fix(pre_commit): 🎨 auto format pre-commit hooks
This commit is contained in:
pre-commit-ci[bot]
parent
4c903cf7f2
commit
d6d71a0366
|
|
@ -1,2 +1,2 @@
|
|||
from supervision.metrics.core import UnsupportedMetricTargetError, Metric, MetricTarget
|
||||
from supervision.metrics.intersection_over_union import IntersectionOverUnion
|
||||
from supervision.metrics.core import Metric, MetricTarget, UnsupportedMetricTargetError
|
||||
from supervision.metrics.intersection_over_union import IntersectionOverUnion
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
from __future__ import annotations
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from enum import Enum
|
||||
from typing import Any, Dict, Iterator, Optional, Tuple, Union
|
||||
|
|
@ -12,6 +13,7 @@ from supervision.detection.core import Detections
|
|||
"""Used by metrics module as class ID, when none is present"""
|
||||
CLASS_ID_NONE = -1
|
||||
|
||||
|
||||
class Metric(ABC):
|
||||
"""
|
||||
The base class for all supervision metrics.
|
||||
|
|
@ -43,7 +45,7 @@ class Metric(ABC):
|
|||
Compute the metric from the internal state and return the result.
|
||||
"""
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
# TODO: determine if this is necessary.
|
||||
# @abstractmethod
|
||||
# def to_pandas(self, *args, **kwargs) -> Any:
|
||||
|
|
@ -91,11 +93,7 @@ class InternalMetricDataStore:
|
|||
* Provides iteration by class
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
metric_target: MetricTarget,
|
||||
class_agnostic: bool
|
||||
):
|
||||
def __init__(self, metric_target: MetricTarget, class_agnostic: bool):
|
||||
self._metric_target = metric_target
|
||||
self._class_agnostic = class_agnostic
|
||||
self._data_1: Dict[int, npt.NDArray]
|
||||
|
|
@ -133,8 +131,10 @@ class InternalMetricDataStore:
|
|||
if class_id not in self._data_1:
|
||||
self._data_1[class_id] = content_of_class
|
||||
continue
|
||||
self._data_1[class_id] = np.vstack((self._data_1[class_id], content_of_class))
|
||||
|
||||
self._data_1[class_id] = np.vstack(
|
||||
(self._data_1[class_id], content_of_class)
|
||||
)
|
||||
|
||||
if content_2 is not None and len(content_2) > 0:
|
||||
assert len(content_2) == len(class_ids_2)
|
||||
for class_id in set(class_ids_2):
|
||||
|
|
@ -142,17 +142,26 @@ class InternalMetricDataStore:
|
|||
if class_id not in self._data_2:
|
||||
self._data_2[class_id] = content_of_class
|
||||
continue
|
||||
self._data_2[class_id] = np.vstack((self._data_2[class_id], content_of_class))
|
||||
self._data_2[class_id] = np.vstack(
|
||||
(self._data_2[class_id], content_of_class)
|
||||
)
|
||||
|
||||
def __iter__(self) -> Iterator[Tuple[int, Optional[npt.NDArray], Optional[npt.NDArray]]]:
|
||||
class_ids = sorted(set.union(
|
||||
set(self._data_1.keys()),
|
||||
set(self._data_2.keys())
|
||||
))
|
||||
def __iter__(
|
||||
self,
|
||||
) -> Iterator[Tuple[int, Optional[npt.NDArray], Optional[npt.NDArray]]]:
|
||||
class_ids = sorted(
|
||||
set.union(set(self._data_1.keys()), set(self._data_2.keys()))
|
||||
)
|
||||
for class_id in class_ids:
|
||||
yield class_id, self._data_1.get(class_id, None), self._data_2.get(class_id, None)
|
||||
yield (
|
||||
class_id,
|
||||
self._data_1.get(class_id, None),
|
||||
self._data_2.get(class_id, None),
|
||||
)
|
||||
|
||||
def _get_content(self, data: Union[npt.NDArray, Detections]) -> Optional[npt.NDArray]:
|
||||
def _get_content(
|
||||
self, data: Union[npt.NDArray, Detections]
|
||||
) -> Optional[npt.NDArray]:
|
||||
"""Return boxes, masks or oriented bounding boxes from the data."""
|
||||
if isinstance(data, np.ndarray):
|
||||
return data
|
||||
|
|
@ -169,7 +178,9 @@ class InternalMetricDataStore:
|
|||
return obb
|
||||
raise ValueError(f"Invalid metric target: {self._metric_target}")
|
||||
|
||||
def _get_class_ids(self, data: Union[npt.NDArray, Detections]) -> npt.NDArray[np.int_]:
|
||||
def _get_class_ids(
|
||||
self, data: Union[npt.NDArray, Detections]
|
||||
) -> npt.NDArray[np.int_]:
|
||||
if self._class_agnostic or isinstance(data, np.ndarray):
|
||||
return np.array([CLASS_ID_NONE] * len(data), dtype=int)
|
||||
assert isinstance(data, Detections)
|
||||
|
|
@ -180,7 +191,9 @@ class InternalMetricDataStore:
|
|||
def _validate_class_ids(self, class_id: npt.NDArray[np.int_]) -> None:
|
||||
class_set = set(class_id)
|
||||
if len(class_set) >= 2 and -1 in class_set:
|
||||
raise ValueError("Metrics store received results with partially defined classes.")
|
||||
raise ValueError(
|
||||
"Metrics store received results with partially defined classes."
|
||||
)
|
||||
|
||||
def _validate_shape(self, data: npt.NDArray) -> None:
|
||||
if self._datapoint_shape is None:
|
||||
|
|
|
|||
|
|
@ -1,14 +1,13 @@
|
|||
from typing import Dict, Union, TYPE_CHECKING
|
||||
from typing import TYPE_CHECKING, Dict, Union
|
||||
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
|
||||
import supervision.config as config
|
||||
from supervision.detection.core import Detections
|
||||
from supervision.metrics.core import InternalMetricDataStore, Metric, MetricTarget
|
||||
|
||||
if TYPE_CHECKING:
|
||||
import pandas as pd
|
||||
pass
|
||||
|
||||
|
||||
class IntersectionOverUnion(Metric):
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
from contextlib import ExitStack as DoesNotRaise
|
||||
from test.test_utils import mock_detections
|
||||
from typing import List, Optional, Tuple, Union
|
||||
|
||||
import numpy as np
|
||||
|
|
@ -8,144 +7,117 @@ import pytest
|
|||
|
||||
from supervision.detection.core import Detections
|
||||
from supervision.metrics.core import (
|
||||
CLASS_ID_NONE,
|
||||
InternalMetricDataStore,
|
||||
MetricTarget,
|
||||
CLASS_ID_NONE
|
||||
)
|
||||
|
||||
# Boxes, class-agnostic
|
||||
|
||||
# Boxes, class-agnostic
|
||||
@pytest.mark.parametrize(
|
||||
"data_1, data_2, expected_result, exception",
|
||||
[
|
||||
# Empty
|
||||
(
|
||||
Detections.empty(),
|
||||
Detections.empty(),
|
||||
[],
|
||||
DoesNotRaise()
|
||||
),
|
||||
(Detections.empty(), Detections.empty(), [], DoesNotRaise()),
|
||||
(
|
||||
np.empty((0, 4), dtype=np.float32),
|
||||
np.empty((0, 4), dtype=np.float32),
|
||||
[],
|
||||
DoesNotRaise()
|
||||
DoesNotRaise(),
|
||||
),
|
||||
(
|
||||
Detections.empty(),
|
||||
np.empty((0, 4), dtype=np.float32),
|
||||
[],
|
||||
DoesNotRaise()
|
||||
),
|
||||
|
||||
(Detections.empty(), np.empty((0, 4), dtype=np.float32), [], DoesNotRaise()),
|
||||
# Single box + Empty
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1]], dtype=np.float32)
|
||||
),
|
||||
Detections(xyxy=np.array([[0, 0, 1, 1]], dtype=np.float32)),
|
||||
Detections.empty(),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
np.array([[0, 0, 1, 1]], dtype=np.float32),
|
||||
None
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
[(CLASS_ID_NONE, np.array([[0, 0, 1, 1]], dtype=np.float32), None)],
|
||||
DoesNotRaise(),
|
||||
),
|
||||
(
|
||||
Detections.empty(),
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1]], dtype=np.float32)
|
||||
),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
None,
|
||||
np.array([[0, 0, 1, 1]], dtype=np.float32)
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
Detections(xyxy=np.array([[0, 0, 1, 1]], dtype=np.float32)),
|
||||
[(CLASS_ID_NONE, None, np.array([[0, 0, 1, 1]], dtype=np.float32))],
|
||||
DoesNotRaise(),
|
||||
),
|
||||
|
||||
# Multiple boxes
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
),
|
||||
Detections(xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)),
|
||||
Detections.empty(),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
None
|
||||
None,
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
DoesNotRaise(),
|
||||
),
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
),
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
),
|
||||
Detections(xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)),
|
||||
Detections(xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
DoesNotRaise(),
|
||||
),
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
),
|
||||
Detections(xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
DoesNotRaise(),
|
||||
),
|
||||
|
||||
# with classes - should be ignored.
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
class_id=np.array([1, 2], dtype=int)
|
||||
class_id=np.array([1, 2], dtype=int),
|
||||
),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
DoesNotRaise(),
|
||||
),
|
||||
]
|
||||
],
|
||||
)
|
||||
def test_store_boxes_class_agnostic(
|
||||
data_1: Union[npt.NDArray, Detections],
|
||||
data_2: Union[npt.NDArray, Detections],
|
||||
expected_result: List[Tuple[int, Optional[npt.NDArray], Optional[npt.NDArray]]],
|
||||
exception: Exception
|
||||
exception: Exception,
|
||||
) -> None:
|
||||
store = InternalMetricDataStore(MetricTarget.BOXES, class_agnostic=True)
|
||||
store.update(data_1, data_2)
|
||||
result = [result for result in store]
|
||||
assert len(result) == len(expected_result)
|
||||
with exception:
|
||||
for (class_id, content_1, content_2), (expected_class_id, expected_content_1, expected_content_2) in zip(result, expected_result):
|
||||
for (class_id, content_1, content_2), (
|
||||
expected_class_id,
|
||||
expected_content_1,
|
||||
expected_content_2,
|
||||
) in zip(result, expected_result):
|
||||
assert class_id == expected_class_id
|
||||
assert (content_1 is None and expected_content_1 is None) or np.array_equal(content_1, expected_content_1)
|
||||
assert (content_2 is None and expected_content_2 is None) or np.array_equal(content_2, expected_content_2)
|
||||
assert (content_1 is None and expected_content_1 is None) or np.array_equal(
|
||||
content_1, expected_content_1
|
||||
)
|
||||
assert (content_2 is None and expected_content_2 is None) or np.array_equal(
|
||||
content_2, expected_content_2
|
||||
)
|
||||
|
||||
|
||||
# Boxes, by-class
|
||||
@pytest.mark.parametrize(
|
||||
|
|
@ -155,128 +127,104 @@ def test_store_boxes_class_agnostic(
|
|||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1]], dtype=np.float32),
|
||||
class_id=np.array([1], dtype=int)
|
||||
class_id=np.array([1], dtype=int),
|
||||
),
|
||||
Detections.empty(),
|
||||
[
|
||||
(
|
||||
1,
|
||||
np.array([[0, 0, 1, 1]], dtype=np.float32),
|
||||
None
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
[(1, np.array([[0, 0, 1, 1]], dtype=np.float32), None)],
|
||||
DoesNotRaise(),
|
||||
),
|
||||
(
|
||||
Detections.empty(),
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1]], dtype=np.float32),
|
||||
class_id=np.array([1], dtype=int)
|
||||
class_id=np.array([1], dtype=int),
|
||||
),
|
||||
[
|
||||
(
|
||||
1,
|
||||
None,
|
||||
np.array([[0, 0, 1, 1]], dtype=np.float32)
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
[(1, None, np.array([[0, 0, 1, 1]], dtype=np.float32))],
|
||||
DoesNotRaise(),
|
||||
),
|
||||
|
||||
# Multiple classes
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
class_id=np.array([1, 2], dtype=int)
|
||||
class_id=np.array([1, 2], dtype=int),
|
||||
),
|
||||
Detections.empty(),
|
||||
[
|
||||
(
|
||||
1,
|
||||
np.array([[0, 0, 1, 1]], dtype=np.float32),
|
||||
None
|
||||
),
|
||||
(
|
||||
2,
|
||||
np.array([[0, 0, 2, 2]], dtype=np.float32),
|
||||
None
|
||||
)
|
||||
(1, np.array([[0, 0, 1, 1]], dtype=np.float32), None),
|
||||
(2, np.array([[0, 0, 2, 2]], dtype=np.float32), None),
|
||||
],
|
||||
DoesNotRaise()
|
||||
DoesNotRaise(),
|
||||
),
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
class_id=np.array([1, 2], dtype=int)
|
||||
class_id=np.array([1, 2], dtype=int),
|
||||
),
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
class_id=np.array([2, 3], dtype=int)
|
||||
class_id=np.array([2, 3], dtype=int),
|
||||
),
|
||||
[
|
||||
(
|
||||
1,
|
||||
np.array([[0, 0, 1, 1]], dtype=np.float32),
|
||||
None
|
||||
),
|
||||
(1, np.array([[0, 0, 1, 1]], dtype=np.float32), None),
|
||||
(
|
||||
2,
|
||||
np.array([[0, 0, 2, 2]], dtype=np.float32),
|
||||
np.array([[0, 0, 1, 1]], dtype=np.float32)
|
||||
np.array([[0, 0, 1, 1]], dtype=np.float32),
|
||||
),
|
||||
(
|
||||
3,
|
||||
None,
|
||||
np.array([[0, 0, 2, 2]], dtype=np.float32)
|
||||
)
|
||||
(3, None, np.array([[0, 0, 2, 2]], dtype=np.float32)),
|
||||
],
|
||||
DoesNotRaise()
|
||||
DoesNotRaise(),
|
||||
),
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
),
|
||||
Detections(xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
DoesNotRaise(),
|
||||
),
|
||||
|
||||
# with classes - should be ignored.
|
||||
(
|
||||
Detections(
|
||||
xyxy=np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
class_id=np.array([1, 2], dtype=int)
|
||||
class_id=np.array([1, 2], dtype=int),
|
||||
),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
[
|
||||
(
|
||||
CLASS_ID_NONE,
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32)
|
||||
np.array([[0, 0, 1, 1], [0, 0, 2, 2]], dtype=np.float32),
|
||||
)
|
||||
],
|
||||
DoesNotRaise()
|
||||
DoesNotRaise(),
|
||||
),
|
||||
]
|
||||
],
|
||||
)
|
||||
def test_store_boxes_by_class(
|
||||
data_1: Union[npt.NDArray, Detections],
|
||||
data_2: Union[npt.NDArray, Detections],
|
||||
expected_result: List[Tuple[int, Optional[npt.NDArray], Optional[npt.NDArray]]],
|
||||
exception: Exception
|
||||
exception: Exception,
|
||||
) -> None:
|
||||
store = InternalMetricDataStore(MetricTarget.BOXES, class_agnostic=False)
|
||||
store.update(data_1, data_2)
|
||||
result = [result for result in store]
|
||||
assert len(result) == len(expected_result)
|
||||
with exception:
|
||||
for (class_id, content_1, content_2), (expected_class_id, expected_content_1, expected_content_2) in zip(result, expected_result):
|
||||
for (class_id, content_1, content_2), (
|
||||
expected_class_id,
|
||||
expected_content_1,
|
||||
expected_content_2,
|
||||
) in zip(result, expected_result):
|
||||
assert class_id == expected_class_id
|
||||
assert (content_1 is None and expected_content_1 is None) or np.array_equal(content_1, expected_content_1)
|
||||
assert (content_2 is None and expected_content_2 is None) or np.array_equal(content_2, expected_content_2)
|
||||
assert (content_1 is None and expected_content_1 is None) or np.array_equal(
|
||||
content_1, expected_content_1
|
||||
)
|
||||
assert (content_2 is None and expected_content_2 is None) or np.array_equal(
|
||||
content_2, expected_content_2
|
||||
)
|
||||
|
|
|
|||
Loading…
Reference in New Issue