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docs: Wrap all placeholder paths in quotes and code expressions (#2128) 

* Wrap all placeholder paths in quotes across documentation and code examples for consistency
* Update pre-commit config to exclude `docs/deprecated.md` from mdformat check
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3
.pre-commit-config.yaml
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@ -49,8 +49,9 @@ repos:
- id: mdformat
additional_dependencies:
- "mdformat-mkdocs[recommended]>=2.1.0"
- "mdformat-ruff"
args: ["--number"]
exclude: ^docs/
exclude: ^(docs/changelog\.md|docs/deprecated\.md)$
- repo: https://github.com/pre-commit/mirrors-mypy
rev: v1.19.1

1
docs/datasets/core.md
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@ -5,6 +5,7 @@ comments: true
# Datasets
!!! warning
Dataset API is still fluid and may change. If you use Dataset API in your project until further notice, freeze the
`supervision` version in your `requirements.txt` or `setup.py`.

114
docs/detection/annotators.md
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@ -17,7 +17,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
box_annotator = sv.BoxAnnotator()
annotated_frame = box_annotator.annotate(
scene=image.copy(),
detections=detections
detections=detections,
)
```
@ -38,7 +38,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
round_box_annotator = sv.RoundBoxAnnotator()
annotated_frame = round_box_annotator.annotate(
scene=image.copy(),
detections=detections
detections=detections,
)
```
@ -59,7 +59,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
corner_annotator = sv.BoxCornerAnnotator()
annotated_frame = corner_annotator.annotate(
scene=image.copy(),
detections=detections
detections=detections,
)
```
@ -80,7 +80,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
color_annotator = sv.ColorAnnotator()
annotated_frame = color_annotator.annotate(
scene=image.copy(),
detections=detections
detections=detections,
)
```
@ -101,7 +101,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
circle_annotator = sv.CircleAnnotator()
annotated_frame = circle_annotator.annotate(
scene=image.copy(),
detections=detections
detections=detections,
)
```
@ -122,7 +122,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
dot_annotator = sv.DotAnnotator()
annotated_frame = dot_annotator.annotate(
scene=image.copy(),
detections=detections
detections=detections,
)
```
@ -143,7 +143,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
triangle_annotator = sv.TriangleAnnotator()
annotated_frame = triangle_annotator.annotate(
scene=image.copy(),
detections=detections
detections=detections,
)
```
@ -164,7 +164,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
ellipse_annotator = sv.EllipseAnnotator()
annotated_frame = ellipse_annotator.annotate(
scene=image.copy(),
detections=detections
detections=detections,
)
```
@ -185,7 +185,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
halo_annotator = sv.HaloAnnotator()
annotated_frame = halo_annotator.annotate(
scene=image.copy(),
detections=detections
detections=detections,
)
```
@ -206,7 +206,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
percentage_bar_annotator = sv.PercentageBarAnnotator()
annotated_frame = percentage_bar_annotator.annotate(
scene=image.copy(),
detections=detections
detections=detections,
)
```
@ -227,7 +227,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
mask_annotator = sv.MaskAnnotator()
annotated_frame = mask_annotator.annotate(
scene=image.copy(),
detections=detections
detections=detections,
)
```
@ -248,7 +248,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
polygon_annotator = sv.PolygonAnnotator()
annotated_frame = polygon_annotator.annotate(
scene=image.copy(),
detections=detections
detections=detections,
)
```
@ -268,15 +268,15 @@ Annotators accept detections and apply box or mask visualizations to the detecti
labels = [
f"{class_name} {confidence:.2f}"
for class_name, confidence
in zip(detections['class_name'], detections.confidence)
for class_name, confidence in zip(
detections["class_name"],
detections.confidence,
)
]
label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
annotated_frame = label_annotator.annotate(
scene=image.copy(),
detections=detections,
labels=labels
scene=image.copy(), detections=detections, labels=labels
)
```
@ -296,18 +296,20 @@ Annotators accept detections and apply box or mask visualizations to the detecti
labels = [
f"{class_name} {confidence:.2f}"
for class_name, confidence
in zip(detections['class_name'], detections.confidence)
for class_name, confidence in zip(
detections["class_name"],
detections.confidence,
)
]
rich_label_annotator = sv.RichLabelAnnotator(
font_path="<TTF_FONT_PATH>",
text_position=sv.Position.CENTER
font_path="TTF_FONT_PATH",
text_position=sv.Position.CENTER,
)
annotated_frame = rich_label_annotator.annotate(
scene=image.copy(),
detections=detections,
labels=labels
labels=labels,
)
```
@ -325,16 +327,13 @@ Annotators accept detections and apply box or mask visualizations to the detecti
image = ...
detections = sv.Detections(...)
icon_paths = [
"<ICON_PATH>"
for _ in detections
]
icon_paths = ["<ICON_PATH>" for _ in detections]
icon_annotator = sv.IconAnnotator()
annotated_frame = icon_annotator.annotate(
scene=image.copy(),
detections=detections,
icon_path=icon_paths
icon_path=icon_paths,
)
```
@ -346,23 +345,25 @@ Annotators accept detections and apply box or mask visualizations to the detecti
<!-- === "Crop"
```python
import supervision as sv
```python
import supervision as sv
image = ...
detections = sv.Detections(...)
image = ...
detections = sv.Detections(...)
crop_annotator = sv.CropAnnotator()
annotated_frame = crop_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
crop_annotator = sv.CropAnnotator()
annotated_frame = crop_annotator.annotate(
scene=image.copy(),
detections=detections,
)
```
![crop-annotator-example](https://media.roboflow.com/supervision-annotator-examples/crop-annotator-example.png){ align=center width="800" }
<div class="result" markdown>
![crop-annotator-example](https://media.roboflow.com/supervision-annotator-examples/crop-annotator-example.png){ align=center width="800" }
</div>
</div>
-->
=== "Blur"
@ -374,10 +375,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
detections = sv.Detections(...)
blur_annotator = sv.BlurAnnotator()
annotated_frame = blur_annotator.annotate(
scene=image.copy(),
detections=detections
)
annotated_frame = (blur_annotator.annotate(scene=image.copy(), detections=detections),)
```
<div class="result" markdown>
@ -397,7 +395,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
pixelate_annotator = sv.PixelateAnnotator()
annotated_frame = pixelate_annotator.annotate(
scene=image.copy(),
detections=detections
detections=detections,
)
```
@ -413,22 +411,23 @@ Annotators accept detections and apply box or mask visualizations to the detecti
import supervision as sv
from ultralytics import YOLO
model = YOLO('yolov8x.pt')
model = YOLO("yolov8x.pt")
trace_annotator = sv.TraceAnnotator()
video_info = sv.VideoInfo.from_video_path(video_path='...')
frames_generator = sv.get_video_frames_generator(source_path='...')
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:
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)
detections=detections,
)
sink.write_frame(frame=annotated_frame)
```
@ -444,20 +443,21 @@ Annotators accept detections and apply box or mask visualizations to the detecti
import supervision as sv
from ultralytics import YOLO
model = YOLO('yolov8x.pt')
model = YOLO("yolov8x.pt")
heat_map_annotator = sv.HeatMapAnnotator()
video_info = sv.VideoInfo.from_video_path(video_path='...')
frames_generator = sv.get_video_frames_generator(source_path='...')
video_info = sv.VideoInfo.from_video_path(video_path="...")
frames_generator = sv.get_video_frames_generator(source_path="...")
with sv.VideoSink(target_path='...', video_info=video_info) as sink:
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)
detections=detections,
)
sink.write_frame(frame=annotated_frame)
```
@ -478,7 +478,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
background_overlay_annotator = sv.BackgroundOverlayAnnotator()
annotated_frame = background_overlay_annotator.annotate(
scene=image.copy(),
detections=detections
detections=detections,
)
```
@ -501,7 +501,7 @@ Annotators accept detections and apply box or mask visualizations to the detecti
annotated_frame = comparison_annotator.annotate(
scene=image.copy(),
detections_1=detections_1,
detections_2=detections_2
detections_2=detections_2,
)
```

44
docs/how_to/benchmark_a_model.md
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@ -51,7 +51,7 @@ from roboflow import Roboflow
rf = Roboflow(api_key="<YOUR_API_KEY>")
project = rf.workspace("<WORKSPACE_NAME>").project("<PROJECT_NAME>")
dataset = project.version(<DATASET_VERSION_NUMBER>).download("<FORMAT>")
dataset = project.version("<DATASET_VERSION_NUMBER>").download("<FORMAT>")
```
If your dataset is from Universe, go to `Dataset` > `Download Dataset` > select the format (e.g. `YOLOv11`) > `Show download code`.
@ -148,7 +148,7 @@ We'll use `supervision` to create a dataset iterator, and then run the model on
test_set = sv.DetectionDataset.from_yolo(
images_directory_path=f"{dataset.location}/test/images",
annotations_directory_path=f"{dataset.location}/test/labels",
data_yaml_path=f"{dataset.location}/data.yaml"
data_yaml_path=f"{dataset.location}/data.yaml",
)
image_paths = []
@ -172,7 +172,7 @@ We'll use `supervision` to create a dataset iterator, and then run the model on
test_set = sv.DetectionDataset.from_yolo(
images_directory_path=f"{dataset.location}/test/images",
annotations_directory_path=f"{dataset.location}/test/labels",
data_yaml_path=f"{dataset.location}/data.yaml"
data_yaml_path=f"{dataset.location}/data.yaml",
)
image_paths = []
@ -199,14 +199,16 @@ We need to remap them to match the dataset classes. Here's how to do it:
def remap_classes(
detections: sv.Detections,
class_ids_from_to: dict[int, int],
class_names_from_to: dict[str, str]
class_names_from_to: dict[str, str],
) -> None:
new_class_ids = [
class_ids_from_to.get(class_id, class_id) for class_id in detections.class_id]
class_ids_from_to.get(class_id, class_id) for class_id in detections.class_id
]
detections.class_id = np.array(new_class_ids)
new_class_names = [
class_names_from_to.get(name, name) for name in detections["class_name"]]
class_names_from_to.get(name, name) for name in detections["class_name"]
]
predictions["class_name"] = np.array(new_class_names)
```
@ -222,7 +224,7 @@ Let's also remove the predictions that are not in the dataset classes.
test_set = sv.DetectionDataset.from_yolo(
images_directory_path=f"{dataset.location}/test/images",
annotations_directory_path=f"{dataset.location}/test/labels",
data_yaml_path=f"{dataset.location}/data.yaml"
data_yaml_path=f"{dataset.location}/data.yaml",
)
image_paths = []
@ -236,11 +238,9 @@ Let's also remove the predictions that are not in the dataset classes.
remap_classes(
detections=predictions,
class_ids_from_to={16: 0},
class_names_from_to={"dog": "Corgi"}
class_names_from_to={"dog": "Corgi"},
)
predictions = predictions[
np.isin(predictions["class_name"], test_set.classes)
]
predictions = predictions[np.isin(predictions["class_name"], test_set.classes),]
image_paths.append(image_path)
predictions_list.append(predictions)
@ -260,7 +260,7 @@ Let's also remove the predictions that are not in the dataset classes.
test_set = sv.DetectionDataset.from_yolo(
images_directory_path=f"{dataset.location}/test/images",
annotations_directory_path=f"{dataset.location}/test/labels",
data_yaml_path=f"{dataset.location}/data.yaml"
data_yaml_path=f"{dataset.location}/data.yaml",
)
image_paths = []
@ -274,11 +274,9 @@ Let's also remove the predictions that are not in the dataset classes.
remap_classes(
detections=predictions,
class_ids_from_to={16: 0},
class_names_from_to={"dog": "Corgi"}
class_names_from_to={"dog": "Corgi"},
)
predictions = predictions[
np.isin(predictions["class_name"], test_set.classes)
]
predictions = predictions[np.isin(predictions["class_name"], test_set.classes),]
image_paths.append(image_path)
predictions_list.append(predictions)
@ -297,16 +295,22 @@ N = 9
GRID_SIZE = (3, 3)
target_annotator = sv.PolygonAnnotator(color=sv.Color.from_hex("#8315f9"), thickness=8)
prediction_annotator = sv.PolygonAnnotator(color=sv.Color.from_hex("#00cfc6"), thickness=6)
prediction_annotator = sv.PolygonAnnotator(
color=sv.Color.from_hex("#00cfc6"), thickness=6
)
annotated_images = []
for image_path, predictions, targets in zip(
image_paths[:N], predictions_list[:N], targets_list[:N]
image_paths[:N], predictions_list[:N], targets_list[:N]
):
annotated_image = cv2.imread(image_path)
annotated_image = target_annotator.annotate(scene=annotated_image, detections=targets)
annotated_image = prediction_annotator.annotate(scene=annotated_image, detections=prediction)
annotated_image = target_annotator.annotate(
scene=annotated_image, detections=targets
)
annotated_image = prediction_annotator.annotate(
scene=annotated_image, detections=prediction
)
annotated_images.append(annotated_image)
sv.plot_images_grid(images=annotated_images, grid_size=GRID_SIZE)

83
docs/how_to/detect_and_annotate.md
View File

@ -20,26 +20,29 @@ First, you'll need to obtain predictions from your object detection or segmentat
model.
=== "Inference"
```python
import cv2
from inference import get_model
model = get_model(model_id="yolov8n-640")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model.infer(image)[0]
```
=== "Ultralytics"
```python
import cv2
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model(image)[0]
```
=== "Transformers"
```python
import torch
from PIL import Image
@ -48,7 +51,7 @@ model.
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
image = Image.open(<SOURCE_IMAGE_PATH>)
image = Image.open("<SOURCE_IMAGE_PATH>")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
@ -57,7 +60,8 @@ model.
width, height = image.size
target_size = torch.tensor([[height, width]])
results = processor.post_process_object_detection(
outputs=outputs, target_sizes=target_size)[0]
outputs=outputs, target_sizes=target_size
)[0]
```
## Load Predictions into Supervision
@ -65,6 +69,7 @@ model.
Now that we have predictions from a model, we can load them into Supervision.
=== "Inference"
We can do so using the [`sv.Detections.from_inference`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_inference) method, which accepts model results from both detection and segmentation models.
```{ .py hl_lines="2 8" }
@ -73,12 +78,13 @@ Now that we have predictions from a model, we can load them into Supervision.
from inference import get_model
model = get_model(model_id="yolov8n-640")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model.infer(image)[0]
detections = sv.Detections.from_inference(results)
```
=== "Ultralytics"
We can do so using the [`sv.Detections.from_ultralytics`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_ultralytics) method, which accepts model results from both detection and segmentation models.
```{ .py hl_lines="2 8" }
@ -87,12 +93,13 @@ Now that we have predictions from a model, we can load them into Supervision.
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model(image)[0]
detections = sv.Detections.from_ultralytics(results)
```
=== "Transformers"
We can do so using the [`sv.Detections.from_transformers`](https://supervision.roboflow.com/latest/detection/core/#supervision.detection.core.Detections.from_transformers) method, which accepts model results from both detection and segmentation models.
```{ .py hl_lines="2 19-21" }
@ -104,7 +111,7 @@ Now that we have predictions from a model, we can load them into Supervision.
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
image = Image.open(<SOURCE_IMAGE_PATH>)
image = Image.open("<SOURCE_IMAGE_PATH>")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
@ -132,13 +139,14 @@ You can load predictions from other computer vision frameworks and libraries usi
Finally, we can annotate the image with the predictions. Since we are working with an object detection model, we will use the [`sv.BoxAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.BoxAnnotator) and [`sv.LabelAnnotator`](https://supervision.roboflow.com/latest/detection/annotators/#supervision.annotators.core.LabelAnnotator) classes.
=== "Inference"
```{ .py hl_lines="10-16" }
import cv2
import supervision as sv
from inference import get_model
model = get_model(model_id="yolov8n-640")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model.infer(image)[0]
detections = sv.Detections.from_inference(results)
@ -152,13 +160,14 @@ Finally, we can annotate the image with the predictions. Since we are working wi
```
=== "Ultralytics"
```{ .py hl_lines="10-16" }
import cv2
import supervision as sv
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model(image)[0]
detections = sv.Detections.from_ultralytics(results)
@ -172,6 +181,7 @@ Finally, we can annotate the image with the predictions. Since we are working wi
```
=== "Transformers"
```{ .py hl_lines="23-30" }
import torch
import supervision as sv
@ -181,7 +191,7 @@ Finally, we can annotate the image with the predictions. Since we are working wi
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
image = Image.open(<SOURCE_IMAGE_PATH>)
image = Image.open("<SOURCE_IMAGE_PATH>")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
@ -213,13 +223,14 @@ will label each detection with its `class_name` (if possible) or `class_id`. You
override this behavior by passing a list of custom `labels` to the `annotate` method.
=== "Inference"
```{ .py hl_lines="13-17 22" }
import cv2
import supervision as sv
from inference import get_model
model = get_model(model_id="yolov8n-640")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model.infer(image)[0]
detections = sv.Detections.from_inference(results)
@ -239,13 +250,14 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
```
=== "Ultralytics"
```{ .py hl_lines="13-17 22" }
import cv2
import supervision as sv
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model(image)[0]
detections = sv.Detections.from_ultralytics(results)
@ -265,6 +277,7 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
```
=== "Transformers"
```{ .py hl_lines="26-30 35" }
import torch
import supervision as sv
@ -274,7 +287,7 @@ override this behavior by passing a list of custom `labels` to the `annotate` me
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
image = Image.open(<SOURCE_IMAGE_PATH>)
image = Image.open("<SOURCE_IMAGE_PATH>")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
@ -314,13 +327,14 @@ is a drop-in replacement for
that will allow you to draw masks instead of boxes.
=== "Inference"
```python
import cv2
import supervision as sv
from inference import get_model
model = get_model(model_id="yolov8n-seg-640")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model.infer(image)[0]
detections = sv.Detections.from_inference(results)
@ -328,19 +342,24 @@ that will allow you to draw masks instead of boxes.
label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER_OF_MASS)
annotated_image = mask_annotator.annotate(
scene=image, detections=detections)
scene=image,
detections=detections,
)
annotated_image = label_annotator.annotate(
scene=annotated_image, detections=detections)
scene=annotated_image,
detections=detections,
)
```
=== "Ultralytics"
```python
import cv2
import supervision as sv
from ultralytics import YOLO
model = YOLO("yolov8n-seg.pt")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model(image)[0]
detections = sv.Detections.from_ultralytics(results)
@ -348,12 +367,17 @@ that will allow you to draw masks instead of boxes.
label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER_OF_MASS)
annotated_image = mask_annotator.annotate(
scene=image, detections=detections)
scene=image,
detections=detections,
)
annotated_image = label_annotator.annotate(
scene=annotated_image, detections=detections)
scene=annotated_image,
detections=detections,
)
```
=== "Transformers"
```python
import torch
import supervision as sv
@ -363,7 +387,7 @@ that will allow you to draw masks instead of boxes.
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic")
model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50-panoptic")
image = Image.open(<SOURCE_IMAGE_PATH>)
image = Image.open("<SOURCE_IMAGE_PATH>")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
@ -372,24 +396,27 @@ that will allow you to draw masks instead of boxes.
width, height = image.size
target_size = torch.tensor([[height, width]])
results = processor.post_process_segmentation(
outputs=outputs, target_sizes=target_size)[0]
outputs=outputs, target_sizes=target_size
)[0]
detections = sv.Detections.from_transformers(
transformers_results=results,
id2label=model.config.id2label)
transformers_results=results, id2label=model.config.id2label
)
mask_annotator = sv.MaskAnnotator()
label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER_OF_MASS)
labels = [
f"{class_name} {confidence:.2f}"
for class_name, confidence
in zip(detections['class_name'], detections.confidence)
for class_name, confidence in zip(
detections["class_name"],
detections.confidence,
)
]
annotated_image = mask_annotator.annotate(
scene=image, detections=detections)
annotated_image = mask_annotator.annotate(scene=image, detections=detections)
annotated_image = label_annotator.annotate(
scene=annotated_image, detections=detections, labels=labels)
scene=annotated_image, detections=detections, labels=labels
)
```
![segmentation-annotation](https://media.roboflow.com/supervision_detect_and_annotate_example_3.png)

61
docs/how_to/detect_small_objects.md
View File

@ -20,13 +20,14 @@ Small object detection in high-resolution images presents challenges due to the
size relative to the image resolution.
=== "Inference"
```python
import cv2
import supervision as sv
from inference import get_model
model = get_model(model_id="yolov8x-640")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model.infer(image)[0]
detections = sv.Detections.from_inference(results)
@ -34,19 +35,24 @@ size relative to the image resolution.
label_annotator = sv.LabelAnnotator()
annotated_image = box_annotator.annotate(
scene=image, detections=detections)
scene=image,
detections=detections,
)
annotated_image = label_annotator.annotate(
scene=annotated_image, detections=detections)
scene=annotated_image,
detections=detections,
)
```
=== "Ultralytics"
```python
import cv2
import supervision as sv
from ultralytics import YOLO
model = YOLO("yolov8x.pt")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model(image)[0]
detections = sv.Detections.from_ultralytics(results)
@ -54,12 +60,17 @@ size relative to the image resolution.
label_annotator = sv.LabelAnnotator()
annotated_image = box_annotator.annotate(
scene=image, detections=detections)
scene=image,
detections=detections,
)
annotated_image = label_annotator.annotate(
scene=annotated_image, detections=detections)
scene=annotated_image,
detections=detections,
)
```
=== "Transformers"
```python
import torch
import supervision as sv
@ -69,7 +80,7 @@ size relative to the image resolution.
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForSegmentation.from_pretrained("facebook/detr-resnet-50")
image = Image.open(<SOURCE_IMAGE_PATH>)
image = Image.open("<SOURCE_IMAGE_PATH>")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
@ -78,22 +89,19 @@ size relative to the image resolution.
width, height = image_slice.size
target_size = torch.tensor([[width, height]])
results = processor.post_process_object_detection(
outputs=outputs, target_sizes=target_size)[0]
outputs=outputs, target_sizes=target_size
)[0]
detections = sv.Detections.from_transformers(results)
box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()
labels = [
model.config.id2label[class_id]
for class_id
in detections.class_id
]
labels = [model.config.id2label[class_id] for class_id in detections.class_id]
annotated_image = box_annotator.annotate(
scene=image, detections=detections)
annotated_image = box_annotator.annotate(scene=image, detections=detections)
annotated_image = label_annotator.annotate(
scene=annotated_image, detections=detections, labels=labels)
scene=annotated_image, detections=detections, labels=labels
)
```
![basic-detection](https://media.roboflow.com/supervision_detect_small_objects_example_1.png)
@ -105,13 +113,14 @@ identification at the cost of processing speed and increased memory usage. This
is less effective for ultra-high-resolution images (4K and above).
=== "Inference"
```{ .py hl_lines="5" }
import cv2
import supervision as sv
from inference import get_model
model = get_model(model_id="yolov8x-1280")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model.infer(image)[0]
detections = sv.Detections.from_inference(results)
@ -125,13 +134,14 @@ is less effective for ultra-high-resolution images (4K and above).
```
=== "Ultralytics"
```{ .py hl_lines="7" }
import cv2
import supervision as sv
from ultralytics import YOLO
model = YOLO("yolov8x.pt")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model(image, imgsz=1280)[0]
detections = sv.Detections.from_ultralytics(results)
@ -157,6 +167,7 @@ objects within each, and aggregating the results.
</video>
=== "Inference"
```{ .py hl_lines="9-14" }
import cv2
import numpy as np
@ -164,7 +175,7 @@ objects within each, and aggregating the results.
from inference import get_model
model = get_model(model_id="yolov8x-640")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
def callback(image_slice: np.ndarray) -> sv.Detections:
results = model.infer(image_slice)[0]
@ -183,6 +194,7 @@ objects within each, and aggregating the results.
```
=== "Ultralytics"
```{ .py hl_lines="9-14" }
import cv2
import numpy as np
@ -190,7 +202,7 @@ objects within each, and aggregating the results.
from ultralytics import YOLO
model = YOLO("yolov8x.pt")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
def callback(image_slice: np.ndarray) -> sv.Detections:
result = model(image_slice)[0]
@ -209,6 +221,7 @@ objects within each, and aggregating the results.
```
=== "Transformers"
```{ .py hl_lines="13-28" }
import cv2
import torch
@ -220,7 +233,7 @@ objects within each, and aggregating the results.
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
def callback(image_slice: np.ndarray) -> sv.Detections:
image_slice = cv2.cvtColor(image_slice, cv2.COLOR_BGR2RGB)
@ -261,6 +274,7 @@ objects within each, and aggregating the results.
[`InferenceSlicer`](https://supervision.roboflow.com/latest/detection/tools/inference_slicer/#supervision.detection.tools.inference_slicer.InferenceSlicer) can perform segmentation tasks too.
=== "Inference"
```{ .py hl_lines="6 16 19-20" }
import cv2
import numpy as np
@ -268,7 +282,7 @@ objects within each, and aggregating the results.
from inference import get_model
model = get_model(model_id="yolov8x-seg-640")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
def callback(image_slice: np.ndarray) -> sv.Detections:
results = model.infer(image_slice)[0]
@ -287,6 +301,7 @@ objects within each, and aggregating the results.
```
=== "Ultralytics"
```{ .py hl_lines="6 16 19-20" }
import cv2
import numpy as np
@ -294,7 +309,7 @@ objects within each, and aggregating the results.
from ultralytics import YOLO
model = YOLO("yolov8x-seg.pt")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
def callback(image_slice: np.ndarray) -> sv.Detections:
result = model(image_slice)[0]

16
docs/how_to/filter_detections.md
View File

@ -15,6 +15,7 @@ the filters in their applications.
Allows you to select detections that belong only to one selected class.
=== "After"
```python
import supervision as sv
@ -29,6 +30,7 @@ Allows you to select detections that belong only to one selected class.
</div>
=== "Before"
```python
import supervision as sv
@ -47,6 +49,7 @@ Allows you to select detections that belong only to one selected class.
Allows you to select detections that belong only to selected set of classes.
=== "After"
```python
import numpy as np
import supervision as sv
@ -63,6 +66,7 @@ Allows you to select detections that belong only to selected set of classes.
</div>
=== "Before"
```python
import numpy as np
import supervision as sv
@ -83,6 +87,7 @@ Allows you to select detections that belong only to selected set of classes.
Allows you to select detections with specific confidence value, for example higher than selected threshold.
=== "After"
```python
import supervision as sv
@ -97,6 +102,7 @@ Allows you to select detections with specific confidence value, for example high
</div>
=== "Before"
```python
import supervision as sv
@ -116,6 +122,7 @@ Allows you to select detections based on their size. We define the area as the n
detection in the image. In the example below, we have sifted out the detections that are too small.
=== "After"
```python
import supervision as sv
@ -130,6 +137,7 @@ detection in the image. In the example below, we have sifted out the detections
</div>
=== "Before"
```python
import supervision as sv
@ -151,6 +159,7 @@ but small on a 3840x2160 image. In such cases, we can filter out detections base
occupied by them. In the example below, we remove too large detections.
=== "After"
```python
import supervision as sv
@ -169,6 +178,7 @@ occupied by them. In the example below, we remove too large detections.
</div>
=== "Before"
```python
import supervision as sv
@ -193,6 +203,7 @@ can be criteria for rejecting detection. Implementing such filtering requires a
simple and fast.
=== "After"
```python
import supervision as sv
@ -209,6 +220,7 @@ simple and fast.
</div>
=== "Before"
```python
import supervision as sv
@ -230,6 +242,7 @@ Allows you to use `Detections` in combination with `PolygonZone` to weed out bou
zone. In the example below you can see how to filter out all detections located in the lower part of the image.
=== "After"
```python
import supervision as sv
@ -246,6 +259,7 @@ zone. In the example below you can see how to filter out all detections located
</div>
=== "Before"
```python
import supervision as sv
@ -266,6 +280,7 @@ zone. In the example below you can see how to filter out all detections located
`Detections`' greatest strength, however, is that you can build arbitrarily complex logical conditions by simply combining separate conditions using `&` or `|`.
=== "After"
```python
import supervision as sv
@ -282,6 +297,7 @@ zone. In the example below you can see how to filter out all detections located
</div>
=== "Before"
```python
import supervision as sv

86
docs/how_to/process_datasets.md
View File

@ -32,8 +32,8 @@ your workspace ID, project ID, and version number.
roboflow.login()
rf = roboflow.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")
```
=== "YOLO"
@ -44,8 +44,8 @@ your workspace ID, project ID, and version number.
roboflow.login()
rf = roboflow.Roboflow()
project = rf.workspace('<WORKSPACE_ID>').project('<PROJECT_ID>')
dataset = project.version('<PROJECT_VERSION>').download("yolov8")
project = rf.workspace("<WORKSPACE_ID>").project("<PROJECT_ID>")
dataset = project.version("<PROJECT_VERSION>").download("yolov8")
```
=== "Pascal VOC"
@ -56,8 +56,8 @@ your workspace ID, project ID, and version number.
roboflow.login()
rf = roboflow.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")
```
## Load Dataset
@ -75,16 +75,16 @@ instances.
import supervision as sv
ds_train = sv.DetectionDataset.from_coco(
images_directory_path=f'{dataset.location}/train',
annotations_path=f'{dataset.location}/train/_annotations.coco.json',
images_directory_path=f"{dataset.location}/train",
annotations_path=f"{dataset.location}/train/_annotations.coco.json",
)
ds_valid = sv.DetectionDataset.from_coco(
images_directory_path=f'{dataset.location}/valid',
annotations_path=f'{dataset.location}/valid/_annotations.coco.json',
images_directory_path=f"{dataset.location}/valid",
annotations_path=f"{dataset.location}/valid/_annotations.coco.json",
)
ds_test = sv.DetectionDataset.from_coco(
images_directory_path=f'{dataset.location}/test',
annotations_path=f'{dataset.location}/test/_annotations.coco.json',
images_directory_path=f"{dataset.location}/test",
annotations_path=f"{dataset.location}/test/_annotations.coco.json",
)
ds_train.classes
@ -102,19 +102,19 @@ instances.
import supervision as sv
ds_train = 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'
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_valid = sv.DetectionDataset.from_yolo(
images_directory_path=f'{dataset.location}/valid/images',
annotations_directory_path=f'{dataset.location}/valid/labels',
data_yaml_path=f'{dataset.location}/data.yaml'
images_directory_path=f"{dataset.location}/valid/images",
annotations_directory_path=f"{dataset.location}/valid/labels",
data_yaml_path=f"{dataset.location}/data.yaml",
)
ds_test = sv.DetectionDataset.from_yolo(
images_directory_path=f'{dataset.location}/test/images',
annotations_directory_path=f'{dataset.location}/test/labels',
data_yaml_path=f'{dataset.location}/data.yaml'
images_directory_path=f"{dataset.location}/test/images",
annotations_directory_path=f"{dataset.location}/test/labels",
data_yaml_path=f"{dataset.location}/data.yaml",
)
ds_train.classes
@ -132,16 +132,16 @@ instances.
import supervision as sv
ds_train = sv.DetectionDataset.from_pascal_voc(
images_directory_path=f'{dataset.location}/train/images',
annotations_directory_path=f'{dataset.location}/train/labels'
images_directory_path=f"{dataset.location}/train/images",
annotations_directory_path=f"{dataset.location}/train/labels",
)
ds_valid = sv.DetectionDataset.from_pascal_voc(
images_directory_path=f'{dataset.location}/valid/images',
annotations_directory_path=f'{dataset.location}/valid/labels'
images_directory_path=f"{dataset.location}/valid/images",
annotations_directory_path=f"{dataset.location}/valid/labels",
)
ds_test = sv.DetectionDataset.from_pascal_voc(
images_directory_path=f'{dataset.location}/test/images',
annotations_directory_path=f'{dataset.location}/test/labels'
images_directory_path=f"{dataset.location}/test/images",
annotations_directory_path=f"{dataset.location}/test/labels",
)
ds_train.classes
@ -294,12 +294,12 @@ ds = sv.DetectionDataset(...)
# Option 1
for image_path, image, annotations in ds:
... # Process each image and its annotations
... # Process each image and its annotations
# Option 2
for idx in range(len(ds)):
image_path, image, annotations = ds[idx]
... # Process the image and annotations at index `idx`
... # Process the image and annotations at index `idx`
```
## Visualize Dataset
@ -351,8 +351,8 @@ sv.plot_images_grid(
ds = sv.DetectionDataset(...)
ds.as_coco(
images_directory_path='<IMAGE_DIRECTORY_PATH>',
annotations_path='<ANNOTATIONS_PATH>'
images_directory_path="<IMAGE_DIRECTORY_PATH>",
annotations_path="<ANNOTATIONS_PATH>",
)
```
@ -366,9 +366,9 @@ sv.plot_images_grid(
ds = sv.DetectionDataset(...)
ds.as_yolo(
images_directory_path='<IMAGE_DIRECTORY_PATH>',
annotations_directory_path='<ANNOTATIONS_DIRECTORY_PATH>',
data_yaml_path='<DATA_YAML_PATH>'
images_directory_path="<IMAGE_DIRECTORY_PATH>",
annotations_directory_path="<ANNOTATIONS_DIRECTORY_PATH>",
data_yaml_path="<DATA_YAML_PATH>",
)
```
@ -382,8 +382,8 @@ sv.plot_images_grid(
ds = sv.DetectionDataset(...)
ds.as_pascal_voc(
images_directory_path='<IMAGE_DIRECTORY_PATH>',
annotations_directory_path='<ANNOTATIONS_DIRECTORY_PATH>'
images_directory_path="<IMAGE_DIRECTORY_PATH>",
annotations_directory_path="<ANNOTATIONS_DIRECTORY_PATH>",
)
```
@ -413,11 +413,11 @@ augmentation = A.Compose(
transforms=[
A.Perspective(p=0.1),
A.HorizontalFlip(p=0.5),
A.RandomBrightnessContrast(p=0.5)
A.RandomBrightnessContrast(p=0.5),
],
bbox_params=A.BboxParams(
format='pascal_voc',
label_fields=['category']
format="pascal_voc",
label_fields=["category"],
),
)
```
@ -437,14 +437,14 @@ _, original_image, original_annotations = ds[0]
output = augmentation(
image=original_image,
bboxes=original_annotations.xyxy,
category=original_annotations.class_id
category=original_annotations.class_id,
)
augmented_image = output['image']
augmented_image = output["image"]
augmented_annotations = replace(
original_annotations,
xyxy=np.array(output['bboxes']),
class_id=np.array(output['category'])
xyxy=np.array(output["bboxes"]),
class_id=np.array(output["category"]),
)
```

60
docs/how_to/save_detections.md
View File

@ -19,34 +19,35 @@ model. You can learn more on this topic in our
[How to Detect and Annotate](https://supervision.roboflow.com/latest/how_to/detect_and_annotate/) guide.
=== "Inference"
```python
import supervision as sv
from inference import get_model
model = get_model(model_id="yolov8n-640")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
frames_generator = sv.get_video_frames_generator("<SOURCE_VIDEO_PATH>")
for frame in frames_generator:
results = model.infer(image)[0]
detections = sv.Detections.from_inference(results)
```
=== "Ultralytics"
```python
import supervision as sv
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
frames_generator = sv.get_video_frames_generator("<SOURCE_VIDEO_PATH>")
for frame in frames_generator:
results = model(frame)[0]
detections = sv.Detections.from_ultralytics(results)
```
=== "Transformers"
```python
import torch
import supervision as sv
@ -54,10 +55,9 @@ model. You can learn more on this topic in our
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
frames_generator = sv.get_video_frames_generator("<SOURCE_VIDEO_PATH>")
for frame in frames_generator:
frame = sv.cv2_to_pillow(frame)
inputs = processor(images=frame, return_tensors="pt")
@ -67,7 +67,8 @@ model. You can learn more on this topic in our
width, height = frame.size
target_size = torch.tensor([[height, width]])
results = processor.post_process_object_detection(
outputs=outputs, target_sizes=target_size)[0]
outputs=outputs, target_sizes=target_size
)[0]
detections = sv.Detections.from_transformers(results)
```
@ -80,14 +81,15 @@ and then pass the
object resulting from the inference to it. Its fields are parsed and saved on disk.
=== "Inference"
```{ .py hl_lines="7 12" }
import supervision as sv
from inference import get_model
model = get_model(model_id="yolov8n-640")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
frames_generator = sv.get_video_frames_generator("<SOURCE_VIDEO_PATH>")
with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
with sv.CSVSink("<TARGET_CSV_PATH>") as sink:
for frame in frames_generator:
results = model.infer(image)[0]
@ -96,14 +98,15 @@ object resulting from the inference to it. Its fields are parsed and saved on di
```
=== "Ultralytics"
```{ .py hl_lines="7 12" }
import supervision as sv
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
frames_generator = sv.get_video_frames_generator("<SOURCE_VIDEO_PATH>")
with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
with sv.CSVSink("<TARGET_CSV_PATH>") as sink:
for frame in frames_generator:
results = model(frame)[0]
@ -112,6 +115,7 @@ object resulting from the inference to it. Its fields are parsed and saved on di
```
=== "Transformers"
```{ .py hl_lines="9 23" }
import torch
import supervision as sv
@ -119,9 +123,9 @@ object resulting from the inference to it. Its fields are parsed and saved on di
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
frames_generator = sv.get_video_frames_generator("<SOURCE_VIDEO_PATH>")
with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
with sv.CSVSink("<TARGET_CSV_PATH>") as sink:
for frame in frames_generator:
frame = sv.cv2_to_pillow(frame)
@ -154,14 +158,15 @@ also allows you to add custom information to each row, which can be passed via t
frame index from which the detections originate.
=== "Inference"
```{ .py hl_lines="8 12" }
import supervision as sv
from inference import get_model
model = get_model(model_id="yolov8n-640")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
frames_generator = sv.get_video_frames_generator("<SOURCE_VIDEO_PATH>")
with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
with sv.CSVSink("<TARGET_CSV_PATH>") as sink:
for frame_index, frame in enumerate(frames_generator):
results = model.infer(image)[0]
@ -170,14 +175,15 @@ frame index from which the detections originate.
```
=== "Ultralytics"
```{ .py hl_lines="8 12" }
import supervision as sv
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
frames_generator = sv.get_video_frames_generator("<SOURCE_VIDEO_PATH>")
with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
with sv.CSVSink("<TARGET_CSV_PATH>") as sink:
for frame_index, frame in enumerate(frames_generator):
results = model(frame)[0]
@ -186,6 +192,7 @@ frame index from which the detections originate.
```
=== "Transformers"
```{ .py hl_lines="10 23" }
import torch
import supervision as sv
@ -193,9 +200,9 @@ frame index from which the detections originate.
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
frames_generator = sv.get_video_frames_generator("<SOURCE_VIDEO_PATH>")
with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
with sv.CSVSink("<TARGET_CSV_PATH>") as sink:
for frame_index, frame in enumerate(frames_generator):
frame = sv.cv2_to_pillow(frame)
@ -227,14 +234,15 @@ with
[`sv.JSONSink`](https://supervision.roboflow.com/latest/detection/tools/save_detections/#supervision.detection.tools.csv_sink.JSONSink).
=== "Inference"
```{ .py hl_lines="7" }
import supervision as sv
from inference import get_model
model = get_model(model_id="yolov8n-640")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
frames_generator = sv.get_video_frames_generator("<SOURCE_VIDEO_PATH>")
with sv.JSONSink(<TARGET_JSON_PATH>) as sink:
with sv.JSONSink("<TARGET_JSON_PATH>") as sink:
for frame_index, frame in enumerate(frames_generator):
results = model.infer(image)[0]
@ -243,14 +251,15 @@ with
```
=== "Ultralytics"
```{ .py hl_lines="7" }
import supervision as sv
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
frames_generator = sv.get_video_frames_generator("<SOURCE_VIDEO_PATH>")
with sv.JSONSink(<TARGET_JSON_PATH>) as sink:
with sv.JSONSink("<TARGET_JSON_PATH>") as sink:
for frame_index, frame in enumerate(frames_generator):
results = model(frame)[0]
@ -259,6 +268,7 @@ with
```
=== "Transformers"
```{ .py hl_lines="9" }
import torch
import supervision as sv
@ -266,9 +276,9 @@ with
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
frames_generator = sv.get_video_frames_generator("<SOURCE_VIDEO_PATH>")
with sv.JSONSink(<TARGET_JSON_PATH>) as sink:
with sv.JSONSink("<TARGET_JSON_PATH>") as sink:
for frame_index, frame in enumerate(frames_generator):
frame = sv.cv2_to_pillow(frame)

16
docs/how_to/track_objects.md
View File

@ -75,7 +75,7 @@ it will be modified to include tracking, labeling, and trace annotations.
import supervision as sv
from inference.models.utils import get_roboflow_model
model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
model = get_roboflow_model(model_id="yolov8n-640", api_key="<ROBOFLOW_API_KEY>")
box_annotator = sv.BoxAnnotator()
def callback(frame: np.ndarray, _: int) -> np.ndarray:
@ -133,7 +133,7 @@ enabling the continuous following of the object's motion path across different f
import supervision as sv
from inference.models.utils import get_roboflow_model
model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
model = get_roboflow_model(model_id="yolov8n-640", api_key="<ROBOFLOW_API_KEY>")
tracker = sv.ByteTrack()
box_annotator = sv.BoxAnnotator()
@ -200,7 +200,7 @@ offering a clear visual representation of each object's class and unique identif
import supervision as sv
from inference.models.utils import get_roboflow_model
model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
model = get_roboflow_model(model_id="yolov8n-640", api_key="<ROBOFLOW_API_KEY>")
tracker = sv.ByteTrack()
box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()
@ -285,7 +285,7 @@ movement patterns and interactions between objects in the video.
import supervision as sv
from inference.models.utils import get_roboflow_model
model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
model = get_roboflow_model(model_id="yolov8n-640", api_key="<ROBOFLOW_API_KEY>")
tracker = sv.ByteTrack()
box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()
@ -381,7 +381,7 @@ Let's immediately visualize the results with our [`EdgeAnnotator`](https://super
from inference.models.utils import get_roboflow_model
model = get_roboflow_model(
model_id="yolov8m-pose-640", api_key=<ROBOFLOW API KEY>)
model_id="yolov8m-pose-640", api_key="<ROBOFLOW_API_KEY>")
edge_annotator = sv.EdgeAnnotator()
vertex_annotator = sv.VertexAnnotator()
@ -454,7 +454,7 @@ Let's convert to detections and visualize the results with our [`BoxAnnotator`](
from inference.models.utils import get_roboflow_model
model = get_roboflow_model(
model_id="yolov8m-pose-640", api_key=<ROBOFLOW API KEY>)
model_id="yolov8m-pose-640", api_key="<ROBOFLOW_API_KEY>")
edge_annotator = sv.EdgeAnnotator()
vertex_annotator = sv.VertexAnnotator()
box_annotator = sv.BoxAnnotator()
@ -531,7 +531,7 @@ Now that we have a `Detections` object, we can track it throughout the video. Ut
from inference.models.utils import get_roboflow_model
model = get_roboflow_model(
model_id="yolov8m-pose-640", api_key=<ROBOFLOW API KEY>)
model_id="yolov8m-pose-640", api_key="<ROBOFLOW_API_KEY>")
edge_annotator = sv.EdgeAnnotator()
vertex_annotator = sv.VertexAnnotator()
box_annotator = sv.BoxAnnotator()
@ -616,7 +616,7 @@ We could stop here as we have successfully tracked the object detected by the ke
from inference.models.utils import get_roboflow_model
model = get_roboflow_model(
model_id="yolov8m-pose-640", api_key=<ROBOFLOW API KEY>)
model_id="yolov8m-pose-640", api_key="<ROBOFLOW_API_KEY>")
edge_annotator = sv.EdgeAnnotator()
vertex_annotator = sv.VertexAnnotator()
box_annotator = sv.BoxAnnotator()

11
docs/index.md
View File

@ -42,6 +42,7 @@ You can install `supervision` in a
!!! example "Installation"
=== "pip (recommended)"
[![version](https://badge.fury.io/py/supervision.svg)](https://badge.fury.io/py/supervision)
[![downloads](https://img.shields.io/pypi/dm/supervision)](https://pypistats.org/packages/supervision)
[![license](https://img.shields.io/pypi/l/supervision)](../LICENSE.md)
@ -52,6 +53,7 @@ You can install `supervision` in a
```
=== "poetry"
[![version](https://badge.fury.io/py/supervision.svg)](https://badge.fury.io/py/supervision)
[![downloads](https://img.shields.io/pypi/dm/supervision)](https://pypistats.org/packages/supervision)
[![license](https://img.shields.io/pypi/l/supervision)](../LICENSE.md)
@ -62,6 +64,7 @@ You can install `supervision` in a
```
=== "uv"
[![version](https://badge.fury.io/py/supervision.svg)](https://badge.fury.io/py/supervision)
[![downloads](https://img.shields.io/pypi/dm/supervision)](https://pypistats.org/packages/supervision)
[![license](https://img.shields.io/pypi/l/supervision)](../LICENSE.md)
@ -78,6 +81,7 @@ You can install `supervision` in a
```
=== "rye"
[![version](https://badge.fury.io/py/supervision.svg)](https://badge.fury.io/py/supervision)
[![downloads](https://img.shields.io/pypi/dm/supervision)](https://pypistats.org/packages/supervision)
[![license](https://img.shields.io/pypi/l/supervision)](../LICENSE.md)
@ -87,9 +91,10 @@ You can install `supervision` in a
rye add supervision
```
!!! example "conda/mamba install"
=== "conda"
[![conda-recipe](https://img.shields.io/badge/recipe-supervision-green.svg)](https://anaconda.org/conda-forge/supervision) [![conda-downloads](https://img.shields.io/conda/dn/conda-forge/supervision.svg)](https://anaconda.org/conda-forge/supervision) [![conda-version](https://img.shields.io/conda/vn/conda-forge/supervision.svg)](https://anaconda.org/conda-forge/supervision) [![conda-platforms](https://img.shields.io/conda/pn/conda-forge/supervision.svg)](https://anaconda.org/conda-forge/supervision)
```bash
@ -97,6 +102,7 @@ You can install `supervision` in a
```
=== "mamba"
[![mamba-recipe](https://img.shields.io/badge/recipe-supervision-green.svg)](https://anaconda.org/conda-forge/supervision) [![mamba-downloads](https://img.shields.io/conda/dn/conda-forge/supervision.svg)](https://anaconda.org/conda-forge/supervision) [![mamba-version](https://img.shields.io/conda/vn/conda-forge/supervision.svg)](https://anaconda.org/conda-forge/supervision) [![mamba-platforms](https://img.shields.io/conda/pn/conda-forge/supervision.svg)](https://anaconda.org/conda-forge/supervision)
```bash
@ -104,7 +110,9 @@ You can install `supervision` in a
```
!!! example "git clone (for development)"
=== "virtualenv"
```bash
# clone repository and navigate to root directory
git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
@ -120,6 +128,7 @@ You can install `supervision` in a
```
=== "uv"
```bash
# clone repository and navigate to root directory
git clone --depth 1 -b develop https://github.com/roboflow/supervision.git

12
docs/keypoint/annotators.md
View File

@ -14,11 +14,11 @@ comments: true
vertex_annotator = sv.VertexAnnotator(
color=sv.Color.GREEN,
radius=10
radius=10,
)
annotated_frame = vertex_annotator.annotate(
scene=image.copy(),
key_points=key_points
key_points=key_points,
)
```
@ -38,11 +38,11 @@ comments: true
edge_annotator = sv.EdgeAnnotator(
color=sv.Color.GREEN,
thickness=5
thickness=5,
)
annotated_frame = edge_annotator.annotate(
scene=image.copy(),
key_points=key_points
key_points=key_points,
)
```
@ -63,11 +63,11 @@ comments: true
vertex_label_annotator = sv.VertexLabelAnnotator(
color=sv.Color.GREEN,
text_color=sv.Color.BLACK,
border_radius=5
border_radius=5,
)
annotated_frame = vertex_label_annotator.annotate(
scene=image.copy(),
key_points=key_points
key_points=key_points,
)
```

2
supervision/annotators/core.py
View File

@ -291,7 +291,7 @@ class OrientedBoxAnnotator(BaseAnnotator):
import supervision as sv
from ultralytics import YOLO
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
model = YOLO("yolov8n-obb.pt")
result = model(image)[0]

34
supervision/detection/core.py
View File

@ -74,7 +74,7 @@ class Detections:
from inference import get_model
model = get_model(model_id="yolov8n-640")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model.infer(image)[0]
detections = sv.Detections.from_inference(results)
```
@ -90,7 +90,7 @@ class Detections:
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
results = model(image)[0]
detections = sv.Detections.from_ultralytics(results)
```
@ -109,7 +109,7 @@ class Detections:
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
image = Image.open(<SOURCE_IMAGE_PATH>)
image = Image.open("<SOURCE_IMAGE_PATH>")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
@ -225,7 +225,7 @@ 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)
@ -265,7 +265,7 @@ class Detections:
import supervision as sv
from ultralytics import YOLO
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
model = YOLO('yolov8s.pt')
results = model(image)[0]
detections = sv.Detections.from_ultralytics(results)
@ -338,7 +338,7 @@ 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]
@ -452,8 +452,8 @@ class Detections:
import supervision as sv
from mmdet.apis import init_detector, inference_detector
image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = init_detector(<CONFIG_PATH>, <WEIGHTS_PATH>, device=<DEVICE>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
model = init_detector("<CONFIG_PATH>", "<WEIGHTS_PATH>", device="<DEVICE>")
result = inference_detector(model, image)
detections = sv.Detections.from_mmdetection(result)
@ -501,7 +501,7 @@ class Detections:
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
image = Image.open(<SOURCE_IMAGE_PATH>)
image = Image.open("<SOURCE_IMAGE_PATH>")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
@ -570,10 +570,10 @@ class Detections:
from detectron2.config import get_cfg
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
cfg = get_cfg()
cfg.merge_from_file(<CONFIG_PATH>)
cfg.MODEL.WEIGHTS = <WEIGHTS_PATH>
cfg.merge_from_file("<CONFIG_PATH>")
cfg.MODEL.WEIGHTS = "<WEIGHTS_PATH>"
predictor = DefaultPredictor(cfg)
result = predictor(image)
@ -616,7 +616,7 @@ class Detections:
import supervision as sv
from inference import get_model
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
model = get_model(model_id="yolov8s-640")
result = model.infer(image)[0]
@ -1689,7 +1689,7 @@ class Detections:
import easyocr
reader = easyocr.Reader(['en'])
results = reader.readtext(<SOURCE_IMAGE_PATH>)
results = reader.readtext("<SOURCE_IMAGE_PATH>")
detections = sv.Detections.from_easyocr(results)
detected_text = detections["class_name"]
```
@ -1734,7 +1734,7 @@ class Detections:
from ncnn.model_zoo import get_model
import supervision as sv
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
model = get_model(
"yolov8s",
target_size=640
@ -2037,7 +2037,7 @@ class Detections:
import supervision as sv
from ultralytics import YOLO
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
model = YOLO('yolov8s.pt')
result = model(image)[0]
@ -2300,7 +2300,7 @@ def merge_inner_detection_object_pair(
import supervision as sv
from inference import get_model
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
model = get_model(model_id="yolov8s-640")
result = model.infer(image)[0]

4
supervision/detection/line_zone.py
View File

@ -55,9 +55,9 @@ class LineZone:
import supervision as sv
from ultralytics import YOLO
model = YOLO(<SOURCE_MODEL_PATH>)
model = YOLO("<SOURCE_MODEL_PATH>")
tracker = sv.ByteTrack()
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
frames_generator = sv.get_video_frames_generator("<SOURCE_VIDEO_PATH>")
start, end = sv.Point(x=0, y=1080), sv.Point(x=3840, y=1080)
line_zone = sv.LineZone(start=start, end=end)

6
supervision/detection/tools/json_sink.py
View File

@ -28,15 +28,15 @@ class JSONSink:
import supervision as sv
from ultralytics import YOLO
model = YOLO(<SOURCE_MODEL_PATH>)
model = YOLO("<SOURCE_MODEL_PATH>")
json_sink = sv.JSONSink(<RESULT_JSON_FILE_PATH>)
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
frames_generator = sv.get_video_frames_generator("<SOURCE_VIDEO_PATH>")
with json_sink as sink:
for frame in frames_generator:
result = model(frame)[0]
detections = sv.Detections.from_ultralytics(result)
sink.append(detections, custom_data={'<CUSTOM_LABEL>':'<CUSTOM_DATA>'})
sink.append(detections, custom_data={"<CUSTOM_LABEL>":"<CUSTOM_DATA>"})
```
"""

2
supervision/detection/tools/polygon_zone.py
View File

@ -43,7 +43,7 @@ class PolygonZone:
import numpy as np
import cv2
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
model = YOLO("yolo11s")
tracker = sv.ByteTrack()

9
supervision/detection/tools/smoother.py
View File

@ -35,16 +35,17 @@ class DetectionsSmoother:
from ultralytics import YOLO
video_info = sv.VideoInfo.from_video_path(video_path=<SOURCE_FILE_PATH>)
frame_generator = sv.get_video_frames_generator(source_path=<SOURCE_FILE_PATH>)
video_info = sv.VideoInfo.from_video_path(video_path="<SOURCE_FILE_PATH>")
frame_generator = sv.get_video_frames_generator(
source_path="<SOURCE_FILE_PATH>")
model = YOLO(<MODEL_PATH>)
model = YOLO("<MODEL_PATH>")
tracker = sv.ByteTrack(frame_rate=video_info.fps)
smoother = sv.DetectionsSmoother()
box_annotator = sv.BoxAnnotator()
with sv.VideoSink(<TARGET_FILE_PATH>, video_info=video_info) as sink:
with sv.VideoSink("<TARGET_FILE_PATH>", video_info=video_info) as sink:
for frame in frame_generator:
result = model(frame)[0]
detections = sv.Detections.from_ultralytics(result)

38
supervision/key_points/core.py
View File

@ -32,7 +32,7 @@ class KeyPoints:
import supervision as sv
from ultralytics import YOLO
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
model = YOLO('yolo11s-pose.pt')
result = model(image)[0]
@ -49,8 +49,8 @@ class KeyPoints:
import supervision as sv
from inference import get_model
image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = get_model(model_id=<POSE_MODEL_ID>, api_key=<ROBOFLOW_API_KEY>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
model = get_model(model_id="<POSE_MODEL_ID>", api_key="<ROBOFLOW_API_KEY>")
result = model.infer(image)[0]
key_points = sv.KeyPoints.from_inference(result)
@ -68,7 +68,7 @@ class KeyPoints:
import mediapipe as mp
import supervision as sv
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
image_height, image_width, _ = image.shape
mediapipe_image = mp.Image(
image_format=mp.ImageFormat.SRGB,
@ -106,7 +106,7 @@ class KeyPoints:
)
device = "cuda" if torch.cuda.is_available() else "cpu"
image = Image.open(<SOURCE_IMAGE_PATH>)
image = Image.open("<SOURCE_IMAGE_PATH>")
DETECTION_MODEL_ID = "PekingU/rtdetr_r50vd_coco_o365"
@ -228,8 +228,8 @@ class KeyPoints:
import supervision as sv
from inference import get_model
image = cv2.imread(<SOURCE_IMAGE_PATH>)
model = get_model(model_id=<POSE_MODEL_ID>, api_key=<ROBOFLOW_API_KEY>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
model = get_model(model_id="<POSE_MODEL_ID>", api_key="<ROBOFLOW_API_KEY>")
result = model.infer(image)[0]
key_points = sv.KeyPoints.from_inference(result)
@ -240,13 +240,13 @@ class KeyPoints:
import supervision as sv
from inference_sdk import InferenceHTTPClient
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
client = InferenceHTTPClient(
api_url="https://detect.roboflow.com",
api_key=<ROBOFLOW_API_KEY>
api_key="<ROBOFLOW_API_KEY>"
)
result = client.infer(image, model_id=<POSE_MODEL_ID>)
result = client.infer(image, model_id="<POSE_MODEL_ID>")
key_points = sv.KeyPoints.from_inference(result)
```
"""
@ -320,7 +320,7 @@ class KeyPoints:
import mediapipe as mp
import supervision as sv
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
image_height, image_width, _ = image.shape
mediapipe_image = mp.Image(
image_format=mp.ImageFormat.SRGB,
@ -346,7 +346,7 @@ class KeyPoints:
import mediapipe as mp
import supervision as sv
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
image_height, image_width, _ = image.shape
mediapipe_image = mp.Image(
image_format=mp.ImageFormat.SRGB,
@ -436,7 +436,7 @@ class KeyPoints:
import supervision as sv
from ultralytics import YOLO
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
model = YOLO('yolov8s-pose.pt')
result = model(image)[0]
@ -475,7 +475,7 @@ class KeyPoints:
import supervision as sv
import super_gradients
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
device = "cuda" if torch.cuda.is_available() else "cpu"
model = super_gradients.training.models.get(
@ -535,10 +535,10 @@ class KeyPoints:
from detectron2.config import get_cfg
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
cfg = get_cfg()
cfg.merge_from_file(<CONFIG_PATH>)
cfg.MODEL.WEIGHTS = <WEIGHTS_PATH>
cfg.merge_from_file("<CONFIG_PATH>")
cfg.MODEL.WEIGHTS = "<WEIGHTS_PATH>"
predictor = DefaultPredictor(cfg)
result = predictor(image)
@ -591,7 +591,7 @@ class KeyPoints:
)
device = "cuda" if torch.cuda.is_available() else "cpu"
image = Image.open(<SOURCE_IMAGE_PATH>)
image = Image.open("<SOURCE_IMAGE_PATH>")
DETECTION_MODEL_ID = "PekingU/rtdetr_r50vd_coco_o365"
@ -728,7 +728,7 @@ class KeyPoints:
import supervision as sv
from ultralytics import YOLO
image = cv2.imread(<SOURCE_IMAGE_PATH>)
image = cv2.imread("<SOURCE_IMAGE_PATH>")
model = YOLO('yolov8s.pt')
result = model(image)[0]

6
supervision/tracker/byte_tracker/core.py
View File

@ -75,7 +75,7 @@ class ByteTrack:
import supervision as sv
from ultralytics import YOLO
model = YOLO(<MODEL_PATH>)
model = YOLO("<MODEL_PATH>")
tracker = sv.ByteTrack()
box_annotator = sv.BoxAnnotator()
@ -95,8 +95,8 @@ class ByteTrack:
return annotated_frame
sv.process_video(
source_path=<SOURCE_VIDEO_PATH>,
target_path=<TARGET_VIDEO_PATH>,
source_path="<SOURCE_VIDEO_PATH>",
target_path="<TARGET_VIDEO_PATH>",
callback=callback
)
```

15
supervision/utils/video.py
View File

@ -31,7 +31,7 @@ class VideoInfo:
```python
import supervision as sv
video_info = sv.VideoInfo.from_video_path(video_path=<SOURCE_VIDEO_FILE>)
video_info = sv.VideoInfo.from_video_path(video_path="<SOURCE_VIDEO_FILE>")
video_info
# VideoInfo(width=3840, height=2160, fps=25, total_frames=538)
@ -78,10 +78,10 @@ class VideoSink:
```python
import supervision as sv
video_info = sv.VideoInfo.from_video_path(<SOURCE_VIDEO_PATH>)
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)
video_info = sv.VideoInfo.from_video_path("<SOURCE_VIDEO_PATH>")
frames_generator = sv.get_video_frames_generator("<SOURCE_VIDEO_PATH>")
with sv.VideoSink(target_path=<TARGET_VIDEO_PATH>, video_info=video_info) as sink:
with sv.VideoSink(target_path="<TARGET_VIDEO_PATH>", video_info=video_info) as sink:
for frame in frames_generator:
sink.write_frame(frame=frame)
```
@ -182,7 +182,7 @@ def get_video_frames_generator(
```python
import supervision as sv
for frame in sv.get_video_frames_generator(source_path=<SOURCE_VIDEO_PATH>):
for frame in sv.get_video_frames_generator(source_path="<SOURCE_VIDEO_PATH>"):
...
```
"""
@ -379,7 +379,8 @@ class FPSMonitor:
```python
import supervision as sv
frames_generator = sv.get_video_frames_generator(source_path=<SOURCE_FILE_PATH>)
frames_generator = sv.get_video_frames_generator(
source_path="<SOURCE_FILE_PATH>")
fps_monitor = sv.FPSMonitor()
for frame in frames_generator:
@ -387,7 +388,7 @@ class FPSMonitor:
fps_monitor.tick()
fps = fps_monitor.fps
```
""" # noqa: E501 // docs
"""
self.all_timestamps: deque[float] = deque(maxlen=sample_size)
@property