基于【Lama Cleaner】一键秒去水印，轻松移除不想要的内容！

一、项目背景

革命性的AI图像编辑技术,让您的图片焕然一新!无论水印、logo、不想要的人物或物体,都能被神奇地移除,只留下纯净的画面。操作简单,效果出众,给你全新的视觉体验。开启图像编辑新纪元,尽在掌控!

利用去水印开源工具Lama Cleaner对照片中"杂质"进行去除！

可以去AI擦除一切应用体验!

先看效果：

 

二、Lama Cleaner是什么？

Lama Cleaner是一款开源且免费的人工学习图片去水印程序（个人主要学习用途），没有图片分辨率限制（个人使用暂未发现），并且保存的图片质量很高（个人觉得跟原图差不多），还能下载处理后的图片到本地。

三、操作

1、安装

In [1]

!pip install litelama==0.1.7
Looking in indexes: https://mirror.baidu.com/pypi/simple/, https://mirrors.aliyun.com/pypi/simple/
Collecting litelama==0.1.7Downloading https://mirrors.aliyun.com/pypi/packages/6e/59/873f5cbaeae1f2b17e6d1ae6c74e1efde28783db4d7442346a77a6140673/litelama-0.1.7-py3-none-any.whl (21 kB)
Collecting kornia>=0.7.0 (from litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/ac/fa/5612c4b1ad83b3044062e9dd0ca3c91937d8023cff0836269e18573655b0/kornia-0.7.2-py2.py3-none-any.whl (825 kB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 825.4/825.4 kB 1.1 MB/s eta 0:00:0000:0100:01
Requirement already satisfied: numpy>=1.24.4 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from litelama==0.1.7) (1.26.2)
Collecting omegaconf>=2.3.0 (from litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/e3/94/1843518e420fa3ed6919835845df698c7e27e183cb997394e4a670973a65/omegaconf-2.3.0-py3-none-any.whl (79 kB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 79.5/79.5 kB 1.1 MB/s eta 0:00:00a 0:00:01
Requirement already satisfied: opencv-python>=4.8.0.76 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from litelama==0.1.7) (4.8.1.78)
Requirement already satisfied: pillow>=10.0.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from litelama==0.1.7) (10.1.0)
Requirement already satisfied: requests>=2.31.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from litelama==0.1.7) (2.31.0)
Requirement already satisfied: safetensors>=0.3.3 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from litelama==0.1.7) (0.4.1)
Collecting torch>=2.0.1 (from litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/33/b3/1fcc3bccfddadfd6845dcbfe26eb4b099f1dfea5aa0e5cfb92b3c98dba5b/torch-2.2.2-cp310-cp310-manylinux1_x86_64.whl (755.5 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 755.5/755.5 MB 769.7 kB/s eta 0:00:0000:0100:16
Collecting kornia-rs>=0.1.0 (from kornia>=0.7.0->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/7b/ef/eec16e87bc8893f608a42c96739ad0c35e30877b0f64bd19d95971534cef/kornia_rs-0.1.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.4 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.4/2.4 MB 1.3 MB/s eta 0:00:0000:0100:01
Requirement already satisfied: packaging in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from kornia>=0.7.0->litelama==0.1.7) (23.2)
Collecting antlr4-python3-runtime==4.9.* (from omegaconf>=2.3.0->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz (117 kB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 117.0/117.0 kB 1.3 MB/s eta 0:00:00a 0:00:01Preparing metadata (setup.py) ... done
Requirement already satisfied: PyYAML>=5.1.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from omegaconf>=2.3.0->litelama==0.1.7) (6.0.1)
Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from requests>=2.31.0->litelama==0.1.7) (3.3.2)
Requirement already satisfied: idna<4,>=2.5 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from requests>=2.31.0->litelama==0.1.7) (3.6)
Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from requests>=2.31.0->litelama==0.1.7) (2.1.0)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from requests>=2.31.0->litelama==0.1.7) (2023.11.17)
Requirement already satisfied: filelock in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from torch>=2.0.1->litelama==0.1.7) (3.13.1)
Requirement already satisfied: typing-extensions>=4.8.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from torch>=2.0.1->litelama==0.1.7) (4.9.0)
Collecting sympy (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/d2/05/e6600db80270777c4a64238a98d442f0fd07cc8915be2a1c16da7f2b9e74/sympy-1.12-py3-none-any.whl (5.7 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.7/5.7 MB 1.3 MB/s eta 0:00:0000:0100:01
Collecting networkx (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/d5/f0/8fbc882ca80cf077f1b246c0e3c3465f7f415439bdea6b899f6b19f61f70/networkx-3.2.1-py3-none-any.whl (1.6 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.6/1.6 MB 1.3 MB/s eta 0:00:0000:0100:01
Requirement already satisfied: jinja2 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from torch>=2.0.1->litelama==0.1.7) (3.1.2)
Requirement already satisfied: fsspec in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from torch>=2.0.1->litelama==0.1.7) (2023.10.0)
Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/b6/9f/c64c03f49d6fbc56196664d05dba14e3a561038a81a638eeb47f4d4cfd48/nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 23.7/23.7 MB 1.3 MB/s eta 0:00:0000:0100:01
Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/eb/d5/c68b1d2cdfcc59e72e8a5949a37ddb22ae6cade80cd4a57a84d4c8b55472/nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 823.6/823.6 kB 1.2 MB/s eta 0:00:0000:0100:01
Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/7e/00/6b218edd739ecfc60524e585ba8e6b00554dd908de2c9c66c1af3e44e18d/nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 14.1/14.1 MB 1.2 MB/s eta 0:00:0000:0100:01
Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/ff/74/a2e2be7fb83aaedec84f391f082cf765dfb635e7caa9b49065f73e4835d8/nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 731.7/731.7 MB 737.2 kB/s eta 0:00:0000:0100:16
Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/37/6d/121efd7382d5b0284239f4ab1fc1590d86d34ed4a4a2fdb13b30ca8e5740/nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 410.6/410.6 MB 1.0 MB/s eta 0:00:0000:0100:08
Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/86/94/eb540db023ce1d162e7bea9f8f5aa781d57c65aed513c33ee9a5123ead4d/nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 121.6/121.6 MB 1.3 MB/s eta 0:00:0000:0100:03
Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/44/31/4890b1c9abc496303412947fc7dcea3d14861720642b49e8ceed89636705/nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 56.5/56.5 MB 1.4 MB/s eta 0:00:0000:0100:01
Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/bc/1d/8de1e5c67099015c834315e333911273a8c6aaba78923dd1d1e25fc5f217/nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 124.2/124.2 MB 1.4 MB/s eta 0:00:0000:0100:03
Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/65/5b/cfaeebf25cd9fdec14338ccb16f6b2c4c7fa9163aefcf057d86b9cc248bb/nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 196.0/196.0 MB 1.2 MB/s eta 0:00:0000:0100:04
Collecting nvidia-nccl-cu12==2.19.3 (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/38/00/d0d4e48aef772ad5aebcf70b73028f88db6e5640b36c38e90445b7a57c45/nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 166.0/166.0 MB 1.2 MB/s eta 0:00:0000:0100:04
Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/da/d3/8057f0587683ed2fcd4dbfbdfdfa807b9160b809976099d36b8f60d08f03/nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 99.1/99.1 kB 1.0 MB/s eta 0:00:00a 0:00:01
Collecting triton==2.2.0 (from torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/95/05/ed974ce87fe8c8843855daa2136b3409ee1c126707ab54a8b72815c08b49/triton-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (167.9 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 167.9/167.9 MB 1.2 MB/s eta 0:00:0000:0100:04
Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/58/d1/d1c80553f9d5d07b6072bc132607d75a0ef3600e28e1890e11c0f55d7346/nvidia_nvjitlink_cu12-12.4.99-py3-none-manylinux2014_x86_64.whl (21.1 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 21.1/21.1 MB 1.4 MB/s eta 0:00:0000:0100:01
Requirement already satisfied: MarkupSafe>=2.0 in /opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages (from jinja2->torch>=2.0.1->litelama==0.1.7) (2.1.3)
Collecting mpmath>=0.19 (from sympy->torch>=2.0.1->litelama==0.1.7)Downloading https://mirrors.aliyun.com/pypi/packages/43/e3/7d92a15f894aa0c9c4b49b8ee9ac9850d6e63b03c9c32c0367a13ae62209/mpmath-1.3.0-py3-none-any.whl (536 kB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 536.2/536.2 kB 1.3 MB/s eta 0:00:0000:0100:01
Building wheels for collected packages: antlr4-python3-runtimeBuilding wheel for antlr4-python3-runtime (setup.py) ... doneCreated wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144554 sha256=077a76af915c8b5e871c1a81a6cbda25ccce15c65326cd9d79be4d51a5141f99Stored in directory: /home/aistudio/.cache/pip/wheels/79/82/b1/b79d6e90f34257cd436860ed4f4a09f9e1ea8cd32da7046ea4
Successfully built antlr4-python3-runtime
Installing collected packages: mpmath, antlr4-python3-runtime, triton, sympy, omegaconf, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, networkx, kornia-rs, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, kornia, litelama
Successfully installed antlr4-python3-runtime-4.9.3 kornia-0.7.2 kornia-rs-0.1.2 litelama-0.1.7 mpmath-1.3.0 networkx-3.2.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 omegaconf-2.3.0 sympy-1.12 torch-2.2.2 triton-2.2.0

2、clean_object

In [2]

from litelama import LiteLama
from litelama.model import download_file
import os
from fastapi import FastAPI, BodyMODEL_PATH = "work/models/"def clean_object_init_img_with_mask(init_img_with_mask):return clean_object(init_img_with_mask['image'],init_img_with_mask['mask'])def clean_object(image,mask):Lama = LiteLama2()init_image = imagemask_image = maskinit_image = init_image.convert("RGB")mask_image = mask_image.convert("RGB")device = "cuda:0"result = Nonetry:Lama.to(device)result = Lama.predict(init_image, mask_image)except:passfinally:Lama.to("cpu")return [result]class LiteLama2(LiteLama):_instance = Nonedef __new__(cls, *args, **kw):if cls._instance is None:cls._instance = object.__new__(cls, *args, **kw)return cls._instancedef __init__(self, checkpoint_path=None, config_path=None):self._checkpoint_path = checkpoint_pathself._config_path = config_pathself._model = Noneif self._checkpoint_path is None:checkpoint_path = os.path.join(MODEL_PATH, "big-lama.safetensors")if  os.path.exists(checkpoint_path) and os.path.isfile(checkpoint_path):passelse:download_file("https://huggingface.co/anyisalin/big-lama/resolve/main/big-lama.safetensors", checkpoint_path)self._checkpoint_path = checkpoint_pathself.load(location="cpu")

3、去除标记物

 

In [3]

from PIL import Image
from work.scripts import lama
# 打开图片文件
image = Image.open("work/scripts/1.jpg")
mask = Image.open("work/scripts/image.png")
_output = clean_object(image,mask)
print(_output)
/opt/conda/envs/python35-paddle120-env/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.htmlfrom .autonotebook import tqdm as notebook_tqdm

[<PIL.Image.Image image mode=RGB size=464x712 at 0x7F5007677B20>]

4、查看结果

In [4]

_output[0].show()

<PIL.Image.Image image mode=RGB size=464x712>

四、Gradio应用部署

本文开头所示的Gradio应用已经打包在work/scripts目录下的app.gradio.py文件内，大家可按照aistudio应用部署的方法进行在线部署，也可下载文件到本地进行本地运行。

具体步骤如下：

  1. 编辑器右上角找到部署按钮

  1. 选择Gradio部署

  1. 填写应用信息，执行文件选择 app.gradio.py，部署环境选择 GPU 即可，最后点击部署，接下来耐心等待部署完成。

本文来自互联网用户投稿，该文观点仅代表作者本人，不代表本站立场。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如若转载，请注明出处：http://www.mzph.cn/diannao/26302.shtml

如若内容造成侵权/违法违规/事实不符，请联系多彩编程网进行投诉反馈email:809451989@qq.com，一经查实，立即删除！

相关文章

【Vue】获取模块内的actions方法

【Vue】获取模块内的actions方法

目标&#xff1a; 掌握模块中 action 的调用语法 (同理 - 直接类比 mutation 即可) 注意&#xff1a; 默认模块中的 mutation 和 actions 会被挂载到全局&#xff0c;需要开启命名空间&#xff0c;才会挂载到子模块。 调用语法&#xff1a; 直接通过 store 调用 $store.di…
阅读更多...
【Go语言】面向对象编程（一）：类的定义、初始化和成员方法

【Go语言】面向对象编程（一）：类的定义、初始化和成员方法

面向对象编程&#xff08;一&#xff09;&#xff1a;类的定义、初始化和成员方法 1 类的定义和初始化 Go 语言的面向对象编程没有 class 、 extends 、implements 之类的关键字和相应的概念&#xff0c;而是借助结构体来实现类的声明&#xff0c;如下是定义一个学生类的方法…
阅读更多...
Virtual Memory Primitives for User Program翻译

Virtual Memory Primitives for User Program翻译

Virtual Memory Primitives for User Program 安德鲁阿普尔&#xff08;Andrew Appel&#xff09;和李凯&#xff08;Kai Li&#xff09; 普林斯顿大学计算机科学系 摘要 传统上&#xff0c;内存管理单元&#xff08;MMUS&#xff09;被操作系统用于实现磁盘分页的虚拟内存…
阅读更多...
FullCalendar日历组件集成实战（9）

FullCalendar日历组件集成实战（9）

背景 有一些应用系统或应用功能&#xff0c;如日程管理、任务管理需要使用到日历组件。虽然Element Plus也提供了日历组件&#xff0c;但功能比较简单&#xff0c;用来做数据展现勉强可用。但如果需要进行复杂的数据展示&#xff0c;以及互动操作如通过点击添加事件&#xff0…
阅读更多...
一文详谈大模型 RAG 优化方案与实践

一文详谈大模型 RAG 优化方案与实践

暑期实习基本结束了&#xff0c;校招即将开启。 不同以往的是&#xff0c;当前职场环境已不再是那个双向奔赴时代了。求职者在变多&#xff0c;HC 在变少&#xff0c;岗位要求还更高了。提前准备才是完全之策。 最近&#xff0c;我们又陆续整理了很多大厂的面试题&#xff0c…
阅读更多...
肾合与出汗：一场你不得不关注的健康对话

肾合与出汗：一场你不得不关注的健康对话

设想一下&#xff0c;我们的身体就像是一部精妙复杂的交响乐&#xff0c;每一个细胞、每一个组织都是乐符&#xff0c;共同编织出生命的旋律&#xff0c;演绎着我们的过去与未来。而汗水&#xff0c;就如同交响乐中的琴弦振动&#xff0c;它流淌在我们的体表&#xff0c;记录着…
阅读更多...
电商API接口接入||电商比价项目比价系统搭建需要注意哪些？

电商API接口接入||电商比价项目比价系统搭建需要注意哪些？

在搭建一个淘宝/京东比价系统时&#xff0c;需要注意以下几个方面&#xff0c;以确保系统的有效性、准确性和用户友好性&#xff1a; 确定平台和商品范围&#xff1a; 明确系统覆盖的电商平台&#xff0c;如淘宝、京东等。确定要比较的商品类别和范围&#xff0c;以确保数据的…
阅读更多...
JFinal学习06 控制器——getPara()接收数据

JFinal学习06 控制器——getPara()接收数据

JFinal学习06 控制器——getPara()接收数据 视频来源https://www.bilibili.com/video/BV1Bt411H7J9/?spm_id_from333.337.search-card.all.click 文章目录 JFinal学习06 控制器——getPara()接收数据零、JFinal数据提交的三种方式一、get提交二、post提交三、url参数化提交四、…
阅读更多...
北航数据结构与程序设计第五次作业选填题复习

北航数据结构与程序设计第五次作业选填题复习

选填题考的很多都是基础概念&#xff0c;对于巩固复习一些仡佬拐角的知识点是很有用的。非北航学生也可以来看看这些题&#xff0c;这一节主要是树方面的习题&#xff1a; 一、 我们首先需要知道一个公式 这是证明&#xff1a; 知道了这个公式&#xff0c;我们把题目中的数据…
阅读更多...
猫头虎分享：2024应届生择业在大模型和智能机器人之间该如何选择？

猫头虎分享：2024应届生择业在大模型和智能机器人之间该如何选择？

猫头虎分享&#xff1a;2024应届生择业在大模型和智能机器人之间该如何选择&#xff1f; 博主猫头虎的技术世界 &#x1f31f; 欢迎来到猫头虎的博客 — 探索技术的无限可能&#xff01; 专栏链接&#xff1a; &#x1f517; 精选专栏&#xff1a; 《面试题大全》 — 面试准备的…
阅读更多...
如何在Bing搜索进行广告推广？2024年必应广告投放怎么做？【附开户攻略】

如何在Bing搜索进行广告推广？2024年必应广告投放怎么做？【附开户攻略】

必应&#xff08;Bing&#xff09;作为全球领先的搜索引擎之一&#xff0c;拥有一个独特且庞大的用户群体&#xff0c;尤其在美国和欧洲市场&#xff0c;很多用户选择必应作为他们的主要搜索引擎。通过必应广告&#xff0c;企业可以触达那些在其他搜索引擎上难以接触到的潜在客…
阅读更多...
自定义打印外观

自定义打印外观

以下示例代码演示了如何将打印外观和背景颜色自定义应用于网格控件中的偶数行。 gridView1.OptionsPrint.UsePrintStyles true; // Enable the AppearancePrint.EvenRow propertys settings. gridView1.OptionsPrint.EnableAppearanceEvenRow true; // Set the background c…
阅读更多...
【机器学习】基于3D CNN通过CT图像分类预测肺炎

【机器学习】基于3D CNN通过CT图像分类预测肺炎

1. 引言 1.1. 研究背景 在医学诊断中&#xff0c;医生通过分析CT影像来预测疾病时&#xff0c;面临一些挑战和局限性&#xff1a; 图像信息的广度与复杂性&#xff1a; CT扫描生成的大量图像对医生来说既是信息的宝库也是处理上的负担。每组CT数据可能包含数百张切片&#xf…
阅读更多...
【Excel技巧】Excel打开密码的两种设置方法！

【Excel技巧】Excel打开密码的两种设置方法！

excel文件打开密码可以再打开文件时输入密码查看文件内容&#xff0c;这样就可以保护文件内容不被任何人查看了&#xff0c;今天分享excel打开密码的两种设置方法给大家。 方法一&#xff1a; 点击excel中的【文件】功能&#xff0c;找到【信息】-【保护工作表】-【用密码进行…
阅读更多...
Spring6

Spring6

一 概述 1.1、Spring是什么&#xff1f; Spring 是一款主流的 Java EE 轻量级开源框架 &#xff0c;Spring 由“Spring 之父”Rod Johnson 提出并创立&#xff0c;其目的是用于简化 Java 企业级应用的开发难度和开发周期。Spring的用途不仅限于服务器端的开发。从简单性、可测…
阅读更多...
2024年计算机相关专业是否还值得选择

2024年计算机相关专业是否还值得选择

目录 1.概述 1.1.就业前景 1.2.个人兴趣与能力 1.3.专业发展与趋势 1.4.市场饱和度与竞争 1.5.建议与展望 2.行业竞争现状 2.1.行业饱和度 2.2.新兴技术的影响 2.3.人才需求的变化 2.4.行业内的创新动态 2.5.保持从业者的竞争力 2.6.小结 3.专业与个人的匹配度判断…
阅读更多...
高光谱成像光源 实现对细微色差的分类--51camera

高光谱成像光源 实现对细微色差的分类--51camera

光源在机器视觉中的重要性不容小觑&#xff0c;它直接影响到图像的质量&#xff0c;进而影响整个系统的性能。然而自然光LED光源不能完全满足实际需求&#xff0c;比如对细微的色差进行分类&#xff0c;我们就需要考虑红外高光谱光源。 所谓高光谱成像&#xff0c;是指使用具有…
阅读更多...
【数学建模】微分方程的数值求解

【数学建模】微分方程的数值求解

微分方程的数值求解 一阶差分求解微分方程原理:四阶龙格-库塔方法应用:小船渡河问题: 进阶求二阶微分方程 一阶差分求解微分方程原理: d y d x f ( x n , y n ) \dfrac{dy}{dx}f(x_n,y_n) dxdy​f(xn​,yn​) y n 1 − y n x n 1 − x n f ( x n , y n ) \dfrac{y_{n1}-y_n…
阅读更多...
人工智能对聊天机器人训练数据的“淘金热”可能会耗尽人类编写的文本

人工智能对聊天机器人训练数据的“淘金热”可能会耗尽人类编写的文本

人工智能对聊天机器人训练数据的“淘金热”可能会耗尽人类编写的文本 像ChatGPT这样的人工智能系统可能很快就会耗尽让它们变得更聪明的东西——人们在网上写下和分享的数万亿字。 Epoch AI研究集团发布的一项新研究预计&#xff0c;科技公司将在大约十年之交——2026年至203…
阅读更多...
【Linux】ls命令

【Linux】ls命令

这个命令主要是用于显示指定工作目录下之内容&#xff08;列出目前工作目录所含的文件及子目录)。 掌握几个重点的常使用的就可以&#xff1a; ls -l # 以长格式显示当前目录中的文件和目录 ls -a # 显示当前目录中的所有文件和目录&am…
阅读更多...
推荐文章
最新文章

Copyright @ 2022~2023