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Blue

FTC Team #14473 Future - Image processing Framework [Currently in Intense Development]

Kitten

This library was made to simplify the agony in doing FTC computer vision in Java.
It follows very simple rules:

  • It can be used for all seasons. This code will always be useful unless FTC changes their software setup.

  • If you don't need it, you should never see it. All the boilerpalce nitty gritty is handled by magick.

Features

  • OpenCV Foundation Detection
  • Opencv Stone Detection (Pretty good 90%)
  • Opencv SkyStone Detection (Bestest one)
  • simplified interface for Vuforia and tensorflow
  • All the above running simultaneously

Maximizing Accuracy

  • Angle the phone so that the field covers as much of the camera image as possible (best performance with elements on field)
  • Don't let stones overlap vertically. The stone detector is optimized for horozontal rows of stones.
  • Don't let elements get too far away. Small shapes will be calssified as noise
  • Don't tilt the camera (like when you steer the car in asphalt 8). The shape classifier will get confused.

Setup

  1. Clone and make a new project at new>project>from version control>git in Android Studio. Code is in the Teamcode folder and bluejay module. We might do bintray hosting but haven't had time yet :p
  2. Sync Gradle. Search for this command with ctrl+shift+a.
  3. Examples are in the teamcode folder.

Using the Library

I've been lax on documentation, so ask if there is ambiguity First off, working code for everyone:

*Stuff here is outdated, bleh. Check teamcode for example.

All classes that the user uses follow a simple guideline: a constructor with clear parameter requirements, a start() method to begin computing, a stop() method to stop computing, and one method with get at the beginning of its name. This returns whatever the class is used for.

For example:

ImageDetector detector = new ImageDetector(this, false);
detector.start();

Is used to get the Vuforia localizer running. It requires an opmode instance and a boolean for whether the RC should display its view.

Please realize the purpose of the start() and stop() methods. We've made a careful decision to include them even though they make the library more complicated. Keeping all the detectors active (ie. calling start() for each) will cause your framerate to plummet and latency to increase.

To request data, simply do:

detector.getPosition();

For the OpenCV Element detector, there are some special rules since I haven't standardized it yet:

  • To toggle detection for each element, set the Pipeline.doSkyStones, Pipeline.doStones, and Pipeline.doFoundations static booleans to True or False.
  • To turn on/off the ENTIRE detector, usestart() and stop(). Note that turning off all individual element detections will still consume resources as there are common shared algorithms that run regardless of whether element detection is on or off.

A note: the IMU class, as a non-absolute Localizer, will always return the difference in position since the last time you called its getter method. That means that if the robot spins more than 360 degrees between that time, you will have an unreliable rotational reading.

Contact

If you got questions, email me at xchenbox@gmail.com or alternatively, find us on Facebook https://www.facebook.com/future14473 We don't have a discord (yet). If there's any problems or ambiguity, shoot me an email

Gallery

hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!

About

Complete game element detector made with OpenCV for the 2019 FIRST Tech Challenge game.

Resources

Readme

Contributing

Contributing
Activity
Custom properties

Stars

0 stars

Watchers

0 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
Blue
FTC Team #14473 Future - Image processing Framework [Currently in Intense Development]
Kitten
This library was made to simplify the agony in doing FTC computer vision in Java.

It follows very simple rules:
  • It can be used for all seasons. This code will always be useful unless FTC changes their software setup.
  • If you don't need it, you should never see it. All the boilerpalce nitty gritty is handled by magick.
Features
  • OpenCV Foundation Detection
  • Opencv Stone Detection (Pretty good 90%)
  • Opencv SkyStone Detection (Bestest one)
  • simplified interface for Vuforia and tensorflow
  • All the above running simultaneously
Maximizing Accuracy
  • Angle the phone so that the field covers as much of the camera image as possible (best performance with elements on field)
  • Don't let stones overlap vertically. The stone detector is optimized for horozontal rows of stones.
  • Don't let elements get too far away. Small shapes will be calssified as noise
  • Don't tilt the camera (like when you steer the car in asphalt 8). The shape classifier will get confused.
Setup
  1. Clone and make a new project at new>project>from version control>git in Android Studio. Code is in the Teamcode folder and bluejay module. We might do bintray hosting but haven't had time yet :p
  2. Sync Gradle. Search for this command with ctrl+shift+a.
  3. Examples are in the teamcode folder.
Using the Library
I've been lax on documentation, so ask if there is ambiguity
First off, working code for everyone:
*Stuff here is outdated, bleh. Check teamcode for example. 
All classes that the user uses follow a simple guideline: a constructor with clear parameter requirements, a start() method to begin computing, a stop() method to stop computing, and one method with get at the beginning of its name. This returns whatever the class is used for.
For example:
ImageDetector detector = new ImageDetector(this, false);
detector.start();

Is used to get the Vuforia localizer running. It requires an opmode instance and a boolean for whether the RC should display its view.
Please realize the purpose of the start() and stop() methods. We've made a careful decision to include them even though they make the library more complicated. Keeping all the detectors active (ie. calling start() for each) will cause your framerate to plummet and latency to increase.
To request data, simply do:
detector.getPosition();

For the OpenCV Element detector, there are some special rules since I haven't standardized it yet:
  • To toggle detection for each element, set the Pipeline.doSkyStones, Pipeline.doStones, and Pipeline.doFoundations static booleans to True or False.
  • To turn on/off the ENTIRE detector, usestart() and stop(). Note that turning off all individual element detections will still consume resources as there are common shared algorithms that run regardless of whether element detection is on or off.
A note: the IMU class, as a non-absolute Localizer, will always return the difference in position since the last time you called its getter method. That means that if the robot spins more than 360 degrees between that time, you will have an unreliable rotational reading.
Contact
If you got questions, email me at xchenbox@gmail.com or alternatively, find us on Facebook https://www.facebook.com/future14473
We don't have a discord (yet). If there's any problems or ambiguity, shoot me an email
Gallery
hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!
About
Complete game element detector made with OpenCV for the 2019 FIRST Tech Challenge game.
Resources
Readme
Contributing
Contributing
Activity
Custom properties
Stars
0 stars
Watchers
0 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
Blue
FTC Team #14473 Future - Image processing Framework [Currently in Intense Development]
Kitten
This library was made to simplify the agony in doing FTC computer vision in Java.

It follows very simple rules:
  • It can be used for all seasons. This code will always be useful unless FTC changes their software setup.
  • If you don't need it, you should never see it. All the boilerpalce nitty gritty is handled by magick.
Features
  • OpenCV Foundation Detection
  • Opencv Stone Detection (Pretty good 90%)
  • Opencv SkyStone Detection (Bestest one)
  • simplified interface for Vuforia and tensorflow
  • All the above running simultaneously
Maximizing Accuracy
  • Angle the phone so that the field covers as much of the camera image as possible (best performance with elements on field)
  • Don't let stones overlap vertically. The stone detector is optimized for horozontal rows of stones.
  • Don't let elements get too far away. Small shapes will be calssified as noise
  • Don't tilt the camera (like when you steer the car in asphalt 8). The shape classifier will get confused.
Setup
  1. Clone and make a new project at new>project>from version control>git in Android Studio. Code is in the Teamcode folder and bluejay module. We might do bintray hosting but haven't had time yet :p
  2. Sync Gradle. Search for this command with ctrl+shift+a.
  3. Examples are in the teamcode folder.
Using the Library
I've been lax on documentation, so ask if there is ambiguity
First off, working code for everyone:
*Stuff here is outdated, bleh. Check teamcode for example. 
All classes that the user uses follow a simple guideline: a constructor with clear parameter requirements, a start() method to begin computing, a stop() method to stop computing, and one method with get at the beginning of its name. This returns whatever the class is used for.
For example:
ImageDetector detector = new ImageDetector(this, false);
detector.start();

Is used to get the Vuforia localizer running. It requires an opmode instance and a boolean for whether the RC should display its view.
Please realize the purpose of the start() and stop() methods. We've made a careful decision to include them even though they make the library more complicated. Keeping all the detectors active (ie. calling start() for each) will cause your framerate to plummet and latency to increase.
To request data, simply do:
detector.getPosition();

For the OpenCV Element detector, there are some special rules since I haven't standardized it yet:
  • To toggle detection for each element, set the Pipeline.doSkyStones, Pipeline.doStones, and Pipeline.doFoundations static booleans to True or False.
  • To turn on/off the ENTIRE detector, usestart() and stop(). Note that turning off all individual element detections will still consume resources as there are common shared algorithms that run regardless of whether element detection is on or off.
A note: the IMU class, as a non-absolute Localizer, will always return the difference in position since the last time you called its getter method. That means that if the robot spins more than 360 degrees between that time, you will have an unreliable rotational reading.
Contact
If you got questions, email me at xchenbox@gmail.com or alternatively, find us on Facebook https://www.facebook.com/future14473
We don't have a discord (yet). If there's any problems or ambiguity, shoot me an email
Gallery
hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!
About
Complete game element detector made with OpenCV for the 2019 FIRST Tech Challenge game.
Resources
Readme
Contributing
Contributing
Activity
Custom properties
Stars
0 stars
Watchers
0 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
Blue
FTC Team #14473 Future - Image processing Framework [Currently in Intense Development]
Kitten
This library was made to simplify the agony in doing FTC computer vision in Java.

It follows very simple rules:
  • It can be used for all seasons. This code will always be useful unless FTC changes their software setup.
  • If you don't need it, you should never see it. All the boilerpalce nitty gritty is handled by magick.
Features
  • OpenCV Foundation Detection
  • Opencv Stone Detection (Pretty good 90%)
  • Opencv SkyStone Detection (Bestest one)
  • simplified interface for Vuforia and tensorflow
  • All the above running simultaneously
Maximizing Accuracy
  • Angle the phone so that the field covers as much of the camera image as possible (best performance with elements on field)
  • Don't let stones overlap vertically. The stone detector is optimized for horozontal rows of stones.
  • Don't let elements get too far away. Small shapes will be calssified as noise
  • Don't tilt the camera (like when you steer the car in asphalt 8). The shape classifier will get confused.
Setup
  1. Clone and make a new project at new>project>from version control>git in Android Studio. Code is in the Teamcode folder and bluejay module. We might do bintray hosting but haven't had time yet :p
  2. Sync Gradle. Search for this command with ctrl+shift+a.
  3. Examples are in the teamcode folder.
Using the Library
I've been lax on documentation, so ask if there is ambiguity
First off, working code for everyone:
*Stuff here is outdated, bleh. Check teamcode for example. 
All classes that the user uses follow a simple guideline: a constructor with clear parameter requirements, a start() method to begin computing, a stop() method to stop computing, and one method with get at the beginning of its name. This returns whatever the class is used for.
For example:
ImageDetector detector = new ImageDetector(this, false);
detector.start();

Is used to get the Vuforia localizer running. It requires an opmode instance and a boolean for whether the RC should display its view.
Please realize the purpose of the start() and stop() methods. We've made a careful decision to include them even though they make the library more complicated. Keeping all the detectors active (ie. calling start() for each) will cause your framerate to plummet and latency to increase.
To request data, simply do:
detector.getPosition();

For the OpenCV Element detector, there are some special rules since I haven't standardized it yet:
  • To toggle detection for each element, set the Pipeline.doSkyStones, Pipeline.doStones, and Pipeline.doFoundations static booleans to True or False.
  • To turn on/off the ENTIRE detector, usestart() and stop(). Note that turning off all individual element detections will still consume resources as there are common shared algorithms that run regardless of whether element detection is on or off.
A note: the IMU class, as a non-absolute Localizer, will always return the difference in position since the last time you called its getter method. That means that if the robot spins more than 360 degrees between that time, you will have an unreliable rotational reading.
Contact
If you got questions, email me at xchenbox@gmail.com or alternatively, find us on Facebook https://www.facebook.com/future14473
We don't have a discord (yet). If there's any problems or ambiguity, shoot me an email
Gallery
hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!
About
Complete game element detector made with OpenCV for the 2019 FIRST Tech Challenge game.
Resources
Readme
Contributing
Contributing
Activity
Custom properties
Stars
0 stars
Watchers
0 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
Blue
FTC Team #14473 Future - Image processing Framework [Currently in Intense Development]
Kitten
This library was made to simplify the agony in doing FTC computer vision in Java.

It follows very simple rules:
  • It can be used for all seasons. This code will always be useful unless FTC changes their software setup.
  • If you don't need it, you should never see it. All the boilerpalce nitty gritty is handled by magick.
Features
  • OpenCV Foundation Detection
  • Opencv Stone Detection (Pretty good 90%)
  • Opencv SkyStone Detection (Bestest one)
  • simplified interface for Vuforia and tensorflow
  • All the above running simultaneously
Maximizing Accuracy
  • Angle the phone so that the field covers as much of the camera image as possible (best performance with elements on field)
  • Don't let stones overlap vertically. The stone detector is optimized for horozontal rows of stones.
  • Don't let elements get too far away. Small shapes will be calssified as noise
  • Don't tilt the camera (like when you steer the car in asphalt 8). The shape classifier will get confused.
Setup
  1. Clone and make a new project at new>project>from version control>git in Android Studio. Code is in the Teamcode folder and bluejay module. We might do bintray hosting but haven't had time yet :p
  2. Sync Gradle. Search for this command with ctrl+shift+a.
  3. Examples are in the teamcode folder.
Using the Library
I've been lax on documentation, so ask if there is ambiguity
First off, working code for everyone:
*Stuff here is outdated, bleh. Check teamcode for example. 
All classes that the user uses follow a simple guideline: a constructor with clear parameter requirements, a start() method to begin computing, a stop() method to stop computing, and one method with get at the beginning of its name. This returns whatever the class is used for.
For example:
ImageDetector detector = new ImageDetector(this, false);
detector.start();

Is used to get the Vuforia localizer running. It requires an opmode instance and a boolean for whether the RC should display its view.
Please realize the purpose of the start() and stop() methods. We've made a careful decision to include them even though they make the library more complicated. Keeping all the detectors active (ie. calling start() for each) will cause your framerate to plummet and latency to increase.
To request data, simply do:
detector.getPosition();

For the OpenCV Element detector, there are some special rules since I haven't standardized it yet:
  • To toggle detection for each element, set the Pipeline.doSkyStones, Pipeline.doStones, and Pipeline.doFoundations static booleans to True or False.
  • To turn on/off the ENTIRE detector, usestart() and stop(). Note that turning off all individual element detections will still consume resources as there are common shared algorithms that run regardless of whether element detection is on or off.
A note: the IMU class, as a non-absolute Localizer, will always return the difference in position since the last time you called its getter method. That means that if the robot spins more than 360 degrees between that time, you will have an unreliable rotational reading.
Contact
If you got questions, email me at xchenbox@gmail.com or alternatively, find us on Facebook https://www.facebook.com/future14473
We don't have a discord (yet). If there's any problems or ambiguity, shoot me an email
Gallery
hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!
About
Complete game element detector made with OpenCV for the 2019 FIRST Tech Challenge game.
Resources
Readme
Contributing
Contributing
Activity
Custom properties
Stars
0 stars
Watchers
0 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
Blue
FTC Team #14473 Future - Image processing Framework [Currently in Intense Development]
Kitten
This library was made to simplify the agony in doing FTC computer vision in Java.

It follows very simple rules:
  • It can be used for all seasons. This code will always be useful unless FTC changes their software setup.
  • If you don't need it, you should never see it. All the boilerpalce nitty gritty is handled by magick.
Features
  • OpenCV Foundation Detection
  • Opencv Stone Detection (Pretty good 90%)
  • Opencv SkyStone Detection (Bestest one)
  • simplified interface for Vuforia and tensorflow
  • All the above running simultaneously
Maximizing Accuracy
  • Angle the phone so that the field covers as much of the camera image as possible (best performance with elements on field)
  • Don't let stones overlap vertically. The stone detector is optimized for horozontal rows of stones.
  • Don't let elements get too far away. Small shapes will be calssified as noise
  • Don't tilt the camera (like when you steer the car in asphalt 8). The shape classifier will get confused.
Setup
  1. Clone and make a new project at new>project>from version control>git in Android Studio. Code is in the Teamcode folder and bluejay module. We might do bintray hosting but haven't had time yet :p
  2. Sync Gradle. Search for this command with ctrl+shift+a.
  3. Examples are in the teamcode folder.
Using the Library
I've been lax on documentation, so ask if there is ambiguity
First off, working code for everyone:
*Stuff here is outdated, bleh. Check teamcode for example. 
All classes that the user uses follow a simple guideline: a constructor with clear parameter requirements, a start() method to begin computing, a stop() method to stop computing, and one method with get at the beginning of its name. This returns whatever the class is used for.
For example:
ImageDetector detector = new ImageDetector(this, false);
detector.start();

Is used to get the Vuforia localizer running. It requires an opmode instance and a boolean for whether the RC should display its view.
Please realize the purpose of the start() and stop() methods. We've made a careful decision to include them even though they make the library more complicated. Keeping all the detectors active (ie. calling start() for each) will cause your framerate to plummet and latency to increase.
To request data, simply do:
detector.getPosition();

For the OpenCV Element detector, there are some special rules since I haven't standardized it yet:
  • To toggle detection for each element, set the Pipeline.doSkyStones, Pipeline.doStones, and Pipeline.doFoundations static booleans to True or False.
  • To turn on/off the ENTIRE detector, usestart() and stop(). Note that turning off all individual element detections will still consume resources as there are common shared algorithms that run regardless of whether element detection is on or off.
A note: the IMU class, as a non-absolute Localizer, will always return the difference in position since the last time you called its getter method. That means that if the robot spins more than 360 degrees between that time, you will have an unreliable rotational reading.
Contact
If you got questions, email me at xchenbox@gmail.com or alternatively, find us on Facebook https://www.facebook.com/future14473
We don't have a discord (yet). If there's any problems or ambiguity, shoot me an email
Gallery
hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!
About
Complete game element detector made with OpenCV for the 2019 FIRST Tech Challenge game.
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, '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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Blue
FTC Team #14473 Future - Image processing Framework [Currently in Intense Development]
Kitten
This library was made to simplify the agony in doing FTC computer vision in Java.

It follows very simple rules:
  • It can be used for all seasons. This code will always be useful unless FTC changes their software setup.
  • If you don't need it, you should never see it. All the boilerpalce nitty gritty is handled by magick.
Features
  • OpenCV Foundation Detection
  • Opencv Stone Detection (Pretty good 90%)
  • Opencv SkyStone Detection (Bestest one)
  • simplified interface for Vuforia and tensorflow
  • All the above running simultaneously
Maximizing Accuracy
  • Angle the phone so that the field covers as much of the camera image as possible (best performance with elements on field)
  • Don't let stones overlap vertically. The stone detector is optimized for horozontal rows of stones.
  • Don't let elements get too far away. Small shapes will be calssified as noise
  • Don't tilt the camera (like when you steer the car in asphalt 8). The shape classifier will get confused.
Setup
  1. Clone and make a new project at new>project>from version control>git in Android Studio. Code is in the Teamcode folder and bluejay module. We might do bintray hosting but haven't had time yet :p
  2. Sync Gradle. Search for this command with ctrl+shift+a.
  3. Examples are in the teamcode folder.
Using the Library
I've been lax on documentation, so ask if there is ambiguity
First off, working code for everyone:
*Stuff here is outdated, bleh. Check teamcode for example. 
All classes that the user uses follow a simple guideline: a constructor with clear parameter requirements, a start() method to begin computing, a stop() method to stop computing, and one method with get at the beginning of its name. This returns whatever the class is used for.
For example:
ImageDetector detector = new ImageDetector(this, false);
detector.start();

Is used to get the Vuforia localizer running. It requires an opmode instance and a boolean for whether the RC should display its view.
Please realize the purpose of the start() and stop() methods. We've made a careful decision to include them even though they make the library more complicated. Keeping all the detectors active (ie. calling start() for each) will cause your framerate to plummet and latency to increase.
To request data, simply do:
detector.getPosition();

For the OpenCV Element detector, there are some special rules since I haven't standardized it yet:
  • To toggle detection for each element, set the Pipeline.doSkyStones, Pipeline.doStones, and Pipeline.doFoundations static booleans to True or False.
  • To turn on/off the ENTIRE detector, usestart() and stop(). Note that turning off all individual element detections will still consume resources as there are common shared algorithms that run regardless of whether element detection is on or off.
A note: the IMU class, as a non-absolute Localizer, will always return the difference in position since the last time you called its getter method. That means that if the robot spins more than 360 degrees between that time, you will have an unreliable rotational reading.
Contact
If you got questions, email me at xchenbox@gmail.com or alternatively, find us on Facebook https://www.facebook.com/future14473
We don't have a discord (yet). If there's any problems or ambiguity, shoot me an email
Gallery
hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!
About
Complete game element detector made with OpenCV for the 2019 FIRST Tech Challenge game.
Resources
Readme
Contributing
Contributing
Activity
Custom properties
Stars
0 stars
Watchers
0 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
Blue
FTC Team #14473 Future - Image processing Framework [Currently in Intense Development]
Kitten
This library was made to simplify the agony in doing FTC computer vision in Java.

It follows very simple rules:
  • It can be used for all seasons. This code will always be useful unless FTC changes their software setup.
  • If you don't need it, you should never see it. All the boilerpalce nitty gritty is handled by magick.
Features
  • OpenCV Foundation Detection
  • Opencv Stone Detection (Pretty good 90%)
  • Opencv SkyStone Detection (Bestest one)
  • simplified interface for Vuforia and tensorflow
  • All the above running simultaneously
Maximizing Accuracy
  • Angle the phone so that the field covers as much of the camera image as possible (best performance with elements on field)
  • Don't let stones overlap vertically. The stone detector is optimized for horozontal rows of stones.
  • Don't let elements get too far away. Small shapes will be calssified as noise
  • Don't tilt the camera (like when you steer the car in asphalt 8). The shape classifier will get confused.
Setup
  1. Clone and make a new project at new>project>from version control>git in Android Studio. Code is in the Teamcode folder and bluejay module. We might do bintray hosting but haven't had time yet :p
  2. Sync Gradle. Search for this command with ctrl+shift+a.
  3. Examples are in the teamcode folder.
Using the Library
I've been lax on documentation, so ask if there is ambiguity
First off, working code for everyone:
*Stuff here is outdated, bleh. Check teamcode for example. 
All classes that the user uses follow a simple guideline: a constructor with clear parameter requirements, a start() method to begin computing, a stop() method to stop computing, and one method with get at the beginning of its name. This returns whatever the class is used for.
For example:
ImageDetector detector = new ImageDetector(this, false);
detector.start();

Is used to get the Vuforia localizer running. It requires an opmode instance and a boolean for whether the RC should display its view.
Please realize the purpose of the start() and stop() methods. We've made a careful decision to include them even though they make the library more complicated. Keeping all the detectors active (ie. calling start() for each) will cause your framerate to plummet and latency to increase.
To request data, simply do:
detector.getPosition();

For the OpenCV Element detector, there are some special rules since I haven't standardized it yet:
  • To toggle detection for each element, set the Pipeline.doSkyStones, Pipeline.doStones, and Pipeline.doFoundations static booleans to True or False.
  • To turn on/off the ENTIRE detector, usestart() and stop(). Note that turning off all individual element detections will still consume resources as there are common shared algorithms that run regardless of whether element detection is on or off.
A note: the IMU class, as a non-absolute Localizer, will always return the difference in position since the last time you called its getter method. That means that if the robot spins more than 360 degrees between that time, you will have an unreliable rotational reading.
Contact
If you got questions, email me at xchenbox@gmail.com or alternatively, find us on Facebook https://www.facebook.com/future14473
We don't have a discord (yet). If there's any problems or ambiguity, shoot me an email
Gallery
hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!hi!
About
Complete game element detector made with OpenCV for the 2019 FIRST Tech Challenge game.
Resources
Readme
Contributing
Contributing
Activity
Custom properties
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages