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pygco

travis status

Python wrappers for GCO alpha-expansion and alpha-beta-swaps. These wrappers provide a high level interface for graph cut inference for multi-label problems.

See my blog for examples and comments: peekaboo-vision.blogspot.com

Installation

With Pip

  • Run pip install git+git://github.com/amueller/gco_python

For Linux

  • Download and install Cython (use your package manager).

  • run make

  • Run example.py for a simple example.

For Windows

  • Make sure Cython is installed (included in enthought Python distribution for example)

  • Download original source from http://vision.csd.uwo.ca/code/gco-v3.0.zip

  • Build gco with your compiler of choice. Create a dynamic library at libgco.so.

  • Adjust the path to gco in setup.py.

  • run python setup.py build.

  • run example.py for a simple example.

Troubleshooting

There have been some problems compiling gco (not my wrappers) using gcc4.7. Please install gcc-4.6 and adjust the call in Makefile accordingly.

Usage

GCO implements alpha expansion and alpha beta swaps using graphcuts. These can be used to efficiently find low energy configurations of certain energy functions. Note that from a probabilistic viewpoint, GCO works in log-space.

Note that all input arrays are assumed to be in int32. This means that float potentials must be rounded!

These algorithms can only deal with certain energies. Unfortunately I have not figured out yet how to convert C++ errors to Python. If an unknown error is raised, it probably means that you used an invalid energy function. Look at the gco README for details.

This package gives a high level interface to gco, providing the following functions:

cut_simple: Graph cut on a 2D grid using a global label affinity-matrix.

cut_VH: NOT DONE YET Graph cut on a 2D grid using a global label affinity-matrix and edge-weights. V contains the weights for vertical edges, H for horizontal ones.

cut_from_graph: Graph cut on an arbitrary graph with global label affinity-matrix.

cut_from_graph_weighted: NOT DONE YET Graph cut on an arbitrary graph with global label affinity-matrix and edgeweights.

See example.py and example_middlebury.py for examples and the gco README for more details.

About

Python wrappers for GCO alpha-expansion and alpha-beta-swaps

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Readme
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138 stars

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5 watching

Forks

67 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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pygco
travis status
Python wrappers for GCO alpha-expansion and alpha-beta-swaps.
These wrappers provide a high level interface for graph cut
inference for multi-label problems.
See my blog for examples and comments: peekaboo-vision.blogspot.com
Installation
With Pip
  • Run pip install git+git://github.com/amueller/gco_python
For Linux
  • Download and install Cython (use your package manager).
  • run make
  • Run example.py for a simple example.
For Windows
  • Make sure Cython is installed (included in enthought Python distribution for example)
  • Download original source from http://vision.csd.uwo.ca/code/gco-v3.0.zip
  • Build gco with your compiler of choice. Create a dynamic library at libgco.so.
  • Adjust the path to gco in setup.py.
  • run python setup.py build.
  • run example.py for a simple example.
Troubleshooting
There have been some problems compiling gco (not my wrappers) using gcc4.7.
Please install gcc-4.6 and adjust the call in Makefile accordingly.
Usage
GCO implements alpha expansion and alpha beta swaps using graphcuts.
These can be used to efficiently find low energy configurations of certain energy functions.
Note that from a probabilistic viewpoint, GCO works in log-space.
Note that all input arrays are assumed to be in int32.
This means that float potentials must be rounded!
These algorithms can only deal with certain energies. Unfortunately
I have not figured out yet how to convert C++ errors to Python. If an unknown
error is raised, it probably means that you used an invalid energy function.
Look at the gco README for details.
This package gives a high level interface to gco, providing the following functions:
cut_simple:
Graph cut on a 2D grid using a global label affinity-matrix.
cut_VH:
NOT DONE YET
Graph cut on a 2D grid using a global label affinity-matrix and edge-weights.
V contains the weights for vertical edges, H for horizontal ones.
cut_from_graph:
Graph cut on an arbitrary graph with global label affinity-matrix.
cut_from_graph_weighted:
NOT DONE YET
Graph cut on an arbitrary graph with global label affinity-matrix and
edgeweights.
See example.py and example_middlebury.py for examples and the gco README
for more details.
About
Python wrappers for GCO alpha-expansion and alpha-beta-swaps
Resources
Readme
Activity
Stars
138 stars
Watchers
5 watching
Forks
67 forks
Report repository
Releases
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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");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
pygco
travis status
Python wrappers for GCO alpha-expansion and alpha-beta-swaps.
These wrappers provide a high level interface for graph cut
inference for multi-label problems.
See my blog for examples and comments: peekaboo-vision.blogspot.com
Installation
With Pip
  • Run pip install git+git://github.com/amueller/gco_python
For Linux
  • Download and install Cython (use your package manager).
  • run make
  • Run example.py for a simple example.
For Windows
  • Make sure Cython is installed (included in enthought Python distribution for example)
  • Download original source from http://vision.csd.uwo.ca/code/gco-v3.0.zip
  • Build gco with your compiler of choice. Create a dynamic library at libgco.so.
  • Adjust the path to gco in setup.py.
  • run python setup.py build.
  • run example.py for a simple example.
Troubleshooting
There have been some problems compiling gco (not my wrappers) using gcc4.7.
Please install gcc-4.6 and adjust the call in Makefile accordingly.
Usage
GCO implements alpha expansion and alpha beta swaps using graphcuts.
These can be used to efficiently find low energy configurations of certain energy functions.
Note that from a probabilistic viewpoint, GCO works in log-space.
Note that all input arrays are assumed to be in int32.
This means that float potentials must be rounded!
These algorithms can only deal with certain energies. Unfortunately
I have not figured out yet how to convert C++ errors to Python. If an unknown
error is raised, it probably means that you used an invalid energy function.
Look at the gco README for details.
This package gives a high level interface to gco, providing the following functions:
cut_simple:
Graph cut on a 2D grid using a global label affinity-matrix.
cut_VH:
NOT DONE YET
Graph cut on a 2D grid using a global label affinity-matrix and edge-weights.
V contains the weights for vertical edges, H for horizontal ones.
cut_from_graph:
Graph cut on an arbitrary graph with global label affinity-matrix.
cut_from_graph_weighted:
NOT DONE YET
Graph cut on an arbitrary graph with global label affinity-matrix and
edgeweights.
See example.py and example_middlebury.py for examples and the gco README
for more details.
About
Python wrappers for GCO alpha-expansion and alpha-beta-swaps
Resources
Readme
Activity
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138 stars
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5 watching
Forks
67 forks
Report repository
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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('^' + ".*" + '

Skip to content
BranchesTags
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pygco
travis status
Python wrappers for GCO alpha-expansion and alpha-beta-swaps.
These wrappers provide a high level interface for graph cut
inference for multi-label problems.
See my blog for examples and comments: peekaboo-vision.blogspot.com
Installation
With Pip
  • Run pip install git+git://github.com/amueller/gco_python
For Linux
  • Download and install Cython (use your package manager).
  • run make
  • Run example.py for a simple example.
For Windows
  • Make sure Cython is installed (included in enthought Python distribution for example)
  • Download original source from http://vision.csd.uwo.ca/code/gco-v3.0.zip
  • Build gco with your compiler of choice. Create a dynamic library at libgco.so.
  • Adjust the path to gco in setup.py.
  • run python setup.py build.
  • run example.py for a simple example.
Troubleshooting
There have been some problems compiling gco (not my wrappers) using gcc4.7.
Please install gcc-4.6 and adjust the call in Makefile accordingly.
Usage
GCO implements alpha expansion and alpha beta swaps using graphcuts.
These can be used to efficiently find low energy configurations of certain energy functions.
Note that from a probabilistic viewpoint, GCO works in log-space.
Note that all input arrays are assumed to be in int32.
This means that float potentials must be rounded!
These algorithms can only deal with certain energies. Unfortunately
I have not figured out yet how to convert C++ errors to Python. If an unknown
error is raised, it probably means that you used an invalid energy function.
Look at the gco README for details.
This package gives a high level interface to gco, providing the following functions:
cut_simple:
Graph cut on a 2D grid using a global label affinity-matrix.
cut_VH:
NOT DONE YET
Graph cut on a 2D grid using a global label affinity-matrix and edge-weights.
V contains the weights for vertical edges, H for horizontal ones.
cut_from_graph:
Graph cut on an arbitrary graph with global label affinity-matrix.
cut_from_graph_weighted:
NOT DONE YET
Graph cut on an arbitrary graph with global label affinity-matrix and
edgeweights.
See example.py and example_middlebury.py for examples and the gco README
for more details.
About
Python wrappers for GCO alpha-expansion and alpha-beta-swaps
Resources
Readme
Activity
Stars
138 stars
Watchers
5 watching
Forks
67 forks
Report repository
Releases
Packages
Used by
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");
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} 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
pygco
travis status
Python wrappers for GCO alpha-expansion and alpha-beta-swaps.
These wrappers provide a high level interface for graph cut
inference for multi-label problems.
See my blog for examples and comments: peekaboo-vision.blogspot.com
Installation
With Pip
  • Run pip install git+git://github.com/amueller/gco_python
For Linux
  • Download and install Cython (use your package manager).
  • run make
  • Run example.py for a simple example.
For Windows
  • Make sure Cython is installed (included in enthought Python distribution for example)
  • Download original source from http://vision.csd.uwo.ca/code/gco-v3.0.zip
  • Build gco with your compiler of choice. Create a dynamic library at libgco.so.
  • Adjust the path to gco in setup.py.
  • run python setup.py build.
  • run example.py for a simple example.
Troubleshooting
There have been some problems compiling gco (not my wrappers) using gcc4.7.
Please install gcc-4.6 and adjust the call in Makefile accordingly.
Usage
GCO implements alpha expansion and alpha beta swaps using graphcuts.
These can be used to efficiently find low energy configurations of certain energy functions.
Note that from a probabilistic viewpoint, GCO works in log-space.
Note that all input arrays are assumed to be in int32.
This means that float potentials must be rounded!
These algorithms can only deal with certain energies. Unfortunately
I have not figured out yet how to convert C++ errors to Python. If an unknown
error is raised, it probably means that you used an invalid energy function.
Look at the gco README for details.
This package gives a high level interface to gco, providing the following functions:
cut_simple:
Graph cut on a 2D grid using a global label affinity-matrix.
cut_VH:
NOT DONE YET
Graph cut on a 2D grid using a global label affinity-matrix and edge-weights.
V contains the weights for vertical edges, H for horizontal ones.
cut_from_graph:
Graph cut on an arbitrary graph with global label affinity-matrix.
cut_from_graph_weighted:
NOT DONE YET
Graph cut on an arbitrary graph with global label affinity-matrix and
edgeweights.
See example.py and example_middlebury.py for examples and the gco README
for more details.
About
Python wrappers for GCO alpha-expansion and alpha-beta-swaps
Resources
Readme
Activity
Stars
138 stars
Watchers
5 watching
Forks
67 forks
Report repository
Releases
Packages
Used by
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
pygco
travis status
Python wrappers for GCO alpha-expansion and alpha-beta-swaps.
These wrappers provide a high level interface for graph cut
inference for multi-label problems.
See my blog for examples and comments: peekaboo-vision.blogspot.com
Installation
With Pip
  • Run pip install git+git://github.com/amueller/gco_python
For Linux
  • Download and install Cython (use your package manager).
  • run make
  • Run example.py for a simple example.
For Windows
  • Make sure Cython is installed (included in enthought Python distribution for example)
  • Download original source from http://vision.csd.uwo.ca/code/gco-v3.0.zip
  • Build gco with your compiler of choice. Create a dynamic library at libgco.so.
  • Adjust the path to gco in setup.py.
  • run python setup.py build.
  • run example.py for a simple example.
Troubleshooting
There have been some problems compiling gco (not my wrappers) using gcc4.7.
Please install gcc-4.6 and adjust the call in Makefile accordingly.
Usage
GCO implements alpha expansion and alpha beta swaps using graphcuts.
These can be used to efficiently find low energy configurations of certain energy functions.
Note that from a probabilistic viewpoint, GCO works in log-space.
Note that all input arrays are assumed to be in int32.
This means that float potentials must be rounded!
These algorithms can only deal with certain energies. Unfortunately
I have not figured out yet how to convert C++ errors to Python. If an unknown
error is raised, it probably means that you used an invalid energy function.
Look at the gco README for details.
This package gives a high level interface to gco, providing the following functions:
cut_simple:
Graph cut on a 2D grid using a global label affinity-matrix.
cut_VH:
NOT DONE YET
Graph cut on a 2D grid using a global label affinity-matrix and edge-weights.
V contains the weights for vertical edges, H for horizontal ones.
cut_from_graph:
Graph cut on an arbitrary graph with global label affinity-matrix.
cut_from_graph_weighted:
NOT DONE YET
Graph cut on an arbitrary graph with global label affinity-matrix and
edgeweights.
See example.py and example_middlebury.py for examples and the gco README
for more details.
About
Python wrappers for GCO alpha-expansion and alpha-beta-swaps
Resources
Readme
Activity
Stars
138 stars
Watchers
5 watching
Forks
67 forks
Report repository
Releases
Packages
Used by
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");
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})();
(function(){
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var __m = "*";
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pygco
travis status
Python wrappers for GCO alpha-expansion and alpha-beta-swaps.
These wrappers provide a high level interface for graph cut
inference for multi-label problems.
See my blog for examples and comments: peekaboo-vision.blogspot.com
Installation
With Pip
  • Run pip install git+git://github.com/amueller/gco_python
For Linux
  • Download and install Cython (use your package manager).
  • run make
  • Run example.py for a simple example.
For Windows
  • Make sure Cython is installed (included in enthought Python distribution for example)
  • Download original source from http://vision.csd.uwo.ca/code/gco-v3.0.zip
  • Build gco with your compiler of choice. Create a dynamic library at libgco.so.
  • Adjust the path to gco in setup.py.
  • run python setup.py build.
  • run example.py for a simple example.
Troubleshooting
There have been some problems compiling gco (not my wrappers) using gcc4.7.
Please install gcc-4.6 and adjust the call in Makefile accordingly.
Usage
GCO implements alpha expansion and alpha beta swaps using graphcuts.
These can be used to efficiently find low energy configurations of certain energy functions.
Note that from a probabilistic viewpoint, GCO works in log-space.
Note that all input arrays are assumed to be in int32.
This means that float potentials must be rounded!
These algorithms can only deal with certain energies. Unfortunately
I have not figured out yet how to convert C++ errors to Python. If an unknown
error is raised, it probably means that you used an invalid energy function.
Look at the gco README for details.
This package gives a high level interface to gco, providing the following functions:
cut_simple:
Graph cut on a 2D grid using a global label affinity-matrix.
cut_VH:
NOT DONE YET
Graph cut on a 2D grid using a global label affinity-matrix and edge-weights.
V contains the weights for vertical edges, H for horizontal ones.
cut_from_graph:
Graph cut on an arbitrary graph with global label affinity-matrix.
cut_from_graph_weighted:
NOT DONE YET
Graph cut on an arbitrary graph with global label affinity-matrix and
edgeweights.
See example.py and example_middlebury.py for examples and the gco README
for more details.
About
Python wrappers for GCO alpha-expansion and alpha-beta-swaps
Resources
Readme
Activity
Stars
138 stars
Watchers
5 watching
Forks
67 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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pygco
travis status
Python wrappers for GCO alpha-expansion and alpha-beta-swaps.
These wrappers provide a high level interface for graph cut
inference for multi-label problems.
See my blog for examples and comments: peekaboo-vision.blogspot.com
Installation
With Pip
  • Run pip install git+git://github.com/amueller/gco_python
For Linux
  • Download and install Cython (use your package manager).
  • run make
  • Run example.py for a simple example.
For Windows
  • Make sure Cython is installed (included in enthought Python distribution for example)
  • Download original source from http://vision.csd.uwo.ca/code/gco-v3.0.zip
  • Build gco with your compiler of choice. Create a dynamic library at libgco.so.
  • Adjust the path to gco in setup.py.
  • run python setup.py build.
  • run example.py for a simple example.
Troubleshooting
There have been some problems compiling gco (not my wrappers) using gcc4.7.
Please install gcc-4.6 and adjust the call in Makefile accordingly.
Usage
GCO implements alpha expansion and alpha beta swaps using graphcuts.
These can be used to efficiently find low energy configurations of certain energy functions.
Note that from a probabilistic viewpoint, GCO works in log-space.
Note that all input arrays are assumed to be in int32.
This means that float potentials must be rounded!
These algorithms can only deal with certain energies. Unfortunately
I have not figured out yet how to convert C++ errors to Python. If an unknown
error is raised, it probably means that you used an invalid energy function.
Look at the gco README for details.
This package gives a high level interface to gco, providing the following functions:
cut_simple:
Graph cut on a 2D grid using a global label affinity-matrix.
cut_VH:
NOT DONE YET
Graph cut on a 2D grid using a global label affinity-matrix and edge-weights.
V contains the weights for vertical edges, H for horizontal ones.
cut_from_graph:
Graph cut on an arbitrary graph with global label affinity-matrix.
cut_from_graph_weighted:
NOT DONE YET
Graph cut on an arbitrary graph with global label affinity-matrix and
edgeweights.
See example.py and example_middlebury.py for examples and the gco README
for more details.
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