fabbff2b60
* Fix security issues and remove noqa comments - Replace MD5 hash_new with md5() and add usedforsecurity=False flag - Add documentation explaining MD5 is for file integrity, not security - Add timeout parameter to requests.get call - Add explicit shell=False and check=False to subprocess.run - Remove all security-related noqa comments - Add per-file ignores for S311 in tests and S603 in examples --------- Co-authored-by: jirka <jirka.borovec@seznam.cz> Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com> Co-authored-by: Borda <6035284+Borda@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
from __future__ import annotations
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from typing import Any
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import numpy as np
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from supervision.detection.core import Detections
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from supervision.key_points.core import KeyPoints
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def mock_detections(
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xyxy: list[list[float]],
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mask: list[np.ndarray] | None = None,
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confidence: list[float] | None = None,
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class_id: list[int] | None = None,
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tracker_id: list[int] | None = None,
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data: dict[str, list[Any]] | None = None,
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) -> Detections:
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def convert_data(data: dict[str, list[Any]]):
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return {k: np.array(v) for k, v in data.items()}
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return Detections(
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xyxy=np.array(xyxy, dtype=np.float32),
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mask=(mask if mask is None else np.array(mask, dtype=bool)),
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confidence=(
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confidence if confidence is None else np.array(confidence, dtype=np.float32)
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),
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class_id=(class_id if class_id is None else np.array(class_id, dtype=int)),
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tracker_id=(
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tracker_id if tracker_id is None else np.array(tracker_id, dtype=int)
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),
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data=convert_data(data) if data else {},
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)
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def mock_key_points(
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xy: list[list[list[float]]],
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confidence: list[list[float]] | None = None,
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class_id: list[int] | None = None,
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data: dict[str, list[Any]] | None = None,
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) -> KeyPoints:
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def convert_data(data: dict[str, list[Any]]):
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return {k: np.array(v) for k, v in data.items()}
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return KeyPoints(
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xy=np.array(xy, dtype=np.float32),
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confidence=(
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confidence if confidence is None else np.array(confidence, dtype=np.float32)
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),
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class_id=(class_id if class_id is None else np.array(class_id, dtype=int)),
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data=convert_data(data) if data else {},
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)
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def random_boxes(
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count: int,
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image_size: tuple[int, int] = (1920, 1080),
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min_box_size: int = 20,
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max_box_size: int = 200,
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seed: int | None = None,
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) -> np.ndarray:
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"""
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Generate random bounding boxes within given image dimensions and size constraints.
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Creates `count` bounding boxes randomly positioned and sized, ensuring each
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stays within image bounds and has width and height in the specified range.
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Args:
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count (`int`): Number of random bounding boxes to generate.
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image_size (`tuple[int, int]`): Image size as `(width, height)`.
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min_box_size (`int`): Minimum side length (pixels) for generated boxes.
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max_box_size (`int`): Maximum side length (pixels) for generated boxes.
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seed (`int` or `None`): Optional random seed for reproducibility.
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Returns:
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(`numpy.ndarray`): Array of shape `(count, 4)` with bounding boxes as
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`(x_min, y_min, x_max, y_max)`.
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"""
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rng = np.random.default_rng(seed)
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img_w, img_h = image_size
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out = np.zeros((count, 4), dtype=np.float32)
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for i in range(count):
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w = rng.uniform(min_box_size, max_box_size)
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h = rng.uniform(min_box_size, max_box_size)
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x_min = rng.uniform(0, img_w - w)
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y_min = rng.uniform(0, img_h - h)
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x_max = x_min + w
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y_max = y_min + h
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out[i] = (x_min, y_min, x_max, y_max)
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return out
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def assert_almost_equal(actual, expected, tolerance=1e-5):
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assert abs(actual - expected) < tolerance, f"Expected {expected}, but got {actual}."
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