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VisionEye View Object Mapping using Ultralytics YOLO11 🚀

VisionEye Object Mappingとは?

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.

サンプル

ビジョンアイビュー物体追跡機能付きVisionEyeビュー距離計算付きVisionEyeビュー
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 論争

名称タイプデフォルト説明
colortuple(235, 219, 11)線とオブジェクトの重心の色
pin_colortuple(255, 0, 255)ビジョンアイ・ピンポイントカラー

お問い合わせは、Ultralytics Issue Sectionまたは下記のディスカッション・セクションまでお気軽にお寄せください。

よくあるご質問

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. リアルタイム物体追跡:オブジェクトの動きに追従します。
  2. Object Identification: Utilizes YOLO11's powerful detection algorithms.
  3. 距離計算:オブジェクトと指定したポイント間の距離を計算します。
  4. 注釈と視覚化:追跡されたオブジェクトの視覚的マーカーを提供します。

以下は、VisionEyeによるトラッキングを実演する簡単なコード・スニペットです:

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()

包括的なガイドについては、VisionEye Object Mapping with Object Trackingをご覧ください。

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.

簡単な例を挙げよう:

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()

詳細な手順については、距離計算機能付きVisionEyeを参照してください。

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. 最先端の性能:リアルタイムで高精度の物体検出を実現。
  2. 柔軟性:検出、追跡、距離計算など様々なタスクをサポート。
  3. コミュニティとサポート:豊富なドキュメントと活発なGitHubコミュニティによるトラブルシューティングと機能拡張。
  4. 使いやすさ:直感的なAPIは複雑なタスクを簡素化し、迅速な展開と反復を可能にします。

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.

さらに詳しい説明と統合例については、Ultralytics 統合ガイドをご覧ください。

📅 Created 10 months ago ✏️ Updated 22 days ago

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