matrice-analytics
Post-processing analytics for Matrice.ai inference pipelines
Decision gist · record as of 2026-08-14
Yes, with conditions. The package is actively maintained, has low install friction, and solves a real problem for developers building analytics on inference results. However, the proprietary license is unclear—verify licensing terms before use. No known security vulnerabilities. Best suited for projects already using the Matrice SDK or similar inference pipelines where standardized post-processing is valuable.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low install friction with a pure-Python wheel and only numpy and scipy as runtime dependencies.
- Active maintenance with a recent release (3 days old as of the fact sheet date), supporting Python 3.8 through 3.12.
License · maintenance · safety
LicenseRef-Proprietary (unclear) — Licensed as proprietary (LicenseRef-Proprietary) with unclear treatment. Verify licensing terms and restrictions before integrating into commercial or open-source projects.
last release 2026-08-11 (3 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,163,821 downloads/mo, #3,237 on PyPI
Alternatives
Verify before relying
pip install matrice-analytics
from matrice_analytics.post_processing import PostProcessor
processor = PostProcessor()
result = processor.process_simple(
raw_results,
usecase="people_counting",
confidence_threshold=0.5
)
print(result.summary)- Whether proprietary license permits use in commercial applications or requires licensing agreement
- Actual performance characteristics and scalability limits for large inference datasets
- Whether zone configuration and tracking features work reliably across different camera angles and environments
What it is and what it does
matrice-analytics is a post-processing system for the Matrice Python SDK that transforms raw inference results into structured analytics. It wraps numpy and scipy to provide standardized processing pipelines for two main use cases: people counting (with zone analysis, occupancy tracking, and alerts) and customer service analytics (staff utilization, customer-staff interactions, queue insights). All operations return ProcessingResult objects with status, metrics, insights, and warnings.
The package uses a registry pattern with type-safe dataclass configurations that support JSON/YAML file loading. It's designed for developers building analytics on top of computer vision inference—typical workflows involve loading raw detections, applying a use case configuration, and extracting business metrics. The system includes error handling that returns structured results even on partial failures, plus automatic insight generation and performance statistics.
Use it for
- Count people in zones (entrance, checkout areas) with occupancy alerts and temporal trends for retail analytics
- Analyze staff utilization and customer-staff interactions to measure service quality and queue efficiency
- Track unique individuals across frames to measure dwell time and movement patterns in defined areas
- Generate business intelligence metrics from detection results without writing custom post-processing logic
- Load and apply pre-configured analytics pipelines from JSON/YAML files across multiple inference runs
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
The package is actively maintained, has low install friction, and solves a real problem for developers building analytics on inference results. However, the proprietary license is unclear—verify licensing terms before use. No known security vulnerabilities. Best suited for projects already using the Matrice SDK or similar inference pipelines where standardized post-processing is valuable.
Install
matrice-analytics on PyPI
Before you install
Low install friction with a pure-Python wheel and only numpy and scipy as runtime dependencies. Active maintenance with a recent release (3 days old as of the fact sheet date), supporting Python 3.8 through 3.12.
License in practice
Licensed as proprietary (LicenseRef-Proprietary) with unclear treatment. Verify licensing terms and restrictions before integrating into commercial or open-source projects.
Quickstart
pip install matrice-analytics
from matrice_analytics.post_processing import PostProcessor
processor = PostProcessor()
result = processor.process_simple(
raw_results,
usecase="people_counting",
confidence_threshold=0.5
)
print(result.summary)Verify before relying
- Whether proprietary license permits use in commercial applications or requires licensing agreement
- Actual performance characteristics and scalability limits for large inference datasets
- Whether zone configuration and tracking features work reliably across different camera angles and environments
Package facts
| License | LicenseRef-Proprietary unclear |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 2 packagesnumpyscipy |
| Maintenance | Actively maintained 3 days since the last release |
| First released | |
| Downloads | 2,163,821 / month, #3,237 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersOperating System :: MacOSOperating System :: Microsoft :: WindowsOperating System :: OS IndependentOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Software Development :: Libraries :: Python ModulesTyping :: Typed |
Evidence: matrice_analytics-0.1.458-py3-none-any.whl
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