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<div class="title">transformations Namespace Reference</div> </div>
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="func-members"></a>
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Functions</h2></td></tr>
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<tr class="memitem:add0ba9b9fec4d378887b341e61b7c2fb"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#add0ba9b9fec4d378887b341e61b7c2fb">int_image</a> (<a class="el" href="namespacetransformations.html#aa0e40dced4103d3ff9d319d23b975e54">float_image</a>)</td></tr>
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<tr class="memdesc:add0ba9b9fec4d378887b341e61b7c2fb"><td class="mdescLeft"> </td><td class="mdescRight">Image type cast ##. <a href="#add0ba9b9fec4d378887b341e61b7c2fb">More...</a><br /></td></tr>
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<tr class="separator:add0ba9b9fec4d378887b341e61b7c2fb"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:aa0e40dced4103d3ff9d319d23b975e54"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#aa0e40dced4103d3ff9d319d23b975e54">float_image</a> (<a class="el" href="namespacetransformations.html#add0ba9b9fec4d378887b341e61b7c2fb">int_image</a>)</td></tr>
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<tr class="separator:aa0e40dced4103d3ff9d319d23b975e54"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:acc25897f51cd3d3df0ed6e29e563046c"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#acc25897f51cd3d3df0ed6e29e563046c">rand_floats</a> (size, mag, maxval, minval=None)</td></tr>
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<tr class="memdesc:acc25897f51cd3d3df0ed6e29e563046c"><td class="mdescLeft"> </td><td class="mdescRight">Parameters utils ##. <a href="#acc25897f51cd3d3df0ed6e29e563046c">More...</a><br /></td></tr>
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<tr class="separator:acc25897f51cd3d3df0ed6e29e563046c"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:ac49802d45ee247e10c3adfcc7cb86cbf"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#ac49802d45ee247e10c3adfcc7cb86cbf">invScale_rand_floats</a> (size, mag, maxval, minval)</td></tr>
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<tr class="separator:ac49802d45ee247e10c3adfcc7cb86cbf"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a380988bd0d3afb343b8b63fe52703180"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#a380988bd0d3afb343b8b63fe52703180">zero_stack</a> (tensor, zero_pos)</td></tr>
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<tr class="separator:a380988bd0d3afb343b8b63fe52703180"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:ac908826415e01ded574dccdd9373e868"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#ac908826415e01ded574dccdd9373e868">float_parameter</a> (level, maxval)</td></tr>
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<tr class="separator:ac908826415e01ded574dccdd9373e868"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a3b82d4e0c1638f26db9227dd391548d8"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#a3b82d4e0c1638f26db9227dd391548d8">flipLR</a> (x)</td></tr>
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<tr class="memdesc:a3b82d4e0c1638f26db9227dd391548d8"><td class="mdescLeft"> </td><td class="mdescRight">Tranformations ##. <a href="#a3b82d4e0c1638f26db9227dd391548d8">More...</a><br /></td></tr>
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<tr class="separator:a3b82d4e0c1638f26db9227dd391548d8"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:afaf4841f6fad18e431c5bb1eca51390a"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#afaf4841f6fad18e431c5bb1eca51390a">flipUD</a> (x)</td></tr>
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<tr class="memitem:a013eae7739ef034ed3d07bde95a76084"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#a013eae7739ef034ed3d07bde95a76084">rotate</a> (x, angle)</td></tr>
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<tr class="separator:a013eae7739ef034ed3d07bde95a76084"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a6aeaef035ff9c1c0977e7ef49b7de735"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#a6aeaef035ff9c1c0977e7ef49b7de735">translate</a> (x, translation)</td></tr>
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<tr class="separator:a6aeaef035ff9c1c0977e7ef49b7de735"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a9afae86f07c95502ab6aa79374f9418e"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#a9afae86f07c95502ab6aa79374f9418e">shear</a> (x, shear)</td></tr>
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<tr class="separator:a9afae86f07c95502ab6aa79374f9418e"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a68fca7bf43a6ba94baad9d7145ee6093"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#a68fca7bf43a6ba94baad9d7145ee6093">contrast</a> (x, contrast_factor)</td></tr>
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<tr class="separator:a68fca7bf43a6ba94baad9d7145ee6093"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:acb86ae3a568a9f88afe87cebe10af747"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#acb86ae3a568a9f88afe87cebe10af747">color</a> (x, color_factor)</td></tr>
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<tr class="separator:acb86ae3a568a9f88afe87cebe10af747"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a3612ce0d406daaf2506f204079497e41"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#a3612ce0d406daaf2506f204079497e41">brightness</a> (x, brightness_factor)</td></tr>
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<tr class="separator:a3612ce0d406daaf2506f204079497e41"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a10a9e3221a2c76824d7d7f305b2aa9aa"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#a10a9e3221a2c76824d7d7f305b2aa9aa">sharpeness</a> (x, sharpness_factor)</td></tr>
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<tr class="separator:a10a9e3221a2c76824d7d7f305b2aa9aa"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:ab666bac350d962bc28e91ac91a04921b"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#ab666bac350d962bc28e91ac91a04921b">posterize</a> (x, bits)</td></tr>
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<tr class="separator:ab666bac350d962bc28e91ac91a04921b"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a0a93f2875e5a3d32f64581666ad03774"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#a0a93f2875e5a3d32f64581666ad03774">solarize</a> (x, thresholds)</td></tr>
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<tr class="separator:a0a93f2875e5a3d32f64581666ad03774"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:aef656b4360dd8029508a01df751ccbf5"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#aef656b4360dd8029508a01df751ccbf5">blend</a> (x, y, alpha)</td></tr>
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<tr class="separator:aef656b4360dd8029508a01df751ccbf5"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a439d5faa6aecf40097061f38d172da7a"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#a439d5faa6aecf40097061f38d172da7a">auto_contrast</a> (x)</td></tr>
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<tr class="separator:a439d5faa6aecf40097061f38d172da7a"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:aa0b8ad06be9731533adce977214e044c"><td class="memItemLeft" align="right" valign="top">def </td><td class="memItemRight" valign="bottom"><a class="el" href="namespacetransformations.html#aa0b8ad06be9731533adce977214e044c">equalize</a> (x)</td></tr>
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<tr class="separator:aa0b8ad06be9731533adce977214e044c"><td class="memSeparator" colspan="2"> </td></tr>
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</table><table class="memberdecls">
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<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="var-members"></a>
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Variables</h2></td></tr>
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<tr class="memitem:a02f3400e169a6dfbdc63dc6d8907f525"><td class="memItemLeft" align="right" valign="top"><a id="a02f3400e169a6dfbdc63dc6d8907f525"></a>
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dictionary </td><td class="memItemRight" valign="bottom"><b>TF_no_mag</b> = {'Identity', 'FlipUD', 'FlipLR', 'Random', 'RandBlend'}</td></tr>
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<tr class="separator:a02f3400e169a6dfbdc63dc6d8907f525"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a9e58090c28b0b0e9adddaaf8dd7ae4a5"><td class="memItemLeft" align="right" valign="top"><a id="a9e58090c28b0b0e9adddaaf8dd7ae4a5"></a>
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dictionary </td><td class="memItemRight" valign="bottom"><b>TF_no_grad</b> = {'Solarize', 'Posterize', '=Solarize', '=Posterize'}</td></tr>
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<tr class="separator:a9e58090c28b0b0e9adddaaf8dd7ae4a5"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:af95bb9f754eba835acc504184ffbd5bd"><td class="memItemLeft" align="right" valign="top"><a id="af95bb9f754eba835acc504184ffbd5bd"></a>
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dictionary </td><td class="memItemRight" valign="bottom"><b>TF_ignore_mag</b> = TF_no_mag | TF_no_grad</td></tr>
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<tr class="separator:af95bb9f754eba835acc504184ffbd5bd"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a4f338dd8fd9c0cbcdbd0072ed3b2a519"><td class="memItemLeft" align="right" valign="top"><a id="a4f338dd8fd9c0cbcdbd0072ed3b2a519"></a>
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int </td><td class="memItemRight" valign="bottom"><b>PARAMETER_MAX</b> = 1</td></tr>
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<tr class="separator:a4f338dd8fd9c0cbcdbd0072ed3b2a519"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:af0e977475e691db60c155db65e8cd88b"><td class="memItemLeft" align="right" valign="top"><a id="af0e977475e691db60c155db65e8cd88b"></a>
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float </td><td class="memItemRight" valign="bottom"><b>PARAMETER_MIN</b> = 0.1</td></tr>
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<tr class="separator:af0e977475e691db60c155db65e8cd88b"><td class="memSeparator" colspan="2"> </td></tr>
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<tr class="memitem:a550637c287bd6ab6df055f26b16daba1"><td class="memItemLeft" align="right" valign="top"><a id="a550637c287bd6ab6df055f26b16daba1"></a>
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dictionary </td><td class="memItemRight" valign="bottom"><b>TF_dict</b></td></tr>
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<tr class="separator:a550637c287bd6ab6df055f26b16daba1"><td class="memSeparator" colspan="2"> </td></tr>
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</table>
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<a name="details" id="details"></a><h2 class="groupheader">Detailed Description</h2>
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<div class="textblock"><pre class="fragment">PyTorch implementation of some PIL image transformations.
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Those implementation are thinked to take advantages of batched computation of PyTorch on GPU.
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Based on Kornia library.
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See: https://github.com/kornia/kornia
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And PIL.
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See:
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https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py
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https://github.com/python-pillow/Pillow/blob/9c78c3f97291bd681bc8637922d6a2fa9415916c/src/PIL/Image.py#L2818
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Inspired from AutoAugment.
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See: https://github.com/tensorflow/models/blob/fc2056bce6ab17eabdc139061fef8f4f2ee763ec/research/autoaugment/augmentation_transforms.py
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</pre> </div><h2 class="groupheader">Function Documentation</h2>
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<a id="a439d5faa6aecf40097061f38d172da7a"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a439d5faa6aecf40097061f38d172da7a">◆ </a></span>auto_contrast()</h2>
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<div class="memitem">
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<div class="memproto">
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<table class="memname">
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<tr>
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<td class="memname">def transformations.auto_contrast </td>
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<td>(</td>
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<td class="paramtype"> </td>
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<td class="paramname"><em>x</em></td><td>)</td>
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<td></td>
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</table>
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</div><div class="memdoc">
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<pre class="fragment">NOT TESTED - EXTRA SLOW</pre>
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</div>
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</div>
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<a id="aef656b4360dd8029508a01df751ccbf5"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#aef656b4360dd8029508a01df751ccbf5">◆ </a></span>blend()</h2>
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<div class="memitem">
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<div class="memproto">
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<table class="memname">
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<tr>
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<td class="memname">def transformations.blend </td>
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<td>(</td>
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<td class="paramtype"> </td>
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<td class="paramname"><em>x</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype"> </td>
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<td class="paramname"><em>y</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype"> </td>
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<td class="paramname"><em>alpha</em> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</div><div class="memdoc">
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<pre class="fragment">Creates a new images by interpolating between two input images, using a constant alpha.
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x and y should have the same size.
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alpha should have the same batch size as the images.
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Apply batch wise :
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out = image1 * (1.0 - alpha) + image2 * alpha
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Args:
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x (Tensor): Batch of images.
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y (Tensor): Batch of images.
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alpha (Tensor): The interpolation alpha factor for each images.
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Returns:
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(Tensor): Batch of solarized images.
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</pre>
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</div>
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</div>
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<a id="a3612ce0d406daaf2506f204079497e41"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a3612ce0d406daaf2506f204079497e41">◆ </a></span>brightness()</h2>
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<div class="memitem">
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<div class="memproto">
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<table class="memname">
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<tr>
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<td class="memname">def transformations.brightness </td>
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<td>(</td>
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<td class="paramtype"> </td>
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<td class="paramname"><em>x</em>, </td>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype"> </td>
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<td class="paramname"><em>brightness_factor</em> </td>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</table>
|
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</div><div class="memdoc">
|
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<pre class="fragment">Adjust brightness of images.
|
|
|
|
Args:
|
|
x (Tensor): Batch of images.
|
|
brightness_factor (Tensor): Brightness factor for each images.
|
|
0.0 gives a black image. A factor of 1.0 gives the original image.
|
|
|
|
Returns:
|
|
(Tensor): Batch of adjusted images.
|
|
</pre>
|
|
</div>
|
|
</div>
|
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<a id="acb86ae3a568a9f88afe87cebe10af747"></a>
|
|
<h2 class="memtitle"><span class="permalink"><a href="#acb86ae3a568a9f88afe87cebe10af747">◆ </a></span>color()</h2>
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<div class="memitem">
|
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<div class="memproto">
|
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<table class="memname">
|
|
<tr>
|
|
<td class="memname">def transformations.color </td>
|
|
<td>(</td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>x</em>, </td>
|
|
</tr>
|
|
<tr>
|
|
<td class="paramkey"></td>
|
|
<td></td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>color_factor</em> </td>
|
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</tr>
|
|
<tr>
|
|
<td></td>
|
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
|
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</div><div class="memdoc">
|
|
<pre class="fragment">Adjust color of images.
|
|
|
|
Args:
|
|
x (Tensor): Batch of images.
|
|
color_factor (Tensor): Color factor for each images.
|
|
0.0 gives a black and white image. A factor of 1.0 gives the original image.
|
|
|
|
Returns:
|
|
(Tensor): Batch of adjusted images.
|
|
</pre>
|
|
</div>
|
|
</div>
|
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<a id="a68fca7bf43a6ba94baad9d7145ee6093"></a>
|
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<h2 class="memtitle"><span class="permalink"><a href="#a68fca7bf43a6ba94baad9d7145ee6093">◆ </a></span>contrast()</h2>
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<div class="memitem">
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<div class="memproto">
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<table class="memname">
|
|
<tr>
|
|
<td class="memname">def transformations.contrast </td>
|
|
<td>(</td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>x</em>, </td>
|
|
</tr>
|
|
<tr>
|
|
<td class="paramkey"></td>
|
|
<td></td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>contrast_factor</em> </td>
|
|
</tr>
|
|
<tr>
|
|
<td></td>
|
|
<td>)</td>
|
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<td></td><td></td>
|
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</tr>
|
|
</table>
|
|
</div><div class="memdoc">
|
|
<pre class="fragment">Adjust contast of images.
|
|
|
|
Args:
|
|
x (FloatTensor): Batch of images.
|
|
contrast_factor (FloatTensor): Contrast adjust factor per element in the batch.
|
|
0 generates a compleatly black image, 1 does not modify the input image while any other non-negative number modify the brightness by this factor.
|
|
|
|
Returns:
|
|
(Tensor): Batch of adjusted images.
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
<a id="aa0b8ad06be9731533adce977214e044c"></a>
|
|
<h2 class="memtitle"><span class="permalink"><a href="#aa0b8ad06be9731533adce977214e044c">◆ </a></span>equalize()</h2>
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<div class="memitem">
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<div class="memproto">
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<table class="memname">
|
|
<tr>
|
|
<td class="memname">def transformations.equalize </td>
|
|
<td>(</td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>x</em></td><td>)</td>
|
|
<td></td>
|
|
</tr>
|
|
</table>
|
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</div><div class="memdoc">
|
|
<pre class="fragment">NOT WORKING</pre>
|
|
</div>
|
|
</div>
|
|
<a id="a3b82d4e0c1638f26db9227dd391548d8"></a>
|
|
<h2 class="memtitle"><span class="permalink"><a href="#a3b82d4e0c1638f26db9227dd391548d8">◆ </a></span>flipLR()</h2>
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|
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<div class="memitem">
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|
<div class="memproto">
|
|
<table class="memname">
|
|
<tr>
|
|
<td class="memname">def transformations.flipLR </td>
|
|
<td>(</td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>x</em></td><td>)</td>
|
|
<td></td>
|
|
</tr>
|
|
</table>
|
|
</div><div class="memdoc">
|
|
|
|
<p>Tranformations ##. </p>
|
|
<pre class="fragment">Flip horizontaly/Left-Right images.
|
|
|
|
Args:
|
|
x (Tensor): Batch of images.
|
|
|
|
Returns:
|
|
(Tensor): Batch of fliped images.
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
<a id="afaf4841f6fad18e431c5bb1eca51390a"></a>
|
|
<h2 class="memtitle"><span class="permalink"><a href="#afaf4841f6fad18e431c5bb1eca51390a">◆ </a></span>flipUD()</h2>
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<div class="memitem">
|
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<div class="memproto">
|
|
<table class="memname">
|
|
<tr>
|
|
<td class="memname">def transformations.flipUD </td>
|
|
<td>(</td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>x</em></td><td>)</td>
|
|
<td></td>
|
|
</tr>
|
|
</table>
|
|
</div><div class="memdoc">
|
|
<pre class="fragment">Flip vertically/Up-Down images.
|
|
|
|
Args:
|
|
x (Tensor): Batch of images.
|
|
|
|
Returns:
|
|
(Tensor): Batch of fliped images.
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
<a id="aa0e40dced4103d3ff9d319d23b975e54"></a>
|
|
<h2 class="memtitle"><span class="permalink"><a href="#aa0e40dced4103d3ff9d319d23b975e54">◆ </a></span>float_image()</h2>
|
|
|
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<div class="memitem">
|
|
<div class="memproto">
|
|
<table class="memname">
|
|
<tr>
|
|
<td class="memname">def transformations.float_image </td>
|
|
<td>(</td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>int_image</em></td><td>)</td>
|
|
<td></td>
|
|
</tr>
|
|
</table>
|
|
</div><div class="memdoc">
|
|
<pre class="fragment">Convert a int Tensor/Image to an float Tensor/Image.
|
|
|
|
Args:
|
|
int_image (ByteTensor): Image tensor.
|
|
|
|
Returns:
|
|
(FloatTensor) Converted tensor.
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
<a id="ac908826415e01ded574dccdd9373e868"></a>
|
|
<h2 class="memtitle"><span class="permalink"><a href="#ac908826415e01ded574dccdd9373e868">◆ </a></span>float_parameter()</h2>
|
|
|
|
<div class="memitem">
|
|
<div class="memproto">
|
|
<table class="memname">
|
|
<tr>
|
|
<td class="memname">def transformations.float_parameter </td>
|
|
<td>(</td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>level</em>, </td>
|
|
</tr>
|
|
<tr>
|
|
<td class="paramkey"></td>
|
|
<td></td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>maxval</em> </td>
|
|
</tr>
|
|
<tr>
|
|
<td></td>
|
|
<td>)</td>
|
|
<td></td><td></td>
|
|
</tr>
|
|
</table>
|
|
</div><div class="memdoc">
|
|
<pre class="fragment">Scale level between 0 and maxval.
|
|
|
|
Args:
|
|
level (float): Level of the operation that will be between [PARAMETER_MIN, PARAMETER_MAX].
|
|
maxval: Maximum value that the operation can have. This will be scaled to level/PARAMETER_MAX.
|
|
Returns:
|
|
A float that results from scaling `maxval` according to `level`.
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
<a id="add0ba9b9fec4d378887b341e61b7c2fb"></a>
|
|
<h2 class="memtitle"><span class="permalink"><a href="#add0ba9b9fec4d378887b341e61b7c2fb">◆ </a></span>int_image()</h2>
|
|
|
|
<div class="memitem">
|
|
<div class="memproto">
|
|
<table class="memname">
|
|
<tr>
|
|
<td class="memname">def transformations.int_image </td>
|
|
<td>(</td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>float_image</em></td><td>)</td>
|
|
<td></td>
|
|
</tr>
|
|
</table>
|
|
</div><div class="memdoc">
|
|
|
|
<p>Image type cast ##. </p>
|
|
<pre class="fragment">Convert a float Tensor/Image to an int Tensor/Image.
|
|
|
|
Be warry that this transformation isn't bijective, each conversion will result in small loss of information.
|
|
Granularity: 1/256 = 0.0039.
|
|
|
|
This will also result in the loss of the gradient associated to input as gradient cannot be tracked on int Tensor.
|
|
|
|
Args:
|
|
float_image (FloatTensor): Image tensor.
|
|
|
|
Returns:
|
|
(ByteTensor) Converted tensor.
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
<a id="ac49802d45ee247e10c3adfcc7cb86cbf"></a>
|
|
<h2 class="memtitle"><span class="permalink"><a href="#ac49802d45ee247e10c3adfcc7cb86cbf">◆ </a></span>invScale_rand_floats()</h2>
|
|
|
|
<div class="memitem">
|
|
<div class="memproto">
|
|
<table class="memname">
|
|
<tr>
|
|
<td class="memname">def transformations.invScale_rand_floats </td>
|
|
<td>(</td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>size</em>, </td>
|
|
</tr>
|
|
<tr>
|
|
<td class="paramkey"></td>
|
|
<td></td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>mag</em>, </td>
|
|
</tr>
|
|
<tr>
|
|
<td class="paramkey"></td>
|
|
<td></td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>maxval</em>, </td>
|
|
</tr>
|
|
<tr>
|
|
<td class="paramkey"></td>
|
|
<td></td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>minval</em> </td>
|
|
</tr>
|
|
<tr>
|
|
<td></td>
|
|
<td>)</td>
|
|
<td></td><td></td>
|
|
</tr>
|
|
</table>
|
|
</div><div class="memdoc">
|
|
<pre class="fragment">Generate a batch of random values.
|
|
|
|
Similar to rand_floats() except that the mag is used in an inversed scale.
|
|
|
|
Mag:[0,PARAMETER_MAX] => [PARAMETER_MAX, 0]
|
|
|
|
Args:
|
|
size (int): Number of value to generate.
|
|
mag (float): Level of the operation that will be between [PARAMETER_MIN, PARAMETER_MAX].
|
|
maxval (float): Maximum value that can be generated. This will be scaled to mag/PARAMETER_MAX.
|
|
minval (float): Minimum value that can be generated. (default: -maxval)
|
|
|
|
Returns:
|
|
(Tensor) Generated batch of float values between [minval, maxval].
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
<a id="ab666bac350d962bc28e91ac91a04921b"></a>
|
|
<h2 class="memtitle"><span class="permalink"><a href="#ab666bac350d962bc28e91ac91a04921b">◆ </a></span>posterize()</h2>
|
|
|
|
<div class="memitem">
|
|
<div class="memproto">
|
|
<table class="memname">
|
|
<tr>
|
|
<td class="memname">def transformations.posterize </td>
|
|
<td>(</td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>x</em>, </td>
|
|
</tr>
|
|
<tr>
|
|
<td class="paramkey"></td>
|
|
<td></td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>bits</em> </td>
|
|
</tr>
|
|
<tr>
|
|
<td></td>
|
|
<td>)</td>
|
|
<td></td><td></td>
|
|
</tr>
|
|
</table>
|
|
</div><div class="memdoc">
|
|
<pre class="fragment">Reduce the number of bits for each color channel.
|
|
|
|
Be warry that the cast to integers block the gradient propagation.
|
|
Args:
|
|
x (Tensor): Batch of images.
|
|
bits (Tensor): The number of bits to keep for each channel (1-8).
|
|
|
|
Returns:
|
|
(Tensor): Batch of posterized images.
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
<a id="acc25897f51cd3d3df0ed6e29e563046c"></a>
|
|
<h2 class="memtitle"><span class="permalink"><a href="#acc25897f51cd3d3df0ed6e29e563046c">◆ </a></span>rand_floats()</h2>
|
|
|
|
<div class="memitem">
|
|
<div class="memproto">
|
|
<table class="memname">
|
|
<tr>
|
|
<td class="memname">def transformations.rand_floats </td>
|
|
<td>(</td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>size</em>, </td>
|
|
</tr>
|
|
<tr>
|
|
<td class="paramkey"></td>
|
|
<td></td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>mag</em>, </td>
|
|
</tr>
|
|
<tr>
|
|
<td class="paramkey"></td>
|
|
<td></td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>maxval</em>, </td>
|
|
</tr>
|
|
<tr>
|
|
<td class="paramkey"></td>
|
|
<td></td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>minval</em> = <code>None</code> </td>
|
|
</tr>
|
|
<tr>
|
|
<td></td>
|
|
<td>)</td>
|
|
<td></td><td></td>
|
|
</tr>
|
|
</table>
|
|
</div><div class="memdoc">
|
|
|
|
<p>Parameters utils ##. </p>
|
|
<pre class="fragment">Generate a batch of random values.
|
|
|
|
Args:
|
|
size (int): Number of value to generate.
|
|
mag (float): Level of the operation that will be between [PARAMETER_MIN, PARAMETER_MAX].
|
|
maxval (float): Maximum value that can be generated. This will be scaled to mag/PARAMETER_MAX.
|
|
minval (float): Minimum value that can be generated. (default: -maxval)
|
|
|
|
Returns:
|
|
(Tensor) Generated batch of float values between [minval, maxval].
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
<a id="a013eae7739ef034ed3d07bde95a76084"></a>
|
|
<h2 class="memtitle"><span class="permalink"><a href="#a013eae7739ef034ed3d07bde95a76084">◆ </a></span>rotate()</h2>
|
|
|
|
<div class="memitem">
|
|
<div class="memproto">
|
|
<table class="memname">
|
|
<tr>
|
|
<td class="memname">def transformations.rotate </td>
|
|
<td>(</td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>x</em>, </td>
|
|
</tr>
|
|
<tr>
|
|
<td class="paramkey"></td>
|
|
<td></td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>angle</em> </td>
|
|
</tr>
|
|
<tr>
|
|
<td></td>
|
|
<td>)</td>
|
|
<td></td><td></td>
|
|
</tr>
|
|
</table>
|
|
</div><div class="memdoc">
|
|
<pre class="fragment">Rotate images.
|
|
|
|
Args:
|
|
x (Tensor): Batch of images.
|
|
angle (Tensor): Angles (degrees) of rotation for each images.
|
|
|
|
Returns:
|
|
(Tensor): Batch of rotated images.
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
<a id="a10a9e3221a2c76824d7d7f305b2aa9aa"></a>
|
|
<h2 class="memtitle"><span class="permalink"><a href="#a10a9e3221a2c76824d7d7f305b2aa9aa">◆ </a></span>sharpeness()</h2>
|
|
|
|
<div class="memitem">
|
|
<div class="memproto">
|
|
<table class="memname">
|
|
<tr>
|
|
<td class="memname">def transformations.sharpeness </td>
|
|
<td>(</td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>x</em>, </td>
|
|
</tr>
|
|
<tr>
|
|
<td class="paramkey"></td>
|
|
<td></td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>sharpness_factor</em> </td>
|
|
</tr>
|
|
<tr>
|
|
<td></td>
|
|
<td>)</td>
|
|
<td></td><td></td>
|
|
</tr>
|
|
</table>
|
|
</div><div class="memdoc">
|
|
<pre class="fragment">Adjust sharpness of images.
|
|
|
|
Args:
|
|
x (Tensor): Batch of images.
|
|
sharpness_factor (Tensor): Sharpness factor for each images.
|
|
0.0 gives a black image. A factor of 1.0 gives the original image.
|
|
|
|
Returns:
|
|
(Tensor): Batch of adjusted images.
|
|
</pre>
|
|
</div>
|
|
</div>
|
|
<a id="a9afae86f07c95502ab6aa79374f9418e"></a>
|
|
<h2 class="memtitle"><span class="permalink"><a href="#a9afae86f07c95502ab6aa79374f9418e">◆ </a></span>shear()</h2>
|
|
|
|
<div class="memitem">
|
|
<div class="memproto">
|
|
<table class="memname">
|
|
<tr>
|
|
<td class="memname">def transformations.shear </td>
|
|
<td>(</td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>x</em>, </td>
|
|
</tr>
|
|
<tr>
|
|
<td class="paramkey"></td>
|
|
<td></td>
|
|
<td class="paramtype"> </td>
|
|
<td class="paramname"><em>shear</em> </td>
|
|
</tr>
|
|
<tr>
|
|
<td></td>
|
|
<td>)</td>
|
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<pre class="fragment">Shear images.
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Args:
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x (Tensor): Batch of images.
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shear (Tensor): Angle of shear for each images.
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Returns:
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(Tensor): Batch of skewed images.
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</pre>
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</div>
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</div>
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<a id="a0a93f2875e5a3d32f64581666ad03774"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a0a93f2875e5a3d32f64581666ad03774">◆ </a></span>solarize()</h2>
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<div class="memitem">
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<div class="memproto">
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<table class="memname">
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<tr>
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<td class="memname">def transformations.solarize </td>
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<td>(</td>
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<td class="paramtype"> </td>
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<td class="paramname"><em>x</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype"> </td>
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<td class="paramname"><em>thresholds</em> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<pre class="fragment">Invert all pixel values above a threshold.
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Be warry that the use of the inequality (x>tresholds) block the gradient propagation.
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Args:
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x (Tensor): Batch of images.
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thresholds (Tensor): All pixels above this level are inverted
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Returns:
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(Tensor): Batch of solarized images.
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|
</pre>
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</div>
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</div>
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<a id="a6aeaef035ff9c1c0977e7ef49b7de735"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a6aeaef035ff9c1c0977e7ef49b7de735">◆ </a></span>translate()</h2>
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<div class="memitem">
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<div class="memproto">
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<table class="memname">
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<tr>
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<td class="memname">def transformations.translate </td>
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<td>(</td>
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<td class="paramtype"> </td>
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<td class="paramname"><em>x</em>, </td>
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</tr>
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<tr>
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<td class="paramkey"></td>
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<td></td>
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<td class="paramtype"> </td>
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<td class="paramname"><em>translation</em> </td>
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</tr>
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<tr>
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<td></td>
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<pre class="fragment">Translate images.
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Args:
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x (Tensor): Batch of images.
|
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translation (Tensor): Distance (pixels) of translation for each images.
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|
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|
Returns:
|
|
(Tensor): Batch of translated images.
|
|
</pre>
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</div>
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</div>
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<a id="a380988bd0d3afb343b8b63fe52703180"></a>
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<h2 class="memtitle"><span class="permalink"><a href="#a380988bd0d3afb343b8b63fe52703180">◆ </a></span>zero_stack()</h2>
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<div class="memitem">
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<div class="memproto">
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<table class="memname">
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<tr>
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<td class="memname">def transformations.zero_stack </td>
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|
<td>(</td>
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<td class="paramtype"> </td>
|
|
<td class="paramname"><em>tensor</em>, </td>
|
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</tr>
|
|
<tr>
|
|
<td class="paramkey"></td>
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<td></td>
|
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<td class="paramtype"> </td>
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<td class="paramname"><em>zero_pos</em> </td>
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</tr>
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<tr>
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<td></td>
|
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<td>)</td>
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<td></td><td></td>
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</tr>
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</table>
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</div><div class="memdoc">
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<pre class="fragment">Add a row of zeros to a Tensor.
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This function is intended to be used with single row Tensor, thus returning a 2 dimension Tensor.
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|
Args:
|
|
tensor (Tensor): Tensor to be stacked with zeros.
|
|
zero_pos (int): Wheter the zeros should be added before or after the Tensor. Either 0 or 1.
|
|
|
|
Returns:
|
|
Stacked Tensor.
|
|
</pre>
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</div>
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