Merge branch 'develop' of github.com:roboflow/supervision into sec/improve-github-workflow-security
This commit is contained in:
Alex Norell
commit
dab2194f47
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|
@ -32,7 +32,7 @@ repos:
|
|||
additional_dependencies: ["bandit[toml]"]
|
||||
|
||||
- repo: https://github.com/astral-sh/ruff-pre-commit
|
||||
rev: v0.8.1
|
||||
rev: v0.8.2
|
||||
hooks:
|
||||
- id: ruff
|
||||
args: [--fix, --exit-non-zero-on-fix]
|
||||
|
|
|
|||
|
|
@ -28,26 +28,28 @@ Annotators accept detections and apply box or mask visualizations to the detecti
|
|||
|
||||
</div>
|
||||
|
||||
=== "RoundBox"
|
||||
<!--
|
||||
=== "RoundBox"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
image = ...
|
||||
detections = sv.Detections(...)
|
||||
image = ...
|
||||
detections = sv.Detections(...)
|
||||
|
||||
round_box_annotator = sv.RoundBoxAnnotator()
|
||||
annotated_frame = round_box_annotator.annotate(
|
||||
scene=image.copy(),
|
||||
detections=detections
|
||||
)
|
||||
```
|
||||
round_box_annotator = sv.RoundBoxAnnotator()
|
||||
annotated_frame = round_box_annotator.annotate(
|
||||
scene=image.copy(),
|
||||
detections=detections
|
||||
)
|
||||
```
|
||||
|
||||
<div class="result" markdown>
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||||
<div class="result" markdown>
|
||||
|
||||
{ align=center width="800" }
|
||||
{ align=center width="800" }
|
||||
|
||||
</div>
|
||||
</div>
|
||||
-->
|
||||
|
||||
=== "BoxCorner"
|
||||
|
||||
|
|
@ -234,7 +236,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
|
|||
|
||||
<div class="result" markdown>
|
||||
|
||||
{ align=center width="800" }
|
||||
{ align=center width="800" }
|
||||
|
||||
</div>
|
||||
|
||||
|
|
@ -255,7 +258,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
|
|||
|
||||
<div class="result" markdown>
|
||||
|
||||
{ align=center width="800" }
|
||||
{ align=center width="800" }
|
||||
|
||||
</div>
|
||||
|
||||
|
|
@ -283,7 +287,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
|
|||
|
||||
<div class="result" markdown>
|
||||
|
||||
{ align=center width="800" }
|
||||
{ align=center width="800" }
|
||||
|
||||
</div>
|
||||
|
||||
|
|
@ -314,7 +319,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
|
|||
|
||||
<div class="result" markdown>
|
||||
|
||||
{ align=center width="800" }
|
||||
{ align=center width="800" }
|
||||
|
||||
</div>
|
||||
|
||||
|
|
@ -341,24 +347,32 @@ Annotators accept detections and apply box or mask visualizations to the detecti
|
|||
|
||||
<div class="result" markdown>
|
||||
|
||||
{ align=center width="800" }
|
||||
{ align=center width="800" }
|
||||
|
||||
</div>
|
||||
|
||||
=== "Crop"
|
||||
<!-- === "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
|
||||
)
|
||||
```
|
||||
crop_annotator = sv.CropAnnotator()
|
||||
annotated_frame = crop_annotator.annotate(
|
||||
scene=image.copy(),
|
||||
detections=detections
|
||||
)
|
||||
```
|
||||
<div class="result" markdown>
|
||||
|
||||
{ align=center width="800" }
|
||||
|
||||
</div>
|
||||
-->
|
||||
|
||||
=== "Blur"
|
||||
|
||||
|
|
@ -377,7 +391,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
|
|||
|
||||
<div class="result" markdown>
|
||||
|
||||
{ align=center width="800" }
|
||||
{ align=center width="800" }
|
||||
|
||||
</div>
|
||||
|
||||
|
|
@ -398,7 +413,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
|
|||
|
||||
<div class="result" markdown>
|
||||
|
||||
{ align=center width="800" }
|
||||
{ align=center width="800" }
|
||||
|
||||
</div>
|
||||
|
||||
|
|
@ -429,7 +445,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
|
|||
|
||||
<div class="result" markdown>
|
||||
|
||||
{ align=center width="800" }
|
||||
{ align=center width="800" }
|
||||
|
||||
</div>
|
||||
|
||||
|
|
@ -458,7 +475,8 @@ Annotators accept detections and apply box or mask visualizations to the detecti
|
|||
|
||||
<div class="result" markdown>
|
||||
|
||||
{ align=center width="800" }
|
||||
{ align=center width="800" }
|
||||
|
||||
</div>
|
||||
|
||||
|
|
@ -479,7 +497,31 @@ Annotators accept detections and apply box or mask visualizations to the detecti
|
|||
|
||||
<div class="result" markdown>
|
||||
|
||||
|
||||
{ align=center width="800" }
|
||||
|
||||
</div>
|
||||
|
||||
=== "Comparison"
|
||||
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
image = ...
|
||||
detections_1 = sv.Detections(...)
|
||||
detections_2 = sv.Detections(...)
|
||||
|
||||
comparison_annotator = sv.ComparisonAnnotator()
|
||||
annotated_frame = comparison_annotator.annotate(
|
||||
scene=image.copy(),
|
||||
detections_1=detections_1,
|
||||
detections_2=detections_2
|
||||
)
|
||||
```
|
||||
|
||||
<div class="result" markdown>
|
||||
|
||||
{ align=center width="800" }
|
||||
|
||||
</div>
|
||||
|
||||
|
|
@ -622,6 +664,12 @@ Annotators accept detections and apply box or mask visualizations to the detecti
|
|||
|
||||
:::supervision.annotators.core.BackgroundOverlayAnnotator
|
||||
|
||||
<div class="md-typeset">
|
||||
<h2><a href="#supervision.annotators.core.ComparisonAnnotator">ComparisonAnnotator</a></h2>
|
||||
</div>
|
||||
|
||||
:::supervision.annotators.core.ComparisonAnnotator
|
||||
|
||||
<div class="md-typeset">
|
||||
<h2><a href="#supervision.annotators.core.ColorLookup">ColorLookup</a></h2>
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -178,7 +178,6 @@ markdown_extensions:
|
|||
anchor_linenums: true
|
||||
|
||||
extra_javascript:
|
||||
- "https://widget.kapa.ai/kapa-widget.bundle.js"
|
||||
- "javascripts/init_kapa_widget.js"
|
||||
- "javascripts/cookbooks-card.js"
|
||||
- "https://cdnjs.cloudflare.com/ajax/libs/dompurify/3.0.8/purify.min.js"
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "anyio"
|
||||
|
|
@ -1762,7 +1762,7 @@ files = [
|
|||
|
||||
[package.dependencies]
|
||||
async-lru = ">=1.0.0"
|
||||
httpx = ">=0.25.0"
|
||||
httpx = ">=0.28.0,<0.29.0"
|
||||
importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""}
|
||||
importlib-resources = {version = ">=1.4", markers = "python_version < \"3.9\""}
|
||||
ipykernel = ">=6.5.0"
|
||||
|
|
|
|||
|
|
@ -14,6 +14,7 @@ from supervision.annotators.core import (
|
|||
BoxCornerAnnotator,
|
||||
CircleAnnotator,
|
||||
ColorAnnotator,
|
||||
ComparisonAnnotator,
|
||||
CropAnnotator,
|
||||
DotAnnotator,
|
||||
EllipseAnnotator,
|
||||
|
|
@ -76,6 +77,7 @@ from supervision.detection.utils import (
|
|||
scale_boxes,
|
||||
xcycwh_to_xyxy,
|
||||
xywh_to_xyxy,
|
||||
xyxy_to_polygons,
|
||||
)
|
||||
from supervision.draw.color import Color, ColorPalette
|
||||
from supervision.draw.utils import (
|
||||
|
|
@ -136,6 +138,7 @@ __all__ = [
|
|||
"ColorAnnotator",
|
||||
"ColorLookup",
|
||||
"ColorPalette",
|
||||
"ComparisonAnnotator",
|
||||
"ConfusionMatrix",
|
||||
"CropAnnotator",
|
||||
"DetectionDataset",
|
||||
|
|
@ -222,4 +225,5 @@ __all__ = [
|
|||
"scale_image",
|
||||
"xcycwh_to_xyxy",
|
||||
"xywh_to_xyxy",
|
||||
"xyxy_to_polygons",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -16,10 +16,16 @@ from supervision.annotators.utils import (
|
|||
)
|
||||
from supervision.config import CLASS_NAME_DATA_FIELD, ORIENTED_BOX_COORDINATES
|
||||
from supervision.detection.core import Detections
|
||||
from supervision.detection.utils import clip_boxes, mask_to_polygons, spread_out_boxes
|
||||
from supervision.detection.utils import (
|
||||
clip_boxes,
|
||||
mask_to_polygons,
|
||||
polygon_to_mask,
|
||||
spread_out_boxes,
|
||||
xyxy_to_polygons,
|
||||
)
|
||||
from supervision.draw.color import Color, ColorPalette
|
||||
from supervision.draw.utils import draw_polygon
|
||||
from supervision.geometry.core import Position
|
||||
from supervision.draw.utils import draw_polygon, draw_rounded_rectangle, draw_text
|
||||
from supervision.geometry.core import Point, Position, Rect
|
||||
from supervision.utils.conversion import (
|
||||
ensure_cv2_image_for_annotation,
|
||||
ensure_pil_image_for_annotation,
|
||||
|
|
@ -2527,6 +2533,9 @@ class CropAnnotator(BaseAnnotator):
|
|||
detections=detections
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
"""
|
||||
assert isinstance(scene, np.ndarray)
|
||||
crops = [
|
||||
|
|
@ -2683,3 +2692,261 @@ class BackgroundOverlayAnnotator(BaseAnnotator):
|
|||
|
||||
np.copyto(scene, colored_mask)
|
||||
return scene
|
||||
|
||||
|
||||
class ComparisonAnnotator:
|
||||
"""
|
||||
Highlights the differences between two sets of detections.
|
||||
Useful for comparing results from two different models, or the difference
|
||||
between a ground truth and a prediction.
|
||||
|
||||
If present, uses the oriented bounding box data.
|
||||
Otherwise, if present, uses a mask.
|
||||
Otherwise, uses the bounding box data.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
color_1: Color = Color.RED,
|
||||
color_2: Color = Color.GREEN,
|
||||
color_overlap: Color = Color.BLUE,
|
||||
*,
|
||||
opacity: float = 0.75,
|
||||
label_1: str = "",
|
||||
label_2: str = "",
|
||||
label_overlap: str = "",
|
||||
label_scale: float = 1.0,
|
||||
):
|
||||
"""
|
||||
Args:
|
||||
color_1 (Color): Color of areas only present in the first set of
|
||||
detections.
|
||||
color_2 (Color): Color of areas only present in the second set of
|
||||
detections.
|
||||
color_overlap (Color): Color of areas present in both sets of detections.
|
||||
opacity (float): Annotator opacity, from `0` to `1`.
|
||||
label_1 (str): Label for the first set of detections.
|
||||
label_2 (str): Label for the second set of detections.
|
||||
label_overlap (str): Label for areas present in both sets of detections.
|
||||
label_scale (float): Controls how large the labels are.
|
||||
"""
|
||||
|
||||
self.color_1 = color_1
|
||||
self.color_2 = color_2
|
||||
self.color_overlap = color_overlap
|
||||
|
||||
self.opacity = opacity
|
||||
self.label_1 = label_1
|
||||
self.label_2 = label_2
|
||||
self.label_overlap = label_overlap
|
||||
self.label_scale = label_scale
|
||||
self.text_thickness = int(self.label_scale + 1.2)
|
||||
|
||||
@ensure_cv2_image_for_annotation
|
||||
def annotate(
|
||||
self, scene: ImageType, detections_1: Detections, detections_2: Detections
|
||||
) -> ImageType:
|
||||
"""
|
||||
Highlights the differences between two sets of detections.
|
||||
|
||||
Args:
|
||||
scene (ImageType): The image where detections will be drawn.
|
||||
`ImageType` is a flexible type, accepting either `numpy.ndarray`
|
||||
or `PIL.Image.Image`.
|
||||
detections_1 (Detections): The first set of detections or predictions.
|
||||
detections_2 (Detections): The second set of detections to compare or
|
||||
ground truth.
|
||||
|
||||
Returns:
|
||||
The annotated image.
|
||||
|
||||
Example:
|
||||
```python
|
||||
import supervision as sv
|
||||
|
||||
image = ...
|
||||
detections_1 = sv.Detections(...)
|
||||
detections_2 = sv.Detections(...)
|
||||
|
||||
comparison_annotator = sv.ComparisonAnnotator()
|
||||
annotated_frame = comparison_annotator.annotate(
|
||||
scene=image.copy(),
|
||||
detections_1=detections_1,
|
||||
detections_2=detections_2
|
||||
)
|
||||
```
|
||||
|
||||
|
||||
"""
|
||||
assert isinstance(scene, np.ndarray)
|
||||
if detections_1.is_empty() and detections_2.is_empty():
|
||||
return scene
|
||||
|
||||
use_obb = self._use_obb(detections_1, detections_2)
|
||||
use_mask = self._use_mask(detections_1, detections_2)
|
||||
|
||||
if use_obb:
|
||||
mask_1 = self._mask_from_obb(scene, detections_1)
|
||||
mask_2 = self._mask_from_obb(scene, detections_2)
|
||||
|
||||
elif use_mask:
|
||||
mask_1 = self._mask_from_mask(scene, detections_1)
|
||||
mask_2 = self._mask_from_mask(scene, detections_2)
|
||||
|
||||
else:
|
||||
mask_1 = self._mask_from_xyxy(scene, detections_1)
|
||||
mask_2 = self._mask_from_xyxy(scene, detections_2)
|
||||
|
||||
mask_overlap = mask_1 & mask_2
|
||||
mask_1 = mask_1 & ~mask_overlap
|
||||
mask_2 = mask_2 & ~mask_overlap
|
||||
|
||||
color_layer = np.zeros_like(scene, dtype=np.uint8)
|
||||
color_layer[mask_overlap] = self.color_overlap.as_bgr()
|
||||
color_layer[mask_1] = self.color_1.as_bgr()
|
||||
color_layer[mask_2] = self.color_2.as_bgr()
|
||||
|
||||
scene[mask_overlap] = (1 - self.opacity) * scene[
|
||||
mask_overlap
|
||||
] + self.opacity * color_layer[mask_overlap]
|
||||
scene[mask_1] = (1 - self.opacity) * scene[mask_1] + self.opacity * color_layer[
|
||||
mask_1
|
||||
]
|
||||
scene[mask_2] = (1 - self.opacity) * scene[mask_2] + self.opacity * color_layer[
|
||||
mask_2
|
||||
]
|
||||
|
||||
self._draw_labels(scene)
|
||||
|
||||
return scene
|
||||
|
||||
@staticmethod
|
||||
def _use_obb(detections_1: Detections, detections_2: Detections) -> bool:
|
||||
assert not detections_1.is_empty() or not detections_2.is_empty()
|
||||
is_obb_1 = ORIENTED_BOX_COORDINATES in detections_1.data
|
||||
is_obb_2 = ORIENTED_BOX_COORDINATES in detections_2.data
|
||||
return (
|
||||
(is_obb_1 and is_obb_2)
|
||||
or (is_obb_1 and detections_2.is_empty())
|
||||
or (detections_1.is_empty() and is_obb_2)
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _use_mask(detections_1: Detections, detections_2: Detections) -> bool:
|
||||
assert not detections_1.is_empty() or not detections_2.is_empty()
|
||||
is_mask_1 = detections_1.mask is not None
|
||||
is_mask_2 = detections_2.mask is not None
|
||||
return (
|
||||
(is_mask_1 and is_mask_2)
|
||||
or (is_mask_1 and detections_2.is_empty())
|
||||
or (detections_1.is_empty() and is_mask_2)
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def _mask_from_xyxy(scene: np.ndarray, detections: Detections) -> np.ndarray:
|
||||
mask = np.zeros(scene.shape[:2], dtype=np.bool_)
|
||||
if detections.is_empty():
|
||||
return mask
|
||||
|
||||
resolution_wh = scene.shape[1], scene.shape[0]
|
||||
polygons = xyxy_to_polygons(detections.xyxy)
|
||||
|
||||
for polygon in polygons:
|
||||
polygon_mask = polygon_to_mask(polygon, resolution_wh=resolution_wh)
|
||||
mask |= polygon_mask.astype(np.bool_)
|
||||
return mask
|
||||
|
||||
@staticmethod
|
||||
def _mask_from_obb(scene: np.ndarray, detections: Detections) -> np.ndarray:
|
||||
mask = np.zeros(scene.shape[:2], dtype=np.bool_)
|
||||
if detections.is_empty():
|
||||
return mask
|
||||
|
||||
resolution_wh = scene.shape[1], scene.shape[0]
|
||||
|
||||
for polygon in detections.data[ORIENTED_BOX_COORDINATES]:
|
||||
polygon_mask = polygon_to_mask(polygon, resolution_wh=resolution_wh)
|
||||
mask |= polygon_mask.astype(np.bool_)
|
||||
return mask
|
||||
|
||||
@staticmethod
|
||||
def _mask_from_mask(scene: np.ndarray, detections: Detections) -> np.ndarray:
|
||||
mask = np.zeros(scene.shape[:2], dtype=np.bool_)
|
||||
if detections.is_empty():
|
||||
return mask
|
||||
assert detections.mask is not None
|
||||
|
||||
for detections_mask in detections.mask:
|
||||
mask |= detections_mask.astype(np.bool_)
|
||||
return mask
|
||||
|
||||
def _draw_labels(self, scene: np.ndarray) -> None:
|
||||
"""
|
||||
Draw the labels, explaining what each color represents, with automatically
|
||||
computed positions.
|
||||
|
||||
Args:
|
||||
scene (np.ndarray): The image where the labels will be drawn.
|
||||
"""
|
||||
margin = int(50 * self.label_scale)
|
||||
gap = int(40 * self.label_scale)
|
||||
y0 = int(50 * self.label_scale)
|
||||
height = int(50 * self.label_scale)
|
||||
|
||||
marker_size = int(20 * self.label_scale)
|
||||
padding = int(10 * self.label_scale)
|
||||
text_box_corner_radius = int(10 * self.label_scale)
|
||||
marker_corner_radius = int(4 * self.label_scale)
|
||||
text_scale = self.label_scale
|
||||
|
||||
label_color_pairs = [
|
||||
(self.label_1, self.color_1),
|
||||
(self.label_2, self.color_2),
|
||||
(self.label_overlap, self.color_overlap),
|
||||
]
|
||||
|
||||
x0 = margin
|
||||
for text, color in label_color_pairs:
|
||||
if not text:
|
||||
continue
|
||||
|
||||
(text_w, _) = cv2.getTextSize(
|
||||
text=text,
|
||||
fontFace=CV2_FONT,
|
||||
fontScale=self.label_scale,
|
||||
thickness=self.text_thickness,
|
||||
)[0]
|
||||
|
||||
width = text_w + marker_size + padding * 4
|
||||
center_x = x0 + width // 2
|
||||
center_y = y0 + height // 2
|
||||
|
||||
draw_rounded_rectangle(
|
||||
scene=scene,
|
||||
rect=Rect(x=x0, y=y0, width=width, height=height),
|
||||
color=Color.WHITE,
|
||||
border_radius=text_box_corner_radius,
|
||||
)
|
||||
|
||||
draw_rounded_rectangle(
|
||||
scene=scene,
|
||||
rect=Rect(
|
||||
x=x0 + padding,
|
||||
y=center_y - marker_size / 2,
|
||||
width=marker_size,
|
||||
height=marker_size,
|
||||
),
|
||||
color=color,
|
||||
border_radius=marker_corner_radius,
|
||||
)
|
||||
|
||||
draw_text(
|
||||
scene,
|
||||
text,
|
||||
text_anchor=Point(x=center_x + marker_size, y=center_y),
|
||||
text_scale=text_scale,
|
||||
text_thickness=self.text_thickness,
|
||||
)
|
||||
|
||||
x0 += width + gap
|
||||
|
|
|
|||
|
|
@ -4,19 +4,10 @@ from pathlib import Path
|
|||
from shutil import copyfileobj
|
||||
from typing import Union
|
||||
|
||||
from supervision.assets.list import VIDEO_ASSETS, VideoAssets
|
||||
from requests import get
|
||||
from tqdm.auto import tqdm
|
||||
|
||||
try:
|
||||
from requests import get
|
||||
from tqdm.auto import tqdm
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"\n"
|
||||
"Please install requests and tqdm to download assets \n"
|
||||
"or install supervision with assets \n"
|
||||
"pip install supervision[assets] \n"
|
||||
"\n"
|
||||
)
|
||||
from supervision.assets.list import VIDEO_ASSETS, VideoAssets
|
||||
|
||||
|
||||
def is_md5_hash_matching(filename: str, original_md5_hash: str) -> bool:
|
||||
|
|
|
|||
|
|
@ -11,6 +11,25 @@ from supervision.geometry.core import Vector
|
|||
MIN_POLYGON_POINT_COUNT = 3
|
||||
|
||||
|
||||
def xyxy_to_polygons(box: np.ndarray) -> np.ndarray:
|
||||
"""
|
||||
Convert an array of boxes to an array of polygons.
|
||||
Retains the input datatype.
|
||||
|
||||
Args:
|
||||
box (np.ndarray): An array of boxes (N, 4), where each box is represented as a
|
||||
list of four coordinates in the format `(x_min, y_min, x_max, y_max)`.
|
||||
|
||||
Returns:
|
||||
np.ndarray: An array of polygons (N, 4, 2), where each polygon is
|
||||
represented as a list of four coordinates in the format `(x, y)`.
|
||||
"""
|
||||
polygon = np.zeros((box.shape[0], 4, 2), dtype=box.dtype)
|
||||
polygon[:, :, 0] = box[:, [0, 2, 2, 0]]
|
||||
polygon[:, :, 1] = box[:, [1, 1, 3, 3]]
|
||||
return polygon
|
||||
|
||||
|
||||
def polygon_to_mask(polygon: np.ndarray, resolution_wh: Tuple[int, int]) -> np.ndarray:
|
||||
"""Generate a mask from a polygon.
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue