cloud-annotations | 🐝 A fast , easy and collaborative open source image | Data Labeling library

Β by Β  cloud-annotations TypeScript Version: v1.3.2 License: MIT

kandi X-RAY | cloud-annotations Summary

cloud-annotations is a TypeScript library typically used in Artificial Intelligence, Data Labeling, Deep Learning, Tensorflow applications. cloud-annotations has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.
A fast, easy and collaborative open source image annotation tool for teams and individuals.
    Support
      Quality
        Security
          License
            Reuse
            Support
              Quality
                Security
                  License
                    Reuse

                      kandi-support Support

                        summary
                        cloud-annotations has a medium active ecosystem.
                        summary
                        It has 2653 star(s) with 467 fork(s). There are 97 watchers for this library.
                        summary
                        It had no major release in the last 12 months.
                        summary
                        There are 20 open issues and 168 have been closed. On average issues are closed in 417 days. There are 8 open pull requests and 0 closed requests.
                        summary
                        It has a neutral sentiment in the developer community.
                        summary
                        The latest version of cloud-annotations is v1.3.2
                        cloud-annotations Support
                          Best in #Data Labeling
                            Average in #Data Labeling
                            cloud-annotations Support
                              Best in #Data Labeling
                                Average in #Data Labeling

                                  kandi-Quality Quality

                                    summary
                                    cloud-annotations has 0 bugs and 0 code smells.
                                    cloud-annotations Quality
                                      Best in #Data Labeling
                                        Average in #Data Labeling
                                        cloud-annotations Quality
                                          Best in #Data Labeling
                                            Average in #Data Labeling

                                              kandi-Security Security

                                                summary
                                                cloud-annotations has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
                                                summary
                                                cloud-annotations code analysis shows 0 unresolved vulnerabilities.
                                                summary
                                                There are 0 security hotspots that need review.
                                                cloud-annotations Security
                                                  Best in #Data Labeling
                                                    Average in #Data Labeling
                                                    cloud-annotations Security
                                                      Best in #Data Labeling
                                                        Average in #Data Labeling

                                                          kandi-License License

                                                            summary
                                                            cloud-annotations is licensed under the MIT License. This license is Permissive.
                                                            summary
                                                            Permissive licenses have the least restrictions, and you can use them in most projects.
                                                            cloud-annotations License
                                                              Best in #Data Labeling
                                                                Average in #Data Labeling
                                                                cloud-annotations License
                                                                  Best in #Data Labeling
                                                                    Average in #Data Labeling

                                                                      kandi-Reuse Reuse

                                                                        summary
                                                                        cloud-annotations releases are available to install and integrate.
                                                                        cloud-annotations Reuse
                                                                          Best in #Data Labeling
                                                                            Average in #Data Labeling
                                                                            cloud-annotations Reuse
                                                                              Best in #Data Labeling
                                                                                Average in #Data Labeling
                                                                                  Top functions reviewed by kandi - BETA
                                                                                  kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
                                                                                  Currently covering the most popular Java, JavaScript and Python libraries. See a Sample Here
                                                                                  Get all kandi verified functions for this library.
                                                                                  Get all kandi verified functions for this library.

                                                                                  cloud-annotations Key Features

                                                                                  🐝 A fast, easy and collaborative open source image annotation tool for teams and individuals.

                                                                                  cloud-annotations Examples and Code Snippets

                                                                                  No Code Snippets are available at this moment for cloud-annotations.
                                                                                  Community Discussions

                                                                                  Trending Discussions on Data Labeling

                                                                                  How can I do this split process in Python?
                                                                                  chevron right
                                                                                  Replacing a character with a space and dividing the string into two words in R
                                                                                  chevron right
                                                                                  Azure ML FileDataset registers, but cannot be accessed for Data Labeling project
                                                                                  chevron right

                                                                                  QUESTION

                                                                                  How can I do this split process in Python?
                                                                                  Asked 2021-Dec-30 at 14:06

                                                                                  I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

                                                                                  What I've done so far is make this representation with the same enumerator class.

                                                                                  A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?

                                                                                  import pandas as pd
                                                                                  from enum import Enum
                                                                                  
                                                                                  
                                                                                  df = pd.DataFrame({'first': ['product and other', 'product2 and other', 'price'], 'second':['product and prices', 'price2', 'product3 and price']})
                                                                                  df
                                                                                  
                                                                                  class Tipos(Enum):
                                                                                      B = 1
                                                                                      I = 2
                                                                                      L = 3
                                                                                  
                                                                                  for index, row in df.iterrows():
                                                                                      sentencas = row.values
                                                                                      for sentenca in sentencas:
                                                                                          for pos, palavra in enumerate(sentenca.split()):
                                                                                              print(f"{palavra} {Tipos(pos+1).name}")
                                                                                  
                                                                                  

                                                                                  Results:

                                                                                                  first              second
                                                                                  0   product and other  product and prices
                                                                                  1  product2 and other              price2
                                                                                  2               price  product3 and price
                                                                                  
                                                                                  product B
                                                                                  and I
                                                                                  other L
                                                                                  product B
                                                                                  and I
                                                                                  prices L
                                                                                  product2 B
                                                                                  and I
                                                                                  other L
                                                                                  price2 B
                                                                                  price B
                                                                                  product3 B
                                                                                  and I
                                                                                  price L
                                                                                  

                                                                                  Desired Results:

                                                                                          Word Ent
                                                                                  0    product B_first
                                                                                  1        and I_first
                                                                                  2      other L_first
                                                                                  3    product B_second
                                                                                  4        and I_second
                                                                                  5     prices L_second
                                                                                  6   product2 B_first
                                                                                  7        and I_first
                                                                                  8      other L_first
                                                                                  9     price2 B_second
                                                                                  10     price B_first
                                                                                  11  product3 B_second
                                                                                  12       and I_second
                                                                                  13     price L_second
                                                                                  
                                                                                  # In that case, the sequence is like that: (B_first, I_first, L_first, L_first...) and if changes the column gets B_second, I_second, L_second...
                                                                                  

                                                                                  ANSWER

                                                                                  Answered 2021-Dec-30 at 13:57

                                                                                  Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:

                                                                                  out = df.unstack().str.split().explode().sort_index(level=1).to_frame('Word')
                                                                                  out['Ent'] = out.groupby(level=[0, 1]).cumcount().map(Tipos) \
                                                                                                   + '_' + out.index.get_level_values(0)
                                                                                  out = out.reset_index(drop=True)
                                                                                  

                                                                                  Output:

                                                                                  >>> out
                                                                                          Word       Ent
                                                                                  0    product   B_first
                                                                                  1        and   I_first
                                                                                  2      other   L_first
                                                                                  3    product  B_second
                                                                                  4        and  I_second
                                                                                  5     prices  L_second
                                                                                  6   product2   B_first
                                                                                  7        and   I_first
                                                                                  8      other   L_first
                                                                                  9     price2  B_second
                                                                                  10     price   B_first
                                                                                  11  product3  B_second
                                                                                  12       and  I_second
                                                                                  13     price  L_second
                                                                                  

                                                                                  Source https://stackoverflow.com/questions/70532286

                                                                                  QUESTION

                                                                                  Replacing a character with a space and dividing the string into two words in R
                                                                                  Asked 2020-Nov-18 at 07:32

                                                                                  I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.

                                                                                  In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space

                                                                                  i have this:

                                                                                          datacolumn1
                                                                                   row 1  knowledge; information; data
                                                                                   row 2  digital;transmission; interoperability; data labeling
                                                                                   row 3  library catalogs; libraries; mobile;libraries
                                                                                  

                                                                                  I need this output:

                                                                                         datacolumn1
                                                                                  row 1  knowledge; information; data
                                                                                  row 2  digital transmission; interoperability; data labeling
                                                                                  row 3  library catalogs; libraries; mobile libraries
                                                                                  

                                                                                  ANSWER

                                                                                  Answered 2020-Nov-16 at 07:24

                                                                                  Try something like:

                                                                                  library(stringr)
                                                                                  str_replace_all(datacolumn1, "(\\w);(\\w)", "\\1 \\2")
                                                                                  

                                                                                  This is probably a neater regex out there, but this will do!

                                                                                  Source https://stackoverflow.com/questions/64853962

                                                                                  QUESTION

                                                                                  Azure ML FileDataset registers, but cannot be accessed for Data Labeling project
                                                                                  Asked 2020-Oct-28 at 20:31

                                                                                  Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.

                                                                                  Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.

                                                                                  Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.

                                                                                  import azureml.core
                                                                                  from azureml.core import Dataset, Datastore, Workspace
                                                                                  
                                                                                  # Load in work space from saved config file
                                                                                  ws = Workspace.from_config()
                                                                                  
                                                                                  # Define full dataset of interest and retrieve it
                                                                                  dataset_name = 'complete_2017'
                                                                                  data = Dataset.get_by_name(ws, dataset_name)
                                                                                  
                                                                                  # Extract file references from dataset as relative paths
                                                                                  rel_filepaths = data.to_path()
                                                                                  
                                                                                  # Stitch back in base directory path to get a list of absolute paths
                                                                                  src_folder = '/raw-data/2017'
                                                                                  abs_filepaths = [src_folder + path for path in rel_filepaths]
                                                                                  
                                                                                  # Define regular expression pattern for extracting source section
                                                                                  import re
                                                                                  pattern = re.compile('\/(S.+)_image\d+.jpg')
                                                                                  
                                                                                  # Create new list of all unique source sections
                                                                                  sections = sorted(set([m.group(1) for m in map(pattern.match, rel_filepaths) if m]))
                                                                                  
                                                                                  # Randomly select sections
                                                                                  num_sections = 5
                                                                                  set_seed = 221020
                                                                                  random.seed(set_seed)   # for repeatibility
                                                                                  sample_sections = random.choices(sections, k = num_sections)
                                                                                  
                                                                                  # Extract images related to the selected sections
                                                                                  matching_images = [filename for filename in abs_filepaths if any(section in filename for section in sample_sections)]
                                                                                  
                                                                                  # Define datastore of interest
                                                                                  datastore = Datastore.get(ws, 'ml-datastore')
                                                                                  
                                                                                  # Convert string paths to Azure Datapath objects and relate back to datastore
                                                                                  from azureml.data.datapath import DataPath
                                                                                  datastore_path = [DataPath(datastore, filepath) for filepath in matching_images]
                                                                                  
                                                                                  # Generate new dataset using from_files() and filtered list of paths
                                                                                  sample = Dataset.File.from_files(datastore_path)
                                                                                  
                                                                                  sample_name = 'random-section-sample'
                                                                                  sample_dataset = sample.register(workspace = ws, name = sample_name, description = 'Sampled sections from full dataset using set seed.')
                                                                                  

                                                                                  Issue: The code I've written in Python SDK runs and the new FileDataset registers, but when I try to look at the dataset details or use it for a Data Labeling project I get the following error even as Owner.

                                                                                  Access denied: Failed to authenticate data access with Workspace system assigned identity. Make sure to add the identity as Reader of the data service.
                                                                                  

                                                                                  Additionally, under the details tab Files in dataset is Unknown and Total size of files in dataset is Unavailable.

                                                                                  I haven't come across this issue anywhere else. I'm able to generate datasets in other ways, so I suspect it's an issue with the code given that I'm working with the data in an unconventional way.

                                                                                  Additional Notes:

                                                                                  • Azure ML version is 1.15.0

                                                                                  ANSWER

                                                                                  Answered 2020-Oct-27 at 22:39

                                                                                  Is the data behind virtual network by any chance?

                                                                                  Source https://stackoverflow.com/questions/64546521

                                                                                  Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                                                                                  Vulnerabilities

                                                                                  No vulnerabilities reported

                                                                                  Install cloud-annotations

                                                                                  You can download it from GitHub.

                                                                                  Support

                                                                                  For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
                                                                                  Find more information at:
                                                                                  Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
                                                                                  Find more libraries
                                                                                  Explore Kits - Develop, implement, customize Projects, Custom Functions and Applications with kandi kits​
                                                                                  Save this library and start creating your kit
                                                                                  CLONE
                                                                                • HTTPS

                                                                                  https://github.com/cloud-annotations/cloud-annotations.git

                                                                                • CLI

                                                                                  gh repo clone cloud-annotations/cloud-annotations

                                                                                • sshUrl

                                                                                  git@github.com:cloud-annotations/cloud-annotations.git

                                                                                • Share this Page

                                                                                  share link

                                                                                  Consider Popular Data Labeling Libraries

                                                                                  label-studio

                                                                                  by heartexlabs

                                                                                  cvat

                                                                                  by openvinotoolkit

                                                                                  VoTT

                                                                                  by microsoft

                                                                                  cloud-annotations

                                                                                  by cloud-annotations

                                                                                  labelbox

                                                                                  by Labelbox

                                                                                  Try Top Libraries by cloud-annotations

                                                                                  training

                                                                                  by cloud-annotationsGo

                                                                                  docusaurus-openapi

                                                                                  by cloud-annotationsTypeScript

                                                                                  object-detection-react

                                                                                  by cloud-annotationsJavaScript

                                                                                  object-tracking-js

                                                                                  by cloud-annotationsJavaScript

                                                                                  docusaurus-plugin-openapi

                                                                                  by cloud-annotationsTypeScript

                                                                                  Compare Data Labeling Libraries with Highest Support

                                                                                  label-studio

                                                                                  by heartexlabs

                                                                                  cvat

                                                                                  by openvinotoolkit

                                                                                  VoTT

                                                                                  by microsoft

                                                                                  cloud-annotations

                                                                                  by cloud-annotations

                                                                                  universal-data-tool

                                                                                  by UniversalDataTool

                                                                                  Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
                                                                                  Find more libraries
                                                                                  Explore Kits - Develop, implement, customize Projects, Custom Functions and Applications with kandi kits​
                                                                                  Save this library and start creating your kit