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Image labeling tool

This application is used to label things in images.
With the goal to be used as a tool for labeling things later to be trained for object detection using Tensorflow or similar tools.
Labels has a related geometry, using the geomtry id, as of right now the id is a faked uuid.
Focus from the start has been on plant counting, but there's no reason not to use it for something else.

Use instructions

The application is separated in two parts. They work about the same. Geometries created for plant counting can be used for training as well. I have tried to keep as much of the data in GeoJson as possible.

Count plants

Start by drawing geometries with the tools on the top right corner. Right click on a geometry to select it.
Select a model and click process.
A API request is now simulated and points are added to the map.

That's about it for plant counting.

Training models

Draw or use old geometries by right clicking on them and select them.
Click inside geometry to create annotations with the selected annotation type.
Several geometries can be created.
Click train to simulate the API request.

A new fake model will be added to the list of models.

Install instructions

To start:

$ npm install

or

$ yarn

To develop:

$ npm run dev

or

$ yarn run dev

To build for production:

$ npm run build

Issues

The mock API is not the best, I didn't want to waste time there. So it only works one time. So if you train more than one model the created fake uuid will be the same and a therefore there's a bug.
Easily solved with a proper API.

About

A small tool for labeling images, to be used for training models that will do object detection in images.

Resources

Readme
Activity

Stars

1 star

Watchers

1 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, '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" + '

Skip to content
BranchesTags
Repository files navigation
Image labeling tool
This application is used to label things in images.

With the goal to be used as a tool for labeling things later to be trained for object detection using Tensorflow or similar tools.

Labels has a related geometry, using the geomtry id, as of right now the id is a faked uuid.

Focus from the start has been on plant counting, but there's no reason not to use it for something else.
Use instructions
The application is separated in two parts. They work about the same. Geometries created for plant counting can be used for training as well.
I have tried to keep as much of the data in GeoJson as possible.
Count plants
Start by drawing geometries with the tools on the top right corner.
Right click on a geometry to select it.

Select a model and click process.

A API request is now simulated and points are added to the map.
That's about it for plant counting.
Training models
Draw or use old geometries by right clicking on them and select them.

Click inside geometry to create annotations with the selected annotation type.

Several geometries can be created.

Click train to simulate the API request.
A new fake model will be added to the list of models.
Install instructions
To start:
$ npm install
or
$ yarn
To develop:
$ npm run dev

or
$ yarn run dev
To build for production:
$ npm run build
Issues
The mock API is not the best, I didn't want to waste time there. So it only works one time. So if you train more than one model the created fake uuid will be the same and a therefore there's a bug.

Easily solved with a proper API.
About
A small tool for labeling images, to be used for training models that will do object detection in images.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Image labeling tool
This application is used to label things in images.

With the goal to be used as a tool for labeling things later to be trained for object detection using Tensorflow or similar tools.

Labels has a related geometry, using the geomtry id, as of right now the id is a faked uuid.

Focus from the start has been on plant counting, but there's no reason not to use it for something else.
Use instructions
The application is separated in two parts. They work about the same. Geometries created for plant counting can be used for training as well.
I have tried to keep as much of the data in GeoJson as possible.
Count plants
Start by drawing geometries with the tools on the top right corner.
Right click on a geometry to select it.

Select a model and click process.

A API request is now simulated and points are added to the map.
That's about it for plant counting.
Training models
Draw or use old geometries by right clicking on them and select them.

Click inside geometry to create annotations with the selected annotation type.

Several geometries can be created.

Click train to simulate the API request.
A new fake model will be added to the list of models.
Install instructions
To start:
$ npm install
or
$ yarn
To develop:
$ npm run dev

or
$ yarn run dev
To build for production:
$ npm run build
Issues
The mock API is not the best, I didn't want to waste time there. So it only works one time. So if you train more than one model the created fake uuid will be the same and a therefore there's a bug.

Easily solved with a proper API.
About
A small tool for labeling images, to be used for training models that will do object detection in images.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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 > 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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Image labeling tool
This application is used to label things in images.

With the goal to be used as a tool for labeling things later to be trained for object detection using Tensorflow or similar tools.

Labels has a related geometry, using the geomtry id, as of right now the id is a faked uuid.

Focus from the start has been on plant counting, but there's no reason not to use it for something else.
Use instructions
The application is separated in two parts. They work about the same. Geometries created for plant counting can be used for training as well.
I have tried to keep as much of the data in GeoJson as possible.
Count plants
Start by drawing geometries with the tools on the top right corner.
Right click on a geometry to select it.

Select a model and click process.

A API request is now simulated and points are added to the map.
That's about it for plant counting.
Training models
Draw or use old geometries by right clicking on them and select them.

Click inside geometry to create annotations with the selected annotation type.

Several geometries can be created.

Click train to simulate the API request.
A new fake model will be added to the list of models.
Install instructions
To start:
$ npm install
or
$ yarn
To develop:
$ npm run dev

or
$ yarn run dev
To build for production:
$ npm run build
Issues
The mock API is not the best, I didn't want to waste time there. So it only works one time. So if you train more than one model the created fake uuid will be the same and a therefore there's a bug.

Easily solved with a proper API.
About
A small tool for labeling images, to be used for training models that will do object detection in images.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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" + '

Skip to content
BranchesTags
Repository files navigation
Image labeling tool
This application is used to label things in images.

With the goal to be used as a tool for labeling things later to be trained for object detection using Tensorflow or similar tools.

Labels has a related geometry, using the geomtry id, as of right now the id is a faked uuid.

Focus from the start has been on plant counting, but there's no reason not to use it for something else.
Use instructions
The application is separated in two parts. They work about the same. Geometries created for plant counting can be used for training as well.
I have tried to keep as much of the data in GeoJson as possible.
Count plants
Start by drawing geometries with the tools on the top right corner.
Right click on a geometry to select it.

Select a model and click process.

A API request is now simulated and points are added to the map.
That's about it for plant counting.
Training models
Draw or use old geometries by right clicking on them and select them.

Click inside geometry to create annotations with the selected annotation type.

Several geometries can be created.

Click train to simulate the API request.
A new fake model will be added to the list of models.
Install instructions
To start:
$ npm install
or
$ yarn
To develop:
$ npm run dev

or
$ yarn run dev
To build for production:
$ npm run build
Issues
The mock API is not the best, I didn't want to waste time there. So it only works one time. So if you train more than one model the created fake uuid will be the same and a therefore there's a bug.

Easily solved with a proper API.
About
A small tool for labeling images, to be used for training models that will do object detection in images.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Image labeling tool
This application is used to label things in images.

With the goal to be used as a tool for labeling things later to be trained for object detection using Tensorflow or similar tools.

Labels has a related geometry, using the geomtry id, as of right now the id is a faked uuid.

Focus from the start has been on plant counting, but there's no reason not to use it for something else.
Use instructions
The application is separated in two parts. They work about the same. Geometries created for plant counting can be used for training as well.
I have tried to keep as much of the data in GeoJson as possible.
Count plants
Start by drawing geometries with the tools on the top right corner.
Right click on a geometry to select it.

Select a model and click process.

A API request is now simulated and points are added to the map.
That's about it for plant counting.
Training models
Draw or use old geometries by right clicking on them and select them.

Click inside geometry to create annotations with the selected annotation type.

Several geometries can be created.

Click train to simulate the API request.
A new fake model will be added to the list of models.
Install instructions
To start:
$ npm install
or
$ yarn
To develop:
$ npm run dev

or
$ yarn run dev
To build for production:
$ npm run build
Issues
The mock API is not the best, I didn't want to waste time there. So it only works one time. So if you train more than one model the created fake uuid will be the same and a therefore there's a bug.

Easily solved with a proper API.
About
A small tool for labeling images, to be used for training models that will do object detection in images.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Image labeling tool
This application is used to label things in images.

With the goal to be used as a tool for labeling things later to be trained for object detection using Tensorflow or similar tools.

Labels has a related geometry, using the geomtry id, as of right now the id is a faked uuid.

Focus from the start has been on plant counting, but there's no reason not to use it for something else.
Use instructions
The application is separated in two parts. They work about the same. Geometries created for plant counting can be used for training as well.
I have tried to keep as much of the data in GeoJson as possible.
Count plants
Start by drawing geometries with the tools on the top right corner.
Right click on a geometry to select it.

Select a model and click process.

A API request is now simulated and points are added to the map.
That's about it for plant counting.
Training models
Draw or use old geometries by right clicking on them and select them.

Click inside geometry to create annotations with the selected annotation type.

Several geometries can be created.

Click train to simulate the API request.
A new fake model will be added to the list of models.
Install instructions
To start:
$ npm install
or
$ yarn
To develop:
$ npm run dev

or
$ yarn run dev
To build for production:
$ npm run build
Issues
The mock API is not the best, I didn't want to waste time there. So it only works one time. So if you train more than one model the created fake uuid will be the same and a therefore there's a bug.

Easily solved with a proper API.
About
A small tool for labeling images, to be used for training models that will do object detection in images.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
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); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Image labeling tool
This application is used to label things in images.

With the goal to be used as a tool for labeling things later to be trained for object detection using Tensorflow or similar tools.

Labels has a related geometry, using the geomtry id, as of right now the id is a faked uuid.

Focus from the start has been on plant counting, but there's no reason not to use it for something else.
Use instructions
The application is separated in two parts. They work about the same. Geometries created for plant counting can be used for training as well.
I have tried to keep as much of the data in GeoJson as possible.
Count plants
Start by drawing geometries with the tools on the top right corner.
Right click on a geometry to select it.

Select a model and click process.

A API request is now simulated and points are added to the map.
That's about it for plant counting.
Training models
Draw or use old geometries by right clicking on them and select them.

Click inside geometry to create annotations with the selected annotation type.

Several geometries can be created.

Click train to simulate the API request.
A new fake model will be added to the list of models.
Install instructions
To start:
$ npm install
or
$ yarn
To develop:
$ npm run dev

or
$ yarn run dev
To build for production:
$ npm run build
Issues
The mock API is not the best, I didn't want to waste time there. So it only works one time. So if you train more than one model the created fake uuid will be the same and a therefore there's a bug.

Easily solved with a proper API.
About
A small tool for labeling images, to be used for training models that will do object detection in images.
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages