Object Cropping using Ultralytics YOLO11

Cắt xén đối tượng là gì?

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.

Xem: Object Cropping using Ultralytics YOLO

Ưu điểm của việc cắt xén đối tượng?

• Focused Analysis: YOLO11 facilitates targeted object cropping, allowing for in-depth examination or processing of individual items within a scene.
• Giảm khối lượng dữ liệu: Bằng cách chỉ trích xuất các đối tượng có liên quan, cắt xén đối tượng giúp giảm thiểu kích thước dữ liệu, làm cho nó hiệu quả cho việc lưu trữ, truyền tải hoặc các tác vụ tính toán tiếp theo.
• 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.

Hình ảnh

Hành lý sân bay
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()

Lập luận model.predict

FAQ

What is object cropping in Ultralytics YOLO11 and how does it work?

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.

Why should I use Ultralytics YOLO11 for object cropping over other solutions?

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.

Làm cách nào để giảm khối lượng dữ liệu của tập dữ liệu bằng cách cắt xén đối tượng?

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.

Can I use Ultralytics YOLO11 for real-time video analysis and object cropping?

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.

What are the hardware requirements for efficiently running YOLO11 for object cropping?

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.

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