ultralytics 8.3.28 new Solutions CLI commands (#17233)
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
d049e22769
commit
3c976807b8
17 changed files with 310 additions and 48 deletions
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@ -1,6 +1,6 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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__version__ = "8.3.27"
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__version__ = "8.3.28"
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import os
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@ -7,11 +7,15 @@ from pathlib import Path
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from types import SimpleNamespace
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from typing import Dict, List, Union
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import cv2
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from ultralytics.utils import (
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ASSETS,
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ASSETS_URL,
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DEFAULT_CFG,
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DEFAULT_CFG_DICT,
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DEFAULT_CFG_PATH,
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DEFAULT_SOL_DICT,
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IS_VSCODE,
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LOGGER,
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RANK,
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@ -30,6 +34,17 @@ from ultralytics.utils import (
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yaml_print,
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)
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# Define valid solutions
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SOLUTION_MAP = {
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"count": ("ObjectCounter", "count"),
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"heatmap": ("Heatmap", "generate_heatmap"),
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"queue": ("QueueManager", "process_queue"),
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"speed": ("SpeedEstimator", "estimate_speed"),
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"workout": ("AIGym", "monitor"),
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"analytics": ("Analytics", "process_data"),
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"help": None,
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}
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# Define valid tasks and modes
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MODES = {"train", "val", "predict", "export", "track", "benchmark"}
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TASKS = {"detect", "segment", "classify", "pose", "obb"}
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@ -57,6 +72,31 @@ TASK2METRIC = {
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MODELS = {TASK2MODEL[task] for task in TASKS}
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ARGV = sys.argv or ["", ""] # sometimes sys.argv = []
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SOLUTIONS_HELP_MSG = f"""
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Arguments received: {str(['yolo'] + ARGV[1:])}. Ultralytics 'yolo solutions' usage overview:
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yolo SOLUTIONS SOLUTION ARGS
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Where SOLUTIONS (required) is a keyword
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SOLUTION (optional) is one of {list(SOLUTION_MAP.keys())}
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ARGS (optional) are any number of custom 'arg=value' pairs like 'show_in=True' that override defaults.
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See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'
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1. Call object counting solution
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yolo solutions count source="path/to/video/file.mp4" region=[(20, 400), (1080, 404), (1080, 360), (20, 360)]
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2. Call heatmaps solution
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yolo solutions heatmap colormap=cv2.COLORMAP_PARAULA model=yolo11n.pt
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3. Call queue management solution
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yolo solutions queue region=[(20, 400), (1080, 404), (1080, 360), (20, 360)] model=yolo11n.pt
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4. Call workouts monitoring solution for push-ups
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yolo solutions workout model=yolo11n-pose.pt kpts=[6, 8, 10]
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5. Generate analytical graphs
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yolo solutions analytics analytics_type="pie"
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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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@ -78,19 +118,24 @@ 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. Run special commands:
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6. Ultralytics solutions usage
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yolo solutions count or in {list(SOLUTION_MAP.keys())} source="path/to/video/file.mp4"
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7. 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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yolo settings
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yolo copy-cfg
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yolo cfg
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yolo solutions help
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Docs: https://docs.ultralytics.com
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Solutions: https://docs.ultralytics.com/solutions/
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Community: https://community.ultralytics.com
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GitHub: https://github.com/ultralytics/ultralytics
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"""
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@ -568,6 +613,100 @@ def handle_yolo_settings(args: List[str]) -> None:
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LOGGER.warning(f"WARNING ⚠️ settings error: '{e}'. Please see {url} for help.")
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def handle_yolo_solutions(args: List[str]) -> None:
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"""
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Processes YOLO solutions arguments and runs the specified computer vision solutions pipeline.
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Args:
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args (List[str]): Command-line arguments for configuring and running the Ultralytics YOLO
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solutions: https://docs.ultralytics.com/solutions/, It can include solution name, source,
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and other configuration parameters.
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Returns:
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None: The function processes video frames and saves the output but doesn't return any value.
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Examples:
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Run people counting solution with default settings:
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>>> handle_yolo_solutions(["count"])
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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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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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- Available solutions are defined in SOLUTION_MAP with their respective classes and methods
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- If an invalid solution is provided, defaults to 'count' solution
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- Output videos are saved in 'runs/solution/{solution_name}' directory
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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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"""
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full_args_dict = {**DEFAULT_SOL_DICT, **DEFAULT_CFG_DICT} # arguments dictionary
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overrides = {}
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# check dictionary alignment
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for arg in merge_equals_args(args):
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arg = arg.lstrip("-").rstrip(",")
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if "=" in arg:
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try:
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k, v = parse_key_value_pair(arg)
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overrides[k] = v
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except (NameError, SyntaxError, ValueError, AssertionError) as e:
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check_dict_alignment(full_args_dict, {arg: ""}, e)
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elif arg in full_args_dict and isinstance(full_args_dict.get(arg), bool):
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overrides[arg] = True
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check_dict_alignment(full_args_dict, overrides) # dict alignment
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# Get solution name
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if args and args[0] in SOLUTION_MAP:
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if args[0] != "help":
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s_n = args.pop(0) # Extract the solution name directly
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else:
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LOGGER.info(SOLUTIONS_HELP_MSG)
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else:
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LOGGER.warning(
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f"⚠️ No valid solution provided. Using default 'count'. Available: {', '.join(SOLUTION_MAP.keys())}"
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)
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s_n = "count" # Default solution if none provided
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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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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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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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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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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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@ -709,6 +848,7 @@ def entrypoint(debug=""):
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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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@ -19,7 +19,6 @@ class AIGym(BaseSolution):
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up_angle (float): Angle threshold for considering the 'up' position of an exercise.
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down_angle (float): Angle threshold for considering the 'down' position of an exercise.
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kpts (List[int]): Indices of keypoints used for angle calculation.
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lw (int): Line width for drawing annotations.
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annotator (Annotator): Object for drawing annotations on the image.
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Methods:
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@ -51,7 +50,6 @@ class AIGym(BaseSolution):
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self.up_angle = float(self.CFG["up_angle"]) # Pose up predefined angle to consider up pose
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self.down_angle = float(self.CFG["down_angle"]) # Pose down predefined angle to consider down pose
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self.kpts = self.CFG["kpts"] # User selected kpts of workouts storage for further usage
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self.lw = self.CFG["line_width"] # Store line_width for usage
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def monitor(self, im0):
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"""
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@ -84,14 +82,14 @@ class AIGym(BaseSolution):
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self.stage += ["-"] * new_human
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# Initialize annotator
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self.annotator = Annotator(im0, line_width=self.lw)
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self.annotator = Annotator(im0, line_width=self.line_width)
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# Enumerate over keypoints
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for ind, k in enumerate(reversed(tracks.keypoints.data)):
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# Get keypoints and estimate the angle
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kpts = [k[int(self.kpts[i])].cpu() for i in range(3)]
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self.angle[ind] = self.annotator.estimate_pose_angle(*kpts)
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im0 = self.annotator.draw_specific_points(k, self.kpts, radius=self.lw * 3)
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im0 = self.annotator.draw_specific_points(k, self.kpts, radius=self.line_width * 3)
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# Determine stage and count logic based on angle thresholds
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if self.angle[ind] < self.down_angle:
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@ -5,7 +5,7 @@ from collections import defaultdict
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import cv2
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from ultralytics import YOLO
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from ultralytics.utils import DEFAULT_CFG_DICT, DEFAULT_SOL_DICT, LOGGER
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from ultralytics.utils import ASSETS_URL, DEFAULT_CFG_DICT, DEFAULT_SOL_DICT, LOGGER
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from ultralytics.utils.checks import check_imshow, check_requirements
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@ -42,8 +42,12 @@ class BaseSolution:
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>>> solution.display_output(image)
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"""
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def __init__(self, **kwargs):
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"""Initializes the BaseSolution class with configuration settings and YOLO model for Ultralytics solutions."""
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def __init__(self, IS_CLI=False, **kwargs):
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"""
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Initializes the `BaseSolution` class with configuration settings and the YOLO model for Ultralytics solutions.
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IS_CLI (optional): Enables CLI mode if set.
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"""
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check_requirements("shapely>=2.0.0")
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from shapely.geometry import LineString, Point, Polygon
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@ -63,9 +67,20 @@ class BaseSolution:
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) # Store line_width for usage
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# Load Model and store classes names
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self.model = YOLO(self.CFG["model"] if self.CFG["model"] else "yolov8n.pt")
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if self.CFG["model"] is None:
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self.CFG["model"] = "yolo11n.pt"
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self.model = YOLO(self.CFG["model"])
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self.names = self.model.names
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if IS_CLI: # for CLI, download the source and init video writer
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if self.CFG["source"] is None:
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d_s = "solutions_ci_demo.mp4" if "-pose" not in self.CFG["model"] else "solution_ci_pose_demo.mp4"
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LOGGER.warning(f"⚠️ WARNING: source not provided. using default source {ASSETS_URL}/{d_s}")
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from ultralytics.utils.downloads import safe_download
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safe_download(f"{ASSETS_URL}/{d_s}") # download source from ultralytics assets
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self.CFG["source"] = d_s # set default source
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# Initialize environment and region setup
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self.env_check = check_imshow(warn=True)
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self.track_history = defaultdict(list)
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@ -37,6 +37,7 @@ ARGV = sys.argv or ["", ""] # sometimes sys.argv = []
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[1] # YOLO
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ASSETS = ROOT / "assets" # default images
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ASSETS_URL = "https://github.com/ultralytics/assets/releases/download/v0.0.0" # assets GitHub URL
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DEFAULT_CFG_PATH = ROOT / "cfg/default.yaml"
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DEFAULT_SOL_CFG_PATH = ROOT / "cfg/solutions/default.yaml" # Ultralytics solutions yaml path
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NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLO multiprocessing threads
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