Object Counting using Ultralytics YOLO11

オブジェクト・カウントとは何か？

Object counting with Ultralytics YOLO11 involves accurate identification and counting of specific objects in videos and camera streams. YOLO11 excels in real-time applications, providing efficient and precise object counting for various scenarios like crowd analysis and surveillance, thanks to its state-of-the-art algorithms and deep learning capabilities.

見るんだ： Object Counting using Ultralytics YOLO11

見るんだ： Class-wise Object Counting using Ultralytics YOLO11

オブジェクト・カウントの利点

リソースの最適化：オブジェクトカウンティングは、正確なカウントを提供し、在庫管理などのアプリケーションでリソース割り当てを最適化することで、効率的なリソース管理を容易にします。
セキュリティの強化：物体計数は、実体を正確に追跡して計数することにより、セキュリティと監視を強化し、事前の脅威検知を支援します。
情報に基づいた意思決定オブジェクトカウンティングは、小売、交通管理、その他様々な領域において、意思決定やプロセスの最適化のための貴重な洞察を提供します。

実世界での応用

物流	水産養殖

Conveyor Belt Packets Counting Using Ultralytics YOLO11	Fish Counting in Sea using Ultralytics YOLO11

Object Counting using YOLO11 Example

地域のカウントOBB Object Countingポリゴン数列に並ぶ特定のクラス

import cv2

from ultralytics import solutions

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))

# Define region points
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]

# Video writer
video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Init Object Counter
counter = solutions.ObjectCounter(
    show=True,
    region=region_points,
    model="yolo11n.pt",
)

# Process video
while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    im0 = counter.count(im0)
    video_writer.write(im0)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

import cv2

from ultralytics import solutions

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))

# line or region points
line_points = [(20, 400), (1080, 400)]

# Video writer
video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Init Object Counter
counter = solutions.ObjectCounter(
    show=True,
    region=line_points,
    model="yolo11n-obb.pt",
)

# Process video
while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    im0 = counter.count(im0)
    video_writer.write(im0)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

import cv2

from ultralytics import solutions

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))

# Define region points
region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360), (20, 400)]

# Video writer
video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Init Object Counter
counter = solutions.ObjectCounter(
    show=True,
    region=region_points,
    model="yolo11n.pt",
)

# Process video
while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    im0 = counter.count(im0)
    video_writer.write(im0)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

import cv2

from ultralytics import solutions

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))

# Define region points
line_points = [(20, 400), (1080, 400)]

# Video writer
video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Init Object Counter
counter = solutions.ObjectCounter(
    show=True,
    region=line_points,
    model="yolo11n.pt",
)

# Process video
while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    im0 = counter.count(im0)
    video_writer.write(im0)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

import cv2

from ultralytics import solutions

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))

# Video writer
video_writer = cv2.VideoWriter("object_counting_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Init Object Counter
counter = solutions.ObjectCounter(
    show=True,
    model="yolo11n.pt",
    classes=[0, 1],
)

# Process video
while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break
    im0 = counter.count(im0)
    video_writer.write(im0)

cap.release()
video_writer.release()
cv2.destroyAllWindows()

議論 `ObjectCounter`

以下はその表である。 ObjectCounter という議論がある：

名称	タイプ	デフォルト	説明
`model`	`str`	`None`	Path to Ultralytics YOLO Model File
`region`	`list`	`[(20, 400), (1260, 400)]`	カウント領域を定義する点のリスト。
`line_width`	`int`	`2`	バウンディングボックスの線の太さ。
`show`	`bool`	`False`	ビデオストリームを表示するかどうかを制御するフラグ。
`show_in`	`bool`	`True`	ビデオストリームにインカウントを表示するかどうかを制御するフラグ。
`show_out`	`bool`	`True`	ビデオストリームにアウトカウントを表示するかどうかを制御するフラグ。

論争 `model.track`

議論	タイプ	デフォルト	説明
`source`	`str`	`None`	Specifies the source directory for images or videos. Supports file paths and URLs.
`persist`	`bool`	`False`	Enables persistent tracking of objects between frames, maintaining IDs across video sequences.
`tracker`	`str`	`botsort.yaml`	Specifies the tracking algorithm to use, e.g., `bytetrack.yaml` または `botsort.yaml`.
`conf`	`float`	`0.3`	Sets the confidence threshold for detections; lower values allow more objects to be tracked but may include false positives.
`iou`	`float`	`0.5`	Sets the Intersection over Union (IoU) threshold for filtering overlapping detections.
`classes`	`list`	`None`	Filters results by class index. For example, `classes=[0, 2, 3]` only tracks the specified classes.
`verbose`	`bool`	`True`	Controls the display of tracking results, providing a visual output of tracked objects.

よくあるご質問

How do I count objects in a video using Ultralytics YOLO11?

To count objects in a video using Ultralytics YOLO11, you can follow these steps:

必要なライブラリをインポートするcv2, ultralytics).
計数領域を定義する（多角形、線など）。
ビデオキャプチャを設定し、オブジェクトカウンターを初期化する。
各フレームを処理してオブジェクトを追跡し、定義された領域内でカウントする。

以下は、地域でカウントする簡単な例である：

import cv2

from ultralytics import solutions


def count_objects_in_region(video_path, output_video_path, model_path):
    """Count objects in a specific region within a video."""
    cap = cv2.VideoCapture(video_path)
    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))
    video_writer = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

    region_points = [(20, 400), (1080, 404), (1080, 360), (20, 360)]
    counter = solutions.ObjectCounter(show=True, region=region_points, model=model_path)

    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            print("Video frame is empty or video processing has been successfully completed.")
            break
        im0 = counter.count(im0)
        video_writer.write(im0)

    cap.release()
    video_writer.release()
    cv2.destroyAllWindows()


count_objects_in_region("path/to/video.mp4", "output_video.avi", "yolo11n.pt")

その他の設定やオプションについては、オブジェクト・カウントのセクションを参照してください。

What are the advantages of using Ultralytics YOLO11 for object counting?

Using Ultralytics YOLO11 for object counting offers several advantages:

リソースの最適化正確な計数を提供することで効率的なリソース管理を促進し、在庫管理などの産業におけるリソース配分の最適化を支援する。
セキュリティの強化：エンティティの正確な追跡とカウントにより、セキュリティと監視を強化し、脅威の事前検知を支援します。
情報に基づいた意思決定意思決定のための貴重な洞察を提供し、小売、交通管理などの領域におけるプロセスを最適化します。

実際のアプリケーションとコード例については、オブジェクト・カウントの利点のセクションをご覧ください。

How can I count specific classes of objects using Ultralytics YOLO11?

To count specific classes of objects using Ultralytics YOLO11, you need to specify the classes you are interested in during the tracking phase. Below is a Python example:

import cv2

from ultralytics import solutions


def count_specific_classes(video_path, output_video_path, model_path, classes_to_count):
    """Count specific classes of objects in a video."""
    cap = cv2.VideoCapture(video_path)
    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))
    video_writer = cv2.VideoWriter(output_video_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

    line_points = [(20, 400), (1080, 400)]
    counter = solutions.ObjectCounter(show=True, region=line_points, model=model_path, classes=classes_to_count)

    while cap.isOpened():
        success, im0 = cap.read()
        if not success:
            print("Video frame is empty or video processing has been successfully completed.")
            break
        im0 = counter.count(im0)
        video_writer.write(im0)

    cap.release()
    video_writer.release()
    cv2.destroyAllWindows()


count_specific_classes("path/to/video.mp4", "output_specific_classes.avi", "yolo11n.pt", [0, 2])

この例では classes_to_count=[0, 2]というクラスのオブジェクトを数える。 0 そして 2 (例：人と車）。

Why should I use YOLO11 over other object detection models for real-time applications?

Ultralytics YOLO11 provides several advantages over other object detection models like Faster R-CNN, SSD, and previous YOLO versions:

Speed and Efficiency: YOLO11 offers real-time processing capabilities, making it ideal for applications requiring high-speed inference, such as surveillance and autonomous driving.
Accuracy: It provides state-of-the-art accuracy for object detection and tracking tasks, reducing the number of false positives and improving overall system reliability.
Ease of Integration: YOLO11 offers seamless integration with various platforms and devices, including mobile and edge devices, which is crucial for modern AI applications.
柔軟性：特定のユースケースの要件を満たすために設定可能なモデルで、オブジェクト検出、セグメンテーション、トラッキングなどのさまざまなタスクをサポートします。

Check out Ultralytics YOLO11 Documentation for a deeper dive into its features and performance comparisons.

Can I use YOLO11 for advanced applications like crowd analysis and traffic management?

Yes, Ultralytics YOLO11 is perfectly suited for advanced applications like crowd analysis and traffic management due to its real-time detection capabilities, scalability, and integration flexibility. Its advanced features allow for high-accuracy object tracking, counting, and classification in dynamic environments. Example use cases include: