اقتصاص الكائنات باستخدام Ultralytics YOLO11

ما هو اقتصاص الكائن؟

Object cropping with Ultralytics YOLO11 involves isolating and extracting specific detected objects from an image or video. The YOLO11 model capabilities are utilized to accurately identify and delineate objects, enabling precise cropping for further analysis or manipulation.

شاهد: Object Cropping using Ultralytics YOLO

مزايا اقتصاص الكائن؟

• Focused Analysis: YOLO11 facilitates targeted object cropping, allowing for in-depth examination or processing of individual items within a scene.
• انخفاض حجم البيانات: من خلال استخراج الكائنات ذات الصلة فقط ، يساعد اقتصاص الكائنات في تقليل حجم البيانات ، مما يجعلها فعالة للتخزين أو الإرسال أو المهام الحسابية اللاحقة.
• Enhanced Precision: YOLO11's object detection accuracy ensures that the cropped objects maintain their spatial relationships, preserving the integrity of the visual information for detailed analysis.

صور

أمتعة المطار
Suitcases Cropping at airport conveyor belt using Ultralytics YOLO11

import os

import cv2

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

model = YOLO("yolo11n.pt")
names = model.names

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))

crop_dir_name = "ultralytics_crop"
if not os.path.exists(crop_dir_name):
    os.mkdir(crop_dir_name)

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

idx = 0
while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or video processing has been successfully completed.")
        break

    results = model.predict(im0, show=False)
    boxes = results[0].boxes.xyxy.cpu().tolist()
    clss = results[0].boxes.cls.cpu().tolist()
    annotator = Annotator(im0, line_width=2, example=names)

    if boxes is not None:
        for box, cls in zip(boxes, clss):
            idx += 1
            annotator.box_label(box, color=colors(int(cls), True), label=names[int(cls)])

            crop_obj = im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])]

            cv2.imwrite(os.path.join(crop_dir_name, str(idx) + ".png"), crop_obj)

    cv2.imshow("ultralytics", im0)
    video_writer.write(im0)

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

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

الحجج model.predict

الأسئلة المتداولة

ما هو اقتصاص الكائنات في Ultralytics YOLO11 وكيف يعمل؟

Object cropping using Ultralytics YOLO11 involves isolating and extracting specific objects from an image or video based on YOLO11's detection capabilities. This process allows for focused analysis, reduced data volume, and enhanced precision by leveraging YOLO11 to identify objects with high accuracy and crop them accordingly. For an in-depth tutorial, refer to the object cropping example.

لماذا يجب استخدام Ultralytics YOLO11 لاقتصاص الكائنات بدلاً من الحلول الأخرى؟

Ultralytics YOLO11 stands out due to its precision, speed, and ease of use. It allows detailed and accurate object detection and cropping, essential for focused analysis and applications needing high data integrity. Moreover, YOLO11 integrates seamlessly with tools like OpenVINO and TensorRT for deployments requiring real-time capabilities and optimization on diverse hardware. Explore the benefits in the guide on model export.

كيف يمكنني تقليل حجم بيانات مجموعة البيانات الخاصة بي باستخدام اقتصاص الكائنات؟

By using Ultralytics YOLO11 to crop only relevant objects from your images or videos, you can significantly reduce the data size, making it more efficient for storage and processing. This process involves training the model to detect specific objects and then using the results to crop and save these portions only. For more information on exploiting Ultralytics YOLO11's capabilities, visit our quickstart guide.

هل يمكنني استخدام Ultralytics YOLO11 لتحليل الفيديو في الوقت الفعلي واقتصاص الأجسام؟

Yes, Ultralytics YOLO11 can process real-time video feeds to detect and crop objects dynamically. The model's high-speed inference capabilities make it ideal for real-time applications such as surveillance, sports analysis, and automated inspection systems. Check out the tracking and prediction modes to understand how to implement real-time processing.

ما هي متطلبات الأجهزة اللازمة لتشغيل YOLO11 بكفاءة من أجل اقتصاص الكائنات؟

Ultralytics YOLO11 is optimized for both CPU and GPU environments, but to achieve optimal performance, especially for real-time or high-volume inference, a dedicated GPU (e.g., NVIDIA Tesla, RTX series) is recommended. For deployment on lightweight devices, consider using CoreML for iOS or TFLite for Android. More details on supported devices and formats can be found in our model deployment options.

📅 تم إنشاؤه منذ 10 أشهر ✏️ تم التحديث منذ 1 شهر

التعليقات