Threaded inference docs improvements (#16313)
Signed-off-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher
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5 changed files with 70 additions and 95 deletions
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@ -61,7 +61,7 @@ Ultralytics YOLO models return either a Python list of `Results` objects, or a m
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model = YOLO("yolov8n.pt") # pretrained YOLOv8n model
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# Run batched inference on a list of images
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results = model(["im1.jpg", "im2.jpg"]) # return a list of Results objects
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results = model(["image1.jpg", "image2.jpg"]) # return a list of Results objects
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# Process results list
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for result in results:
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@ -83,7 +83,7 @@ Ultralytics YOLO models return either a Python list of `Results` objects, or a m
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model = YOLO("yolov8n.pt") # pretrained YOLOv8n model
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# Run batched inference on a list of images
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results = model(["im1.jpg", "im2.jpg"], stream=True) # return a generator of Results objects
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results = model(["image1.jpg", "image2.jpg"], stream=True) # return a generator of Results objects
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# Process results generator
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for result in results:
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@ -109,8 +109,8 @@ YOLOv8 can process different types of input sources for inference, as shown in t
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| image | `'image.jpg'` | `str` or `Path` | Single image file. |
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| URL | `'https://ultralytics.com/images/bus.jpg'` | `str` | URL to an image. |
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| screenshot | `'screen'` | `str` | Capture a screenshot. |
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| PIL | `Image.open('im.jpg')` | `PIL.Image` | HWC format with RGB channels. |
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| OpenCV | `cv2.imread('im.jpg')` | `np.ndarray` | HWC format with BGR channels `uint8 (0-255)`. |
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| PIL | `Image.open('image.jpg')` | `PIL.Image` | HWC format with RGB channels. |
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| OpenCV | `cv2.imread('image.jpg')` | `np.ndarray` | HWC format with BGR channels `uint8 (0-255)`. |
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| numpy | `np.zeros((640,1280,3))` | `np.ndarray` | HWC format with BGR channels `uint8 (0-255)`. |
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| torch | `torch.zeros(16,3,320,640)` | `torch.Tensor` | BCHW format with RGB channels `float32 (0.0-1.0)`. |
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| CSV | `'sources.csv'` | `str` or `Path` | CSV file containing paths to images, videos, or directories. |
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@ -710,16 +710,16 @@ When using YOLO models in a multi-threaded application, it's important to instan
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from ultralytics import YOLO
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def thread_safe_predict(image_path):
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def thread_safe_predict(model, image_path):
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"""Performs thread-safe prediction on an image using a locally instantiated YOLO model."""
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local_model = YOLO("yolov8n.pt")
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results = local_model.predict(image_path)
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model = YOLO(model)
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results = model.predict(image_path)
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# Process results
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# Starting threads that each have their own model instance
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Thread(target=thread_safe_predict, args=("image1.jpg",)).start()
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Thread(target=thread_safe_predict, args=("image2.jpg",)).start()
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Thread(target=thread_safe_predict, args=("yolov8n.pt", "image1.jpg")).start()
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Thread(target=thread_safe_predict, args=("yolov8n.pt", "image2.jpg")).start()
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```
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For an in-depth look at thread-safe inference with YOLO models and step-by-step instructions, please refer to our [YOLO Thread-Safe Inference Guide](../guides/yolo-thread-safe-inference.md). This guide will provide you with all the necessary information to avoid common pitfalls and ensure that your multi-threaded inference runs smoothly.
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@ -290,63 +290,50 @@ Finally, after all threads have completed their task, the windows displaying the
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from ultralytics import YOLO
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# Define model names and video sources
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MODEL_NAMES = ["yolov8n.pt", "yolov8n-seg.pt"]
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SOURCES = ["path/to/video1.mp4", 0] # local video, 0 for webcam
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def run_tracker_in_thread(filename, model, file_index):
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def run_tracker_in_thread(model_name, filename, index):
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"""
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Runs a video file or webcam stream concurrently with the YOLOv8 model using threading.
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This function captures video frames from a given file or camera source and utilizes the YOLOv8 model for object
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tracking. The function runs in its own thread for concurrent processing.
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Runs a video file or webcam stream concurrently with the YOLOv8 model using threading. This function captures video
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frames from a given file or camera source and utilizes the YOLOv8 model for object tracking. The function runs in
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its own thread for concurrent processing.
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Args:
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filename (str): The path to the video file or the identifier for the webcam/external camera source.
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model (obj): The YOLOv8 model object.
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file_index (int): An index to uniquely identify the file being processed, used for display purposes.
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Note:
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Press 'q' to quit the video display window.
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index (int): An index to uniquely identify the file being processed, used for display purposes.
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"""
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video = cv2.VideoCapture(filename) # Read the video file
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model = YOLO(model_name)
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video = cv2.VideoCapture(filename)
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while True:
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ret, frame = video.read() # Read the video frames
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# Exit the loop if no more frames in either video
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ret, frame = video.read()
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if not ret:
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break
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# Track objects in frames if available
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results = model.track(frame, persist=True)
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res_plotted = results[0].plot()
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cv2.imshow(f"Tracking_Stream_{file_index}", res_plotted)
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cv2.imshow(f"Tracking_Stream_{index}", res_plotted)
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key = cv2.waitKey(1)
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if key == ord("q"):
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if cv2.waitKey(1) == ord("q"):
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break
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# Release video sources
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video.release()
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# Load the models
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model1 = YOLO("yolov8n.pt")
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model2 = YOLO("yolov8n-seg.pt")
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# Create and start tracker threads using a for loop
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tracker_threads = []
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for i, (video_file, model_name) in enumerate(zip(SOURCES, MODEL_NAMES), start=1):
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thread = threading.Thread(target=run_tracker_in_thread, args=(model_name, video_file, i), daemon=True)
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tracker_threads.append(thread)
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thread.start()
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# Define the video files for the trackers
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video_file1 = "path/to/video1.mp4" # Path to video file, 0 for webcam
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video_file2 = 0 # Path to video file, 0 for webcam, 1 for external camera
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# Create the tracker threads
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tracker_thread1 = threading.Thread(target=run_tracker_in_thread, args=(video_file1, model1, 1), daemon=True)
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tracker_thread2 = threading.Thread(target=run_tracker_in_thread, args=(video_file2, model2, 2), daemon=True)
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# Start the tracker threads
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tracker_thread1.start()
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tracker_thread2.start()
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# Wait for the tracker threads to finish
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tracker_thread1.join()
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tracker_thread2.join()
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# Wait for all tracker threads to finish
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for thread in tracker_threads:
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thread.join()
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# Clean up and close windows
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cv2.destroyAllWindows()
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@ -44,8 +44,8 @@ Harness `detect.py` for versatile inference on various sources. It automatically
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```bash
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python detect.py --weights yolov5s.pt --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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image.jpg # image
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video.mp4 # video
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screen # screenshot
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path/ # directory
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list.txt # list of images
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