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Removed '>>>' and '...' from docs - Merge PR #761 from RaghavvGupta/improved-code-usability 

Removed '>>>' and '...' from examples and docs and improved some indentation.
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Onuralp SEZER 2024-01-24 17:21:00 +03:00 committed by GitHub
parent 80b7eecfaf d4e0d8d46d
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17 changed files with 824 additions and 827 deletions
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300
docs/annotators.md
View File

@ -6,16 +6,16 @@ status: new
=== "BoundingBox"
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> bounding_box_annotator = sv.BoundingBoxAnnotator()
>>> annotated_frame = bounding_box_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
bounding_box_annotator = sv.BoundingBoxAnnotator()
annotated_frame = bounding_box_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
@ -27,16 +27,16 @@ status: new
=== "RoundBox"
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> round_box_annotator = sv.RoundBoxAnnotator()
>>> annotated_frame = round_box_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
round_box_annotator = sv.RoundBoxAnnotator()
annotated_frame = round_box_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
@ -48,16 +48,16 @@ status: new
=== "BoxCorner"
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> corner_annotator = sv.BoxCornerAnnotator()
>>> annotated_frame = corner_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
corner_annotator = sv.BoxCornerAnnotator()
annotated_frame = corner_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
@ -69,16 +69,16 @@ status: new
=== "Color"
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> color_annotator = sv.ColorAnnotator()
>>> annotated_frame = color_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
color_annotator = sv.ColorAnnotator()
annotated_frame = color_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
@ -90,16 +90,16 @@ status: new
=== "Circle"
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> circle_annotator = sv.CircleAnnotator()
>>> annotated_frame = circle_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
circle_annotator = sv.CircleAnnotator()
annotated_frame = circle_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
@ -111,16 +111,16 @@ status: new
=== "Dot"
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> dot_annotator = sv.DotAnnotator()
>>> annotated_frame = dot_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
dot_annotator = sv.DotAnnotator()
annotated_frame = dot_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
@ -132,16 +132,16 @@ status: new
=== "Triangle"
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> triangle_annotator = sv.TriangleAnnotator()
>>> annotated_frame = triangle_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
triangle_annotator = sv.TriangleAnnotator()
annotated_frame = triangle_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
@ -153,16 +153,16 @@ status: new
=== "Ellipse"
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> ellipse_annotator = sv.EllipseAnnotator()
>>> annotated_frame = ellipse_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
ellipse_annotator = sv.EllipseAnnotator()
annotated_frame = ellipse_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
@ -174,16 +174,16 @@ status: new
=== "Halo"
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> halo_annotator = sv.HaloAnnotator()
>>> annotated_frame = halo_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
halo_annotator = sv.HaloAnnotator()
annotated_frame = halo_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
@ -195,16 +195,16 @@ status: new
=== "PercentageBar"
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> percentage_bar_annotator = sv.PercentageBarAnnotator()
>>> annotated_frame = percentage_bar_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
percentage_bar_annotator = sv.PercentageBarAnnotator()
annotated_frame = percentage_bar_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
@ -216,16 +216,16 @@ status: new
=== "Mask"
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> mask_annotator = sv.MaskAnnotator()
>>> annotated_frame = mask_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
mask_annotator = sv.MaskAnnotator()
annotated_frame = mask_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
@ -237,16 +237,16 @@ status: new
=== "Polygon"
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> polygon_annotator = sv.PolygonAnnotator()
>>> annotated_frame = polygon_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
polygon_annotator = sv.PolygonAnnotator()
annotated_frame = polygon_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
@ -258,16 +258,16 @@ status: new
=== "Label"
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
>>> annotated_frame = label_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
annotated_frame = label_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
@ -279,16 +279,16 @@ status: new
=== "Blur"
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> blur_annotator = sv.BlurAnnotator()
>>> annotated_frame = blur_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
blur_annotator = sv.BlurAnnotator()
annotated_frame = blur_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
@ -300,16 +300,16 @@ status: new
=== "Pixelate"
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> pixelate_annotator = sv.PixelateAnnotator()
>>> annotated_frame = pixelate_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
pixelate_annotator = sv.PixelateAnnotator()
annotated_frame = pixelate_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
@ -321,26 +321,26 @@ status: new
=== "Trace"
```python
>>> import supervision as sv
>>> from ultralytics import YOLO
import supervision as sv
from ultralytics import YOLO
>>> model = YOLO('yolov8x.pt')
model = YOLO('yolov8x.pt')
>>> trace_annotator = sv.TraceAnnotator()
trace_annotator = sv.TraceAnnotator()
>>> video_info = sv.VideoInfo.from_video_path(video_path='...')
>>> frames_generator = get_video_frames_generator(source_path='...')
>>> tracker = sv.ByteTrack()
video_info = sv.VideoInfo.from_video_path(video_path='...')
frames_generator = get_video_frames_generator(source_path='...')
tracker = sv.ByteTrack()
>>> with sv.VideoSink(target_path='...', video_info=video_info) as sink:
... for frame in frames_generator:
... result = model(frame)[0]
... detections = sv.Detections.from_ultralytics(result)
... detections = tracker.update_with_detections(detections)
... annotated_frame = trace_annotator.annotate(
... scene=frame.copy(),
... detections=detections)
... sink.write_frame(frame=annotated_frame)
with sv.VideoSink(target_path='...', video_info=video_info) as sink:
for frame in frames_generator:
result = model(frame)[0]
detections = sv.Detections.from_ultralytics(result)
detections = tracker.update_with_detections(detections)
annotated_frame = trace_annotator.annotate(
scene=frame.copy(),
detections=detections)
sink.write_frame(frame=annotated_frame)
```
<div class="result" markdown>
@ -352,24 +352,24 @@ status: new
=== "HeatMap"
```python
>>> import supervision as sv
>>> from ultralytics import YOLO
import supervision as sv
from ultralytics import YOLO
>>> model = YOLO('yolov8x.pt')
model = YOLO('yolov8x.pt')
>>> heat_map_annotator = sv.HeatMapAnnotator()
heat_map_annotator = sv.HeatMapAnnotator()
>>> video_info = sv.VideoInfo.from_video_path(video_path='...')
>>> frames_generator = get_video_frames_generator(source_path='...')
video_info = sv.VideoInfo.from_video_path(video_path='...')
frames_generator = get_video_frames_generator(source_path='...')
>>> with sv.VideoSink(target_path='...', video_info=video_info) as sink:
... for frame in frames_generator:
... result = model(frame)[0]
... detections = sv.Detections.from_ultralytics(result)
... annotated_frame = heat_map_annotator.annotate(
... scene=frame.copy(),
... detections=detections)
... sink.write_frame(frame=annotated_frame)
with sv.VideoSink(target_path='...', video_info=video_info) as sink:
for frame in frames_generator:
result = model(frame)[0]
detections = sv.Detections.from_ultralytics(result)
annotated_frame = heat_map_annotator.annotate(
scene=frame.copy(),
detections=detections)
sink.write_frame(frame=annotated_frame)
```
<div class="result" markdown>

301
supervision/annotators/core.py
View File

@ -57,16 +57,16 @@ class BoundingBoxAnnotator(BaseAnnotator):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> bounding_box_annotator = sv.BoundingBoxAnnotator()
>>> annotated_frame = bounding_box_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
bounding_box_annotator = sv.BoundingBoxAnnotator()
annotated_frame = bounding_box_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
![bounding-box-annotator-example](https://media.roboflow.com/
@ -139,16 +139,16 @@ class MaskAnnotator(BaseAnnotator):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> mask_annotator = sv.MaskAnnotator()
>>> annotated_frame = mask_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
mask_annotator = sv.MaskAnnotator()
annotated_frame = mask_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
![mask-annotator-example](https://media.roboflow.com/
@ -222,16 +222,16 @@ class PolygonAnnotator(BaseAnnotator):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> polygon_annotator = sv.PolygonAnnotator()
>>> annotated_frame = polygon_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
polygon_annotator = sv.PolygonAnnotator()
annotated_frame = polygon_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
![polygon-annotator-example](https://media.roboflow.com/
@ -304,16 +304,16 @@ class ColorAnnotator(BaseAnnotator):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> color_annotator = sv.ColorAnnotator()
>>> annotated_frame = color_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
color_annotator = sv.ColorAnnotator()
annotated_frame = color_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
![box-mask-annotator-example](https://media.roboflow.com/
@ -394,16 +394,16 @@ class HaloAnnotator(BaseAnnotator):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> halo_annotator = sv.HaloAnnotator()
>>> annotated_frame = halo_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
halo_annotator = sv.HaloAnnotator()
annotated_frame = halo_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
![halo-annotator-example](https://media.roboflow.com/
@ -488,16 +488,16 @@ class EllipseAnnotator(BaseAnnotator):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> ellipse_annotator = sv.EllipseAnnotator()
>>> annotated_frame = ellipse_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
ellipse_annotator = sv.EllipseAnnotator()
annotated_frame = ellipse_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
![ellipse-annotator-example](https://media.roboflow.com/
@ -575,16 +575,16 @@ class BoxCornerAnnotator(BaseAnnotator):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> corner_annotator = sv.BoxCornerAnnotator()
>>> annotated_frame = corner_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
corner_annotator = sv.BoxCornerAnnotator()
annotated_frame = corner_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
![box-corner-annotator-example](https://media.roboflow.com/
@ -659,16 +659,16 @@ class CircleAnnotator(BaseAnnotator):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> circle_annotator = sv.CircleAnnotator()
>>> annotated_frame = circle_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
circle_annotator = sv.CircleAnnotator()
annotated_frame = circle_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
@ -745,16 +745,16 @@ class DotAnnotator(BaseAnnotator):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> dot_annotator = sv.DotAnnotator()
>>> annotated_frame = dot_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
dot_annotator = sv.DotAnnotator()
annotated_frame = dot_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
![dot-annotator-example](https://media.roboflow.com/
@ -872,16 +872,16 @@ class LabelAnnotator:
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
>>> annotated_frame = label_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
annotated_frame = label_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
![label-annotator-example](https://media.roboflow.com/
@ -986,16 +986,16 @@ class BlurAnnotator(BaseAnnotator):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> blur_annotator = sv.BlurAnnotator()
>>> annotated_frame = circle_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
blur_annotator = sv.BlurAnnotator()
annotated_frame = circle_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
![blur-annotator-example](https://media.roboflow.com/
@ -1071,26 +1071,25 @@ class TraceAnnotator:
Example:
```python
>>> import supervision as sv
>>> from ultralytics import YOLO
import supervision as sv
from ultralytics import YOLO
>>> model = YOLO('yolov8x.pt')
model = YOLO('yolov8x.pt')
trace_annotator = sv.TraceAnnotator()
>>> trace_annotator = sv.TraceAnnotator()
video_info = sv.VideoInfo.from_video_path(video_path='...')
frames_generator = sv.get_video_frames_generator(source_path='...')
tracker = sv.ByteTrack()
>>> video_info = sv.VideoInfo.from_video_path(video_path='...')
>>> frames_generator = sv.get_video_frames_generator(source_path='...')
>>> tracker = sv.ByteTrack()
>>> with sv.VideoSink(target_path='...', video_info=video_info) as sink:
... for frame in frames_generator:
... result = model(frame)[0]
... detections = sv.Detections.from_ultralytics(result)
... detections = tracker.update_with_detections(detections)
... annotated_frame = trace_annotator.annotate(
... scene=frame.copy(),
... detections=detections)
... sink.write_frame(frame=annotated_frame)
with sv.VideoSink(target_path='...', video_info=video_info) as sink:
for frame in frames_generator:
result = model(frame)[0]
detections = sv.Detections.from_ultralytics(result)
detections = tracker.update_with_detections(detections)
annotated_frame = trace_annotator.annotate(
scene=frame.copy(),
detections=detections)
sink.write_frame(frame=annotated_frame)
```
![trace-annotator-example](https://media.roboflow.com/
@ -1167,24 +1166,24 @@ class HeatMapAnnotator:
Example:
```python
>>> import supervision as sv
>>> from ultralytics import YOLO
import supervision as sv
from ultralytics import YOLO
>>> model = YOLO('yolov8x.pt')
model = YOLO('yolov8x.pt')
>>> heat_map_annotator = sv.HeatMapAnnotator()
heat_map_annotator = sv.HeatMapAnnotator()
>>> video_info = sv.VideoInfo.from_video_path(video_path='...')
>>> frames_generator = get_video_frames_generator(source_path='...')
video_info = sv.VideoInfo.from_video_path(video_path='...')
frames_generator = get_video_frames_generator(source_path='...')
>>> with sv.VideoSink(target_path='...', video_info=video_info) as sink:
... for frame in frames_generator:
... result = model(frame)[0]
... detections = sv.Detections.from_ultralytics(result)
... annotated_frame = heat_map_annotator.annotate(
... scene=frame.copy(),
... detections=detections)
... sink.write_frame(frame=annotated_frame)
with sv.VideoSink(target_path='...', video_info=video_info) as sink:
for frame in frames_generator:
result = model(frame)[0]
detections = sv.Detections.from_ultralytics(result)
annotated_frame = heat_map_annotator.annotate(
scene=frame.copy(),
detections=detections)
sink.write_frame(frame=annotated_frame)
```
![heatmap-annotator-example](https://media.roboflow.com/
@ -1244,16 +1243,16 @@ class PixelateAnnotator(BaseAnnotator):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> pixelate_annotator = sv.PixelateAnnotator()
>>> annotated_frame = pixelate_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
pixelate_annotator = sv.PixelateAnnotator()
annotated_frame = pixelate_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
![pixelate-annotator-example](https://media.roboflow.com/
@ -1330,16 +1329,16 @@ class TriangleAnnotator(BaseAnnotator):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> triangle_annotator = sv.TriangleAnnotator()
>>> annotated_frame = triangle_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
triangle_annotator = sv.TriangleAnnotator()
annotated_frame = triangle_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
![triangle-annotator-example](https://media.roboflow.com/
@ -1423,16 +1422,16 @@ class RoundBoxAnnotator(BaseAnnotator):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> round_box_annotator = sv.RoundBoxAnnotator()
>>> annotated_frame = round_box_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
round_box_annotator = sv.RoundBoxAnnotator()
annotated_frame = round_box_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
![round-box-annotator-example](https://media.roboflow.com/
@ -1563,16 +1562,16 @@ class PercentageBarAnnotator(BaseAnnotator):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> image = ...
>>> detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
>>> percentage_bar_annotator = sv.BoundingBoxAnnotator()
>>> annotated_frame = percentage_bar_annotator.annotate(
... scene=image.copy(),
... detections=detections
... )
percentage_bar_annotator = sv.BoundingBoxAnnotator()
annotated_frame = percentage_bar_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
![percentage-bar-example](https://media.roboflow.com/

4
supervision/assets/downloader.py
View File

@ -54,9 +54,9 @@ def download_assets(asset_name: Union[VideoAssets, str]) -> str:
Example:
```python
>>> from supervision.assets import download_assets, VideoAssets
from supervision.assets import download_assets, VideoAssets
>>> download_assets(VideoAssets.VEHICLES)
download_assets(VideoAssets.VEHICLES)
"vehicles.mp4"
```
"""

66
supervision/classification/core.py
View File

@ -59,18 +59,18 @@ class Classifications:
Example:
```python
>>> from PIL import Image
>>> import clip
>>> import supervision as sv
from PIL import Image
import clip
import supervision as sv
>>> model, preprocess = clip.load('ViT-B/32')
model, preprocess = clip.load('ViT-B/32')
>>> image = cv2.imread(SOURCE_IMAGE_PATH)
>>> image = preprocess(image).unsqueeze(0)
image = cv2.imread(SOURCE_IMAGE_PATH)
image = preprocess(image).unsqueeze(0)
>>> text = clip.tokenize(["a diagram", "a dog", "a cat"])
>>> output, _ = model(image, text)
>>> classifications = sv.Classifications.from_clip(output)
text = clip.tokenize(["a diagram", "a dog", "a cat"])
output, _ = model(image, text)
classifications = sv.Classifications.from_clip(output)
```
"""
@ -97,15 +97,15 @@ class Classifications:
Example:
```python
>>> import cv2
>>> from ultralytics import YOLO
>>> import supervision as sv
import cv2
from ultralytics import YOLO
import supervision as sv
>>> image = cv2.imread(SOURCE_IMAGE_PATH)
>>> model = YOLO('yolov8n-cls.pt')
image = cv2.imread(SOURCE_IMAGE_PATH)
model = YOLO('yolov8n-cls.pt')
>>> output = model(image)[0]
>>> classifications = sv.Classifications.from_ultralytics(output)
output = model(image)[0]
classifications = sv.Classifications.from_ultralytics(output)
```
"""
confidence = ultralytics_results.probs.data.cpu().numpy()
@ -125,25 +125,25 @@ class Classifications:
Example:
```python
>>> from PIL import Image
>>> import timm
>>> from timm.data import resolve_data_config, create_transform
>>> import supervision as sv
from PIL import Image
import timm
from timm.data import resolve_data_config, create_transform
import supervision as sv
>>> model = timm.create_model(
... model_name='hf-hub:nateraw/resnet50-oxford-iiit-pet',
... pretrained=True
... ).eval()
model = timm.create_model(
model_name='hf-hub:nateraw/resnet50-oxford-iiit-pet',
pretrained=True
).eval()
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
config = resolve_data_config({}, model=model)
transform = create_transform(**config)
>>> image = Image.open(SOURCE_IMAGE_PATH).convert('RGB')
>>> x = transform(image).unsqueeze(0)
image = Image.open(SOURCE_IMAGE_PATH).convert('RGB')
x = transform(image).unsqueeze(0)
>>> output = model(x)
output = model(x)
>>> classifications = sv.Classifications.from_timm(output)
classifications = sv.Classifications.from_timm(output)
```
"""
confidence = timm_results.cpu().detach().numpy()[0]
@ -168,11 +168,11 @@ class Classifications:
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> classifications = sv.Classifications(...)
classifications = sv.Classifications(...)
>>> classifications.get_top_k(1)
classifications.get_top_k(1)
(array([1]), array([0.9]))
```

159
supervision/dataset/core.py
View File

@ -118,13 +118,13 @@ class DetectionDataset(BaseDataset):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> ds = sv.DetectionDataset(...)
>>> train_ds, test_ds = ds.split(split_ratio=0.7,
... random_state=42, shuffle=True)
>>> len(train_ds), len(test_ds)
(700, 300)
ds = sv.DetectionDataset(...)
train_ds, test_ds = ds.split(split_ratio=0.7,
random_state=42, shuffle=True)
len(train_ds), len(test_ds)
# (700, 300)
```
"""
@ -231,24 +231,24 @@ class DetectionDataset(BaseDataset):
Example:
```python
>>> import roboflow
>>> from roboflow import Roboflow
>>> import supervision as sv
import roboflow
from roboflow import Roboflow
import supervision as sv
>>> roboflow.login()
roboflow.login()
>>> rf = Roboflow()
rf = Roboflow()
>>> project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
>>> dataset = project.version(PROJECT_VERSION).download("voc")
project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
dataset = project.version(PROJECT_VERSION).download("voc")
>>> ds = sv.DetectionDataset.from_pascal_voc(
... images_directory_path=f"{dataset.location}/train/images",
... annotations_directory_path=f"{dataset.location}/train/labels"
... )
ds = sv.DetectionDataset.from_pascal_voc(
images_directory_path=f"{dataset.location}/train/images",
annotations_directory_path=f"{dataset.location}/train/labels"
)
>>> ds.classes
['dog', 'person']
ds.classes
# ['dog', 'person']
```
"""
@ -288,25 +288,24 @@ class DetectionDataset(BaseDataset):
Example:
```python
>>> import roboflow
>>> from roboflow import Roboflow
>>> import supervision as sv
import roboflow
from roboflow import Roboflow
import supervision as sv
>>> roboflow.login()
roboflow.login()
rf = Roboflow()
>>> rf = Roboflow()
project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
dataset = project.version(PROJECT_VERSION).download("yolov5")
>>> project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
>>> dataset = project.version(PROJECT_VERSION).download("yolov5")
ds = sv.DetectionDataset.from_yolo(
images_directory_path=f"{dataset.location}/train/images",
annotations_directory_path=f"{dataset.location}/train/labels",
data_yaml_path=f"{dataset.location}/data.yaml"
)
>>> ds = sv.DetectionDataset.from_yolo(
... images_directory_path=f"{dataset.location}/train/images",
... annotations_directory_path=f"{dataset.location}/train/labels",
... data_yaml_path=f"{dataset.location}/data.yaml"
... )
>>> ds.classes
['dog', 'person']
ds.classes
# ['dog', 'person']
```
"""
classes, images, annotations = load_yolo_annotations(
@ -394,24 +393,23 @@ class DetectionDataset(BaseDataset):
Example:
```python
>>> import roboflow
>>> from roboflow import Roboflow
>>> import supervision as sv
import roboflow
from roboflow import Roboflow
import supervision as sv
>>> roboflow.login()
roboflow.login()
rf = Roboflow()
>>> rf = Roboflow()
project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
dataset = project.version(PROJECT_VERSION).download("coco")
>>> project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
>>> dataset = project.version(PROJECT_VERSION).download("coco")
ds = sv.DetectionDataset.from_coco(
images_directory_path=f"{dataset.location}/train",
annotations_path=f"{dataset.location}/train/_annotations.coco.json",
)
>>> ds = sv.DetectionDataset.from_coco(
... images_directory_path=f"{dataset.location}/train",
... annotations_path=f"{dataset.location}/train/_annotations.coco.json",
... )
>>> ds.classes
['dog', 'person']
ds.classes
# ['dog', 'person']
```
"""
classes, images, annotations = load_coco_annotations(
@ -486,25 +484,25 @@ class DetectionDataset(BaseDataset):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> ds_1 = sv.DetectionDataset(...)
>>> len(ds_1)
100
>>> ds_1.classes
['dog', 'person']
ds_1 = sv.DetectionDataset(...)
len(ds_1)
# 100
ds_1.classes
# ['dog', 'person']
>>> ds_2 = sv.DetectionDataset(...)
>>> len(ds_2)
200
>>> ds_2.classes
['cat']
ds_2 = sv.DetectionDataset(...)
len(ds_2)
# 200
ds_2.classes
# ['cat']
>>> ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
>>> len(ds_merged)
300
>>> ds_merged.classes
['cat', 'dog', 'person']
ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
len(ds_merged)
# 300
ds_merged.classes
# ['cat', 'dog', 'person']
```
"""
merged_images, merged_annotations = {}, {}
@ -571,13 +569,13 @@ class ClassificationDataset(BaseDataset):
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> cd = sv.ClassificationDataset(...)
>>> train_cd,test_cd = cd.split(split_ratio=0.7,
... random_state=42,shuffle=True)
>>> len(train_cd), len(test_cd)
(700, 300)
cd = sv.ClassificationDataset(...)
train_cd,test_cd = cd.split(split_ratio=0.7,
random_state=42,shuffle=True)
len(train_cd), len(test_cd)
# (700, 300)
```
"""
image_names = list(self.images.keys())
@ -639,20 +637,19 @@ class ClassificationDataset(BaseDataset):
Example:
```python
>>> import roboflow
>>> from roboflow import Roboflow
>>> import supervision as sv
import roboflow
from roboflow import Roboflow
import supervision as sv
>>> roboflow.login()
roboflow.login()
rf = Roboflow()
>>> rf = Roboflow()
project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
dataset = project.version(PROJECT_VERSION).download("folder")
>>> project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
>>> dataset = project.version(PROJECT_VERSION).download("folder")
>>> cd = sv.ClassificationDataset.from_folder_structure(
... root_directory_path=f"{dataset.location}/train"
... )
cd = sv.ClassificationDataset.from_folder_structure(
root_directory_path=f"{dataset.location}/train"
)
```
"""
classes = os.listdir(root_directory_path)

29
supervision/detection/annotate.py
View File

@ -63,23 +63,22 @@ class BoxAnnotator:
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> classes = ['person', ...]
>>> image = ...
>>> detections = sv.Detections(...)
classes = ['person', ...]
image = ...
detections = sv.Detections(...)
>>> box_annotator = sv.BoxAnnotator()
>>> labels = [
... f"{classes[class_id]} {confidence:0.2f}"
... for _, _, confidence, class_id, _
... in detections
... ]
>>> annotated_frame = box_annotator.annotate(
... scene=image.copy(),
... detections=detections,
... labels=labels
... )
box_annotator = sv.BoxAnnotator()
labels = [
f"{classes[class_id]} {confidence:0.2f}"
for _, _, confidence, class_id, _ in detections
]
annotated_frame = box_annotator.annotate(
scene=image.copy(),
detections=detections,
labels=labels
)
```
"""
font = cv2.FONT_HERSHEY_SIMPLEX

290
supervision/detection/core.py
View File

@ -126,14 +126,14 @@ class Detections:
Example:
```python
>>> import cv2
>>> import torch
>>> import supervision as sv
import cv2
import torch
import supervision as sv
>>> image = cv2.imread(SOURCE_IMAGE_PATH)
>>> model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
>>> result = model(image)
>>> detections = sv.Detections.from_yolov5(result)
image = cv2.imread(SOURCE_IMAGE_PATH)
model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
result = model(image)
detections = sv.Detections.from_yolov5(result)
```
"""
yolov5_detections_predictions = yolov5_results.pred[0].cpu().cpu().numpy()
@ -159,14 +159,14 @@ class Detections:
Example:
```python
>>> import cv2
>>> import supervision as sv
>>> from ultralytics import YOLO
import cv2
import supervision as sv
from ultralytics import YOLO
>>> image = cv2.imread(...)
>>> model = YOLO('yolov8s.pt')
>>> result = model(image)[0]
>>> detections = sv.Detections.from_ultralytics(result)
image = cv2.imread()
model = YOLO('yolov8s.pt')
result = model(image)[0]
detections = sv.Detections.from_ultralytics(result)
```
"""
@ -198,14 +198,14 @@ class Detections:
Example:
```python
>>> import cv2
>>> from super_gradients.training import models
>>> import supervision as sv
import cv2
from super_gradients.training import models
import supervision as sv
>>> 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)
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)
```
"""
if np.asarray(yolo_nas_results.prediction.bboxes_xyxy).shape[0] == 0:
@ -235,20 +235,16 @@ class Detections:
Example:
```python
>>> import tensorflow as tf
>>> import tensorflow_hub as hub
>>> import numpy as np
>>> import cv2
import tensorflow as tf
import tensorflow_hub as hub
import numpy as np
import cv2
>>> module_handle = "https://tfhub.dev/tensorflow/centernet/hourglass_512x512_kpts/1"
>>> model = hub.load(module_handle)
>>> img = np.array(cv2.imread(SOURCE_IMAGE_PATH))
>>> result = model(img)
>>> detections = sv.Detections.from_tensorflow(result)
module_handle = "https://tfhub.dev/tensorflow/centernet/hourglass_512x512_kpts/1"
model = hub.load(module_handle)
img = np.array(cv2.imread(SOURCE_IMAGE_PATH))
result = model(img)
detections = sv.Detections.from_tensorflow(result)
```
""" # noqa: E501 // docs
@ -278,15 +274,15 @@ class Detections:
Example:
```python
>>> import supervision as sv
>>> from deepsparse import Pipeline
import supervision as sv
from deepsparse import Pipeline
>>> yolo_pipeline = Pipeline.create(
... task="yolo",
... model_path = "zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned80_quant-none"
... )
>>> result = yolo_pipeline(<SOURCE IMAGE PATH>)
>>> detections = sv.Detections.from_deepsparse(result)
yolo_pipeline = Pipeline.create(
task="yolo",
model_path = "zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned80_quant-none"
)
result = yolo_pipeline(<SOURCE IMAGE PATH>)
detections = sv.Detections.from_deepsparse(result)
```
""" # noqa: E501 // docs
@ -315,14 +311,14 @@ class Detections:
Example:
```python
>>> import cv2
>>> import supervision as sv
>>> from mmdet.apis import DetInferencer
import cv2
import supervision as sv
from mmdet.apis import DetInferencer
>>> 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)
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)
```
"""
@ -364,18 +360,18 @@ class Detections:
Example:
```python
>>> import cv2
>>> from detectron2.engine import DefaultPredictor
>>> from detectron2.config import get_cfg
>>> import supervision as sv
import cv2
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
import supervision as sv
>>> 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"
>>> predictor = DefaultPredictor(cfg)
>>> result = predictor(image)
>>> detections = sv.Detections.from_detectron2(result)
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"
predictor = DefaultPredictor(cfg)
result = predictor(image)
detections = sv.Detections.from_detectron2(result)
```
"""
@ -412,14 +408,14 @@ class Detections:
Example:
```python
>>> import cv2
>>> import supervision as sv
>>> from inference.models.utils import get_roboflow_model
import cv2
import supervision as sv
from inference.models.utils import get_roboflow_model
>>> image = cv2.imread(...)
>>> model = get_roboflow_model(model_id="yolov8s-640")
>>> result = model.infer(image)[0]
>>> detections = sv.Detections.from_inference(result)
image = cv2.imread()
model = get_roboflow_model(model_id="yolov8s-640")
result = model.infer(image)[0]
detections = sv.Detections.from_inference(result)
```
"""
with suppress(AttributeError):
@ -461,14 +457,14 @@ class Detections:
Example:
```python
>>> import cv2
>>> import supervision as sv
>>> from inference.models.utils import get_roboflow_model
import cv2
import supervision as sv
from inference.models.utils import get_roboflow_model
>>> image = cv2.imread(...)
>>> model = get_roboflow_model(model_id="yolov8s-640")
>>> result = model.infer(image)[0]
>>> detections = sv.Detections.from_roboflow(result)
image = cv2.imread()
model = get_roboflow_model(model_id="yolov8s-640")
result = model.infer(image)[0]
detections = sv.Detections.from_roboflow(result)
```
"""
return cls.from_inference(roboflow_result)
@ -488,17 +484,17 @@ class Detections:
Example:
```python
>>> import supervision as sv
>>> from segment_anything import (
... sam_model_registry,
... SamAutomaticMaskGenerator
... )
import supervision as sv
from segment_anything import (
sam_model_registry,
SamAutomaticMaskGenerator
)
>>> sam_model_reg = sam_model_registry[MODEL_TYPE]
>>> sam = sam_model_reg(checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
>>> mask_generator = SamAutomaticMaskGenerator(sam)
>>> sam_result = mask_generator.generate(IMAGE)
>>> detections = sv.Detections.from_sam(sam_result=sam_result)
sam_model_reg = sam_model_registry[MODEL_TYPE]
sam = sam_model_reg(checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
mask_generator = SamAutomaticMaskGenerator(sam)
sam_result = mask_generator.generate(IMAGE)
detections = sv.Detections.from_sam(sam_result=sam_result)
```
"""
@ -535,25 +531,25 @@ class Detections:
Example:
```python
>>> import requests
>>> import supervision as sv
import requests
import supervision as sv
>>> image = open(input, "rb").read()
image = open(input, "rb").read()
>>> endpoint = "https://.cognitiveservices.azure.com/"
>>> subscription_key = "..."
endpoint = "https://.cognitiveservices.azure.com/"
subscription_key = ""
>>> headers = {
... "Content-Type": "application/octet-stream",
... "Ocp-Apim-Subscription-Key": subscription_key
... }
headers = {
"Content-Type": "application/octet-stream",
"Ocp-Apim-Subscription-Key": subscription_key
}
>>> response = requests.post(endpoint,
... headers=self.headers,
... data=image
... ).json()
response = requests.post(endpoint,
headers=self.headers,
data=image
).json()
>>> detections = sv.Detections.from_azure_analyze_image(response)
detections = sv.Detections.from_azure_analyze_image(response)
```
"""
if "error" in azure_result:
@ -617,21 +613,21 @@ class Detections:
Example:
```python
>>> import supervision as sv
>>> import paddle
>>> from ppdet.engine import Trainer
>>> from ppdet.core.workspace import load_config
import supervision as sv
import paddle
from ppdet.engine import Trainer
from ppdet.core.workspace import load_config
>>> weights = (...)
>>> config = (...)
weights = ()
config = ()
>>> cfg = load_config(config)
>>> trainer = Trainer(cfg, mode='test')
>>> trainer.load_weights(weights)
cfg = load_config(config)
trainer = Trainer(cfg, mode='test')
trainer.load_weights(weights)
>>> paddledet_result = trainer.predict([images])[0]
paddledet_result = trainer.predict([images])[0]
>>> detections = sv.Detections.from_paddledet(paddledet_result)
detections = sv.Detections.from_paddledet(paddledet_result)
```
"""
@ -655,9 +651,9 @@ class Detections:
Example:
```python
>>> from supervision import Detections
from supervision import Detections
>>> empty_detections = Detections.empty()
empty_detections = Detections.empty()
```
"""
return cls(
@ -689,29 +685,29 @@ class Detections:
import numpy as np
import supervision as sv
>>> detections_1 = sv.Detections(
... xyxy=np.array([[15, 15, 100, 100], [200, 200, 300, 300]]),
... class_id=np.array([1, 2]),
... data={'feature_vector': np.array([0.1, 0.2)])}
... )
detections_1 = sv.Detections(
xyxy=np.array([[15, 15, 100, 100], [200, 200, 300, 300]]),
class_id=np.array([1, 2]),
data={'feature_vector': np.array([0.1, 0.2)])}
)
>>> detections_2 = sv.Detections(
... xyxy=np.array([[30, 30, 120, 120]]),
... class_id=np.array([1]),
... data={'feature_vector': [np.array([0.3])]}
... )
detections_2 = sv.Detections(
xyxy=np.array([[30, 30, 120, 120]]),
class_id=np.array([1]),
data={'feature_vector': [np.array([0.3])]}
)
>>> merged_detections = Detections.merge([detections_1, detections_2])
merged_detections = Detections.merge([detections_1, detections_2])
>>> merged_detections.xyxy
merged_detections.xyxy
array([[ 15, 15, 100, 100],
[200, 200, 300, 300],
[ 30, 30, 120, 120]])
>>> merged_detections.class_id
merged_detections.class_id
array([1, 2, 1])
>>> merged_detections.data['feature_vector']
merged_detections.data['feature_vector']
array([0.1, 0.2, 0.3])
```
"""
@ -844,17 +840,17 @@ class Detections:
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> detections = sv.Detections(...)
detections = sv.Detections()
>>> first_detection = detections[0]
>>> first_10_detections = detections[0:10]
>>> some_detections = detections[[0, 2, 4]]
>>> class_0_detections = detections[detections.class_id == 0]
>>> high_confidence_detections = detections[detections.confidence > 0.5]
first_detection = detections[0]
first_10_detections = detections[0:10]
some_detections = detections[[0, 2, 4]]
class_0_detections = detections[detections.class_id == 0]
high_confidence_detections = detections[detections.confidence > 0.5]
>>> feature_vector = detections['feature_vector']
feature_vector = detections['feature_vector']
```
"""
if isinstance(index, str):
@ -880,22 +876,22 @@ class Detections:
Example:
```python
>>> import cv2
>>> from ultralytics import YOLO
>>> import supervision as sv
import cv2
from ultralytics import YOLO
import supervision as sv
>>> model = YOLO('yolov8s.pt')
model = YOLO('yolov8s.pt')
>>> image = cv2.imread(SOURCE_IMAGE_PATH)
image = cv2.imread(SOURCE_IMAGE_PATH)
>>> result = model(image)[0]
>>> detections = sv.Detections.from_ultralytics(result)
result = model(image)[0]
detections = sv.Detections.from_ultralytics(result)
>>> detections['names'] = [
... model.model.names[class_id]
... for class_id
... in detections.class_id
... ]
detections['names'] = [
model.model.names[class_id]
for class_id
in detections.class_id
]
```
"""
if not isinstance(value, (np.ndarray, list)):
@ -915,7 +911,7 @@ class Detections:
Returns:
np.ndarray: An array of floats containing the area of each detection
in the format of `(area_1, area_2, ..., area_n)`,
in the format of `(area_1, area_2, , area_n)`,
where n is the number of detections.
"""
if self.mask is not None:
@ -930,7 +926,7 @@ class Detections:
Returns:
np.ndarray: An array of floats containing the area of each bounding
box in the format of `(area_1, area_2, ..., area_n)`,
box in the format of `(area_1, area_2, , area_n)`,
where n is the number of detections.
"""
return (self.xyxy[:, 3] - self.xyxy[:, 1]) * (self.xyxy[:, 2] - self.xyxy[:, 0])

20
supervision/detection/tools/inference_slicer.py
View File

@ -77,20 +77,20 @@ class InferenceSlicer:
Example:
```python
>>> import cv2
>>> import supervision as sv
>>> from ultralytics import YOLO
import cv2
import supervision as sv
from ultralytics import YOLO
>>> image = cv2.imread(SOURCE_IMAGE_PATH)
>>> model = YOLO(...)
image = cv2.imread(SOURCE_IMAGE_PATH)
model = YOLO(...)
>>> def callback(image_slice: np.ndarray) -> sv.Detections:
... result = model(image_slice)[0]
... return sv.Detections.from_ultralytics(result)
def callback(image_slice: np.ndarray) -> sv.Detections:
result = model(image_slice)[0]
return sv.Detections.from_ultralytics(result)
>>> slicer = sv.InferenceSlicer(callback = callback)
slicer = sv.InferenceSlicer(callback = callback)
>>> detections = slicer(image)
detections = slicer(image)
```
"""
detections_list = []

38
supervision/detection/utils.py
View File

@ -415,16 +415,17 @@ def move_boxes(xyxy: np.ndarray, offset: np.ndarray) -> np.ndarray:
Example:
```python
>>> import numpy as np
>>> import supervision as sv
import numpy as np
import supervision as sv
>>> boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
>>> offset = np.array([5, 5])
>>> sv.move_boxes(boxes, offset)
... array([
... [15, 15, 25, 25],
... [35, 35, 45, 45]
... ])
boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
offset = np.array([5, 5])
moved_box = sv.move_boxes(boxes, offset)
print(moved_box)
# np.array([
# [15, 15, 25, 25],
# [35, 35, 45, 45]
# ])
```
"""
return xyxy + np.hstack([offset, offset])
@ -446,16 +447,17 @@ def scale_boxes(xyxy: np.ndarray, factor: float) -> np.ndarray:
Example:
```python
>>> import numpy as np
>>> import supervision as sv
import numpy as np
import supervision as sv
>>> boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
>>> factor = 1.5
>>> sv.scale_boxes(boxes, factor)
... array([
... [ 7.5, 7.5, 22.5, 22.5],
... [27.5, 27.5, 42.5, 42.5]
... ])
boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
factor = 1.5
scaled_bb = sv.scale_boxes(boxes, factor)
print(scaled_bb)
# np.array([
# [ 7.5, 7.5, 22.5, 22.5],
# [27.5, 27.5, 42.5, 42.5]
# ])
```
"""
centers = (xyxy[:, :2] + xyxy[:, 2:]) / 2

8
supervision/draw/utils.py
View File

@ -135,9 +135,11 @@ def draw_text(
Examples:
```python
>>> scene = np.zeros((100, 100, 3), dtype=np.uint8)
>>> text_anchor = Point(x=50, y=50)
>>> scene = draw_text(scene=scene, text="Hello, world!",text_anchor=text_anchor)
import numpy as np
scene = np.zeros((100, 100, 3), dtype=np.uint8)
text_anchor = Point(x=50, y=50)
scene = draw_text(scene=scene, text="Hello, world!",text_anchor=text_anchor)
```
"""
text_width, text_height = cv2.getTextSize(

7
supervision/geometry/utils.py
View File

@ -22,10 +22,11 @@ def get_polygon_center(polygon: np.ndarray) -> Point:
Examples:
```python
>>> from supervision.geometry.utils import get_polygon_center
from supervision.geometry.utils import get_polygon_center
import numpy as np
>>> vertices = np.array([[0, 0], [0, 1], [1, 1], [1, 0]])
>>> get_center(vertices)
vertices = np.array([[0, 0], [0, 1], [1, 1], [1, 0]])
get_center(vertices)
Point(x=0.5, y=0.5)
```
"""

268
supervision/metrics/detection.py
View File

@ -116,31 +116,31 @@ class ConfusionMatrix:
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> targets = [
... sv.Detections(...),
... sv.Detections(...)
... ]
targets = [
sv.Detections(...),
sv.Detections(...)
]
>>> predictions = [
... sv.Detections(...),
... sv.Detections(...)
... ]
predictions = [
sv.Detections(...),
sv.Detections(...)
]
>>> confusion_matrix = sv.ConfusionMatrix.from_detections(
... predictions=predictions,
... targets=target,
... classes=['person', ...]
... )
confusion_matrix = sv.ConfusionMatrix.from_detections(
predictions=predictions,
targets=target,
classes=['person', ...]
)
>>> confusion_matrix.matrix
array([
[0., 0., 0., 0.],
[0., 1., 0., 1.],
[0., 1., 1., 0.],
[1., 1., 0., 0.]
])
print(confusion_matrix.matrix)
# np.array([
# [0., 0., 0., 0.],
# [0., 1., 0., 1.],
# [0., 1., 1., 0.],
# [1., 1., 0., 0.]
# ])
```
"""
@ -191,46 +191,47 @@ class ConfusionMatrix:
Example:
```python
>>> import supervision as sv
import supervision as sv
import numpy as np
>>> targets = (
... [
... array(
... [
... [0.0, 0.0, 3.0, 3.0, 1],
... [2.0, 2.0, 5.0, 5.0, 1],
... [6.0, 1.0, 8.0, 3.0, 2],
... ]
... ),
... array([1.0, 1.0, 2.0, 2.0, 2]),
... ]
... )
targets = (
[
np.array(
[
[0.0, 0.0, 3.0, 3.0, 1],
[2.0, 2.0, 5.0, 5.0, 1],
[6.0, 1.0, 8.0, 3.0, 2],
]
),
np.array([1.0, 1.0, 2.0, 2.0, 2]),
]
)
>>> predictions = [
... array(
... [
... [0.0, 0.0, 3.0, 3.0, 1, 0.9],
... [0.1, 0.1, 3.0, 3.0, 0, 0.9],
... [6.0, 1.0, 8.0, 3.0, 1, 0.8],
... [1.0, 6.0, 2.0, 7.0, 1, 0.8],
... ]
... ),
... array([[1.0, 1.0, 2.0, 2.0, 2, 0.8]])
... ]
predictions = [
np.array(
[
[0.0, 0.0, 3.0, 3.0, 1, 0.9],
[0.1, 0.1, 3.0, 3.0, 0, 0.9],
[6.0, 1.0, 8.0, 3.0, 1, 0.8],
[1.0, 6.0, 2.0, 7.0, 1, 0.8],
]
),
np.array([[1.0, 1.0, 2.0, 2.0, 2, 0.8]])
]
>>> confusion_matrix = sv.ConfusionMatrix.from_tensors(
... predictions=predictions,
... targets=targets,
... classes=['person', ...]
... )
confusion_matrix = sv.ConfusionMatrix.from_tensors(
predictions=predictions,
targets=targets,
classes=['person', ...]
)
>>> confusion_matrix.matrix
array([
[0., 0., 0., 0.],
[0., 1., 0., 1.],
[0., 1., 1., 0.],
[1., 1., 0., 0.]
])
print(confusion_matrix.matrix)
# np.array([
# [0., 0., 0., 0.],
# [0., 1., 0., 1.],
# [0., 1., 1., 0.],
# [1., 1., 0., 0.]
# ])
```
"""
validate_input_tensors(predictions, targets)
@ -365,28 +366,28 @@ class ConfusionMatrix:
Example:
```python
>>> import supervision as sv
>>> from ultralytics import YOLO
import supervision as sv
from ultralytics import YOLO
>>> dataset = sv.DetectionDataset.from_yolo(...)
dataset = sv.DetectionDataset.from_yolo(...)
>>> model = YOLO(...)
>>> def callback(image: np.ndarray) -> sv.Detections:
... result = model(image)[0]
... return sv.Detections.from_ultralytics(result)
model = YOLO(...)
def callback(image: np.ndarray) -> sv.Detections:
result = model(image)[0]
return sv.Detections.from_ultralytics(result)
>>> confusion_matrix = sv.ConfusionMatrix.benchmark(
... dataset = dataset,
... callback = callback
... )
confusion_matrix = sv.ConfusionMatrix.benchmark(
dataset = dataset,
callback = callback
)
>>> confusion_matrix.matrix
array([
[0., 0., 0., 0.],
[0., 1., 0., 1.],
[0., 1., 1., 0.],
[1., 1., 0., 0.]
])
print(confusion_matrix.matrix)
# np.array([
# [0., 0., 0., 0.],
# [0., 1., 0., 1.],
# [0., 1., 1., 0.],
# [1., 1., 0., 0.]
# ])
```
"""
predictions, targets = [], []
@ -532,25 +533,25 @@ class MeanAveragePrecision:
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> targets = [
... sv.Detections(...),
... sv.Detections(...)
... ]
targets = [
sv.Detections(...),
sv.Detections(...)
]
>>> predictions = [
... sv.Detections(...),
... sv.Detections(...)
... ]
predictions = [
sv.Detections(...),
sv.Detections(...)
]
>>> mean_average_precision = sv.MeanAveragePrecision.from_detections(
... predictions=predictions,
... targets=target,
... )
mean_average_precision = sv.MeanAveragePrecision.from_detections(
predictions=predictions,
targets=target,
)
>>> mean_average_precison.map50_95
0.2899
print(mean_average_precison.map50_95)
# 0.2899
```
"""
prediction_tensors = []
@ -583,23 +584,23 @@ class MeanAveragePrecision:
Example:
```python
>>> import supervision as sv
>>> from ultralytics import YOLO
import supervision as sv
from ultralytics import YOLO
>>> dataset = sv.DetectionDataset.from_yolo(...)
dataset = sv.DetectionDataset.from_yolo(...)
>>> model = YOLO(...)
>>> def callback(image: np.ndarray) -> sv.Detections:
... result = model(image)[0]
... return sv.Detections.from_ultralytics(result)
model = YOLO(...)
def callback(image: np.ndarray) -> sv.Detections:
result = model(image)[0]
return sv.Detections.from_ultralytics(result)
>>> mean_average_precision = sv.MeanAveragePrecision.benchmark(
... dataset = dataset,
... callback = callback
... )
mean_average_precision = sv.MeanAveragePrecision.benchmark(
dataset = dataset,
callback = callback
)
>>> mean_average_precision.map50_95
0.433
print(mean_average_precision.map50_95)
# 0.433
```
"""
predictions, targets = [], []
@ -637,40 +638,41 @@ class MeanAveragePrecision:
Example:
```python
>>> import supervision as sv
import supervision as sv
import numpy as np
>>> targets = (
... [
... array(
... [
... [0.0, 0.0, 3.0, 3.0, 1],
... [2.0, 2.0, 5.0, 5.0, 1],
... [6.0, 1.0, 8.0, 3.0, 2],
... ]
... ),
... array([1.0, 1.0, 2.0, 2.0, 2]),
... ]
... )
targets = (
[
np.array(
[
[0.0, 0.0, 3.0, 3.0, 1],
[2.0, 2.0, 5.0, 5.0, 1],
[6.0, 1.0, 8.0, 3.0, 2],
]
),
np.array([[1.0, 1.0, 2.0, 2.0, 2]]),
]
)
>>> predictions = [
... array(
... [
... [0.0, 0.0, 3.0, 3.0, 1, 0.9],
... [0.1, 0.1, 3.0, 3.0, 0, 0.9],
... [6.0, 1.0, 8.0, 3.0, 1, 0.8],
... [1.0, 6.0, 2.0, 7.0, 1, 0.8],
... ]
... ),
... array([[1.0, 1.0, 2.0, 2.0, 2, 0.8]])
... ]
predictions = [
np.array(
[
[0.0, 0.0, 3.0, 3.0, 1, 0.9],
[0.1, 0.1, 3.0, 3.0, 0, 0.9],
[6.0, 1.0, 8.0, 3.0, 1, 0.8],
[1.0, 6.0, 2.0, 7.0, 1, 0.8],
]
),
np.array([[1.0, 1.0, 2.0, 2.0, 2, 0.8]])
]
>>> mean_average_precison = sv.MeanAveragePrecision.from_tensors(
... predictions=predictions,
... targets=targets,
... )
mean_average_precison = sv.MeanAveragePrecision.from_tensors(
predictions=predictions,
targets=targets,
)
>>> mean_average_precison.map50_95
0.2899
print(mean_average_precison.map50_95)
# 0.6649
```
"""
validate_input_tensors(predictions, targets)

41
supervision/tracker/byte_tracker/core.py
View File

@ -205,30 +205,29 @@ class ByteTrack:
Detection: The updated detection results that now include tracking IDs.
Example:
```python
>>> import supervision as sv
>>> from ultralytics import YOLO
import supervision as sv
from ultralytics import YOLO
>>> model = YOLO(...)
>>> byte_tracker = sv.ByteTrack()
>>> annotator = sv.BoxAnnotator()
model = YOLO(...)
byte_tracker = sv.ByteTrack()
annotator = sv.BoxAnnotator()
>>> 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)
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)
>>> sv.process_video(
... source_path='...',
... target_path='...',
... callback=callback
... )
sv.process_video(
source_path='...',
target_path='...',
callback=callback
)
```
"""

12
supervision/utils/file.py
View File

@ -34,14 +34,14 @@ def list_files_with_extensions(
Examples:
```python
>>> import supervision as sv
import supervision as sv
>>> # List all files in the directory
>>> files = sv.list_files_with_extensions(directory='my_directory')
# List all files in the directory
files = sv.list_files_with_extensions(directory='my_directory')
>>> # List only files with '.txt' and '.md' extensions
>>> files = sv.list_files_with_extensions(
... directory='my_directory', extensions=['txt', 'md'])
# List only files with '.txt' and '.md' extensions
files = sv.list_files_with_extensions(
directory='my_directory', extensions=['txt', 'md'])
```
"""

24
supervision/utils/image.py
View File

@ -20,13 +20,13 @@ def crop_image(image: np.ndarray, xyxy: np.ndarray) -> np.ndarray:
Examples:
```python
>>> import supervision as sv
import supervision as sv
>>> detection = sv.Detections(...)
>>> with sv.ImageSink(target_dir_path='target/directory/path') as sink:
... for xyxy in detection.xyxy:
... cropped_image = sv.crop_image(image=image, xyxy=xyxy)
... sink.save_image(image=image)
detection = sv.Detections(...)
with sv.ImageSink(target_dir_path='target/directory/path') as sink:
for xyxy in detection.xyxy:
cropped_image = sv.crop_image(image=image, xyxy=xyxy)
sink.save_image(image=image)
```
"""
@ -55,13 +55,13 @@ class ImageSink:
Examples:
```python
>>> import supervision as sv
import supervision as sv
>>> with sv.ImageSink(target_dir_path='target/directory/path',
... overwrite=True) as sink:
... for image in sv.get_video_frames_generator(
... source_path='source_video.mp4', stride=2):
... sink.save_image(image=image)
with sv.ImageSink(target_dir_path='target/directory/path',
overwrite=True) as sink:
for image in sv.get_video_frames_generator(
source_path='source_video.mp4', stride=2):
sink.save_image(image=image)
```
"""

24
supervision/utils/notebook.py
View File

@ -18,13 +18,13 @@ def plot_image(
Examples:
```python
>>> import cv2
>>> import supervision as sv
import cv2
import supervision as sv
>>> image = cv2.imread("path/to/image.jpg")
image = cv2.imread("path/to/image.jpg")
%matplotlib inline
>>> sv.plot_image(image=image, size=(16, 16))
sv.plot_image(image=image, size=(16, 16))
```
"""
plt.figure(figsize=size)
@ -63,18 +63,18 @@ def plot_images_grid(
Examples:
```python
>>> import cv2
>>> import supervision as sv
import cv2
import supervision as sv
>>> image1 = cv2.imread("path/to/image1.jpg")
>>> image2 = cv2.imread("path/to/image2.jpg")
>>> image3 = cv2.imread("path/to/image3.jpg")
image1 = cv2.imread("path/to/image1.jpg")
image2 = cv2.imread("path/to/image2.jpg")
image3 = cv2.imread("path/to/image3.jpg")
>>> images = [image1, image2, image3]
>>> titles = ["Image 1", "Image 2", "Image 3"]
images = [image1, image2, image3]
titles = ["Image 1", "Image 2", "Image 3"]
%matplotlib inline
>>> plot_images_grid(images, grid_size=(2, 2), titles=titles, size=(16, 16))
plot_images_grid(images, grid_size=(2, 2), titles=titles, size=(16, 16))
```
"""
nrows, ncols = grid_size

60
supervision/utils/video.py
View File

@ -24,15 +24,15 @@ class VideoInfo:
Examples:
```python
>>> import supervision as sv
import supervision as sv
>>> video_info = sv.VideoInfo.from_video_path(video_path='video.mp4')
video_info = sv.VideoInfo.from_video_path(video_path='video.mp4')
>>> video_info
VideoInfo(width=3840, height=2160, fps=25, total_frames=538)
video_info
# VideoInfo(width=3840, height=2160, fps=25, total_frames=538)
>>> video_info.resolution_wh
(3840, 2160)
video_info.resolution_wh
# (3840, 2160)
```
"""
@ -71,14 +71,14 @@ class VideoSink:
Example:
```python
>>> import supervision as sv
import supervision as sv
>>> video_info = sv.VideoInfo.from_video_path('source.mp4')
>>> frames_generator = sv.get_video_frames_generator('source.mp4')
video_info = sv.VideoInfo.from_video_path('source.mp4')
frames_generator = sv.get_video_frames_generator('source.mp4')
>>> with sv.VideoSink(target_path='target.mp4', video_info=video_info) as sink:
... for frame in frames_generator:
... sink.write_frame(frame=frame)
with sv.VideoSink(target_path='target.mp4', video_info=video_info) as sink:
for frame in frames_generator:
sink.write_frame(frame=frame)
```
"""
@ -143,10 +143,10 @@ def get_video_frames_generator(
Examples:
```python
>>> import supervision as sv
import supervision as sv
>>> for frame in sv.get_video_frames_generator(source_path='source_video.mp4'):
... ...
for frame in sv.get_video_frames_generator(source_path='source_video.mp4'):
...
```
"""
video, start, end = _validate_and_setup_video(source_path, start, end)
@ -183,16 +183,16 @@ def process_video(
Examples:
```python
>>> import supervision as sv
import supervision as sv
>>> def callback(scene: np.ndarray, index: int) -> np.ndarray:
... ...
def callback(scene: np.ndarray, index: int) -> np.ndarray:
...
>>> process_video(
... source_path='...',
... target_path='...',
... callback=callback
... )
process_video(
source_path='...',
target_path='...',
callback=callback
)
```
"""
source_video_info = VideoInfo.from_video_path(video_path=source_path)
@ -217,15 +217,15 @@ class FPSMonitor:
Examples:
```python
>>> import supervision as sv
import supervision as sv
>>> frames_generator = sv.get_video_frames_generator('source.mp4')
>>> fps_monitor = sv.FPSMonitor()
frames_generator = sv.get_video_frames_generator('source.mp4')
fps_monitor = sv.FPSMonitor()
>>> for frame in frames_generator:
... # your processing code here
... fps_monitor.tick()
... fps = fps_monitor()
for frame in frames_generator:
# your processing code here
fps_monitor.tick()
fps = fps_monitor()
```
"""
self.all_timestamps = deque(maxlen=sample_size)