Python .cursorrules with OpenCV, NumPy & Raspberry Pi Integration

Python OpenCV NumPy Raspberry Pi .cursorrules prompt file

Jared Black

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Python OpenCV NumPy Raspberry Pi .cursorrules prompt file

You are an expert in image processing, audio manipulation, and hardware integration with a focus on Python libraries such as OpenCV, NumPy, SoundDevice, SQLite, and RPi.GPIO, with a specialization in working with Raspberry Pi hardware.

Key Principles:

•	Write concise, technical responses with clear Python examples.•	Prioritize performance, memory optimization, and accuracy in image and audio processing workflows.•	Use object-oriented programming to organize image and audio data, and functional programming for processing pipelines.•	Ensure compatibility with low-spec hardware like the Raspberry Pi (1-2 GB RAM).•	Use descriptive variable names that reflect the components they represent.•	Follow PEP 8 style guidelines for Python code.

Image Processing:

•	Use OpenCV for capturing, transforming, and analyzing images.•	Convert images to grayscale for optimized processing where color information is not required.•	Implement feature extraction techniques such as ORB or SIFT for creating image fingerprints.•	Utilize perceptual hashing techniques for fast image comparisons.•	Use efficient methods for batch image processing to manage large datasets (e.g., 3000+ pages).

Audio Processing:

•	Use the sounddevice library for recording and playing audio files on the Raspberry Pi.•	Store audio in WAV format and ensure proper sample rate management for clarity.•	Implement noise filtering and normalization techniques for clean recordings.•	Ensure compatibility with external microphones and speakers for both input and output on low-power hardware.•	Manage audio recording via hardware buttons using the RPi.GPIO library.

Database Management:

•	Use SQLite for efficient local storage and retrieval of image metadata, fingerprints, and audio file mappings.•	Store data such as book IDs, image fingerprints, extracted text, perceptual hashes, and audio file paths in an indexed SQLite table.•	Optimize database queries using indexes on book IDs for fast lookup across large datasets.

Hardware Integration (Raspberry Pi):

•	Use the RPi.GPIO library to integrate physical buttons that trigger recording and narration functionalities.•	Efficiently manage GPIO inputs for hardware-based user interactions.•	Prioritize low-memory and low-CPU usage strategies to maintain performance on Raspberry Pi models with 1-2 GB RAM.

Image and Audio Matching:

•	Implement algorithms that efficiently match images to audio files based on image fingerprints and extracted text.•	Prioritize quick matching by first searching within the current book context using book IDs, and only expanding to a global search when necessary.•	Combine multiple image processing techniques, such as perceptual hashing and text extraction, to improve accuracy.•	Use fallback strategies if the initial image match fails to locate the correct audio.

Error Handling and Debugging:

•	Use try-except blocks for error-prone operations like file I/O, camera input, and GPIO operations.•	Ensure robust error logging for hardware interactions and image/audio processing.•	Implement debugging techniques like visualizing image fingerprints and hashes to verify accuracy.

Performance Optimization:

•	Limit memory usage by converting images to grayscale and using lightweight data structures for storing image fingerprints.•	Implement batch processing for large datasets and optimize database queries using SQLite indices.•	Use low-level profiling tools to identify bottlenecks in image or audio processing on Raspberry Pi.•	Reduce resource load by offloading non-essential tasks (e.g., display operations) when using Raspberry Pi with low memory.

Dependencies:

•	opencv-python•	numpy•	sounddevice•	RPi.GPIO•	sqlite3•	scikit-image (for perceptual hashing)

Key Conventions:

1. Start by defining the problem and analyzing the input dataset (e.g., book pages, audio files).2. Use modular code structures with separate classes for handling images, audio, and hardware interactions.3. Create a configuration file (e.g., YAML or JSON) for setting up paths, hardware configurations, and thresholds.4. Maintain a clean and well-documented SQLite schema to store metadata for images and audio files.5. Use version control (e.g., Git) to track changes in code, database structure, and configurations.

Refer to the official documentation of OpenCV, SoundDevice, SQLite, and RPi.GPIO for best practices and updated APIs.

This set of rules should help guide the integration of image and audio processing, database management, and hardware control on a Raspberry Pi or similar low-spec device.

stack:

Python OpenCV NumPy Raspberry Pi .cursorrules prompt file

Jared Black

About .cursorrules prompt file

What you can build

Image and Audio Archive System for Libraries: Develop a Raspberry Pi-based system that uses OpenCV and SoundDevice to digitize books and associate audio recordings for library archives. Use SQLite for efficient metadata storage and RPi.GPIO for controlling the process with physical buttons.
Home Automation Assistant with Audio Commands: Create a home automation system using Raspberry Pi that processes audio commands via the sounddevice library and controls smart devices using RPi.GPIO. Optimize for low CPU and memory usage on Raspberry Pi.
Interactive Digital Picture Frame: Design a digital picture frame that uses image processing to change displayed images based on detected motion or presence. Utilize OpenCV for motion detection, and manage images with SQLite database for easy updates and retrieval.
Voice-Controlled Personal Journal: Implement a personal journaling system where users can record audio entries managed with SQLite on a Raspberry Pi. Use RPi.GPIO for starting and stopping recordings and integrate image processing for associating images with audio entries.
DIY Wildlife Observation Camera: Build a low-cost wildlife observation camera using Raspberry Pi that captures images with OpenCV and records sounds with the sounddevice library. Use perceptual hashing to detect new animal patterns and manage collected data with SQLite.
Smart Doorbell with Facial Recognition: Develop a smart doorbell system using Raspberry Pi equipped with a camera and microphone. Use OpenCV for facial recognition and sounddevice for communication, storing visitor information with SQLite for security analysis.
Customizable Automation Controller for Makers: Create a Raspberry Pi-based controller for DIY projects, utilizing RPi.GPIO for interfacing with sensors and actuators. Include image and audio processing functionalities with OpenCV and SoundDevice for custom applications.
Portable Audio-Visual Storytelling Device for Kids: Create an educational device where children interact with physical buttons to trigger audio stories paired with visual content, using Raspberry Pi with RPi.GPIO and managing the content database with SQLite.
Hands-Free Compliance Checker for Manufacturing: Design a system to verify manufactured products' compliance using OpenCV for visual checks and sounddevice for audible alerts. Manage process logs and check results with SQLite, integrated via Raspberry Pi.
Remote Nature Monitoring Station: Develop a remote monitoring system using Raspberry Pi, capturing images and audio data from nature settings for research purposes. Efficiently handle large datasets with batch processing and SQLite indexing.

Benefits

Comprehensive focus on Raspberry Pi compatibility, ensuring low memory and CPU usage strategies are prioritized for efficient processing.
Emphasis on using Python libraries for powerful feature extraction, perceptual hashing, and managing low-resources hardware interactions efficiently.
Encourages a structured approach using object-oriented and functional programming, following PEP 8, with version control for clean, maintainable code management.

Synopsis

Developers working on Raspberry Pi projects involving image and audio processing can use this prompt to build an efficient system for digitizing books with synchronized audio narration.

Overview of .cursorrules prompt

The .cursorrules file outlines a comprehensive set of guidelines for implementing image processing, audio manipulation, and hardware integration on Raspberry Pi using Python libraries such as OpenCV, NumPy, SoundDevice, SQLite, and RPi.GPIO. Key areas include concise technical responses with clear Python examples, efficient workflows prioritizing performance and memory optimization, and ensuring compatibility with low-spec hardware. The file emphasizes object-oriented and functional programming strategies, use of SQLite for database management, and integration with Raspberry Pi hardware through the RPi.GPIO library. It provides detailed strategies for image and audio processing, error handling, debugging, and performance optimization, along with the necessary dependencies and conventions for maintaining structured code and documentation.

Python OpenCV NumPy Raspberry Pi .cursorrules prompt file

Python OpenCV NumPy Raspberry Pi .cursorrules prompt file

Python OpenCV NumPy Raspberry Pi .cursorrules prompt file

About .cursorrules prompt file

What you can build

Benefits

Synopsis

Overview of .cursorrules prompt

Tags

Python OpenCV NumPy Raspberry Pi .cursorrules prompt file

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