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A class to perform object detection, image classification, image segmentation and pose estimation inference.
This class provides functionalities for loading models, configuring settings, uploading video files, and performing
real-time inference using Streamlit and Ultralytics YOLO models.
Attributes:
st (module): Streamlit module for UI creation.
temp_dict (dict): Temporary dictionary to store the model path and other configuration.
model_path (str): Path to the loaded model.
model (YOLO): The YOLO model instance.
source (str): Selected video source (webcam or video file).
enable_trk (bool): Enable tracking option.
conf (float): Confidence threshold for detection.
iou (float): IoU threshold for non-maximum suppression.
org_frame (Any): Container for the original frame to be displayed.
ann_frame (Any): Container for the annotated frame to be displayed.
vid_file_name (str | int): Name of the uploaded video file or webcam index.
selected_ind (list[int]): List of selected class indices for detection.
Methods:
web_ui: Set up the Streamlit web interface with custom HTML elements.
sidebar: Configure the Streamlit sidebar for model and inference settings.
source_upload: Handle video file uploads through the Streamlit interface.
configure: Configure the model and load selected classes for inference.
inference: Perform real-time object detection inference.
Examples:
Create an Inference instance with a custom model
>>> inf = Inference(model="path/to/model.pt")
>>> inf.inference()
Create an Inference instance with default settings
>>> inf = Inference()
>>> inf.inference()
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Initialize the Inference class, checking Streamlit requirements and setting up the model path.
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