SAHI_Yolov8:小目标检测

 懒得推gitee，记录一下

import os
import time
import shutil
os.getcwd()

# arrange an instance segmentation model for test
from sahi.utils.yolov8 import (
    download_yolov8s_model,
)
import cv2
from sahi import AutoDetectionModel
from sahi.utils.cv import read_image
from sahi.utils.file import download_from_url
from sahi.predict import get_prediction, get_sliced_prediction, predict
from IPython.display import Image


detection_model = AutoDetectionModel.from_pretrained(
    model_type='yolov8',
    # YOLOv8模型的路径
    model_path="/home/hxzh/PycharmProjects/SAHI_YOLOv8/best.pt",
    # YOLOv8模型的路径
    confidence_threshold=0.3,
    # 设备类型。
    # 如果您的计算机配备 NVIDIA GPU，则可以通过将 'device' 标志更改为'cuda:0'来启用 CUDA 加速；否则，将其保留为'cpu'
    device="cuda:0", # or 'cuda:0'
)
start_time=time.time()
images_path="/media/hxzh/D/Dataset/plean_test/0926"
for i in os.listdir(images_path):

    image=cv2.imread(f"{images_path}/{i}")[:,:,::-1]
# image=cv2.resize(image,(1000,700))[:,:,::-1]
    result = get_sliced_prediction(
        # "/media/hxzh/D/Dataset/plean_test/0926/DJI_20230919085432_0018_W.JPG",
        image,
        detection_model,
        slice_height = 1000,
        slice_width = 750,
        overlap_height_ratio = 0.2,
        overlap_width_ratio = 0.2
    )


    result.export_visuals(export_dir="demo_data/",hide_labels=True,hide_conf=True)
    os.rename("demo_data/prediction_visual.png",f"demo_data/{i}")
    print(time.time()-start_time)
    Image(f"demo_data/{i}")