├── .gitignore
├── BingSiteAuth.xml
├── Gifs
├── Anno_Img_Apple_33.gif
├── Anno_Img_Astronaut_33.gif
├── Anno_Img_Lantern_33.gif
├── Bg_Img_Apple_33.gif
├── Bg_Img_Astronaut_33.gif
├── Bg_Img_Lantern_33.gif
├── Random_Rotation_Apple_33.gif
├── Random_Rotation_Astronaut_33.gif
├── Random_Rotation_Lantern_33.gif
├── Rotation_Apple_33.gif
├── Rotation_Astronaut_33.gif
├── Rotation_Lantern_33.gif
├── YApple_33.gif
├── YAstronaut_33.gif
├── YLantern_33.gif
├── YN_Apple_33.gif
├── YN_Astronaut_33.gif
└── YN_Lantern_33.gif
├── Images
├── 1.png
├── 2.png
├── 3.png
├── O_1.png
├── O_2.png
└── O_3.png
├── Input_Images
├── demo1.jpg
├── demo2.jpg
└── demo3.jpg
├── Input_JSONS
├── demo1.json
├── demo2.json
└── demo3.json
├── LICENSE
├── README.md
├── Scripts
├── __init__.py
├── iseg_aug_yaml.py
├── shape_adjustment.py
└── transforms.py
├── background_images
├── b1.jpg
├── b2.jpg
├── b3.jpg
└── b4.jpg
└── input.yaml
/.gitignore:
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1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | share/python-wheels/
24 | *.egg-info/
25 | .installed.cfg
26 | *.egg
27 | MANIFEST
28 |
29 | # PyInstaller
30 | # Usually these files are written by a python script from a template
31 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
32 | *.manifest
33 | *.spec
34 |
35 | # Installer logs
36 | pip-log.txt
37 | pip-delete-this-directory.txt
38 |
39 | # Unit test / coverage reports
40 | htmlcov/
41 | .tox/
42 | .nox/
43 | .coverage
44 | .coverage.*
45 | .cache
46 | nosetests.xml
47 | coverage.xml
48 | *.cover
49 | *.py,cover
50 | .hypothesis/
51 | .pytest_cache/
52 | cover/
53 |
54 | # Translations
55 | *.mo
56 | *.pot
57 |
58 | # Django stuff:
59 | *.log
60 | local_settings.py
61 | db.sqlite3
62 | db.sqlite3-journal
63 |
64 | # Flask stuff:
65 | instance/
66 | .webassets-cache
67 |
68 | # Scrapy stuff:
69 | .scrapy
70 |
71 | # Sphinx documentation
72 | docs/_build/
73 |
74 | # PyBuilder
75 | .pybuilder/
76 | target/
77 |
78 | # Jupyter Notebook
79 | .ipynb_checkpoints
80 |
81 | # IPython
82 | profile_default/
83 | ipython_config.py
84 |
85 | # pyenv
86 | # For a library or package, you might want to ignore these files since the code is
87 | # intended to run in multiple environments; otherwise, check them in:
88 | # .python-version
89 |
90 | # pipenv
91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
94 | # install all needed dependencies.
95 | #Pipfile.lock
96 |
97 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow
98 | __pypackages__/
99 |
100 | # Celery stuff
101 | celerybeat-schedule
102 | celerybeat.pid
103 |
104 | # SageMath parsed files
105 | *.sage.py
106 |
107 | # Environments
108 | .env
109 | .venv
110 | env/
111 | venv/
112 | ENV/
113 | env.bak/
114 | venv.bak/
115 |
116 | # Spyder project settings
117 | .spyderproject
118 | .spyproject
119 |
120 | # Rope project settings
121 | .ropeproject
122 |
123 | # mkdocs documentation
124 | /site
125 |
126 | # mypy
127 | .mypy_cache/
128 | .dmypy.json
129 | dmypy.json
130 |
131 | # Pyre type checker
132 | .pyre/
133 |
134 | # pytype static type analyzer
135 | .pytype/
136 |
137 | # Cython debug symbols
138 | cython_debug/
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/BingSiteAuth.xml:
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1 |
2 |
3 | 56E5C0648855C13712ECDA7A57DE5F5E
4 |
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/Gifs/Anno_Img_Apple_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/Anno_Img_Apple_33.gif
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/Gifs/Anno_Img_Astronaut_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/Anno_Img_Astronaut_33.gif
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/Gifs/Anno_Img_Lantern_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/Anno_Img_Lantern_33.gif
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/Gifs/Bg_Img_Apple_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/Bg_Img_Apple_33.gif
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/Gifs/Bg_Img_Astronaut_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/Bg_Img_Astronaut_33.gif
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/Gifs/Bg_Img_Lantern_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/Bg_Img_Lantern_33.gif
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/Gifs/Random_Rotation_Apple_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/Random_Rotation_Apple_33.gif
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/Gifs/Random_Rotation_Astronaut_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/Random_Rotation_Astronaut_33.gif
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/Gifs/Random_Rotation_Lantern_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/Random_Rotation_Lantern_33.gif
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/Gifs/Rotation_Apple_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/Rotation_Apple_33.gif
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/Gifs/Rotation_Astronaut_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/Rotation_Astronaut_33.gif
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/Gifs/Rotation_Lantern_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/Rotation_Lantern_33.gif
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/Gifs/YApple_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/YApple_33.gif
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/Gifs/YAstronaut_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/YAstronaut_33.gif
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/Gifs/YLantern_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/YLantern_33.gif
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/Gifs/YN_Apple_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/YN_Apple_33.gif
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/Gifs/YN_Astronaut_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/YN_Astronaut_33.gif
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/Gifs/YN_Lantern_33.gif:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Gifs/YN_Lantern_33.gif
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/Images/1.png:
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/Images/2.png:
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/Images/3.png:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Images/3.png
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/Images/O_1.png:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Images/O_1.png
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/Images/O_2.png:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Images/O_2.png
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/Images/O_3.png:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Images/O_3.png
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Input_Images/demo1.jpg
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Input_Images/demo2.jpg
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Input_Images/demo3.jpg
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500 | "imageHeight": 338,
501 | "imageWidth": 338
502 | }
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/LICENSE:
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1 | MIT License
2 |
3 | Copyright (c) 2021 Parul Parima
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
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/README.md:
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1 | # Image Data Augmentation
2 |
3 | ## Introduction
4 |
5 | This script is used to augment image data created using **LabelMe-MIT**.
6 |
7 | **It also creates new json files for the newly generated images, i.e., it augments both the image as well as it's annotation**.
8 |
9 | ### Constraints
10 |
11 | - LabelMe should be used for annotating images
12 | - Annotation should be a closed polygon/bounding box
13 | - There should be one annotation in an image
14 |
15 | *This script can work for images with multiple annotation as well, but it will only take into account the first annotation or the first user-specified class annotation*.
16 |
17 | ## Description
18 |
19 | It copies the annotated portion from the reference image(input annotated image), processes it according to the user's instructions, which can be provided in the YAML file. And then paste it on one of the given random background images.
20 |
21 | - **iseg_aug_yaml.py**
22 | - **input.yaml**
23 |
24 | ### Transforms
25 |
26 | 1. Downscale
27 |
28 | 2. Upscale
29 |
30 | 3. Rotation
31 |
32 | 4. Horizontal Flip
33 |
34 | 5. Vertical Flip
35 |
36 | 6. Random Shift
37 |
38 | 7. Blur - Averaging, Gaussian Blurring, Median Blurring, Bilateral Filtering
39 |
40 | 8. Noise - Gauss, Salt and Pepper, Poisson, Speckle
41 |
42 | 9. Grayscale
43 |
44 | 10. Brightness and Contrast
45 |
46 | 11. Canny Edge Detection
47 |
48 | ### Options other than transforms
49 |
50 | 1. Threshold Ratio - Ratio of annotated area to background image area. Combinations below this ratio will be neglected.
51 |
52 | 2. User Class - Choose a specific class on which to do transformations
53 |
54 | 3. Pad Annotation - Amount of padding you want to add to the annotation
55 |
56 |
57 |
58 |
59 |
60 | | |
61 | Lantern |
62 | Apple |
63 | Astronaut |
64 |
65 |
66 |
67 |
68 | | Inputs |
69 |  |
70 |  |
71 |  |
72 |
73 |
74 |
75 |
76 | | Backgrounds |
77 |  |
78 |  |
79 |  |
80 |
81 |
82 |
83 |
84 | | Output |
85 |  |
86 |  |
87 |  |
88 |
89 |
90 |
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/Scripts/__init__.py:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/Scripts/__init__.py
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/Scripts/iseg_aug_yaml.py:
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1 | import numpy as np
2 | import math
3 | import random
4 | import cv2
5 | import json
6 | import base64
7 | import os
8 | import argparse
9 | from pathlib import Path
10 | import yaml
11 | from transforms import *
12 | from shape_adjustment import *
13 |
14 | class ImageAugmentation:
15 |
16 | def __init__(self, yamldata):
17 | self.yamldata = yamldata
18 | self.aimg_folderpath = self.yamldata['inputs']['aimg_folderpath']
19 | self.ajson_folderpath = self.yamldata['inputs']['ajson_folderpath']
20 | self.bgimg_folderpath = self.yamldata['inputs']['bgimg_folderpath']
21 | self.output_folderpath = self.yamldata['inputs']['output_folderpath']
22 | self.bg_count = self.yamldata['inputs']['bg_count']
23 | self.ntimes_perbg = self.yamldata['inputs']['ntimes_perbg']
24 | self.ratio_threshold = self.yamldata['inputs']['ratio_threshold']
25 | self.user_class = self.yamldata['inputs']['user_class']
26 | self.pad_annotation = self.yamldata['inputs']['pad_annotation']
27 |
28 | def dataread(self,img, aimg_folderpath, ajson_folderpath, output_folderpath, user_class, pad):
29 | image_name = Path(img).stem
30 | anno_img_path = os.path.join(aimg_folderpath, img)
31 | anno_img_json = os.path.join(ajson_folderpath, image_name + ".json")
32 | aug_path = os.path.join(output_folderpath,image_name + "_aug_")
33 | anno_img = cv2.imread(anno_img_path)
34 | height, width = anno_img.shape[0], anno_img.shape[1]
35 |
36 | # Access original coordinates
37 | f = open(anno_img_json,)
38 | data = json.load(f)
39 | f.close()
40 |
41 | coordinates = [[]]
42 | shape_type = [[]]
43 |
44 | if user_class == "default":
45 | coordinates[0] = data['shapes'][0]['points']
46 | shape_type[0] = data['shapes'][0]['shape_type']
47 | if data['shapes'][0]['shape_type']=="rectangle":
48 | y_min, y_max, x_min, x_max = findminmax(coordinates[0])
49 | x_min = x_min - pad if x_min - pad>=0 else 0
50 | y_min = y_min - pad if y_min - pad>=0 else 0
51 | x_max = x_max + pad if x_max + pad<=height else height
52 | y_max = y_max + pad if y_max + pad<=width else width
53 | coordinates[0] = [[y_min,x_min],[y_max,x_min],[y_max,x_max],[y_min,x_max]]
54 | else:
55 | i = 0
56 | j = 0
57 | for k in data['shapes']:
58 | if data['shapes'][i]['label'] == user_class:
59 | coordinates.append( data['shapes'][i]['points'])
60 | shape_type.append( data['shapes'][i]['shape_type'])
61 |
62 | # Condition specific for bounding box
63 | if data['shapes'][i]['shape_type']=="rectangle":
64 | y_min, y_max, x_min, x_max = findminmax(coordinates[j])
65 | x_min = x_min - pad if x_min - pad>=0 else 0
66 | y_min = y_min - pad if y_min - pad>=0 else 0
67 | x_max = x_max + pad if x_max + pad<=height else height
68 | y_max = y_max + pad if y_max + pad<=width else width
69 | coordinates[j] = [[y_min,x_min],[y_max,x_min],[y_max,x_max],[y_min,x_max]]
70 | j = j + 1
71 | i = i + 1
72 |
73 | first_value=data["shapes"][0]
74 | data["shapes"] = []
75 | data["shapes"].append(first_value)
76 |
77 | return (data, coordinates, shape_type, aug_path, anno_img)
78 |
79 | def checkarea(self, bg_img, coordinates, ratio_threshold = 0.0):
80 | arr = np.array(coordinates)
81 | correction = arr[-1][1] * arr[0][0] - arr[-1][0] * arr[0][1]
82 | main_area = np.dot(arr[::,1][:-1],arr[::,0][1:]) - np.dot(arr[::,0][:-1],arr[::,1][1:])
83 | area = 0.5 * np.abs(main_area + correction)
84 | ratio = area /(bg_img.shape[0]*bg_img.shape[1]) * 100
85 |
86 | if ratio >= ratio_threshold:
87 | return True
88 | else:
89 | return False
90 |
91 | def findminmax(self,coordinates):
92 | arr = np.array(coordinates)
93 | y_min = min(arr[::,0])
94 | y_max = max(arr[::,0])
95 | x_min = min(arr[::,1])
96 | x_max = max(arr[::,1])
97 |
98 | return y_min, y_max, x_min, x_max
99 |
100 | # Remove the coordinates values exceeding the image
101 | def cropitup(self,coordinates, width, height):
102 | dummy = []
103 | index = []
104 | for i in range(0,len(coordinates)):
105 | if coordinates[i][0]<=width and coordinates[i][1]<=height:
106 | index.append(i)
107 |
108 | if len(index) == 0:
109 | return dummy
110 | elif len(index) == len(coordinates):
111 | return coordinates
112 | else:
113 | return dummy
114 |
115 | def dataformation(self,aug_img, aug_path, data, shape_type, rchoice, new_coordinates1, counter, user_class):
116 | new_path = aug_path + str(counter) + ".jpg"
117 | cv2.imwrite(new_path, aug_img.astype(np.uint8))
118 |
119 | json_path = aug_path + str(counter) + ".json"
120 |
121 | # Condition specific for bounding box
122 | if shape_type[rchoice]=="rectangle":
123 | ymin,ymax,xmin,xmax = findminmax(new_coordinates1)
124 | new_coordinates1 = [[ymin, xmin], [ymax, xmax]]
125 |
126 | if user_class!="default":
127 | data["shapes"][0]["label"] = user_class
128 | data["shapes"][0]["shape_type"] = shape_type[rchoice]
129 | data["shapes"][0]["points"] = new_coordinates1
130 | data["imagePath"] = ".." + os.path.basename(new_path)
131 | data["imageData"] = str(base64.b64encode(open(new_path,'rb').read()))[2:-1]
132 | data["imageHeight"] = aug_img.shape[0]
133 | data["imageWidth"] = aug_img.shape[1]
134 |
135 | with open(json_path, 'w') as outfile:
136 | json.dump(data, outfile, indent=2)
137 |
138 | def pipeline(self):
139 |
140 | # Extracting name of images
141 | inp_imgs = os.listdir(self.aimg_folderpath)
142 | bg_imgs = os.listdir(self.bgimg_folderpath)
143 |
144 | ts = self.yamldata['transforms']
145 |
146 | if ts['rotation']['rotation_state'] == True:
147 | rotlimitangle = ts['rotation']['rotlimitangle']
148 | if rotlimitangle>=360:
149 | rotlimitangle = 359
150 |
151 | flag = 0
152 |
153 | # Iterating on annotated images
154 | for img in inp_imgs:
155 |
156 | data, coordinates, shape_type, aug_path, anno_img = obj.dataread(img, self.aimg_folderpath, self.ajson_folderpath, self.output_folderpath, self.user_class, self.pad_annotation)
157 |
158 | length = len(coordinates)
159 |
160 | if length==0:
161 | print("%s does not contain given class" % (Path(img).stem))
162 | continue
163 |
164 | height, width = anno_img.shape[0], anno_img.shape[1]
165 | counter = 1 # Counts the number of augmentation per annotated image
166 |
167 | # Iterating through a given no. of background images
168 | for backgrounds in range(0, self.bg_count):
169 |
170 | bg = os.path.join(self.bgimg_folderpath, random.choice(bg_imgs)) # Selecting a random background image
171 | bg_img = cv2.imread(bg)
172 | dummy_bg = bg_img.copy()
173 |
174 |
175 | # Checks whether annotated area when pasted in background image is above a threshold or not
176 | list_choice = []
177 | for i in range(0,len(coordinates)):
178 | if obj.checkarea(bg_img, coordinates[i], self.ratio_threshold) == True:
179 | list_choice.append(i)
180 |
181 | if len(list_choice)==0:
182 | continue
183 |
184 | # Transforms
185 | for j in range(0, self.ntimes_perbg):
186 |
187 | rchoice = random.choice(list_choice)
188 | aug_coordinates = coordinates[rchoice]
189 | aug_anno_img = anno_img
190 |
191 | # Downscale
192 | if ts['scaling']['scaling_state'] == True:
193 | downscaleprob = random.random()
194 | if ts['scaling']['downscale_prob'] == 1.0 or (downscaleprob <= ts['scaling']['downscale_prob'] and downscaleprob>0):
195 | new_coordinates = [[i[0]/ts['scaling']['downscale_factor'], i[1]/ts['scaling']['downscale_factor']] for i in aug_coordinates]
196 | if obj.checkarea(bg_img, new_coordinates, self.ratio_threshold) == True:
197 | aug_anno_img, aug_coordinates = transforms().downscale(aug_anno_img, aug_coordinates, ts['scaling']['downscale_factor'])
198 |
199 | # Rotate
200 | if ts['rotation']['rotation_state'] == True:
201 | rotationprob = random.random()
202 | if ts['rotation']['rotation_prob'] == 1.0 or (rotationprob <= ts['rotation']['rotation_prob'] and rotationprob>0):
203 | if rotlimitangle != 0:
204 | aug_anno_img, aug_coordinates = transforms().rotation(aug_anno_img, aug_coordinates, rotlimitangle)
205 |
206 | # Upscale
207 | if ts['scaling']['scaling_state'] == True:
208 | upscaleprob = random.random()
209 | if ts['scaling']['upscale_prob'] == 1.0 or (upscaleprob <= ts['scaling']['upscale_prob'] and upscaleprob>0):
210 | new_coordinates = [[i[0]*ts['scaling']['upscale_factor'], i[1]*ts['scaling']['upscale_factor']] for i in aug_coordinates]
211 | y_min, y_max, x_min, x_max = obj.findminmax(new_coordinates)
212 | if (y_max < bg_img.shape[1] and y_min > 0 and x_max < bg_img.shape[0] and x_min > 0):
213 | aug_anno_img, aug_coordinates = transforms().upscale(aug_anno_img, aug_coordinates,ts['scaling']['upscale_factor'])
214 |
215 |
216 | # Flip
217 | if ts['flipping']['flipping_state'] == True:
218 | verticalprob = random.random()
219 | if ts['flipping']['vertical_flip_prob'] == 1.0 or (verticalprob <= ts['flipping']['vertical_flip_prob'] and verticalprob>0):
220 | aug_anno_img, aug_coordinates = transforms().flipvertical(aug_anno_img, aug_coordinates)
221 |
222 | horizontalprob = random.random()
223 | if ts['flipping']['horizontal_flip_prob'] == 1.0 or (horizontalprob <= ts['flipping']['horizontal_flip_prob'] and horizontalprob>0):
224 | aug_anno_img, aug_coordinates = transforms().fliphorizontal(aug_anno_img, aug_coordinates)
225 |
226 | # Brightness and Contrast
227 | if ts['brightness-contrast']['brightness-contrast_state'] == True:
228 | bcprob = random.random()
229 | if ts['brightness-contrast']['brightness-contrast_prob'] == 1.0 or (bcprob <= ts['brightness-contrast']['brightness-contrast_prob'] and bcprob>0):
230 | aug_anno_img = cv2.convertScaleAbs(aug_anno_img, alpha=ts['brightness-contrast']['alpha'], beta=ts['brightness-contrast']['beta'])
231 |
232 | # BLur
233 | if ts['blur']['blur_state'] == True:
234 | blurprob = random.random()
235 | if ts['blur']['blur_prob'] == 1.0 or (blurprob <= ts['blur']['blur_prob'] and blurprob>0):
236 | aug_anno_img = transforms().blur(aug_anno_img, ts['blur']['blur_choice'])
237 |
238 | # Noise
239 | if ts['noise']['noise_state'] == True:
240 | noiseprob = random.random()
241 | if ts['noise']['noise_prob'] == 1.0 or (noiseprob <=ts['noise']['noise_prob'] and noiseprob>0):
242 | aug_anno_img = transforms().noise(aug_anno_img, ts['noise']['noise_choice'], ts['noise']['gauss'], ts['noise']['salt&pepper'], ts['noise']['speckle'])
243 |
244 | # Shift
245 | if ts['randomshift']['shift_state'] == True:
246 | shiftprob = random.random()
247 | if ts['randomshift']['shift_prob'] == 1.0 or (shiftprob <= ts['randomshift']['shift_prob'] and shiftprob>0):
248 | aug_anno_img, aug_coordinates = transforms().shift(aug_coordinates, aug_anno_img, bg_img)
249 |
250 | aug_anno_img = shape_adjustment().shape_adjust(aug_anno_img, bg_img.shape[1], bg_img.shape[0])
251 | aug_coordinates = obj.cropitup(aug_coordinates,bg_img.shape[1], bg_img.shape[0])
252 |
253 | if len(aug_coordinates)==0:
254 | continue
255 |
256 | mask = np.zeros((aug_anno_img.shape[0], aug_anno_img.shape[1]), dtype=np.uint8)
257 | points1 = np.round(np.expand_dims(np.array(aug_coordinates),0)).astype('int32')
258 | cv2.fillPoly(mask, points1, 255)
259 | cv2.fillPoly(bg_img, points1, 0)
260 | res = cv2.bitwise_and(aug_anno_img, aug_anno_img, mask = mask)
261 |
262 | # Final image
263 | if obj.checkarea(bg_img, aug_coordinates,self.ratio_threshold) == True:
264 |
265 | # Grayscale
266 | if ts['grayscale']['grayscale_state'] == True:
267 | grayscaleprob = random.random()
268 | if ts['grayscale']['grayscale_prob'] == 1.0 or (grayscaleprob <= ts['grayscale']['grayscale_prob'] and grayscaleprob>0):
269 | bg_img, res = transforms().grayscale(bg_img, res, ts['grayscale']['grayscale_choice'])
270 |
271 | # Edge Detection
272 | if ts['edgedetection']['edgedetection_state'] == True:
273 | edgedetectionprob = random.random()
274 | if ts['edgedetection']['edgedetection_prob'] == 1.0 or (edgedetectionprob <= ts['edgedetection']['edgedetection_prob'] and edgedetectionprob>0):
275 | bg_img, res = transforms().edgedetection(bg_img, res, ts['edgedetection']['edgedetection_choice'])
276 |
277 | aug_img = bg_img + res
278 | obj.dataformation(aug_img, aug_path, data, shape_type, rchoice, aug_coordinates, counter, self.user_class)
279 | counter+=1
280 | flag +=1
281 | bg_img = dummy_bg.copy()
282 |
283 | print("%d files formed!" % (flag))
284 |
285 | if __name__ == '__main__':
286 |
287 | parser = argparse.ArgumentParser()
288 | parser.add_argument("--yaml_path", help="Path of YAML file", type=str)
289 | args = parser.parse_args()
290 | if os.path.exists(args.yaml_path):
291 | stream = open(args.yaml_path, 'r')
292 | try:
293 | yamldata = yaml.safe_load(stream)
294 | except:
295 | print("Not a YAML File!")
296 | try:
297 | obj = ImageAugmentation(yamldata)
298 |
299 | except:
300 | print("YAML File does not contain expected data.")
301 | obj.pipeline()
302 | else:
303 | print("Path does not exist")
--------------------------------------------------------------------------------
/Scripts/shape_adjustment.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 |
3 | class shape_adjustment:
4 |
5 | def __init__(self):
6 | pass
7 |
8 | # Adds shape_adjust or crops the background image accordingly to match dimensions of the annotated image
9 | def shape_adjust(self,res, bg_width, bg_height):
10 | pad_w_tot = res.shape[1] - bg_width
11 | pad_h_tot = res.shape[0] - bg_height
12 |
13 | pad_bottom, pad_right = pad_h_tot, pad_w_tot
14 |
15 | if pad_w_tot<=0:
16 | pad_right = -pad_w_tot
17 |
18 | if pad_h_tot<=0:
19 | pad_bottom = -pad_h_tot
20 |
21 | # Same dimensions
22 | if pad_w_tot==0 and pad_h_tot==0:
23 | pass
24 |
25 | # Annotated Image dimensions (height, width) > Background Image dimensions
26 | elif pad_w_tot>0 and pad_h_tot>0:
27 | res = res[0:res.shape[0]-pad_bottom, 0:res.shape[1]-pad_right, :]
28 |
29 | # Annotated Image's width >= Background Image's width; Annotated Image's height <= Background Image's height
30 | elif pad_w_tot>=0 and pad_h_tot<=0:
31 | rb = np.pad(res[:,:,0],((0,pad_bottom),(0,0)), mode='constant', constant_values=0)
32 | gb = np.pad(res[:,:,1],((0,pad_bottom),(0,0)), mode='constant', constant_values=0)
33 | bb = np.pad(res[:,:,2],((0,pad_bottom),(0,0)), mode='constant', constant_values=0)
34 | res = np.dstack(tup=(rb, gb, bb))
35 | res = res[:, 0:res.shape[1]-pad_right, :]
36 |
37 | # Annotated Image's width <= Background Image's width; Annotated Image's height >= Background Image's height
38 | elif pad_w_tot<=0 and pad_h_tot>=0:
39 | rb = np.pad(res[:,:,0],((0,0),(0,pad_right)), mode='constant', constant_values=0)
40 | gb = np.pad(res[:,:,1],((0,0),(0,pad_right)), mode='constant', constant_values=0)
41 | bb = np.pad(res[:,:,2],((0,0),(0,pad_right)), mode='constant', constant_values=0)
42 | res = np.dstack(tup=(rb, gb, bb))
43 | res = res[0:res.shape[0]-pad_bottom, :, :]
44 |
45 | # Annotated Image dimensions (height, width) < Background Image dimensions
46 | else:
47 | rb = np.pad(res[:,:,0],((0,pad_bottom),(0,pad_right)), mode='constant', constant_values=0)
48 | gb = np.pad(res[:,:,1],((0,pad_bottom),(0,pad_right)), mode='constant', constant_values=0)
49 | bb = np.pad(res[:,:,2],((0,pad_bottom),(0,pad_right)), mode='constant', constant_values=0)
50 | res = np.dstack(tup=(rb, gb, bb))
51 |
52 | return res
--------------------------------------------------------------------------------
/Scripts/transforms.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 | import cv2
3 | import random
4 | from scipy import ndimage
5 | import math
6 | from shape_adjustment import *
7 |
8 | class transforms:
9 |
10 | def __init__(self):
11 | pass
12 |
13 | #Downscales the image by a user defined factor
14 | def downscale(self, input_img, coordinates, bg_img, scale=2):
15 | if input_img.shape[0]%scale!=0:
16 | pad_bottom = input_img.shape[0]%scale
17 | else:
18 | pad_bottom = 0
19 | if input_img.shape[1]%scale!=0:
20 | pad_right = input_img.shape[1]%scale
21 | else:
22 | pad_right = 0
23 |
24 | rb = np.pad(input_img[:,:,0],((0,pad_bottom),(0,pad_right)), mode='constant', constant_values=0)
25 | gb = np.pad(input_img[:,:,1],((0,pad_bottom),(0,pad_right)), mode='constant', constant_values=0)
26 | bb = np.pad(input_img[:,:,2],((0,pad_bottom),(0,pad_right)), mode='constant', constant_values=0)
27 | input_img = np.dstack(tup=(rb, gb, bb))
28 |
29 | new_coordinates = [[i[0]/scale, i[1]/scale] for i in coordinates]
30 | aug_img = input_img[::scale,::scale]
31 |
32 | return (aug_img, new_coordinates)
33 |
34 | #Upscales the image by a user defined factor
35 | def upscale(self, input_img, coordinates, bg_img, scale=2):
36 | if input_img.shape[0]%scale!=0:
37 | pad_bottom = input_img.shape[0]%scale
38 | else:
39 | pad_bottom = 0
40 | if input_img.shape[1]%scale!=0:
41 | pad_right = input_img.shape[1]%scale
42 | else:
43 | pad_right = 0
44 |
45 | rb = np.pad(input_img[:,:,0],((0,pad_bottom),(0,pad_right)), mode='constant', constant_values=0)
46 | gb = np.pad(input_img[:,:,1],((0,pad_bottom),(0,pad_right)), mode='constant', constant_values=0)
47 | bb = np.pad(input_img[:,:,2],((0,pad_bottom),(0,pad_right)), mode='constant', constant_values=0)
48 | input_img = np.dstack(tup=(rb, gb, bb))
49 |
50 | new_coordinates = [[i[0]*scale, i[1]*scale] for i in coordinates]
51 | aug_img = cv2.pyrUp(input_img, scale)
52 |
53 | return (aug_img, new_coordinates)
54 |
55 | # Rotates coordinates
56 | def rotation(self, input_img, coordinates, rotlimitangle):
57 | temp_coordinates = np.asarray(coordinates)
58 | temp_coordinates = np.asarray([temp_coordinates[:,1],temp_coordinates[:,0]])
59 |
60 | alpha = np.random.randint(0,rotlimitangle+1)
61 | theta = alpha % 90
62 |
63 | cos_alpha = math.cos(math.radians(alpha))
64 | sin_alpha = math.sin(math.radians(alpha))
65 | cos_theta = math.cos(math.radians(theta))
66 | sin_theta = math.sin(math.radians(theta))
67 |
68 | bias = np.zeros(temp_coordinates.shape)
69 | bias[0] = input_img.shape[0]
70 | bias[1] = input_img.shape[1]
71 |
72 | if alpha>=0 and alpha<90:
73 | R_b = np.array([[0, sin_theta],[0, 0]])
74 | elif alpha>=90 and alpha<180:
75 | R_b = np.array([[sin_theta, cos_theta],[0, sin_theta]])
76 | elif alpha>=180 and alpha<270:
77 | R_b = np.array([[cos_theta, 0],[sin_theta, cos_theta]])
78 | elif alpha>=270 and alpha<360:
79 | R_b = np.array([[0, 0],[cos_theta, 0]])
80 |
81 | R = np.array([[cos_alpha, -sin_alpha],[sin_alpha, cos_alpha]])
82 |
83 | new_matrix = np.dot(R, temp_coordinates) + np.dot(R_b, bias)
84 | rotated_coordinates = new_matrix[::-1].T.tolist()
85 |
86 | aug_img = ndimage.rotate(input_img, alpha, reshape=True)
87 |
88 | return (aug_img, rotated_coordinates)
89 |
90 | # Flips the image about horizontal axis
91 | def fliphorizontal(self, input_img, coordinates):
92 | aug_img = input_img[::-1,::]
93 | aug_coordinates = [[i[0], input_img.shape[0] - i[1]] for i in coordinates]
94 |
95 | return (aug_img, aug_coordinates)
96 |
97 | # Flips the image about vertical axis
98 | def flipvertical(self, input_img, coordinates):
99 | aug_img = input_img[::,::-1]
100 | aug_coordinates = [[input_img.shape[1] - i[0], i[1]] for i in coordinates]
101 |
102 | return (aug_img, aug_coordinates)
103 |
104 | # Blur
105 | def blur(self,input_img, blurchoice):
106 | blurtype = ['Averaging', 'Gaussian Blurring', 'Median Blurring', 'Bilateral Filtering']
107 | if(blurchoice!=0 and blurchoice!=1 and blurchoice!=2 and blurchoice!=3):
108 | ch = np.random.randint(0,4)
109 | blurchoice = ch
110 | if blurchoice == 0:
111 | aug_img = cv2.blur(input_img, (2,2))
112 | elif blurchoice == 1:
113 | aug_img = cv2.GaussianBlur(input_img,(5,5),0)
114 | elif blurchoice == 2:
115 | aug_img = cv2.medianBlur(input_img,5)
116 | elif blurchoice == 3:
117 | aug_img = cv2.bilateralFilter(input_img,9,75,75)
118 |
119 | return aug_img
120 |
121 | # Noise
122 | def noise(self, input_img, noise_choice, p_gauss =[0, 0.1], p_sp =[0.5, 0.004], p_speckle =[0.1]):
123 | noise_type = ["gauss", "salt&pepper", "poisson", "speckle"]
124 | if(noise_choice!=0 and noise_choice!=1 and noise_choice!=2 and noise_choice!=3):
125 | ch = np.random.randint(0,4)
126 | noise_choice = ch
127 |
128 | if noise_choice == 0:
129 | mean,variance = p_gauss[0], p_gauss[1]
130 | sigma = variance**0.5
131 | gauss = np.random.normal(mean,sigma,input_img.shape)
132 | aug_img = input_img + gauss
133 | aug_img = np.clip(aug_img, 0, 255)
134 |
135 | elif noise_choice == 1:
136 | s_vs_p, amount = p_sp[0], p_sp[1]
137 | # Salt mode
138 | num_salt = np.ceil(amount * input_img.size * s_vs_p)
139 | coords = [np.random.randint(0, i - 1, int(num_salt))
140 | for i in input_img.shape]
141 | input_img[tuple(coords)] = 255
142 | # Pepper mode
143 | num_pepper = np.ceil(amount* input_img.size * (1. - s_vs_p))
144 | coords = [np.random.randint(0, i - 1, int(num_pepper))
145 | for i in input_img.shape]
146 | input_img[tuple(coords)] = 0
147 | aug_img = input_img
148 |
149 | elif noise_choice == 2:
150 | vals = len(np.unique(input_img))
151 | vals = 2 ** np.ceil(np.log2(vals))
152 | aug_img = np.random.poisson(input_img * vals) / float(vals)
153 |
154 | elif noise_choice == 3:
155 | gauss = np.random.randn(input_img.shape[0], input_img.shape[1], input_img.shape[2])
156 | aug_img = input_img + p_speckle[0]*(input_img * gauss)
157 | aug_img = np.clip(aug_img, 0, 255)
158 |
159 | return aug_img
160 |
161 |
162 | # Random shift
163 | def shift(self, aug_coordinates, input_img, bg_img):
164 | x_min = int(min([sublist[1] for sublist in aug_coordinates]))
165 | y_min = int(min(([sublist[0] for sublist in aug_coordinates])))
166 | new_coordinates = []
167 | new_coordinates = [[i[0]-y_min, i[1]-x_min] for i in aug_coordinates]
168 |
169 | # Annotated area's maximum height and width
170 | x_max = int(max([sublist[1] for sublist in new_coordinates]))
171 | y_max = int(max(([sublist[0] for sublist in new_coordinates])))
172 |
173 | if x_max>bg_img.shape[0] or y_max>bg_img.shape[1]:
174 | return (input_img, aug_coordinates)
175 |
176 | # Shift the annotated object to origin in original image
177 | aug_img = np.roll(input_img, -x_min, axis = 0)
178 | aug_img = np.roll(aug_img, -y_min, axis = 1)
179 |
180 | # Random Shift
181 | x_shift = np.random.randint(0, bg_img.shape[0] - x_max)
182 | y_shift = np.random.randint(0, bg_img.shape[1] - y_max)
183 | aug_coordinates = []
184 | aug_coordinates = [[i[0]+y_shift, i[1]+x_shift] for i in new_coordinates]
185 | aug_img = shape_adjustment().shape_adjust(aug_img, bg_img.shape[1], bg_img.shape[0])
186 | aug_img = np.roll(aug_img, x_shift, axis=0)
187 | aug_img = np.roll(aug_img, y_shift, axis=1)
188 |
189 | return (aug_img, aug_coordinates)
190 |
191 | # Grayscale
192 | def grayscale(self, bg_img, res, gray_choice):
193 | if(gray_choice!=0 and gray_choice!=1 and gray_choice!=2):
194 | ch = np.random.randint(0,3)
195 | gray_choice = ch
196 | if gray_choice == 0:
197 | bg_img = bg_img[:, :, 0]
198 | res = res[:, :, 0]
199 | elif gray_choice == 1:
200 | rb = res[:, :, 0]
201 | gb = res[:, :, 0]
202 | bb = res[:, :, 0]
203 | res = np.dstack(tup=(rb, gb, bb))
204 | elif gray_choice == 2:
205 | rb = bg_img[:, :, 0]
206 | gb = bg_img[:, :, 0]
207 | bb = bg_img[:, :, 0]
208 | bg_img = np.dstack(tup=(rb, gb, bb))
209 | return (bg_img, res)
210 |
211 | # Edge Detection
212 | def edgedetection(self, bg_img, res,edge_choice):
213 | if(edge_choice!=0 and edge_choice!=1):
214 | ch = np.random.randint(0,2)
215 | edge_choice = ch
216 |
217 | if len(res.shape)==2:
218 | if edge_choice == 0:
219 | bg_img = np.uint8(bg_img)
220 | bg_img = cv2.GaussianBlur(bg_img, (3,3), 0)
221 | bg_img = cv2.Canny(image= bg_img, threshold1=100, threshold2=200)
222 | res = np.uint8(res)
223 | res = cv2.GaussianBlur(res, (3,3), 0)
224 | res = cv2.Canny(image=res, threshold1=100, threshold2=200)
225 |
226 | elif len(res.shape)==3:
227 | if edge_choice == 0:
228 | bg_img = bg_img[:, :, 0]
229 | bg_img = np.uint8(bg_img)
230 | bg_img = cv2.GaussianBlur(bg_img, (3,3), 0)
231 | bg_img = cv2.Canny(image=bg_img, threshold1=100, threshold2=200)
232 | bg_img = np.dstack(tup=(bg_img, bg_img, bg_img))
233 | res = res[:, :, 0]
234 | res = np.uint8(res)
235 | res = cv2.GaussianBlur(res, (3,3), 0)
236 | res = cv2.Canny(image=res, threshold1=100, threshold2=200)
237 | res = np.dstack(tup=(res, res, res))
238 | return (bg_img, res)
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/background_images/b1.jpg:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/background_images/b1.jpg
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/background_images/b2.jpg:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/background_images/b2.jpg
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/background_images/b3.jpg:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/background_images/b3.jpg
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/background_images/b4.jpg:
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https://raw.githubusercontent.com/ParulParima/LabelMe-Image-Data-Augment-/0d37c8844a2bec7bc0b69e1414e7cb3dfc387098/background_images/b4.jpg
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/input.yaml:
--------------------------------------------------------------------------------
1 | ---
2 | inputs:
3 | aimg_folderpath: C:\Users\91954\Documents\GitHub\LabelMe-Image-Data-Augment-\Input_Images
4 | ajson_folderpath: C:\Users\91954\Documents\GitHub\LabelMe-Image-Data-Augment-\Input_JSONS
5 | bgimg_folderpath: C:\Users\91954\Documents\GitHub\LabelMe-Image-Data-Augment-\background_images
6 | output_folderpath: C:\Users\91954\Desktop\seal\Freshoutput
7 | bg_count: 10
8 | ntimes_perbg: 5
9 | ratio_threshold : 0.1 # Ratio of annotated area to background image area. Combinations below this ratio will be neglected.
10 | user_class: default # Specify a specific class or keep it default (takes the first annotated class)
11 | pad_annotation: 5 # Amount(in pixels) of padding you want to add to the annotation (Works only on bounding box annotation)
12 | transforms:
13 | scaling:
14 | scaling_state: True
15 | downscale_prob: 0.5
16 | downscale_factor: 2
17 | upscale_prob: 0.5
18 | upscale_factor: 2
19 | rotation:
20 | rotation_state: True
21 | rotation_prob: 0.5
22 | rotlimitangle: 78
23 | flipping:
24 | flipping_state: True
25 | horizontal_flip_prob: 0.5
26 | vertical_flip_prob: 0.5
27 | randomshift:
28 | shift_state: True
29 | shift_prob: 1
30 | blur:
31 | blur_state: True
32 | blur_prob: 0.5
33 | blur_choice: random #0 - 'Averaging', 1 - 'Gaussian Blurring', 2 - 'Median Blurring', 3 - 'Bilateral Filtering', random - for any random blur
34 | noise:
35 | noise_state: True
36 | noise_prob: 0.5
37 | noise_choice: random #0 - 'gauss', 1 - 'salt&pepper', 2 - 'poisson', 3 - 'speckle', random - for any random noise
38 | gauss:
39 | - 45 # Enter the sigma value if you have chosen gauss
40 | - 50 # Enter the variance value if you have chosen gauss
41 | salt&pepper:
42 | - 0.5 # Enter the s_vs_p value if you have chosen salt&pepper
43 | - 0.1 # Enter the amount value if you have chosen salt&pepper
44 | speckle:
45 | - 0.1 # Enter the amount value if you have chosen speckle
46 | grayscale:
47 | grayscale_state: True
48 | grayscale_prob: 0.2
49 | grayscale_choice: random # 0 - grayscale the whole output image, 1 - grayscale only the annotated part in the output image, 2 - grayscale the output image except the annotated area, random - any of the three
50 | brightness-contrast:
51 | brightness-contrast_state: True
52 | brightness-contrast_prob: 0.5
53 | alpha: 1 # Contrast control (1.0-3.0)
54 | beta: 0 # Brightness control (0-100)
55 | edgedetection:
56 | edgedetection_state: True
57 | edgedetection_prob: 0.2
58 | edgedetection_choice: random # 0 - Canny Edge Detection on the whole output image, 1 - Canny Edge Detection only on the annotated part in the output image, random - any of the two
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