可视化脚本用于使用MMDetection库进行图像的目标检测

# Copyright (c) OpenMMLab. All rights reserved.
import asyncio
from argparse import ArgumentParserfrom mmdet.apis import (async_inference_detector, inference_detector,init_detector, show_result_pyplot)
import denseclip# 解析命令行参数
def parse_args():parser = ArgumentParser()parser.add_argument('img', help='Image file')  # 图像文件路径parser.add_argument('config', help='Config file')  # 配置文件路径parser.add_argument('checkpoint', help='Checkpoint file')  # 检查点文件路径parser.add_argument('--out-file', default=None, help='Path to output file')  # 输出结果文件路径parser.add_argument('--device', default='cuda:0', help='Device used for inference')  # 使用进行推理的设备parser.add_argument('--palette',default='coco',choices=['coco', 'voc', 'citys', 'random'],help='Color palette used for visualization')  # 可视化使用的颜色调色板parser.add_argument('--score-thr', type=float, default=0.3, help='bbox score threshold')  # 框得分阈值parser.add_argument('--async-test',action='store_true',help='whether to set async options for async inference.')  # 是否进行异步推理args = parser.parse_args()return args# 主函数
def main(args):# 从配置文件和检查点文件构建模型model = init_detector(args.config, args.checkpoint, device=args.device)# 对单张图像进行测试result = inference_detector(model, args.img)# 显示结果show_result_pyplot(model,args.img,result,palette=args.palette,score_thr=args.score_thr,out_file=args.out_file)# 异步主函数
async def async_main(args):# 从配置文件和检查点文件构建模型model = init_detector(args.config, args.checkpoint, device=args.device)# 对单张图像进行异步测试tasks = asyncio.create_task(async_inference_detector(model, args.img))result = await asyncio.gather(tasks)# 显示结果show_result_pyplot(model,args.img,result[0],palette=args.palette,score_thr=args.score_thr,out_file=args.out_file)# 主程序入口
if __name__ == '__main__':args = parse_args()if args.async_test:asyncio.run(async_main(args))  # 如果设置了异步选项，则运行异步主函数else:main(args)  # 否则运行同步主函数

用命令行指定具体文件

基本示例

python your_script.py path/to/image.jpg path/to/config.py path/to/checkpoint.pth

指定输出文件

python your_script.py path/to/image.jpg path/to/config.py path/to/checkpoint.pth --out-file path/to/output.jpg

设置边界框得分阈值

python your_script.py path/to/image.jpg path/to/config.py path/to/checkpoint.pth --score-thr 0.5

或者用下面的分布式测试

import argparse
import os
import warningsimport mmcv
import torch
from mmcv import Config, DictAction
from mmcv.cnn import fuse_conv_bn
from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,wrap_fp16_model)from mmdet.apis import multi_gpu_test, single_gpu_test
from mmdet.datasets import (build_dataloader, build_dataset,replace_ImageToTensor)
from mmdet.models import build_detector
import denseclipdef parse_args():parser = argparse.ArgumentParser(description='MMDet test (and eval) a model')parser.add_argument('config', help='test config file path')parser.add_argument('checkpoint', help='checkpoint file')parser.add_argument('--out', help='output result file in pickle format')parser.add_argument('--fuse-conv-bn',action='store_true',help='Whether to fuse conv and bn, this will slightly increase''the inference speed')parser.add_argument('--format-only',action='store_true',help='Format the output results without perform evaluation. It is''useful when you want to format the result to a specific format and ''submit it to the test server')parser.add_argument('--eval',type=str,nargs='+',help='evaluation metrics, which depends on the dataset, e.g., "bbox",'' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')parser.add_argument('--show', action='store_true', help='show results')parser.add_argument('--show-dir', help='directory where painted images will be saved')parser.add_argument('--show-score-thr',type=float,default=0.3,help='score threshold (default: 0.3)')parser.add_argument('--gpu-collect',action='store_true',help='whether to use gpu to collect results.')parser.add_argument('--tmpdir',help='tmp directory used for collecting results from multiple ''workers, available when gpu-collect is not specified')parser.add_argument('--cfg-options',nargs='+',action=DictAction,help='override some settings in the used config, the key-value pair ''in xxx=yyy format will be merged into config file. If the value to ''be overwritten is a list, it should be like key="[a,b]" or key=a,b ''It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ''Note that the quotation marks are necessary and that no white space ''is allowed.')parser.add_argument('--options',nargs='+',action=DictAction,help='custom options for evaluation, the key-value pair in xxx=yyy ''format will be kwargs for dataset.evaluate() function (deprecate), ''change to --eval-options instead.')parser.add_argument('--eval-options',nargs='+',action=DictAction,help='custom options for evaluation, the key-value pair in xxx=yyy ''format will be kwargs for dataset.evaluate() function')parser.add_argument('--launcher',choices=['none', 'pytorch', 'slurm', 'mpi'],default='none',help='job launcher')parser.add_argument('--local_rank', type=int, default=0)args = parser.parse_args()if 'LOCAL_RANK' not in os.environ:os.environ['LOCAL_RANK'] = str(args.local_rank)if args.options and args.eval_options:raise ValueError('--options and --eval-options cannot be both ''specified, --options is deprecated in favor of --eval-options')if args.options:warnings.warn('--options is deprecated in favor of --eval-options')args.eval_options = args.optionsreturn argsdef main():args = parse_args()assert args.out or args.eval or args.format_only or args.show \or args.show_dir, \('Please specify at least one operation (save/eval/format/show the ''results / save the results) with the argument "--out", "--eval"'', "--format-only", "--show" or "--show-dir"')if args.eval and args.format_only:raise ValueError('--eval and --format_only cannot be both specified')if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):raise ValueError('The output file must be a pkl file.')cfg = Config.fromfile(args.config)if args.cfg_options is not None:cfg.merge_from_dict(args.cfg_options)# import modules from string list.if cfg.get('custom_imports', None):from mmcv.utils import import_modules_from_stringsimport_modules_from_strings(**cfg['custom_imports'])# set cudnn_benchmarkif cfg.get('cudnn_benchmark', False):torch.backends.cudnn.benchmark = Truecfg.model.pretrained = Noneif cfg.model.get('neck'):if isinstance(cfg.model.neck, list):for neck_cfg in cfg.model.neck:if neck_cfg.get('rfp_backbone'):if neck_cfg.rfp_backbone.get('pretrained'):neck_cfg.rfp_backbone.pretrained = Noneelif cfg.model.neck.get('rfp_backbone'):if cfg.model.neck.rfp_backbone.get('pretrained'):cfg.model.neck.rfp_backbone.pretrained = None# in case the test dataset is concatenatedif isinstance(cfg.data.test, dict):cfg.data.test.test_mode = Trueelif isinstance(cfg.data.test, list):for ds_cfg in cfg.data.test:ds_cfg.test_mode = True# init distributed env first, since logger depends on the dist info.if args.launcher == 'none':distributed = Falseelse:distributed = Trueinit_dist(args.launcher, **cfg.dist_params)# build the dataloadersamples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1)if samples_per_gpu > 1:# Replace 'ImageToTensor' to 'DefaultFormatBundle'cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)dataset = build_dataset(cfg.data.test)data_loader = build_dataloader(dataset,samples_per_gpu=samples_per_gpu,workers_per_gpu=cfg.data.workers_per_gpu,dist=distributed,shuffle=False)# build the model and load checkpointif 'DenseCLIP' in cfg.model.type:cfg.model.class_names = list(dataset.CLASSES)if not hasattr(cfg, 'test_cfg'):cfg.test_cfg = Nonemodel = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)fp16_cfg = cfg.get('fp16', None)if fp16_cfg is not None:wrap_fp16_model(model)checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')if args.fuse_conv_bn:model = fuse_conv_bn(model)# old versions did not save class info in checkpoints, this walkaround is# for backward compatibilityif 'CLASSES' in checkpoint['meta']:model.CLASSES = checkpoint['meta']['CLASSES']else:model.CLASSES = dataset.CLASSESif not distributed:model = MMDataParallel(model, device_ids=[0])outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,args.show_score_thr)else:model = MMDistributedDataParallel(model.cuda(),device_ids=[torch.cuda.current_device()],broadcast_buffers=False)outputs = multi_gpu_test(model, data_loader, args.tmpdir,args.gpu_collect)rank, _ = get_dist_info()if rank == 0:if args.out:print(f'\nwriting results to {args.out}')mmcv.dump(outputs, args.out)kwargs = {} if args.eval_options is None else args.eval_optionsif args.format_only:dataset.format_results(outputs, **kwargs)if args.eval:eval_kwargs = cfg.get('evaluation', {}).copy()# hard-code way to remove EvalHook argsfor key in ['interval', 'tmpdir', 'start', 'gpu_collect', 'save_best','rule']:eval_kwargs.pop(key, None)eval_kwargs.update(dict(metric=args.eval, **kwargs))print(dataset.evaluate(outputs, **eval_kwargs))if __name__ == '__main__':main()