ultralytics/docs/en/guides/vision-eye.md

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true	Discover VisionEye's object mapping and tracking powered by Ultralytics YOLOv8. Simulate human eye precision, track objects, and calculate distances effortlessly.	VisionEye, YOLOv8, Ultralytics, object mapping, object tracking, distance calculation, computer vision, AI, machine learning, Python, tutorial

VisionEye View Object Mapping using Ultralytics YOLOv8 🚀

What is VisionEye Object Mapping?

Ultralytics YOLOv8 VisionEye offers the capability for computers to identify and pinpoint objects, simulating the observational precision of the human eye. This functionality enables computers to discern and focus on specific objects, much like the way the human eye observes details from a particular viewpoint.

Samples

VisionEye View	VisionEye View With Object Tracking	VisionEye View With Distance Calculation
VisionEye View Object Mapping using Ultralytics YOLOv8	VisionEye View Object Mapping with Object Tracking using Ultralytics YOLOv8	VisionEye View with Distance Calculation using Ultralytics YOLOv8

!!! Example "VisionEye Object Mapping using YOLOv8"

=== "VisionEye Object Mapping"

    ```python
    import cv2

    from ultralytics import YOLO
    from ultralytics.utils.plotting import Annotator, colors

    model = YOLO("yolov8n.pt")
    names = model.model.names
    cap = cv2.VideoCapture("path/to/video/file.mp4")
    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("visioneye-pinpoint.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

    center_point = (-10, h)

    while True:
        ret, im0 = cap.read()
        if not ret:
            print("Video frame is empty or video processing has been successfully completed.")
            break

        results = model.predict(im0)
        boxes = results[0].boxes.xyxy.cpu()
        clss = results[0].boxes.cls.cpu().tolist()

        annotator = Annotator(im0, line_width=2)

        for box, cls in zip(boxes, clss):
            annotator.box_label(box, label=names[int(cls)], color=colors(int(cls)))
            annotator.visioneye(box, center_point)

        out.write(im0)
        cv2.imshow("visioneye-pinpoint", im0)

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

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

=== "VisionEye Object Mapping with Object Tracking"

    ```python
    import cv2

    from ultralytics import YOLO
    from ultralytics.utils.plotting import Annotator, colors

    model = YOLO("yolov8n.pt")
    cap = cv2.VideoCapture("path/to/video/file.mp4")
    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("visioneye-pinpoint.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

    center_point = (-10, h)

    while True:
        ret, im0 = cap.read()
        if not ret:
            print("Video frame is empty or video processing has been successfully completed.")
            break

        annotator = Annotator(im0, line_width=2)

        results = model.track(im0, persist=True)
        boxes = results[0].boxes.xyxy.cpu()

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

            for box, track_id in zip(boxes, track_ids):
                annotator.box_label(box, label=str(track_id), color=colors(int(track_id)))
                annotator.visioneye(box, center_point)

        out.write(im0)
        cv2.imshow("visioneye-pinpoint", im0)

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

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

=== "VisionEye with Distance Calculation"

    ```python
    import math

    import cv2

    from ultralytics import YOLO
    from ultralytics.utils.plotting import Annotator, colors

    model = YOLO("yolov8s.pt")
    cap = cv2.VideoCapture("Path/to/video/file.mp4")

    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("visioneye-distance-calculation.avi", cv2.VideoWriter_fourcc(*"MJPG"), fps, (w, h))

    center_point = (0, h)
    pixel_per_meter = 10

    txt_color, txt_background, bbox_clr = ((0, 0, 0), (255, 255, 255), (255, 0, 255))

    while True:
        ret, im0 = cap.read()
        if not ret:
            print("Video frame is empty or video processing has been successfully completed.")
            break

        annotator = Annotator(im0, line_width=2)

        results = model.track(im0, persist=True)
        boxes = results[0].boxes.xyxy.cpu()

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

            for box, track_id in zip(boxes, track_ids):
                annotator.box_label(box, label=str(track_id), color=bbox_clr)
                annotator.visioneye(box, center_point)

                x1, y1 = int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2)  # Bounding box centroid

                distance = (math.sqrt((x1 - center_point[0]) ** 2 + (y1 - center_point[1]) ** 2)) / pixel_per_meter

                text_size, _ = cv2.getTextSize(f"Distance: {distance:.2f} m", cv2.FONT_HERSHEY_SIMPLEX, 1.2, 3)
                cv2.rectangle(im0, (x1, y1 - text_size[1] - 10), (x1 + text_size[0] + 10, y1), txt_background, -1)
                cv2.putText(im0, f"Distance: {distance:.2f} m", (x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 1.2, txt_color, 3)

        out.write(im0)
        cv2.imshow("visioneye-distance-calculation", im0)

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

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

visioneye Arguments

Name	Type	Default	Description
color	tuple	(235, 219, 11)	Line and object centroid color
pin_color	tuple	(255, 0, 255)	VisionEye pinpoint color

Note

For any inquiries, feel free to post your questions in the Ultralytics Issue Section or the discussion section mentioned below.