BranchesTags

Repository files navigation

YOLOpython -- Please reference to VGG , this project is closed

Introduction

This is a Keras implementation of the YOLO:Real-Time Object Detection

 YOLO paper please reference to http://pjreddie.com/darknet/yolo/
also reference http://guanghan.info/blog/en/my-works/train-yolo/

Usage

Training

Python src/yolo.py train [pretrained_Keras_model.h5]

If saved_Keras_model.h5 option used, it will read in pretrained model and do incrmentally training Otherwise train from scratch

Predict

Python src/yolo.py test pretrained_Keras_model.h5 predicted_image.jpg

Demo video (need install opencv)

Python src/yolo.py demo_video pretrained_Keras_model.h5 videofile

Need to know about code

Code explanation:

workingcfg.txt : put your cfg file in this file

yolo.py : main function

yolodata.py : read train_data/train.txt, then generate resized X_train and proper Y_train numpy matrix

ddd.py : create custom YOLO loss function (the equation reference to http://pjreddie.com/media/files/papers/yolo_1.pdf)

kerasmodel.py : create Keras model according to cfg file

parse.py : parse cfg file

cfgconst.py : read workingcfg.txt then call parse.py to parse the correcponding cfg file

utils.py, darknet.py : misc

Running enviroment

Keras with tensorflow backend

train_data/train.txt : contain training image data file list

train_data/[images/labels] folder : contain training image files and corresponding label text files

Loss function code explanation

Truth table format arrangement as follow for one training sample : truth atble format is [confidence,x,y,w,h,classes], and [confidence,x,y,w,h,classes] each element size is gridcell^2

Given a example : gridcell =7, classes =2, then truth table dimension is (7^2) * (5 + 2) = 343. In this truth vector, index 048 represent confidence, 4997 represent x, 98146 represent y, 147195 represent w, 196244 represent h, 245293 represent class 0, 294~342 represent class 1

The reason for the trurh table arrangement is for YOLO custom loss implementation.

OPENCV enviroment setup (ubuntu 16.04 + CUDA 8.0)

Please reference to http://www.pyimagesearch.com/2015/06/22/install-opencv-3-0-and-python-2-7-on-ubuntu/

follow its steps will fail if you use CUDA 8.0 , need to do some modification. First following the STEP 1 ~ 9 , then at STEP 10, use follows instead

 1.cd ~
2.git clone https://github.com/daveselinger/opencv
3.cd opencv
4.git checkout 3.1.0-with-cuda8
at opencv_contrib part also use "git checkout 3.1.0"
please reference to https://github.com/opencv/opencv/issues/6677

modify cmake command to add "-D WITH_GTK=ON"

	cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_C_EXAMPLES=OFF \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
-D WITH_GTK=ON \
-D BUILD_EXAMPLES=ON ..

then follow 3.1.0 setup is OK.

Be caution at STEP 11, "ln -s /usr/local/lib/python2.7/site-packages/cv2.so cv2.so" may not work because of the wrong cv2.so path. If something wrong, check out the path where your cv2.so be installed. In my case cv2.so at /usr/local/lib/python2.7/dist-packages/cv2/cv2.so

About

create YOLO network via keras / python (closed, move to VGG)

Resources

Readme
Activity

Stars

5 stars

Watchers

2 watching

Forks

2 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
YOLOpython -- Please reference to VGG , this project is closed
Introduction
This is a Keras implementation of the YOLO:Real-Time Object Detection
 YOLO paper please reference to http://pjreddie.com/darknet/yolo/
also reference http://guanghan.info/blog/en/my-works/train-yolo/

Usage
Training
Python src/yolo.py train [pretrained_Keras_model.h5]

If saved_Keras_model.h5 option used, it will read in pretrained model and do incrmentally training
Otherwise train from scratch
Predict
Python src/yolo.py test pretrained_Keras_model.h5 predicted_image.jpg

Demo video (need install opencv)
Python src/yolo.py demo_video pretrained_Keras_model.h5 videofile

Need to know about code
Code explanation:
workingcfg.txt : put your cfg file in this file
yolo.py : main function
yolodata.py : read train_data/train.txt, then generate resized X_train and proper Y_train numpy matrix
ddd.py : create custom YOLO loss function (the equation reference to http://pjreddie.com/media/files/papers/yolo_1.pdf)
kerasmodel.py : create Keras model according to cfg file
parse.py : parse cfg file
cfgconst.py : read workingcfg.txt then call parse.py to parse the correcponding cfg file
utils.py, darknet.py : misc
Running enviroment
Keras with tensorflow backend
train_data/train.txt : contain training image data file list
train_data/[images/labels] folder : contain training image files and corresponding label text files
Loss function code explanation
Truth table format arrangement as follow
for one training sample : truth atble format is [confidence,x,y,w,h,classes], and [confidence,x,y,w,h,classes] each element
size is gridcell^2
Given a example : gridcell =7, classes =2, then truth table dimension is (7^2) * (5 + 2) = 343.
In this truth vector, index 048 represent confidence, 4997 represent x, 98146 represent y,
147195 represent w, 196244 represent h, 245293 represent class 0, 294~342 represent class 1
The reason for the trurh table arrangement is for YOLO custom loss implementation.
OPENCV enviroment setup (ubuntu 16.04 + CUDA 8.0)
Please reference to http://www.pyimagesearch.com/2015/06/22/install-opencv-3-0-and-python-2-7-on-ubuntu/
follow its steps will fail if you use CUDA 8.0 , need to do some modification.
First following the STEP 1 ~ 9 , then at STEP 10, use follows instead
 1.cd ~
2.git clone https://github.com/daveselinger/opencv
3.cd opencv
4.git checkout 3.1.0-with-cuda8
at opencv_contrib part also use "git checkout 3.1.0"
please reference to https://github.com/opencv/opencv/issues/6677

modify cmake command to add "-D WITH_GTK=ON"
	cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_C_EXAMPLES=OFF \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
-D WITH_GTK=ON \
-D BUILD_EXAMPLES=ON ..

then follow 3.1.0 setup is OK.
Be caution at STEP 11, "ln -s /usr/local/lib/python2.7/site-packages/cv2.so cv2.so" may not work
because of the wrong cv2.so path. If something wrong, check out the path where your cv2.so be installed.
In my case cv2.so at /usr/local/lib/python2.7/dist-packages/cv2/cv2.so
About
create YOLO network via keras / python (closed, move to VGG)
Resources
Readme
Activity
Stars
5 stars
Watchers
2 watching
Forks
2 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
YOLOpython -- Please reference to VGG , this project is closed
Introduction
This is a Keras implementation of the YOLO:Real-Time Object Detection
 YOLO paper please reference to http://pjreddie.com/darknet/yolo/
also reference http://guanghan.info/blog/en/my-works/train-yolo/

Usage
Training
Python src/yolo.py train [pretrained_Keras_model.h5]

If saved_Keras_model.h5 option used, it will read in pretrained model and do incrmentally training
Otherwise train from scratch
Predict
Python src/yolo.py test pretrained_Keras_model.h5 predicted_image.jpg

Demo video (need install opencv)
Python src/yolo.py demo_video pretrained_Keras_model.h5 videofile

Need to know about code
Code explanation:
workingcfg.txt : put your cfg file in this file
yolo.py : main function
yolodata.py : read train_data/train.txt, then generate resized X_train and proper Y_train numpy matrix
ddd.py : create custom YOLO loss function (the equation reference to http://pjreddie.com/media/files/papers/yolo_1.pdf)
kerasmodel.py : create Keras model according to cfg file
parse.py : parse cfg file
cfgconst.py : read workingcfg.txt then call parse.py to parse the correcponding cfg file
utils.py, darknet.py : misc
Running enviroment
Keras with tensorflow backend
train_data/train.txt : contain training image data file list
train_data/[images/labels] folder : contain training image files and corresponding label text files
Loss function code explanation
Truth table format arrangement as follow
for one training sample : truth atble format is [confidence,x,y,w,h,classes], and [confidence,x,y,w,h,classes] each element
size is gridcell^2
Given a example : gridcell =7, classes =2, then truth table dimension is (7^2) * (5 + 2) = 343.
In this truth vector, index 048 represent confidence, 4997 represent x, 98146 represent y,
147195 represent w, 196244 represent h, 245293 represent class 0, 294~342 represent class 1
The reason for the trurh table arrangement is for YOLO custom loss implementation.
OPENCV enviroment setup (ubuntu 16.04 + CUDA 8.0)
Please reference to http://www.pyimagesearch.com/2015/06/22/install-opencv-3-0-and-python-2-7-on-ubuntu/
follow its steps will fail if you use CUDA 8.0 , need to do some modification.
First following the STEP 1 ~ 9 , then at STEP 10, use follows instead
 1.cd ~
2.git clone https://github.com/daveselinger/opencv
3.cd opencv
4.git checkout 3.1.0-with-cuda8
at opencv_contrib part also use "git checkout 3.1.0"
please reference to https://github.com/opencv/opencv/issues/6677

modify cmake command to add "-D WITH_GTK=ON"
	cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_C_EXAMPLES=OFF \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
-D WITH_GTK=ON \
-D BUILD_EXAMPLES=ON ..

then follow 3.1.0 setup is OK.
Be caution at STEP 11, "ln -s /usr/local/lib/python2.7/site-packages/cv2.so cv2.so" may not work
because of the wrong cv2.so path. If something wrong, check out the path where your cv2.so be installed.
In my case cv2.so at /usr/local/lib/python2.7/dist-packages/cv2/cv2.so
About
create YOLO network via keras / python (closed, move to VGG)
Resources
Readme
Activity
Stars
5 stars
Watchers
2 watching
Forks
2 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
YOLOpython -- Please reference to VGG , this project is closed
Introduction
This is a Keras implementation of the YOLO:Real-Time Object Detection
 YOLO paper please reference to http://pjreddie.com/darknet/yolo/
also reference http://guanghan.info/blog/en/my-works/train-yolo/

Usage
Training
Python src/yolo.py train [pretrained_Keras_model.h5]

If saved_Keras_model.h5 option used, it will read in pretrained model and do incrmentally training
Otherwise train from scratch
Predict
Python src/yolo.py test pretrained_Keras_model.h5 predicted_image.jpg

Demo video (need install opencv)
Python src/yolo.py demo_video pretrained_Keras_model.h5 videofile

Need to know about code
Code explanation:
workingcfg.txt : put your cfg file in this file
yolo.py : main function
yolodata.py : read train_data/train.txt, then generate resized X_train and proper Y_train numpy matrix
ddd.py : create custom YOLO loss function (the equation reference to http://pjreddie.com/media/files/papers/yolo_1.pdf)
kerasmodel.py : create Keras model according to cfg file
parse.py : parse cfg file
cfgconst.py : read workingcfg.txt then call parse.py to parse the correcponding cfg file
utils.py, darknet.py : misc
Running enviroment
Keras with tensorflow backend
train_data/train.txt : contain training image data file list
train_data/[images/labels] folder : contain training image files and corresponding label text files
Loss function code explanation
Truth table format arrangement as follow
for one training sample : truth atble format is [confidence,x,y,w,h,classes], and [confidence,x,y,w,h,classes] each element
size is gridcell^2
Given a example : gridcell =7, classes =2, then truth table dimension is (7^2) * (5 + 2) = 343.
In this truth vector, index 048 represent confidence, 4997 represent x, 98146 represent y,
147195 represent w, 196244 represent h, 245293 represent class 0, 294~342 represent class 1
The reason for the trurh table arrangement is for YOLO custom loss implementation.
OPENCV enviroment setup (ubuntu 16.04 + CUDA 8.0)
Please reference to http://www.pyimagesearch.com/2015/06/22/install-opencv-3-0-and-python-2-7-on-ubuntu/
follow its steps will fail if you use CUDA 8.0 , need to do some modification.
First following the STEP 1 ~ 9 , then at STEP 10, use follows instead
 1.cd ~
2.git clone https://github.com/daveselinger/opencv
3.cd opencv
4.git checkout 3.1.0-with-cuda8
at opencv_contrib part also use "git checkout 3.1.0"
please reference to https://github.com/opencv/opencv/issues/6677

modify cmake command to add "-D WITH_GTK=ON"
	cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_C_EXAMPLES=OFF \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
-D WITH_GTK=ON \
-D BUILD_EXAMPLES=ON ..

then follow 3.1.0 setup is OK.
Be caution at STEP 11, "ln -s /usr/local/lib/python2.7/site-packages/cv2.so cv2.so" may not work
because of the wrong cv2.so path. If something wrong, check out the path where your cv2.so be installed.
In my case cv2.so at /usr/local/lib/python2.7/dist-packages/cv2/cv2.so
About
create YOLO network via keras / python (closed, move to VGG)
Resources
Readme
Activity
Stars
5 stars
Watchers
2 watching
Forks
2 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
YOLOpython -- Please reference to VGG , this project is closed
Introduction
This is a Keras implementation of the YOLO:Real-Time Object Detection
 YOLO paper please reference to http://pjreddie.com/darknet/yolo/
also reference http://guanghan.info/blog/en/my-works/train-yolo/

Usage
Training
Python src/yolo.py train [pretrained_Keras_model.h5]

If saved_Keras_model.h5 option used, it will read in pretrained model and do incrmentally training
Otherwise train from scratch
Predict
Python src/yolo.py test pretrained_Keras_model.h5 predicted_image.jpg

Demo video (need install opencv)
Python src/yolo.py demo_video pretrained_Keras_model.h5 videofile

Need to know about code
Code explanation:
workingcfg.txt : put your cfg file in this file
yolo.py : main function
yolodata.py : read train_data/train.txt, then generate resized X_train and proper Y_train numpy matrix
ddd.py : create custom YOLO loss function (the equation reference to http://pjreddie.com/media/files/papers/yolo_1.pdf)
kerasmodel.py : create Keras model according to cfg file
parse.py : parse cfg file
cfgconst.py : read workingcfg.txt then call parse.py to parse the correcponding cfg file
utils.py, darknet.py : misc
Running enviroment
Keras with tensorflow backend
train_data/train.txt : contain training image data file list
train_data/[images/labels] folder : contain training image files and corresponding label text files
Loss function code explanation
Truth table format arrangement as follow
for one training sample : truth atble format is [confidence,x,y,w,h,classes], and [confidence,x,y,w,h,classes] each element
size is gridcell^2
Given a example : gridcell =7, classes =2, then truth table dimension is (7^2) * (5 + 2) = 343.
In this truth vector, index 048 represent confidence, 4997 represent x, 98146 represent y,
147195 represent w, 196244 represent h, 245293 represent class 0, 294~342 represent class 1
The reason for the trurh table arrangement is for YOLO custom loss implementation.
OPENCV enviroment setup (ubuntu 16.04 + CUDA 8.0)
Please reference to http://www.pyimagesearch.com/2015/06/22/install-opencv-3-0-and-python-2-7-on-ubuntu/
follow its steps will fail if you use CUDA 8.0 , need to do some modification.
First following the STEP 1 ~ 9 , then at STEP 10, use follows instead
 1.cd ~
2.git clone https://github.com/daveselinger/opencv
3.cd opencv
4.git checkout 3.1.0-with-cuda8
at opencv_contrib part also use "git checkout 3.1.0"
please reference to https://github.com/opencv/opencv/issues/6677

modify cmake command to add "-D WITH_GTK=ON"
	cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_C_EXAMPLES=OFF \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
-D WITH_GTK=ON \
-D BUILD_EXAMPLES=ON ..

then follow 3.1.0 setup is OK.
Be caution at STEP 11, "ln -s /usr/local/lib/python2.7/site-packages/cv2.so cv2.so" may not work
because of the wrong cv2.so path. If something wrong, check out the path where your cv2.so be installed.
In my case cv2.so at /usr/local/lib/python2.7/dist-packages/cv2/cv2.so
About
create YOLO network via keras / python (closed, move to VGG)
Resources
Readme
Activity
Stars
5 stars
Watchers
2 watching
Forks
2 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
YOLOpython -- Please reference to VGG , this project is closed
Introduction
This is a Keras implementation of the YOLO:Real-Time Object Detection
 YOLO paper please reference to http://pjreddie.com/darknet/yolo/
also reference http://guanghan.info/blog/en/my-works/train-yolo/

Usage
Training
Python src/yolo.py train [pretrained_Keras_model.h5]

If saved_Keras_model.h5 option used, it will read in pretrained model and do incrmentally training
Otherwise train from scratch
Predict
Python src/yolo.py test pretrained_Keras_model.h5 predicted_image.jpg

Demo video (need install opencv)
Python src/yolo.py demo_video pretrained_Keras_model.h5 videofile

Need to know about code
Code explanation:
workingcfg.txt : put your cfg file in this file
yolo.py : main function
yolodata.py : read train_data/train.txt, then generate resized X_train and proper Y_train numpy matrix
ddd.py : create custom YOLO loss function (the equation reference to http://pjreddie.com/media/files/papers/yolo_1.pdf)
kerasmodel.py : create Keras model according to cfg file
parse.py : parse cfg file
cfgconst.py : read workingcfg.txt then call parse.py to parse the correcponding cfg file
utils.py, darknet.py : misc
Running enviroment
Keras with tensorflow backend
train_data/train.txt : contain training image data file list
train_data/[images/labels] folder : contain training image files and corresponding label text files
Loss function code explanation
Truth table format arrangement as follow
for one training sample : truth atble format is [confidence,x,y,w,h,classes], and [confidence,x,y,w,h,classes] each element
size is gridcell^2
Given a example : gridcell =7, classes =2, then truth table dimension is (7^2) * (5 + 2) = 343.
In this truth vector, index 048 represent confidence, 4997 represent x, 98146 represent y,
147195 represent w, 196244 represent h, 245293 represent class 0, 294~342 represent class 1
The reason for the trurh table arrangement is for YOLO custom loss implementation.
OPENCV enviroment setup (ubuntu 16.04 + CUDA 8.0)
Please reference to http://www.pyimagesearch.com/2015/06/22/install-opencv-3-0-and-python-2-7-on-ubuntu/
follow its steps will fail if you use CUDA 8.0 , need to do some modification.
First following the STEP 1 ~ 9 , then at STEP 10, use follows instead
 1.cd ~
2.git clone https://github.com/daveselinger/opencv
3.cd opencv
4.git checkout 3.1.0-with-cuda8
at opencv_contrib part also use "git checkout 3.1.0"
please reference to https://github.com/opencv/opencv/issues/6677

modify cmake command to add "-D WITH_GTK=ON"
	cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_C_EXAMPLES=OFF \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
-D WITH_GTK=ON \
-D BUILD_EXAMPLES=ON ..

then follow 3.1.0 setup is OK.
Be caution at STEP 11, "ln -s /usr/local/lib/python2.7/site-packages/cv2.so cv2.so" may not work
because of the wrong cv2.so path. If something wrong, check out the path where your cv2.so be installed.
In my case cv2.so at /usr/local/lib/python2.7/dist-packages/cv2/cv2.so
About
create YOLO network via keras / python (closed, move to VGG)
Resources
Readme
Activity
Stars
5 stars
Watchers
2 watching
Forks
2 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
YOLOpython -- Please reference to VGG , this project is closed
Introduction
This is a Keras implementation of the YOLO:Real-Time Object Detection
 YOLO paper please reference to http://pjreddie.com/darknet/yolo/
also reference http://guanghan.info/blog/en/my-works/train-yolo/

Usage
Training
Python src/yolo.py train [pretrained_Keras_model.h5]

If saved_Keras_model.h5 option used, it will read in pretrained model and do incrmentally training
Otherwise train from scratch
Predict
Python src/yolo.py test pretrained_Keras_model.h5 predicted_image.jpg

Demo video (need install opencv)
Python src/yolo.py demo_video pretrained_Keras_model.h5 videofile

Need to know about code
Code explanation:
workingcfg.txt : put your cfg file in this file
yolo.py : main function
yolodata.py : read train_data/train.txt, then generate resized X_train and proper Y_train numpy matrix
ddd.py : create custom YOLO loss function (the equation reference to http://pjreddie.com/media/files/papers/yolo_1.pdf)
kerasmodel.py : create Keras model according to cfg file
parse.py : parse cfg file
cfgconst.py : read workingcfg.txt then call parse.py to parse the correcponding cfg file
utils.py, darknet.py : misc
Running enviroment
Keras with tensorflow backend
train_data/train.txt : contain training image data file list
train_data/[images/labels] folder : contain training image files and corresponding label text files
Loss function code explanation
Truth table format arrangement as follow
for one training sample : truth atble format is [confidence,x,y,w,h,classes], and [confidence,x,y,w,h,classes] each element
size is gridcell^2
Given a example : gridcell =7, classes =2, then truth table dimension is (7^2) * (5 + 2) = 343.
In this truth vector, index 048 represent confidence, 4997 represent x, 98146 represent y,
147195 represent w, 196244 represent h, 245293 represent class 0, 294~342 represent class 1
The reason for the trurh table arrangement is for YOLO custom loss implementation.
OPENCV enviroment setup (ubuntu 16.04 + CUDA 8.0)
Please reference to http://www.pyimagesearch.com/2015/06/22/install-opencv-3-0-and-python-2-7-on-ubuntu/
follow its steps will fail if you use CUDA 8.0 , need to do some modification.
First following the STEP 1 ~ 9 , then at STEP 10, use follows instead
 1.cd ~
2.git clone https://github.com/daveselinger/opencv
3.cd opencv
4.git checkout 3.1.0-with-cuda8
at opencv_contrib part also use "git checkout 3.1.0"
please reference to https://github.com/opencv/opencv/issues/6677

modify cmake command to add "-D WITH_GTK=ON"
	cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_C_EXAMPLES=OFF \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
-D WITH_GTK=ON \
-D BUILD_EXAMPLES=ON ..

then follow 3.1.0 setup is OK.
Be caution at STEP 11, "ln -s /usr/local/lib/python2.7/site-packages/cv2.so cv2.so" may not work
because of the wrong cv2.so path. If something wrong, check out the path where your cv2.so be installed.
In my case cv2.so at /usr/local/lib/python2.7/dist-packages/cv2/cv2.so
About
create YOLO network via keras / python (closed, move to VGG)
Resources
Readme
Activity
Stars
5 stars
Watchers
2 watching
Forks
2 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
YOLOpython -- Please reference to VGG , this project is closed
Introduction
This is a Keras implementation of the YOLO:Real-Time Object Detection
 YOLO paper please reference to http://pjreddie.com/darknet/yolo/
also reference http://guanghan.info/blog/en/my-works/train-yolo/

Usage
Training
Python src/yolo.py train [pretrained_Keras_model.h5]

If saved_Keras_model.h5 option used, it will read in pretrained model and do incrmentally training
Otherwise train from scratch
Predict
Python src/yolo.py test pretrained_Keras_model.h5 predicted_image.jpg

Demo video (need install opencv)
Python src/yolo.py demo_video pretrained_Keras_model.h5 videofile

Need to know about code
Code explanation:
workingcfg.txt : put your cfg file in this file
yolo.py : main function
yolodata.py : read train_data/train.txt, then generate resized X_train and proper Y_train numpy matrix
ddd.py : create custom YOLO loss function (the equation reference to http://pjreddie.com/media/files/papers/yolo_1.pdf)
kerasmodel.py : create Keras model according to cfg file
parse.py : parse cfg file
cfgconst.py : read workingcfg.txt then call parse.py to parse the correcponding cfg file
utils.py, darknet.py : misc
Running enviroment
Keras with tensorflow backend
train_data/train.txt : contain training image data file list
train_data/[images/labels] folder : contain training image files and corresponding label text files
Loss function code explanation
Truth table format arrangement as follow
for one training sample : truth atble format is [confidence,x,y,w,h,classes], and [confidence,x,y,w,h,classes] each element
size is gridcell^2
Given a example : gridcell =7, classes =2, then truth table dimension is (7^2) * (5 + 2) = 343.
In this truth vector, index 048 represent confidence, 4997 represent x, 98146 represent y,
147195 represent w, 196244 represent h, 245293 represent class 0, 294~342 represent class 1
The reason for the trurh table arrangement is for YOLO custom loss implementation.
OPENCV enviroment setup (ubuntu 16.04 + CUDA 8.0)
Please reference to http://www.pyimagesearch.com/2015/06/22/install-opencv-3-0-and-python-2-7-on-ubuntu/
follow its steps will fail if you use CUDA 8.0 , need to do some modification.
First following the STEP 1 ~ 9 , then at STEP 10, use follows instead
 1.cd ~
2.git clone https://github.com/daveselinger/opencv
3.cd opencv
4.git checkout 3.1.0-with-cuda8
at opencv_contrib part also use "git checkout 3.1.0"
please reference to https://github.com/opencv/opencv/issues/6677

modify cmake command to add "-D WITH_GTK=ON"
	cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_C_EXAMPLES=OFF \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \
-D WITH_GTK=ON \
-D BUILD_EXAMPLES=ON ..

then follow 3.1.0 setup is OK.
Be caution at STEP 11, "ln -s /usr/local/lib/python2.7/site-packages/cv2.so cv2.so" may not work
because of the wrong cv2.so path. If something wrong, check out the path where your cv2.so be installed.
In my case cv2.so at /usr/local/lib/python2.7/dist-packages/cv2/cv2.so
About
create YOLO network via keras / python (closed, move to VGG)
Resources
Readme
Activity
Stars
5 stars
Watchers
2 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages