Here's a Python script using OpenCV (cv2) and YOLOv8 to run inference on video frames. Whether to plot classification probability. Whether to plot the label of bounding boxes. Useful for indicating the expected format of the output. If None, it is scaled to the image size.Īn example string to display. Whether to plot the detection confidence score. imshow ( "result", res_plotted ) Argument Results.path: Path containing the path to the input imageĮach result is composed of a torch.Tensor by default, which allows for easy manipulation:.Results.orig_img: Original image loaded in memory.Results.probs: Probs object for containing class probabilities.Results.keypoints: Keypoints object for with properties and methods for manipulating predicted keypoints.Results.masks: Masks object for indexing masks or getting segment coordinates.Results.boxes: Boxes object with properties and methods for manipulating bounding boxes.The Results object contains the following components: The below table contains valid Ultralytics video formats. The below table contains valid Ultralytics image formats. See the tables below for the valid suffixes and example predict commands. YOLOv8 supports various image and video formats, as specified in yolo/data/utils.py. If None, it is scaled to the image size.Īpply image augmentation to prediction sourcesįilter results by class, i.e. Intersection over union (IoU) threshold for NMSĭevice to run on, i.e. Object confidence threshold for detection predict ( source, save = True, imgsz = 320, conf = 0.5 ) URL for streaming protocols such as RTSP, RTMP, or an IP address.īelow are code examples for using each source type: Path to a directory containing images or videos. Video file in formats like MP4, AVI, etc. SourceĬSV file containing paths to images, videos, or directories. In contrast, stream=True utilizes a generator, which only keeps the results of the current frame or data point in memory, significantly reducing memory consumption and preventing out-of-memory issues. When stream=False, the results for all frames or data points are stored in memory, which can quickly add up and cause out-of-memory errors for large inputs. Use stream=True for processing long videos or large datasets to efficiently manage memory. Streaming mode is beneficial for processing videos or live streams as it creates a generator of results instead of loading all frames into memory. The table also indicates whether each source can be used in streaming mode with the argument stream=True ✅. The sources include static images, video streams, and various data formats. YOLOv8 can process different types of input sources for inference, as shown in the table below. probs # Class probabilities for classification outputs Inference Sources keypoints # Keypoints object for pose outputs probs = result. masks # Masks object for segmentation masks outputs keypoints = result. boxes # Boxes object for bbox outputs masks = result. From ultralytics import YOLO # Load a model model = YOLO ( 'yolov8n.pt' ) # pretrained YOLOv8n model # Run batched inference on a list of images results = model (, stream = True ) # return a generator of Results objects # Process results generator for result in results : boxes = result.
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