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Image Processing and Augmentation Tool

Description

This project is an image processing and augmentation tool designed to apply various transformations to images. Data augmentation in the context of machine learning and image processing is a technique used to enhance the size and quality of training datasets by creating modified versions of the data. This process helps in improving the robustness and effectiveness of models, especially in tasks like image recognition and classification.

Features

  • Apply multiple image augmentation techniques like flipping, brightness enhancement, contrast enhancement, sharpening, edge enhancement, gamma correction, and equalization.
  • Ability to load processing parameters from a JSON file.
  • Process images in bulk from a specified input directory.

Getting Started

Requirements

  • Python 3.x
  • Pip package manager

Setup and Installation

  1. Clone the Repository
git clone https://github.com/renan-siqueira/python-data-augmentation-tool.git
cd python-data-augmentation-tool
  1. Create and Activate a Virtual Environment (Optional but recommended)
  • For Windows:
python -m venv venv
.\venv\Scripts\activate
  • For Unix or MacOS:
python3 -m venv venv
source venv/bin/activate
  1. Install Required Dependencies
pip install -r requirements.txt

How to Use

  1. Place your images in the input directory specified in the settings/config.py file.

  2. Modify the json/params.json file to set your desired augmentation parameters.

  3. Run the main script to process the images:

python main.py
  1. Processed images will be saved in the output directory specified in the settings/config.py file.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions to this project are welcome. Please adhere to this project's Code of Conduct.

Authors

  • Renan Siqueira Antonio

About

This project is an image processing and augmentation tool

Topics

data-augmentationdata-scienceimage-processingimage-transformationsmit-licensenumpyopencvopencv-pythonpillowpylintpythontoolkit

Resources

Readme
MIT license
Activity

Stars

2 stars

Watchers

1 watching

Forks

1 fork
Report repository

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all \u003cpre\u003e\u003ccode\u003e blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks"); } } catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); } })(); (function(){ try { var __m = "github.com"; var __re = new RegExp('^' + "github\\.com" + '
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Image Processing and Augmentation Tool

Description

This project is an image processing and augmentation tool designed to apply various transformations to images. Data augmentation in the context of machine learning and image processing is a technique used to enhance the size and quality of training datasets by creating modified versions of the data. This process helps in improving the robustness and effectiveness of models, especially in tasks like image recognition and classification.

Features

  • Apply multiple image augmentation techniques like flipping, brightness enhancement, contrast enhancement, sharpening, edge enhancement, gamma correction, and equalization.
  • Ability to load processing parameters from a JSON file.
  • Process images in bulk from a specified input directory.

Getting Started

Requirements

  • Python 3.x
  • Pip package manager

Setup and Installation

  1. Clone the Repository
git clone https://github.com/renan-siqueira/python-data-augmentation-tool.git
cd python-data-augmentation-tool
  1. Create and Activate a Virtual Environment (Optional but recommended)
  • For Windows:
python -m venv venv
.\venv\Scripts\activate
  • For Unix or MacOS:
python3 -m venv venv
source venv/bin/activate
  1. Install Required Dependencies
pip install -r requirements.txt

How to Use

  1. Place your images in the input directory specified in the settings/config.py file.

  2. Modify the json/params.json file to set your desired augmentation parameters.

  3. Run the main script to process the images:

python main.py
  1. Processed images will be saved in the output directory specified in the settings/config.py file.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions to this project are welcome. Please adhere to this project's Code of Conduct.

Authors

  • Renan Siqueira Antonio

About

This project is an image processing and augmentation tool

Topics

data-augmentationdata-scienceimage-processingimage-transformationsmit-licensenumpyopencvopencv-pythonpillowpylintpythontoolkit

Resources

Readme
MIT license
Activity

Stars

2 stars

Watchers

1 watching

Forks

1 fork
Report repository

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Packages

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Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Image Processing and Augmentation Tool

Description

This project is an image processing and augmentation tool designed to apply various transformations to images. Data augmentation in the context of machine learning and image processing is a technique used to enhance the size and quality of training datasets by creating modified versions of the data. This process helps in improving the robustness and effectiveness of models, especially in tasks like image recognition and classification.

Features

  • Apply multiple image augmentation techniques like flipping, brightness enhancement, contrast enhancement, sharpening, edge enhancement, gamma correction, and equalization.
  • Ability to load processing parameters from a JSON file.
  • Process images in bulk from a specified input directory.

Getting Started

Requirements

  • Python 3.x
  • Pip package manager

Setup and Installation

  1. Clone the Repository
git clone https://github.com/renan-siqueira/python-data-augmentation-tool.git
cd python-data-augmentation-tool
  1. Create and Activate a Virtual Environment (Optional but recommended)
  • For Windows:
python -m venv venv
.\venv\Scripts\activate
  • For Unix or MacOS:
python3 -m venv venv
source venv/bin/activate
  1. Install Required Dependencies
pip install -r requirements.txt

How to Use

  1. Place your images in the input directory specified in the settings/config.py file.

  2. Modify the json/params.json file to set your desired augmentation parameters.

  3. Run the main script to process the images:

python main.py
  1. Processed images will be saved in the output directory specified in the settings/config.py file.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions to this project are welcome. Please adhere to this project's Code of Conduct.

Authors

  • Renan Siqueira Antonio

About

This project is an image processing and augmentation tool

Topics

data-augmentationdata-scienceimage-processingimage-transformationsmit-licensenumpyopencvopencv-pythonpillowpylintpythontoolkit

Resources

Readme
MIT license
Activity

Stars

2 stars

Watchers

1 watching

Forks

1 fork
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length \u003e 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Image Processing and Augmentation Tool

Description

This project is an image processing and augmentation tool designed to apply various transformations to images. Data augmentation in the context of machine learning and image processing is a technique used to enhance the size and quality of training datasets by creating modified versions of the data. This process helps in improving the robustness and effectiveness of models, especially in tasks like image recognition and classification.

Features

  • Apply multiple image augmentation techniques like flipping, brightness enhancement, contrast enhancement, sharpening, edge enhancement, gamma correction, and equalization.
  • Ability to load processing parameters from a JSON file.
  • Process images in bulk from a specified input directory.

Getting Started

Requirements

  • Python 3.x
  • Pip package manager

Setup and Installation

  1. Clone the Repository
git clone https://github.com/renan-siqueira/python-data-augmentation-tool.git
cd python-data-augmentation-tool
  1. Create and Activate a Virtual Environment (Optional but recommended)
  • For Windows:
python -m venv venv
.\venv\Scripts\activate
  • For Unix or MacOS:
python3 -m venv venv
source venv/bin/activate
  1. Install Required Dependencies
pip install -r requirements.txt

How to Use

  1. Place your images in the input directory specified in the settings/config.py file.

  2. Modify the json/params.json file to set your desired augmentation parameters.

  3. Run the main script to process the images:

python main.py
  1. Processed images will be saved in the output directory specified in the settings/config.py file.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions to this project are welcome. Please adhere to this project's Code of Conduct.

Authors

  • Renan Siqueira Antonio

About

This project is an image processing and augmentation tool

Topics

data-augmentationdata-scienceimage-processingimage-transformationsmit-licensenumpyopencvopencv-pythonpillowpylintpythontoolkit

Resources

Readme
MIT license
Activity

Stars

2 stars

Watchers

1 watching

Forks

1 fork
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
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Image Processing and Augmentation Tool

Description

This project is an image processing and augmentation tool designed to apply various transformations to images. Data augmentation in the context of machine learning and image processing is a technique used to enhance the size and quality of training datasets by creating modified versions of the data. This process helps in improving the robustness and effectiveness of models, especially in tasks like image recognition and classification.

Features

  • Apply multiple image augmentation techniques like flipping, brightness enhancement, contrast enhancement, sharpening, edge enhancement, gamma correction, and equalization.
  • Ability to load processing parameters from a JSON file.
  • Process images in bulk from a specified input directory.

Getting Started

Requirements

  • Python 3.x
  • Pip package manager

Setup and Installation

  1. Clone the Repository
git clone https://github.com/renan-siqueira/python-data-augmentation-tool.git
cd python-data-augmentation-tool
  1. Create and Activate a Virtual Environment (Optional but recommended)
  • For Windows:
python -m venv venv
.\venv\Scripts\activate
  • For Unix or MacOS:
python3 -m venv venv
source venv/bin/activate
  1. Install Required Dependencies
pip install -r requirements.txt

How to Use

  1. Place your images in the input directory specified in the settings/config.py file.

  2. Modify the json/params.json file to set your desired augmentation parameters.

  3. Run the main script to process the images:

python main.py
  1. Processed images will be saved in the output directory specified in the settings/config.py file.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions to this project are welcome. Please adhere to this project's Code of Conduct.

Authors

  • Renan Siqueira Antonio

About

This project is an image processing and augmentation tool

Topics

data-augmentationdata-scienceimage-processingimage-transformationsmit-licensenumpyopencvopencv-pythonpillowpylintpythontoolkit

Resources

Readme
MIT license
Activity

Stars

2 stars

Watchers

1 watching

Forks

1 fork
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Image Processing and Augmentation Tool

Description

This project is an image processing and augmentation tool designed to apply various transformations to images. Data augmentation in the context of machine learning and image processing is a technique used to enhance the size and quality of training datasets by creating modified versions of the data. This process helps in improving the robustness and effectiveness of models, especially in tasks like image recognition and classification.

Features

  • Apply multiple image augmentation techniques like flipping, brightness enhancement, contrast enhancement, sharpening, edge enhancement, gamma correction, and equalization.
  • Ability to load processing parameters from a JSON file.
  • Process images in bulk from a specified input directory.

Getting Started

Requirements

  • Python 3.x
  • Pip package manager

Setup and Installation

  1. Clone the Repository
git clone https://github.com/renan-siqueira/python-data-augmentation-tool.git
cd python-data-augmentation-tool
  1. Create and Activate a Virtual Environment (Optional but recommended)
  • For Windows:
python -m venv venv
.\venv\Scripts\activate
  • For Unix or MacOS:
python3 -m venv venv
source venv/bin/activate
  1. Install Required Dependencies
pip install -r requirements.txt

How to Use

  1. Place your images in the input directory specified in the settings/config.py file.

  2. Modify the json/params.json file to set your desired augmentation parameters.

  3. Run the main script to process the images:

python main.py
  1. Processed images will be saved in the output directory specified in the settings/config.py file.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions to this project are welcome. Please adhere to this project's Code of Conduct.

Authors

  • Renan Siqueira Antonio

About

This project is an image processing and augmentation tool

Topics

data-augmentationdata-scienceimage-processingimage-transformationsmit-licensenumpyopencvopencv-pythonpillowpylintpythontoolkit

Resources

Readme
MIT license
Activity

Stars

2 stars

Watchers

1 watching

Forks

1 fork
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
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Image Processing and Augmentation Tool

Description

This project is an image processing and augmentation tool designed to apply various transformations to images. Data augmentation in the context of machine learning and image processing is a technique used to enhance the size and quality of training datasets by creating modified versions of the data. This process helps in improving the robustness and effectiveness of models, especially in tasks like image recognition and classification.

Features

  • Apply multiple image augmentation techniques like flipping, brightness enhancement, contrast enhancement, sharpening, edge enhancement, gamma correction, and equalization.
  • Ability to load processing parameters from a JSON file.
  • Process images in bulk from a specified input directory.

Getting Started

Requirements

  • Python 3.x
  • Pip package manager

Setup and Installation

  1. Clone the Repository
git clone https://github.com/renan-siqueira/python-data-augmentation-tool.git
cd python-data-augmentation-tool
  1. Create and Activate a Virtual Environment (Optional but recommended)
  • For Windows:
python -m venv venv
.\venv\Scripts\activate
  • For Unix or MacOS:
python3 -m venv venv
source venv/bin/activate
  1. Install Required Dependencies
pip install -r requirements.txt

How to Use

  1. Place your images in the input directory specified in the settings/config.py file.

  2. Modify the json/params.json file to set your desired augmentation parameters.

  3. Run the main script to process the images:

python main.py
  1. Processed images will be saved in the output directory specified in the settings/config.py file.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions to this project are welcome. Please adhere to this project's Code of Conduct.

Authors

  • Renan Siqueira Antonio

About

This project is an image processing and augmentation tool

Topics

data-augmentationdata-scienceimage-processingimage-transformationsmit-licensenumpyopencvopencv-pythonpillowpylintpythontoolkit

Resources

Readme
MIT license
Activity

Stars

2 stars

Watchers

1 watching

Forks

1 fork
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
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Image Processing and Augmentation Tool

Description

This project is an image processing and augmentation tool designed to apply various transformations to images. Data augmentation in the context of machine learning and image processing is a technique used to enhance the size and quality of training datasets by creating modified versions of the data. This process helps in improving the robustness and effectiveness of models, especially in tasks like image recognition and classification.

Features

  • Apply multiple image augmentation techniques like flipping, brightness enhancement, contrast enhancement, sharpening, edge enhancement, gamma correction, and equalization.
  • Ability to load processing parameters from a JSON file.
  • Process images in bulk from a specified input directory.

Getting Started

Requirements

  • Python 3.x
  • Pip package manager

Setup and Installation

  1. Clone the Repository
git clone https://github.com/renan-siqueira/python-data-augmentation-tool.git
cd python-data-augmentation-tool
  1. Create and Activate a Virtual Environment (Optional but recommended)
  • For Windows:
python -m venv venv
.\venv\Scripts\activate
  • For Unix or MacOS:
python3 -m venv venv
source venv/bin/activate
  1. Install Required Dependencies
pip install -r requirements.txt

How to Use

  1. Place your images in the input directory specified in the settings/config.py file.

  2. Modify the json/params.json file to set your desired augmentation parameters.

  3. Run the main script to process the images:

python main.py
  1. Processed images will be saved in the output directory specified in the settings/config.py file.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions to this project are welcome. Please adhere to this project's Code of Conduct.

Authors

  • Renan Siqueira Antonio

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This project is an image processing and augmentation tool

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data-augmentationdata-scienceimage-processingimage-transformationsmit-licensenumpyopencvopencv-pythonpillowpylintpythontoolkit

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