dev(narugo): add docs (31a1d232) · Commits · git-mirror / Imgutils

docs/source/api_doc/generic/yolo.rst

+1 −1

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		@@ -11,7 +11,7 @@ YOLOModel
		-----------------------------------------

		.. autoclass:: YOLOModel
		:members: __init__, predict, clear
		:members: __init__, predict, clear, make_ui, launch_demo

imgutils/generic/yolo.py

+71 −0

Original line number	Diff line number	Diff line
		@@ -41,16 +41,37 @@ __all__ = [


		def _check_gradio_env():
		"""
		Check if the Gradio library is installed and available.

		:raises EnvironmentError: If Gradio is not installed.
		"""
		if gr is None:
		raise EnvironmentError(f'Gradio required for launching webui-based demo.\n'
		f'Please install it with `pip install dghs-imgutils[demo]`.')


		def _v_fix(v):
		"""
		Round and convert a float value to an integer.

		:param v: The float value to be rounded and converted.
		:type v: float
		:return: The rounded integer value.
		:rtype: int
		"""
		return int(round(v))


		def _bbox_fix(bbox):
		"""
		Fix the bounding box coordinates by rounding them to integers.

		:param bbox: The bounding box coordinates.
		:type bbox: tuple
		:return: A tuple of fixed (rounded to integer) bounding box coordinates.
		:rtype: tuple
		"""
		return tuple(map(_v_fix, bbox))


		@@ -425,11 +446,36 @@ class YOLOModel:
		def clear(self):
		"""
		Clear cached model and metadata.

		This method removes all cached models and their associated metadata from memory.
		It's useful for freeing up memory or ensuring that the latest versions of models are loaded.
		"""
		self._models.clear()

		def make_ui(self, default_model_name: Optional[str] = None,
		default_conf_threshold: float = 0.25, default_iou_threshold: float = 0.7):
		"""
		Create a Gradio-based user interface for object detection.

		This method sets up an interactive UI that allows users to upload images,
		select models, and adjust detection parameters. It uses the Gradio library
		to create the interface.

		:param default_model_name: The name of the default model to use.
		If None, the most recently updated model is selected.
		:type default_model_name: Optional[str]
		:param default_conf_threshold: Default confidence threshold for the UI. Default is 0.25.
		:type default_conf_threshold: float
		:param default_iou_threshold: Default IoU threshold for the UI. Default is 0.7.
		:type default_iou_threshold: float

		:raises ImportError: If Gradio is not installed in the environment.

		:Example:

		>>> model = YOLOModel("username/repo_name")
		>>> model.make_ui(default_model_name="yolov5s")
		"""
		_check_gradio_env()
		model_list = self.model_names
		if not default_model_name:
		@@ -493,6 +539,31 @@ class YOLOModel:
		def launch_demo(self, default_model_name: Optional[str] = None,
		default_conf_threshold: float = 0.25, default_iou_threshold: float = 0.7,
		server_name: Optional[str] = None, server_port: Optional[int] = None, **kwargs):
		"""
		Launch a Gradio demo for object detection.

		This method creates and launches a Gradio demo that allows users to interactively
		perform object detection on uploaded images using the YOLO model.

		:param default_model_name: The name of the default model to use.
		If None, the most recently updated model is selected.
		:type default_model_name: Optional[str]
		:param default_conf_threshold: Default confidence threshold for the demo. Default is 0.25.
		:type default_conf_threshold: float
		:param default_iou_threshold: Default IoU threshold for the demo. Default is 0.7.
		:type default_iou_threshold: float
		:param server_name: The name of the server to run the demo on. Default is None.
		:type server_name: Optional[str]
		:param server_port: The port to run the demo on. Default is None.
		:type server_port: Optional[int]
		:param kwargs: Additional keyword arguments to pass to gr.Blocks.launch().

		:raises EnvironmentError: If Gradio is not installed in the environment.

		Example:
		>>> model = YOLOModel("username/repo_name")
		>>> model.launch_demo(default_model_name="yolov5s", server_name="0.0.0.0", server_port=7860)
		"""
		_check_gradio_env()
		with gr.Blocks() as demo:
		with gr.Row():