ultralytics 8.0.141 create new SettingsManager (#3790)
This commit is contained in:
Glenn Jocher
GitHub
parent
42afe772d5
commit
20f5efd40a
215 changed files with 917 additions and 749 deletions
|
|
@ -21,7 +21,7 @@ passing `stream=True` in the predictor's call method.
|
|||
|
||||
# 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 bbox outputs
|
||||
|
|
@ -39,7 +39,7 @@ passing `stream=True` in the predictor's call method.
|
|||
|
||||
# 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 bbox outputs
|
||||
|
|
@ -65,7 +65,7 @@ YOLOv8 can process different types of input sources for inference, as shown in t
|
|||
| OpenCV | `cv2.imread('im.jpg')` | `np.ndarray` of `uint8 (0-255)` | HWC format with BGR channels. |
|
||||
| numpy | `np.zeros((640,1280,3))` | `np.ndarray` of `uint8 (0-255)` | HWC format with BGR channels. |
|
||||
| torch | `torch.zeros(16,3,320,640)` | `torch.Tensor` of `float32 (0.0-1.0)` | BCHW format with RGB channels. |
|
||||
| CSV | `'sources.csv'` | `str` or `Path` | CSV file containing paths to images, videos, or directories. |
|
||||
| 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. |
|
||||
|
|
@ -77,204 +77,204 @@ Below are code examples for using each source type:
|
|||
!!! example "Prediction sources"
|
||||
|
||||
=== "image"
|
||||
Run inference on an image file.
|
||||
Run inference on an image file.
|
||||
```python
|
||||
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.
|
||||
```python
|
||||
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.
|
||||
```python
|
||||
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).
|
||||
```python
|
||||
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.
|
||||
```python
|
||||
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.
|
||||
```python
|
||||
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.
|
||||
```python
|
||||
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.
|
||||
```python
|
||||
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
|
||||
```
|
||||
|
||||
|
||||
=== "video"
|
||||
Run inference on a video file. By using `stream=True`, you can create a generator of Results objects to reduce memory usage.
|
||||
```python
|
||||
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/**/*`.
|
||||
```python
|
||||
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.
|
||||
```python
|
||||
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.
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
|
||||
# Load a pretrained YOLOv8n model
|
||||
model = YOLO('yolov8n.pt')
|
||||
|
||||
|
||||
# Define source as YouTube video URL
|
||||
source = 'https://youtu.be/Zgi9g1ksQHc'
|
||||
|
||||
|
||||
# Run inference on the source
|
||||
results = model(source, stream=True) # generator of Results objects
|
||||
```
|
||||
|
||||
|
||||
=== "Stream"
|
||||
Run inference on remote streaming sources using RTSP, RTMP, and IP address protocols.
|
||||
```python
|
||||
from ultralytics import YOLO
|
||||
|
||||
|
||||
# Load a pretrained YOLOv8n model
|
||||
model = YOLO('yolov8n.pt')
|
||||
|
||||
|
||||
# Define source as RTSP, RTMP or IP streaming address
|
||||
source = 'rtsp://example.com/media.mp4'
|
||||
|
||||
|
||||
# Run inference on the source
|
||||
results = model(source, stream=True) # generator of Results objects
|
||||
```
|
||||
|
|
@ -417,7 +417,7 @@ operations are cached, meaning they're only calculated once per object, and thos
|
|||
masks = results[0].masks # Masks object
|
||||
masks.xy # x, y segments (pixels), List[segment] * N
|
||||
masks.xyn # x, y segments (normalized), List[segment] * N
|
||||
masks.data # raw masks tensor, (N, H, W) or masks.masks
|
||||
masks.data # raw masks tensor, (N, H, W) or masks.masks
|
||||
```
|
||||
|
||||
### Keypoints
|
||||
|
|
@ -432,7 +432,7 @@ operations are cached, meaning they're only calculated once per object, and thos
|
|||
keypoints.xy # x, y keypoints (pixels), (num_dets, num_kpts, 2/3), the last dimension can be 2 or 3, depends the model.
|
||||
keypoints.xyn # x, y keypoints (normalized), (num_dets, num_kpts, 2/3)
|
||||
keypoints.conf # confidence score(num_dets, num_kpts) of each keypoint if the last dimension is 3.
|
||||
keypoints.data # raw keypoints tensor, (num_dets, num_kpts, 2/3)
|
||||
keypoints.data # raw keypoints tensor, (num_dets, num_kpts, 2/3)
|
||||
```
|
||||
|
||||
### probs
|
||||
|
|
@ -448,7 +448,7 @@ operations are cached, meaning they're only calculated once per object, and thos
|
|||
probs.top1 # The top1 indices of classification, a value with Int type.
|
||||
probs.top5conf # The top5 scores of classification, a tensor with shape (5, ).
|
||||
probs.top1conf # The top1 scores of classification. a value with torch.tensor type.
|
||||
keypoints.data # raw probs tensor, (num_class, )
|
||||
keypoints.data # raw probs tensor, (num_class, )
|
||||
```
|
||||
|
||||
Class reference documentation for `Results` module and its components can be found [here](../reference/engine/results.md)
|
||||
|
|
@ -489,37 +489,37 @@ Here's a Python script using OpenCV (cv2) and YOLOv8 to run inference on video f
|
|||
```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()
|
||||
```
|
||||
```
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue