ultralytics/docs/en/solutions/index.md

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Ultralytics Solutions: Harness YOLO11 to Solve Real-World Problems

Ultralytics Solutions provide cutting-edge applications of YOLO models, offering real-world solutions like object counting, blurring, and security systems, enhancing efficiency and accuracy in diverse industries. Discover the power of YOLO11 for practical, impactful implementations.

Watch: How to Run Ultralytics Solutions from the Command Line (CLI) | Ultralytics YOLO11 🚀

Solutions

Here's our curated list of Ultralytics solutions that can be used to create awesome computer vision projects.

• Object Counting: Learn to perform real-time object counting with YOLO11. Gain the expertise to accurately count objects in live video streams.
• Object Cropping: Master object cropping with YOLO11 for precise extraction of objects from images and videos.
• Object Blurring: Apply object blurring using YOLO11 to protect privacy in image and video processing.
• Workouts Monitoring: Discover how to monitor workouts using YOLO11. Learn to track and analyze various fitness routines in real time.
• Objects Counting in Regions: Count objects in specific regions using YOLO11 for accurate detection in varied areas.
• Security Alarm System: Create a security alarm system with YOLO11 that triggers alerts upon detecting new objects. Customize the system to fit your specific needs.
• Heatmaps: Utilize detection heatmaps to visualize data intensity across a matrix, providing clear insights in computer vision tasks.
• Instance Segmentation with Object Tracking: Implement instance segmentation and object tracking with YOLO11 to achieve precise object boundaries and continuous monitoring.
• VisionEye View Objects Mapping: Develop systems that mimic human eye focus on specific objects, enhancing the computer's ability to discern and prioritize details.
• Speed Estimation: Estimate object speed using YOLO11 and object tracking techniques, crucial for applications like autonomous vehicles and traffic monitoring.
• Distance Calculation: Calculate distances between objects using bounding box centroids in YOLO11, essential for spatial analysis.
• Queue Management: Implement efficient queue management systems to minimize wait times and improve productivity using YOLO11.
• Parking Management: Organize and direct vehicle flow in parking areas with YOLO11, optimizing space utilization and user experience.
• Analytics: Conduct comprehensive data analysis to discover patterns and make informed decisions, leveraging YOLO11 for descriptive, predictive, and prescriptive analytics.
• Live Inference with Streamlit: Leverage the power of YOLO11 for real-time object detection directly through your web browser with a user-friendly Streamlit interface.
• Track Objects in Zone: Learn how to track objects within specific zones of video frames using YOLO11 for precise and efficient monitoring.
• Similarity search 🚀 NEW: Enable intelligent image retrieval by combining OpenAI CLIP embeddings with Meta FAISS, allowing natural language queries like "person holding a bag" or "vehicles in motion."

Solutions Arguments

{% from "macros/solutions-args.md" import param_table %} {{ param_table() }}

!!! note "Track args"

 Solutions also support some of the arguments from `track`, including parameters such as `conf`, `line_width`, `tracker`, `model`, `show`, `verbose` and `classes`.

{% from "macros/track-args.md" import param_table %} {{ param_table(["tracker", "conf", "iou", "classes", "verbose", "device"]) }}

!!! note "Visualization args"

You can use `show_conf`, `show_labels`, and other mentioned arguments to customize the visualization.

{% from "macros/visualization-args.md" import param_table %} {{ param_table(["show", "line_width", "show_conf", "show_labels"]) }}

Usage of SolutionAnnotator

All Ultralytics Solutions use the separate class SolutionAnnotator, that extends the main Annotator class, and have the following methods:

Method	Return Type	Description
draw_region()	None	Draws a region using specified points, colors, and thickness.
queue_counts_display()	None	Displays queue counts in the specified region.
display_analytics()	None	Displays overall statistics for parking lot management.
estimate_pose_angle()	float	Calculates the angle between three points in an object pose.
draw_specific_points()	None	Draws specific keypoints on the image.
plot_workout_information()	None	Draws a labeled text box on the image.
plot_angle_and_count_and_stage()	None	Visualizes angle, step count, and stage for workout monitoring.
plot_distance_and_line()	None	Displays the distance between centroids and connects them with a line.
display_objects_labels()	None	Annotates bounding boxes with object class labels.
sweep_annotator()	None	Visualize a vertical sweep line and optional label.
visioneye()	None	Maps and connects object centroids to a visual "eye" point.
adaptive_label()	None	Draw a circular or rectangle background shape label in center of a bounding box.

Working with SolutionResults

Except Similarity Search, each Solution calls return a list of SolutionResults object.

• For object counting, the results include in_count, out_count, and classwise_count.

!!! example "SolutionResults"

```python
import cv2

from ultralytics import solutions

im0 = cv2.imread("path/to/img")

region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360)]

counter = solutions.ObjectCounter(
    show=True,  # display the output
    region=region_points,  # pass region points
    model="yolo11n.pt",  # model="yolo11n-obb.pt" for object counting with OBB model.
    # classes=[0, 2],  # count specific classes i.e. person and car with COCO pretrained model.
    # tracker="botsort.yaml"  # Choose trackers i.e "bytetrack.yaml"
)
results = counter(im0)
print(results.in_count)  # display in_counts
print(results.out_count)  # display out_counts
print(results.classwise_count)  # display classwise_count
```

SolutionResults object have the following attributes:

Attribute	Type	Description
plot_im	np.ndarray	Image with visual overlays such as counts, blur effects, or solution-specific enhancements.
in_count	int	Total number of objects detected entering the defined zone in the video stream.
out_count	int	Total number of objects detected exiting the defined zone in the video stream.
classwise_count	Dict[str, int]	Dictionary recording class-wise in/out object counts for advanced analytics.
queue_count	int	Number of objects currently within a predefined queue or waiting area (suitable for queue management).
workout_count	int	Total number of workout repetitions completed during exercise tracking.
workout_angle	float	Calculated joint or pose angle during workout for form assessment.
workout_stage	str	Current workout stage or movement phase (e.g., 'up', 'down').
pixels_distance	float	Pixel-based distance between two objects or points e.g., bounding boxes. (Suitable for distance calculation).
available_slots	int	Number of unoccupied slots in a monitored area (suitable for parking management).
filled_slots	int	Number of occupied slots in a monitored area. (suitable for parking management)
email_sent	bool	Indicates whether a notification or alert email has been successfully sent (suitable for security alarm).
total_tracks	int	Total number of unique object tracks observed during video analysis.
region_counts	Dict[str, int]	Object counts within user-defined regions or zones.
speed_dict	Dict[str, float]	Track-wise dictionary of calculated object speeds, useful for velocity analysis.
total_crop_objects	int	Total number of cropped object images generated by the ObjectCropper solution.
speed	Dict[str, float]	Dictionary containing performance metrics for tracking and solution processing.

For more details, refer to the SolutionResults class documentation.

Solutions Usage via CLI

!!! tip "Command Info"

Most of the Solutions can be used directly through the command-line interface, including:

`Count`, `Crop`, `Blur`, `Workout`, `Heatmap`, `Isegment`, `Visioneye`, `Speed`, `Queue`, `Analytics`, `Inference`

**Syntax**

    yolo SOLUTIONS SOLUTION_NAME ARGS

- **SOLUTIONS** is a required keyword.
- **SOLUTION_NAME** is one of: `['count', 'crop', 'blur', 'workout', 'heatmap', 'isegment', 'queue', 'speed', 'analytics', 'trackzone', 'inference', 'visioneye']`.
- **ARGS** (optional) are custom `arg=value` pairs, such as `show_in=True`, to override default settings.

yolo solutions count show=True # for object counting

yolo solutions source="path/to/video.mp4" # specify video file path

Contribute to Our Solutions

We welcome contributions from the community! If you've mastered a particular aspect of Ultralytics YOLO that's not yet covered in our solutions, we encourage you to share your expertise. Writing a guide is a great way to give back to the community and help us make our documentation more comprehensive and user-friendly.

To get started, please read our Contributing Guide for guidelines on how to open up a Pull Request (PR) 🛠️. We look forward to your contributions!

Let's work together to make the Ultralytics YOLO ecosystem more robust and versatile 🙏!

FAQ

How can I use Ultralytics YOLO for real-time object counting?

Ultralytics YOLO11 can be used for real-time object counting by leveraging its advanced object detection capabilities. You can follow our detailed guide on Object Counting to set up YOLO11 for live video stream analysis. Simply install YOLO11, load your model, and process video frames to count objects dynamically.

What are the benefits of using Ultralytics YOLO for security systems?

Ultralytics YOLO11 enhances security systems by offering real-time object detection and alert mechanisms. By employing YOLO11, you can create a security alarm system that triggers alerts when new objects are detected in the surveillance area. Learn how to set up a Security Alarm System with YOLO11 for robust security monitoring.

How can Ultralytics YOLO improve queue management systems?

Ultralytics YOLO11 can significantly improve queue management systems by accurately counting and tracking people in queues, thus helping to reduce wait times and optimize service efficiency. Follow our detailed guide on Queue Management to learn how to implement YOLO11 for effective queue monitoring and analysis.

Can Ultralytics YOLO be used for workout monitoring?

Yes, Ultralytics YOLO11 can be effectively used for monitoring workouts by tracking and analyzing fitness routines in real-time. This allows for precise evaluation of exercise form and performance. Explore our guide on Workouts Monitoring to learn how to set up an AI-powered workout monitoring system using YOLO11.

How does Ultralytics YOLO help in creating heatmaps for data visualization?

Ultralytics YOLO11 can generate heatmaps to visualize data intensity across a given area, highlighting regions of high activity or interest. This feature is particularly useful in understanding patterns and trends in various computer vision tasks. Learn more about creating and using Heatmaps with YOLO11 for comprehensive data analysis and visualization.