acmenra-cv 0.1.4

Production-ready computer vision utilities for ADAS and multi-object tracking

🚀 acmenra-cv

Production-ready computer vision utilities for ADAS, multi-object tracking, and embedded vision systems

pip install acmenra-cv

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📦 Overview

acmenra-cv is a high-performance, type-safe computer vision library engineered for real-time applications on resource-constrained embedded systems (Raspberry Pi 5, Jetson, etc.). Built following Clean Architecture principles, it provides three cohesive modules:

Module	Purpose	Key Features
YOLOUtils	Spatial primitives for YOLO outputs	Normalized coordinates, strict validation, immutable transformations
tracker	Multi-object tracking with trajectories	Persistent IDs, configurable history, BoT-SORT integration
render	Type-safe visualization layer	Alpha-blended overlays, embedded optimizations, graceful degradation

All components operate in normalized coordinate space [0.0, 1.0] by default, ensuring resolution independence across varying camera inputs and inference resolutions.

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✨ Key Features

🔹 Unified Architecture

• Strict type & range validation ([0.0, 1.0] with safe() clamping factory)
• Seamless YOLO integration (boxes, masks.xyn, obb.xyxyxyxyn)
• Immutable geometric transformations (scale, translate, smooth)
• Full type safety with IDE autocomplete and consistent API across all primitives

🔹 Embedded-Ready Performance

• Zero-crash OpenCV integration with @validate_frame decorator
• Global show=False toggle to bypass all rendering for headless/embedded deployments
• Memory-efficient trajectory queues with O(1) average calculation
• Alpha-blended compositing (cv2.addWeighted) and anti-aliased geometry (LINE_AA)

🔹 ADAS & Safety-Critical Design

• Trajectory history management for zone crossing and collision detection
• Temporal metadata (TimedPoint) for velocity/direction estimation
• Configurable thresholds (conf, iou, max_length) for dynamic adaptation
• Graceful degradation on invalid inputs — no exceptions, just safe fallbacks

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🧩 Module Documentation

🔷 utils — Spatial Primitives

Validated geometric containers for YOLO detection outputs

Classes

Point — Validated 3D normalized coordinates

• __init__(): Initializes with X, Y, Z. Validates float type and [0.0, 1.0] range.
• X, Y, Z: Properties with strict type and range validation.
• get_distance(): Euclidean distance to another point (includes Z).
• scale(), translate(): Immutable transformations returning new instances.
• safe(): Class method factory with coordinate clamping — no exceptions.

Box — Axis-aligned 3D bounding box

• __init__(): Six boundaries (left, right, top, bottom, front, back).
• center, bottom_center: Computed properties for tracking.
• width, height, depth: Dimension properties.
• get_area(), get_volume(): Geometric calculations.
• to_absolute_array(): Converts to pixel corners for OpenCV.

Polygon — Segmentation mask container

• from_yolo_xyn(): Static factory from YOLO masks.xyn.
• smooth(): Vertex smoothing via moving average.
• get_area(): Shoelace formula for normalized area.
• __getitem__(): Supports slicing — returns new Polygon.

Obb — Oriented (rotated) bounding box

• from_yolo_obb(): Static factory from YOLO obb.xyxyxyxyn.
• angle: Cached rotation angle (-90° to +90°).
• width, height: Average edge lengths for rotated rectangles.

YOLOInstance — Unified detection container

• Combines id, class_id, category, conf, box, polygon, obb.
• Strict validation on all properties.
• Designed for safe pipeline integration.

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🔷 tracker — Multi-Object Tracking

Persistent IDs, trajectory management, ADAS integration

Classes

Tracker — Main tracking engine

• __init__(): Configurable with YOLO model, device, thresholds, max_length.
• track(): Main entry point — returns List[TrackedObject] with persistent IDs.
• predict(), double_predict(): Detection modes (single / panoramic).
• Properties: model, device, conf, iou, max_length — all validated.

TrackedObject — Single tracked entity

• id: Tracking identifier (Optional[int]).
• instance: Latest YOLOInstance detection data.
• trajectory: TimedPointQueue with historical positions.

TimedPointQueue — Fixed-length trajectory history

• enqueue(), dequeue(): FIFO with auto-eviction.
• average_x/y/z: O(1) incremental centroid calculation.
• get_values(): Deep-copy snapshot for safe external access.

TimedPoint — Time-stamped spatial point

• Extends Point with timestamp: Optional[datetime].
• to_point(): Discards temporal metadata for geometry-only ops.

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🔷 render — Visualization Layer

Type-safe drawing operations for embedded systems

Classes

Drawer — Main rendering engine

• draw_instances(): Renders multiple objects with alpha-blended overlays 🔥
• draw_box(), draw_obb(), draw_polygon(), draw_trajectory(): Individual element rendering.
• draw_text(): Absolute pixel coordinate text rendering.
• @validate_frame decorator on all public methods — zero-crash guarantee.

Style — Centralized visualization config

• palette: Unique RGB tuples with [0, 255] validation.
• thickness, rounding, segment, smooth, alpha: All range-validated.
• show: Global toggle — False bypasses all rendering for performance.

Font — OpenCV typography config

• color: 3-element RGB tuple validation.
• font: OpenCV font identifier [0, 7].
• font_scale, thickness: Positive integer validation.

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💡 Quick Start

from acmenra_cv.utils import Point, Box, YOLOInstance
from acmenra_cv.tracker import Tracker, TimedPointQueue
from acmenra_cv.render import Drawer, Style, Font
from ultralytics import YOLO

# 1. Initialize components
model = YOLO("yolov8n.pt")
style = Style(
    palette=[(255, 0, 0), (0, 255, 0), (0, 0, 255)],
    font=Font(color=(255, 255, 255)),
    show=True
)
tracker = Tracker(model=model, category=YourEnum, max_length=50)
drawer = Drawer(style=style)

# 2. Process a frame
frame = ...  # Your BGR frame (numpy array)
tracked_objects = tracker.track(frame, enable_tracking=True)

# 3. Render results
output = drawer.draw_instances(
    frame=frame,
    tracked_objects=tracked_objects,
    is_box=True,
    is_trajectory=True
)

# 4. Use spatial data for business logic
for obj in tracked_objects:
    if obj.trajectory.count >= 5:
        speed = estimate_speed(obj.trajectory)  # Your logic
        if obj.instance.category == YourEnum.car and speed > threshold:
            trigger_alert(obj)

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📋 Requirements

numpy>=1.21.0
opencv-python>=4.5.0
ultralytics>=8.0.0

Optional for development:

pytest>=7.0.0
black>=23.0.0
mypy>=1.0.0

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🔐 License

© 2026 acmenra.studio. All rights reserved.

This software is proprietary and confidential. Unauthorized copying, distribution, or use is strictly prohibited.

For commercial licensing inquiries: [e-mail]

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🌐 Links

• PyPI: https://pypi.org/project/acmenra-cv/
• Source: https://github.com/acmenra/acmenra-cv
• Documentation: https://github.com/acmenra/acmenra-cv#readme
• Issues: https://github.com/acmenra/acmenra-cv/issues

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