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
5b76bed7d0
commit
51026a9a4a
13 changed files with 251 additions and 188 deletions
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@ -1,6 +1,6 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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__version__ = "8.3.53"
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__version__ = "8.3.54"
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import os
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@ -42,6 +42,7 @@ SOLUTION_MAP = {
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"workout": ("AIGym", "monitor"),
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"analytics": ("Analytics", "process_data"),
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"trackzone": ("TrackZone", "trackzone"),
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"inference": ("Inference", "inference"),
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"help": None,
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}
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@ -97,7 +98,10 @@ SOLUTIONS_HELP_MSG = f"""
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yolo solutions analytics analytics_type="pie"
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6. Track objects within specific zones
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yolo solutions trackzone source="path/to/video/file.mp4" region=[(150, 150), (1130, 150), (1130, 570), (150, 570)]
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yolo solutions trackzone source="path/to/video/file.mp4" region=[(150, 150), (1130, 150), (1130, 570), (150, 570)]
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7. Streamlit real-time webcam inference GUI
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yolo streamlit-predict
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"""
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CLI_HELP_MSG = f"""
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Arguments received: {str(['yolo'] + ARGV[1:])}. Ultralytics 'yolo' commands use the following syntax:
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@ -121,13 +125,10 @@ CLI_HELP_MSG = f"""
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4. Export a YOLO11n classification model to ONNX format at image size 224 by 128 (no TASK required)
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yolo export model=yolo11n-cls.pt format=onnx imgsz=224,128
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5. Streamlit real-time webcam inference GUI
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yolo streamlit-predict
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6. Ultralytics solutions usage
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5. Ultralytics solutions usage
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yolo solutions count or in {list(SOLUTION_MAP.keys())[1:-1]} source="path/to/video/file.mp4"
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7. Run special commands:
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6. Run special commands:
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yolo help
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yolo checks
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yolo version
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@ -636,6 +637,9 @@ def handle_yolo_solutions(args: List[str]) -> None:
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Run analytics with custom configuration:
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>>> handle_yolo_solutions(["analytics", "conf=0.25", "source=path/to/video/file.mp4"])
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Run inference with custom configuration, requires Streamlit version 1.29.0 or higher.
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>>> handle_yolo_solutions(["inference", "model=yolo11n.pt"])
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Notes:
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- Default configurations are merged from DEFAULT_SOL_DICT and DEFAULT_CFG_DICT
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- Arguments can be provided in the format 'key=value' or as boolean flags
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@ -645,7 +649,9 @@ def handle_yolo_solutions(args: List[str]) -> None:
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- For 'analytics' solution, frame numbers are tracked for generating analytical graphs
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- Video processing can be interrupted by pressing 'q'
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- Processes video frames sequentially and saves output in .avi format
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- If no source is specified, downloads and uses a default sample video
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- If no source is specified, downloads and uses a default sample video\
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- The inference solution will be launched using the 'streamlit run' command.
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- The Streamlit app file is located in the Ultralytics package directory.
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"""
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full_args_dict = {**DEFAULT_SOL_DICT, **DEFAULT_CFG_DICT} # arguments dictionary
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overrides = {}
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@ -678,60 +684,56 @@ def handle_yolo_solutions(args: List[str]) -> None:
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if args and args[0] == "help": # Add check for return if user call `yolo solutions help`
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return
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cls, method = SOLUTION_MAP[s_n] # solution class name, method name and default source
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if s_n == "inference":
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checks.check_requirements("streamlit>=1.29.0")
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LOGGER.info("💡 Loading Ultralytics live inference app...")
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subprocess.run(
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[ # Run subprocess with Streamlit custom argument
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"streamlit",
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"run",
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str(ROOT / "solutions/streamlit_inference.py"),
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"--server.headless",
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"true",
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overrides["model"],
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]
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)
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else:
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cls, method = SOLUTION_MAP[s_n] # solution class name, method name and default source
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from ultralytics import solutions # import ultralytics solutions
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from ultralytics import solutions # import ultralytics solutions
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solution = getattr(solutions, cls)(IS_CLI=True, **overrides) # get solution class i.e ObjectCounter
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process = getattr(solution, method) # get specific function of class for processing i.e, count from ObjectCounter
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solution = getattr(solutions, cls)(IS_CLI=True, **overrides) # get solution class i.e ObjectCounter
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process = getattr(
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solution, method
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) # get specific function of class for processing i.e, count from ObjectCounter
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cap = cv2.VideoCapture(solution.CFG["source"]) # read the video file
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cap = cv2.VideoCapture(solution.CFG["source"]) # read the video file
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# extract width, height and fps of the video file, create save directory and initialize video writer
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import os # for directory creation
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from pathlib import Path
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# extract width, height and fps of the video file, create save directory and initialize video writer
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import os # for directory creation
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from pathlib import Path
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from ultralytics.utils.files import increment_path # for output directory path update
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from ultralytics.utils.files import increment_path # for output directory path update
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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if s_n == "analytics": # analytical graphs follow fixed shape for output i.e w=1920, h=1080
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w, h = 1920, 1080
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save_dir = increment_path(Path("runs") / "solutions" / "exp", exist_ok=False)
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save_dir.mkdir(parents=True, exist_ok=True) # create the output directory
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vw = cv2.VideoWriter(os.path.join(save_dir, "solution.avi"), cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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if s_n == "analytics": # analytical graphs follow fixed shape for output i.e w=1920, h=1080
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w, h = 1920, 1080
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save_dir = increment_path(Path("runs") / "solutions" / "exp", exist_ok=False)
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save_dir.mkdir(parents=True, exist_ok=True) # create the output directory
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vw = cv2.VideoWriter(os.path.join(save_dir, "solution.avi"), cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
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try: # Process video frames
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f_n = 0 # frame number, required for analytical graphs
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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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break
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frame = process(frame, f_n := f_n + 1) if s_n == "analytics" else process(frame)
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vw.write(frame)
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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finally:
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cap.release()
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def handle_streamlit_inference():
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"""
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Open the Ultralytics Live Inference Streamlit app for real-time object detection.
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This function initializes and runs a Streamlit application designed for performing live object detection using
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Ultralytics models. It checks for the required Streamlit package and launches the app.
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Examples:
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>>> handle_streamlit_inference()
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Notes:
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- Requires Streamlit version 1.29.0 or higher.
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- The app is launched using the 'streamlit run' command.
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- The Streamlit app file is located in the Ultralytics package directory.
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"""
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checks.check_requirements("streamlit>=1.29.0")
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LOGGER.info("💡 Loading Ultralytics Live Inference app...")
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subprocess.run(["streamlit", "run", ROOT / "solutions/streamlit_inference.py", "--server.headless", "true"])
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try: # Process video frames
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f_n = 0 # frame number, required for analytical graphs
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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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break
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frame = process(frame, f_n := f_n + 1) if s_n == "analytics" else process(frame)
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vw.write(frame)
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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finally:
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cap.release()
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def parse_key_value_pair(pair: str = "key=value"):
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@ -853,7 +855,6 @@ def entrypoint(debug=""):
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"login": lambda: handle_yolo_hub(args),
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"logout": lambda: handle_yolo_hub(args),
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"copy-cfg": copy_default_cfg,
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"streamlit-predict": lambda: handle_streamlit_inference(),
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"solutions": lambda: handle_yolo_solutions(args[1:]),
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}
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full_args_dict = {**DEFAULT_CFG_DICT, **{k: None for k in TASKS}, **{k: None for k in MODES}, **special}
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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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# 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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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
|
||||
with open("ultralytics.mp4", "wb") as out: # Open temporary file as bytes
|
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out.write(g.read()) # Read bytes into file
|
||||
self.vid_file_name = "ultralytics.mp4"
|
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elif self.source == "webcam":
|
||||
self.vid_file_name = 0
|
||||
|
||||
if st.sidebar.button("Start"):
|
||||
videocapture = cv2.VideoCapture(vid_file_name) # Capture the video
|
||||
def configure(self):
|
||||
"""Configures the model and loads 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")]
|
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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)
|
||||
|
||||
if not videocapture.isOpened():
|
||||
st.error("Could not open webcam.")
|
||||
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!")
|
||||
|
||||
stop_button = st.button("Stop") # Button to stop the inference
|
||||
# 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]
|
||||
|
||||
while videocapture.isOpened():
|
||||
success, frame = videocapture.read()
|
||||
if not success:
|
||||
st.warning("Failed to read frame from webcam. Please make sure the webcam is connected properly.")
|
||||
break
|
||||
if not isinstance(self.selected_ind, list): # Ensure selected_options is a list
|
||||
self.selected_ind = list(self.selected_ind)
|
||||
|
||||
prev_time = time.time() # Store initial time for FPS calculation
|
||||
def inference(self):
|
||||
"""Performs real-time object detection inference."""
|
||||
self.web_ui() # Initialize the web interface
|
||||
self.sidebar() # Create the sidebar
|
||||
self.source_upload() # Upload the video source
|
||||
self.configure() # Configure the app
|
||||
|
||||
# Store model predictions
|
||||
if enable_trk == "Yes":
|
||||
results = model.track(frame, conf=conf, iou=iou, classes=selected_ind, persist=True)
|
||||
else:
|
||||
results = model(frame, conf=conf, iou=iou, classes=selected_ind)
|
||||
annotated_frame = results[0].plot() # Add annotations on frame
|
||||
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.")
|
||||
while cap.isOpened():
|
||||
success, frame = cap.read()
|
||||
if not success:
|
||||
st.warning("Failed to read frame from webcam. Please make sure the webcam is connected properly.")
|
||||
break
|
||||
|
||||
# Calculate model FPS
|
||||
curr_time = time.time()
|
||||
fps = 1 / (curr_time - prev_time)
|
||||
prev_time = time.time() # Store initial time for FPS calculation
|
||||
|
||||
# display frame
|
||||
org_frame.image(frame, channels="BGR")
|
||||
ann_frame.image(annotated_frame, channels="BGR")
|
||||
# Store model predictions
|
||||
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:
|
||||
videocapture.release() # Release the capture
|
||||
torch.cuda.empty_cache() # Clear CUDA memory
|
||||
st.stop() # Stop streamlit app
|
||||
fps = 1 / (time.time() - prev_time) # Calculate model FPS
|
||||
|
||||
# Display FPS in sidebar
|
||||
fps_display.metric("FPS", f"{fps:.2f}")
|
||||
if stop_button:
|
||||
cap.release() # Release the capture
|
||||
self.st.stop() # Stop streamlit app
|
||||
|
||||
# Release the capture
|
||||
videocapture.release()
|
||||
self.fps_display.metric("FPS", f"{fps:.2f}") # Display FPS in sidebar
|
||||
self.org_frame.image(frame, channels="BGR") # Display original frame
|
||||
self.ann_frame.image(annotated_frame, channels="BGR") # Display processed frame
|
||||
|
||||
# Clear CUDA memory
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
# Destroy window
|
||||
cv2.destroyAllWindows()
|
||||
cap.release() # Release the capture
|
||||
cv2.destroyAllWindows() # Destroy window
|
||||
|
||||
|
||||
# Main function call
|
||||
if __name__ == "__main__":
|
||||
inference()
|
||||
import sys # Import the sys module for accessing command-line arguments
|
||||
|
||||
model = None # Initialize the model variable as None
|
||||
|
||||
# Check if a model name is provided as a command-line argument
|
||||
args = len(sys.argv)
|
||||
if args > 1:
|
||||
model = args # Assign the first argument as the model name
|
||||
|
||||
# Create an instance of the Inference class and run inference
|
||||
Inference(model=model).inference()
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue