# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license import io from typing import Any import cv2 from ultralytics import YOLO from ultralytics.utils import LOGGER from ultralytics.utils.checks import check_requirements from ultralytics.utils.downloads import GITHUB_ASSETS_STEMS class Inference: """ 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 (str): Enable tracking option ("Yes" or "No"). 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: Sets up the Streamlit web interface with custom HTML elements. sidebar: Configures the Streamlit sidebar for model and inference settings. source_upload: Handles video file uploads through the Streamlit interface. configure: Configures the model and loads selected classes for inference. inference: Performs real-time object detection inference. Examples: >>> inf = Inference(model="path/to/model.pt") # Model is an optional argument >>> inf.inference() """ def __init__(self, **kwargs: Any): """ Initialize the Inference class, checking Streamlit requirements and setting up the model path. Args: **kwargs (Any): Additional keyword arguments for model configuration. """ check_requirements("streamlit>=1.29.0") # scope imports for faster ultralytics package load speeds import streamlit as st self.st = st # Reference to the Streamlit module self.source = None # Video source selection (webcam or video file) self.enable_trk = False # Flag to toggle object tracking self.conf = 0.25 # Confidence threshold for detection self.iou = 0.45 # Intersection-over-Union (IoU) threshold for non-maximum suppression self.org_frame = None # Container for the original frame display self.ann_frame = None # Container for the annotated frame display self.vid_file_name = None # Video file name or webcam index self.selected_ind = [] # List of selected class indices for detection self.model = None # YOLO model instance self.temp_dict = {"model": None, **kwargs} self.model_path = None # Model file path if self.temp_dict["model"] is not None: self.model_path = self.temp_dict["model"] LOGGER.info(f"Ultralytics Solutions: ✅ {self.temp_dict}") def web_ui(self): """Sets up the Streamlit web interface with custom HTML elements.""" menu_style_cfg = """""" # Hide main menu style # Main title of streamlit application main_title_cfg = """

Ultralytics YOLO Streamlit Application

""" # Subtitle of streamlit application sub_title_cfg = """

Experience real-time object detection on your webcam with the power of Ultralytics YOLO! 🚀

""" # Set html page configuration and append custom HTML self.st.set_page_config(page_title="Ultralytics Streamlit App", layout="wide") self.st.markdown(menu_style_cfg, unsafe_allow_html=True) self.st.markdown(main_title_cfg, unsafe_allow_html=True) self.st.markdown(sub_title_cfg, unsafe_allow_html=True) def sidebar(self): """Configure the Streamlit sidebar for model and inference settings.""" with self.st.sidebar: # Add Ultralytics LOGO logo = "https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg" self.st.image(logo, width=250) self.st.sidebar.title("User Configuration") # Add elements to vertical setting menu self.source = self.st.sidebar.selectbox( "Video", ("webcam", "video"), ) # Add source selection dropdown self.enable_trk = self.st.sidebar.radio("Enable Tracking", ("Yes", "No")) # Enable object tracking self.conf = float( self.st.sidebar.slider("Confidence Threshold", 0.0, 1.0, self.conf, 0.01) ) # Slider for confidence self.iou = float(self.st.sidebar.slider("IoU Threshold", 0.0, 1.0, self.iou, 0.01)) # Slider for NMS threshold col1, col2 = self.st.columns(2) # Create two columns for displaying frames self.org_frame = col1.empty() # Container for original frame self.ann_frame = col2.empty() # Container for annotated frame def source_upload(self): """Handle video file uploads through the Streamlit interface.""" self.vid_file_name = "" if self.source == "video": vid_file = self.st.sidebar.file_uploader("Upload Video File", type=["mp4", "mov", "avi", "mkv"]) if vid_file is not None: g = io.BytesIO(vid_file.read()) # BytesIO Object with open("ultralytics.mp4", "wb") as out: # Open temporary file as bytes out.write(g.read()) # Read bytes into file self.vid_file_name = "ultralytics.mp4" elif self.source == "webcam": self.vid_file_name = 0 # Use webcam index 0 def configure(self): """Configure the model and load selected classes for inference.""" # Add dropdown menu for model selection available_models = [x.replace("yolo", "YOLO") for x in GITHUB_ASSETS_STEMS if x.startswith("yolo11")] if self.model_path: # If user provided the custom model, insert model without suffix as *.pt is added later available_models.insert(0, self.model_path.split(".pt")[0]) selected_model = self.st.sidebar.selectbox("Model", available_models) with self.st.spinner("Model is downloading..."): self.model = YOLO(f"{selected_model.lower()}.pt") # Load the YOLO model class_names = list(self.model.names.values()) # Convert dictionary to list of class names self.st.success("Model loaded successfully!") # Multiselect box with class names and get indices of selected classes selected_classes = self.st.sidebar.multiselect("Classes", class_names, default=class_names[:3]) self.selected_ind = [class_names.index(option) for option in selected_classes] if not isinstance(self.selected_ind, list): # Ensure selected_options is a list self.selected_ind = list(self.selected_ind) def inference(self): """Perform real-time object detection inference on video or webcam feed.""" self.web_ui() # Initialize the web interface self.sidebar() # Create the sidebar self.source_upload() # Upload the video source self.configure() # Configure the app if self.st.sidebar.button("Start"): stop_button = self.st.button("Stop") # Button to stop the inference cap = cv2.VideoCapture(self.vid_file_name) # Capture the video if not cap.isOpened(): self.st.error("Could not open webcam or video source.") return while cap.isOpened(): success, frame = cap.read() if not success: self.st.warning("Failed to read frame from webcam. Please verify the webcam is connected properly.") break # Process frame with model if self.enable_trk == "Yes": results = self.model.track( frame, conf=self.conf, iou=self.iou, classes=self.selected_ind, persist=True ) else: results = self.model(frame, conf=self.conf, iou=self.iou, classes=self.selected_ind) annotated_frame = results[0].plot() # Add annotations on frame if stop_button: cap.release() # Release the capture self.st.stop() # Stop streamlit app self.org_frame.image(frame, channels="BGR") # Display original frame self.ann_frame.image(annotated_frame, channels="BGR") # Display processed frame cap.release() # Release the capture cv2.destroyAllWindows() # Destroy all OpenCV windows if __name__ == "__main__": import sys # Import the sys module for accessing command-line arguments # Check if a model name is provided as a command-line argument args = len(sys.argv) model = sys.argv[1] if args > 1 else None # Assign first argument as the model name if provided # Create an instance of the Inference class and run inference Inference(model=model).inference()