init - 初始化项目

doc/js_tutorials/js_imgproc/js_gradients/js_gradients.markdown

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+Image Gradients {#tutorial_js_gradients}
+===============
+
+Goal
+----
+
+-   Find Image gradients, edges etc
+-   We will learn following functions : **cv.Sobel()**, **cv.Scharr()**, **cv.Laplacian()** etc
+
+Theory
+------
+
+OpenCV provides three types of gradient filters or High-pass filters, Sobel, Scharr and Laplacian.
+We will see each one of them.
+
+### 1. Sobel and Scharr Derivatives
+
+Sobel operators is a joint Gausssian smoothing plus differentiation operation, so it is more
+resistant to noise. You can specify the direction of derivatives to be taken, vertical or horizontal
+(by the arguments, yorder and xorder respectively). You can also specify the size of kernel by the
+argument ksize. If ksize = -1, a 3x3 Scharr filter is used which gives better results than 3x3 Sobel
+filter. Please see the docs for kernels used.
+
+We use the functions: **cv.Sobel (src, dst, ddepth, dx, dy, ksize = 3, scale = 1, delta = 0, borderType = cv.BORDER_DEFAULT)**
+@param src         input image.
+@param dst         output image of the same size and the same number of channels as src.
+@param ddepth      output image depth(see cv.combinations); in the case of 8-bit input images it will result in truncated derivatives.
+@param dx          order of the derivative x.
+@param dy          order of the derivative y.
+@param ksize       size of the extended Sobel kernel; it must be 1, 3, 5, or 7.
+@param scale       optional scale factor for the computed derivative values.
+@param delta       optional delta value that is added to the results prior to storing them in dst.
+@param borderType  pixel extrapolation method(see cv.BorderTypes).
+
+**cv.Scharr (src, dst, ddepth, dx, dy, scale = 1, delta = 0, borderType = cv.BORDER_DEFAULT)**
+@param src         input image.
+@param dst         output image of the same size and the same number of channels as src.
+@param ddepth      output image depth(see cv.combinations).
+@param dx          order of the derivative x.
+@param dy          order of the derivative y.
+@param scale       optional scale factor for the computed derivative values.
+@param delta       optional delta value that is added to the results prior to storing them in dst.
+@param borderType  pixel extrapolation method(see cv.BorderTypes).
+
+Try it
+------
+
+\htmlonly
+<iframe src="../../js_gradients_Sobel.html" width="100%"
+        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
+</iframe>
+\endhtmlonly
+
+### 2. Laplacian Derivatives
+
+It calculates the Laplacian of the image given by the relation,
+\f$\Delta src = \frac{\partial ^2{src}}{\partial x^2} + \frac{\partial ^2{src}}{\partial y^2}\f$ where
+each derivative is found using Sobel derivatives. If ksize = 1, then following kernel is used for
+filtering:
+
+\f[kernel = \begin{bmatrix} 0 & 1 & 0 \\ 1 & -4 & 1 \\ 0 & 1 & 0  \end{bmatrix}\f]
+
+We use the function: **cv.Laplacian (src, dst, ddepth, ksize = 1, scale = 1, delta = 0, borderType = cv.BORDER_DEFAULT)**
+@param src         input image.
+@param dst         output image of the same size and the same number of channels as src.
+@param ddepth      output image depth.
+@param ksize       aperture size used to compute the second-derivative filters.
+@param scale       optional scale factor for the computed Laplacian values.
+@param delta       optional delta value that is added to the results prior to storing them in dst.
+@param borderType  pixel extrapolation method(see cv.BorderTypes).
+
+Try it
+------
+
+\htmlonly
+<iframe src="../../js_gradients_Laplacian.html" width="100%"
+        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
+</iframe>
+\endhtmlonly
+
+One Important Matter!
+---------------------
+
+In our last example, output datatype is cv.CV_8U. But there is a slight problem with
+that. Black-to-White transition is taken as Positive slope (it has a positive value) while
+White-to-Black transition is taken as a Negative slope (It has negative value). So when you convert
+data to cv.CV_8U, all negative slopes are made zero. In simple words, you miss that edge.
+
+If you want to detect both edges, better option is to keep the output datatype to some higher forms,
+like cv.CV_16S, cv.CV_64F etc, take its absolute value and then convert back to cv.CV_8U.
+Below code demonstrates this procedure for a horizontal Sobel filter and difference in results.
+
+Try it
+------
+
+\htmlonly
+<iframe src="../../js_gradients_absSobel.html" width="100%"
+        onload="this.style.height=this.contentDocument.body.scrollHeight +'px';">
+</iframe>
+\endhtmlonly