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A Rust module using OpenCV bindings for real-time movement detection from video streams, ideal for security systems and smart environments.

License: Apache License 2.0

open-source opencv opencv-rust rust rust-lang rust-library

morv's Introduction

morv

This Rust module utilizes OpenCV bindings to implement real-time movement detection from video streams.

It provides algorithms for background subtraction, contour detection, and bounding box visualization, suitable for applications in security systems, smart environments, monitoring systems and more.

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morv's Issues

Profiling and Benchmarking the System

Description:

Implement profiling and benchmarking tools to identify bottlenecks and measure the performance of different components in the system.

Implementation Steps:

Select and integrate profiling tools compatible with Rust (e.g., cargo flamegraph, perf, criterion) to measure execution times and resource usage.
Identify key areas of the codebase that may impact performance, such as video capture, background subtraction, contour detection, and bounding box visualization.
Create benchmarks for these components, focusing on metrics such as frame processing time, memory usage, and CPU/GPU utilization.
Run the profiling tools on different hardware configurations to gather performance data across various environments.
Document the results, highlighting areas that require optimization, and create baseline performance metrics for future comparisons.

Background Subtraction Algorithm

Aim:

Implement background subtraction using MOG2/KNN

Description:

Develop a background subtraction algorithm using OpenCV's MOG2 or KNN to isolate moving objects from the background.
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Create Unit Tests for Bounding Box Visualization

Description:

Develop unit tests to validate the bounding box visualization functionality, ensuring it performs accurately and efficiently.

Implementation Steps:

Set up a testing framework in the Rust project using cargo test.
Write unit tests to verify that bounding boxes are correctly calculated for a variety of contours, including edge cases like very small or irregularly shaped contours.
Write unit tests to ensure that bounding boxes are drawn correctly on the original frame, maintaining accurate positions and sizes across different frames.
Write unit tests to validate the color-coding system, ensuring that it assigns and maintains unique colors for different objects.
Run all tests to confirm that the bounding box visualization is functioning as expected, documenting any issues and refining the implementation as needed.

Filter Out Small or Irrelevant Contours

Description:

Determine the criteria for filtering contours, such as a minimum contour area or perimeter.

Implementation Steps:

Determine the criteria for filtering contours, such as a minimum contour area or perimeter.
Implement logic to iterate through the detected contours and filter out those that do not meet the specified criteria.
Store only the filtered contours that represent significant objects in the scene for further processing.
Test the filtering process by displaying only the filtered contours on the original frame to ensure that noise is effectively removed.
Experiment with different filtering criteria to balance sensitivity and accuracy, documenting the optimal settings.

Document and Review Integration Testing Results

Description:

Document the results of the integration testing process, including any issues found and how they were resolved, and review the findings with the team.

Implementation Steps:

Compile the results from both automated and manual integration testing, including details on test cases executed, outcomes, and any issues encountered.
Create a report that summarizes the overall performance and reliability of the integrated system, highlighting areas of strength and potential risks.
Document the steps taken to resolve any integration issues, including code changes, optimizations, and retesting efforts.
Review the integration testing report with the development team, discussing any unresolved issues and planning further actions if necessary.
Store the documentation in the project repository, making it accessible for future reference and for use in ongoing development and maintenance.

Allow Dynamic Switching Between MOG2 and KNN

Description:

Implement functionality to dynamically switch between MOG2 and KNN background subtraction algorithms at runtime.

Implementation Steps:

Define a configuration option or command-line parameter to specify which background subtraction algorithm to use (MOG2 or KNN).
Implement logic to initialize the selected background subtractor (MOG2 or KNN) based on user input at the start of the program.
Allow users to switch between algorithms during runtime without restarting the application, possibly through key bindings or a GUI option.
Ensure the switching process is smooth, with minimal disruption to the video processing loop.
Test the dynamic switching functionality by running the module, switching between MOG2 and KNN during execution, and observing the results.

Bounding Box Visualization

Aim:

Implement bounding box visualization around detected objects

Description:

Draw bounding boxes around the detected objects to highlight movement in the video stream.
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Allow Switching Between Camera and Video File Input

Description:

Implement functionality that allows the user to switch between live camera input and video file input dynamically during runtime.

Implementation Steps:

Design a configuration or command-line interface that allows users to specify whether they want to use a camera or a video file as input.
Implement logic in the module to switch between camera capture and video file loading based on the user’s input.
Ensure the switch is seamless, without requiring the application to restart.
Provide clear instructions and examples in the documentation on how users can switch inputs.
Test the switching functionality by running the module with both input types and toggling between them during execution.

Validate Performance Under Integrated Conditions

Description:

Validate the performance of the system when all modules are integrated, ensuring that the system meets performance requirements in real-time operation.

Implementation Steps:

Set up performance benchmarks that simulate full system operation, measuring key metrics such as frame rate, latency, and resource usage.
Run the integrated system on different hardware configurations to assess how well it performs under various conditions.
Test the system's ability to handle high-load scenarios, such as processing high-resolution video streams with multiple moving objects.
Compare the performance data against the baseline metrics established during individual module testing, identifying any regressions or bottlenecks.
Optimize the system as needed to ensure it meets the required performance standards, retesting until the desired performance is achieved.

Create Documentation and Guidelines for Parameter Tuning

Description:

Develop comprehensive documentation and guidelines to help users understand and effectively tune parameters for different environments and use cases.

Implementation Steps:

Document each parameter in detail, including its impact on the system, typical use cases, and suggested ranges.
Provide step-by-step guidelines on how to manually tune parameters for common scenarios, such as detecting slow-moving objects or handling sudden lighting changes.
Include a troubleshooting section in the documentation to help users address issues such as poor detection accuracy or high false positive rates.
Create example configuration files for different environments (e.g., indoor, outdoor, low-light) to serve as starting points for users.
Publish the documentation as part of the project’s official documentation, ensuring it is easily accessible through the UI and online resources.

Implement Color-Coding for Multiple Objects

Description:

Implement a system to assign different colors to bounding boxes based on the object they represent, enhancing visual differentiation between multiple objects.

Implementation Steps:

Define a color scheme where each unique object (tracked contour) is assigned a specific color.
Implement logic to track which color corresponds to which object ID and ensure consistency across frames.
Modify the bounding box drawing function to use the assigned color for each object.
Handle cases where new objects enter the frame, ensuring they are assigned a new color without conflicting with existing objects.
Test the color-coding system by running the module on video sequences with multiple moving objects and verifying that each object is consistently colored.

Implement a System for Saving and Loading Parameter Configurations

Description:

Develop functionality to save tuned parameters into configuration files and load them during subsequent sessions, allowing users to maintain optimized settings.

Implementation Steps:

Design a configuration file format (e.g., JSON, TOML) to store parameter values, including comments explaining each parameter.
Implement functionality to save the current parameter settings to a file via the UI or command-line options.
Implement functionality to load parameter settings from a saved configuration file when starting the application.
Ensure that loading a configuration file correctly updates the parameters in real-time, reflecting changes in the UI and video processing pipeline.
Test the save and load functionality by creating, saving, loading, and applying different parameter configurations, ensuring the application behaves as expected.

Implement Dynamic Parameter Adjustment

Description:

Implement functionality to allow dynamic adjustment of key parameters during runtime, enabling real-time tuning and optimization.

Implementation Steps:

Create a system that allows parameters to be adjusted dynamically through the UI, without needing to restart the application.
Implement real-time listeners or callbacks that trigger updates in the video processing pipeline when a parameter is changed
Ensure that changes in parameters immediately affect the behavior of the background subtraction, contour detection, and bounding box visualization algorithms.
Test the dynamic adjustment by changing parameters in the UI and observing how these changes impact the video output.
Implement safeguards to prevent setting parameters to values that could cause the system to crash or behave unpredictably.

Performance Optimization

Aim:

Optimize the module for real-time performance

Description:

Ensure the module runs efficiently in real-time, minimizing latency and maximizing frame rate.
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Add Unit Tests for Video Capture Module

Description:

Create comprehensive unit tests to ensure the video capture functionality works correctly and handles edge cases.

Implementation Steps:

Set up a testing framework in the Rust project using cargo test.
Write unit tests to verify that the camera capture initializes and retrieves frames correctly.
Write unit tests to verify that video file loading works with different file formats and scenarios.
Write unit tests to ensure error handling mechanisms work as expected, simulating common failures.
Run all tests to ensure the video capture functionality is robust and ready for deployment.

Implement Settings Panel

Description:

Develop a settings panel where users can configure advanced options and customize the behavior of the application.

Implementation Steps:

Identify and list the advanced settings that should be configurable, such as video source selection, algorithm parameters, and UI preferences.
Implement a separate section or popup window within the UI for the settings panel, accessible via a settings button in the main interface.
Create input fields, sliders, and dropdowns within the settings panel to allow users to configure each option.
Implement functionality to save and load these settings, ensuring that user preferences are persisted across sessions.
Test the settings panel by adjusting various options and verifying that they take effect immediately or after a restart, as appropriate.

Contour Detection

Aim:

Implement contour detection for isolated objects

Description:

Detect the contours of moving objects in the video stream using OpenCV’s contour detection methods.
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Create Unit Tests for Contour Detection

Description:

Develop unit tests to validate the contour detection and tracking system, ensuring its accuracy and robustness in different scenarios.

Implementation Steps:

Set up a testing framework in the Rust project using cargo test.
Write unit tests to verify that the findContours function accurately detects contours in a variety of test images with different shapes and sizes.
Write unit tests to ensure that the contour filtering process correctly removes noise and irrelevant contours while preserving meaningful ones.
Write unit tests to validate the contour tracking system, ensuring that it consistently assigns and updates IDs across frames.
Run all tests to confirm that the contour detection and tracking functionality works as expected, documenting any issues and iterating on the implementation as necessary.

User Interface

Aim:

Implement a simple UI for real-time video display

Description:

Use OpenCV’s imshow to display the processed video with bounding boxes in real-time.
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Track and Label Detected Contours

Description:

Implement a system to track detected contours across multiple frames and assign unique labels to each moving object.

Implementation Steps:

Create a tracking system that assigns unique IDs to each detected contour based on its position and movement across frames.
Implement logic to update the position of each contour in subsequent frames, maintaining the same ID if the object is still in view.
Handle cases where contours merge, split, or exit the frame, ensuring that labels are updated or removed as necessary.
Display the label for each tracked contour on the original frame, showing the object’s ID and position.
Test the tracking and labeling system by running the module on video sequences with multiple moving objects and verifying that the labels remain consistent.

Optimize Memory Usage

Description:

Optimize memory management to reduce the application’s memory footprint, preventing leaks and ensuring efficient resource utilization.

Implementation Steps:

Review the codebase for memory-intensive operations, such as storing video frames, contours, and bounding boxes.
Implement memory pooling or recycling techniques to reuse allocated memory buffers for video frames, reducing the need for frequent allocations and deallocations.
Minimize the usage of large data structures by processing data in chunks or using more memory-efficient representations (e.g., storing contours as approximated polygons instead of raw points).
Use Rust's ownership and borrowing system to ensure memory is correctly managed, preventing leaks and double-free errors.
Test memory usage by running the application over extended periods and in scenarios with high object density to ensure stable performance.

Optimize Algorithmic Efficiency

Description:

Review and optimize the algorithms used in the system to reduce computational complexity, improving both speed and accuracy.

Implementation Steps:

Analyze the current algorithms for background subtraction, contour detection, and bounding box visualization to identify inefficiencies or redundancies.
Explore alternative algorithms or optimizations, such as using approximations or simplified models, to achieve similar results with lower computational cost.
Implement algorithmic enhancements like adaptive thresholding, dynamic contour filtering, or region-based processing to reduce the number of operations per frame.
Evaluate the trade-offs between accuracy and performance, ensuring that optimizations do not significantly degrade the quality of detection and visualization.
Test the optimized algorithms across a variety of video sources and conditions, verifying that they consistently improve performance without sacrificing accuracy.

Implement KNN Background Subtraction

Description:

Implement the KNN (K-Nearest Neighbors) algorithm as an alternative method for background subtraction.

Implementation Steps:

Import the BackgroundSubtractorKNN module from OpenCV.
Initialize the KNN background subtractor using BackgroundSubtractorKNN::default.
Modify the video processing loop to apply the KNN background subtractor to each frame, similar to the MOG2 implementation.
Retrieve and display the foreground mask generated by the KNN algorithm, comparing it with the MOG2 output.
Experiment with and adjust KNN-specific parameters such as the history, distance threshold, and detect shadows to optimize performance.

Implement Video Display Area

Description:

Develop the UI component that displays the video stream, including real-time rendering of the processed frames with bounding boxes and contours.

Implementation Steps:

Create a widget or component in the UI to serve as the video display area, where the video frames will be rendered.
Implement functionality to stream the processed video frames from the background subtraction and contour detection pipeline to this display area.
Ensure the video display area updates in real-time, reflecting changes such as bounding box visualization and contour detection.
Handle different video resolutions and aspect ratios, ensuring the video is displayed correctly without distortion.
Test the video display area by running the module with live video input and checking that the video stream appears correctly and smoothly.

Implement Automated Integration Tests

Description:

Develop automated integration tests that verify the correct interaction between modules, ensuring the system behaves as expected when components are combined.

Implementation Steps:

Set up an automated testing framework compatible with Rust (e.g., cargo test, proptest, rstest) to run integration tests.
Write test cases that simulate interactions between modules, such as passing video frames through the entire processing pipeline and verifying the output.
Implement tests that cover key scenarios like starting and stopping video capture, switching algorithms, and adjusting parameters in real-time.
Include tests that simulate error conditions, such as loss of video input or invalid parameter values, to ensure the system handles them gracefully.
Run the automated tests regularly as part of the CI/CD pipeline, ensuring that new changes do not introduce integration issues.

Apply Thresholding to Foreground Mask

Description:

Import the necessary OpenCV functions for thresholding, such as threshold.

Implementation Steps:

Import the necessary OpenCV functions for thresholding, such as threshold.
Apply a binary threshold to the foreground mask where pixel values greater than a set threshold are converted to white (255) and the rest to black (0).
Experiment with different threshold values to determine the optimal level for isolating the moving objects from the background.
Ensure the thresholding process is applied efficiently in the video processing loop.
Test the thresholding by displaying the binary image and checking that it accurately represents the areas of movement.

Draw Bounding Boxes on the Original Frame

Description:

Implement functionality to overlay the calculated bounding boxes onto the original video frame for visual representation.

Implementation Steps:

Import the necessary OpenCV functions for drawing, particularly rectangle.
Iterate over the list of bounding boxes and use the rectangle function to draw each one on the original frame.
Choose appropriate colors, thickness, and styles for the bounding boxes to ensure they are clearly visible.
Ensure that the drawing process is integrated into the main video processing loop, so the boxes update in real-time.
Test the drawing functionality by running the module and verifying that the bounding boxes appear correctly around the moving objects in each frame.

Video Capture Implementation

Aim:

Implement video capture from camera or video file

Description:

Implement functionality to capture video streams from a connected camera or load a video file for processing.
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Create Unit Tests for Background Subtraction

Description:

Develop comprehensive unit tests to validate the functionality and robustness of both the MOG2 and KNN background subtraction implementations.

Implementation Steps:

Set up a testing framework in the Rust project using cargo test.
Write unit tests to verify that the MOG2 background subtractor correctly identifies moving objects and generates accurate foreground masks.
Write unit tests to verify the KNN background subtractor's performance under various conditions (e.g., different lighting, motion speeds).
Create test cases that simulate common scenarios, such as sudden changes in lighting or the introduction of new static objects, to ensure the algorithms handle these cases gracefully.
Run all tests to validate the background subtraction functionality, ensuring that both MOG2 and KNN produce consistent and reliable results.

Implement camera capture functionality

Description:

Set up the module to capture video from a connected camera using OpenCV.
Ensure that the camera capture initializes correctly and retrieves frames in real-time.

Implementation Steps:

Import the necessary OpenCV crates and modules into the Rust project.
Use VideoCapture::new to open the default camera (ID 0) and start capturing video.
Implement a loop that continuously captures frames from the camera and processes them.
Test the camera capture functionality by displaying the captured frames using OpenCV’s imshow.

Implement Contour Detection Using OpenCV

Description:

Detect contours in the binary image created by the thresholding step to identify the boundaries of moving objects.

Implementation Steps:

Import the findContours function from OpenCV, which is used to detect contours in binary images.
Apply the findContours function to the thresholded binary image to extract the contours of the moving objects.
Configure the contour retrieval mode (e.g., RETR_EXTERNAL to retrieve only the external contours) and contour approximation method (e.g., CHAIN_APPROX_SIMPLE).
Store the detected contours in a data structure for further processing, such as drawing bounding boxes.
Test the contour detection by drawing the detected contours on the original frame using drawContours and displaying the result.

Develop Integration Test Plan

Description:

Create a detailed integration test plan outlining the scope, objectives, and approach for testing the interactions between different modules.

Implementation Steps:

Identify the key modules and components that need to be tested in combination, such as video capture, background subtraction, contour detection, and bounding box visualization.
Define the objectives of integration testing, focusing on validating the correctness of interactions and ensuring seamless integration between modules.
Outline test scenarios that cover typical workflows, edge cases, and potential failure points, such as switching background subtraction algorithms during a live stream.
Create a timeline for executing the integration tests, specifying when each module should be tested in relation to others.
Document the integration test plan, including test cases, expected results, and acceptance criteria, and review it with the development team for feedback.

Implement Control Panel for User Interactions

Description:

Develop a control panel in the UI that allows users to interact with the application, such as starting/stopping the video stream, switching algorithms, and adjusting settings.

Implementation Steps:

Identify the key controls needed in the panel, such as play/pause buttons, algorithm selection dropdown, and sliders for adjusting parameters.
Implement buttons for starting and stopping the video stream, linking them to the underlying video capture logic.
Create dropdown menus or toggles to allow users to switch between different background subtraction algorithms (MOG2, KNN).
Implement sliders or input fields to adjust parameters like threshold values, contour filtering criteria, and bounding box settings.
Test the control panel by interacting with each control and verifying that the corresponding changes are reflected in the video processing and visualization.

Leverage Hardware Acceleration

Description:

Implement hardware acceleration techniques to offload processing tasks to the GPU, improving the overall performance of the application.

Implementation Steps:

Identify components of the system that can benefit from GPU acceleration, such as background subtraction, contour detection, and video decoding.
Integrate GPU-accelerated libraries (e.g., OpenCV’s CUDA modules, Vulkan) into the Rust project, ensuring compatibility with the existing pipeline.
Implement fallbacks for systems that do not support GPU acceleration, allowing the application to run on both high-end and low-end hardware.
Test the GPU-accelerated version of the application on compatible hardware, comparing its performance to the CPU-only version.
Document the requirements and setup process for enabling hardware acceleration, making it easy for users to take advantage of this feature.

Design the User Interface Layout

Description:

Create a design layout for the user interface, ensuring it is user-friendly and provides easy access to all the features of the application.

Implementation Steps:

Outline the key components that need to be displayed on the UI, such as the video stream, control buttons, and settings panel.
Design a mockup of the UI layout using tools like Figma or Sketch, detailing where each component will be positioned.
Define the layout structure in code using a Rust GUI framework (e.g., egui, gtk-rs, or imgui-rs).
Ensure the layout is responsive and adapts to different screen sizes, maintaining usability on various devices.
Review the layout with the team or stakeholders for feedback and refine it based on their input before moving to implementation.

Handle Video Stream Errors

Description:

Implement robust error handling for potential issues during video capture, such as loss of camera connection or corrupted video files.

Implementation Steps:

Identify possible error scenarios during video capture (e.g., camera disconnects, video file corruption).
Implement error detection in the video capture loop, using checks like is_opened() and read() returning Result.
Create custom error messages that provide clear feedback to the user about what went wrong.
Implement retry logic or graceful degradation (e.g., attempt to reconnect to the camera, skip corrupted frames).
Write unit tests to simulate and handle these error scenarios.

Implement Real-Time Feedback and Status Display

Description:

Add a status bar or notification area in the UI that provides real-time feedback to the user about the current state of the application.

Implementation Steps:

Design a status bar or notification area that fits within the overall UI layout, ensuring it is easily visible without obstructing other components.
Implement functionality to display real-time status messages, such as "Video Stream Started," "Switched to MOG2 Algorithm," or "Error: Camera Not Found."
Add indicators for current settings, such as the selected background subtraction algorithm, video source, and any active filters.
Implement error handling and feedback mechanisms to notify the user of issues, such as video stream interruptions or unsupported video formats.
Test the status display by running the application and verifying that all relevant feedback is shown promptly and clearly during different operations.

Develop an Automated Parameter Optimization Tool

Description:

Create a tool that automatically optimizes parameters based on predefined criteria, such as maximizing detection accuracy or minimizing false positives.

Implementation Steps:

Define the criteria for optimization, such as maximizing the accuracy of detected contours or minimizing the processing time.
Implement an optimization algorithm (e.g., grid search, random search, or genetic algorithms) that iterates through different parameter combinations to find the optimal settings.
Run the optimization tool against sample video streams, logging the results and identifying the best-performing parameter sets.
Integrate the optimization tool into the application, allowing users to trigger automated tuning sessions from the UI.
Test the tool by running it in different environments (e.g., indoor, outdoor) and verifying that it consistently improves performance.

Implement MOG2 Background Subtraction

Description:

Implement the MOG2 (Mixture of Gaussians) algorithm for background subtraction to isolate moving objects from the background.

Implementation Steps:

Import the necessary OpenCV modules required for background subtraction, particularly the BackgroundSubtractorMOG2.
Initialize the MOG2 background subtractor using BackgroundSubtractorMOG2::default.
Integrate the background subtractor into the video processing loop, applying it to each captured frame.
Retrieve the foreground mask (binary image) where the moving objects are white, and the background is black.
Test the implementation by displaying the foreground mask and adjusting parameters such as the history, variance threshold, and detect shadows.

Documentation

Aim:

Write comprehensive documentation for the module

Description:

Document the module’s setup, usage, and customization options to make it easy for others to integrate into their systems.
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Identify Key Parameters for Tuning

Description:

Identify and list the key parameters that significantly impact the performance and accuracy of the background subtraction, contour detection, and bounding box visualization algorithms.

Implementation Steps:

Review the algorithms implemented for background subtraction (MOG2, KNN), contour detection, and bounding box visualization.
Identify critical parameters such as history, threshold, detect_shadows, min_contour_area, and bounding box padding.
Document each parameter, explaining its role, range of acceptable values, and how it influences the outcome.
Group the parameters based on their association with specific components (e.g., background subtraction, contour detection).
Create a baseline configuration file or structure in the code that holds default values for these parameters, which can be modified later.

Integration Testing

Aim:

Test the module with various video sources

Description:

Test the module across different lighting conditions and environments to ensure robustness.
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Implement Video File Loading

Description:

Add functionality to load and process video from a file instead of live camera input.
This will allow users to run the module on pre-recorded videos for testing or analysis.

Implementation Steps:

Modify the existing video capture code to support loading video files using VideoCapture::from_file.
Implement a mechanism to check if the input source is a camera or a video file, and load the appropriate source accordingly.
Ensure the module can handle different video file formats (e.g., MP4, AVI).
Implement error handling for cases where the video file is missing or cannot be loaded.
Test the video file loading functionality by running the module with various video files and displaying the frames.

CI/CD Setup

Aim:

Set up continuous integration and deployment

Description:

Implement CI/CD pipelines for automated testing and deployment of the module.
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Display Object Information Within Bounding Boxes

Description:

Enhance the bounding box visualization by displaying additional information (e.g., object ID, size) within or near the bounding boxes.

Implementation Steps:

Decide on the information to be displayed within the bounding boxes, such as object ID, position, or area.
Import the necessary OpenCV functions for text rendering, particularly putText.
Implement logic to calculate the position for the text to be displayed within or near the bounding box without obstructing the visualization.
Ensure the text is clearly readable by choosing an appropriate font size, color, and background.
Test the display functionality by running the module and verifying that the information appears correctly for each object in real-time.

Perform Manual Integration Testing

Description:

Conduct manual integration testing to complement automated tests, focusing on complex scenarios and user interactions that may not be fully covered by automation.

Implementation Steps:

Execute the test scenarios outlined in the integration test plan manually, focusing on areas where automated tests may not fully capture user experience.
Test the entire workflow from video capture to visualization, checking for issues like delays, incorrect detections, or UI glitches during transitions.
Simulate real-world use cases, such as adjusting parameters while the system is processing a live stream, and observe the behavior of the application.
Document any issues or anomalies encountered during manual testing, providing detailed descriptions, steps to reproduce, and possible causes.
Work closely with developers to debug and resolve issues found during manual testing, iterating on the process until the system functions as expected.

Parameter Tuning

Aim:

Fine-tune background subtraction and contour detection parameters

Description:

Adjust the parameters for background subtraction and contour detection to optimize performance in different scenarios.
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Calculate Bounding Boxes for Detected Contours

Description:

Implement functionality to calculate bounding boxes around each detected contour, which will visually represent the moving objects.

Implementation Steps:

Import the necessary OpenCV functions for bounding box calculation, specifically boundingRect.
Iterate over the filtered contours and apply boundingRect to each, generating a rectangle that encloses the contour.
Store the calculated bounding boxes in a data structure that can be easily accessed and modified later.
Ensure the bounding boxes are updated in real-time as new contours are detected or existing ones move.
Test the bounding box calculation by drawing the rectangles on the original frame and verifying they accurately enclose the detected objects.

Optimize Background Subtraction Performance

Description:

Analyze and optimize the performance of the background subtraction algorithms to ensure they run efficiently in real-time.

Implementation Steps:

Profile the performance of the MOG2 and KNN algorithms using tools like perf or valgrind to identify bottlenecks.
Experiment with different parameter settings (e.g., history length, shadow detection) to find the optimal balance between accuracy and speed.
Implement optimizations such as multi-threading or parallel processing to improve performance, especially for high-resolution video streams.
Minimize memory usage by optimizing the handling of foreground masks and reducing unnecessary allocations.
Document the performance benchmarks and provide guidelines on how to tune the parameters for different environments (e.g., indoor vs. outdoor).

Optimize Video Processing Pipeline

Description:

Optimize the video processing pipeline to reduce latency and increase the frame processing rate, ensuring smooth real-time operation.

Implementation Steps:

Analyze the current video processing pipeline to identify stages that introduce delays, such as frame capture, processing, and rendering.
Implement optimizations like frame skipping, where frames are dropped under high load to maintain real-time performance.
Leverage multi-threading or parallel processing to offload intensive tasks, such as background subtraction or contour detection, to separate threads.
Consider using Rust's asynchronous features (async/await) to handle I/O-bound tasks, reducing blocking operations.
Test the optimized pipeline under different conditions (e.g., high-resolution video, multiple moving objects) to ensure consistent performance improvements.