5 | 
6 |
7 |
8 |
9 | 
12 |
13 |
14 |
15 | PyLabel is a Python package to help you prepare image datasets for computer vision models including PyTorch and YOLOv5. It can translate bounding box annotations between different formats. (For example, COCO to YOLO.) And it includes an AI-assisted labeling tool that runs in a Jupyter notebook.
16 |
17 | - **Translate:** Convert annotation formats with a single line of code:
18 | ```
19 | importer.ImportCoco(path_to_annotations).export.ExportToYoloV5()
20 | ```
21 | - **Analyze:** PyLabel stores annotatations in a pandas dataframe so you can easily perform analysis on image datasets.
22 | - **Split:** Divide image datasets into train, test, and val with stratification to get consistent class distribution. 
23 | - **Label:** PyLabel also includes an image labeling tool that runs in a Jupyter notebook that can annotate images manually or perform automatic labeling using a pre-trained model.
24 | - **Visualize:** Render images from your dataset with bounding boxes overlaid so you can confirm the accuracy of the annotations.
25 |
26 |
27 | ## Tutorial Notebooks
28 | See PyLabel in action in these [sample Jupyter notebooks](https://github.com/pylabel-project/samples/):
29 | - [Convert COCO to YOLO](https://github.com/pylabel-project/samples/blob/main/coco2yolov5.ipynb)
30 | - [Convert COCO to VOC](https://github.com/pylabel-project/samples/blob/main/coco2voc.ipynb)
31 | - [Convert VOC to COCO](https://github.com/pylabel-project/samples/blob/main/voc2coco.ipynb)
32 | - [Convert YOLO to COCO](https://github.com/pylabel-project/samples/blob/main/yolo2coco.ipynb)
33 | - [Convert YOLO to VOC](https://github.com/pylabel-project/samples/blob/main/yolo2voc.ipynb)
34 | - [Import a YOLO YAML File](https://github.com/pylabel-project/samples/blob/main/yolo_with_yaml_importer.ipynb)
35 | - [Splitting Images Datasets into Train, Test, Val](https://github.com/pylabel-project/samples/blob/main/dataset_splitting.ipynb)
36 | - [Labeling Tool Demo with AI Assisted Labeling](https://github.com/pylabel-project/samples/blob/main/pylabeler.ipynb)
37 |
38 | Find more docs at https://pylabel.readthedocs.io.
39 |
40 | ## About PyLabel
41 | PyLabel was developed by Jeremy Fraenkel, Alex Heaton, and Derek Topper as the Capstope project for the Master of Information and Data Science (MIDS) at the UC Berkeley School of Information. If you have any questions or feedback please [create an issue](https://github.com/pylabel-project/pylabel/issues). Please let us know how we can make PyLabel more useful.
42 |
43 |
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1 | rm -r build/
2 | rm -r pylabel.egg-info/
3 | rm -r dist/
4 | Python setup.py sdist bdist_wheel
5 | twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
6 |
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1 | # Minimal makefile for Sphinx documentation
2 | #
3 |
4 | # You can set these variables from the command line, and also
5 | # from the environment for the first two.
6 | SPHINXOPTS ?=
7 | SPHINXBUILD ?= sphinx-build
8 | SOURCEDIR = .
9 | BUILDDIR = _build
10 |
11 | # Put it first so that "make" without argument is like "make help".
12 | help:
13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
14 |
15 | .PHONY: help Makefile
16 |
17 | # Catch-all target: route all unknown targets to Sphinx using the new
18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
19 | %: Makefile
20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
21 |
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1 | # Sphinx build info version 1
2 | # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
3 | config: 1313101fa6f04cd3f284c7e8344acc16
4 | tags: 645f666f9bcd5a90fca523b33c5a78b7
5 |
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1 |
2 |
3 |
80 | """The dataset is the primary object that you will interactive with when using PyLabel.
81 | All other modules are sub-modules of the dataset object.
82 | """
83 |
84 | from pylabel.analyze import Analyze
85 | from pylabel.exporter import Export
86 | from pylabel.visualize import Visualize
87 | from pylabel.labeler import Labeler
88 | from pylabel.splitter import Split
89 | from pylabel.shared import _ReindexCatIds
90 |
91 | import numpy as np
92 |
93 |
94 | [docs]class Dataset:
95 | def __init__(self, df):
96 | self.df = df
97 | """Pandas Dataframe: The dataframe where annotations are stored. This dataframe can be read directly
98 | to query the contents of the dataset. You can also edit this dataframe to filter records or edit the
99 | annotations directly.
100 |
101 | Example:
102 | >>> dataset.df
103 | """
104 | self.name = "dataset"
105 | """string: Default is 'dataset'. A friendly name for your dataset that is used as part of the filename(s)
106 | when exporting annotation files.
107 | """
108 | self.path_to_annotations = ""
109 | """string: Default is ''. The path to the annotation files associated with the dataset. When importing,
110 | this will be path to the directory where the annotations are stored. By default, annotations will be exported
111 | to the same directory. Changing this value will change where the annotations are exported to.
112 | """
113 | self.export = Export(dataset=self)
114 | """See pylabel.export module."""
115 | self.visualize = Visualize(dataset=self)
116 | """See pylabel.visualize module."""
117 | self.analyze = Analyze(dataset=self)
118 | """See pylabel.analyze module."""
119 | self.labeler = Labeler(self)
120 | """See pylabel.labeler module."""
121 | self.splitter = Split(dataset=self)
122 | """See pylabel.splitter module."""
123 |
124 | [docs] def ReindexCatIds(self, cat_id_index=0):
125 | """
126 | Reindex the values of the cat_id column so that that they start from an int (usually 0 or 1) and
127 | then increment the cat_ids to index + number of categories.
128 | It's useful if the cat_ids are not continuous, especially for dataset subsets,
129 | or combined multiple datasets. Some models like Yolo require starting from 0 and others
130 | like Detectron require starting from 1.
131 |
132 | Args:
133 | cat_id_index (int): Defaults to 0.
134 | The cat ids will increment sequentially the cat_index value. For example if there are 10
135 | classes then the cat_ids will be a range from 0-9.
136 |
137 | Example:
138 | >>> dataset.analyze.class_ids
139 | [1,2,4,5,6,7,8,9,11,12]
140 | >>> dataset.ReindexCatIds(cat_id_index) = 0
141 | >>> dataset.analyze.class_ids
142 | [0,1,2,3,4,5,6,7,8,9]
143 | """
144 | assert isinstance(cat_id_index, int), "cat_id_index must be an int."
145 | _ReindexCatIds(self.df, cat_id_index)
146 |
80 | import bbox_visualizer as bbv
81 | import cv2
82 | from PIL import Image
83 | from pathlib import Path
84 |
85 | [docs]class Visualize:
86 | def __init__(self, dataset=None):
87 | self.dataset = dataset
88 |
89 | """Functions to visualize inspect images and annotations."""
90 | [docs] def ShowBoundingBoxes(self, img_id:int=0, img_filename:str="") -> Image:
91 | """Enter a filename or index number and return the image with the bounding boxes drawn."""
92 |
93 | ds = self.dataset
94 |
95 | #Handle cases where user enters image name in default field
96 | if type(img_id) == str:
97 | img_filename = img_id
98 |
99 | if img_filename == "":
100 | df_single_img_annots = ds.df.loc[ds.df.img_id == img_id]
101 | else:
102 | df_single_img_annots = ds.df.loc[ds.df.img_filename == img_filename]
103 |
104 | full_image_path = str(Path(ds.path_to_annotations, df_single_img_annots.iloc[0].img_folder, df_single_img_annots.iloc[0].img_filename))
105 | img = cv2.imread(str(full_image_path))
106 | img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
107 |
108 | labels = []
109 | bboxes = []
110 |
111 | for index, row in df_single_img_annots.iterrows():
112 | labels.append(row['cat_name'])
113 | bboxes.append([int(row['ann_bbox_xmin']),int(row['ann_bbox_ymin']),int(row['ann_bbox_xmax']),int(row['ann_bbox_ymax'])])
114 |
115 | img_with_boxes = bbv.draw_multiple_rectangles(img, bboxes)
116 | img_with_boxes = bbv.add_multiple_labels(img_with_boxes, labels, bboxes)
117 |
118 | rendered_img = Image.fromarray(img_with_boxes)
119 | #rendered_img.save("bbox-visualizer/jpeg.jpg")
120 | return rendered_img
121 |
122 | ' + _('Hide Search Matches') + '
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274 | }, 275 | 276 | /** 277 | * get the current relative url 278 | */ 279 | getCurrentURL : function() { 280 | var path = document.location.pathname; 281 | var parts = path.split(/\//); 282 | $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { 283 | if (this === '..') 284 | parts.pop(); 285 | }); 286 | var url = parts.join('/'); 287 | return path.substring(url.lastIndexOf('/') + 1, path.length - 1); 288 | }, 289 | 290 | initOnKeyListeners: function() { 291 | $(document).keydown(function(event) { 292 | var activeElementType = document.activeElement.tagName; 293 | // don't navigate when in search box, textarea, dropdown or button 294 | if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT' 295 | && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey 296 | && !event.shiftKey) { 297 | switch (event.keyCode) { 298 | case 37: // left 299 | var prevHref = $('link[rel="prev"]').prop('href'); 300 | if (prevHref) { 301 | window.location.href = prevHref; 302 | return false; 303 | } 304 | break; 305 | case 39: // right 306 | var nextHref = $('link[rel="next"]').prop('href'); 307 | if (nextHref) { 308 | window.location.href = nextHref; 309 | return false; 310 | } 311 | break; 312 | } 313 | } 314 | }); 315 | } 316 | }; 317 | 318 | // quick alias for translations 319 | _ = Documentation.gettext; 320 | 321 | $(document).ready(function() { 322 | Documentation.init(); 323 | }); 324 | -------------------------------------------------------------------------------- /docs/_build/html/_static/documentation_options.js: -------------------------------------------------------------------------------- 1 | var DOCUMENTATION_OPTIONS = { 2 | URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), 3 | VERSION: '0.1.30', 4 | LANGUAGE: 'None', 5 | COLLAPSE_INDEX: false, 6 | BUILDER: 'html', 7 | FILE_SUFFIX: '.html', 8 | LINK_SUFFIX: '.html', 9 | HAS_SOURCE: true, 10 | SOURCELINK_SUFFIX: '.txt', 11 | NAVIGATION_WITH_KEYS: false 12 | }; 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Documentation for the the PyLabel package at https://github.com/pylabel-project/pylabel/
89 |Documentation :
91 |Integrations :
109 || 96 | p | ||
| 100 | |
101 | pylabel | 102 | |
| 105 | |
106 | pylabel.analyze | 107 | |
| 110 | |
111 | pylabel.dataset | 112 | |
| 115 | |
116 | pylabel.exporter | 117 | |
| 120 | |
121 | pylabel.importer | 122 | |
| 125 | |
126 | pylabel.labeler | 127 | |
| 130 | |
131 | pylabel.shared | 132 | |
| 135 | |
136 | pylabel.splitter | 137 | |
| 140 | |
141 | pylabel.visualize | 142 | |
