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VisionEye View Object Mapping using Ultralytics YOLO11 🚀

ما هو رسم خرائط كائن VisionEye؟

Ultralytics YOLO11 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.

العينات

فيجن آي فيوعرض VisionEye مع تتبع الكائنعرض VisionEye مع حساب المسافة
VisionEye View Object Mapping using Ultralytics YOLO11VisionEye View Object Mapping with Object Tracking using Ultralytics YOLO11VisionEye View with Distance Calculation using Ultralytics YOLO11
VisionEye View Object Mapping using Ultralytics YOLO11VisionEye View Object Mapping with Object Tracking using Ultralytics YOLO11VisionEye View with Distance Calculation using Ultralytics YOLO11

VisionEye Object Mapping using YOLO11

import cv2

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

model = YOLO("yolo11n.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()

import cv2

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

model = YOLO("yolo11n.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()

import math

import cv2

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

model = YOLO("yolo11n.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 الحجج

اسمنوعافتراضيوصف
colortuple(235, 219, 11)لون الخط والكائن المركزي
pin_colortuple(255, 0, 255)فيجن آي لون محدد

ملاحظه

لأية استفسارات ، لا تتردد في نشر أسئلتك في Ultralytics قسم المشكلة أو قسم المناقشة المذكور أدناه.

الأسئلة المتداولة

How do I start using VisionEye Object Mapping with Ultralytics YOLO11?

To start using VisionEye Object Mapping with Ultralytics YOLO11, first, you'll need to install the Ultralytics YOLO package via pip. Then, you can use the sample code provided in the documentation to set up object detection with VisionEye. Here's a simple example to get you started:

import cv2

from ultralytics import YOLO

model = YOLO("yolo11n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")

while True:
    ret, frame = cap.read()
    if not ret:
        break

    results = model.predict(frame)
    for result in results:
        # Perform custom logic with result
        pass

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

cap.release()
cv2.destroyAllWindows()

What are the key features of VisionEye's object tracking capability using Ultralytics YOLO11?

VisionEye's object tracking with Ultralytics YOLO11 allows users to follow the movement of objects within a video frame. Key features include:

  1. تتبع الأجسام في الوقت الحقيقي: يواكب الأجسام أثناء تحركها.
  2. Object Identification: Utilizes YOLO11's powerful detection algorithms.
  3. حساب المسافات: حساب المسافات بين الأجسام والنقاط المحددة.
  4. التعليق التوضيحي والتصور: يوفر علامات مرئية للأجسام المتعقبة.

إليك مقتطف رمز موجز يوضح التتبع باستخدام VisionEye:

import cv2

from ultralytics import YOLO

model = YOLO("yolo11n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")

while True:
    ret, frame = cap.read()
    if not ret:
        break

    results = model.track(frame, persist=True)
    for result in results:
        # Annotate and visualize tracking
        pass

    cv2.imshow("visioneye-tracking", frame)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()

للاطلاع على دليل شامل، قم بزيارة دليل VisionEye لتخطيط الكائنات مع تتبع الكائنات.

How can I calculate distances with VisionEye's YOLO11 model?

Distance calculation with VisionEye and Ultralytics YOLO11 involves determining the distance of detected objects from a specified point in the frame. It enhances spatial analysis capabilities, useful in applications such as autonomous driving and surveillance.

إليك مثالاً مبسطاً:

import math

import cv2

from ultralytics import YOLO

model = YOLO("yolo11n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
center_point = (0, 480)  # Example center point
pixel_per_meter = 10

while True:
    ret, frame = cap.read()
    if not ret:
        break

    results = model.track(frame, persist=True)
    for result in results:
        # Calculate distance logic
        distances = [
            (math.sqrt((box[0] - center_point[0]) ** 2 + (box[1] - center_point[1]) ** 2)) / pixel_per_meter
            for box in results
        ]

    cv2.imshow("visioneye-distance", frame)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

cap.release()
cv2.destroyAllWindows()

للحصول على تعليمات مفصلة، ارجع إلى VisionEye مع حساب المسافة.

Why should I use Ultralytics YOLO11 for object mapping and tracking?

Ultralytics YOLO11 is renowned for its speed, accuracy, and ease of integration, making it a top choice for object mapping and tracking. Key advantages include:

  1. أداء متطور للغاية: يوفر دقة عالية في اكتشاف الأجسام في الوقت الحقيقي.
  2. المرونة: يدعم مهام مختلفة مثل الكشف والتتبع وحساب المسافة.
  3. المجتمع والدعم: وثائق موسعة ومجتمع GitHub نشط لاستكشاف الأخطاء وإصلاحها والتحسينات.
  4. سهولة الاستخدام: تعمل واجهة برمجة التطبيقات البديهية على تبسيط المهام المعقدة، مما يسمح بالنشر والتكرار السريع.

For more information on applications and benefits, check out the Ultralytics YOLO11 documentation.

How can I integrate VisionEye with other machine learning tools like Comet or ClearML?

Ultralytics YOLO11 can integrate seamlessly with various machine learning tools like Comet and ClearML, enhancing experiment tracking, collaboration, and reproducibility. Follow the detailed guides on how to use YOLOv5 with Comet and integrate YOLO11 with ClearML to get started.

لمزيد من الاستكشاف وأمثلة التكامل، راجع دليل التكاملUltralytics .

📅 Created 10 months ago ✏️ Updated 22 days ago
ألترالتيكسيستانتست غلين-جوتشر MatthewNoyce رضوان منور IvorZhu331

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