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Add millimeters in solutions/distance_caculation.py + object-cropping.md visuals (#7860)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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docs/en/guides/index.md
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@ -37,7 +37,7 @@ Here's a compilation of in-depth guides to help you master different aspects of
- [Objects Counting in Regions](region-counting.md) 🚀 NEW: Explore counting objects in specific regions with Ultralytics YOLOv8 for precise and efficient object detection in varied areas.
- [Security Alarm System](security-alarm-system.md) 🚀 NEW: Discover the process of creating a security alarm system with Ultralytics YOLOv8. This system triggers alerts upon detecting new objects in the frame. Subsequently, you can customize the code to align with your specific use case.
- [Heatmaps](heatmaps.md) 🚀 NEW: Elevate your understanding of data with our Detection Heatmaps! These intuitive visual tools use vibrant color gradients to vividly illustrate the intensity of data values across a matrix. Essential in computer vision, heatmaps are skillfully designed to highlight areas of interest, providing an immediate, impactful way to interpret spatial information.
- [Instance Segmentation with Object Tracking](instance-segmentation-and-tracking.md) 🚀 NEW: Explore our feature on Object Segmentation in Bounding Boxes Shape, providing a visual representation of precise object boundaries for enhanced understanding and analysis.
- [Instance Segmentation with Object Tracking](instance-segmentation-and-tracking.md) 🚀 NEW: Explore our feature on [Object Segmentation](https://docs.ultralytics.com/tasks/segment/) in Bounding Boxes Shape, providing a visual representation of precise object boundaries for enhanced understanding and analysis.
- [VisionEye View Objects Mapping](vision-eye.md) 🚀 NEW: This feature aim computers to discern and focus on specific objects, much like the way the human eye observes details from a particular viewpoint.
- [Speed Estimation](speed-estimation.md) 🚀 NEW: Speed estimation in computer vision relies on analyzing object motion through techniques like [object tracking](https://docs.ultralytics.com/modes/track/), crucial for applications like autonomous vehicles and traffic monitoring.
- [Distance Calculation](distance-calculation.md) 🚀 NEW: Distance calculation, which involves measuring the separation between two objects within a defined space, is a crucial aspect. In the context of Ultralytics YOLOv8, the method employed for this involves using the bounding box centroid to determine the distance associated with user-highlighted bounding boxes.

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docs/en/guides/object-cropping.md
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@ -16,6 +16,15 @@ Object cropping with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
- **Reduced Data Volume**: By extracting only relevant objects, object cropping helps in minimizing data size, making it efficient for storage, transmission, or subsequent computational tasks.
- **Enhanced Precision**: YOLOv8's object detection accuracy ensures that the cropped objects maintain their spatial relationships, preserving the integrity of the visual information for detailed analysis.
## Visuals
| Airport Luggage |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| ![Conveyor Belt at Airport Suitcases Cropping using Ultralytics YOLOv8](https://github.com/RizwanMunawar/RizwanMunawar/assets/62513924/648f46be-f233-4307-a8e5-046eea38d2e4) |
| Suitcases Cropping at airport conveyor belt using Ultralytics YOLOv8 |
!!! Example "Object Cropping using YOLOv8 Example"
=== "Object Cropping"