12 KiB

Raw Blame History

comments	description	keywords
true	Comprehensive Guide to Understanding and Creating Line Graphs, Bar Plots, and Pie Charts	Analytics, Data Visualization, Line Graphs, Bar Plots, Pie Charts, Quickstart Guide, Data Analysis, Python, Visualization Tools

Analytics using Ultralytics YOLOv8 📊

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.

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("yolov8s.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("yolov8s.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("yolov8s.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("yolov8s.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()
    ```

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.

Arguments `model.track`

Name	Type	Default	Description
`source`	`im0`	`None`	source directory for images or videos
`persist`	`bool`	`False`	persisting tracks between frames
`tracker`	`str`	`botsort.yaml`	Tracking method 'bytetrack' or 'botsort'
`conf`	`float`	`0.3`	Confidence Threshold
`iou`	`float`	`0.5`	IOU Threshold
`classes`	`list`	`None`	filter results by class, i.e. classes=0, or classes=[0,2,3]
`verbose`	`bool`	`True`	Display the object tracking results

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.

12 KiB Raw Blame History