标签：training ultralytics image YOLO Yolov8 源码 video model 解析

Yolov8 源码解析（十七）

---

comments: true
description: Harness the power of Ultralytics YOLOv8 for real-time, high-speed inference on various data sources. Learn about predict mode, key features, and practical applications.
keywords: Ultralytics, YOLOv8, model prediction, inference, predict mode, real-time inference, computer vision, machine learning, streaming, high performance

Model Prediction with Ultralytics YOLO

Introduction

In the world of machine learning and computer vision, the process of making sense out of visual data is called 'inference' or 'prediction'. Ultralytics YOLOv8 offers a powerful feature known as predict mode that is tailored for high-performance, real-time inference on a wide range of data sources.

Watch: How to Extract the Outputs from Ultralytics YOLOv8 Model for Custom Projects.

Real-world Applications

Manufacturing	Sports	Safety
Vehicle Spare Parts Detection	Football Player Detection	People Fall Detection

Why Use Ultralytics YOLO for Inference?

Here's why you should consider YOLOv8's predict mode for your various inference needs:

• Versatility: Capable of making inferences on images, videos, and even live streams.
• Performance: Engineered for real-time, high-speed processing without sacrificing accuracy.
• Ease of Use: Intuitive Python and CLI interfaces for rapid deployment and testing.
• Highly Customizable: Various settings and parameters to tune the model's inference behavior according to your specific requirements.

Key Features of Predict Mode

YOLOv8's predict mode is designed to be robust and versatile, featuring:

• Multiple Data Source Compatibility: Whether your data is in the form of individual images, a collection of images, video files, or real-time video streams, predict mode has you covered.
• Streaming Mode: Use the streaming feature to generate a memory-efficient generator of Results objects. Enable this by setting stream=True in the predictor's call method.
• Batch Processing: The ability to process multiple images or video frames in a single batch, further speeding up inference time.
• Integration Friendly: Easily integrate with existing data pipelines and other software components, thanks to its flexible API.

Ultralytics YOLO models return either a Python list of Results objects, or a memory-efficient Python generator of Results objects when stream=True is passed to the model during inference:

!!! Example "Predict"

=== "Return a list with `stream=False`"

    ```py
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolov8n.pt")  # pretrained YOLOv8n model

    # Run batched inference on a list of images
    results = model(["im1.jpg", "im2.jpg"])  # return a list of Results objects

    # Process results list
    for result in results:
        boxes = result.boxes  # Boxes object for bounding box outputs
        masks = result.masks  # Masks object for segmentation masks outputs
        keypoints = result.keypoints  # Keypoints object for pose outputs
        probs = result.probs  # Probs object for classification outputs
        obb = result.obb  # Oriented boxes object for OBB outputs
        result.show()  # display to screen
        result.save(filename="result.jpg")  # save to disk
    ```

=== "Return a generator with `stream=True`"

    ```py
    from ultralytics import YOLO

    # Load a model
    model = YOLO("yolov8n.pt")  # pretrained YOLOv8n model

    # Run batched inference on a list of images
    results = model(["im1.jpg", "im2.jpg"], stream=True)  # return a generator of Results objects

    # Process results generator
    for result in results:
        boxes = result.boxes  # Boxes object for bounding box outputs
        masks = result.masks  # Masks object for segmentation masks outputs
        keypoints = result.keypoints  # Keypoints object for pose outputs
        probs = result.probs  # Probs object for classification outputs
        obb = result.obb  # Oriented boxes object for OBB outputs
        result.show()  # display to screen
        result.save(filename="result.jpg")  # save to disk
    ```

Inference Sources

YOLOv8 can process different types of input sources for inference, as shown in the table below. The sources include static images, video streams, and various data formats. The table also indicates whether each source can be used in streaming mode with the argument stream=True ✅. Streaming mode is beneficial for processing videos or live streams as it creates a generator of results instead of loading all frames into memory.

!!! Tip "Tip"

Use `stream=True` for processing long videos or large datasets to efficiently manage memory. 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. 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.

Source	Argument	Type	Notes
image	'image.jpg'	str or Path	Single image file.
URL	'https://ultralytics.com/images/bus.jpg'	str	URL to an image.
screenshot	'screen'	str	Capture a screenshot.
PIL	Image.open('im.jpg')	PIL.Image	HWC format with RGB channels.
OpenCV	cv2.imread('im.jpg')	np.ndarray	HWC format with BGR channels uint8 (0-255).
numpy	np.zeros((640,1280,3))	np.ndarray	HWC format with BGR channels uint8 (0-255).
torch	torch.zeros(16,3,320,640)	torch.Tensor	BCHW format with RGB channels float32 (0.0-1.0).
CSV	'sources.csv'	str or Path	CSV file containing paths to images, videos, or directories.
video ✅	'video.mp4'	str or Path	Video file in formats like MP4, AVI, etc.
directory ✅	'path/'	str or Path	Path to a directory containing images or videos.
glob ✅	'path/*.jpg'	str	Glob pattern to match multiple files. Use the * character as a wildcard.
YouTube ✅	'https://youtu.be/LNwODJXcvt4'	str	URL to a YouTube video.
stream ✅	'rtsp://example.com/media.mp4'	str	URL for streaming protocols such as RTSP, RTMP, TCP, or an IP address.
multi-stream ✅	'list.streams'	str or Path	*.streams text file with one stream URL per row, i.e. 8 streams will run at batch-size 8.

Below are code examples for using each source type:

!!! Example "Prediction sources"

=== "image"

    Run inference on an image file.
    ```py
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO("yolov8n.pt")

    # Define path to the image file
    source = "path/to/image.jpg"

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "screenshot"

    Run inference on the current screen content as a screenshot.
    ```py
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO("yolov8n.pt")

    # Define current screenshot as source
    source = "screen"

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "URL"

    Run inference on an image or video hosted remotely via URL.
    ```py
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO("yolov8n.pt")

    # Define remote image or video URL
    source = "https://ultralytics.com/images/bus.jpg"

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "PIL"

    Run inference on an image opened with Python Imaging Library (PIL).
    ```py
    from PIL import Image

    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO("yolov8n.pt")

    # Open an image using PIL
    source = Image.open("path/to/image.jpg")

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "OpenCV"

    Run inference on an image read with OpenCV.
    ```py
    import cv2

    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO("yolov8n.pt")

    # Read an image using OpenCV
    source = cv2.imread("path/to/image.jpg")

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "numpy"

    Run inference on an image represented as a numpy array.
    ```py
    import numpy as np

    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO("yolov8n.pt")

    # Create a random numpy array of HWC shape (640, 640, 3) with values in range [0, 255] and type uint8
    source = np.random.randint(low=0, high=255, size=(640, 640, 3), dtype="uint8")

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "torch"

    Run inference on an image represented as a PyTorch tensor.
    ```py
    import torch

    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO("yolov8n.pt")

    # Create a random torch tensor of BCHW shape (1, 3, 640, 640) with values in range [0, 1] and type float32
    source = torch.rand(1, 3, 640, 640, dtype=torch.float32)

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "CSV"

    Run inference on a collection of images, URLs, videos and directories listed in a CSV file.
    ```py
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO("yolov8n.pt")

    # Define a path to a CSV file with images, URLs, videos and directories
    source = "path/to/file.csv"

    # Run inference on the source
    results = model(source)  # list of Results objects
    ```

=== "video"

    Run inference on a video file. By using `stream=True`, you can create a generator of Results objects to reduce memory usage.
    ```py
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO("yolov8n.pt")

    # Define path to video file
    source = "path/to/video.mp4"

    # Run inference on the source
    results = model(source, stream=True)  # generator of Results objects
    ```

=== "directory"

    Run inference on all images and videos in a directory. To also capture images and videos in subdirectories use a glob pattern, i.e. `path/to/dir/**/*`.
    ```py
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO("yolov8n.pt")

    # Define path to directory containing images and videos for inference
    source = "path/to/dir"

    # Run inference on the source
    results = model(source, stream=True)  # generator of Results objects
    ```

=== "glob"

    Run inference on all images and videos that match a glob expression with `*` characters.
    ```py
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO("yolov8n.pt")

    # Define a glob search for all JPG files in a directory
    source = "path/to/dir/*.jpg"

    # OR define a recursive glob search for all JPG files including subdirectories
    source = "path/to/dir/**/*.jpg"

    # Run inference on the source
    results = model(source, stream=True)  # generator of Results objects
    ```

=== "YouTube"

    Run inference on a YouTube video. By using `stream=True`, you can create a generator of Results objects to reduce memory usage for long videos.
    ```py
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO("yolov8n.pt")

    # Define source as YouTube video URL
    source = "https://youtu.be/LNwODJXcvt4"

    # Run inference on the source
    results = model(source, stream=True)  # generator of Results objects
    ```

=== "Streams"

    Run inference on remote streaming sources using RTSP, RTMP, TCP and IP address protocols. If multiple streams are provided in a `*.streams` text file then batched inference will run, i.e. 8 streams will run at batch-size 8, otherwise single streams will run at batch-size 1.
    ```py
    from ultralytics import YOLO

    # Load a pretrained YOLOv8n model
    model = YOLO("yolov8n.pt")

    # Single stream with batch-size 1 inference
    source = "rtsp://example.com/media.mp4"  # RTSP, RTMP, TCP or IP streaming address

    # Multiple streams with batched inference (i.e. batch-size 8 for 8 streams)
    source = "path/to/list.streams"  # *.streams text file with one streaming address per row

    # Run inference on the source
    results = model(source, stream=True)  # generator of Results objects
    ```

Inference Arguments

model.predict() accepts multiple arguments that can be passed at inference time to override defaults:

!!! Example

```py
from ultralytics import YOLO

# Load a pretrained YOLOv8n model
model = YOLO("yolov8n.pt")

# Run inference on 'bus.jpg' with arguments
model.predict("bus.jpg", save=True, imgsz=320, conf=0.5)
```

Inference arguments:

Argument	Type	Default	Description
source	str	'ultralytics/assets'	Specifies the data source for inference. Can be an image path, video file, directory, URL, or device ID for live feeds. Supports a wide range of formats and sources, enabling flexible application across different types of input.
conf	float	0.25	Sets the minimum confidence threshold for detections. Objects detected with confidence below this threshold will be disregarded. Adjusting this value can help reduce false positives.
iou	float	0.7	Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Lower values result in fewer detections by eliminating overlapping boxes, useful for reducing duplicates.
imgsz	int or tuple	640	Defines the image size for inference. Can be a single integer 640 for square resizing or a (height, width) tuple. Proper sizing can improve detection accuracy and processing speed.
half	bool	False	Enables half-precision (FP16) inference, which can speed up model inference on supported GPUs with minimal impact on accuracy.
device	str	None	Specifies the device for inference (e.g., cpu, cuda:0 or 0). Allows users to select between CPU, a specific GPU, or other compute devices for model execution.
max_det	int	300	Maximum number of detections allowed per image. Limits the total number of objects the model can detect in a single inference, preventing excessive outputs in dense scenes.
vid_stride	int	1	Frame stride for video inputs. Allows skipping frames in videos to speed up processing at the cost of temporal resolution. A value of 1 processes every frame, higher values skip frames.
stream_buffer	bool	False	Determines if all frames should be buffered when processing video streams (True), or if the model should return the most recent frame (False). Useful for real-time applications.
visualize	bool	False	Activates visualization of model features during inference, providing insights into what the model is "seeing". Useful for debugging and model interpretation.
augment	bool	False	Enables test-time augmentation (TTA) for predictions, potentially improving detection robustness at the cost of inference speed.
agnostic_nms	bool	False	Enables class-agnostic Non-Maximum Suppression (NMS), which merges overlapping boxes of different classes. Useful in multi-class detection scenarios where class overlap is common.
classes	list[int]	None	Filters predictions to a set of class IDs. Only detections belonging to the specified classes will be returned. Useful for focusing on relevant objects in multi-class detection tasks.
retina_masks	bool	False	Uses high-resolution segmentation masks if available in the model. This can enhance mask quality for segmentation tasks, providing finer detail.
embed	list[int]	None	Specifies the layers from which to extract feature vectors or embeddings. Useful for downstream tasks like clustering or similarity search.

Visualization arguments:

Argument	Type	Default	Description
show	bool	False	If True, displays the annotated images or videos in a window. Useful for immediate visual feedback during development or testing.
save	bool	False	Enables saving of the annotated images or videos to file. Useful for documentation, further analysis, or sharing results.
save_frames	bool	False	When processing videos, saves individual frames as images. Useful for extracting specific frames or for detailed frame-by-frame analysis.
save_txt	bool	False	Saves detection results in a text file, following the format [class] [x_center] [y_center] [width] [height] [confidence]. Useful for integration with other analysis tools.
save_conf	bool	False	Includes confidence scores in the saved text files. Enhances the detail available for post-processing and analysis.
save_crop	bool	False	Saves cropped images of detections. Useful for dataset augmentation, analysis, or creating focused datasets for specific objects.
show_labels	bool	True	Displays labels for each detection in the visual output. Provides immediate understanding of detected objects.
show_conf	bool	True	Displays the confidence score for each detection alongside the label. Gives insight into the model's certainty for each detection.
show_boxes	bool	True	Draws bounding boxes around detected objects. Essential for visual identification and location of objects in images or video frames.
line_width	None or int	None	Specifies the line width of bounding boxes. If None, the line width is automatically adjusted based on the image size. Provides visual customization for clarity.

Image and Video Formats

YOLOv8 supports various image and video formats, as specified in ultralytics/data/utils.py. See the tables below for the valid suffixes and example predict commands.

Images

The below table contains valid Ultralytics image formats.

Image Suffixes	Example Predict Command	Reference
.bmp	yolo predict source=image.bmp	Microsoft BMP File Format
.dng	yolo predict source=image.dng	Adobe DNG
.jpeg	yolo predict source=image.jpeg	JPEG
.jpg	yolo predict source=image.jpg	JPEG
.mpo	yolo predict source=image.mpo	Multi Picture Object
.png	yolo predict source=image.png	Portable Network Graphics
.tif	yolo predict source=image.tif	Tag Image File Format
.tiff	yolo predict source=image.tiff	Tag Image File Format
.webp	yolo predict source=image.webp	WebP
.pfm	yolo predict source=image.pfm	Portable FloatMap

Videos

The below table contains valid Ultralytics video formats.

Video Suffixes	Example Predict Command	Reference
.asf	yolo predict source=video.asf	Advanced Systems Format
.avi	yolo predict source=video.avi	Audio Video Interleave
.gif	yolo predict source=video.gif	Graphics Interchange Format
.m4v	yolo predict source=video.m4v	MPEG-4 Part 14
.mkv	yolo predict source=video.mkv	Matroska
.mov	yolo predict source=video.mov	QuickTime File Format
.mp4	yolo predict source=video.mp4	MPEG-4 Part 14 - Wikipedia
.mpeg	yolo predict source=video.mpeg	MPEG-1 Part 2
.mpg	yolo predict source=video.mpg	MPEG-1 Part 2
.ts	yolo predict source=video.ts	MPEG Transport Stream
.wmv	yolo predict source=video.wmv	Windows Media Video
.webm	yolo predict source=video.webm	WebM Project

Working with Results

All Ultralytics predict() calls will return a list of Results objects:

!!! Example "Results"

```py
from ultralytics import YOLO

# Load a pretrained YOLOv8n model
model = YOLO("yolov8n.pt")

# Run inference on an image
results = model("bus.jpg")  # list of 1 Results object
results = model(["bus.jpg", "zidane.jpg"])  # list of 2 Results objects
```

Results objects have the following attributes:

Attribute	Type	Description
orig_img	numpy.ndarray	The original image as a numpy array.
orig_shape	tuple	The original image shape in (height, width) format.
boxes	Boxes, optional	A Boxes object containing the detection bounding boxes.
masks	Masks, optional	A Masks object containing the detection masks.
probs	Probs, optional	A Probs object containing probabilities of each class for classification task.
keypoints	Keypoints, optional	A Keypoints object containing detected keypoints for each object.
obb	OBB, optional	An OBB object containing oriented bounding boxes.
speed	dict	A dictionary of preprocess, inference, and postprocess speeds in milliseconds per image.
names	dict	A dictionary of class names.
path	str	The path to the image file.

Results objects have the following methods:

Method	Return Type	Description
update()	None	Update the boxes, masks, and probs attributes of the Results object.
cpu()	Results	Return a copy of the Results object with all tensors on CPU memory.
numpy()	Results	Return a copy of the Results object with all tensors as numpy arrays.
cuda()	Results	Return a copy of the Results object with all tensors on GPU memory.
to()	Results	Return a copy of the Results object with tensors on the specified device and dtype.
new()	Results	Return a new Results object with the same image, path, and names.
plot()	numpy.ndarray	Plots the detection results. Returns a numpy array of the annotated image.
show()	None	Show annotated results to screen.
save()	None	Save annotated results to file.
verbose()	str	Return log string for each task.
save_txt()	None	Save predictions into a txt file.
save_crop()	None	Save cropped predictions to save_dir/cls/file_name.jpg.
tojson()	str	Convert the object to JSON format.

For more details see the Results class documentation.

Boxes

Boxes object can be used to index, manipulate, and convert bounding boxes to different formats.

!!! Example "Boxes"

```py
from ultralytics import YOLO

# Load a pretrained YOLOv8n model
model = YOLO("yolov8n.pt")

# Run inference on an image
results = model("bus.jpg")  # results list

# View results
for r in results:
    print(r.boxes)  # print the Boxes object containing the detection bounding boxes
```

Here is a table for the Boxes class methods and properties, including their name, type, and description:

Name	Type	Description
cpu()	Method	Move the object to CPU memory.
numpy()	Method	Convert the object to a numpy array.
cuda()	Method	Move the object to CUDA memory.
to()	Method	Move the object to the specified device.
xyxy	Property (torch.Tensor)	Return the boxes in xyxy format.
conf	Property (torch.Tensor)	Return the confidence values of the boxes.
cls	Property (torch.Tensor)	Return the class values of the boxes.
id	Property (torch.Tensor)	Return the track IDs of the boxes (if available).
xywh	Property (torch.Tensor)	Return the boxes in xywh format.
xyxyn	Property (torch.Tensor)	Return the boxes in xyxy format normalized by original image size.
xywhn	Property (torch.Tensor)	Return the boxes in xywh format normalized by original image size.

For more details see the Boxes class documentation.

Masks

Masks object can be used index, manipulate and convert masks to segments.

!!! Example "Masks"

```py
from ultralytics import YOLO

# Load a pretrained YOLOv8n-seg Segment model
model = YOLO("yolov8n-seg.pt")

# Run inference on an image
results = model("bus.jpg")  # results list

# View results
for r in results:
    print(r.masks)  # print the Masks object containing the detected instance masks
```

Here is a table for the Masks class methods and properties, including their name, type, and description:

Name	Type	Description
cpu()	Method	Returns the masks tensor on CPU memory.
numpy()	Method	Returns the masks tensor as a numpy array.
cuda()	Method	Returns the masks tensor on GPU memory.
to()	Method	Returns the masks tensor with the specified device and dtype.
xyn	Property (torch.Tensor)	A list of normalized segments represented as tensors.
xy	Property (torch.Tensor)	A list of segments in pixel coordinates represented as tensors.

For more details see the Masks class documentation.

Keypoints

Keypoints object can be used index, manipulate and normalize coordinates.

!!! Example "Keypoints"

```py
from ultralytics import YOLO

# Load a pretrained YOLOv8n-pose Pose model
model = YOLO("yolov8n-pose.pt")

# Run inference on an image
results = model("bus.jpg")  # results list

# View results
for r in results:
    print(r.keypoints)  # print the Keypoints object containing the detected keypoints
```

Here is a table for the Keypoints class methods and properties, including their name, type, and description:

Name	Type	Description
cpu()	Method	Returns the keypoints tensor on CPU memory.
numpy()	Method	Returns the keypoints tensor as a numpy array.
cuda()	Method	Returns the keypoints tensor on GPU memory.
to()	Method	Returns the keypoints tensor with the specified device and dtype.
xyn	Property (torch.Tensor)	A list of normalized keypoints represented as tensors.
xy	Property (torch.Tensor)	A list of keypoints in pixel coordinates represented as tensors.
conf	Property (torch.Tensor)	Returns confidence values of keypoints if available, else None.

For more details see the Keypoints class documentation.

Probs

Probs object can be used index, get top1 and top5 indices and scores of classification.

!!! Example "Probs"

```py
from ultralytics import YOLO

# Load a pretrained YOLOv8n-cls Classify model
model = YOLO("yolov8n-cls.pt")

# Run inference on an image
results = model("bus.jpg")  # results list

# View results
for r in results:
    print(r.probs)  # print the Probs object containing the detected class probabilities
```

Here's a table summarizing the methods and properties for the Probs class:

Name	Type	Description
cpu()	Method	Returns a copy of the probs tensor on CPU memory.
numpy()	Method	Returns a copy of the probs tensor as a numpy array.
cuda()	Method	Returns a copy of the probs tensor on GPU memory.
to()	Method	Returns a copy of the probs tensor with the specified device and dtype.
top1	Property (int)	Index of the top 1 class.
top5	Property (list[int])	Indices of the top 5 classes.
top1conf	Property (torch.Tensor)	Confidence of the top 1 class.
top5conf	Property (torch.Tensor)	Confidences of the top 5 classes.

For more details see the Probs class documentation.

OBB

OBB object can be used to index, manipulate, and convert oriented bounding boxes to different formats.

!!! Example "OBB"

```py
from ultralytics import YOLO

# Load a pretrained YOLOv8n model
model = YOLO("yolov8n-obb.pt")

# Run inference on an image
results = model("bus.jpg")  # results list

# View results
for r in results:
    print(r.obb)  # print the OBB object containing the oriented detection bounding boxes
```

Here is a table for the OBB class methods and properties, including their name, type, and description:

Name	Type	Description
cpu()	Method	Move the object to CPU memory.
numpy()	Method	Convert the object to a numpy array.
cuda()	Method	Move the object to CUDA memory.
to()	Method	Move the object to the specified device.
conf	Property (torch.Tensor)	Return the confidence values of the boxes.
cls	Property (torch.Tensor)	Return the class values of the boxes.
id	Property (torch.Tensor)	Return the track IDs of the boxes (if available).
xyxy	Property (torch.Tensor)	Return the horizontal boxes in xyxy format.
xywhr	Property (torch.Tensor)	Return the rotated boxes in xywhr format.
xyxyxyxy	Property (torch.Tensor)	Return the rotated boxes in xyxyxyxy format.
xyxyxyxyn	Property (torch.Tensor)	Return the rotated boxes in xyxyxyxy format normalized by image size.

For more details see the OBB class documentation.

Plotting Results

The plot() method in Results objects facilitates visualization of predictions by overlaying detected objects (such as bounding boxes, masks, keypoints, and probabilities) onto the original image. This method returns the annotated image as a NumPy array, allowing for easy display or saving.

!!! Example "Plotting"

```py
from PIL import Image

from ultralytics import YOLO

# Load a pretrained YOLOv8n model
model = YOLO("yolov8n.pt")

# Run inference on 'bus.jpg'
results = model(["bus.jpg", "zidane.jpg"])  # results list

# Visualize the results
for i, r in enumerate(results):
    # Plot results image
    im_bgr = r.plot()  # BGR-order numpy array
    im_rgb = Image.fromarray(im_bgr[..., ::-1])  # RGB-order PIL image

    # Show results to screen (in supported environments)
    r.show()

    # Save results to disk
    r.save(filename=f"results{i}.jpg")
```

plot() Method Parameters

The plot() method supports various arguments to customize the output:

Argument	Type	Description	Default
conf	bool	Include detection confidence scores.	True
line_width	float	Line width of bounding boxes. Scales with image size if None.	None
font_size	float	Text font size. Scales with image size if None.	None
font	str	Font name for text annotations.	'Arial.ttf'
pil	bool	Return image as a PIL Image object.	False
img	numpy.ndarray	Alternative image for plotting. Uses the original image if None.	None
im_gpu	torch.Tensor	GPU-accelerated image for faster mask plotting. Shape: (1, 3, 640, 640).	None
kpt_radius	int	Radius for drawn keypoints.	5
kpt_line	bool	Connect keypoints with lines.	True
labels	bool	Include class labels in annotations.	True
boxes	bool	Overlay bounding boxes on the image.	True
masks	bool	Overlay masks on the image.	True
probs	bool	Include classification probabilities.	True
show	bool	Display the annotated image directly using the default image viewer.	False
save	bool	Save the annotated image to a file specified by filename.	False
filename	str	Path and name of the file to save the annotated image if save is True.	None

Thread-Safe Inference

Ensuring thread safety during inference is crucial when you are running multiple YOLO models in parallel across different threads. Thread-safe inference guarantees that each thread's predictions are isolated and do not interfere with one another, avoiding race conditions and ensuring consistent and reliable outputs.

When using YOLO models in a multi-threaded application, it's important to instantiate separate model objects for each thread or employ thread-local storage to prevent conflicts:

!!! Example "Thread-Safe Inference"

Instantiate a single model inside each thread for thread-safe inference:
```py
from threading import Thread

from ultralytics import YOLO


def thread_safe_predict(image_path):
    """Performs thread-safe prediction on an image using a locally instantiated YOLO model."""
    local_model = YOLO("yolov8n.pt")
    results = local_model.predict(image_path)
    # Process results


# Starting threads that each have their own model instance
Thread(target=thread_safe_predict, args=("image1.jpg",)).start()
Thread(target=thread_safe_predict, args=("image2.jpg",)).start()
```

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. This guide will provide you with all the necessary information to avoid common pitfalls and ensure that your multi-threaded inference runs smoothly.

Streaming Source for-loop

Here's a Python script using OpenCV (cv2) and YOLOv8 to run inference on video frames. This script assumes you have already installed the necessary packages (opencv-python and ultralytics).

!!! Example "Streaming for-loop"

```py
import cv2

from ultralytics import YOLO

# Load the YOLOv8 model
model = YOLO("yolov8n.pt")

# Open the video file
video_path = "path/to/your/video/file.mp4"
cap = cv2.VideoCapture(video_path)

# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()

    if success:
        # Run YOLOv8 inference on the frame
        results = model(frame)

        # Visualize the results on the frame
        annotated_frame = results[0].plot()

        # Display the annotated frame
        cv2.imshow("YOLOv8 Inference", annotated_frame)

        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Break the loop if the end of the video is reached
        break

# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()
```

This script will run predictions on each frame of the video, visualize the results, and display them in a window. The loop can be exited by pressing 'q'.

FAQ

What is Ultralytics YOLOv8 and its predict mode for real-time inference?

Ultralytics YOLOv8 is a state-of-the-art model for real-time object detection, segmentation, and classification. Its predict mode allows users to perform high-speed inference on various data sources such as images, videos, and live streams. Designed for performance and versatility, it also offers batch processing and streaming modes. For more details on its features, check out the Ultralytics YOLOv8 predict mode.

How can I run inference using Ultralytics YOLOv8 on different data sources?

Ultralytics YOLOv8 can process a wide range of data sources, including individual images, videos, directories, URLs, and streams. You can specify the data source in the model.predict() call. For example, use 'image.jpg' for a local image or 'https://ultralytics.com/images/bus.jpg' for a URL. Check out the detailed examples for various inference sources in the documentation.

How do I optimize YOLOv8 inference speed and memory usage?

To optimize inference speed and manage memory efficiently, you can use the streaming mode by setting stream=True in the predictor's call method. The streaming mode generates a memory-efficient generator of Results objects instead of loading all frames into memory. For processing long videos or large datasets, streaming mode is particularly useful. Learn more about streaming mode.

What inference arguments does Ultralytics YOLOv8 support?

The model.predict() method in YOLOv8 supports various arguments such as conf, iou, imgsz, device, and more. These arguments allow you to customize the inference process, setting parameters like confidence thresholds, image size, and the device used for computation. Detailed descriptions of these arguments can be found in the inference arguments section.

How can I visualize and save the results of YOLOv8 predictions?

After running inference with YOLOv8, the Results objects contain methods for displaying and saving annotated images. You can use methods like result.show() and result.save(filename="result.jpg") to visualize and save the results. For a comprehensive list of these methods, refer to the working with results section.

---

comments: true
description: Discover efficient, flexible, and customizable multi-object tracking with Ultralytics YOLO. Learn to track real-time video streams with ease.
keywords: multi-object tracking, Ultralytics YOLO, video analytics, real-time tracking, object detection, AI, machine learning

Multi-Object Tracking with Ultralytics YOLO

Object tracking in the realm of video analytics is a critical task that not only identifies the location and class of objects within the frame but also maintains a unique ID for each detected object as the video progresses. The applications are limitless—ranging from surveillance and security to real-time sports analytics.

Why Choose Ultralytics YOLO for Object Tracking?

The output from Ultralytics trackers is consistent with standard object detection but has the added value of object IDs. This makes it easy to track objects in video streams and perform subsequent analytics. Here's why you should consider using Ultralytics YOLO for your object tracking needs:

• Efficiency: Process video streams in real-time without compromising accuracy.
• Flexibility: Supports multiple tracking algorithms and configurations.
• Ease of Use: Simple Python API and CLI options for quick integration and deployment.
• Customizability: Easy to use with custom trained YOLO models, allowing integration into domain-specific applications.

Watch: Object Detection and Tracking with Ultralytics YOLOv8.

Real-world Applications

Transportation	Retail	Aquaculture
Vehicle Tracking	People Tracking	Fish Tracking

Features at a Glance

Ultralytics YOLO extends its object detection features to provide robust and versatile object tracking:

• Real-Time Tracking: Seamlessly track objects in high-frame-rate videos.
• Multiple Tracker Support: Choose from a variety of established tracking algorithms.
• Customizable Tracker Configurations: Tailor the tracking algorithm to meet specific requirements by adjusting various parameters.

Available Trackers

Ultralytics YOLO supports the following tracking algorithms. They can be enabled by passing the relevant YAML configuration file such as tracker=tracker_type.yaml:

• BoT-SORT - Use botsort.yaml to enable this tracker.
• ByteTrack - Use bytetrack.yaml to enable this tracker.

The default tracker is BoT-SORT.

Tracking

!!! Warning "Tracker Threshold Information"

If object confidence score will be low, i.e lower than [`track_high_thresh`](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/trackers/bytetrack.yaml#L5), then there will be no tracks successfully returned and updated.

To run the tracker on video streams, use a trained Detect, Segment or Pose model such as YOLOv8n, YOLOv8n-seg and YOLOv8n-pose.

!!! Example

=== "Python"

    ```py
    from ultralytics import YOLO

    # Load an official or custom model
    model = YOLO("yolov8n.pt")  # Load an official Detect model
    model = YOLO("yolov8n-seg.pt")  # Load an official Segment model
    model = YOLO("yolov8n-pose.pt")  # Load an official Pose model
    model = YOLO("path/to/best.pt")  # Load a custom trained model

    # Perform tracking with the model
    results = model.track("https://youtu.be/LNwODJXcvt4", show=True)  # Tracking with default tracker
    results = model.track("https://youtu.be/LNwODJXcvt4", show=True, tracker="bytetrack.yaml")  # with ByteTrack
    ```

=== "CLI"

    ```py
    # Perform tracking with various models using the command line interface
    yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4"  # Official Detect model
    yolo track model=yolov8n-seg.pt source="https://youtu.be/LNwODJXcvt4"  # Official Segment model
    yolo track model=yolov8n-pose.pt source="https://youtu.be/LNwODJXcvt4"  # Official Pose model
    yolo track model=path/to/best.pt source="https://youtu.be/LNwODJXcvt4"  # Custom trained model

    # Track using ByteTrack tracker
    yolo track model=path/to/best.pt tracker="bytetrack.yaml"
    ```

As can be seen in the above usage, tracking is available for all Detect, Segment and Pose models run on videos or streaming sources.

Configuration

!!! Warning "Tracker Threshold Information"

If object confidence score will be low, i.e lower than [`track_high_thresh`](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/trackers/bytetrack.yaml#L5), then there will be no tracks successfully returned and updated.

Tracking Arguments

Tracking configuration shares properties with Predict mode, such as conf, iou, and show. For further configurations, refer to the Predict model page.

!!! Example

=== "Python"

    ```py
    from ultralytics import YOLO

    # Configure the tracking parameters and run the tracker
    model = YOLO("yolov8n.pt")
    results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True)
    ```

=== "CLI"

    ```py
    # Configure tracking parameters and run the tracker using the command line interface
    yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" conf=0.3, iou=0.5 show
    ```

Tracker Selection

Ultralytics also allows you to use a modified tracker configuration file. To do this, simply make a copy of a tracker config file (for example, custom_tracker.yaml) from ultralytics/cfg/trackers and modify any configurations (except the tracker_type) as per your needs.

!!! Example

=== "Python"

    ```py
    from ultralytics import YOLO

    # Load the model and run the tracker with a custom configuration file
    model = YOLO("yolov8n.pt")
    results = model.track(source="https://youtu.be/LNwODJXcvt4", tracker="custom_tracker.yaml")
    ```

=== "CLI"

    ```py
    # Load the model and run the tracker with a custom configuration file using the command line interface
    yolo track model=yolov8n.pt source="https://youtu.be/LNwODJXcvt4" tracker='custom_tracker.yaml'
    ```

For a comprehensive list of tracking arguments, refer to the ultralytics/cfg/trackers page.

Python Examples

Persisting Tracks Loop

Here is a Python script using OpenCV (cv2) and YOLOv8 to run object tracking on video frames. This script still assumes you have already installed the necessary packages (opencv-python and ultralytics). The persist=True argument tells the tracker that the current image or frame is the next in a sequence and to expect tracks from the previous image in the current image.

!!! Example "Streaming for-loop with tracking"

```py
import cv2

from ultralytics import YOLO

# Load the YOLOv8 model
model = YOLO("yolov8n.pt")

# Open the video file
video_path = "path/to/video.mp4"
cap = cv2.VideoCapture(video_path)

# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()

    if success:
        # Run YOLOv8 tracking on the frame, persisting tracks between frames
        results = model.track(frame, persist=True)

        # Visualize the results on the frame
        annotated_frame = results[0].plot()

        # Display the annotated frame
        cv2.imshow("YOLOv8 Tracking", annotated_frame)

        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Break the loop if the end of the video is reached
        break

# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()
```

Please note the change from model(frame) to model.track(frame), which enables object tracking instead of simple detection. This modified script will run the tracker on each frame of the video, visualize the results, and display them in a window. The loop can be exited by pressing 'q'.

Plotting Tracks Over Time

Visualizing object tracks over consecutive frames can provide valuable insights into the movement patterns and behavior of detected objects within a video. With Ultralytics YOLOv8, plotting these tracks is a seamless and efficient process.

In the following example, we demonstrate how to utilize YOLOv8's tracking capabilities to plot the movement of detected objects across multiple video frames. This script involves opening a video file, reading it frame by frame, and utilizing the YOLO model to identify and track various objects. By retaining the center points of the detected bounding boxes and connecting them, we can draw lines that represent the paths followed by the tracked objects.

!!! Example "Plotting tracks over multiple video frames"

```py
from collections import defaultdict

import cv2
import numpy as np

from ultralytics import YOLO

# Load the YOLOv8 model
model = YOLO("yolov8n.pt")

# Open the video file
video_path = "path/to/video.mp4"
cap = cv2.VideoCapture(video_path)

# Store the track history
track_history = defaultdict(lambda: [])

# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()

    if success:
        # Run YOLOv8 tracking on the frame, persisting tracks between frames
        results = model.track(frame, persist=True)

        # Get the boxes and track IDs
        boxes = results[0].boxes.xywh.cpu()
        track_ids = results[0].boxes.id.int().cpu().tolist()

        # Visualize the results on the frame
        annotated_frame = results[0].plot()

        # Plot the tracks
        for box, track_id in zip(boxes, track_ids):
            x, y, w, h = box
            track = track_history[track_id]
            track.append((float(x), float(y)))  # x, y center point
            if len(track) > 30:  # retain 90 tracks for 90 frames
                track.pop(0)

            # Draw the tracking lines
            points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
            cv2.polylines(annotated_frame, [points], isClosed=False, color=(230, 230, 230), thickness=10)

        # Display the annotated frame
        cv2.imshow("YOLOv8 Tracking", annotated_frame)

        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Break the loop if the end of the video is reached
        break

# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()
```

Multithreaded Tracking

Multithreaded tracking provides the capability to run object tracking on multiple video streams simultaneously. This is particularly useful when handling multiple video inputs, such as from multiple surveillance cameras, where concurrent processing can greatly enhance efficiency and performance.

In the provided Python script, we make use of Python's threading module to run multiple instances of the tracker concurrently. Each thread is responsible for running the tracker on one video file, and all the threads run simultaneously in the background.

To ensure that each thread receives the correct parameters (the video file, the model to use and the file index), we define a function run_tracker_in_thread that accepts these parameters and contains the main tracking loop. This function reads the video frame by frame, runs the tracker, and displays the results.

Two different models are used in this example: yolov8n.pt and yolov8n-seg.pt, each tracking objects in a different video file. The video files are specified in video_file1 and video_file2.

The daemon=True parameter in threading.Thread means that these threads will be closed as soon as the main program finishes. We then start the threads with start() and use join() to make the main thread wait until both tracker threads have finished.

Finally, after all threads have completed their task, the windows displaying the results are closed using cv2.destroyAllWindows().

!!! Example "Streaming for-loop with tracking"

```py
import threading

import cv2

from ultralytics import YOLO


def run_tracker_in_thread(filename, model, file_index):
    """
    Runs a video file or webcam stream concurrently with the YOLOv8 model using threading.

    This function captures video frames from a given file or camera source and utilizes the YOLOv8 model for object
    tracking. The function runs in its own thread for concurrent processing.

    Args:
        filename (str): The path to the video file or the identifier for the webcam/external camera source.
        model (obj): The YOLOv8 model object.
        file_index (int): An index to uniquely identify the file being processed, used for display purposes.

    Note:
        Press 'q' to quit the video display window.
    """
    video = cv2.VideoCapture(filename)  # Read the video file

    while True:
        ret, frame = video.read()  # Read the video frames

        # Exit the loop if no more frames in either video
        if not ret:
            break

        # Track objects in frames if available
        results = model.track(frame, persist=True)
        res_plotted = results[0].plot()
        cv2.imshow(f"Tracking_Stream_{file_index}", res_plotted)

        key = cv2.waitKey(1)
        if key == ord("q"):
            break

    # Release video sources
    video.release()


# Load the models
model1 = YOLO("yolov8n.pt")
model2 = YOLO("yolov8n-seg.pt")

# Define the video files for the trackers
video_file1 = "path/to/video1.mp4"  # Path to video file, 0 for webcam
video_file2 = 0  # Path to video file, 0 for webcam, 1 for external camera

# Create the tracker threads
tracker_thread1 = threading.Thread(target=run_tracker_in_thread, args=(video_file1, model1, 1), daemon=True)
tracker_thread2 = threading.Thread(target=run_tracker_in_thread, args=(video_file2, model2, 2), daemon=True)

# Start the tracker threads
tracker_thread1.start()
tracker_thread2.start()

# Wait for the tracker threads to finish
tracker_thread1.join()
tracker_thread2.join()

# Clean up and close windows
cv2.destroyAllWindows()
```

This example can easily be extended to handle more video files and models by creating more threads and applying the same methodology.

Contribute New Trackers

Are you proficient in multi-object tracking and have successfully implemented or adapted a tracking algorithm with Ultralytics YOLO? We invite you to contribute to our Trackers section in ultralytics/cfg/trackers! Your real-world applications and solutions could be invaluable for users working on tracking tasks.

By contributing to this section, you help expand the scope of tracking solutions available within the Ultralytics YOLO framework, adding another layer of functionality and utility for the community.

To initiate your contribution, please refer to our Contributing Guide for comprehensive instructions on submitting a Pull Request (PR)

标签：training,ultralytics,image,YOLO,Yolov8,源码,video,model,解析
From： https://www.cnblogs.com/apachecn/p/18398136