monkeycc

重叠裁剪之后 预测 最后如何合并图像 在原图上 显示缺陷的位置 @juncaipeng

原图 9000*9000 训练图尺寸是640*640 导出模型 input_shape 这个不知道 怎么设置 对应的配置 是否是 python predict.py --is_slide True --stride 640 640 --crop_size 512 512 但是 --stride 640 640 --crop_size 512 512 不清楚 是不是这样改 不知道是否合理 Python...

python 部署 是不是可以直接用 predict.py 来预测 比 infer.py 速度上 是否一样 predict.py 滑动窗口预测 要1-2秒 有没办法缩短这个时间 ？？ @juncaipeng

C# 部署 预测 win环境 nvidia 3060 显卡 GPU来预测 cuda 11.0 有没教程的 还是要用PaddleX的 https://github.com/PaddlePaddle/PaddleX/blob/develop/deploy/cpp/docs/models/paddleseg.md PaddleSeg模型部署 但是我没看到有 滑窗 的api C# 需要滑窗预测 怎么办

How would I now be able to save the augmented polygons back to json labelme annotations format? Have you solved the problem @marvision-ai @Jazzzzie @Latha-13 Thank you for your code...

`https://github.com/rdt2992/Resnet/blob/master/data_augmentation_final` ``` from imgaug import augmenters as iaa import numpy as np import cv2 import os iname = [] folder = 'C:\\Users\\com\\PycharmProjects\\untitled\\test\\auto_test\\aug' def load_images_from_folder(folder): images = [] global iname for...

`https://github.com/guchengxi1994/mask2json` `https://github.com/guchengxi1994/mask2json/blob/master/test_scripts/test_imgAug.py` imgaug   `https://github.com/pureyangcry/tools` `https://github.com/pureyangcry/tools/blob/master/DataAugForObjectSegmentation/DataAugmentforLabelMe.py` opencv 

imgaug data augmentation Output LabelMe JSON Enable offline data augmentation I think you are very technical If you have time and are interested Whether to consider Create a new repository...

Thank you for your help The tutorial is very detailed It can be added to the gallery Help more people I hope more and more people use ttkbootstrap ------------------------------------------ Add...

Expect DataTable control