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41 changes: 41 additions & 0 deletions imagepy/menus/Plugins/Contribute/Contributions/DeepClas4BioPy.md
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,41 @@
# DeepClas4BioPy

**Path:** https://github.com/adines/DeepClas4BioImagePy

**Version:** 0.1

**Author:** adines

**Email:** adines@unirioja.es

**Keyword:** deepclas4bio, classification

**Description:** DeepClas4BioPy is an ImagePy plugin that connects ImagePy with the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio). This plugin allows ImagePy users to use deep learning techniques for object classification abstracting deep learning techniques details.


## Requirements
To use this plugin is necessary to have installed ImagePy with python 3.6 and the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio).

## Installation
To install this plugin you can use the Plugins Manager.

## Using the plugin
In this section, we will see an example of how to use this plugin. For this example we will classify a lion image using the VGG16 model from the Keras framework. In addition, you can use the model and the framework that best suits to your problem.

To use the plugin you must follow the following steps:

1. Load the image that you want to classify
![Loading the image](docs/images/001.png)


2. Run the plugin

Go to Plugins and search the name of the plugin in this case DeepClas4BioPy.


3. Select the framework and the model you want use
![Select framework and model](docs/images/003.png)


4. Visualize the output
![Visualize the output](docs/images/004.png)
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all
 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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41 changes: 41 additions & 0 deletions imagepy/menus/Plugins/Contribute/Contributions/DeepClas4BioPy.md
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,41 @@
# DeepClas4BioPy

**Path:** https://github.com/adines/DeepClas4BioImagePy

**Version:** 0.1

**Author:** adines

**Email:** adines@unirioja.es

**Keyword:** deepclas4bio, classification

**Description:** DeepClas4BioPy is an ImagePy plugin that connects ImagePy with the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio). This plugin allows ImagePy users to use deep learning techniques for object classification abstracting deep learning techniques details.


## Requirements
To use this plugin is necessary to have installed ImagePy with python 3.6 and the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio).

## Installation
To install this plugin you can use the Plugins Manager.

## Using the plugin
In this section, we will see an example of how to use this plugin. For this example we will classify a lion image using the VGG16 model from the Keras framework. In addition, you can use the model and the framework that best suits to your problem.

To use the plugin you must follow the following steps:

1. Load the image that you want to classify
![Loading the image](docs/images/001.png)


2. Run the plugin

Go to Plugins and search the name of the plugin in this case DeepClas4BioPy.


3. Select the framework and the model you want use
![Select framework and model](docs/images/003.png)


4. Visualize the output
![Visualize the output](docs/images/004.png)
, '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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41 changes: 41 additions & 0 deletions imagepy/menus/Plugins/Contribute/Contributions/DeepClas4BioPy.md
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,41 @@
# DeepClas4BioPy

**Path:** https://github.com/adines/DeepClas4BioImagePy

**Version:** 0.1

**Author:** adines

**Email:** adines@unirioja.es

**Keyword:** deepclas4bio, classification

**Description:** DeepClas4BioPy is an ImagePy plugin that connects ImagePy with the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio). This plugin allows ImagePy users to use deep learning techniques for object classification abstracting deep learning techniques details.


## Requirements
To use this plugin is necessary to have installed ImagePy with python 3.6 and the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio).

## Installation
To install this plugin you can use the Plugins Manager.

## Using the plugin
In this section, we will see an example of how to use this plugin. For this example we will classify a lion image using the VGG16 model from the Keras framework. In addition, you can use the model and the framework that best suits to your problem.

To use the plugin you must follow the following steps:

1. Load the image that you want to classify
![Loading the image](docs/images/001.png)


2. Run the plugin

Go to Plugins and search the name of the plugin in this case DeepClas4BioPy.


3. Select the framework and the model you want use
![Select framework and model](docs/images/003.png)


4. Visualize the output
![Visualize the output](docs/images/004.png)
, '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 > 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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41 changes: 41 additions & 0 deletions imagepy/menus/Plugins/Contribute/Contributions/DeepClas4BioPy.md
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,41 @@
# DeepClas4BioPy

**Path:** https://github.com/adines/DeepClas4BioImagePy

**Version:** 0.1

**Author:** adines

**Email:** adines@unirioja.es

**Keyword:** deepclas4bio, classification

**Description:** DeepClas4BioPy is an ImagePy plugin that connects ImagePy with the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio). This plugin allows ImagePy users to use deep learning techniques for object classification abstracting deep learning techniques details.


## Requirements
To use this plugin is necessary to have installed ImagePy with python 3.6 and the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio).

## Installation
To install this plugin you can use the Plugins Manager.

## Using the plugin
In this section, we will see an example of how to use this plugin. For this example we will classify a lion image using the VGG16 model from the Keras framework. In addition, you can use the model and the framework that best suits to your problem.

To use the plugin you must follow the following steps:

1. Load the image that you want to classify
![Loading the image](docs/images/001.png)


2. Run the plugin

Go to Plugins and search the name of the plugin in this case DeepClas4BioPy.


3. Select the framework and the model you want use
![Select framework and model](docs/images/003.png)


4. Visualize the output
![Visualize the output](docs/images/004.png)
, '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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41 changes: 41 additions & 0 deletions imagepy/menus/Plugins/Contribute/Contributions/DeepClas4BioPy.md
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,41 @@
# DeepClas4BioPy

**Path:** https://github.com/adines/DeepClas4BioImagePy

**Version:** 0.1

**Author:** adines

**Email:** adines@unirioja.es

**Keyword:** deepclas4bio, classification

**Description:** DeepClas4BioPy is an ImagePy plugin that connects ImagePy with the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio). This plugin allows ImagePy users to use deep learning techniques for object classification abstracting deep learning techniques details.


## Requirements
To use this plugin is necessary to have installed ImagePy with python 3.6 and the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio).

## Installation
To install this plugin you can use the Plugins Manager.

## Using the plugin
In this section, we will see an example of how to use this plugin. For this example we will classify a lion image using the VGG16 model from the Keras framework. In addition, you can use the model and the framework that best suits to your problem.

To use the plugin you must follow the following steps:

1. Load the image that you want to classify
![Loading the image](docs/images/001.png)


2. Run the plugin

Go to Plugins and search the name of the plugin in this case DeepClas4BioPy.


3. Select the framework and the model you want use
![Select framework and model](docs/images/003.png)


4. Visualize the output
![Visualize the output](docs/images/004.png)
, '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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41 changes: 41 additions & 0 deletions imagepy/menus/Plugins/Contribute/Contributions/DeepClas4BioPy.md
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,41 @@
# DeepClas4BioPy

**Path:** https://github.com/adines/DeepClas4BioImagePy

**Version:** 0.1

**Author:** adines

**Email:** adines@unirioja.es

**Keyword:** deepclas4bio, classification

**Description:** DeepClas4BioPy is an ImagePy plugin that connects ImagePy with the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio). This plugin allows ImagePy users to use deep learning techniques for object classification abstracting deep learning techniques details.


## Requirements
To use this plugin is necessary to have installed ImagePy with python 3.6 and the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio).

## Installation
To install this plugin you can use the Plugins Manager.

## Using the plugin
In this section, we will see an example of how to use this plugin. For this example we will classify a lion image using the VGG16 model from the Keras framework. In addition, you can use the model and the framework that best suits to your problem.

To use the plugin you must follow the following steps:

1. Load the image that you want to classify
![Loading the image](docs/images/001.png)


2. Run the plugin

Go to Plugins and search the name of the plugin in this case DeepClas4BioPy.


3. Select the framework and the model you want use
![Select framework and model](docs/images/003.png)


4. Visualize the output
![Visualize the output](docs/images/004.png)
, '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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41 changes: 41 additions & 0 deletions imagepy/menus/Plugins/Contribute/Contributions/DeepClas4BioPy.md
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,41 @@
# DeepClas4BioPy

**Path:** https://github.com/adines/DeepClas4BioImagePy

**Version:** 0.1

**Author:** adines

**Email:** adines@unirioja.es

**Keyword:** deepclas4bio, classification

**Description:** DeepClas4BioPy is an ImagePy plugin that connects ImagePy with the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio). This plugin allows ImagePy users to use deep learning techniques for object classification abstracting deep learning techniques details.


## Requirements
To use this plugin is necessary to have installed ImagePy with python 3.6 and the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio).

## Installation
To install this plugin you can use the Plugins Manager.

## Using the plugin
In this section, we will see an example of how to use this plugin. For this example we will classify a lion image using the VGG16 model from the Keras framework. In addition, you can use the model and the framework that best suits to your problem.

To use the plugin you must follow the following steps:

1. Load the image that you want to classify
![Loading the image](docs/images/001.png)


2. Run the plugin

Go to Plugins and search the name of the plugin in this case DeepClas4BioPy.


3. Select the framework and the model you want use
![Select framework and model](docs/images/003.png)


4. Visualize the output
![Visualize the output](docs/images/004.png)
, '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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41 changes: 41 additions & 0 deletions imagepy/menus/Plugins/Contribute/Contributions/DeepClas4BioPy.md
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,41 @@
# DeepClas4BioPy

**Path:** https://github.com/adines/DeepClas4BioImagePy

**Version:** 0.1

**Author:** adines

**Email:** adines@unirioja.es

**Keyword:** deepclas4bio, classification

**Description:** DeepClas4BioPy is an ImagePy plugin that connects ImagePy with the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio). This plugin allows ImagePy users to use deep learning techniques for object classification abstracting deep learning techniques details.


## Requirements
To use this plugin is necessary to have installed ImagePy with python 3.6 and the [DeepClas4Bio API](https://github.com/adines/DeepClas4Bio).

## Installation
To install this plugin you can use the Plugins Manager.

## Using the plugin
In this section, we will see an example of how to use this plugin. For this example we will classify a lion image using the VGG16 model from the Keras framework. In addition, you can use the model and the framework that best suits to your problem.

To use the plugin you must follow the following steps:

1. Load the image that you want to classify
![Loading the image](docs/images/001.png)


2. Run the plugin

Go to Plugins and search the name of the plugin in this case DeepClas4BioPy.


3. Select the framework and the model you want use
![Select framework and model](docs/images/003.png)


4. Visualize the output
![Visualize the output](docs/images/004.png)