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final docs updates

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SkalskiP 2024-01-24 17:47:11 +01:00
parent 164b9bedce
commit 6565fae1c2
5 changed files with 72 additions and 66 deletions
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21
docs/annotators.md
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@ -66,27 +66,6 @@ status: new
</div>
=== "OrientedBox"
```python
>>> import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
>>> oriented_box_annotator = sv.OrientedBoxAnnotator()
>>> annotated_frame = oriented_box_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
```
<div class="result" markdown>
![oriented-box-annotator-example](https://media.roboflow.com/supervision-annotator-examples/oriented-box-annotator-example-purple.png){ align=center width="800" }
</div>
=== "Color"
```python

11
docs/index.md
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@ -7,7 +7,7 @@ comments: true
<a align="center" href="" target="_blank">
<img
width="850"
src="https://media.roboflow.com/open-source/supervision/roboflow-supervision-banner.png?ik-sdk-version=javascript-1.4.3&updatedAt=1674062891088"
src="https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png?updatedAt=1678995927529"
>
</a>
</p>
@ -17,6 +17,15 @@ comments: true
We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us!
<video controls>
<source
src="https://media.roboflow.com/traffic_analysis_result.mp4"
type="video/mp4"
>
</video>
## 🚀 Quickstart
<div class="grid cards" markdown>
- __Detect and Annotate__

3
supervision/annotators/core.py
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@ -146,9 +146,6 @@ class OrientedBoxAnnotator(BaseAnnotator):
... detections=detections
... )
```
![oriented-box-annotator-example](https://media.roboflow.com/
supervision-annotator-examples/oriented-box-annotator-example-purple.png)
""" # noqa E501 // docs
if detections.data is None or "xyxyxyxy" not in detections.data:

60
supervision/detection/core.py
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@ -130,7 +130,7 @@ class Detections:
import torch
import supervision as sv
image = cv2.imread(SOURCE_IMAGE_PATH)
image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
result = model(image)
detections = sv.Detections.from_yolov5(result)
@ -150,6 +150,13 @@ class Detections:
Creates a Detections instance from a
[YOLOv8](https://github.com/ultralytics/ultralytics) inference result.
!!! Note
`from_ultralytics` is compatible with
[detection](https://docs.ultralytics.com/tasks/detect/),
[segmentation](https://docs.ultralytics.com/tasks/segment/), and
[OBB](https://docs.ultralytics.com/tasks/obb/) models.
Args:
ultralytics_results (ultralytics.yolo.engine.results.Results):
The output Results instance from YOLOv8
@ -163,12 +170,13 @@ class Detections:
import supervision as sv
from ultralytics import YOLO
image = cv2.imread()
image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = YOLO('yolov8s.pt')
result = model(image)[0]
detections = sv.Detections.from_ultralytics(result)
```
"""
""" # noqa: E501 // docs
if ultralytics_results.obb is not None:
return cls(
@ -213,8 +221,9 @@ class Detections:
from super_gradients.training import models
import supervision as sv
image = cv2.imread(SOURCE_IMAGE_PATH)
image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = models.get('yolo_nas_l', pretrained_weights="coco")
result = list(model.predict(image, conf=0.35))[0]
detections = sv.Detections.from_yolo_nas(result)
```
@ -309,9 +318,9 @@ class Detections:
@classmethod
def from_mmdetection(cls, mmdet_results) -> Detections:
"""
Creates a Detections instance from
a [mmdetection](https://github.com/open-mmlab/mmdetection) inference result.
Also supported for [mmyolo](https://github.com/open-mmlab/mmyolo)
Creates a Detections instance from a
[mmdetection](https://github.com/open-mmlab/mmdetection) and
[mmyolo](https://github.com/open-mmlab/mmyolo) inference result.
Args:
mmdet_results (mmdet.structures.DetDataSample):
@ -324,14 +333,15 @@ class Detections:
```python
import cv2
import supervision as sv
from mmdet.apis import DetInferencer
from mmdet.apis import init_detector, inference_detector
image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = init_detector(<CONFIG_PATH>, <WEIGHTS_PATH>, device=<DEVICE>)
inferencer = DetInferencer(model_name, checkpoint, device)
mmdet_result = inferencer(SOURCE_IMAGE_PATH, out_dir='./output',
return_datasamples=True)["predictions"][0]
detections = sv.Detections.from_mmdetection(mmdet_result)
result = inference_detector(model, image)
detections = sv.Detections.from_mmdetection(result)
```
"""
""" # noqa: E501 // docs
return cls(
xyxy=mmdet_results.pred_instances.bboxes.cpu().numpy(),
@ -372,15 +382,17 @@ class Detections:
Example:
```python
import cv2
import supervision as sv
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
import supervision as sv
image = cv2.imread(SOURCE_IMAGE_PATH)
image = cv2.imread(<SOURCE_IMAGE_PATH>)
cfg = get_cfg()
cfg.merge_from_file("path/to/config.yaml")
cfg.MODEL.WEIGHTS = "path/to/model_weights.pth"
cfg.merge_from_file(<CONFIG_PATH>)
cfg.MODEL.WEIGHTS = <WEIGHTS_PATH>
predictor = DefaultPredictor(cfg)
result = predictor(image)
detections = sv.Detections.from_detectron2(result)
```
@ -423,8 +435,9 @@ class Detections:
import supervision as sv
from inference.models.utils import get_roboflow_model
image = cv2.imread()
image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = get_roboflow_model(model_id="yolov8s-640")
result = model.infer(image)[0]
detections = sv.Detections.from_inference(result)
```
@ -472,8 +485,9 @@ class Detections:
import supervision as sv
from inference.models.utils import get_roboflow_model
image = cv2.imread()
image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = get_roboflow_model(model_id="yolov8s-640")
result = model.infer(image)[0]
detections = sv.Detections.from_roboflow(result)
```
@ -888,13 +902,12 @@ class Detections:
Example:
```python
import cv2
from ultralytics import YOLO
import supervision as sv
from ultralytics import YOLO
image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = YOLO('yolov8s.pt')
image = cv2.imread(SOURCE_IMAGE_PATH)
result = model(image)[0]
detections = sv.Detections.from_ultralytics(result)
@ -950,7 +963,8 @@ class Detections:
Args:
threshold (float, optional): The intersection-over-union threshold
to use for non-maximum suppression. Defaults to 0.5.
to use for non-maximum suppression. I'm the lower the value the more
restrictive the NMS becomes. Defaults to 0.5.
class_agnostic (bool, optional): Whether to perform class-agnostic
non-maximum suppression. If True, the class_id of each detection
will be ignored. Defaults to False.

43
supervision/tracker/byte_tracker/core.py
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@ -166,6 +166,10 @@ def detections2boxes(detections: Detections) -> np.ndarray:
class ByteTrack:
"""
Initialize the ByteTrack object.
<video controls>
<source src="https://media.roboflow.com/supervision/video-examples/how-to/track-objects/annotate-video-with-traces.mp4" type="video/mp4">
</video>
Parameters:
track_thresh (float, optional): Detection confidence threshold
@ -173,7 +177,7 @@ class ByteTrack:
track_buffer (int, optional): Number of frames to buffer when a track is lost.
match_thresh (float, optional): Threshold for matching tracks with detections.
frame_rate (int, optional): The frame rate of the video.
"""
""" # noqa: E501 // docs
def __init__(
self,
@ -196,36 +200,39 @@ class ByteTrack:
def update_with_detections(self, detections: Detections) -> Detections:
"""
Updates the tracker with the provided detections and
returns the updated detection results.
Updates the tracker with the provided detections and returns the updated
detection results.
Args:
detections (Detections): The detections to pass through the tracker.
Parameters:
detections: The new detections to update with.
Returns:
Detection: The updated detection results that now include tracking IDs.
Example:
```python
import supervision as sv
from ultralytics import YOLO
model = YOLO(...)
byte_tracker = sv.ByteTrack()
annotator = sv.BoxAnnotator()
model = YOLO(<MODEL_PATH>)
tracker = sv.ByteTrack()
bounding_box_annotator = sv.BoundingBoxAnnotator()
label_annotator = sv.LabelAnnotator()
def callback(frame: np.ndarray, index: int) -> np.ndarray:
results = model(frame)[0]
detections = sv.Detections.from_ultralytics(results)
detections = byte_tracker.update_with_detections(detections)
labels = [
f"#{tracker_id} {model.model.names[class_id]} {confidence:0.2f}"
for _, _, confidence, class_id, tracker_id in detections
]
return annotator.annotate(scene=frame.copy(),
detections=detections, labels=labels)
labels = [f"#{tracker_id}" for tracker_id in detections.tracker_id]
annotated_frame = bounding_box_annotator.annotate(
scene=frame.copy(), detections=detections)
annotated_frame = label_annotator.annotate(
scene=annotated_frame, detections=detections, labels=labels)
return annotated_frame
sv.process_video(
source_path='...',
target_path='...',
source_path=<SOURCE_VIDEO_PATH>,
target_path=<TARGET_VIDEO_PATH>,
callback=callback
)
```