: list[ObjectPrediction]):
         raise NotImplementedError()
 

sahi/predict.py

@@ -32,7 +32,7 @@
     visualize_object_predictions,
 )
 from sahi.utils.file import Path, increment_path, list_files, save_json, save_pickle
-from sahi.utils.import_utils import check_requirements
+from sahi.utils.package_utils import check_requirements
 
 POSTPROCESS_NAME_TO_CLASS = {
     "GREEDYNMM": GreedyNMMPostprocess,

sahi/scripts/coco2fiftyone.py

@@ -1,8 +1,6 @@
 import time
 from pathlib import Path
 
-import fire
-
 from sahi.utils.file import load_json
 
 
@@ -13,6 +11,8 @@ def main(
     iou_thresh: float = 0.5,
 ):
     """
+    Launch fiftyone app with coco dataset and coco results.
+
     Args:
         image_dir (str): directory for coco images
         dataset_json_path (str): file path for the coco dataset json file
@@ -75,7 +75,3 @@ def main(
         print(f"SAHI has successfully launched a Fiftyone app at http://localhost:{fo.config.default_app_port}")
     while 1:
         time.sleep(3)
-
-
-if __name__ == "__main__":
-    fire.Fire(main)

sahi/scripts/coco2yolo.py

@@ -1,7 +1,5 @@
 from __future__ import annotations
 
-import fire
-
 from sahi.utils.coco import Coco
 from sahi.utils.file import Path, increment_path
 
@@ -15,17 +13,24 @@ def main(
     seed: int = 1,
     disable_symlink=False,
 ):
-    """
+    """Convert COCO dataset to YOLO format.
+
     Args:
-        images_dir (str): directory for coco images
-        dataset_json_path (str): file path for the coco json file to be converted
-        train_split (float or int): set the training split ratio
-        project (str): save results to project/name
-        name (str): save results to project/name"
-        seed (int): fix the seed for reproducibility
-        disable_symlink (bool): required in google colab env
+        image_dir: Directory containing COCO images
+        dataset_json_path: Path to the COCO JSON annotation file
+        train_split: Training split ratio (0.0 to 1.0)
+        project: Project directory for output
+        name: Experiment name for output subdirectory
+        seed: Random seed for reproducibility
+        disable_symlink: Disable symbolic links (required in some environments)
     """
 
+    # Validate required parameters
+    if image_dir is None:
+        raise ValueError("image_dir is required")
+    if dataset_json_path is None:
+        raise ValueError("dataset_json_path is required")
+
     # increment run
     save_dir = Path(increment_path(Path(project) / name, exist_ok=False))
     # load coco dict
@@ -42,7 +47,3 @@ def main(
     )
 
     print(f"COCO to YOLO conversion results are successfully exported to {save_dir}")
-
-
-if __name__ == "__main__":
-    fire.Fire(main)

sahi/scripts/coco_error_analysis.py

@@ -6,7 +6,6 @@
 from multiprocessing import Pool
 from pathlib import Path
 
-import fire
 import numpy as np
 
 from sahi.logger import logger
@@ -452,6 +451,8 @@ def analyse(
     return_dict: bool = False,
 ) -> dict | None:
     """
+    Perform COCO error analysis and export result plots to out_dir.
+
     Args:
         dataset_json_path (str): file path for the coco dataset json file
         result_json_paths (str): file path for the coco result json file
@@ -480,7 +481,3 @@ def analyse(
     )
     if return_dict:
         return result
-
-
-if __name__ == "__main__":
-    fire.Fire(analyse)

sahi/scripts/coco_evaluation.py

@@ -7,7 +7,6 @@
 from pathlib import Path
 from typing import Literal
 
-import fire
 import numpy as np
 from terminaltables import AsciiTable
 
@@ -361,7 +360,8 @@ def evaluate(
     areas: list[int] = [1024, 9216, 10000000000],
     return_dict: bool = False,
 ):
-    """
+    """COCO evaluation entrypoint.
+
     Args:
         dataset_json_path (str): file path for the coco dataset json file
         result_json_path (str): file path for the coco result json file
@@ -373,6 +373,7 @@ def evaluate(
         areas (List[int]): area regions for coco evaluation calculations
         return_dict (bool): If True, returns a dict with 'eval_results' 'export_path' fields.
     """
+
     try:
         from pycocotools.coco import COCO
         from pycocotools.cocoeval import COCOeval
@@ -396,7 +397,3 @@ def evaluate(
     )
     if return_dict:
         return result
-
-
-if __name__ == "__main__":
-    fire.Fire(evaluate)

sahi/scripts/slice_coco.py

@@ -1,7 +1,5 @@
 import os
 
-import fire
-
 from sahi.slicing import slice_coco
 from sahi.utils.file import Path, save_json
 
@@ -16,6 +14,8 @@ def slicer(
     min_area_ratio: float = 0.1,
 ):
     """
+    Slice COCO dataset images and annotations.
+
     Args:
         image_dir (str): directory for coco images
         dataset_json_path (str): file path for the coco dataset json file
@@ -61,7 +61,3 @@ def slicer(
         output_coco_annotation_file_path = os.path.join(output_dir, sliced_coco_name + ".json")
         save_json(coco_dict, output_coco_annotation_file_path)
         print(f"Sliced dataset for 'slice_size: {slice_size}' is exported to {output_dir}")
-
-
-if __name__ == "__main__":
-    fire.Fire(slice)