Highlight the closest target with a different color in predictions

[1260635600]

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This pull request adds a feature to highlight the closest detected target with a different color in the predictions made by YOLOv5. The closest target is determined based on its distance from a reference point.

Changes made:

• Added a function find_closest_target in detect.py to calculate and identify the closest target.
• Updated the drawing function to change the color of the closest target.
• Modified the detect function to incorporate the new highlighting feature.
• Added relevant tests to ensure the functionality works as expected.

Testing:

• Verified the changes using sample images to ensure the closest target is correctly highlighted.
• Ran all existing unit tests and added new ones for the new feature.

This feature enhances the detection capabilities of YOLOv5, making it easier to identify the nearest object, which can be useful in applications like autonomous navigation and object tracking.

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Enhanced object detection in YOLOv5 with closer object highlight and streamlined code for performance.

📊 Key Changes

• Numpy Integration: Added numpy for advanced numerical calculations.
• Code Simplification: Simplified detection algorithms removing redundant conditions, focusing on streamlining and performance optimization.
• Closest Object Highlight: Implemented functionality to calculate and highlight the object closest to the image center, making it stand out by marking it in green.
• Code Cleanup: Removed unnecessary comments and simplified block comments for clarity and conciseness.

🎯 Purpose & Impact

• Purpose:
  • To make the detection process more intuitive by visually emphasizing the object closest to the image center.
  • To improve the readability and performance of the code by removing redundant operations and simplifying complex constructs.
• Impact:
  • Developers: These changes make the codebase cleaner, enhancing maintainability and ease of future enhancements.
  • Users: Users benefit from the new feature that highlights the closest object, potentially improving usability in applications requiring focus on central objects, such as autonomous navigation or surveillance.

📈 Expect upgrades in performance and user experience for applications leveraging YOLOv5 for object detection. 🕵️‍♂️🔍

[github-actions]

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I have read the CLA Document and I sign the CLA

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1 out of 2 committers have signed the CLA.
✅ (UltralyticsAssistant)[https://github.com/UltralyticsAssistant]
❌ @1260635600
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[github-actions]

👋 Hello @1260635600, thank you for submitting a YOLOv5 🚀 PR! To allow your work to be integrated as seamlessly as possible, we advise you to:

• ✅ Verify your PR is up-to-date with ultralytics/yolov5 master branch. If your PR is behind you can update your code by clicking the 'Update branch' button or by running git pull and git merge master locally.
• ✅ Verify all YOLOv5 Continuous Integration (CI) checks are passing.
• ✅ Reduce changes to the absolute minimum required for your bug fix or feature addition. "It is not daily increase but daily decrease, hack away the unessential. The closer to the source, the less wastage there is." — Bruce Lee