Build 2024-12-08 05:10:24. 7dc3840. Event: schedule.

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+self.__SSG_MANIFEST=new Set([]);self.__SSG_MANIFEST_CB&&self.__SSG_MANIFEST_CB()

docs/api_reference/augmentations/transforms/index.html

@@ -5804,7 +5804,7 @@
 </span><span id=__span-0-128><a id=__codelineno-0-128 name=__codelineno-0-128></a><a href=#__codelineno-0-128><span class=linenos data-linenos="128 "></span></a>
 </span><span id=__span-0-129><a id=__codelineno-0-129 name=__codelineno-0-129></a><a href=#__codelineno-0-129><span class=linenos data-linenos="129 "></span></a>    <span class=k>def</span> <span class=nf>get_transform_init_args_names</span><span class=p>(</span><span class=bp>self</span><span class=p>)</span> <span class=o>-&gt;</span> <span class=nb>tuple</span><span class=p>[</span><span class=nb>str</span><span class=p>,</span> <span class=o>...</span><span class=p>]:</span>
 </span><span id=__span-0-130><a id=__codelineno-0-130 name=__codelineno-0-130></a><a href=#__codelineno-0-130><span class=linenos data-linenos="130 "></span></a>        <span class=k>return</span> <span class=s2>&quot;amount&quot;</span><span class=p>,</span> <span class=s2>&quot;salt_vs_pepper&quot;</span>
-</span></code></pre></div> </details> </div> </div> <div class="doc doc-object doc-class"> <h2 id=albumentations.augmentations.transforms.Sharpen class="doc doc-heading" data-toc-label=Sharpen> <code>class <strong> Sharpen</strong></code> <code> (alpha=(0.2, 0.5), lightness=(0.5, 1.0), method=&#39;kernel&#39;, kernel_size=5, sigma=1.0, p=0.5, always_apply=None) </code> <span class=doc-github-link> <a href=https://github.com/albumentations-team/albumentations/blob/main/albumentations/augmentations/transforms.py#L20 target=_blank>[view source on GitHub]</a> </span><a class=headerlink href=#albumentations.augmentations.transforms.Sharpen title="Permanent link">¶</a> </h2> <div class=class-signature> </div> <div class="doc doc-contents "> <p>Sharpen the input image using either kernel-based or Gaussian interpolation method.</p> <p>Implements two different approaches to image sharpening: 1. Traditional kernel-based method using Laplacian operator 2. Gaussian interpolation method (similar to Kornia's approach)</p> <p><strong>Parameters:</strong></p> <table> <thead> <tr> <th>Name</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>alpha</code></td> <td><code>tuple[float, float]</code></td> <td><p>Range for the visibility of sharpening effect. At 0, only the original image is visible, at 1.0 only its processed version is visible. Values should be in the range [0, 1]. Used in both methods. Default: (0.2, 0.5).</p></td> </tr> <tr> <td><code>lightness</code></td> <td><code>tuple[float, float]</code></td> <td><p>Range for the lightness of the sharpened image. Only used in 'kernel' method. Larger values create higher contrast. Values should be greater than 0. Default: (0.5, 1.0).</p></td> </tr> <tr> <td><code>method</code></td> <td><code>str</code></td> <td><p>Sharpening algorithm to use: - 'kernel': Traditional kernel-based sharpening using Laplacian operator - 'gaussian': Interpolation between Gaussian blurred and original image Default: 'kernel'</p></td> </tr> <tr> <td><code>kernel_size</code></td> <td><code>int</code></td> <td><p>Size of the Gaussian blur kernel for 'gaussian' method. Must be odd. Default: 5</p></td> </tr> <tr> <td><code>sigma</code></td> <td><code>float</code></td> <td><p>Standard deviation for Gaussian kernel in 'gaussian' method. Default: 1.0</p></td> </tr> <tr> <td><code>p</code></td> <td><code>float</code></td> <td><p>Probability of applying the transform. Default: 0.5.</p></td> </tr> </tbody> </table> <p>Image types: uint8, float32</p> <p>Number of channels: Any</p> <p>Mathematical Formulation: 1. Kernel Method: The sharpening operation is based on the Laplacian operator L: L = [[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]</p> <div class=codehilite><pre><span></span><code>   The final kernel K is a weighted sum:
+</span></code></pre></div> </details> </div> </div> <div class="doc doc-object doc-class"> <h2 id=albumentations.augmentations.transforms.Sharpen class="doc doc-heading" data-toc-label=Sharpen> <code>class <strong> Sharpen</strong></code> <code> (alpha=(0.2, 0.5), lightness=(0.5, 1.0), method=&#39;kernel&#39;, kernel_size=5, sigma=1.0, p=0.5, always_apply=None) </code> <span class=doc-github-link> <a href=https://github.com/albumentations-team/albumentations/blob/main/albumentations/augmentations/transforms.py#L20 target=_blank>[view source on GitHub]</a> </span><a class=headerlink href=#albumentations.augmentations.transforms.Sharpen title="Permanent link">¶</a> </h2> <div class=class-signature> </div> <div class="doc doc-contents "> <p>Sharpen the input image using either kernel-based or Gaussian interpolation method.</p> <p>Implements two different approaches to image sharpening: 1. Traditional kernel-based method using Laplacian operator 2. Gaussian interpolation method (similar to Kornia's approach)</p> <p><strong>Parameters:</strong></p> <table> <thead> <tr> <th>Name</th> <th>Type</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>alpha</code></td> <td><code>tuple[float, float]</code></td> <td><p>Range for the visibility of sharpening effect. At 0, only the original image is visible, at 1.0 only its processed version is visible. Values should be in the range [0, 1]. Used in both methods. Default: (0.2, 0.5).</p></td> </tr> <tr> <td><code>lightness</code></td> <td><code>tuple[float, float]</code></td> <td><p>Range for the lightness of the sharpened image. Only used in 'kernel' method. Larger values create higher contrast. Values should be greater than 0. Default: (0.5, 1.0).</p></td> </tr> <tr> <td><code>method</code></td> <td><code>Literal[&#39;kernel&#39;, &#39;gaussian&#39;]</code></td> <td><p>Sharpening algorithm to use: - 'kernel': Traditional kernel-based sharpening using Laplacian operator - 'gaussian': Interpolation between Gaussian blurred and original image Default: 'kernel'</p></td> </tr> <tr> <td><code>kernel_size</code></td> <td><code>int</code></td> <td><p>Size of the Gaussian blur kernel for 'gaussian' method. Must be odd. Default: 5</p></td> </tr> <tr> <td><code>sigma</code></td> <td><code>float</code></td> <td><p>Standard deviation for Gaussian kernel in 'gaussian' method. Default: 1.0</p></td> </tr> <tr> <td><code>p</code></td> <td><code>float</code></td> <td><p>Probability of applying the transform. Default: 0.5.</p></td> </tr> </tbody> </table> <p>Image types: uint8, float32</p> <p>Number of channels: Any</p> <p>Mathematical Formulation: 1. Kernel Method: The sharpening operation is based on the Laplacian operator L: L = [[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]]</p> <div class=codehilite><pre><span></span><code>   The final kernel K is a weighted sum:
    K = (1 - a)I + a(L + λI)
 
    where:
@@ -5858,7 +5858,7 @@
 </span><span id=__span-0-15><a id=__codelineno-0-15 name=__codelineno-0-15></a><a href=#__codelineno-0-15><span class=linenos data-linenos=" 15 "></span></a><span class=sd>            Only used in &#39;kernel&#39; method. Larger values create higher contrast.</span>
 </span><span id=__span-0-16><a id=__codelineno-0-16 name=__codelineno-0-16></a><a href=#__codelineno-0-16><span class=linenos data-linenos=" 16 "></span></a><span class=sd>            Values should be greater than 0. Default: (0.5, 1.0).</span>
 </span><span id=__span-0-17><a id=__codelineno-0-17 name=__codelineno-0-17></a><a href=#__codelineno-0-17><span class=linenos data-linenos=" 17 "></span></a>
-</span><span id=__span-0-18><a id=__codelineno-0-18 name=__codelineno-0-18></a><a href=#__codelineno-0-18><span class=linenos data-linenos=" 18 "></span></a><span class=sd>        method (str): Sharpening algorithm to use:</span>
+</span><span id=__span-0-18><a id=__codelineno-0-18 name=__codelineno-0-18></a><a href=#__codelineno-0-18><span class=linenos data-linenos=" 18 "></span></a><span class=sd>        method (Literal[&#39;kernel&#39;, &#39;gaussian&#39;]): Sharpening algorithm to use:</span>
 </span><span id=__span-0-19><a id=__codelineno-0-19 name=__codelineno-0-19></a><a href=#__codelineno-0-19><span class=linenos data-linenos=" 19 "></span></a><span class=sd>            - &#39;kernel&#39;: Traditional kernel-based sharpening using Laplacian operator</span>
 </span><span id=__span-0-20><a id=__codelineno-0-20 name=__codelineno-0-20></a><a href=#__codelineno-0-20><span class=linenos data-linenos=" 20 "></span></a><span class=sd>            - &#39;gaussian&#39;: Interpolation between Gaussian blurred and original image</span>
 </span><span id=__span-0-21><a id=__codelineno-0-21 name=__codelineno-0-21></a><a href=#__codelineno-0-21><span class=linenos data-linenos=" 21 "></span></a><span class=sd>            Default: &#39;kernel&#39;</span>