ultralytics/docs/en/guides/analytics.md

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Analytics using Ultralytics YOLO11

Introduction

This guide provides a comprehensive overview of three fundamental types of data visualizations: line graphs, bar plots, and pie charts. Each section includes step-by-step instructions and code snippets on how to create these visualizations using Python.

Watch: How to generate Analytical Graphs using Ultralytics | Line Graphs, Bar Plots, Area and Pie Charts

Visual Samples

Line Graph	Bar Plot	Pie Chart

Why Graphs are Important

• Line graphs are ideal for tracking changes over short and long periods and for comparing changes for multiple groups over the same period.
• Bar plots, on the other hand, are suitable for comparing quantities across different categories and showing relationships between a category and its numerical value.
• Lastly, pie charts are effective for illustrating proportions among categories and showing parts of a whole.

!!! analytics "Analytics Examples"

=== "Line Graph"

    ```python
    import cv2

    from ultralytics import YOLO, solutions

    model = YOLO("yolo11n.pt")

    cap = cv2.VideoCapture("Path/to/video/file.mp4")
    assert cap.isOpened(), "Error reading video file"
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

    out = cv2.VideoWriter("line_plot.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

    analytics = solutions.Analytics(
        type="line",
        writer=out,
        im0_shape=(w, h),
        view_img=True,
    )
    total_counts = 0
    frame_count = 0

    while cap.isOpened():
        success, frame = cap.read()

        if success:
            frame_count += 1
            results = model.track(frame, persist=True, verbose=True)

            if results[0].boxes.id is not None:
                boxes = results[0].boxes.xyxy.cpu()
                for box in boxes:
                    total_counts += 1

            analytics.update_line(frame_count, total_counts)

            total_counts = 0
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
        else:
            break

    cap.release()
    out.release()
    cv2.destroyAllWindows()
    ```

=== "Multiple Lines"

    ```python
    import cv2

    from ultralytics import YOLO, solutions

    model = YOLO("yolo11n.pt")

    cap = cv2.VideoCapture("Path/to/video/file.mp4")
    assert cap.isOpened(), "Error reading video file"
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
    out = cv2.VideoWriter("multiple_line_plot.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

    analytics = solutions.Analytics(
        type="line",
        writer=out,
        im0_shape=(w, h),
        view_img=True,
        max_points=200,
    )

    frame_count = 0
    data = {}
    labels = []

    while cap.isOpened():
        success, frame = cap.read()

        if success:
            frame_count += 1

            results = model.track(frame, persist=True)

            if results[0].boxes.id is not None:
                boxes = results[0].boxes.xyxy.cpu()
                track_ids = results[0].boxes.id.int().cpu().tolist()
                clss = results[0].boxes.cls.cpu().tolist()

                for box, track_id, cls in zip(boxes, track_ids, clss):
                    # Store each class label
                    if model.names[int(cls)] not in labels:
                        labels.append(model.names[int(cls)])

                    # Store each class count
                    if model.names[int(cls)] in data:
                        data[model.names[int(cls)]] += 1
                    else:
                        data[model.names[int(cls)]] = 0

            # update lines every frame
            analytics.update_multiple_lines(data, labels, frame_count)
            data = {}  # clear the data list for next frame
        else:
            break

    cap.release()
    out.release()
    cv2.destroyAllWindows()
    ```

=== "Pie Chart"

    ```python
    import cv2

    from ultralytics import YOLO, solutions

    model = YOLO("yolo11n.pt")

    cap = cv2.VideoCapture("Path/to/video/file.mp4")
    assert cap.isOpened(), "Error reading video file"
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

    out = cv2.VideoWriter("pie_chart.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

    analytics = solutions.Analytics(
        type="pie",
        writer=out,
        im0_shape=(w, h),
        view_img=True,
    )

    clswise_count = {}

    while cap.isOpened():
        success, frame = cap.read()
        if success:
            results = model.track(frame, persist=True, verbose=True)
            if results[0].boxes.id is not None:
                boxes = results[0].boxes.xyxy.cpu()
                clss = results[0].boxes.cls.cpu().tolist()
                for box, cls in zip(boxes, clss):
                    if model.names[int(cls)] in clswise_count:
                        clswise_count[model.names[int(cls)]] += 1
                    else:
                        clswise_count[model.names[int(cls)]] = 1

                analytics.update_pie(clswise_count)
                clswise_count = {}

            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
        else:
            break

    cap.release()
    out.release()
    cv2.destroyAllWindows()
    ```

=== "Bar Plot"

    ```python
    import cv2

    from ultralytics import YOLO, solutions

    model = YOLO("yolo11n.pt")

    cap = cv2.VideoCapture("Path/to/video/file.mp4")
    assert cap.isOpened(), "Error reading video file"
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

    out = cv2.VideoWriter("bar_plot.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

    analytics = solutions.Analytics(
        type="bar",
        writer=out,
        im0_shape=(w, h),
        view_img=True,
    )

    clswise_count = {}

    while cap.isOpened():
        success, frame = cap.read()
        if success:
            results = model.track(frame, persist=True, verbose=True)
            if results[0].boxes.id is not None:
                boxes = results[0].boxes.xyxy.cpu()
                clss = results[0].boxes.cls.cpu().tolist()
                for box, cls in zip(boxes, clss):
                    if model.names[int(cls)] in clswise_count:
                        clswise_count[model.names[int(cls)]] += 1
                    else:
                        clswise_count[model.names[int(cls)]] = 1

                analytics.update_bar(clswise_count)
                clswise_count = {}

            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
        else:
            break

    cap.release()
    out.release()
    cv2.destroyAllWindows()
    ```

=== "Area chart"

    ```python
    import cv2

    from ultralytics import YOLO, solutions

    model = YOLO("yolo11n.pt")

    cap = cv2.VideoCapture("path/to/video/file.mp4")
    assert cap.isOpened(), "Error reading video file"
    w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

    out = cv2.VideoWriter("area_plot.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

    analytics = solutions.Analytics(
        type="area",
        writer=out,
        im0_shape=(w, h),
        view_img=True,
    )

    clswise_count = {}
    frame_count = 0

    while cap.isOpened():
        success, frame = cap.read()
        if success:
            frame_count += 1
            results = model.track(frame, persist=True, verbose=True)

            if results[0].boxes.id is not None:
                boxes = results[0].boxes.xyxy.cpu()
                clss = results[0].boxes.cls.cpu().tolist()

                for box, cls in zip(boxes, clss):
                    if model.names[int(cls)] in clswise_count:
                        clswise_count[model.names[int(cls)]] += 1
                    else:
                        clswise_count[model.names[int(cls)]] = 1

            analytics.update_area(frame_count, clswise_count)
            clswise_count = {}
            if cv2.waitKey(1) & 0xFF == ord("q"):
                break
        else:
            break

    cap.release()
    out.release()
    cv2.destroyAllWindows()
    ```

Argument Analytics

Here's a table with the Analytics arguments:

Name	Type	Default	Description
type	str	None	Type of data or object.
im0_shape	tuple	None	Shape of the initial image.
writer	cv2.VideoWriter	None	Object for writing video files.
title	str	ultralytics	Title for the visualization.
x_label	str	x	Label for the x-axis.
y_label	str	y	Label for the y-axis.
bg_color	str	white	Background color.
fg_color	str	black	Foreground color.
line_color	str	yellow	Color of the lines.
line_width	int	2	Width of the lines.
fontsize	int	13	Font size for text.
view_img	bool	False	Flag to display the image or video.
save_img	bool	True	Flag to save the image or video.
max_points	int	50	For multiple lines, total points drawn on frame, before deleting initial points.
points_width	int	15	Width of line points highlighter.

Arguments model.track

{% include "macros/track-args.md" %}

Conclusion

Understanding when and how to use different types of visualizations is crucial for effective data analysis. Line graphs, bar plots, and pie charts are fundamental tools that can help you convey your data's story more clearly and effectively.

FAQ

How do I create a line graph using Ultralytics YOLO11 Analytics?

To create a line graph using Ultralytics YOLO11 Analytics, follow these steps:

1. Load a YOLO11 model and open your video file.
2. Initialize the Analytics class with the type set to "line."
3. Iterate through video frames, updating the line graph with relevant data, such as object counts per frame.
4. Save the output video displaying the line graph.

Example:

import cv2

from ultralytics import YOLO, solutions

model = YOLO("yolo11n.pt")
cap = cv2.VideoCapture("Path/to/video/file.mp4")
out = cv2.VideoWriter("line_plot.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

analytics = solutions.Analytics(type="line", writer=out, im0_shape=(w, h), view_img=True)

while cap.isOpened():
    success, frame = cap.read()
    if success:
        results = model.track(frame, persist=True)
        total_counts = sum([1 for box in results[0].boxes.xyxy])
        analytics.update_line(frame_count, total_counts)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
out.release()
cv2.destroyAllWindows()

For further details on configuring the Analytics class, visit the Analytics using Ultralytics YOLO11 📊 section.

What are the benefits of using Ultralytics YOLO11 for creating bar plots?

Using Ultralytics YOLO11 for creating bar plots offers several benefits:

1. Real-time Data Visualization: Seamlessly integrate object detection results into bar plots for dynamic updates.
2. Ease of Use: Simple API and functions make it straightforward to implement and visualize data.
3. Customization: Customize titles, labels, colors, and more to fit your specific requirements.
4. Efficiency: Efficiently handle large amounts of data and update plots in real-time during video processing.

Use the following example to generate a bar plot:

import cv2

from ultralytics import YOLO, solutions

model = YOLO("yolo11n.pt")
cap = cv2.VideoCapture("Path/to/video/file.mp4")
out = cv2.VideoWriter("bar_plot.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

analytics = solutions.Analytics(type="bar", writer=out, im0_shape=(w, h), view_img=True)

while cap.isOpened():
    success, frame = cap.read()
    if success:
        results = model.track(frame, persist=True)
        clswise_count = {
            model.names[int(cls)]: boxes.size(0)
            for cls, boxes in zip(results[0].boxes.cls.tolist(), results[0].boxes.xyxy)
        }
        analytics.update_bar(clswise_count)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
out.release()
cv2.destroyAllWindows()

To learn more, visit the Bar Plot section in the guide.

Why should I use Ultralytics YOLO11 for creating pie charts in my data visualization projects?

Ultralytics YOLO11 is an excellent choice for creating pie charts because:

1. Integration with Object Detection: Directly integrate object detection results into pie charts for immediate insights.
2. User-Friendly API: Simple to set up and use with minimal code.
3. Customizable: Various customization options for colors, labels, and more.
4. Real-time Updates: Handle and visualize data in real-time, which is ideal for video analytics projects.

Here's a quick example:

import cv2

from ultralytics import YOLO, solutions

model = YOLO("yolo11n.pt")
cap = cv2.VideoCapture("Path/to/video/file.mp4")
out = cv2.VideoWriter("pie_chart.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

analytics = solutions.Analytics(type="pie", writer=out, im0_shape=(w, h), view_img=True)

while cap.isOpened():
    success, frame = cap.read()
    if success:
        results = model.track(frame, persist=True)
        clswise_count = {
            model.names[int(cls)]: boxes.size(0)
            for cls, boxes in zip(results[0].boxes.cls.tolist(), results[0].boxes.xyxy)
        }
        analytics.update_pie(clswise_count)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
out.release()
cv2.destroyAllWindows()

For more information, refer to the Pie Chart section in the guide.

Can Ultralytics YOLO11 be used to track objects and dynamically update visualizations?

Yes, Ultralytics YOLO11 can be used to track objects and dynamically update visualizations. It supports tracking multiple objects in real-time and can update various visualizations like line graphs, bar plots, and pie charts based on the tracked objects' data.

Example for tracking and updating a line graph:

import cv2

from ultralytics import YOLO, solutions

model = YOLO("yolo11n.pt")
cap = cv2.VideoCapture("Path/to/video/file.mp4")
out = cv2.VideoWriter("line_plot.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

analytics = solutions.Analytics(type="line", writer=out, im0_shape=(w, h), view_img=True)

while cap.isOpened():
    success, frame = cap.read()
    if success:
        results = model.track(frame, persist=True)
        total_counts = sum([1 for box in results[0].boxes.xyxy])
        analytics.update_line(frame_count, total_counts)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
out.release()
cv2.destroyAllWindows()

To learn about the complete functionality, see the Tracking section.

What makes Ultralytics YOLO11 different from other object detection solutions like OpenCV and TensorFlow?

Ultralytics YOLO11 stands out from other object detection solutions like OpenCV and TensorFlow for multiple reasons:

1. State-of-the-art Accuracy: YOLO11 provides superior accuracy in object detection, segmentation, and classification tasks.
2. Ease of Use: User-friendly API allows for quick implementation and integration without extensive coding.
3. Real-time Performance: Optimized for high-speed inference, suitable for real-time applications.
4. Diverse Applications: Supports various tasks including multi-object tracking, custom model training, and exporting to different formats like ONNX, TensorRT, and CoreML.
5. Comprehensive Documentation: Extensive documentation and blog resources to guide users through every step.

For more detailed comparisons and use cases, explore our Ultralytics Blog.