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 فيو | عرض VisionEye مع تتبع الكائن | عرض VisionEye مع حساب المسافة |
---|---|---|
VisionEye View Object Mapping using Ultralytics YOLO11 | VisionEye View Object Mapping with Object Tracking using Ultralytics YOLO11 | VisionEye 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
اسم | نوع | افتراضي | وصف |
---|---|---|---|
color |
tuple |
(235, 219, 11) |
لون الخط والكائن المركزي |
pin_color |
tuple |
(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:
- تتبع الأجسام في الوقت الحقيقي: يواكب الأجسام أثناء تحركها.
- Object Identification: Utilizes YOLO11's powerful detection algorithms.
- حساب المسافات: حساب المسافات بين الأجسام والنقاط المحددة.
- 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:
- أداء متطور للغاية: يوفر دقة عالية في اكتشاف الأجسام في الوقت الحقيقي.
- المرونة: يدعم مهام مختلفة مثل الكشف والتتبع وحساب المسافة.
- المجتمع والدعم: وثائق موسعة ومجتمع GitHub نشط لاستكشاف الأخطاء وإصلاحها والتحسينات.
- 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 .