mmsegmentation默认将预测得到的mask覆盖在原始图片上进行显示或保存,为了直接输出灰度图,需要对源码进行修改 修改位置mmseg/models/segmentors/base/show_result defshow_result(self,img,result,palette=None,win_name='',show=False,wait_time=0,out_file=None,opacity=0.5):img=mmcv.imread(img)img=img.copy()s...
show_result_pyplot(model, img, result, show=True, save_dir='visualization_results') 图10. 结果 如有问题,烦请斧正。
img = 'demo.png' result = inference_model(model, img) # 在新的窗口可视化结果 show_result_pyplot(model, img, result, show=True) # 或者将可视化结果保存到图像文件夹中 # 您可以修改分割 map 的透明度 (0, 1]. show_result_pyplot(model, img, result, show=True, out_file='result.jpg', opa...
model=init_model(config_file,checkpoint_file,device='cuda:0')# 在单张图像上测试并可视化 img='demo.png'result=inference_model(model,img)# 在新的窗口可视化结果show_result_pyplot(model,img,result,show=True)# 或者将可视化结果保存到图像文件夹中 # 您可以修改分割 map的透明度(0,1].show_result_pyp...
plt.show() result = inference_model(model, img_bgr) result.keys() pred_mask = result.pred_sem_seg.data[0].cpu().numpy() pred_mask.shape np.unique(pred_mask) plt.figure(figsize=(8, 8)) plt.imshow(pred_mask) save_path='./outputs/spaceNet50_'+epoch+'_predict.png' ...
449be17d.pth'model = init_segmentor(config_file, checkpoint_file, device='cuda:0')# 准备输入图像img = 'tests/data/cityscapes/frankfurt_000000_000294_leftImg8bit.png'# 执行分割result = inference_segmentor(model, img)# 显示结果show_result_pyplot(model, img, result, get_palette('cityscapes')...
from mmseg.apis import inference_segmentor, init_segmentor, show_result_pyplot from mmseg.core.evaluation import get_palette config_file = 'configs/pspnet/pspnet_r50-d8_512x1024_40k_cityscapes.py' checkpoint_file = 'checkpoints/pspnet_r50-d8_512x1024_40k_cityscapes_20200605_003338-2966598c.pth'...
file model = init_segmentor(config_file, checkpoint_file, device='cuda:0') # test a single image and show the results img = 'test.jpg' # or img = mmcv.imread(img), which will only load it once result = inference_segmentor(model, img) # visualize the results in a new window model...
input_image='input.jpg' result=model.inference(input_image) # 可视化结果 result.show() 5. 总结 mmsegmentation 是一个功能强大的实例分割工具箱,提供了多种模型和算法,支持模型训练、测试、推理和部署。通过使用 mmsegmentation,我们可以轻松地解决实例分割问题,并在计算机视觉领域取得更好的成果。©...
show_result_pyplot Implemented based on SegLocalVisualizer train_model Removed, use runner.train to train. multi_gpu_test Removed, use runner.test to test. single_gpu_test Removed, use runner.test to test. set_random_seed Removed, use mmengine.runner.set_random_seed. init_random_seed Removed...