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Object detection with tensorflow

Here are some of my experiments with image object detection and more specifically person detection for rpicalarm project. Most use tensorflow which is still a pain (as of June 2018) to install on raspberry pi 3 raspbian distribution for the more recent versions.

Accuracy-wise I got better results with yolov3, darknet cnn and coco dataset.

Pre-requisites

The examples have been tested with tensorflow 1.8.X running on CPU To install

pip3 install tensorflow

And the dependencies

pip3 install -r requirements.txt

darknet (53 layers) model + yolov3 detection + coco dataset

Overview

See the article Implementing Yolov3 using tensorflow slim and the associated github repository tensorflow-yolov3

Note that on a 2.7 Ghz intel bi-processor machine: it takes 1.8s average to analyze a photo of 640x480 pixels. Memory consumed is around 1.2 Gbytes

Running with weight

Download the weights:

wget https://pjreddie.com/media/files/yolov3.weights

Put your source images in the images folder. Result will be written in the results folder

cd yolov3
python3 object_detector.py

Running with a saved model checkpoint

Put your source images in the images folder. Result will be written in the results folder

Manually download the zipped model into the yolov3 folder https://drive.google.com/uc?export=download&confirm=YmSR&id=1uMRe0Z3x4lp3tMutH9ZrL43VS6B1UUa_

cd yolov3
unzip yolov3-coco.zip
python3 object_detector_saved_model.py

darknet model + yolov2 detection

object_detector.py uses a pre-trained yolov2 model. The .pb and .meta files have been generated using darkflow

Current implementation only detects the following objects:

  • aeroplane
  • bicycle
  • bird
  • boat
  • bottle
  • bus
  • car
  • cat
  • chair
  • cow
  • diningtable
  • dog
  • horse
  • motorbike
  • person
  • pottedplant
  • sheep
  • sofa
  • train
  • tvmonitor

Put your source images in the images folder. Result will be written in the results folder

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Deep learning experiments

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

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Repository files navigation
Object detection with tensorflow
Here are some of my experiments with image object detection and more specifically person detection for rpicalarm project.
Most use tensorflow which is still a pain (as of June 2018) to install on raspberry pi 3 raspbian distribution for the more recent versions.
Accuracy-wise I got better results with yolov3, darknet cnn and coco dataset.
Pre-requisites
The examples have been tested with tensorflow 1.8.X running on CPU
To install
pip3 install tensorflow
And the dependencies
pip3 install -r requirements.txt

darknet (53 layers) model + yolov3 detection + coco dataset
Overview
See the article Implementing Yolov3 using tensorflow slim and the associated github repository
tensorflow-yolov3
Note that on a 2.7 Ghz intel bi-processor machine: it takes 1.8s average to analyze a photo of 640x480 pixels.
Memory consumed is around 1.2 Gbytes
Running with weight
Download the weights:
wget https://pjreddie.com/media/files/yolov3.weights

Put your source images in the images folder. Result will be written in the results folder
cd yolov3
python3 object_detector.py

Running with a saved model checkpoint
Put your source images in the images folder. Result will be written in the results folder
Manually download the zipped model into the yolov3 folder https://drive.google.com/uc?export=download&confirm=YmSR&id=1uMRe0Z3x4lp3tMutH9ZrL43VS6B1UUa_
cd yolov3
unzip yolov3-coco.zip
python3 object_detector_saved_model.py

darknet model + yolov2 detection
object_detector.py uses a pre-trained yolov2 model. The .pb and .meta files have been generated using darkflow
Current implementation only detects the following objects:
  • aeroplane
  • bicycle
  • bird
  • boat
  • bottle
  • bus
  • car
  • cat
  • chair
  • cow
  • diningtable
  • dog
  • horse
  • motorbike
  • person
  • pottedplant
  • sheep
  • sofa
  • train
  • tvmonitor
Put your source images in the images folder. Result will be written in the results folder
About
Deep learning experiments
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Object detection with tensorflow
Here are some of my experiments with image object detection and more specifically person detection for rpicalarm project.
Most use tensorflow which is still a pain (as of June 2018) to install on raspberry pi 3 raspbian distribution for the more recent versions.
Accuracy-wise I got better results with yolov3, darknet cnn and coco dataset.
Pre-requisites
The examples have been tested with tensorflow 1.8.X running on CPU
To install
pip3 install tensorflow
And the dependencies
pip3 install -r requirements.txt

darknet (53 layers) model + yolov3 detection + coco dataset
Overview
See the article Implementing Yolov3 using tensorflow slim and the associated github repository
tensorflow-yolov3
Note that on a 2.7 Ghz intel bi-processor machine: it takes 1.8s average to analyze a photo of 640x480 pixels.
Memory consumed is around 1.2 Gbytes
Running with weight
Download the weights:
wget https://pjreddie.com/media/files/yolov3.weights

Put your source images in the images folder. Result will be written in the results folder
cd yolov3
python3 object_detector.py

Running with a saved model checkpoint
Put your source images in the images folder. Result will be written in the results folder
Manually download the zipped model into the yolov3 folder https://drive.google.com/uc?export=download&confirm=YmSR&id=1uMRe0Z3x4lp3tMutH9ZrL43VS6B1UUa_
cd yolov3
unzip yolov3-coco.zip
python3 object_detector_saved_model.py

darknet model + yolov2 detection
object_detector.py uses a pre-trained yolov2 model. The .pb and .meta files have been generated using darkflow
Current implementation only detects the following objects:
  • aeroplane
  • bicycle
  • bird
  • boat
  • bottle
  • bus
  • car
  • cat
  • chair
  • cow
  • diningtable
  • dog
  • horse
  • motorbike
  • person
  • pottedplant
  • sheep
  • sofa
  • train
  • tvmonitor
Put your source images in the images folder. Result will be written in the results folder
About
Deep learning experiments
Resources
Readme
Activity
Stars
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Watchers
1 watching
Forks
0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Object detection with tensorflow
Here are some of my experiments with image object detection and more specifically person detection for rpicalarm project.
Most use tensorflow which is still a pain (as of June 2018) to install on raspberry pi 3 raspbian distribution for the more recent versions.
Accuracy-wise I got better results with yolov3, darknet cnn and coco dataset.
Pre-requisites
The examples have been tested with tensorflow 1.8.X running on CPU
To install
pip3 install tensorflow
And the dependencies
pip3 install -r requirements.txt

darknet (53 layers) model + yolov3 detection + coco dataset
Overview
See the article Implementing Yolov3 using tensorflow slim and the associated github repository
tensorflow-yolov3
Note that on a 2.7 Ghz intel bi-processor machine: it takes 1.8s average to analyze a photo of 640x480 pixels.
Memory consumed is around 1.2 Gbytes
Running with weight
Download the weights:
wget https://pjreddie.com/media/files/yolov3.weights

Put your source images in the images folder. Result will be written in the results folder
cd yolov3
python3 object_detector.py

Running with a saved model checkpoint
Put your source images in the images folder. Result will be written in the results folder
Manually download the zipped model into the yolov3 folder https://drive.google.com/uc?export=download&confirm=YmSR&id=1uMRe0Z3x4lp3tMutH9ZrL43VS6B1UUa_
cd yolov3
unzip yolov3-coco.zip
python3 object_detector_saved_model.py

darknet model + yolov2 detection
object_detector.py uses a pre-trained yolov2 model. The .pb and .meta files have been generated using darkflow
Current implementation only detects the following objects:
  • aeroplane
  • bicycle
  • bird
  • boat
  • bottle
  • bus
  • car
  • cat
  • chair
  • cow
  • diningtable
  • dog
  • horse
  • motorbike
  • person
  • pottedplant
  • sheep
  • sofa
  • train
  • tvmonitor
Put your source images in the images folder. Result will be written in the results folder
About
Deep learning experiments
Resources
Readme
Activity
Stars
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Watchers
1 watching
Forks
0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Repository files navigation
Object detection with tensorflow
Here are some of my experiments with image object detection and more specifically person detection for rpicalarm project.
Most use tensorflow which is still a pain (as of June 2018) to install on raspberry pi 3 raspbian distribution for the more recent versions.
Accuracy-wise I got better results with yolov3, darknet cnn and coco dataset.
Pre-requisites
The examples have been tested with tensorflow 1.8.X running on CPU
To install
pip3 install tensorflow
And the dependencies
pip3 install -r requirements.txt

darknet (53 layers) model + yolov3 detection + coco dataset
Overview
See the article Implementing Yolov3 using tensorflow slim and the associated github repository
tensorflow-yolov3
Note that on a 2.7 Ghz intel bi-processor machine: it takes 1.8s average to analyze a photo of 640x480 pixels.
Memory consumed is around 1.2 Gbytes
Running with weight
Download the weights:
wget https://pjreddie.com/media/files/yolov3.weights

Put your source images in the images folder. Result will be written in the results folder
cd yolov3
python3 object_detector.py

Running with a saved model checkpoint
Put your source images in the images folder. Result will be written in the results folder
Manually download the zipped model into the yolov3 folder https://drive.google.com/uc?export=download&confirm=YmSR&id=1uMRe0Z3x4lp3tMutH9ZrL43VS6B1UUa_
cd yolov3
unzip yolov3-coco.zip
python3 object_detector_saved_model.py

darknet model + yolov2 detection
object_detector.py uses a pre-trained yolov2 model. The .pb and .meta files have been generated using darkflow
Current implementation only detects the following objects:
  • aeroplane
  • bicycle
  • bird
  • boat
  • bottle
  • bus
  • car
  • cat
  • chair
  • cow
  • diningtable
  • dog
  • horse
  • motorbike
  • person
  • pottedplant
  • sheep
  • sofa
  • train
  • tvmonitor
Put your source images in the images folder. Result will be written in the results folder
About
Deep learning experiments
Resources
Readme
Activity
Stars
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Watchers
1 watching
Forks
0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
Object detection with tensorflow
Here are some of my experiments with image object detection and more specifically person detection for rpicalarm project.
Most use tensorflow which is still a pain (as of June 2018) to install on raspberry pi 3 raspbian distribution for the more recent versions.
Accuracy-wise I got better results with yolov3, darknet cnn and coco dataset.
Pre-requisites
The examples have been tested with tensorflow 1.8.X running on CPU
To install
pip3 install tensorflow
And the dependencies
pip3 install -r requirements.txt

darknet (53 layers) model + yolov3 detection + coco dataset
Overview
See the article Implementing Yolov3 using tensorflow slim and the associated github repository
tensorflow-yolov3
Note that on a 2.7 Ghz intel bi-processor machine: it takes 1.8s average to analyze a photo of 640x480 pixels.
Memory consumed is around 1.2 Gbytes
Running with weight
Download the weights:
wget https://pjreddie.com/media/files/yolov3.weights

Put your source images in the images folder. Result will be written in the results folder
cd yolov3
python3 object_detector.py

Running with a saved model checkpoint
Put your source images in the images folder. Result will be written in the results folder
Manually download the zipped model into the yolov3 folder https://drive.google.com/uc?export=download&confirm=YmSR&id=1uMRe0Z3x4lp3tMutH9ZrL43VS6B1UUa_
cd yolov3
unzip yolov3-coco.zip
python3 object_detector_saved_model.py

darknet model + yolov2 detection
object_detector.py uses a pre-trained yolov2 model. The .pb and .meta files have been generated using darkflow
Current implementation only detects the following objects:
  • aeroplane
  • bicycle
  • bird
  • boat
  • bottle
  • bus
  • car
  • cat
  • chair
  • cow
  • diningtable
  • dog
  • horse
  • motorbike
  • person
  • pottedplant
  • sheep
  • sofa
  • train
  • tvmonitor
Put your source images in the images folder. Result will be written in the results folder
About
Deep learning experiments
Resources
Readme
Activity
Stars
1 star
Watchers
1 watching
Forks
0 forks
Report repository
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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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Repository files navigation
Object detection with tensorflow
Here are some of my experiments with image object detection and more specifically person detection for rpicalarm project.
Most use tensorflow which is still a pain (as of June 2018) to install on raspberry pi 3 raspbian distribution for the more recent versions.
Accuracy-wise I got better results with yolov3, darknet cnn and coco dataset.
Pre-requisites
The examples have been tested with tensorflow 1.8.X running on CPU
To install
pip3 install tensorflow
And the dependencies
pip3 install -r requirements.txt

darknet (53 layers) model + yolov3 detection + coco dataset
Overview
See the article Implementing Yolov3 using tensorflow slim and the associated github repository
tensorflow-yolov3
Note that on a 2.7 Ghz intel bi-processor machine: it takes 1.8s average to analyze a photo of 640x480 pixels.
Memory consumed is around 1.2 Gbytes
Running with weight
Download the weights:
wget https://pjreddie.com/media/files/yolov3.weights

Put your source images in the images folder. Result will be written in the results folder
cd yolov3
python3 object_detector.py

Running with a saved model checkpoint
Put your source images in the images folder. Result will be written in the results folder
Manually download the zipped model into the yolov3 folder https://drive.google.com/uc?export=download&confirm=YmSR&id=1uMRe0Z3x4lp3tMutH9ZrL43VS6B1UUa_
cd yolov3
unzip yolov3-coco.zip
python3 object_detector_saved_model.py

darknet model + yolov2 detection
object_detector.py uses a pre-trained yolov2 model. The .pb and .meta files have been generated using darkflow
Current implementation only detects the following objects:
  • aeroplane
  • bicycle
  • bird
  • boat
  • bottle
  • bus
  • car
  • cat
  • chair
  • cow
  • diningtable
  • dog
  • horse
  • motorbike
  • person
  • pottedplant
  • sheep
  • sofa
  • train
  • tvmonitor
Put your source images in the images folder. Result will be written in the results folder
About
Deep learning experiments
Resources
Readme
Activity
Stars
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Object detection with tensorflow
Here are some of my experiments with image object detection and more specifically person detection for rpicalarm project.
Most use tensorflow which is still a pain (as of June 2018) to install on raspberry pi 3 raspbian distribution for the more recent versions.
Accuracy-wise I got better results with yolov3, darknet cnn and coco dataset.
Pre-requisites
The examples have been tested with tensorflow 1.8.X running on CPU
To install
pip3 install tensorflow
And the dependencies
pip3 install -r requirements.txt

darknet (53 layers) model + yolov3 detection + coco dataset
Overview
See the article Implementing Yolov3 using tensorflow slim and the associated github repository
tensorflow-yolov3
Note that on a 2.7 Ghz intel bi-processor machine: it takes 1.8s average to analyze a photo of 640x480 pixels.
Memory consumed is around 1.2 Gbytes
Running with weight
Download the weights:
wget https://pjreddie.com/media/files/yolov3.weights

Put your source images in the images folder. Result will be written in the results folder
cd yolov3
python3 object_detector.py

Running with a saved model checkpoint
Put your source images in the images folder. Result will be written in the results folder
Manually download the zipped model into the yolov3 folder https://drive.google.com/uc?export=download&confirm=YmSR&id=1uMRe0Z3x4lp3tMutH9ZrL43VS6B1UUa_
cd yolov3
unzip yolov3-coco.zip
python3 object_detector_saved_model.py

darknet model + yolov2 detection
object_detector.py uses a pre-trained yolov2 model. The .pb and .meta files have been generated using darkflow
Current implementation only detects the following objects:
  • aeroplane
  • bicycle
  • bird
  • boat
  • bottle
  • bus
  • car
  • cat
  • chair
  • cow
  • diningtable
  • dog
  • horse
  • motorbike
  • person
  • pottedplant
  • sheep
  • sofa
  • train
  • tvmonitor
Put your source images in the images folder. Result will be written in the results folder
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