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

Merge branch 'develop' into feat/metrics-module-initial

This commit is contained in:
Onuralp SEZER 2024-08-19 11:24:22 +03:00 committed by GitHub
parent ab76a47eb8 4ab9ae5a71
commit c31d96dabe
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
9 changed files with 309 additions and 73 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
.pre-commit-config.yaml
View File

@ -45,7 +45,7 @@ repos:
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.5.6
rev: v0.6.0
hooks:
- id: ruff
args: [--fix, --exit-non-zero-on-fix]

27
docs/detection/annotators.md
View File

@ -433,6 +433,27 @@ status: new
</div>
=== "Background Color"
```python
import supervision as sv
image = ...
detections = sv.Detections(...)
background_overlay_annotator = sv.BackgroundOverlayAnnotator()
annotated_frame = background_overlay_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
<div class="result" markdown>
![background-overlay-annotator-example](https://media.roboflow.com/supervision-annotator-examples/background-color-annotator-example-purple.png)
</div>
<div class="md-typeset">
<h2><a href="#supervision.annotators.core.BoxAnnotator">BoxAnnotator</a></h2>
</div>
@ -553,6 +574,12 @@ status: new
:::supervision.annotators.core.CropAnnotator
<div class="md-typeset">
<h2><a href="#supervision.annotators.core.BackgroundOverlayAnnotator">BackgroundOverlayAnnotator</a></h2>
</div>
:::supervision.annotators.core.BackgroundOverlayAnnotator
<div class="md-typeset">
<h2><a href="#supervision.annotators.core.ColorLookup">ColorLookup</a></h2>
</div>

50
poetry.lock generated
View File

@ -2063,13 +2063,13 @@ files = [
[[package]]
name = "mike"
version = "2.1.2"
version = "2.1.3"
description = "Manage multiple versions of your MkDocs-powered documentation"
optional = false
python-versions = "*"
files = [
{file = "mike-2.1.2-py3-none-any.whl", hash = "sha256:d61d9b423ab412d634ca2bd520136d5114e3cc73f4bbd1aa6a0c6625c04918c0"},
{file = "mike-2.1.2.tar.gz", hash = "sha256:d59cc8054c50f9c8a046cfd47f9b700cf9ff1b2b19f420bd8812ca6f94fa8bd3"},
{file = "mike-2.1.3-py3-none-any.whl", hash = "sha256:d90c64077e84f06272437b464735130d380703a76a5738b152932884c60c062a"},
{file = "mike-2.1.3.tar.gz", hash = "sha256:abd79b8ea483fb0275b7972825d3082e5ae67a41820f8d8a0dc7a3f49944e810"},
]
[package.dependencies]
@ -3838,29 +3838,29 @@ files = [
[[package]]
name = "ruff"
version = "0.5.7"
version = "0.6.1"
description = "An extremely fast Python linter and code formatter, written in Rust."
optional = false
python-versions = ">=3.7"
files = [
{file = "ruff-0.5.7-py3-none-linux_armv6l.whl", hash = "sha256:548992d342fc404ee2e15a242cdbea4f8e39a52f2e7752d0e4cbe88d2d2f416a"},
{file = "ruff-0.5.7-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:00cc8872331055ee017c4f1071a8a31ca0809ccc0657da1d154a1d2abac5c0be"},
{file = "ruff-0.5.7-py3-none-macosx_11_0_arm64.whl", hash = "sha256:eaf3d86a1fdac1aec8a3417a63587d93f906c678bb9ed0b796da7b59c1114a1e"},
{file = "ruff-0.5.7-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a01c34400097b06cf8a6e61b35d6d456d5bd1ae6961542de18ec81eaf33b4cb8"},
{file = "ruff-0.5.7-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:fcc8054f1a717e2213500edaddcf1dbb0abad40d98e1bd9d0ad364f75c763eea"},
{file = "ruff-0.5.7-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7f70284e73f36558ef51602254451e50dd6cc479f8b6f8413a95fcb5db4a55fc"},
{file = "ruff-0.5.7-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:a78ad870ae3c460394fc95437d43deb5c04b5c29297815a2a1de028903f19692"},
{file = "ruff-0.5.7-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9ccd078c66a8e419475174bfe60a69adb36ce04f8d4e91b006f1329d5cd44bcf"},
{file = "ruff-0.5.7-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:7e31c9bad4ebf8fdb77b59cae75814440731060a09a0e0077d559a556453acbb"},
{file = "ruff-0.5.7-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8d796327eed8e168164346b769dd9a27a70e0298d667b4ecee6877ce8095ec8e"},
{file = "ruff-0.5.7-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:4a09ea2c3f7778cc635e7f6edf57d566a8ee8f485f3c4454db7771efb692c499"},
{file = "ruff-0.5.7-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:a36d8dcf55b3a3bc353270d544fb170d75d2dff41eba5df57b4e0b67a95bb64e"},
{file = "ruff-0.5.7-py3-none-musllinux_1_2_i686.whl", hash = "sha256:9369c218f789eefbd1b8d82a8cf25017b523ac47d96b2f531eba73770971c9e5"},
{file = "ruff-0.5.7-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:b88ca3db7eb377eb24fb7c82840546fb7acef75af4a74bd36e9ceb37a890257e"},
{file = "ruff-0.5.7-py3-none-win32.whl", hash = "sha256:33d61fc0e902198a3e55719f4be6b375b28f860b09c281e4bdbf783c0566576a"},
{file = "ruff-0.5.7-py3-none-win_amd64.whl", hash = "sha256:083bbcbe6fadb93cd86709037acc510f86eed5a314203079df174c40bbbca6b3"},
{file = "ruff-0.5.7-py3-none-win_arm64.whl", hash = "sha256:2dca26154ff9571995107221d0aeaad0e75a77b5a682d6236cf89a58c70b76f4"},
{file = "ruff-0.5.7.tar.gz", hash = "sha256:8dfc0a458797f5d9fb622dd0efc52d796f23f0a1493a9527f4e49a550ae9a7e5"},
{file = "ruff-0.6.1-py3-none-linux_armv6l.whl", hash = "sha256:b4bb7de6a24169dc023f992718a9417380301b0c2da0fe85919f47264fb8add9"},
{file = "ruff-0.6.1-py3-none-macosx_10_12_x86_64.whl", hash = "sha256:45efaae53b360c81043e311cdec8a7696420b3d3e8935202c2846e7a97d4edae"},
{file = "ruff-0.6.1-py3-none-macosx_11_0_arm64.whl", hash = "sha256:bc60c7d71b732c8fa73cf995efc0c836a2fd8b9810e115be8babb24ae87e0850"},
{file = "ruff-0.6.1-py3-none-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2c7477c3b9da822e2db0b4e0b59e61b8a23e87886e727b327e7dcaf06213c5cf"},
{file = "ruff-0.6.1-py3-none-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:3a0af7ab3f86e3dc9f157a928e08e26c4b40707d0612b01cd577cc84b8905cc9"},
{file = "ruff-0.6.1-py3-none-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:392688dbb50fecf1bf7126731c90c11a9df1c3a4cdc3f481b53e851da5634fa5"},
{file = "ruff-0.6.1-py3-none-manylinux_2_17_ppc64.manylinux2014_ppc64.whl", hash = "sha256:5278d3e095ccc8c30430bcc9bc550f778790acc211865520f3041910a28d0024"},
{file = "ruff-0.6.1-py3-none-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fe6d5f65d6f276ee7a0fc50a0cecaccb362d30ef98a110f99cac1c7872df2f18"},
{file = "ruff-0.6.1-py3-none-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b2e0dd11e2ae553ee5c92a81731d88a9883af8db7408db47fc81887c1f8b672e"},
{file = "ruff-0.6.1-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d812615525a34ecfc07fd93f906ef5b93656be01dfae9a819e31caa6cfe758a1"},
{file = "ruff-0.6.1-py3-none-musllinux_1_2_aarch64.whl", hash = "sha256:faaa4060f4064c3b7aaaa27328080c932fa142786f8142aff095b42b6a2eb631"},
{file = "ruff-0.6.1-py3-none-musllinux_1_2_armv7l.whl", hash = "sha256:99d7ae0df47c62729d58765c593ea54c2546d5de213f2af2a19442d50a10cec9"},
{file = "ruff-0.6.1-py3-none-musllinux_1_2_i686.whl", hash = "sha256:9eb18dfd7b613eec000e3738b3f0e4398bf0153cb80bfa3e351b3c1c2f6d7b15"},
{file = "ruff-0.6.1-py3-none-musllinux_1_2_x86_64.whl", hash = "sha256:c62bc04c6723a81e25e71715aa59489f15034d69bf641df88cb38bdc32fd1dbb"},
{file = "ruff-0.6.1-py3-none-win32.whl", hash = "sha256:9fb4c4e8b83f19c9477a8745e56d2eeef07a7ff50b68a6998f7d9e2e3887bdc4"},
{file = "ruff-0.6.1-py3-none-win_amd64.whl", hash = "sha256:c2ebfc8f51ef4aca05dad4552bbcf6fe8d1f75b2f6af546cc47cc1c1ca916b5b"},
{file = "ruff-0.6.1-py3-none-win_arm64.whl", hash = "sha256:3bc81074971b0ffad1bd0c52284b22411f02a11a012082a76ac6da153536e014"},
{file = "ruff-0.6.1.tar.gz", hash = "sha256:af3ffd8c6563acb8848d33cd19a69b9bfe943667f0419ca083f8ebe4224a3436"},
]
[[package]]
@ -4125,13 +4125,13 @@ files = [
[[package]]
name = "tox"
version = "4.17.1"
version = "4.18.0"
description = "tox is a generic virtualenv management and test command line tool"
optional = false
python-versions = ">=3.8"
files = [
{file = "tox-4.17.1-py3-none-any.whl", hash = "sha256:2974597c0353577126ab014f52d1a399fb761049e165ff34427f84e8cfe6c990"},
{file = "tox-4.17.1.tar.gz", hash = "sha256:2c41565a571e34480bd401d668a4899806169a4633e972ac296c54406d2ded8a"},
{file = "tox-4.18.0-py3-none-any.whl", hash = "sha256:0a457400cf70615dc0627eb70d293e80cd95d8ce174bb40ac011011f0c03a249"},
{file = "tox-4.18.0.tar.gz", hash = "sha256:5dfa1cab9f146becd6e351333a82f9e0ade374451630ba65ee54584624c27b58"},
]
[package.dependencies]

1
supervision/__init__.py
View File

@ -7,6 +7,7 @@ except importlib_metadata.PackageNotFoundError:
__version__ = "development"
from supervision.annotators.core import (
BackgroundOverlayAnnotator,
BlurAnnotator,
BoundingBoxAnnotator,
BoxAnnotator,

119
supervision/annotators/core.py
View File

@ -927,6 +927,7 @@ class DotAnnotator(BaseAnnotator):
position: Position = Position.CENTER,
color_lookup: ColorLookup = ColorLookup.CLASS,
outline_thickness: int = 0,
outline_color: Union[Color, ColorPalette] = Color.BLACK,
):
"""
Args:
@ -937,12 +938,16 @@ class DotAnnotator(BaseAnnotator):
color_lookup (ColorLookup): Strategy for mapping colors to annotations.
Options are `INDEX`, `CLASS`, `TRACK`.
outline_thickness (int): Thickness of the outline of the dot.
outline_color (Union[Color, ColorPalette]): The color or color palette to
use for outline. It is activated by setting outline_thickness to a value
greater than 0.
"""
self.color: Union[Color, ColorPalette] = color
self.radius: int = radius
self.position: Position = position
self.color_lookup: ColorLookup = color_lookup
self.outline_thickness = outline_thickness
self.outline_color: Union[Color, ColorPalette] = outline_color
@ensure_cv2_image_for_annotation
def annotate(
@ -997,8 +1002,20 @@ class DotAnnotator(BaseAnnotator):
cv2.circle(scene, center, self.radius, color.as_bgr(), -1)
if self.outline_thickness:
outline_color = resolve_color(
color=self.outline_color,
detections=detections,
detection_idx=detection_idx,
color_lookup=self.color_lookup
if custom_color_lookup is None
else custom_color_lookup,
)
cv2.circle(
scene, center, self.radius, (0, 0, 0), self.outline_thickness
scene,
center,
self.radius,
outline_color.as_bgr(),
self.outline_thickness,
)
return scene
@ -1744,6 +1761,7 @@ class TriangleAnnotator(BaseAnnotator):
position: Position = Position.TOP_CENTER,
color_lookup: ColorLookup = ColorLookup.CLASS,
outline_thickness: int = 0,
outline_color: Union[Color, ColorPalette] = Color.BLACK,
):
"""
Args:
@ -1755,6 +1773,9 @@ class TriangleAnnotator(BaseAnnotator):
color_lookup (ColorLookup): Strategy for mapping colors to annotations.
Options are `INDEX`, `CLASS`, `TRACK`.
outline_thickness (int): Thickness of the outline of the triangle.
outline_color (Union[Color, ColorPalette]): The color or color palette to
use for outline. It is activated by setting outline_thickness to a value
greater than 0.
"""
self.color: Union[Color, ColorPalette] = color
self.base: int = base
@ -1762,6 +1783,7 @@ class TriangleAnnotator(BaseAnnotator):
self.position: Position = position
self.color_lookup: ColorLookup = color_lookup
self.outline_thickness: int = outline_thickness
self.outline_color: Union[Color, ColorPalette] = outline_color
@ensure_cv2_image_for_annotation
def annotate(
@ -1824,8 +1846,20 @@ class TriangleAnnotator(BaseAnnotator):
cv2.fillPoly(scene, [vertices], color.as_bgr())
if self.outline_thickness:
outline_color = resolve_color(
color=self.outline_color,
detections=detections,
detection_idx=detection_idx,
color_lookup=self.color_lookup
if custom_color_lookup is None
else custom_color_lookup,
)
cv2.polylines(
scene, [vertices], True, (0, 0, 0), thickness=self.outline_thickness
scene,
[vertices],
True,
outline_color.as_bgr(),
thickness=self.outline_thickness,
)
return scene
@ -2283,3 +2317,84 @@ class CropAnnotator(BaseAnnotator):
)
elif position == Position.BOTTOM_RIGHT:
return (anchor_x, anchor_y), (anchor_x + width, anchor_y + height)
class BackgroundOverlayAnnotator(BaseAnnotator):
"""
A class for drawing a colored overlay on the background of an image outside
the region of detections.
If masks are provided, the background is colored outside the masks.
If masks are not provided, the background is colored outside the bounding boxes.
You can use the `force_box` parameter to force the annotator to use bounding boxes.
!!! warning
This annotator uses `sv.Detections.mask`.
"""
def __init__(
self,
color: Color = Color.BLACK,
opacity: float = 0.5,
force_box: bool = False,
):
"""
Args:
color (Color): The color to use for annotating detections.
opacity (float): Opacity of the overlay mask. Must be between `0` and `1`.
force_box (bool): If `True`, forces the annotator to use bounding boxes when
masks are provided in the supplied sv.Detections.
"""
self.color: Color = color
self.opacity = opacity
self.force_box = force_box
@ensure_cv2_image_for_annotation
def annotate(self, scene: ImageType, detections: Detections) -> ImageType:
"""
Applies a colored overlay to the scene outside of the detected regions.
Args:
scene (ImageType): The image where masks will be drawn.
`ImageType` is a flexible type, accepting either `numpy.ndarray`
or `PIL.Image.Image`.
detections (Detections): Object detections to annotate.
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(...)
background_overlay_annotator = sv.BackgroundOverlayAnnotator()
annotated_frame = background_overlay_annotator.annotate(
scene=image.copy(),
detections=detections
)
```
![background-overlay-annotator-example](https://media.roboflow.com/
supervision-annotator-examples/background-color-annotator-example-purple.png)
"""
colored_mask = np.full_like(scene, self.color.as_bgr(), dtype=np.uint8)
cv2.addWeighted(
scene, 1 - self.opacity, colored_mask, self.opacity, 0, dst=colored_mask
)
if detections.mask is None or self.force_box:
for x1, y1, x2, y2 in detections.xyxy.astype(int):
colored_mask[y1:y2, x1:x2] = scene[y1:y2, x1:x2]
else:
for mask in detections.mask:
colored_mask[mask] = scene[mask]
np.copyto(scene, colored_mask)
return scene

9
supervision/detection/core.py
View File

@ -29,6 +29,7 @@ from supervision.detection.utils import (
extract_ultralytics_masks,
get_data_item,
is_data_equal,
mask_to_xyxy,
merge_data,
process_roboflow_result,
xywh_to_xyxy,
@ -267,6 +268,14 @@ class Detections:
},
)
if hasattr(ultralytics_results, "boxes") and ultralytics_results.boxes is None:
masks = extract_ultralytics_masks(ultralytics_results)
return cls(
xyxy=mask_to_xyxy(masks),
mask=masks,
class_id=np.arange(len(ultralytics_results)),
)
class_id = ultralytics_results.boxes.cls.cpu().numpy().astype(int)
class_names = np.array([ultralytics_results.names[i] for i in class_id])
return cls(

31
supervision/detection/overlap_filter.py
View File

@ -1,3 +1,5 @@
from __future__ import annotations
from enum import Enum
from typing import List, Union
@ -248,16 +250,21 @@ class OverlapFilter(Enum):
NON_MAX_SUPPRESSION = "non_max_suppression"
NON_MAX_MERGE = "non_max_merge"
@classmethod
def list(cls):
return list(map(lambda c: c.value, cls))
def validate_overlap_filter(
strategy: Union[OverlapFilter, str],
) -> OverlapFilter:
if isinstance(strategy, str):
try:
strategy = OverlapFilter(strategy.lower())
except ValueError:
raise ValueError(
f"Invalid strategy value: {strategy}. Must be one of "
f"{[e.value for e in OverlapFilter]}"
)
return strategy
@classmethod
def from_value(cls, value: Union[OverlapFilter, str]) -> OverlapFilter:
if isinstance(value, cls):
return value
if isinstance(value, str):
value = value.lower()
try:
return cls(value)
except ValueError:
raise ValueError(f"Invalid value: {value}. Must be one of {cls.list()}")
raise ValueError(
f"Invalid value type: {type(value)}. Must be an instance of "
f"{cls.__name__} or str."
)

122
supervision/detection/tools/inference_slicer.py
View File

@ -6,10 +6,14 @@ import numpy as np
from supervision.config import ORIENTED_BOX_COORDINATES
from supervision.detection.core import Detections
from supervision.detection.overlap_filter import OverlapFilter, validate_overlap_filter
from supervision.detection.overlap_filter import OverlapFilter
from supervision.detection.utils import move_boxes, move_masks, move_oriented_boxes
from supervision.utils.image import crop_image
from supervision.utils.internal import SupervisionWarnings
from supervision.utils.internal import (
SupervisionWarnings,
deprecated_parameter,
warn_deprecated,
)
def move_detections(
@ -54,11 +58,16 @@ class InferenceSlicer:
slice, and then merging the detections.
Args:
slice_wh (Tuple[int, int]): Dimensions of each slice in the format
`(width, height)`.
overlap_ratio_wh (Tuple[float, float]): Overlap ratio between consecutive
slices in the format `(width_ratio, height_ratio)`.
overlap_filter_strategy (Union[OverlapFilter, str]): Strategy for
slice_wh (Tuple[int, int]): Dimensions of each slice measured in pixels. The
tuple should be in the format `(width, height)`.
overlap_ratio_wh (Optional[Tuple[float, float]]): A tuple representing the
desired overlap ratio for width and height between consecutive slices.
Each value should be in the range [0, 1), where 0 means no overlap and
a value close to 1 means high overlap.
overlap_wh (Optional[Tuple[int, int]]): A tuple representing the desired
overlap for width and height between consecutive slices measured in pixels.
Each value should be greater than or equal to 0.
overlap_filter (Union[OverlapFilter, str]): Strategy for
filtering or merging overlapping detections in slices.
iou_threshold (float): Intersection over Union (IoU) threshold
used when filtering by overlap.
@ -73,23 +82,37 @@ class InferenceSlicer:
not a multiple of the slice's width or height minus the overlap.
"""
@deprecated_parameter(
old_parameter="overlap_filter_strategy",
new_parameter="overlap_filter",
map_function=lambda x: x,
warning_message="`{old_parameter}` in `{function_name}` is deprecated and will "
"be removed in `supervision-0.27.0`. Use '{new_parameter}' "
"instead.",
)
def __init__(
self,
callback: Callable[[np.ndarray], Detections],
slice_wh: Tuple[int, int] = (320, 320),
overlap_ratio_wh: Tuple[float, float] = (0.2, 0.2),
overlap_filter_strategy: Union[
OverlapFilter, str
] = OverlapFilter.NON_MAX_SUPPRESSION,
overlap_ratio_wh: Optional[Tuple[float, float]] = (0.2, 0.2),
overlap_wh: Optional[Tuple[int, int]] = None,
overlap_filter: Union[OverlapFilter, str] = OverlapFilter.NON_MAX_SUPPRESSION,
iou_threshold: float = 0.5,
thread_workers: int = 1,
):
overlap_filter_strategy = validate_overlap_filter(overlap_filter_strategy)
if overlap_ratio_wh is not None:
warn_deprecated(
"`overlap_ratio_wh` in `InferenceSlicer.__init__` is deprecated and "
"will be removed in `supervision-0.27.0`. Use `overlap_wh` instead."
)
self._validate_overlap(overlap_ratio_wh, overlap_wh)
self.overlap_ratio_wh = overlap_ratio_wh
self.overlap_wh = overlap_wh
self.slice_wh = slice_wh
self.overlap_ratio_wh = overlap_ratio_wh
self.iou_threshold = iou_threshold
self.overlap_filter_strategy = overlap_filter_strategy
self.overlap_filter = OverlapFilter.from_value(overlap_filter)
self.callback = callback
self.thread_workers = thread_workers
@ -134,6 +157,7 @@ class InferenceSlicer:
resolution_wh=resolution_wh,
slice_wh=self.slice_wh,
overlap_ratio_wh=self.overlap_ratio_wh,
overlap_wh=self.overlap_wh,
)
with ThreadPoolExecutor(max_workers=self.thread_workers) as executor:
@ -144,15 +168,15 @@ class InferenceSlicer:
detections_list.append(future.result())
merged = Detections.merge(detections_list=detections_list)
if self.overlap_filter_strategy == OverlapFilter.NONE:
if self.overlap_filter == OverlapFilter.NONE:
return merged
elif self.overlap_filter_strategy == OverlapFilter.NON_MAX_SUPPRESSION:
elif self.overlap_filter == OverlapFilter.NON_MAX_SUPPRESSION:
return merged.with_nms(threshold=self.iou_threshold)
elif self.overlap_filter_strategy == OverlapFilter.NON_MAX_MERGE:
elif self.overlap_filter == OverlapFilter.NON_MAX_MERGE:
return merged.with_nmm(threshold=self.iou_threshold)
else:
warnings.warn(
f"Invalid overlap filter strategy: {self.overlap_filter_strategy}",
f"Invalid overlap filter strategy: {self.overlap_filter}",
category=SupervisionWarnings,
)
return merged
@ -182,7 +206,8 @@ class InferenceSlicer:
def _generate_offset(
resolution_wh: Tuple[int, int],
slice_wh: Tuple[int, int],
overlap_ratio_wh: Tuple[float, float],
overlap_ratio_wh: Optional[Tuple[float, float]],
overlap_wh: Optional[Tuple[int, int]],
) -> np.ndarray:
"""
Generate offset coordinates for slicing an image based on the given resolution,
@ -191,12 +216,15 @@ class InferenceSlicer:
Args:
resolution_wh (Tuple[int, int]): A tuple representing the width and height
of the image to be sliced.
slice_wh (Tuple[int, int]): A tuple representing the desired width and
height of each slice.
overlap_ratio_wh (Tuple[float, float]): A tuple representing the desired
overlap ratio for width and height between consecutive slices. Each
value should be in the range [0, 1), where 0 means no overlap and a
value close to 1 means high overlap.
slice_wh (Tuple[int, int]): Dimensions of each slice measured in pixels. The
tuple should be in the format `(width, height)`.
overlap_ratio_wh (Optional[Tuple[float, float]]): A tuple representing the
desired overlap ratio for width and height between consecutive slices.
Each value should be in the range [0, 1), where 0 means no overlap and
a value close to 1 means high overlap.
overlap_wh (Optional[Tuple[int, int]]): A tuple representing the desired
overlap for width and height between consecutive slices measured in
pixels. Each value should be greater than or equal to 0.
Returns:
np.ndarray: An array of shape `(n, 4)` containing coordinates for each
@ -211,10 +239,19 @@ class InferenceSlicer:
"""
slice_width, slice_height = slice_wh
image_width, image_height = resolution_wh
overlap_ratio_width, overlap_ratio_height = overlap_ratio_wh
overlap_width = (
overlap_wh[0]
if overlap_wh is not None
else int(overlap_ratio_wh[0] * slice_width)
)
overlap_height = (
overlap_wh[1]
if overlap_wh is not None
else int(overlap_ratio_wh[1] * slice_height)
)
width_stride = slice_width - int(overlap_ratio_width * slice_width)
height_stride = slice_height - int(overlap_ratio_height * slice_height)
width_stride = slice_width - overlap_width
height_stride = slice_height - overlap_height
ws = np.arange(0, image_width, width_stride)
hs = np.arange(0, image_height, height_stride)
@ -226,3 +263,32 @@ class InferenceSlicer:
offsets = np.stack([xmin, ymin, xmax, ymax], axis=-1).reshape(-1, 4)
return offsets
@staticmethod
def _validate_overlap(
overlap_ratio_wh: Optional[Tuple[float, float]],
overlap_wh: Optional[Tuple[int, int]],
) -> None:
if overlap_ratio_wh is not None and overlap_wh is not None:
raise ValueError(
"Both `overlap_ratio_wh` and `overlap_wh` cannot be provided. "
"Please provide only one of them."
)
if overlap_ratio_wh is None and overlap_wh is None:
raise ValueError(
"Either `overlap_ratio_wh` or `overlap_wh` must be provided. "
"Please provide one of them."
)
if overlap_ratio_wh is not None:
if not (0 <= overlap_ratio_wh[0] < 1 and 0 <= overlap_ratio_wh[1] < 1):
raise ValueError(
"Overlap ratios must be in the range [0, 1). "
f"Received: {overlap_ratio_wh}"
)
if overlap_wh is not None:
if not (overlap_wh[0] >= 0 and overlap_wh[1] >= 0):
raise ValueError(
"Overlap values must be greater than or equal to 0. "
f"Received: {overlap_wh}"
)

21
supervision/keypoint/annotators.py
View File

@ -194,7 +194,7 @@ class VertexLabelAnnotator:
def __init__(
self,
color: Union[Color, List[Color]] = Color.ROBOFLOW,
text_color: Color = Color.WHITE,
text_color: Union[Color, List[Color]] = Color.WHITE,
text_scale: float = 0.5,
text_thickness: int = 1,
text_padding: int = 10,
@ -205,7 +205,9 @@ class VertexLabelAnnotator:
color (Union[Color, List[Color]], optional): The color to use for each
keypoint label. If a list is provided, the colors will be used in order
for each keypoint.
text_color (Color, optional): The color to use for the labels.
text_color (Union[Color, List[Color]], optional): The color to use
for the labels. If a list is provided, the colors will be used in order
for each keypoint.
text_scale (float, optional): The scale of the text.
text_thickness (int, optional): The thickness of the text.
text_padding (int, optional): The padding around the text.
@ -214,7 +216,7 @@ class VertexLabelAnnotator:
"""
self.border_radius: int = border_radius
self.color: Union[Color, List[Color]] = color
self.text_color: Color = text_color
self.text_color: Union[Color, List[Color]] = text_color
self.text_scale: float = text_scale
self.text_thickness: int = text_thickness
self.text_padding: int = text_padding
@ -321,12 +323,19 @@ class VertexLabelAnnotator:
skeletons_count=skeletons_count,
)
text_colors = self.preprocess_and_validate_colors(
colors=self.text_color,
points_count=points_count,
skeletons_count=skeletons_count,
)
labels = self.preprocess_and_validate_labels(
labels=labels, points_count=points_count, skeletons_count=skeletons_count
)
anchors = anchors[mask]
colors = colors[mask]
text_colors = text_colors[mask]
labels = labels[mask]
xyxy = np.array(
@ -344,7 +353,9 @@ class VertexLabelAnnotator:
xyxy_padded = pad_boxes(xyxy=xyxy, px=self.text_padding)
for text, color, box, box_padded in zip(labels, colors, xyxy, xyxy_padded):
for text, color, text_color, box, box_padded in zip(
labels, colors, text_colors, xyxy, xyxy_padded
):
draw_rounded_rectangle(
scene=scene,
rect=Rect.from_xyxy(box_padded),
@ -357,7 +368,7 @@ class VertexLabelAnnotator:
org=(box[0], box[3]),
fontFace=font,
fontScale=self.text_scale,
color=self.text_color.as_rgb(),
color=text_color.as_bgr(),
thickness=self.text_thickness,
lineType=cv2.LINE_AA,
)