fromimport inference_detector_by_patches img='demo/dota_demo.jpg' result=1024], [824], [1.0],0.1) def inference_detector_by_patches(model, img, sizes, steps, ratios, merge_iou_thr, bs=1): """inference patches with the detector. Split huge image(s) into patches and inference them wit...
result=inference_detector_by_patches(model, img, [1024], [824], [1.0],0.1) definference_detector_by_patches(model, img, sizes, steps, ratios, merge_iou_thr, bs=1): """inference patches with the detector. Split huge image(s) into patches and inference them with the detector. Finally,...
# build the model from a config file and a checkpoint file model = init_detector(args.config, args.checkpoint, device=args.device) # test a single image result = inference_detector(model, args.img) # show the results show_result_pyplot( model, args.img, result, palette=args.palette, sco...
apis import init_detector, inference_detector import mmrotate config_file = 'oriented_rcnn_r50_fpn_1x_dota_le90.py' checkpoint_file = 'oriented_rcnn_r50_fpn_1x_dota_le90-6d2b2ce0.pth' model = init_detector(config_file, checkpoint_file, device='cuda:0') inference_detector(model, '...
Loss.GWD, KLD, and KFIoU propose different loss to train the rotated object detector. Our experimental results in Table2show that the KLD loss achieved the best mAP in the OpenCV definition method when using RetinaNet as the baseline. However, when using the R3Det as the baseline, the KF...
import numpy as np import torch import torchvision.transforms as T from PIL import Image from mmdet.apis import inference_detector, init_detector import mmrotate # noqa: F401 transform = T.Compose([ T.ToTensor(), ]) # 加载配置文件和模型权重 config_file = "configs/robustRDet/lsk_s_fpn_1x...
(type='Collect', # Pipeline that decides which keys in the data should be passed to the detector keys=['img', 'gt_bboxes', 'gt_labels']) ] test_pipeline = [ dict(type='LoadImageFromFile'), # First pipeline to load images from file path dict( type='MultiScaleFlipAug', # An ...
from mmdet.apis import inference_detector, init_detector, show_result_pyplot import mmrotate # noqa: F401 def parse_args(): parser = ArgumentParser() parser.add_argument('img', help='Image file') parser.add_argument('config', help='Config file') ...
apis import init_detector, inference_detector import mmrotate config_file = 'oriented_rcnn_r50_fpn_1x_dota_le90.py' checkpoint_file = 'oriented_rcnn_r50_fpn_1x_dota_le90-6d2b2ce0.pth' model = init_detector(config_file, checkpoint_file, device='cuda:0') inference_detector(model, '...