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fix(formatting): 🐛 merge latest code from develop for formatting 

Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>
This commit is contained in:
Onuralp SEZER 2023-08-02 15:28:44 +03:00
parent 1645ecf4bc d6d4760fce
commit f9d3aaa67e
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GPG Key ID: CF0835DFDF14CA38
9 changed files with 464 additions and 100 deletions
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2
.pre-commit-config.yaml
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@ -1,5 +1,3 @@
default_language_version:
python: python3.8
ci:
autofix_prs: true

3
CONTRIBUTING.md
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@ -24,7 +24,7 @@ Before you contribute a new feature, consider submitting an Issue to discuss the
## How to Contribute Changes
First, fork this repository to your own GitHub account. Create a new branch that describes your changes (i.e. `line-counter-docs`). Push your changes to the branch on your fork and then submit a pull request to this repository.
First, fork this repository to your own GitHub account. Create a new branch that describes your changes (i.e. `line-counter-docs`). Push your changes to the branch on your fork and then submit a pull request to `develop` branch of this repository.
When creating new functions, please ensure you have the following:
@ -32,6 +32,7 @@ When creating new functions, please ensure you have the following:
2. Unit tests for the function.
3. Examples in the documentation for the function.
4. Created an entry in our docs to autogenerate the documentation for the function.
5. Please share google colab with minimal code to test new feature or reproduce PR whenever it is possible. Please ensure that google colab can be accessed without any issue.
All pull requests will be reviewed by the maintainers of the project. We will provide feedback and ask for changes if necessary.

117
poetry.lock generated
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@ -2156,54 +2156,83 @@ files = [
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version = "9.5.0"
description = "Python Imaging Library (Fork)"
optional = false
python-versions = ">=3.6"
python-versions = ">=3.7"
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{file = "Pillow-9.5.0.tar.gz", hash = "sha256:bf548479d336726d7a0eceb6e767e179fbde37833ae42794602631a070d630f1"},
]
[package.extras]
docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
[[package]]
name = "pkginfo"
version = "1.9.6"
@ -3443,4 +3472,4 @@ desktop = ["opencv-python"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.8,<3.12.0"
content-hash = "be27f05c8857580f327c9d6b89216524c9b8cacf662a24b1d5d4147e4a194f81"
content-hash = "4917c08576fa8226c0593bac637f8442171d16cb50912a0cfe225959dcaa4e5e"

5
pyproject.toml
View File

@ -5,6 +5,7 @@ description = "A set of easy-to-use utils that will come in handy in any Compute
authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
maintainers = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
readme = "README.md"
license = "MIT"
packages = [{include = "supervision"}]
homepage = "https://github.com/roboflow/supervision"
repository = "https://github.com/roboflow/supervision"
@ -14,7 +15,7 @@ keywords = ["machine-learning", "deep-learning", "vision", "ML", "DL", "AI", "YO
classifiers=[
'Intended Audience :: Developers',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: BSD License',
'License :: OSI Approved :: MIT License',
'Programming Language :: Python :: 3',
'Programming Language :: Python :: 3.8',
'Programming Language :: Python :: 3.9',
@ -37,7 +38,7 @@ python = ">=3.8,<3.12.0"
numpy = "^1.20.0"
matplotlib = "^3.7.1"
pyyaml = "^6.0"
pillow = "^8.4.0"
pillow = "^9.4.0"
opencv-python = { version = "^4.8.0.74", optional = true }
opencv-python-headless = "^4.8.0.74"

46
supervision/dataset/core.py
View File

@ -31,7 +31,6 @@ from supervision.dataset.utils import (
train_test_split,
)
from supervision.detection.core import Detections
from supervision.utils.file import list_files_with_extensions
@dataclass
@ -212,16 +211,20 @@ class DetectionDataset(BaseDataset):
@classmethod
def from_pascal_voc(
cls, images_directory_path: str, annotations_directory_path: str
cls,
images_directory_path: str,
annotations_directory_path: str,
force_masks: bool = False,
) -> DetectionDataset:
"""
Creates a Dataset instance from PASCAL VOC formatted data.
Args:
images_directory_path (str): The path to the
directory containing the images.
annotations_directory_path (str): The path to the directory
images_directory_path (str): Path to the directory containing the images.
annotations_directory_path (str): Path to the directory
containing the PASCAL VOC XML annotations.
force_masks (bool, optional): If True, forces masks to
be loaded for all annotations, regardless of whether they are present.
Returns:
DetectionDataset: A DetectionDataset instance containing
@ -240,7 +243,7 @@ class DetectionDataset(BaseDataset):
>>> project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
>>> dataset = project.version(PROJECT_VERSION).download("voc")
>>> ds = sv.DetectionDataset.from_yolo(
>>> ds = sv.DetectionDataset.from_pascal_voc(
... images_directory_path=f"{dataset.location}/train/images",
... annotations_directory_path=f"{dataset.location}/train/labels"
... )
@ -249,34 +252,13 @@ class DetectionDataset(BaseDataset):
['dog', 'person']
```
"""
image_paths = list_files_with_extensions(
directory=images_directory_path, extensions=["jpg", "jpeg", "png"]
)
annotation_paths = list_files_with_extensions(
directory=annotations_directory_path, extensions=["xml"]
classes, images, annotations = load_pascal_voc_annotations(
images_directory_path=images_directory_path,
annotations_directory_path=annotations_directory_path,
force_masks=force_masks,
)
raw_annotations: List[Tuple[str, Detections, List[str]]] = [
load_pascal_voc_annotations(annotation_path=str(annotation_path))
for annotation_path in annotation_paths
]
classes = []
for annotation in raw_annotations:
classes.extend(annotation[2])
classes = list(set(classes))
for annotation in raw_annotations:
class_id = [classes.index(class_name) for class_name in annotation[2]]
annotation[1].class_id = np.array(class_id)
images = {
image_path.name: cv2.imread(str(image_path)) for image_path in image_paths
}
annotations = {
image_name: detections for image_name, detections, _ in raw_annotations
}
return DetectionDataset(classes=classes, images=images, annotations=annotations)
@classmethod

138
supervision/dataset/formats/pascal_voc.py
View File

@ -1,12 +1,16 @@
from typing import List, Optional, Tuple
import os
from pathlib import Path
from typing import Dict, List, Optional, Tuple
from xml.dom.minidom import parseString
from xml.etree.ElementTree import Element, SubElement, parse, tostring
import cv2
import numpy as np
from supervision.dataset.utils import approximate_mask_with_polygons
from supervision.detection.core import Detections
from supervision.detection.utils import polygon_to_xyxy
from supervision.detection.utils import polygon_to_mask, polygon_to_xyxy
from supervision.utils.file import list_files_with_extensions
def object_to_pascal_voc(
@ -126,27 +130,100 @@ def detections_to_pascal_voc(
def load_pascal_voc_annotations(
annotation_path: str,
) -> Tuple[str, Detections, List[str]]:
images_directory_path: str,
annotations_directory_path: str,
force_masks: bool = False,
) -> Tuple[List[str], Dict[str, np.ndarray], Dict[str, Detections]]:
"""
Loads PASCAL VOC XML annotations and returns the image name,
a Detections instance, and a list of class names.
Args:
annotation_path (str): The path to the PASCAL VOC XML annotations file.
images_directory_path (str): The path to the directory containing the images.
annotations_directory_path (str): The path to the directory containing the
PASCAL VOC annotation files.
force_masks (bool, optional): If True, forces masks to be loaded for all
annotations, regardless of whether they are present.
Returns:
Tuple[str, Detections, List[str]]: A tuple containing the image name,
a Detections instance, and a list of class
names of objects in the detections.
Tuple[List[str], Dict[str, np.ndarray], Dict[str, Detections]]: A tuple
containing a list of class names,
a dictionary with image names as keys and
images as values, and a dictionary with image names as
keys and corresponding Detections instances as values.
"""
tree = parse(annotation_path)
root = tree.getroot()
image_name = root.find("filename").text
image_paths = list_files_with_extensions(
directory=images_directory_path, extensions=["jpg", "jpeg", "png"]
)
classes = []
images = {}
annotations = {}
for image_path in image_paths:
image_name = Path(image_path).stem
image = cv2.imread(str(image_path))
annotation_path = os.path.join(annotations_directory_path, f"{image_name}.xml")
if not os.path.exists(annotation_path):
images[image_path.name] = image
annotations[image_path.name] = Detections.empty()
continue
tree = parse(annotation_path)
root = tree.getroot()
resolution_wh = (image.shape[1], image.shape[0])
annotation, classes = detections_from_xml_obj(
root, classes, resolution_wh, force_masks
)
images[image_path.name] = image
annotations[image_path.name] = annotation
return classes, images, annotations
def detections_from_xml_obj(
root: Element, classes: List[str], resolution_wh, force_masks: bool = False
) -> Tuple[Detections, List[str]]:
"""
Converts an XML object in Pascal VOC format to a Detections object.
Expected XML format:
<annotation>
...
<object>
<name>dog</name>
<bndbox>
<xmin>48</xmin>
<ymin>240</ymin>
<xmax>195</xmax>
<ymax>371</ymax>
</bndbox>
<polygon>
<x1>48</x1>
<y1>240</y1>
<x2>195</x2>
<y2>240</y2>
<x3>195</x3>
<y3>371</y3>
<x4>48</x4>
<y4>371</y4>
</polygon>
</object>
</annotation>
Returns:
Tuple[Detections, List[str]]: A tuple containing a Detections object and an
updated list of class names, extended with the class names
from the XML object.
"""
xyxy = []
class_names = []
masks = []
with_masks = False
extended_classes = classes[:]
for obj in root.findall("object"):
class_name = obj.find("name").text
class_names.append(class_name)
@ -159,7 +236,40 @@ def load_pascal_voc_annotations(
xyxy.append([x1, y1, x2, y2])
xyxy = np.array(xyxy)
detections = Detections(xyxy=xyxy)
with_masks = obj.find("polygon") is not None
with_masks = force_masks if force_masks else with_masks
return image_name, detections, class_names
for polygon in obj.findall("polygon"):
polygon_points = parse_polygon_points(polygon)
mask_from_polygon = polygon_to_mask(
polygon=np.array(polygon_points),
resolution_wh=resolution_wh,
)
masks.append(mask_from_polygon)
xyxy = np.array(xyxy) if len(xyxy) > 0 else np.empty((0, 4))
for k in set(class_names):
if k not in extended_classes:
extended_classes.append(k)
class_id = np.array(
[extended_classes.index(class_name) for class_name in class_names]
)
if with_masks:
annotation = Detections(
xyxy=xyxy, mask=np.array(masks).astype(bool), class_id=class_id
)
else:
annotation = Detections(xyxy=xyxy, class_id=class_id)
return annotation, extended_classes
def parse_polygon_points(polygon: Element) -> List[List[int]]:
polygon_points = []
coords = polygon.findall(".//*")
for i in range(0, len(coords), 2):
x = int(coords[i].text)
y = int(coords[i + 1].text)
polygon_points.append([x, y])
return polygon_points

84
supervision/detection/core.py
View File

@ -6,12 +6,13 @@ from typing import Any, Iterator, List, Optional, Tuple, Union
import numpy as np
from supervision.detection.utils import (
extract_yolov8_masks,
extract_ultralytics_masks,
non_max_suppression,
process_roboflow_result,
xywh_to_xyxy,
)
from supervision.geometry.core import Position
from supervision.utils.internal import deprecated
def _validate_xyxy(xyxy: Any, n: int) -> None:
@ -177,6 +178,10 @@ class Detections:
)
@classmethod
@deprecated(
"This method is deprecated and removed in 0.15.0 release. Use "
"sv.Detections.from_ultralytics() instead."
)
def from_yolov8(cls, yolov8_results) -> Detections:
"""
Creates a Detections instance from a
@ -205,7 +210,44 @@ class Detections:
xyxy=yolov8_results.boxes.xyxy.cpu().numpy(),
confidence=yolov8_results.boxes.conf.cpu().numpy(),
class_id=yolov8_results.boxes.cls.cpu().numpy().astype(int),
mask=extract_yolov8_masks(yolov8_results),
mask=extract_ultralytics_masks(yolov8_results),
)
@classmethod
def from_ultralytics(cls, ultralytics_results) -> Detections:
"""
Creates a Detections instance from a
[YOLOv8](https://github.com/ultralytics/ultralytics) inference result.
Args:
yolov8_results (ultralytics.yolo.engine.results.Results): The output
results instance from YOLOv8
Returns:
Detections: A new Detections object.
Example:
```python
>>> import cv2
>>> from ultralytics import YOLO, FastSAM, SAM, RTDETR
>>> import supervision as sv
>>> image = cv2.imread(SOURCE_IMAGE_PATH)
>>> model = YOLO('yolov8s.pt')
>>> model = SAM('sam_b.pt')
>>> model = SAM('mobile_sam.pt')
>>> model = FastSAM('FastSAM-s.pt')
>>> model = RTDETR('rtdetr-l.pt')
>>> result = model(image)[0]
>>> detections = sv.Detections.from_ultralytics(result)
```
"""
return cls(
xyxy=ultralytics_results.boxes.xyxy.cpu().numpy(),
confidence=ultralytics_results.boxes.conf.cpu().numpy(),
class_id=ultralytics_results.boxes.cls.cpu().numpy().astype(int),
mask=extract_ultralytics_masks(ultralytics_results),
)
@classmethod
@ -414,6 +456,44 @@ class Detections:
return Detections(xyxy=xywh_to_xyxy(boxes_xywh=xywh), mask=mask)
@classmethod
def from_paddledet(cls, paddledet_result):
"""
Creates a Detections instance from
[PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection)
inference result.
Args:
paddledet_result (List[dict]): The output Results instance from SAM
Returns:
Detections: A new Detections object.
Example:
```python
>>> import supervision as sv
>>> import paddle
>>> from ppdet.engine import Trainer
>>> from ppdet.core.workspace import load_config
>>> weights = (...)
>>> config = (...)
>>> cfg = load_config(config)
>>> trainer = Trainer(cfg, mode='test')
>>> trainer.load_weights(weights)
>>> paddledet_result = trainer.predict([images])[0]
>>> detections = sv.Detections.from_paddledet(paddledet_result)
```
"""
return cls(
xyxy=paddledet_result["bbox"][:, 2:6],
confidence=paddledet_result["bbox"][:, 1],
class_id=paddledet_result["bbox"][:, 0].astype(int),
)
@classmethod
def empty(cls) -> Detections:
"""

9
supervision/detection/utils.py
View File

@ -19,7 +19,7 @@ def polygon_to_mask(polygon: np.ndarray, resolution_wh: Tuple[int, int]) -> np.n
`1`'s and the rest is filled with `0`'s.
"""
width, height = resolution_wh
mask = np.zeros((height, width), dtype=np.uint8)
mask = np.zeros((height, width))
cv2.fillPoly(mask, [polygon], color=1)
return mask
@ -293,7 +293,7 @@ def approximate_polygon(
return np.squeeze(approximated_points, axis=1)
def extract_yolov8_masks(yolov8_results) -> Optional[np.ndarray]:
def extract_ultralytics_masks(yolov8_results) -> Optional[np.ndarray]:
if not yolov8_results.masks:
return None
@ -321,7 +321,10 @@ def extract_yolov8_masks(yolov8_results) -> Optional[np.ndarray]:
for i in range(masks.shape[0]):
mask = masks[i]
mask = mask[top:bottom, left:right]
mask = cv2.resize(mask, (orig_shape[1], orig_shape[0]))
if mask.shape != orig_shape:
mask = cv2.resize(mask, (orig_shape[1], orig_shape[0]))
mask_maps.append(mask)
return np.asarray(mask_maps, dtype=bool)

160
test/dataset/formats/test_pascal_voc.py Normal file
View File

@ -0,0 +1,160 @@
import xml.etree.ElementTree as ET
from contextlib import ExitStack as DoesNotRaise
from test.utils import mock_detections
from typing import List, Optional
import numpy as np
import pytest
from supervision.dataset.formats.pascal_voc import (
detections_from_xml_obj,
object_to_pascal_voc,
parse_polygon_points,
)
def are_xml_elements_equal(elem1, elem2):
if (
elem1.tag != elem2.tag
or elem1.attrib != elem2.attrib
or elem1.text != elem2.text
or len(elem1) != len(elem2)
):
return False
for child1, child2 in zip(elem1, elem2):
if not are_xml_elements_equal(child1, child2):
return False
return True
@pytest.mark.parametrize(
"xyxy, name, polygon, expected_result, exception",
[
(
[0, 0, 10, 10],
"test",
None,
ET.fromstring(
"""<object><name>test</name><bndbox><xmin>0</xmin><ymin>0</ymin>
<xmax>10</xmax><ymax>10</ymax></bndbox></object>"""
),
DoesNotRaise(),
),
(
[0, 0, 10, 10],
"test",
[[0, 0], [10, 0], [10, 10], [0, 10]],
ET.fromstring(
"""<object><name>test</name><bndbox><xmin>0</xmin><ymin>0</ymin>
<xmax>10</xmax><ymax>10</ymax
></bndbox><polygon><x1>0</x1><y1>0</y1><x2>10</x2>
<y2>0</y2><x3>10</x3><y3>10</y3><x4>0</x4><y4>10
</y4></polygon></object>"""
),
DoesNotRaise(),
),
],
)
def test_object_to_pascal_voc(
xyxy: np.ndarray,
name: str,
polygon: Optional[np.ndarray],
expected_result,
exception: Exception,
):
with exception:
result = object_to_pascal_voc(xyxy=xyxy, name=name, polygon=polygon)
assert are_xml_elements_equal(result, expected_result)
@pytest.mark.parametrize(
"polygon_element, expected_result, exception",
[
(
ET.fromstring(
"""<polygon><x1>0</x1><y1>0</y1><x2>10</x2><y2>0</y2><x3>10</x3>
<y3>10</y3><x4>0</x4><y4>10</y4></polygon>"""
),
[[0, 0], [10, 0], [10, 10], [0, 10]],
DoesNotRaise(),
)
],
)
def test_parse_polygon_points(
polygon_element,
expected_result: List[list],
exception,
):
with exception:
result = parse_polygon_points(polygon_element)
assert result == expected_result
ONE_CLASS_N_BBOX = """<annotation><object><name>test</name><bndbox><xmin>0</xmin
><ymin>0</ymin><xmax>10</xmax><ymax
>10</ymax></bndbox></object><object><name>test</name><bndbox><xmin>10</xmin><ymin>10
</ymin><xmax>20</xmax><ymax>20 </ymax></bndbox></object></annotation>"""
ONE_CLASS_ONE_BBOX = """<annotation><object><name>test</name><bndbox><xmin>0</xmin
><ymin>0</ymin><xmax>10</xmax><ymax >10</ymax></bndbox></object></annotation>"""
N_CLASS_N_BBOX = """<annotation><object><name>test</name><bndbox><xmin>0</xmin><ymin
>0</ymin><xmax>10</xmax><ymax>10
</ymax></bndbox></object><object><name>test</name><bndbox><xmin>20</xmin><ymin>30
</ymin><xmax>30</xmax><ymax>40</ymax
></bndbox></object><object><name>test2</name><bndbox><xmin>10</xmin><ymin>10</ymin
><xmax>20</xmax><ymax>20</ymax ></bndbox></object></annotation>"""
NO_DETECTIONS = "<annotation></annotation>"
@pytest.mark.parametrize(
"xml_string, classes, resolution_wh, force_masks, expected_result, exception",
[
(
ONE_CLASS_ONE_BBOX,
["test"],
(100, 100),
False,
mock_detections(np.array([[0, 0, 10, 10]]), None, [0]),
DoesNotRaise(),
),
(
ONE_CLASS_N_BBOX,
["test"],
(100, 100),
False,
mock_detections(np.array([[0, 0, 10, 10], [10, 10, 20, 20]]), None, [0, 0]),
DoesNotRaise(),
),
(
N_CLASS_N_BBOX,
["test", "test2"],
(100, 100),
False,
mock_detections(
np.array([[0, 0, 10, 10], [20, 30, 30, 40], [10, 10, 20, 20]]),
None,
[0, 0, 1],
),
DoesNotRaise(),
),
(
NO_DETECTIONS,
[],
(100, 100),
False,
mock_detections(np.empty((0, 4)), None, []),
DoesNotRaise(),
),
],
)
def test_detections_from_xml_obj(
xml_string, classes, resolution_wh, force_masks, expected_result, exception
):
with exception:
root = ET.fromstring(xml_string)
result, _ = detections_from_xml_obj(root, classes, resolution_wh, force_masks)
assert result == expected_result