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719 lines
40 KiB
719 lines
40 KiB
--- |
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comments: true |
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description: Discover how to use YOLOv8 predict mode for various tasks. Learn about different inference sources like images, videos, and data formats. |
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keywords: Ultralytics, YOLOv8, predict mode, inference sources, prediction tasks, streaming mode, image processing, video processing, machine learning, AI |
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--- |
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# Model Prediction with Ultralytics YOLO |
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<img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics YOLO ecosystem and integrations"> |
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## Introduction |
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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. |
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<p align="center"> |
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<br> |
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<iframe width="720" height="405" src="https://www.youtube.com/embed/QtsI0TnwDZs?si=ljesw75cMO2Eas14" |
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title="YouTube video player" frameborder="0" |
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" |
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allowfullscreen> |
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</iframe> |
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<br> |
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<strong>Watch:</strong> How to Extract the Outputs from Ultralytics YOLOv8 Model for Custom Projects. |
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</p> |
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## Real-world Applications |
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| Manufacturing | Sports | Safety | |
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|:-------------------------------------------------:|:----------------------------------------------------:|:-------------------------------------------:| |
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| ![Vehicle Spare Parts Detection][car spare parts] | ![Football Player Detection][football player detect] | ![People Fall Detection][human fall detect] | |
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| Vehicle Spare Parts Detection | Football Player Detection | People Fall Detection | |
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## Why Use Ultralytics YOLO for Inference? |
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Here's why you should consider YOLOv8's predict mode for your various inference needs: |
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- **Versatility:** Capable of making inferences on images, videos, and even live streams. |
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- **Performance:** Engineered for real-time, high-speed processing without sacrificing accuracy. |
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- **Ease of Use:** Intuitive Python and CLI interfaces for rapid deployment and testing. |
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- **Highly Customizable:** Various settings and parameters to tune the model's inference behavior according to your specific requirements. |
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### Key Features of Predict Mode |
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YOLOv8's predict mode is designed to be robust and versatile, featuring: |
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- **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. |
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- **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. |
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- **Batch Processing:** The ability to process multiple images or video frames in a single batch, further speeding up inference time. |
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- **Integration Friendly:** Easily integrate with existing data pipelines and other software components, thanks to its flexible API. |
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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: |
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!!! example "Predict" |
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=== "Return a list with `stream=False`" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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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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# Process results list |
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for result in results: |
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boxes = result.boxes # Boxes object for bbox outputs |
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masks = result.masks # Masks object for segmentation masks outputs |
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keypoints = result.keypoints # Keypoints object for pose outputs |
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probs = result.probs # Probs object for classification outputs |
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``` |
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=== "Return a generator with `stream=True`" |
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```python |
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from ultralytics import YOLO |
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# Load a model |
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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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# Process results generator |
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for result in results: |
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boxes = result.boxes # Boxes object for bbox outputs |
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masks = result.masks # Masks object for segmentation masks outputs |
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keypoints = result.keypoints # Keypoints object for pose outputs |
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probs = result.probs # Probs object for classification outputs |
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``` |
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## Inference Sources |
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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. |
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!!! tip "Tip" |
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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. |
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| Source | Argument | Type | Notes | |
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|----------------|--------------------------------------------|-----------------|---------------------------------------------------------------------------------------------| |
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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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| 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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| video ✅ | `'video.mp4'` | `str` or `Path` | Video file in formats like MP4, AVI, etc. | |
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| directory ✅ | `'path/'` | `str` or `Path` | Path to a directory containing images or videos. | |
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| glob ✅ | `'path/*.jpg'` | `str` | Glob pattern to match multiple files. Use the `*` character as a wildcard. | |
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| YouTube ✅ | `'https://youtu.be/LNwODJXcvt4'` | `str` | URL to a YouTube video. | |
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| stream ✅ | `'rtsp://example.com/media.mp4'` | `str` | URL for streaming protocols such as RTSP, RTMP, TCP, or an IP address. | |
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| 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. | |
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Below are code examples for using each source type: |
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!!! example "Prediction sources" |
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=== "image" |
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Run inference on an image file. |
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```python |
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from ultralytics import YOLO |
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# Load a pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Define path to the image file |
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source = 'path/to/image.jpg' |
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# Run inference on the source |
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results = model(source) # list of Results objects |
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``` |
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=== "screenshot" |
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Run inference on the current screen content as a screenshot. |
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```python |
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from ultralytics import YOLO |
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# Load a pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Define current screenshot as source |
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source = 'screen' |
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# Run inference on the source |
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results = model(source) # list of Results objects |
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``` |
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=== "URL" |
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Run inference on an image or video hosted remotely via URL. |
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```python |
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from ultralytics import YOLO |
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# Load a pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Define remote image or video URL |
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source = 'https://ultralytics.com/images/bus.jpg' |
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# Run inference on the source |
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results = model(source) # list of Results objects |
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``` |
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=== "PIL" |
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Run inference on an image opened with Python Imaging Library (PIL). |
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```python |
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from PIL import Image |
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from ultralytics import YOLO |
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# Load a pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Open an image using PIL |
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source = Image.open('path/to/image.jpg') |
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# Run inference on the source |
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results = model(source) # list of Results objects |
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``` |
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=== "OpenCV" |
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Run inference on an image read with OpenCV. |
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```python |
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import cv2 |
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from ultralytics import YOLO |
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# Load a pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Read an image using OpenCV |
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source = cv2.imread('path/to/image.jpg') |
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# Run inference on the source |
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results = model(source) # list of Results objects |
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``` |
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=== "numpy" |
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Run inference on an image represented as a numpy array. |
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```python |
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import numpy as np |
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from ultralytics import YOLO |
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# Load a pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Create a random numpy array of HWC shape (640, 640, 3) with values in range [0, 255] and type uint8 |
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source = np.random.randint(low=0, high=255, size=(640, 640, 3), dtype='uint8') |
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# Run inference on the source |
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results = model(source) # list of Results objects |
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``` |
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=== "torch" |
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Run inference on an image represented as a PyTorch tensor. |
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```python |
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import torch |
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from ultralytics import YOLO |
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# Load a pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Create a random torch tensor of BCHW shape (1, 3, 640, 640) with values in range [0, 1] and type float32 |
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source = torch.rand(1, 3, 640, 640, dtype=torch.float32) |
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# Run inference on the source |
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results = model(source) # list of Results objects |
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``` |
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=== "CSV" |
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Run inference on a collection of images, URLs, videos and directories listed in a CSV file. |
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```python |
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import torch |
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from ultralytics import YOLO |
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# Load a pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Define a path to a CSV file with images, URLs, videos and directories |
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source = 'path/to/file.csv' |
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# Run inference on the source |
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results = model(source) # list of Results objects |
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``` |
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=== "video" |
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Run inference on a video file. By using `stream=True`, you can create a generator of Results objects to reduce memory usage. |
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```python |
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from ultralytics import YOLO |
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# Load a pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Define path to video file |
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source = 'path/to/video.mp4' |
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# Run inference on the source |
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results = model(source, stream=True) # generator of Results objects |
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``` |
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=== "directory" |
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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/**/*`. |
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```python |
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from ultralytics import YOLO |
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# Load a pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Define path to directory containing images and videos for inference |
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source = 'path/to/dir' |
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# Run inference on the source |
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results = model(source, stream=True) # generator of Results objects |
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``` |
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=== "glob" |
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Run inference on all images and videos that match a glob expression with `*` characters. |
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```python |
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from ultralytics import YOLO |
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# Load a pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Define a glob search for all JPG files in a directory |
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source = 'path/to/dir/*.jpg' |
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# OR define a recursive glob search for all JPG files including subdirectories |
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source = 'path/to/dir/**/*.jpg' |
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# Run inference on the source |
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results = model(source, stream=True) # generator of Results objects |
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``` |
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=== "YouTube" |
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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. |
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```python |
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from ultralytics import YOLO |
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# Load a pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Define source as YouTube video URL |
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source = 'https://youtu.be/LNwODJXcvt4' |
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# Run inference on the source |
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results = model(source, stream=True) # generator of Results objects |
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``` |
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=== "Streams" |
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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. |
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```python |
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from ultralytics import YOLO |
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# Load a pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Single stream with batch-size 1 inference |
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source = 'rtsp://example.com/media.mp4' # RTSP, RTMP, TCP or IP streaming address |
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# Multiple streams with batched inference (i.e. batch-size 8 for 8 streams) |
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source = 'path/to/list.streams' # *.streams text file with one streaming address per row |
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# Run inference on the source |
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results = model(source, stream=True) # generator of Results objects |
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``` |
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## Inference Arguments |
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`model.predict()` accepts multiple arguments that can be passed at inference time to override defaults: |
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!!! example |
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```python |
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from ultralytics import YOLO |
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# Load a pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Run inference on 'bus.jpg' with arguments |
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model.predict('bus.jpg', save=True, imgsz=320, conf=0.5) |
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``` |
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All supported arguments: |
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| Name | Type | Default | Description | |
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|-----------------|----------------|------------------------|--------------------------------------------------------------------------------| |
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| `source` | `str` | `'ultralytics/assets'` | source directory for images or videos | |
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| `conf` | `float` | `0.25` | object confidence threshold for detection | |
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| `iou` | `float` | `0.7` | intersection over union (IoU) threshold for NMS | |
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| `imgsz` | `int or tuple` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) | |
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| `half` | `bool` | `False` | use half precision (FP16) | |
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| `device` | `None or str` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu | |
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| `show` | `bool` | `False` | show results if possible | |
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| `save` | `bool` | `False` | save images with results | |
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| `save_txt` | `bool` | `False` | save results as .txt file | |
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| `save_conf` | `bool` | `False` | save results with confidence scores | |
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| `save_crop` | `bool` | `False` | save cropped images with results | |
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| `hide_labels` | `bool` | `False` | hide labels | |
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| `hide_conf` | `bool` | `False` | hide confidence scores | |
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| `max_det` | `int` | `300` | maximum number of detections per image | |
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| `vid_stride` | `bool` | `False` | video frame-rate stride | |
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| `stream_buffer` | `bool` | `False` | buffer all streaming frames (True) or return the most recent frame (False) | |
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| `line_width` | `None or int` | `None` | The line width of the bounding boxes. If None, it is scaled to the image size. | |
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| `visualize` | `bool` | `False` | visualize model features | |
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| `augment` | `bool` | `False` | apply image augmentation to prediction sources | |
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| `agnostic_nms` | `bool` | `False` | class-agnostic NMS | |
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| `retina_masks` | `bool` | `False` | use high-resolution segmentation masks | |
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| `classes` | `None or list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] | |
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| `boxes` | `bool` | `True` | Show boxes in segmentation predictions | |
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## Image and Video Formats |
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YOLOv8 supports various image and video formats, as specified in [data/utils.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/data/utils.py). See the tables below for the valid suffixes and example predict commands. |
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### Images |
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The below table contains valid Ultralytics image formats. |
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| Image Suffixes | Example Predict Command | Reference | |
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|----------------|----------------------------------|-------------------------------------------------------------------------------| |
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| .bmp | `yolo predict source=image.bmp` | [Microsoft BMP File Format](https://en.wikipedia.org/wiki/BMP_file_format) | |
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| .dng | `yolo predict source=image.dng` | [Adobe DNG](https://www.adobe.com/products/photoshop/extend.displayTab2.html) | |
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| .jpeg | `yolo predict source=image.jpeg` | [JPEG](https://en.wikipedia.org/wiki/JPEG) | |
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| .jpg | `yolo predict source=image.jpg` | [JPEG](https://en.wikipedia.org/wiki/JPEG) | |
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| .mpo | `yolo predict source=image.mpo` | [Multi Picture Object](https://fileinfo.com/extension/mpo) | |
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| .png | `yolo predict source=image.png` | [Portable Network Graphics](https://en.wikipedia.org/wiki/PNG) | |
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| .tif | `yolo predict source=image.tif` | [Tag Image File Format](https://en.wikipedia.org/wiki/TIFF) | |
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| .tiff | `yolo predict source=image.tiff` | [Tag Image File Format](https://en.wikipedia.org/wiki/TIFF) | |
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| .webp | `yolo predict source=image.webp` | [WebP](https://en.wikipedia.org/wiki/WebP) | |
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| .pfm | `yolo predict source=image.pfm` | [Portable FloatMap](https://en.wikipedia.org/wiki/Netpbm#File_formats) | |
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### Videos |
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The below table contains valid Ultralytics video formats. |
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| Video Suffixes | Example Predict Command | Reference | |
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|----------------|----------------------------------|----------------------------------------------------------------------------------| |
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| .asf | `yolo predict source=video.asf` | [Advanced Systems Format](https://en.wikipedia.org/wiki/Advanced_Systems_Format) | |
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| .avi | `yolo predict source=video.avi` | [Audio Video Interleave](https://en.wikipedia.org/wiki/Audio_Video_Interleave) | |
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| .gif | `yolo predict source=video.gif` | [Graphics Interchange Format](https://en.wikipedia.org/wiki/GIF) | |
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| .m4v | `yolo predict source=video.m4v` | [MPEG-4 Part 14](https://en.wikipedia.org/wiki/M4V) | |
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| .mkv | `yolo predict source=video.mkv` | [Matroska](https://en.wikipedia.org/wiki/Matroska) | |
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| .mov | `yolo predict source=video.mov` | [QuickTime File Format](https://en.wikipedia.org/wiki/QuickTime_File_Format) | |
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| .mp4 | `yolo predict source=video.mp4` | [MPEG-4 Part 14 - Wikipedia](https://en.wikipedia.org/wiki/MPEG-4_Part_14) | |
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| .mpeg | `yolo predict source=video.mpeg` | [MPEG-1 Part 2](https://en.wikipedia.org/wiki/MPEG-1) | |
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| .mpg | `yolo predict source=video.mpg` | [MPEG-1 Part 2](https://en.wikipedia.org/wiki/MPEG-1) | |
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| .ts | `yolo predict source=video.ts` | [MPEG Transport Stream](https://en.wikipedia.org/wiki/MPEG_transport_stream) | |
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| .wmv | `yolo predict source=video.wmv` | [Windows Media Video](https://en.wikipedia.org/wiki/Windows_Media_Video) | |
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| .webm | `yolo predict source=video.webm` | [WebM Project](https://en.wikipedia.org/wiki/WebM) | |
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## Working with Results |
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All Ultralytics `predict()` calls will return a list of `Results` objects: |
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!!! example "Results" |
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```python |
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from ultralytics import YOLO |
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# Load a pretrained YOLOv8n model |
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model = YOLO('yolov8n.pt') |
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# Run inference on an image |
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results = model('bus.jpg') # list of 1 Results object |
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results = model(['bus.jpg', 'zidane.jpg']) # list of 2 Results objects |
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``` |
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`Results` objects have the following attributes: |
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| Attribute | Type | Description | |
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|--------------|-----------------------|------------------------------------------------------------------------------------------| |
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| `orig_img` | `numpy.ndarray` | The original image as a numpy array. | |
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| `orig_shape` | `tuple` | The original image shape in (height, width) format. | |
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| `boxes` | `Boxes, optional` | A Boxes object containing the detection bounding boxes. | |
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| `masks` | `Masks, optional` | A Masks object containing the detection masks. | |
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| `probs` | `Probs, optional` | A Probs object containing probabilities of each class for classification task. | |
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| `keypoints` | `Keypoints, optional` | A Keypoints object containing detected keypoints for each object. | |
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| `speed` | `dict` | A dictionary of preprocess, inference, and postprocess speeds in milliseconds per image. | |
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| `names` | `dict` | A dictionary of class names. | |
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| `path` | `str` | The path to the image file. | |
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`Results` objects have the following methods: |
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| Method | Return Type | Description | |
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|-----------------|-----------------|-------------------------------------------------------------------------------------| |
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| `__getitem__()` | `Results` | Return a Results object for the specified index. | |
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| `__len__()` | `int` | Return the number of detections in the Results object. | |
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| `update()` | `None` | Update the boxes, masks, and probs attributes of the Results object. | |
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| `cpu()` | `Results` | Return a copy of the Results object with all tensors on CPU memory. | |
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| `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. | |
|
| `keys()` | `List[str]` | Return a list of non-empty attribute names. | |
|
| `plot()` | `numpy.ndarray` | Plots the detection results. Returns a numpy array of the annotated image. | |
|
| `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()` | `None` | Convert the object to JSON format. | |
|
|
|
For more details see the `Results` class [documentation](../reference/engine/results.md). |
|
|
|
### Boxes |
|
|
|
`Boxes` object can be used to index, manipulate, and convert bounding boxes to different formats. |
|
|
|
!!! example "Boxes" |
|
|
|
```python |
|
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](../reference/engine/results.md#ultralytics.engine.results.Boxes). |
|
|
|
### Masks |
|
|
|
`Masks` object can be used index, manipulate and convert masks to segments. |
|
|
|
!!! example "Masks" |
|
|
|
```python |
|
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](../reference/engine/results.md#ultralytics.engine.results.Masks). |
|
|
|
### Keypoints |
|
|
|
`Keypoints` object can be used index, manipulate and normalize coordinates. |
|
|
|
!!! example "Keypoints" |
|
|
|
```python |
|
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](../reference/engine/results.md#ultralytics.engine.results.Keypoints). |
|
|
|
### Probs |
|
|
|
`Probs` object can be used index, get `top1` and `top5` indices and scores of classification. |
|
|
|
!!! example "Probs" |
|
|
|
```python |
|
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](../reference/engine/results.md#ultralytics.engine.results.Probs). |
|
|
|
## Plotting Results |
|
|
|
You can use the `plot()` method of a `Result` objects to visualize predictions. It plots all prediction types (boxes, masks, keypoints, probabilities, etc.) contained in the `Results` object onto a numpy array that can then be shown or saved. |
|
|
|
!!! example "Plotting" |
|
|
|
```python |
|
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') # results list |
|
|
|
# Show the results |
|
for r in results: |
|
im_array = r.plot() # plot a BGR numpy array of predictions |
|
im = Image.fromarray(im_array[..., ::-1]) # RGB PIL image |
|
im.show() # show image |
|
im.save('results.jpg') # save image |
|
``` |
|
|
|
The `plot()` method supports the following arguments: |
|
|
|
| Argument | Type | Description | Default | |
|
|--------------|-----------------|--------------------------------------------------------------------------------|---------------| |
|
| `conf` | `bool` | Whether to plot the detection confidence score. | `True` | |
|
| `line_width` | `float` | The line width of the bounding boxes. If None, it is scaled to the image size. | `None` | |
|
| `font_size` | `float` | The font size of the text. If None, it is scaled to the image size. | `None` | |
|
| `font` | `str` | The font to use for the text. | `'Arial.ttf'` | |
|
| `pil` | `bool` | Whether to return the image as a PIL Image. | `False` | |
|
| `img` | `numpy.ndarray` | Plot to another image. if not, plot to original image. | `None` | |
|
| `im_gpu` | `torch.Tensor` | Normalized image in gpu with shape (1, 3, 640, 640), for faster mask plotting. | `None` | |
|
| `kpt_radius` | `int` | Radius of the drawn keypoints. Default is 5. | `5` | |
|
| `kpt_line` | `bool` | Whether to draw lines connecting keypoints. | `True` | |
|
| `labels` | `bool` | Whether to plot the label of bounding boxes. | `True` | |
|
| `boxes` | `bool` | Whether to plot the bounding boxes. | `True` | |
|
| `masks` | `bool` | Whether to plot the masks. | `True` | |
|
| `probs` | `bool` | Whether to plot classification probability | `True` | |
|
|
|
## 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: |
|
```python |
|
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](../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. |
|
|
|
## 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" |
|
|
|
```python |
|
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'. |
|
|
|
[car spare parts]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/a0f802a8-0776-44cf-8f17-93974a4a28a1 |
|
[football player detect]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/7d320e1f-fc57-4d7f-a691-78ee579c3442 |
|
[human fall detect]: https://github.com/RizwanMunawar/ultralytics/assets/62513924/86437c4a-3227-4eee-90ef-9efb697bdb43
|
|
|