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blurhash-python

importblurhashimportPIL.ImageimportnumpyPIL.Image.open("cool_cat_small.jpg")
# Result:

A picture of a cool cat.

blurhash.encode(numpy.array(PIL.Image.open("cool_cat_small.jpg").convert("RGB")))
# Result: 'UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH'PIL.Image.fromarray(numpy.array(blurhash.decode('UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH', 128, 128)).astype('uint8'))
# Result:

Blurhash example output: A blurred cool cat.

Blurhash is an algorithm that lets you transform image data into a small text representation of a blurred version of the image. This is useful since this small textual representation can be included when sending objects that may have images attached around, which then can be used to quickly create a placeholder for images that are still loading or that should be hidden behind a content warning.

This library contains a pure-python implementation of the blurhash algorithm, closely following the original swift implementation by Dag Ågren. The module has no dependencies (the unit tests require PIL and numpy). You can install it via pip:

$ pip3 install blurhash

It exports five functions:

  • "encode" and "decode" do the actual en- and decoding of blurhash strings
  • "components" returns the number of components x- and y components of a blurhash
  • "srgb_to_linear" and "linear_to_srgb" are colour space conversion helpers

Have a look at example.py for an example of how to use all of these working together.

Documentation for each function:

blurhash.encode(image, components_x=4, components_y=4, linear=False):
"""Calculates the blurhash for an image using the given x and y component counts.Image should be a 3-dimensional array, with the first dimension being y, the secondbeing x, and the third being the three rgb components that are assumed to be 0-255 srgb integers (incidentally, this is the format you will get from a PIL RGB image).You can also pass in already linear data - to do this, set linear to True. This isuseful if you want to encode a version of your image resized to a smaller size (whichyou should ideally do in linear colour)."""blurhash.decode(blurhash, width, height, punch=1.0, linear=False)
"""Decodes the given blurhash to an image of the specified size.Returns the resulting image a list of lists of 3-value sRGB 8 bit integerlists. Set linear to True if you would prefer to get linear floating point RGB back.The punch parameter can be used to de- or increase the contrast of theresulting image.As per the original implementation it is suggested to only decodeto a relatively small size and then scale the result up, as itbasically looks the same anyways."""blurhash.srgb_to_linear(value):
"""srgb 0-255 integer to linear 0.0-1.0 floating point conversion."""blurhash.linear_to_srgb(value):
"""linear 0.0-1.0 floating point to srgb 0-255 integer conversion."""

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Implementation of the blurhash ( https://github.com/woltapp/blurhash ) algorithm in pure python

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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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blurhash-python

importblurhashimportPIL.ImageimportnumpyPIL.Image.open("cool_cat_small.jpg")
# Result:

A picture of a cool cat.

blurhash.encode(numpy.array(PIL.Image.open("cool_cat_small.jpg").convert("RGB")))
# Result: 'UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH'PIL.Image.fromarray(numpy.array(blurhash.decode('UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH', 128, 128)).astype('uint8'))
# Result:

Blurhash example output: A blurred cool cat.

Blurhash is an algorithm that lets you transform image data into a small text representation of a blurred version of the image. This is useful since this small textual representation can be included when sending objects that may have images attached around, which then can be used to quickly create a placeholder for images that are still loading or that should be hidden behind a content warning.

This library contains a pure-python implementation of the blurhash algorithm, closely following the original swift implementation by Dag Ågren. The module has no dependencies (the unit tests require PIL and numpy). You can install it via pip:

$ pip3 install blurhash

It exports five functions:

  • "encode" and "decode" do the actual en- and decoding of blurhash strings
  • "components" returns the number of components x- and y components of a blurhash
  • "srgb_to_linear" and "linear_to_srgb" are colour space conversion helpers

Have a look at example.py for an example of how to use all of these working together.

Documentation for each function:

blurhash.encode(image, components_x=4, components_y=4, linear=False):
"""Calculates the blurhash for an image using the given x and y component counts.Image should be a 3-dimensional array, with the first dimension being y, the secondbeing x, and the third being the three rgb components that are assumed to be 0-255 srgb integers (incidentally, this is the format you will get from a PIL RGB image).You can also pass in already linear data - to do this, set linear to True. This isuseful if you want to encode a version of your image resized to a smaller size (whichyou should ideally do in linear colour)."""blurhash.decode(blurhash, width, height, punch=1.0, linear=False)
"""Decodes the given blurhash to an image of the specified size.Returns the resulting image a list of lists of 3-value sRGB 8 bit integerlists. Set linear to True if you would prefer to get linear floating point RGB back.The punch parameter can be used to de- or increase the contrast of theresulting image.As per the original implementation it is suggested to only decodeto a relatively small size and then scale the result up, as itbasically looks the same anyways."""blurhash.srgb_to_linear(value):
"""srgb 0-255 integer to linear 0.0-1.0 floating point conversion."""blurhash.linear_to_srgb(value):
"""linear 0.0-1.0 floating point to srgb 0-255 integer conversion."""

About

Implementation of the blurhash ( https://github.com/woltapp/blurhash ) algorithm in pure python

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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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blurhash-python

importblurhashimportPIL.ImageimportnumpyPIL.Image.open("cool_cat_small.jpg")
# Result:

A picture of a cool cat.

blurhash.encode(numpy.array(PIL.Image.open("cool_cat_small.jpg").convert("RGB")))
# Result: 'UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH'PIL.Image.fromarray(numpy.array(blurhash.decode('UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH', 128, 128)).astype('uint8'))
# Result:

Blurhash example output: A blurred cool cat.

Blurhash is an algorithm that lets you transform image data into a small text representation of a blurred version of the image. This is useful since this small textual representation can be included when sending objects that may have images attached around, which then can be used to quickly create a placeholder for images that are still loading or that should be hidden behind a content warning.

This library contains a pure-python implementation of the blurhash algorithm, closely following the original swift implementation by Dag Ågren. The module has no dependencies (the unit tests require PIL and numpy). You can install it via pip:

$ pip3 install blurhash

It exports five functions:

  • "encode" and "decode" do the actual en- and decoding of blurhash strings
  • "components" returns the number of components x- and y components of a blurhash
  • "srgb_to_linear" and "linear_to_srgb" are colour space conversion helpers

Have a look at example.py for an example of how to use all of these working together.

Documentation for each function:

blurhash.encode(image, components_x=4, components_y=4, linear=False):
"""Calculates the blurhash for an image using the given x and y component counts.Image should be a 3-dimensional array, with the first dimension being y, the secondbeing x, and the third being the three rgb components that are assumed to be 0-255 srgb integers (incidentally, this is the format you will get from a PIL RGB image).You can also pass in already linear data - to do this, set linear to True. This isuseful if you want to encode a version of your image resized to a smaller size (whichyou should ideally do in linear colour)."""blurhash.decode(blurhash, width, height, punch=1.0, linear=False)
"""Decodes the given blurhash to an image of the specified size.Returns the resulting image a list of lists of 3-value sRGB 8 bit integerlists. Set linear to True if you would prefer to get linear floating point RGB back.The punch parameter can be used to de- or increase the contrast of theresulting image.As per the original implementation it is suggested to only decodeto a relatively small size and then scale the result up, as itbasically looks the same anyways."""blurhash.srgb_to_linear(value):
"""srgb 0-255 integer to linear 0.0-1.0 floating point conversion."""blurhash.linear_to_srgb(value):
"""linear 0.0-1.0 floating point to srgb 0-255 integer conversion."""

About

Implementation of the blurhash ( https://github.com/woltapp/blurhash ) algorithm in pure python

Resources

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98 stars

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3 watching

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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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blurhash-python

importblurhashimportPIL.ImageimportnumpyPIL.Image.open("cool_cat_small.jpg")
# Result:

A picture of a cool cat.

blurhash.encode(numpy.array(PIL.Image.open("cool_cat_small.jpg").convert("RGB")))
# Result: 'UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH'PIL.Image.fromarray(numpy.array(blurhash.decode('UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH', 128, 128)).astype('uint8'))
# Result:

Blurhash example output: A blurred cool cat.

Blurhash is an algorithm that lets you transform image data into a small text representation of a blurred version of the image. This is useful since this small textual representation can be included when sending objects that may have images attached around, which then can be used to quickly create a placeholder for images that are still loading or that should be hidden behind a content warning.

This library contains a pure-python implementation of the blurhash algorithm, closely following the original swift implementation by Dag Ågren. The module has no dependencies (the unit tests require PIL and numpy). You can install it via pip:

$ pip3 install blurhash

It exports five functions:

  • "encode" and "decode" do the actual en- and decoding of blurhash strings
  • "components" returns the number of components x- and y components of a blurhash
  • "srgb_to_linear" and "linear_to_srgb" are colour space conversion helpers

Have a look at example.py for an example of how to use all of these working together.

Documentation for each function:

blurhash.encode(image, components_x=4, components_y=4, linear=False):
"""Calculates the blurhash for an image using the given x and y component counts.Image should be a 3-dimensional array, with the first dimension being y, the secondbeing x, and the third being the three rgb components that are assumed to be 0-255 srgb integers (incidentally, this is the format you will get from a PIL RGB image).You can also pass in already linear data - to do this, set linear to True. This isuseful if you want to encode a version of your image resized to a smaller size (whichyou should ideally do in linear colour)."""blurhash.decode(blurhash, width, height, punch=1.0, linear=False)
"""Decodes the given blurhash to an image of the specified size.Returns the resulting image a list of lists of 3-value sRGB 8 bit integerlists. Set linear to True if you would prefer to get linear floating point RGB back.The punch parameter can be used to de- or increase the contrast of theresulting image.As per the original implementation it is suggested to only decodeto a relatively small size and then scale the result up, as itbasically looks the same anyways."""blurhash.srgb_to_linear(value):
"""srgb 0-255 integer to linear 0.0-1.0 floating point conversion."""blurhash.linear_to_srgb(value):
"""linear 0.0-1.0 floating point to srgb 0-255 integer conversion."""

About

Implementation of the blurhash ( https://github.com/woltapp/blurhash ) algorithm in pure python

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3 watching

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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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blurhash-python

importblurhashimportPIL.ImageimportnumpyPIL.Image.open("cool_cat_small.jpg")
# Result:

A picture of a cool cat.

blurhash.encode(numpy.array(PIL.Image.open("cool_cat_small.jpg").convert("RGB")))
# Result: 'UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH'PIL.Image.fromarray(numpy.array(blurhash.decode('UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH', 128, 128)).astype('uint8'))
# Result:

Blurhash example output: A blurred cool cat.

Blurhash is an algorithm that lets you transform image data into a small text representation of a blurred version of the image. This is useful since this small textual representation can be included when sending objects that may have images attached around, which then can be used to quickly create a placeholder for images that are still loading or that should be hidden behind a content warning.

This library contains a pure-python implementation of the blurhash algorithm, closely following the original swift implementation by Dag Ågren. The module has no dependencies (the unit tests require PIL and numpy). You can install it via pip:

$ pip3 install blurhash

It exports five functions:

  • "encode" and "decode" do the actual en- and decoding of blurhash strings
  • "components" returns the number of components x- and y components of a blurhash
  • "srgb_to_linear" and "linear_to_srgb" are colour space conversion helpers

Have a look at example.py for an example of how to use all of these working together.

Documentation for each function:

blurhash.encode(image, components_x=4, components_y=4, linear=False):
"""Calculates the blurhash for an image using the given x and y component counts.Image should be a 3-dimensional array, with the first dimension being y, the secondbeing x, and the third being the three rgb components that are assumed to be 0-255 srgb integers (incidentally, this is the format you will get from a PIL RGB image).You can also pass in already linear data - to do this, set linear to True. This isuseful if you want to encode a version of your image resized to a smaller size (whichyou should ideally do in linear colour)."""blurhash.decode(blurhash, width, height, punch=1.0, linear=False)
"""Decodes the given blurhash to an image of the specified size.Returns the resulting image a list of lists of 3-value sRGB 8 bit integerlists. Set linear to True if you would prefer to get linear floating point RGB back.The punch parameter can be used to de- or increase the contrast of theresulting image.As per the original implementation it is suggested to only decodeto a relatively small size and then scale the result up, as itbasically looks the same anyways."""blurhash.srgb_to_linear(value):
"""srgb 0-255 integer to linear 0.0-1.0 floating point conversion."""blurhash.linear_to_srgb(value):
"""linear 0.0-1.0 floating point to srgb 0-255 integer conversion."""

About

Implementation of the blurhash ( https://github.com/woltapp/blurhash ) algorithm in pure python

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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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blurhash-python

importblurhashimportPIL.ImageimportnumpyPIL.Image.open("cool_cat_small.jpg")
# Result:

A picture of a cool cat.

blurhash.encode(numpy.array(PIL.Image.open("cool_cat_small.jpg").convert("RGB")))
# Result: 'UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH'PIL.Image.fromarray(numpy.array(blurhash.decode('UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH', 128, 128)).astype('uint8'))
# Result:

Blurhash example output: A blurred cool cat.

Blurhash is an algorithm that lets you transform image data into a small text representation of a blurred version of the image. This is useful since this small textual representation can be included when sending objects that may have images attached around, which then can be used to quickly create a placeholder for images that are still loading or that should be hidden behind a content warning.

This library contains a pure-python implementation of the blurhash algorithm, closely following the original swift implementation by Dag Ågren. The module has no dependencies (the unit tests require PIL and numpy). You can install it via pip:

$ pip3 install blurhash

It exports five functions:

  • "encode" and "decode" do the actual en- and decoding of blurhash strings
  • "components" returns the number of components x- and y components of a blurhash
  • "srgb_to_linear" and "linear_to_srgb" are colour space conversion helpers

Have a look at example.py for an example of how to use all of these working together.

Documentation for each function:

blurhash.encode(image, components_x=4, components_y=4, linear=False):
"""Calculates the blurhash for an image using the given x and y component counts.Image should be a 3-dimensional array, with the first dimension being y, the secondbeing x, and the third being the three rgb components that are assumed to be 0-255 srgb integers (incidentally, this is the format you will get from a PIL RGB image).You can also pass in already linear data - to do this, set linear to True. This isuseful if you want to encode a version of your image resized to a smaller size (whichyou should ideally do in linear colour)."""blurhash.decode(blurhash, width, height, punch=1.0, linear=False)
"""Decodes the given blurhash to an image of the specified size.Returns the resulting image a list of lists of 3-value sRGB 8 bit integerlists. Set linear to True if you would prefer to get linear floating point RGB back.The punch parameter can be used to de- or increase the contrast of theresulting image.As per the original implementation it is suggested to only decodeto a relatively small size and then scale the result up, as itbasically looks the same anyways."""blurhash.srgb_to_linear(value):
"""srgb 0-255 integer to linear 0.0-1.0 floating point conversion."""blurhash.linear_to_srgb(value):
"""linear 0.0-1.0 floating point to srgb 0-255 integer conversion."""

About

Implementation of the blurhash ( https://github.com/woltapp/blurhash ) algorithm in pure python

Resources

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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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blurhash-python

importblurhashimportPIL.ImageimportnumpyPIL.Image.open("cool_cat_small.jpg")
# Result:

A picture of a cool cat.

blurhash.encode(numpy.array(PIL.Image.open("cool_cat_small.jpg").convert("RGB")))
# Result: 'UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH'PIL.Image.fromarray(numpy.array(blurhash.decode('UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH', 128, 128)).astype('uint8'))
# Result:

Blurhash example output: A blurred cool cat.

Blurhash is an algorithm that lets you transform image data into a small text representation of a blurred version of the image. This is useful since this small textual representation can be included when sending objects that may have images attached around, which then can be used to quickly create a placeholder for images that are still loading or that should be hidden behind a content warning.

This library contains a pure-python implementation of the blurhash algorithm, closely following the original swift implementation by Dag Ågren. The module has no dependencies (the unit tests require PIL and numpy). You can install it via pip:

$ pip3 install blurhash

It exports five functions:

  • "encode" and "decode" do the actual en- and decoding of blurhash strings
  • "components" returns the number of components x- and y components of a blurhash
  • "srgb_to_linear" and "linear_to_srgb" are colour space conversion helpers

Have a look at example.py for an example of how to use all of these working together.

Documentation for each function:

blurhash.encode(image, components_x=4, components_y=4, linear=False):
"""Calculates the blurhash for an image using the given x and y component counts.Image should be a 3-dimensional array, with the first dimension being y, the secondbeing x, and the third being the three rgb components that are assumed to be 0-255 srgb integers (incidentally, this is the format you will get from a PIL RGB image).You can also pass in already linear data - to do this, set linear to True. This isuseful if you want to encode a version of your image resized to a smaller size (whichyou should ideally do in linear colour)."""blurhash.decode(blurhash, width, height, punch=1.0, linear=False)
"""Decodes the given blurhash to an image of the specified size.Returns the resulting image a list of lists of 3-value sRGB 8 bit integerlists. Set linear to True if you would prefer to get linear floating point RGB back.The punch parameter can be used to de- or increase the contrast of theresulting image.As per the original implementation it is suggested to only decodeto a relatively small size and then scale the result up, as itbasically looks the same anyways."""blurhash.srgb_to_linear(value):
"""srgb 0-255 integer to linear 0.0-1.0 floating point conversion."""blurhash.linear_to_srgb(value):
"""linear 0.0-1.0 floating point to srgb 0-255 integer conversion."""

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Implementation of the blurhash ( https://github.com/woltapp/blurhash ) algorithm in pure python

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blurhash-python

importblurhashimportPIL.ImageimportnumpyPIL.Image.open("cool_cat_small.jpg")
# Result:

A picture of a cool cat.

blurhash.encode(numpy.array(PIL.Image.open("cool_cat_small.jpg").convert("RGB")))
# Result: 'UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH'PIL.Image.fromarray(numpy.array(blurhash.decode('UBL_:rOpGG-oBUNG,qRj2so|=eE1w^n4S5NH', 128, 128)).astype('uint8'))
# Result:

Blurhash example output: A blurred cool cat.

Blurhash is an algorithm that lets you transform image data into a small text representation of a blurred version of the image. This is useful since this small textual representation can be included when sending objects that may have images attached around, which then can be used to quickly create a placeholder for images that are still loading or that should be hidden behind a content warning.

This library contains a pure-python implementation of the blurhash algorithm, closely following the original swift implementation by Dag Ågren. The module has no dependencies (the unit tests require PIL and numpy). You can install it via pip:

$ pip3 install blurhash

It exports five functions:

  • "encode" and "decode" do the actual en- and decoding of blurhash strings
  • "components" returns the number of components x- and y components of a blurhash
  • "srgb_to_linear" and "linear_to_srgb" are colour space conversion helpers

Have a look at example.py for an example of how to use all of these working together.

Documentation for each function:

blurhash.encode(image, components_x=4, components_y=4, linear=False):
"""Calculates the blurhash for an image using the given x and y component counts.Image should be a 3-dimensional array, with the first dimension being y, the secondbeing x, and the third being the three rgb components that are assumed to be 0-255 srgb integers (incidentally, this is the format you will get from a PIL RGB image).You can also pass in already linear data - to do this, set linear to True. This isuseful if you want to encode a version of your image resized to a smaller size (whichyou should ideally do in linear colour)."""blurhash.decode(blurhash, width, height, punch=1.0, linear=False)
"""Decodes the given blurhash to an image of the specified size.Returns the resulting image a list of lists of 3-value sRGB 8 bit integerlists. Set linear to True if you would prefer to get linear floating point RGB back.The punch parameter can be used to de- or increase the contrast of theresulting image.As per the original implementation it is suggested to only decodeto a relatively small size and then scale the result up, as itbasically looks the same anyways."""blurhash.srgb_to_linear(value):
"""srgb 0-255 integer to linear 0.0-1.0 floating point conversion."""blurhash.linear_to_srgb(value):
"""linear 0.0-1.0 floating point to srgb 0-255 integer conversion."""

About

Implementation of the blurhash ( https://github.com/woltapp/blurhash ) algorithm in pure python

Resources

Stars

98 stars

Watchers

3 watching

Forks

Releases

Packages

Used by

Contributors

Languages