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Object Cropping using Ultralytics YOLOv8 🚀

What is Object Cropping?

Object cropping with Ultralytics YOLOv8 involves isolating and extracting specific detected objects from an image or video. The YOLOv8 model capabilities are utilized to accurately identify and delineate objects, enabling precise cropping for further analysis or manipulation.



Watch: Object Cropping using Ultralytics YOLOv8

Advantages of Object Cropping?

  • Focused Analysis: YOLOv8 facilitates targeted object cropping, allowing for in-depth examination or processing of individual items within a scene.
  • Reduced Data Volume: By extracting only relevant objects, object cropping helps in minimizing data size, making it efficient for storage, transmission, or subsequent computational tasks.
  • Enhanced Precision: YOLOv8's object detection accuracy ensures that the cropped objects maintain their spatial relationships, preserving the integrity of the visual information for detailed analysis.

Visuals

Airport Luggage
Conveyor Belt at Airport Suitcases Cropping using Ultralytics YOLOv8
Suitcases Cropping at airport conveyor belt using Ultralytics YOLOv8

Object Cropping using YOLOv8 Example

import os

import cv2

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

model = YOLO("yolov8n.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()

Arguments model.predict

Argument Type Default Description
source str 'ultralytics/assets' Specifies the data source for inference. Can be an image path, video file, directory, URL, or device ID for live feeds. Supports a wide range of formats and sources, enabling flexible application across different types of input.
conf float 0.25 Sets the minimum confidence threshold for detections. Objects detected with confidence below this threshold will be disregarded. Adjusting this value can help reduce false positives.
iou float 0.7 Intersection Over Union (IoU) threshold for Non-Maximum Suppression (NMS). Lower values result in fewer detections by eliminating overlapping boxes, useful for reducing duplicates.
imgsz int or tuple 640 Defines the image size for inference. Can be a single integer 640 for square resizing or a (height, width) tuple. Proper sizing can improve detection accuracy and processing speed.
half bool False Enables half-precision (FP16) inference, which can speed up model inference on supported GPUs with minimal impact on accuracy.
device str None Specifies the device for inference (e.g., cpu, cuda:0 or 0). Allows users to select between CPU, a specific GPU, or other compute devices for model execution.
max_det int 300 Maximum number of detections allowed per image. Limits the total number of objects the model can detect in a single inference, preventing excessive outputs in dense scenes.
vid_stride int 1 Frame stride for video inputs. Allows skipping frames in videos to speed up processing at the cost of temporal resolution. A value of 1 processes every frame, higher values skip frames.
stream_buffer bool False Determines if all frames should be buffered when processing video streams (True), or if the model should return the most recent frame (False). Useful for real-time applications.
visualize bool False Activates visualization of model features during inference, providing insights into what the model is "seeing". Useful for debugging and model interpretation.
augment bool False Enables test-time augmentation (TTA) for predictions, potentially improving detection robustness at the cost of inference speed.
agnostic_nms bool False Enables class-agnostic Non-Maximum Suppression (NMS), which merges overlapping boxes of different classes. Useful in multi-class detection scenarios where class overlap is common.
classes list[int] None Filters predictions to a set of class IDs. Only detections belonging to the specified classes will be returned. Useful for focusing on relevant objects in multi-class detection tasks.
retina_masks bool False Uses high-resolution segmentation masks if available in the model. This can enhance mask quality for segmentation tasks, providing finer detail.
embed list[int] None Specifies the layers from which to extract feature vectors or embeddings. Useful for downstream tasks like clustering or similarity search.


Created 2024-01-09, Updated 2024-06-13
Authors: RizwanMunawar (2), glenn-jocher (7), IvorZhu331 (1), AyushExel (1)

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