xiaoxue_admin
/
supervision
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

merge 0.26.0 deprecation changes

This commit is contained in:
SkalskiP 2025-02-18 10:04:38 +01:00
parent 59b652d0f2
commit 7e19c633a6
13 changed files with 97 additions and 165 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
docs/how_to/track_objects.md
View File

@ -55,7 +55,7 @@ it will be modified to include tracking, labeling, and trace annotations.
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
box_annotator = sv.BoundingBoxAnnotator()
box_annotator = sv.BoxAnnotator()
def callback(frame: np.ndarray, _: int) -> np.ndarray:
results = model(frame)[0]
@ -77,7 +77,7 @@ it will be modified to include tracking, labeling, and trace annotations.
from inference.models.utils import get_roboflow_model
model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
box_annotator = sv.BoundingBoxAnnotator()
box_annotator = sv.BoxAnnotator()
def callback(frame: np.ndarray, _: int) -> np.ndarray:
results = model.infer(frame)[0]
@ -112,7 +112,7 @@ enabling the continuous following of the object's motion path across different f
model = YOLO("yolov8n.pt")
tracker = sv.ByteTrack()
box_annotator = sv.BoundingBoxAnnotator()
box_annotator = sv.BoxAnnotator()
def callback(frame: np.ndarray, _: int) -> np.ndarray:
results = model(frame)[0]
@ -136,7 +136,7 @@ enabling the continuous following of the object's motion path across different f
model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
tracker = sv.ByteTrack()
box_annotator = sv.BoundingBoxAnnotator()
box_annotator = sv.BoxAnnotator()
def callback(frame: np.ndarray, _: int) -> np.ndarray:
results = model.infer(frame)[0]
@ -168,7 +168,7 @@ offering a clear visual representation of each object's class and unique identif
model = YOLO("yolov8n.pt")
tracker = sv.ByteTrack()
box_annotator = sv.BoundingBoxAnnotator()
box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()
def callback(frame: np.ndarray, _: int) -> np.ndarray:
@ -203,7 +203,7 @@ offering a clear visual representation of each object's class and unique identif
model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
tracker = sv.ByteTrack()
box_annotator = sv.BoundingBoxAnnotator()
box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()
def callback(frame: np.ndarray, _: int) -> np.ndarray:
@ -250,7 +250,7 @@ movement patterns and interactions between objects in the video.
model = YOLO("yolov8n.pt")
tracker = sv.ByteTrack()
box_annotator = sv.BoundingBoxAnnotator()
box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()
trace_annotator = sv.TraceAnnotator()
@ -288,7 +288,7 @@ movement patterns and interactions between objects in the video.
model = get_roboflow_model(model_id="yolov8n-640", api_key=<ROBOFLOW API KEY>)
tracker = sv.ByteTrack()
box_annotator = sv.BoundingBoxAnnotator()
box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()
trace_annotator = sv.TraceAnnotator()

8
examples/count_people_in_zone/inference_example.py
View File

@ -36,7 +36,7 @@ def load_zones_config(file_path: str) -> List[np.ndarray]:
def initiate_annotators(
polygons: List[np.ndarray], resolution_wh: Tuple[int, int]
) -> Tuple[
List[sv.PolygonZone], List[sv.PolygonZoneAnnotator], List[sv.BoundingBoxAnnotator]
List[sv.PolygonZone], List[sv.PolygonZoneAnnotator], List[sv.BoxAnnotator]
]:
line_thickness = sv.calculate_optimal_line_thickness(resolution_wh=resolution_wh)
text_scale = sv.calculate_optimal_text_scale(resolution_wh=resolution_wh)
@ -54,7 +54,7 @@ def initiate_annotators(
text_thickness=line_thickness * 2,
text_scale=text_scale * 2,
)
box_annotator = sv.BoundingBoxAnnotator(
box_annotator = sv.BoxAnnotator(
color=COLORS.by_idx(index), thickness=line_thickness
)
zones.append(zone)
@ -97,7 +97,7 @@ def annotate(
frame: np.ndarray,
zones: List[sv.PolygonZone],
zone_annotators: List[sv.PolygonZoneAnnotator],
box_annotators: List[sv.BoundingBoxAnnotator],
box_annotators: List[sv.BoxAnnotator],
detections: sv.Detections,
) -> np.ndarray:
"""
@ -108,7 +108,7 @@ def annotate(
zones (List[sv.PolygonZone]): A list of polygon zones used for detection.
zone_annotators (List[sv.PolygonZoneAnnotator]): A list of annotators for
drawing zone annotations.
box_annotators (List[sv.BoundingBoxAnnotator]): A list of annotators for
box_annotators (List[sv.BoxAnnotator]): A list of annotators for
drawing box annotations.
detections (sv.Detections): Detections to be used for annotation.

8
examples/count_people_in_zone/ultralytics_example.py
View File

@ -34,7 +34,7 @@ def load_zones_config(file_path: str) -> List[np.ndarray]:
def initiate_annotators(
polygons: List[np.ndarray], resolution_wh: Tuple[int, int]
) -> Tuple[
List[sv.PolygonZone], List[sv.PolygonZoneAnnotator], List[sv.BoundingBoxAnnotator]
List[sv.PolygonZone], List[sv.PolygonZoneAnnotator], List[sv.BoxAnnotator]
]:
line_thickness = sv.calculate_optimal_line_thickness(resolution_wh=resolution_wh)
text_scale = sv.calculate_optimal_text_scale(resolution_wh=resolution_wh)
@ -52,7 +52,7 @@ def initiate_annotators(
text_thickness=line_thickness * 2,
text_scale=text_scale * 2,
)
box_annotator = sv.BoundingBoxAnnotator(
box_annotator = sv.BoxAnnotator(
color=COLORS.by_idx(index), thickness=line_thickness
)
zones.append(zone)
@ -94,7 +94,7 @@ def annotate(
frame: np.ndarray,
zones: List[sv.PolygonZone],
zone_annotators: List[sv.PolygonZoneAnnotator],
box_annotators: List[sv.BoundingBoxAnnotator],
box_annotators: List[sv.BoxAnnotator],
detections: sv.Detections,
) -> np.ndarray:
"""
@ -105,7 +105,7 @@ def annotate(
zones (List[sv.PolygonZone]): A list of polygon zones used for detection.
zone_annotators (List[sv.PolygonZoneAnnotator]): A list of annotators for
drawing zone annotations.
box_annotators (List[sv.BoundingBoxAnnotator]): A list of annotators for
box_annotators (List[sv.BoxAnnotator]): A list of annotators for
drawing box annotations.
detections (sv.Detections): Detections to be used for annotation.

2
examples/tracking/inference_example.py
View File

@ -18,7 +18,7 @@ def process_video(
model = get_roboflow_model(model_id=model_id, api_key=roboflow_api_key)
tracker = sv.ByteTrack()
box_annotator = sv.BoundingBoxAnnotator()
box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()
frame_generator = sv.get_video_frames_generator(source_path=source_video_path)
video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)

2
examples/tracking/ultralytics_example.py
View File

@ -16,7 +16,7 @@ def process_video(
model = YOLO(source_weights_path)
tracker = sv.ByteTrack()
box_annotator = sv.BoundingBoxAnnotator()
box_annotator = sv.BoxAnnotator()
label_annotator = sv.LabelAnnotator()
frame_generator = sv.get_video_frames_generator(source_path=source_video_path)
video_info = sv.VideoInfo.from_video_path(video_path=source_video_path)

2
pyproject.toml
View File

@ -2,7 +2,7 @@
name = "supervision"
description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
license = { text = "MIT" }
version = "0.26.0rc3"
version = "0.26.0rc4"
readme = "README.md"
requires-python = ">=3.8"
authors = [

4
supervision/__init__.py
View File

@ -9,7 +9,6 @@ except importlib_metadata.PackageNotFoundError:
from supervision.annotators.core import (
BackgroundOverlayAnnotator,
BlurAnnotator,
BoundingBoxAnnotator,
BoxAnnotator,
BoxCornerAnnotator,
CircleAnnotator,
@ -46,7 +45,7 @@ from supervision.detection.line_zone import (
LineZoneAnnotator,
LineZoneAnnotatorMulticlass,
)
from supervision.detection.lmm import LMM
from supervision.detection.vlm import LMM, VLM
from supervision.detection.overlap_filter import (
OverlapFilter,
box_non_max_merge,
@ -126,7 +125,6 @@ __all__ = [
"BackgroundOverlayAnnotator",
"BaseDataset",
"BlurAnnotator",
"BoundingBoxAnnotator",
"BoxAnnotator",
"BoxCornerAnnotator",
"ByteTrack",

86
supervision/annotators/core.py
View File

@ -124,92 +124,6 @@ class BoxAnnotator(BaseAnnotator):
return scene
@deprecated(
"`BoundingBoxAnnotator` is deprecated and has been renamed to `BoxAnnotator`."
" `BoundingBoxAnnotator` will be removed in supervision-0.26.0."
)
class BoundingBoxAnnotator(BaseAnnotator):
"""
A class for drawing bounding boxes on an image using provided detections.
"""
def __init__(
self,
color: Union[Color, ColorPalette] = ColorPalette.DEFAULT,
thickness: int = 2,
color_lookup: ColorLookup = ColorLookup.CLASS,
):
"""
Args:
color (Union[Color, ColorPalette]): The color or color palette to use for
annotating detections.
thickness (int): Thickness of the bounding box lines.
color_lookup (ColorLookup): Strategy for mapping colors to annotations.
Options are `INDEX`, `CLASS`, `TRACK`.
"""
self.color: Union[Color, ColorPalette] = color
self.thickness: int = thickness
self.color_lookup: ColorLookup = color_lookup
@ensure_cv2_image_for_annotation
def annotate(
self,
scene: ImageType,
detections: Detections,
custom_color_lookup: Optional[np.ndarray] = None,
) -> ImageType:
"""
Annotates the given scene with bounding boxes based on the provided detections.
Args:
scene (ImageType): The image where bounding boxes will be drawn. `ImageType`
is a flexible type, accepting either `numpy.ndarray` or `PIL.Image.Image`.
detections (Detections): Object detections to annotate.
custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
Allows to override the default color mapping strategy.
Returns:
The annotated image, matching the type of `scene` (`numpy.ndarray`
or `PIL.Image.Image`)
Example:
```python
import supervision as sv
image = ...
detections = sv.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/
supervision-annotator-examples/bounding-box-annotator-example-purple.png)
"""
assert isinstance(scene, np.ndarray)
for detection_idx in range(len(detections)):
x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
color = resolve_color(
color=self.color,
detections=detections,
detection_idx=detection_idx,
color_lookup=self.color_lookup
if custom_color_lookup is None
else custom_color_lookup,
)
cv2.rectangle(
img=scene,
pt1=(x1, y1),
pt2=(x2, y2),
color=color.as_bgr(),
thickness=self.thickness,
)
return scene
class OrientedBoxAnnotator(BaseAnnotator):
"""
A class for drawing oriented bounding boxes on an image using provided detections.

22
supervision/dataset/core.py
View File

@ -705,28 +705,6 @@ class ClassificationDataset(BaseDataset):
"a list of paths `List[str]` instead."
)
@property
@deprecated(
"`DetectionDataset.images` property is deprecated and will be removed in "
"`supervision-0.26.0`. Iterate with `for path, image, annotation in dataset:` "
"instead."
)
def images(self) -> Dict[str, np.ndarray]:
"""
Load all images to memory and return them as a dictionary.
!!! warning
Only use this when you need all images at once.
It is much more memory-efficient to initialize dataset with
image paths and use `for path, image, annotation in dataset:`.
"""
if self._images_in_memory:
return self._images_in_memory
images = {image_path: cv2.imread(image_path) for image_path in self.image_paths}
return images
def _get_image(self, image_path: str) -> np.ndarray:
"""Assumes that image is in dataset"""
if self._images_in_memory:

49
supervision/detection/core.py
View File

@ -9,11 +9,11 @@ from supervision.config import (
CLASS_NAME_DATA_FIELD,
ORIENTED_BOX_COORDINATES,
)
from supervision.detection.lmm import (
from supervision.detection.vlm import (
LMM,
from_florence_2,
from_paligemma,
validate_lmm_parameters,
validate_vlm_parameters, VLM,
)
from supervision.detection.overlap_filter import (
box_non_max_merge,
@ -39,7 +39,7 @@ from supervision.detection.utils import (
xywh_to_xyxy,
)
from supervision.geometry.core import Position
from supervision.utils.internal import get_instance_variables
from supervision.utils.internal import get_instance_variables, deprecated
from supervision.validators import validate_detections_fields
@ -799,6 +799,10 @@ class Detections:
)
@classmethod
@deprecated(
"`Detections.from_lmm` property is deprecated and will be removed in "
"`supervision-0.31.0`. Use Detections.from_vlm instead."
)
def from_lmm(
cls, lmm: Union[LMM, str], result: Union[str, dict], **kwargs: Any
) -> Detections:
@ -837,19 +841,48 @@ class Detections:
# array([0])
```
"""
lmm = validate_lmm_parameters(lmm, result, kwargs)
# filler logic mapping old from_lmm to new from_vlm
lmm_to_vlm = {
LMM.PALIGEMMA: VLM.PALIGEMMA,
LMM.FLORENCE_2: VLM.FLORENCE_2,
LMM.QWEN_2_5_VL: VLM.QWEN_2_5_VL
}
if lmm == LMM.PALIGEMMA:
if isinstance(lmm, LMM):
vlm = lmm_to_vlm[lmm]
elif isinstance(lmm, str):
try:
lmm_parsed = LMM(lmm.lower())
except ValueError:
raise ValueError(
f"Invalid LMM string '{lmm}'. Must be one of "
f"{[m.value for m in LMM]}"
)
vlm = lmm_to_vlm[lmm_parsed]
else:
raise ValueError(
f"Invalid type for 'lmm': {type(lmm)}. Must be LMM or str."
)
return cls.from_vlm(vlm=vlm, result=result, **kwargs)
@classmethod
def from_vlm(cls, vlm: Union[VLM, str], result: Union[str, dict], **kwargs: Any) -> Detections:
vlm = validate_vlm_parameters(vlm, result, kwargs)
if vlm == VLM.PALIGEMMA:
xyxy, class_id, class_name = from_paligemma(result, **kwargs)
data = {CLASS_NAME_DATA_FIELD: class_name}
return cls(xyxy=xyxy, class_id=class_id, data=data)
if lmm == LMM.QWEN_2_5_VL:
if vlm == VLM.QWEN_2_5_VL:
xyxy, class_id, class_name = from_paligemma(result, **kwargs)
data = {CLASS_NAME_DATA_FIELD: class_name}
return cls(xyxy=xyxy, class_id=class_id, data=data)
if lmm == LMM.FLORENCE_2:
if vlm == VLM.FLORENCE_2:
xyxy, labels, mask, xyxyxyxy = from_florence_2(result, **kwargs)
if len(xyxy) == 0:
return cls.empty()
@ -862,8 +895,6 @@ class Detections:
return cls(xyxy=xyxy, mask=mask, data=data)
raise ValueError(f"Unsupported LMM: {lmm}")
@classmethod
def from_easyocr(cls, easyocr_results: list) -> Detections:
"""

59
supervision/detection/lmm.py → supervision/detection/vlm.py
View File

@ -6,30 +6,41 @@ from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
from supervision.detection.utils import polygon_to_mask, polygon_to_xyxy
from supervision.utils.internal import deprecated
@deprecated(
"`LMM` enum is deprecated and will be removed in "
"`supervision-0.31.0`. Use VLM instead."
)
class LMM(Enum):
PALIGEMMA = "paligemma"
FLORENCE_2 = "florence_2"
QWEN_2_5_VL = "qwen_2_5_vl"
RESULT_TYPES: Dict[LMM, type] = {
LMM.PALIGEMMA: str,
LMM.FLORENCE_2: dict,
LMM.QWEN_2_5_VL: str,
class VLM(Enum):
PALIGEMMA = "paligemma"
FLORENCE_2 = "florence_2"
QWEN_2_5_VL = "qwen_2_5_vl"
RESULT_TYPES: Dict[VLM, type] = {
VLM.PALIGEMMA: str,
VLM.FLORENCE_2: dict,
VLM.QWEN_2_5_VL: str,
}
REQUIRED_ARGUMENTS: Dict[LMM, List[str]] = {
LMM.PALIGEMMA: ["resolution_wh"],
LMM.FLORENCE_2: ["resolution_wh"],
LMM.QWEN_2_5_VL: ["input_wh", "resolution_wh"],
REQUIRED_ARGUMENTS: Dict[VLM, List[str]] = {
VLM.PALIGEMMA: ["resolution_wh"],
VLM.FLORENCE_2: ["resolution_wh"],
VLM.QWEN_2_5_VL: ["input_wh", "resolution_wh"],
}
ALLOWED_ARGUMENTS: Dict[LMM, List[str]] = {
LMM.PALIGEMMA: ["resolution_wh", "classes"],
LMM.FLORENCE_2: ["resolution_wh"],
LMM.QWEN_2_5_VL: ["input_wh", "resolution_wh", "classes"],
ALLOWED_ARGUMENTS: Dict[VLM, List[str]] = {
VLM.PALIGEMMA: ["resolution_wh", "classes"],
VLM.FLORENCE_2: ["resolution_wh"],
VLM.QWEN_2_5_VL: ["input_wh", "resolution_wh", "classes"],
}
SUPPORTED_TASKS_FLORENCE_2 = [
@ -46,33 +57,33 @@ SUPPORTED_TASKS_FLORENCE_2 = [
]
def validate_lmm_parameters(
lmm: Union[LMM, str], result: Any, kwargs: Dict[str, Any]
) -> LMM:
if isinstance(lmm, str):
def validate_vlm_parameters(
vlm: Union[VLM, str], result: Any, kwargs: Dict[str, Any]
) -> VLM:
if isinstance(vlm, str):
try:
lmm = LMM(lmm.lower())
vlm = VLM(vlm.lower())
except ValueError:
raise ValueError(
f"Invalid lmm value: {lmm}. Must be one of {[e.value for e in LMM]}"
f"Invalid vlm value: {vlm}. Must be one of {[e.value for e in VLM]}"
)
if not isinstance(result, RESULT_TYPES[lmm]):
if not isinstance(result, RESULT_TYPES[vlm]):
raise ValueError(
f"Invalid LMM result type: {type(result)}. Must be {RESULT_TYPES[lmm]}"
f"Invalid VLM result type: {type(result)}. Must be {RESULT_TYPES[vlm]}"
)
required_args = REQUIRED_ARGUMENTS.get(lmm, [])
required_args = REQUIRED_ARGUMENTS.get(vlm, [])
for arg in required_args:
if arg not in kwargs:
raise ValueError(f"Missing required argument: {arg}")
allowed_args = ALLOWED_ARGUMENTS.get(lmm, [])
allowed_args = ALLOWED_ARGUMENTS.get(vlm, [])
for arg in kwargs:
if arg not in allowed_args:
raise ValueError(f"Argument {arg} is not allowed for {lmm.name}")
raise ValueError(f"Argument {arg} is not allowed for {vlm.name}")
return lmm
return vlm
def from_paligemma(

2
test/detection/test_lmm.py → test/detection/test_vlm.py
View File

@ -4,7 +4,7 @@ from typing import List, Optional, Tuple
import numpy as np
import pytest
from supervision.detection.lmm import from_paligemma, from_qwen_2_5_vl
from supervision.detection.vlm import from_paligemma, from_qwen_2_5_vl
@pytest.mark.parametrize(

2
test/detection/test_lmm_florence_2.py → test/detection/test_vlm_florence_2.py
View File

@ -4,7 +4,7 @@ from typing import Optional, Tuple
import numpy as np
import pytest
from supervision.detection.lmm import from_florence_2
from supervision.detection.vlm import from_florence_2
@pytest.mark.parametrize(