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
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13 changed files with 251 additions and 188 deletions
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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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