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Model Prediction with Ultralytics YOLO

Ultralytics YOLO ecosystem and integrations

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
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:

Predict

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
    result.show()  # display to screen
    result.save(filename='result.jpg')  # save to disk
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
    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

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:

Prediction sources

Run inference on an image file.

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

Run inference on the current screen content as a screenshot.

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

Run inference on an image or video hosted remotely via URL.

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

Run inference on an image opened with Python Imaging Library (PIL).

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

Run inference on an image read with OpenCV.

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

Run inference on an image represented as a numpy array.

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

Run inference on an image represented as a PyTorch tensor.

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

Run inference on a collection of images, URLs, videos and directories listed in a CSV file.

import torch
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

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

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

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/**/*.

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

Run inference on all images and videos that match a glob expression with * characters.

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

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.

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

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.

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

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). Higher 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:

Results

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.

Boxes

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.

Masks

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.

Keypoints

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.

Probs

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.

OBB

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.

Plotting

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_array[..., ::-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:

Thread-Safe Inference

Instantiate a single model inside each thread for thread-safe inference:

from ultralytics import YOLO
from threading import Thread

def thread_safe_predict(image_path):
    # Instantiate a new model inside the thread
    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).

Streaming for-loop

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'.



Created 2023-11-12, Updated 2024-02-13
Authors: glenn-jocher (15), tensorturtle (1), AyushExel (1), Laughing-q (1)

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