ultralytics 8.3.54 New Streamlit inference Solution (#18316)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Signed-off-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Muhammad Rizwan Munawar
GitHub
parent
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commit
51026a9a4a
13 changed files with 251 additions and 188 deletions
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@ -10,7 +10,7 @@ from .queue_management import QueueManager
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from .region_counter import RegionCounter
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from .security_alarm import SecurityAlarm
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from .speed_estimation import SpeedEstimator
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from .streamlit_inference import inference
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from .streamlit_inference import Inference
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from .trackzone import TrackZone
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__all__ = (
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@ -23,7 +23,7 @@ __all__ = (
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"QueueManager",
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"SpeedEstimator",
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"Analytics",
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"inference",
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"Inference",
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"RegionCounter",
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"TrackZone",
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"SecurityAlarm",
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@ -5,7 +5,9 @@ import json
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import cv2
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import numpy as np
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from ultralytics.solutions.solutions import LOGGER, BaseSolution, check_requirements
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from ultralytics.solutions.solutions import BaseSolution
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from ultralytics.utils import LOGGER
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from ultralytics.utils.checks import check_requirements
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from ultralytics.utils.plotting import Annotator
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@ -1,6 +1,7 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from ultralytics.solutions.solutions import LOGGER, BaseSolution
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from ultralytics.solutions.solutions import BaseSolution
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from ultralytics.utils import LOGGER
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from ultralytics.utils.plotting import Annotator, colors
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@ -4,145 +4,188 @@ import io
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import time
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import cv2
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import torch
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from ultralytics import YOLO
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from ultralytics.utils import LOGGER
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from ultralytics.utils.checks import check_requirements
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from ultralytics.utils.downloads import GITHUB_ASSETS_STEMS
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def inference(model=None):
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"""Performs real-time object detection on video input using YOLO in a Streamlit web application."""
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check_requirements("streamlit>=1.29.0") # scope imports for faster ultralytics package load speeds
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import streamlit as st
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class Inference:
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"""
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A class to perform object detection, image classification, image segmentation and pose estimation inference using
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Streamlit and Ultralytics YOLO models. It provides the functionalities such as loading models, configuring settings,
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uploading video files, and performing real-time inference.
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from ultralytics import YOLO
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Attributes:
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st (module): Streamlit module for UI creation.
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temp_dict (dict): Temporary dictionary to store the model path.
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model_path (str): Path to the loaded model.
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model (YOLO): The YOLO model instance.
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source (str): Selected video source.
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enable_trk (str): Enable tracking option.
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conf (float): Confidence threshold.
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iou (float): IoU threshold for non-max suppression.
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vid_file_name (str): Name of the uploaded video file.
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selected_ind (list): List of selected class indices.
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# Hide main menu style
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menu_style_cfg = """<style>MainMenu {visibility: hidden;}</style>"""
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Methods:
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web_ui: Sets up the Streamlit web interface with custom HTML elements.
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sidebar: Configures the Streamlit sidebar for model and inference settings.
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source_upload: Handles video file uploads through the Streamlit interface.
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configure: Configures the model and loads selected classes for inference.
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inference: Performs real-time object detection inference.
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# Main title of streamlit application
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main_title_cfg = """<div><h1 style="color:#FF64DA; text-align:center; font-size:40px;
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font-family: 'Archivo', sans-serif; margin-top:-50px;margin-bottom:20px;">
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Ultralytics YOLO Streamlit Application
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</h1></div>"""
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Examples:
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>>> inf = solutions.Inference(model="path/to/model/file.pt") # Model is not necessary argument.
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>>> inf.inference()
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"""
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# Subtitle of streamlit application
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sub_title_cfg = """<div><h4 style="color:#042AFF; text-align:center;
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font-family: 'Archivo', sans-serif; margin-top:-15px; margin-bottom:50px;">
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Experience real-time object detection on your webcam with the power of Ultralytics YOLO! 🚀</h4>
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</div>"""
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def __init__(self, **kwargs):
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"""
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Initializes the Inference class, checking Streamlit requirements and setting up the model path.
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# Set html page configuration
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st.set_page_config(page_title="Ultralytics Streamlit App", layout="wide", initial_sidebar_state="auto")
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Args:
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**kwargs (Dict): Additional keyword arguments for model configuration.
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"""
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check_requirements("streamlit>=1.29.0") # scope imports for faster ultralytics package load speeds
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import streamlit as st
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# Append the custom HTML
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st.markdown(menu_style_cfg, unsafe_allow_html=True)
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st.markdown(main_title_cfg, unsafe_allow_html=True)
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st.markdown(sub_title_cfg, unsafe_allow_html=True)
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self.st = st
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# Add ultralytics logo in sidebar
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with st.sidebar:
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logo = "https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg"
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st.image(logo, width=250)
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self.temp_dict = {"model": None} # Temporary dict to store the model path
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self.temp_dict.update(kwargs)
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# Add elements to vertical setting menu
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st.sidebar.title("User Configuration")
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self.model_path = None # Store model file name with path
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if self.temp_dict["model"] is not None:
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self.model_path = self.temp_dict["model"]
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# Add video source selection dropdown
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source = st.sidebar.selectbox(
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"Video",
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("webcam", "video"),
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)
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LOGGER.info(f"Ultralytics Solutions: ✅ {self.temp_dict}")
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vid_file_name = ""
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if source == "video":
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vid_file = st.sidebar.file_uploader("Upload Video File", type=["mp4", "mov", "avi", "mkv"])
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if vid_file is not None:
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g = io.BytesIO(vid_file.read()) # BytesIO Object
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vid_location = "ultralytics.mp4"
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with open(vid_location, "wb") as out: # Open temporary file as bytes
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out.write(g.read()) # Read bytes into file
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vid_file_name = "ultralytics.mp4"
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elif source == "webcam":
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vid_file_name = 0
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def web_ui(self):
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"""Sets up the Streamlit web interface with custom HTML elements."""
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menu_style_cfg = """<style>MainMenu {visibility: hidden;}</style>""" # Hide main menu style
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# Add dropdown menu for model selection
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available_models = [x.replace("yolo", "YOLO") for x in GITHUB_ASSETS_STEMS if x.startswith("yolo11")]
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if model:
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available_models.insert(0, model.split(".pt")[0]) # insert model without suffix as *.pt is added later
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# Main title of streamlit application
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main_title_cfg = """<div><h1 style="color:#FF64DA; text-align:center; font-size:40px; margin-top:-50px;
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font-family: 'Archivo', sans-serif; margin-bottom:20px;">Ultralytics YOLO Streamlit Application</h1></div>"""
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selected_model = st.sidebar.selectbox("Model", available_models)
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with st.spinner("Model is downloading..."):
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model = YOLO(f"{selected_model.lower()}.pt") # Load the YOLO model
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class_names = list(model.names.values()) # Convert dictionary to list of class names
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st.success("Model loaded successfully!")
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# Subtitle of streamlit application
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sub_title_cfg = """<div><h4 style="color:#042AFF; text-align:center; font-family: 'Archivo', sans-serif;
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margin-top:-15px; margin-bottom:50px;">Experience real-time object detection on your webcam with the power
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of Ultralytics YOLO! 🚀</h4></div>"""
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# Multiselect box with class names and get indices of selected classes
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selected_classes = st.sidebar.multiselect("Classes", class_names, default=class_names[:3])
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selected_ind = [class_names.index(option) for option in selected_classes]
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# Set html page configuration and append custom HTML
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self.st.set_page_config(page_title="Ultralytics Streamlit App", layout="wide", initial_sidebar_state="auto")
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self.st.markdown(menu_style_cfg, unsafe_allow_html=True)
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self.st.markdown(main_title_cfg, unsafe_allow_html=True)
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self.st.markdown(sub_title_cfg, unsafe_allow_html=True)
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if not isinstance(selected_ind, list): # Ensure selected_options is a list
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selected_ind = list(selected_ind)
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def sidebar(self):
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"""Configures the Streamlit sidebar for model and inference settings."""
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with self.st.sidebar: # Add Ultralytics LOGO
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logo = "https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg"
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self.st.image(logo, width=250)
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enable_trk = st.sidebar.radio("Enable Tracking", ("Yes", "No"))
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conf = float(st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.25, 0.01))
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iou = float(st.sidebar.slider("IoU Threshold", 0.0, 1.0, 0.45, 0.01))
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self.st.sidebar.title("User Configuration") # Add elements to vertical setting menu
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self.source = self.st.sidebar.selectbox(
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"Video",
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("webcam", "video"),
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) # Add source selection dropdown
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self.enable_trk = self.st.sidebar.radio("Enable Tracking", ("Yes", "No")) # Enable object tracking
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self.conf = float(self.st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.25, 0.01)) # Slider for confidence
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self.iou = float(self.st.sidebar.slider("IoU Threshold", 0.0, 1.0, 0.45, 0.01)) # Slider for NMS threshold
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col1, col2 = st.columns(2)
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org_frame = col1.empty()
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ann_frame = col2.empty()
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col1, col2 = self.st.columns(2)
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self.org_frame = col1.empty()
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self.ann_frame = col2.empty()
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self.fps_display = self.st.sidebar.empty() # Placeholder for FPS display
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fps_display = st.sidebar.empty() # Placeholder for FPS display
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def source_upload(self):
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"""Handles video file uploads through the Streamlit interface."""
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self.vid_file_name = ""
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if self.source == "video":
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vid_file = self.st.sidebar.file_uploader("Upload Video File", type=["mp4", "mov", "avi", "mkv"])
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if vid_file is not None:
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g = io.BytesIO(vid_file.read()) # BytesIO Object
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with open("ultralytics.mp4", "wb") as out: # Open temporary file as bytes
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out.write(g.read()) # Read bytes into file
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self.vid_file_name = "ultralytics.mp4"
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elif self.source == "webcam":
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self.vid_file_name = 0
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if st.sidebar.button("Start"):
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videocapture = cv2.VideoCapture(vid_file_name) # Capture the video
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def configure(self):
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"""Configures the model and loads selected classes for inference."""
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# Add dropdown menu for model selection
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available_models = [x.replace("yolo", "YOLO") for x in GITHUB_ASSETS_STEMS if x.startswith("yolo11")]
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if self.model_path: # If user provided the custom model, insert model without suffix as *.pt is added later
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available_models.insert(0, self.model_path.split(".pt")[0])
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selected_model = self.st.sidebar.selectbox("Model", available_models)
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if not videocapture.isOpened():
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st.error("Could not open webcam.")
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with self.st.spinner("Model is downloading..."):
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self.model = YOLO(f"{selected_model.lower()}.pt") # Load the YOLO model
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class_names = list(self.model.names.values()) # Convert dictionary to list of class names
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self.st.success("Model loaded successfully!")
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stop_button = st.button("Stop") # Button to stop the inference
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# Multiselect box with class names and get indices of selected classes
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selected_classes = self.st.sidebar.multiselect("Classes", class_names, default=class_names[:3])
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self.selected_ind = [class_names.index(option) for option in selected_classes]
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while videocapture.isOpened():
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success, frame = videocapture.read()
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if not success:
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st.warning("Failed to read frame from webcam. Please make sure the webcam is connected properly.")
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break
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if not isinstance(self.selected_ind, list): # Ensure selected_options is a list
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self.selected_ind = list(self.selected_ind)
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prev_time = time.time() # Store initial time for FPS calculation
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def inference(self):
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"""Performs real-time object detection inference."""
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self.web_ui() # Initialize the web interface
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self.sidebar() # Create the sidebar
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self.source_upload() # Upload the video source
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self.configure() # Configure the app
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# Store model predictions
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if enable_trk == "Yes":
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results = model.track(frame, conf=conf, iou=iou, classes=selected_ind, persist=True)
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else:
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results = model(frame, conf=conf, iou=iou, classes=selected_ind)
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annotated_frame = results[0].plot() # Add annotations on frame
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if self.st.sidebar.button("Start"):
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stop_button = self.st.button("Stop") # Button to stop the inference
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cap = cv2.VideoCapture(self.vid_file_name) # Capture the video
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if not cap.isOpened():
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self.st.error("Could not open webcam.")
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while cap.isOpened():
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success, frame = cap.read()
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if not success:
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st.warning("Failed to read frame from webcam. Please make sure the webcam is connected properly.")
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break
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# Calculate model FPS
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curr_time = time.time()
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fps = 1 / (curr_time - prev_time)
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prev_time = time.time() # Store initial time for FPS calculation
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# display frame
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org_frame.image(frame, channels="BGR")
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ann_frame.image(annotated_frame, channels="BGR")
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# Store model predictions
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if self.enable_trk == "Yes":
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results = self.model.track(
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frame, conf=self.conf, iou=self.iou, classes=self.selected_ind, persist=True
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)
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else:
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results = self.model(frame, conf=self.conf, iou=self.iou, classes=self.selected_ind)
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annotated_frame = results[0].plot() # Add annotations on frame
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if stop_button:
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videocapture.release() # Release the capture
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torch.cuda.empty_cache() # Clear CUDA memory
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st.stop() # Stop streamlit app
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fps = 1 / (time.time() - prev_time) # Calculate model FPS
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# Display FPS in sidebar
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fps_display.metric("FPS", f"{fps:.2f}")
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if stop_button:
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cap.release() # Release the capture
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self.st.stop() # Stop streamlit app
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# Release the capture
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videocapture.release()
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self.fps_display.metric("FPS", f"{fps:.2f}") # Display FPS in sidebar
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self.org_frame.image(frame, channels="BGR") # Display original frame
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self.ann_frame.image(annotated_frame, channels="BGR") # Display processed frame
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# Clear CUDA memory
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torch.cuda.empty_cache()
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# Destroy window
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cv2.destroyAllWindows()
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cap.release() # Release the capture
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cv2.destroyAllWindows() # Destroy window
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# Main function call
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if __name__ == "__main__":
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inference()
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import sys # Import the sys module for accessing command-line arguments
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model = None # Initialize the model variable as None
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# Check if a model name is provided as a command-line argument
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args = len(sys.argv)
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if args > 1:
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model = args # Assign the first argument as the model name
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# Create an instance of the Inference class and run inference
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Inference(model=model).inference()
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