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.github/workflows/pylint.yml vendored
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name: Pylint
on: [push]
jobs:
build:
runs-on: ubuntu-latest
strategy:
matrix:
python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
steps:
- uses: actions/checkout@v3
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v3
with:
python-version: ${{ matrix.python-version }}
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
pip install pylint
- name: Analysing the code with pylint
run: |
pylint $(git ls-files '*.py')

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# This workflow will upload a Python Package using Twine when a release is created
# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python#publishing-to-package-registries
# This workflow uses actions that are not certified by GitHub.
# They are provided by a third-party and are governed by
# separate terms of service, privacy policy, and support
# documentation.
name: Upload Python Package
on:
release:
types: [published]
permissions:
contents: read
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v3
with:
python-version: '3.x'
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install build
- name: Build package
run: python -m build
- name: Publish package
uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
with:
user: __token__
password: ${{ secrets.PYPI_API_TOKEN }}

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[MAIN]
# Analyse import fallback blocks. This can be used to support both Python 2 and
# 3 compatible code, which means that the block might have code that exists
# only in one or another interpreter, leading to false positives when analysed.
analyse-fallback-blocks=no
# Clear in-memory caches upon conclusion of linting. Useful if running pylint
# in a server-like mode.
clear-cache-post-run=no
# Load and enable all available extensions. Use --list-extensions to see a list
# all available extensions.
#enable-all-extensions=
# In error mode, messages with a category besides ERROR or FATAL are
# suppressed, and no reports are done by default. Error mode is compatible with
# disabling specific errors.
#errors-only=
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# This is primarily useful in continuous integration scripts.
#exit-zero=
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extension-pkg-allow-list=
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# run arbitrary code. (This is an alternative name to extension-pkg-allow-list
# for backward compatibility.)
extension-pkg-whitelist=
# Return non-zero exit code if any of these messages/categories are detected,
# even if score is above --fail-under value. Syntax same as enable. Messages
# specified are enabled, while categories only check already-enabled messages.
fail-on=
# Specify a score threshold under which the program will exit with error.
fail-under=10
# Interpret the stdin as a python script, whose filename needs to be passed as
# the module_or_package argument.
#from-stdin=
# Files or directories to be skipped. They should be base names, not paths.
ignore=CVS
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ignore-paths=
# Files or directories matching the regular expression patterns are skipped.
# The regex matches against base names, not paths. The default value ignores
# Emacs file locks
ignore-patterns=^\.#
# List of module names for which member attributes should not be checked
# (useful for modules/projects where namespaces are manipulated during runtime
# and thus existing member attributes cannot be deduced by static analysis). It
# supports qualified module names, as well as Unix pattern matching.
ignored-modules=
# Python code to execute, usually for sys.path manipulation such as
# pygtk.require().
#init-hook=
# Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the
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jobs=1
# Control the amount of potential inferred values when inferring a single
# object. This can help the performance when dealing with large functions or
# complex, nested conditions.
limit-inference-results=100
# List of plugins (as comma separated values of python module names) to load,
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load-plugins=
# Pickle collected data for later comparisons.
persistent=yes
# Minimum Python version to use for version dependent checks. Will default to
# the version used to run pylint.
py-version=3.10
# Discover python modules and packages in the file system subtree.
recursive=no
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source-roots=
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suggestion-mode=yes
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unsafe-load-any-extension=no
# In verbose mode, extra non-checker-related info will be displayed.
#verbose=
[BASIC]
# Naming style matching correct argument names.
argument-naming-style=snake_case
# Regular expression matching correct argument names. Overrides argument-
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# naming style.
#argument-rgx=
# Naming style matching correct attribute names.
attr-naming-style=snake_case
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#attr-rgx=
# Bad variable names which should always be refused, separated by a comma.
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bad-names-rgxs=
# Naming style matching correct class attribute names.
class-attribute-naming-style=any
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# Naming style matching correct class constant names.
class-const-naming-style=UPPER_CASE
# Regular expression matching correct class constant names. Overrides class-
# const-naming-style. If left empty, class constant names will be checked with
# the set naming style.
#class-const-rgx=
# Naming style matching correct class names.
class-naming-style=PascalCase
# Regular expression matching correct class names. Overrides class-naming-
# style. If left empty, class names will be checked with the set naming style.
#class-rgx=
# Naming style matching correct constant names.
const-naming-style=UPPER_CASE
# Regular expression matching correct constant names. Overrides const-naming-
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# style.
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# ones are exempt.
docstring-min-length=-1
# Naming style matching correct function names.
function-naming-style=snake_case
# Regular expression matching correct function names. Overrides function-
# naming-style. If left empty, function names will be checked with the set
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#function-rgx=
# Good variable names which should always be accepted, separated by a comma.
good-names=i,
j,
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ex,
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_
# Good variable names regexes, separated by a comma. If names match any regex,
# they will always be accepted
good-names-rgxs=
# Include a hint for the correct naming format with invalid-name.
include-naming-hint=no
# Naming style matching correct inline iteration names.
inlinevar-naming-style=any
# Regular expression matching correct inline iteration names. Overrides
# inlinevar-naming-style. If left empty, inline iteration names will be checked
# with the set naming style.
#inlinevar-rgx=
# Naming style matching correct method names.
method-naming-style=snake_case
# Regular expression matching correct method names. Overrides method-naming-
# style. If left empty, method names will be checked with the set naming style.
#method-rgx=
# Naming style matching correct module names.
module-naming-style=snake_case
# Regular expression matching correct module names. Overrides module-naming-
# style. If left empty, module names will be checked with the set naming style.
#module-rgx=
# Colon-delimited sets of names that determine each other's naming style when
# the name regexes allow several styles.
name-group=
# Regular expression which should only match function or class names that do
# not require a docstring.
no-docstring-rgx=^_
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# to this list to register other decorators that produce valid properties.
# These decorators are taken in consideration only for invalid-name.
property-classes=abc.abstractproperty
# Regular expression matching correct type alias names. If left empty, type
# alias names will be checked with the set naming style.
#typealias-rgx=
# Regular expression matching correct type variable names. If left empty, type
# variable names will be checked with the set naming style.
#typevar-rgx=
# Naming style matching correct variable names.
variable-naming-style=snake_case
# Regular expression matching correct variable names. Overrides variable-
# naming-style. If left empty, variable names will be checked with the set
# naming style.
#variable-rgx=
[CLASSES]
# Warn about protected attribute access inside special methods
check-protected-access-in-special-methods=no
# List of method names used to declare (i.e. assign) instance attributes.
defining-attr-methods=__init__,
__new__,
setUp,
asyncSetUp,
__post_init__
# List of member names, which should be excluded from the protected access
# warning.
exclude-protected=_asdict,_fields,_replace,_source,_make,os._exit
# List of valid names for the first argument in a class method.
valid-classmethod-first-arg=cls
# List of valid names for the first argument in a metaclass class method.
valid-metaclass-classmethod-first-arg=mcs
[DESIGN]
# List of regular expressions of class ancestor names to ignore when counting
# public methods (see R0903)
exclude-too-few-public-methods=
# List of qualified class names to ignore when counting class parents (see
# R0901)
ignored-parents=
# Maximum number of arguments for function / method.
max-args=5
# Maximum number of attributes for a class (see R0902).
max-attributes=7
# Maximum number of boolean expressions in an if statement (see R0916).
max-bool-expr=5
# Maximum number of branch for function / method body.
max-branches=12
# Maximum number of locals for function / method body.
max-locals=15
# Maximum number of parents for a class (see R0901).
max-parents=7
# Maximum number of public methods for a class (see R0904).
max-public-methods=20
# Maximum number of return / yield for function / method body.
max-returns=6
# Maximum number of statements in function / method body.
max-statements=50
# Minimum number of public methods for a class (see R0903).
min-public-methods=2
[EXCEPTIONS]
# Exceptions that will emit a warning when caught.
overgeneral-exceptions=builtins.BaseException,builtins.Exception
[FORMAT]
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expected-line-ending-format=
# Regexp for a line that is allowed to be longer than the limit.
ignore-long-lines=^\s*(# )?<?https?://\S+>?$
# Number of spaces of indent required inside a hanging or continued line.
indent-after-paren=4
# String used as indentation unit. This is usually " " (4 spaces) or "\t" (1
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# Maximum number of lines in a module.
max-module-lines=1000
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# contains single statement.
single-line-class-stmt=no
# Allow the body of an if to be on the same line as the test if there is no
# else.
single-line-if-stmt=no
[IMPORTS]
# List of modules that can be imported at any level, not just the top level
# one.
allow-any-import-level=
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allow-reexport-from-package=no
# Allow wildcard imports from modules that define __all__.
allow-wildcard-with-all=no
# Deprecated modules which should not be used, separated by a comma.
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# Output a graph (.gv or any supported image format) of external dependencies
# to the given file (report RP0402 must not be disabled).
ext-import-graph=
# Output a graph (.gv or any supported image format) of all (i.e. internal and
# external) dependencies to the given file (report RP0402 must not be
# disabled).
import-graph=
# Output a graph (.gv or any supported image format) of internal dependencies
# to the given file (report RP0402 must not be disabled).
int-import-graph=
# Force import order to recognize a module as part of the standard
# compatibility libraries.
known-standard-library=
# Force import order to recognize a module as part of a third party library.
known-third-party=enchant
# Couples of modules and preferred modules, separated by a comma.
preferred-modules=
[LOGGING]
# The type of string formatting that logging methods do. `old` means using %
# formatting, `new` is for `{}` formatting.
logging-format-style=old
# Logging modules to check that the string format arguments are in logging
# function parameter format.
logging-modules=logging
[MESSAGES CONTROL]
# Only show warnings with the listed confidence levels. Leave empty to show
# all. Valid levels: HIGH, CONTROL_FLOW, INFERENCE, INFERENCE_FAILURE,
# UNDEFINED.
confidence=HIGH,
CONTROL_FLOW,
INFERENCE,
INFERENCE_FAILURE,
UNDEFINED
# Disable the message, report, category or checker with the given id(s). You
# can either give multiple identifiers separated by comma (,) or put this
# option multiple times (only on the command line, not in the configuration
# file where it should appear only once). You can also use "--disable=all" to
# disable everything first and then re-enable specific checks. For example, if
# you want to run only the similarities checker, you can use "--disable=all
# --enable=similarities". If you want to run only the classes checker, but have
# no Warning level messages displayed, use "--disable=all --enable=classes
# --disable=W".
disable=raw-checker-failed,
bad-inline-option,
locally-disabled,
file-ignored,
suppressed-message,
useless-suppression,
deprecated-pragma,
use-symbolic-message-instead
# Enable the message, report, category or checker with the given id(s). You can
# either give multiple identifier separated by comma (,) or put this option
# multiple time (only on the command line, not in the configuration file where
# it should appear only once). See also the "--disable" option for examples.
enable=c-extension-no-member
[METHOD_ARGS]
# List of qualified names (i.e., library.method) which require a timeout
# parameter e.g. 'requests.api.get,requests.api.post'
timeout-methods=requests.api.delete,requests.api.get,requests.api.head,requests.api.options,requests.api.patch,requests.api.post,requests.api.put,requests.api.request
[MISCELLANEOUS]
# List of note tags to take in consideration, separated by a comma.
notes=FIXME,
XXX,
TODO
# Regular expression of note tags to take in consideration.
notes-rgx=
[REFACTORING]
# Maximum number of nested blocks for function / method body
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# Complete name of functions that never returns. When checking for
# inconsistent-return-statements if a never returning function is called then
# it will be considered as an explicit return statement and no message will be
# printed.
never-returning-functions=sys.exit,argparse.parse_error
[REPORTS]
# Python expression which should return a score less than or equal to 10. You
# have access to the variables 'fatal', 'error', 'warning', 'refactor',
# 'convention', and 'info' which contain the number of messages in each
# category, as well as 'statement' which is the total number of statements
# analyzed. This score is used by the global evaluation report (RP0004).
evaluation=max(0, 0 if fatal else 10.0 - ((float(5 * error + warning + refactor + convention) / statement) * 10))
# Template used to display messages. This is a python new-style format string
# used to format the message information. See doc for all details.
msg-template=
# Set the output format. Available formats are text, parseable, colorized, json
# and msvs (visual studio). You can also give a reporter class, e.g.
# mypackage.mymodule.MyReporterClass.
#output-format=
# Tells whether to display a full report or only the messages.
reports=no
# Activate the evaluation score.
score=yes
[SIMILARITIES]
# Comments are removed from the similarity computation
ignore-comments=yes
# Docstrings are removed from the similarity computation
ignore-docstrings=yes
# Imports are removed from the similarity computation
ignore-imports=yes
# Signatures are removed from the similarity computation
ignore-signatures=yes
# Minimum lines number of a similarity.
min-similarity-lines=4
[SPELLING]
# Limits count of emitted suggestions for spelling mistakes.
max-spelling-suggestions=4
# Spelling dictionary name. No available dictionaries : You need to install
# both the python package and the system dependency for enchant to work..
spelling-dict=
# List of comma separated words that should be considered directives if they
# appear at the beginning of a comment and should not be checked.
spelling-ignore-comment-directives=fmt: on,fmt: off,noqa:,noqa,nosec,isort:skip,mypy:
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spelling-ignore-words=
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spelling-private-dict-file=
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# --spelling-private-dict-file option) instead of raising a message.
spelling-store-unknown-words=no
[STRING]
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# character used as a quote delimiter is used inconsistently within a module.
check-quote-consistency=no
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# on implicit string concatenation in sequences defined over several lines.
check-str-concat-over-line-jumps=no
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# List of decorators that produce context managers, such as
# contextlib.contextmanager. Add to this list to register other decorators that
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contextmanager-decorators=contextlib.contextmanager
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# system, and so shouldn't trigger E1101 when accessed. Python regular
# expressions are accepted.
generated-members=cv2.*
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ignore-none=yes
# This flag controls whether pylint should warn about no-member and similar
# checks whenever an opaque object is returned when inferring. The inference
# can return multiple potential results while evaluating a Python object, but
# some branches might not be evaluated, which results in partial inference. In
# that case, it might be useful to still emit no-member and other checks for
# the rest of the inferred objects.
ignore-on-opaque-inference=yes
# List of symbolic message names to ignore for Mixin members.
ignored-checks-for-mixins=no-member,
not-async-context-manager,
not-context-manager,
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# for classes with dynamically set attributes). This supports the use of
# qualified names.
ignored-classes=optparse.Values,thread._local,_thread._local,argparse.Namespace
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# name must start or end with one of those strings.
callbacks=cb_,
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# Tells whether we should check for unused import in __init__ files.
init-import=no
# List of qualified module names which can have objects that can redefine
# builtins.
redefining-builtins-modules=six.moves,past.builtins,future.builtins,builtins,io

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README.md
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**Forked from [rooneysh/Labelme2YOLO](https://github.com/rooneysh/Labelme2YOLO)**
# Labelme2YOLO
[![PyPI - Version](https://img.shields.io/pypi/v/labelme2yolo.svg)](https://pypi.org/project/labelme2yolo)
![PyPI - Downloads](https://img.shields.io/pypi/dm/labelme2yolo?style=flat)
[![PYPI - Downloads](https://static.pepy.tech/badge/labelme2yolo)](https://pepy.tech/project/labelme2yolo)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/labelme2yolo.svg)](https://pypi.org/project/labelme2yolo)
[![Codacy Badge](https://app.codacy.com/project/badge/Grade/12122fe86f8643c4aa5667c20d528f61)](https://www.codacy.com/gh/GreatV/labelme2yolo/dashboard?utm_source=github.com\&utm_medium=referral\&utm_content=GreatV/labelme2yolo\&utm_campaign=Badge_Grade)
[![Codacy Badge](https://app.codacy.com/project/badge/Grade/12122fe86f8643c4aa5667c20d528f61)](https://www.codacy.com/gh/GreatV/labelme2yolo/dashboard?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=GreatV/labelme2yolo&amp;utm_campaign=Badge_Grade)
Labelme2YOLO is a powerful tool for converting LabelMe's JSON format to [YOLOv5](https://github.com/ultralytics/yolov5) dataset format. This tool can also be used for YOLOv5/YOLOv8 segmentation datasets, if you have already made your segmentation dataset with LabelMe, it is easy to use this tool to help convert to YOLO format dataset.
Help converting LabelMe Annotation Tool JSON format to YOLO text file format.
If you've already marked your segmentation dataset by LabelMe, it's easy to use this tool to help converting to YOLO format dataset.
## New Features
* export data as yolo polygon annotation (for YOLOv5 & YOLOV8 segmentation)
* Now you can choose the output format of the label text. The two available alternatives are `polygon` and bounding box (`bbox`).
---------
## New
- export data as yolo polygon annotation (for YOLOv5 v7.0 segmentation)
- Now you can choose the output format of the label text. The available options are `plygon` and `bbox`.
## Installation
```shell
```console
pip install labelme2yolo
```
## Arguments
## Parameters Explain
**--json_dir** LabelMe JSON files folder path.
**--json\_dir** LabelMe JSON files folder path.
**--val_size (Optional)** Validation dataset size, for example 0.2 means 20% for validation.
**--val\_size (Optional)** Validation dataset size, for example 0.2 means 20% for validation.
**--test_size (Optional)** Test dataset size, for example 0.2 means 20% for Test.
**--test\_size (Optional)** Test dataset size, for example 0.1 means 10% for Test.
**--json_name (Optional)** Convert single LabelMe JSON file.
**--json\_name (Optional)** Convert single LabelMe JSON file.
**--output\_format (Optional)** The output format of label.
**--label\_list (Optional)** The pre-assigned category labels.
**--output_format (Optional)** The output format of label.
## How to Use
### 1. Converting JSON files and splitting training, validation datasets
You may need to place all LabelMe JSON files under **labelme\_json\_dir** and then run the following command:
```shell
labelme2yolo --json_dir /path/to/labelme_json_dir/
```
This tool will generate dataset labels and images with YOLO format in different folders, such as
```plaintext
/path/to/labelme_json_dir/YOLODataset/labels/train/
/path/to/labelme_json_dir/YOLODataset/labels/val/
/path/to/labelme_json_dir/YOLODataset/images/train/
/path/to/labelme_json_dir/YOLODataset/images/val/
/path/to/labelme_json_dir/YOLODataset/dataset.yaml
```
### 2. Converting JSON files and splitting training, validation, and test datasets with --val\_size and --test\_size
You may need to place all LabelMe JSON files under **labelme\_json\_dir** and then run the following command:
```shell
### 1. Convert JSON files, split training, validation and test dataset by --val_size and --test_size
Put all LabelMe JSON files under **labelme_json_dir**, and run this python command.
```bash
labelme2yolo --json_dir /path/to/labelme_json_dir/ --val_size 0.15 --test_size 0.15
```
This tool will generate dataset labels and images with YOLO format in different folders, such as
```plaintext
Script would generate YOLO format dataset labels and images under different folders, for example,
```bash
/path/to/labelme_json_dir/YOLODataset/labels/train/
/path/to/labelme_json_dir/YOLODataset/labels/test/
/path/to/labelme_json_dir/YOLODataset/labels/val/
/path/to/labelme_json_dir/YOLODataset/images/train/
/path/to/labelme_json_dir/YOLODataset/images/test/
/path/to/labelme_json_dir/YOLODataset/images/val/
/path/to/labelme_json_dir/YOLODataset/dataset.yaml
```
### 2. Convert JSON files, split training and validation dataset by folder
If you already split train dataset and validation dataset for LabelMe by yourself, please put these folder under labelme_json_dir, for example,
```bash
/path/to/labelme_json_dir/train/
/path/to/labelme_json_dir/val/
```
Put all LabelMe JSON files under **labelme_json_dir**.
Script would read train and validation dataset by folder.
Run this python command.
```bash
labelme2yolo --json_dir /path/to/labelme_json_dir/
```
Script would generate YOLO format dataset labels and images under different folders, for example,
```bash
/path/to/labelme_json_dir/YOLODataset/labels/train/
/path/to/labelme_json_dir/YOLODataset/labels/val/
/path/to/labelme_json_dir/YOLODataset/images/train/
/path/to/labelme_json_dir/YOLODataset/images/val/
/path/to/labelme_json_dir/YOLODataset/dataset.yaml
```
### 3. Convert single JSON file
Put LabelMe JSON file under **labelme_json_dir**. , and run this python command.
```bash
labelme2yolo --json_dir /path/to/labelme_json_dir/ --json_name 2.json
```
Script would generate YOLO format text label and image under **labelme_json_dir**, for example,
```bash
/path/to/labelme_json_dir/2.text
/path/to/labelme_json_dir/2.png
```
## How to build package/wheel
1. [install hatch](https://hatch.pypa.io/latest/install/)
@ -84,6 +95,4 @@ hatch build
## License
**Forked from [rooneysh/Labelme2YOLO](https://github.com/rooneysh/Labelme2YOLO)**
`labelme2yolo` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.

18
pyproject.toml
View File

@ -6,27 +6,27 @@ build-backend = "hatchling.build"
name = "labelme2yolo"
description = "This script converts the JSON format output by LabelMe to the text format required by YOLO serirs."
readme = "README.md"
requires-python = ">=3.8"
requires-python = ">=3.7"
license = "MIT"
keywords = []
authors = [
{ name = "GreatV(Wang Xin)", email = "xinwang614@gmail.com" },
]
classifiers = [
"Development Status :: 5 - Production/Stable",
"Development Status :: 4 - Beta",
"Programming Language :: Python",
"Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"License :: OSI Approved :: MIT License",
"Programming Language :: Python :: Implementation :: CPython",
"Programming Language :: Python :: Implementation :: PyPy",
]
dependencies = [
"opencv-python>=4.1.2",
"Pillow>=9.2,<10.4",
"numpy>=1.23.1,<1.27.0",
"rich"
"Pillow>=9.2,<9.5",
"scikit-learn>=1.1.1,<1.3.0",
"numpy>=1.23.1,<1.25.0"
]
dynamic = ["version"]
@ -54,7 +54,7 @@ cov = "pytest --cov-report=term-missing --cov-config=pyproject.toml --cov=labelm
no-cov = "cov --no-cov"
[[tool.hatch.envs.test.matrix]]
python = ["38", "39", "310", "311", "312"]
python = ["37", "38", "39", "310"]
[tool.coverage.run]
branch = true

2
requirements.txt
View File

@ -1,4 +1,4 @@
opencv-python
Pillow
scikit-learn
numpy
rich

6
src/labelme2yolo/__about__.py
View File

@ -1,7 +1,5 @@
# SPDX-FileCopyrightText: 2023-present Wang Xin <xinwang614@gmail.com>
#
# SPDX-License-Identifier: MIT
"""
about version
"""
__version__ = "0.1.7"
__version__ = '0.0.9'

3
src/labelme2yolo/__main__.py
View File

@ -1,9 +1,6 @@
# SPDX-FileCopyrightText: 2022-present Wang Xin <xinwang614@gmail.com>
#
# SPDX-License-Identifier: MIT
"""
main
"""
import sys
if __name__ == "__main__":

27
src/labelme2yolo/cli/__init__.py
View File

@ -1,22 +1,13 @@
# SPDX-FileCopyrightText: 2022-present Wang Xin <xinwang614@gmail.com>
#
# SPDX-License-Identifier: MIT
"""
cli init
"""
import argparse
from labelme2yolo.__about__ import __version__
from labelme2yolo.l2y import Labelme2YOLO
def run():
"""
run cli
"""
parser = argparse.ArgumentParser("labelme2yolo")
parser.add_argument(
"-v", "--version", action="version", version="%(prog)s " + __version__
)
parser.add_argument(
"--json_dir", type=str, help="Please input the path of the labelme json files."
)
@ -24,14 +15,14 @@ def run():
"--val_size",
type=float,
nargs="?",
default=0.2,
help="Please input the validation dataset size, for example 0.2.",
default=None,
help="Please input the validation dataset size, for example 0.1.",
)
parser.add_argument(
"--test_size",
type=float,
nargs="?",
default=0.0,
default=None,
help="Please input the test dataset size, for example 0.1.",
)
parser.add_argument(
@ -48,14 +39,6 @@ def run():
help='The default output format for labelme2yolo is "polygon".'
' However, you can choose to output in bbox format by specifying the "bbox" option.',
)
parser.add_argument(
"--label_list",
type=str,
nargs="+",
default=None,
help="The ordered label list, for example --label_list cat dog",
required=False,
)
args = parser.parse_args()
@ -63,7 +46,7 @@ def run():
parser.print_help()
return 0
convertor = Labelme2YOLO(args.json_dir, args.output_format, args.label_list)
convertor = Labelme2YOLO(args.json_dir, args.output_format)
if args.json_name is None:
convertor.convert(val_size=args.val_size, test_size=args.test_size)

403
src/labelme2yolo/l2y.py
View File

@ -5,58 +5,36 @@ Created on Aug 18, 2021
@author: GreatV(Wang Xin)
"""
import base64
import glob
import io
import json
import math
import os
import random
import shutil
import uuid
import logging
from functools import partial
from collections import OrderedDict
from multiprocessing import Pool
import cv2
import numpy as np
import PIL.ExifTags
import PIL.Image
import PIL.ImageOps
import cv2
import numpy as np
from rich.progress import Progress
# set seed
random.seed(12345678)
random.Random().seed(12345678)
np.random.seed(12345678)
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("labelme2yolo")
from sklearn.model_selection import train_test_split
def train_test_split(dataset_index, test_size=0.2):
"""Split dataset into train set and test set with test_size"""
test_size = min(max(0.0, test_size), 1.0)
total_size = len(dataset_index)
train_size = int(round(total_size * (1.0 - test_size)))
random.shuffle(dataset_index)
train_index = dataset_index[:train_size]
test_index = dataset_index[train_size:]
return train_index, test_index
# number of LabelMe2YOLO multiprocessing threads
NUM_THREADS = max(1, os.cpu_count() - 1)
# copy form https://github.com/wkentaro/labelme/blob/main/labelme/utils/image.py
def img_data_to_pil(img_data):
"""Convert img_data(byte) to PIL.Image"""
file = io.BytesIO()
file.write(img_data)
img_pil = PIL.Image.open(file)
f = io.BytesIO()
f.write(img_data)
img_pil = PIL.Image.open(f)
return img_pil
# copy form https://github.com/wkentaro/labelme/blob/main/labelme/utils/image.py
def img_data_to_arr(img_data):
"""Convert img_data(byte) to numpy.ndarray"""
img_pil = img_data_to_pil(img_data)
img_arr = np.array(img_pil)
return img_arr
@ -64,7 +42,6 @@ def img_data_to_arr(img_data):
# copy form https://github.com/wkentaro/labelme/blob/main/labelme/utils/image.py
def img_b64_to_arr(img_b64):
"""Convert img_b64(str) to numpy.ndarray"""
img_data = base64.b64decode(img_b64)
img_arr = img_data_to_arr(img_data)
return img_arr
@ -72,27 +49,27 @@ def img_b64_to_arr(img_b64):
# copy form https://github.com/wkentaro/labelme/blob/main/labelme/utils/image.py
def img_pil_to_data(img_pil):
"""Convert PIL.Image to img_data(byte)"""
file = io.BytesIO()
img_pil.save(file, format="PNG")
img_data = file.getvalue()
f = io.BytesIO()
img_pil.save(f, format="PNG")
img_data = f.getvalue()
return img_data
# copy form https://github.com/wkentaro/labelme/blob/main/labelme/utils/image.py
def img_arr_to_b64(img_arr):
"""Convert numpy.ndarray to img_b64(str)"""
img_pil = PIL.Image.fromarray(img_arr)
file = io.BytesIO()
img_pil.save(file, format="PNG")
img_bin = file.getvalue()
img_b64 = base64.encodebytes(img_bin)
f = io.BytesIO()
img_pil.save(f, format="PNG")
img_bin = f.getvalue()
if hasattr(base64, "encodebytes"):
img_b64 = base64.encodebytes(img_bin)
else:
img_b64 = base64.encodestring(img_bin)
return img_b64
# copy form https://github.com/wkentaro/labelme/blob/main/labelme/utils/image.py
def img_data_to_png_data(img_data):
"""Convert img_data(byte) to png_data(byte)"""
with io.BytesIO() as f_out:
f_out.write(img_data)
img = PIL.Image.open(f_out)
@ -103,186 +80,169 @@ def img_data_to_png_data(img_data):
return f_in.read()
def extend_point_list(point_list, out_format="polygon"):
"""Extend point list to polygon or bbox"""
x_min = min(float(point) for point in point_list[::2])
x_max = max(float(point) for point in point_list[::2])
y_min = min(float(point) for point in point_list[1::2])
y_max = max(float(point) for point in point_list[1::2])
def get_label_id_map(json_dir):
label_set = set()
if out_format == "bbox":
x_i = x_min
y_i = y_min
w_i = x_max - x_min
h_i = y_max - y_min
x_i = x_i + w_i / 2
y_i = y_i + h_i / 2
return np.array([x_i, y_i, w_i, h_i])
for file_name in os.listdir(json_dir):
if file_name.endswith("json"):
json_path = os.path.join(json_dir, file_name)
data = json.load(open(json_path))
for shape in data["shapes"]:
label_set.add(shape["label"])
return np.array([x_min, y_min, x_max, y_min, x_max, y_max, x_min, y_max])
return OrderedDict([(label, label_id) for label_id, label in enumerate(label_set)])
def save_yolo_label(obj_list, label_dir, target_dir, target_name):
"""Save yolo label to txt file"""
txt_path = os.path.join(label_dir, target_dir, target_name)
def extend_point_list(point_list, format="polygon"):
xmin = min([float(point) for point in point_list[::2]])
xmax = max([float(point) for point in point_list[::2]])
ymin = min([float(point) for point in point_list[1::2]])
ymax = max([float(point) for point in point_list[1::2]])
with open(txt_path, "w+", encoding="utf-8") as file:
for label, points in obj_list:
if (format == "polygon"):
return np.array([xmin, ymin, xmax, ymin, xmax, ymax, xmin, ymax])
if (format == "bbox"):
return np.array([xmin, ymin, xmax - xmin, ymax - ymin])
def save_yolo_label(json_name, label_dir_path, target_dir, yolo_obj_list):
txt_path = os.path.join(label_dir_path,
target_dir,
json_name.replace(".json", ".txt"))
with open(txt_path, "w+") as f:
for yolo_obj in yolo_obj_list:
label, points = yolo_obj
points = [str(item) for item in points]
line = f"{label} {' '.join(points)}\n"
file.write(line)
yolo_obj_line = f"{label} {' '.join(points)}\n"
f.write(yolo_obj_line)
def save_yolo_image(json_data, json_dir, image_dir, target_dir, target_name):
"""Save yolo image to image_dir_path/target_dir"""
img_path = os.path.join(image_dir, target_dir, target_name)
def save_yolo_image(json_data, json_name, image_dir_path, target_dir):
img_name = json_name.replace(".json", ".png")
img_path = os.path.join(image_dir_path, target_dir, img_name)
if json_data["imageData"]:
if not os.path.exists(img_path):
img = img_b64_to_arr(json_data["imageData"])
PIL.Image.fromarray(img).save(img_path)
else:
image_name = json_data["imagePath"]
src_image_name = os.path.join(json_dir, image_name)
src_image = cv2.imread(src_image_name)
cv2.imwrite(img_path, src_image)
return img_path
class Labelme2YOLO:
"""Labelme to YOLO format converter"""
class Labelme2YOLO(object):
def __init__(self, json_dir, output_format, label_list):
self._json_dir = os.path.expanduser(json_dir)
def __init__(self, json_dir, output_format):
self._json_dir = json_dir
self._output_format = output_format
self._label_list = []
self._label_id_map = {}
self._label_dir_path = ""
self._image_dir_path = ""
if label_list:
self._label_list = label_list
self._label_id_map = {
label: label_id for label_id, label in enumerate(label_list)
}
else:
logger.info("Searching label list from json files ...")
# get label list from json files for parallel processing
json_files = glob.glob(
os.path.join(self._json_dir, "**", "*.json"), recursive=True
)
for json_file in json_files:
with open(json_file, encoding="utf-8") as file:
json_data = json.load(file)
for shape in json_data["shapes"]:
if shape["label"] not in self._label_list:
self._label_list.append(shape["label"])
self._label_id_map = {
label: label_id for label_id, label in enumerate(self._label_list)
}
self._label_id_map = get_label_id_map(self._json_dir)
def _update_id_map(self, label: str):
if label not in self._label_list:
self._label_list.append(label)
self._label_id_map[label] = len(self._label_id_map)
def _make_train_val_dir(self):
self._label_dir_path = os.path.join(self._json_dir,
'YOLODataset/labels/')
self._image_dir_path = os.path.join(self._json_dir,
'YOLODataset/images/')
def _make_train_val_dir(self, create_test_dir=False):
self._label_dir_path = os.path.join(self._json_dir, "YOLODataset/labels/")
self._image_dir_path = os.path.join(self._json_dir, "YOLODataset/images/")
for yolo_path in (os.path.join(self._label_dir_path + 'train/'),
os.path.join(self._label_dir_path + 'val/'),
os.path.join(self._label_dir_path + 'test/'),
os.path.join(self._image_dir_path + 'train/'),
os.path.join(self._image_dir_path + 'val/'),
os.path.join(self._image_dir_path + 'test/')):
if os.path.exists(yolo_path):
shutil.rmtree(yolo_path)
for yolo_path in [self._label_dir_path, self._image_dir_path]:
shutil.rmtree(yolo_path, ignore_errors=True)
os.makedirs(yolo_path)
parts = ["train", "val", "test"] if create_test_dir else ["train", "val"]
image_dirs = [os.path.join(self._image_dir_path, part) for part in parts]
label_dirs = [os.path.join(self._label_dir_path, part) for part in parts]
dirs = image_dirs + label_dirs
for yolo_path in dirs:
os.makedirs(yolo_path, exist_ok=True)
def _train_test_split(self, folders, json_names, val_size, test_size):
if len(folders) > 0 and 'train' in folders and 'val' in folders and 'test' in folders:
train_folder = os.path.join(self._json_dir, 'train/')
train_json_names = [train_sample_name + '.json'
for train_sample_name in os.listdir(train_folder)
if os.path.isdir(os.path.join(train_folder, train_sample_name))]
def _get_dataset_part_json_names(self, dataset_part: str):
"""Get json names in dataset_part folder"""
set_folder = os.path.join(self._json_dir, dataset_part)
json_names = []
for sample_name in os.listdir(set_folder):
set_dir = os.path.join(set_folder, sample_name)
if os.path.isdir(set_dir):
json_names.append(sample_name + ".json")
return json_names
val_folder = os.path.join(self._json_dir, 'val/')
val_json_names = [val_sample_name + '.json'
for val_sample_name in os.listdir(val_folder)
if os.path.isdir(os.path.join(val_folder, val_sample_name))]
def _train_test_split(self, json_names, val_size, test_size=None):
"""Split json names to train, val, test"""
total_size = len(json_names)
dataset_index = list(range(total_size))
train_ids, val_ids = train_test_split(dataset_index, test_size=val_size)
test_ids = []
test_folder = os.path.join(self._json_dir, 'test/')
test_json_names = [test_sample_name + '.json'
for test_sample_name in os.listdir(test_folder)
if os.path.isdir(os.path.join(test_folder, test_sample_name))]
return train_json_names, val_json_names, test_json_names
train_idxs, val_idxs = train_test_split(range(len(json_names)),
test_size=val_size)
tmp_train_len = len(train_idxs)
test_idxs = []
if test_size is None:
test_size = 0.0
if test_size > 0.0:
train_ids, test_ids = train_test_split(
train_ids, test_size=test_size / (1 - val_size)
)
train_json_names = [json_names[train_idx] for train_idx in train_ids]
val_json_names = [json_names[val_idx] for val_idx in val_ids]
test_json_names = [json_names[test_idx] for test_idx in test_ids]
if test_size > 1e-8:
train_idxs, test_idxs = train_test_split(
range(tmp_train_len), test_size=test_size / (1 - val_size))
train_json_names = [json_names[train_idx] for train_idx in train_idxs]
val_json_names = [json_names[val_idx] for val_idx in val_idxs]
test_json_names = [json_names[test_idx] for test_idx in test_idxs]
return train_json_names, val_json_names, test_json_names
def convert(self, val_size, test_size):
"""Convert labelme format to yolo format"""
json_names = glob.glob(
os.path.join(self._json_dir, "**", "*.json"), recursive=True
)
json_names = sorted(json_names)
json_names = [file_name for file_name in os.listdir(self._json_dir)
if os.path.isfile(os.path.join(self._json_dir, file_name)) and
file_name.endswith('.json')]
folders = [file_name for file_name in os.listdir(self._json_dir)
if os.path.isdir(os.path.join(self._json_dir, file_name))]
train_json_names, val_json_names, test_json_names = self._train_test_split(
json_names, val_size, test_size
)
folders, json_names, val_size, test_size)
self._make_train_val_dir(test_size > 0.0)
self._make_train_val_dir()
# convert labelme object to yolo format object, and save them to files
# also get image from labelme json file and save them under images folder
dirs = ("train", "val", "test")
names = (train_json_names, val_json_names, test_json_names)
for target_dir, json_names in zip(dirs, names):
logger.info("Converting %s set ...", target_dir)
with Pool(os.cpu_count() - 1) as pool, Progress() as progress:
task = progress.add_task("[cyan]Converting...", total=len(json_names))
func = partial(self.covert_json_to_text, target_dir)
for _ in pool.imap_unordered(func, json_names):
progress.update(task, advance=1)
for target_dir, json_names in zip(('train/', 'val/', 'test/'),
(train_json_names, val_json_names, test_json_names)):
pool = Pool(NUM_THREADS)
for json_name in json_names:
pool.apply_async(self.covert_json_to_text,
args=(target_dir, json_name))
pool.close()
pool.join()
print('Generating dataset.yaml file ...')
self._save_dataset_yaml()
def covert_json_to_text(self, target_dir, json_name):
"""Convert json file to yolo format text file and save them to files"""
with open(json_name, encoding="utf-8") as file:
json_data = json.load(file)
json_path = os.path.join(self._json_dir, json_name)
json_data = json.load(open(json_path))
filename: str = uuid.UUID(int=random.Random().getrandbits(128)).hex
image_name = f"{filename}.png"
label_name = f"{filename}.txt"
img_path = save_yolo_image(
json_data, self._json_dir, self._image_dir_path, target_dir, image_name
)
print('Converting %s for %s ...' %
(json_name, target_dir.replace('/', '')))
img_path = save_yolo_image(json_data,
json_name,
self._image_dir_path,
target_dir)
yolo_obj_list = self._get_yolo_object_list(json_data, img_path)
save_yolo_label(yolo_obj_list, self._label_dir_path, target_dir, label_name)
save_yolo_label(json_name,
self._label_dir_path,
target_dir,
yolo_obj_list)
def convert_one(self, json_name):
"""Convert one json file to yolo format text file and save them to files"""
json_path = os.path.join(self._json_dir, json_name)
with open(json_path, encoding="utf-8") as file:
json_data = json.load(file)
json_data = json.load(open(json_path))
image_name = json_name.replace(".json", ".png")
label_name = json_name.replace(".json", ".txt")
img_path = save_yolo_image(
json_data, self._json_dir, self._image_dir_path, "", image_name
)
print('Converting %s ...' % json_name)
img_path = save_yolo_image(json_data, json_name,
self._json_dir, '')
yolo_obj_list = self._get_yolo_object_list(json_data, img_path)
save_yolo_label(yolo_obj_list, self._label_dir_path, "", label_name)
save_yolo_label(json_name, self._json_dir,
'', yolo_obj_list)
def _get_yolo_object_list(self, json_data, img_path):
yolo_obj_list = []
@ -291,86 +251,65 @@ class Labelme2YOLO:
for shape in json_data["shapes"]:
# labelme circle shape is different from others
# it only has 2 points, 1st is circle center, 2nd is drag end point
if shape["shape_type"] == "circle":
yolo_obj = self._get_circle_shape_yolo_object(shape, img_h, img_w)
if shape['shape_type'] == 'circle':
yolo_obj = self._get_circle_shape_yolo_object(
shape, img_h, img_w)
else:
yolo_obj = self._get_other_shape_yolo_object(shape, img_h, img_w)
yolo_obj = self._get_other_shape_yolo_object(
shape, img_h, img_w)
if yolo_obj:
yolo_obj_list.append(yolo_obj)
yolo_obj_list.append(yolo_obj)
return yolo_obj_list
def _get_circle_shape_yolo_object(self, shape, img_h, img_w):
obj_center_x, obj_center_y = shape["points"][0]
obj_center_x, obj_center_y = shape['points'][0]
radius = math.sqrt(
(obj_center_x - shape["points"][1][0]) ** 2
+ (obj_center_y - shape["points"][1][1]) ** 2
)
num_points = 36
points = np.zeros(2 * num_points)
for i in range(num_points):
angle = 2.0 * math.pi * i / num_points
points[2 * i] = (obj_center_x + radius * math.cos(angle)) / img_w
points[2 * i + 1] = (obj_center_y + radius * math.sin(angle)) / img_h
radius = math.sqrt((obj_center_x - shape['points'][1][0]) ** 2 +
(obj_center_y - shape['points'][1][1]) ** 2)
obj_w = 2 * radius
obj_h = 2 * radius
if shape["label"]:
label = shape["label"]
if label not in self._label_list:
self._update_id_map(label)
label_id = self._label_id_map[shape["label"]]
yolo_center_x = round(float(obj_center_x / img_w), 6)
yolo_center_y = round(float(obj_center_y / img_h), 6)
yolo_w = round(float(obj_w / img_w), 6)
yolo_h = round(float(obj_h / img_h), 6)
return label_id, points.tolist()
label_id = self._label_id_map[shape['label']]
return None
return label_id, yolo_center_x, yolo_center_y, yolo_w, yolo_h
def _get_other_shape_yolo_object(self, shape, img_h, img_w):
point_list = shape["points"]
point_list = shape['points']
points = np.zeros(2 * len(point_list))
points[::2] = [float(point[0]) / img_w for point in point_list]
points[1::2] = [float(point[1]) / img_h for point in point_list]
if len(points) == 4:
if self._output_format == "polygon":
points = extend_point_list(points)
if self._output_format == "bbox":
points = extend_point_list(points, "bbox")
label_id = self._label_id_map[shape['label']]
if shape["label"]:
label = shape["label"]
if label not in self._label_list:
self._update_id_map(label)
label_id = self._label_id_map[shape["label"]]
return label_id, points.tolist()
return None
return label_id, points.tolist()
def _save_dataset_yaml(self):
yaml_path = os.path.join(self._json_dir, "YOLODataset/", "dataset.yaml")
yaml_path = os.path.join(
self._json_dir, 'YOLODataset/', 'dataset.yaml')
with open(yaml_path, "w+", encoding="utf-8") as yaml_file:
train_dir = os.path.join(self._image_dir_path, "train/")
val_dir = os.path.join(self._image_dir_path, "val/")
test_dir = os.path.join(self._image_dir_path, "test/")
with open(yaml_path, 'w+') as yaml_file:
yaml_file.write('train: %s\n' %
os.path.join(self._image_dir_path, 'train/'))
yaml_file.write('val: %s\n' %
os.path.join(self._image_dir_path, 'val/'))
yaml_file.write('test: %s\n' %
os.path.join(self._image_dir_path, 'test/'))
yaml_file.write('nc: %i\n' % len(self._label_id_map))
names_str = ''
names_str = ""
for label, _ in self._label_id_map.items():
names_str += f'"{label}", '
names_str += "'%s', " % label
names_str = names_str.rstrip(", ")
if os.path.exists(test_dir):
content = (
f"train: {train_dir}\nval: {val_dir}\ntest: {test_dir}\n"
f"nc: {len(self._label_id_map)}\n"
f"names: [{names_str}]"
)
else:
content = (
f"train: {train_dir}\nval: {val_dir}\n"
f"nc: {len(self._label_id_map)}\n"
f"names: [{names_str}]"
)
yaml_file.write(content)
yaml_file.write("names: [%s]" % names_str)