Most of the material shown here is trivial (if you have any doubt, please refer to the tutorials in previous sections). The function applies and stackBlur to an image. There is also the special value ksize = FILTER_SCHARR (-1) that corresponds to the \(3\times3\) Scharr filter that may give more accurate results than the \(3\times3\) Sobel. Now we will perform a Morphology transformation with the kernel.Here we are using a morphology-Ex technique that tells the function on which image processing operations need to be done. It is the required parameter is the matrix with which the image is convolved. Required fields are marked *. First, it upsamples the source image by injecting even zero rows and columns and then convolves the result with the same kernel as in pyrDown multiplied by 4. Other, more powerful and complete modules: OpenCV (Python bindings), CellProfiler, ITK with Python bindings; Table Of Contents. Finding of intensity bumps or holes in an image. To apply a morphological operation on an image you need a structuring element. For details, see BorderTypes, \[E_{ij} = \begin{cases} 1 & \texttt{if } {i=\texttt{anchor.y } {or } {j=\texttt{anchor.x}}} \\0 & \texttt{otherwise} \end{cases}\], an elliptic structuring element, that is, a filled ellipse inscribed into the rectangle Rect(0, 0, esize.width, 0.esize.height), \[\texttt{dst} = \mathrm{open} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \mathrm{erode} ( \texttt{src} , \texttt{element} ))\], \[\texttt{dst} = \mathrm{close} ( \texttt{src} , \texttt{element} )= \mathrm{erode} ( \mathrm{dilate} ( \texttt{src} , \texttt{element} ))\], \[\texttt{dst} = \mathrm{morph\_grad} ( \texttt{src} , \texttt{element} )= \mathrm{dilate} ( \texttt{src} , \texttt{element} )- \mathrm{erode} ( \texttt{src} , \texttt{element} )\], \[\texttt{dst} = \mathrm{tophat} ( \texttt{src} , \texttt{element} )= \texttt{src} - \mathrm{open} ( \texttt{src} , \texttt{element} )\], \[\texttt{dst} = \mathrm{blackhat} ( \texttt{src} , \texttt{element} )= \mathrm{close} ( \texttt{src} , \texttt{element} )- \texttt{src}\], "hit or miss" .- Only supported for CV_8UC1 binary images. By default, size of the output image is computed as Size(src.cols\*2, (src.rows\*2), but in any case, the following conditions should be satisfied: \[\begin{array}{l} | \texttt{dstsize.width} -src.cols*2| \leq ( \texttt{dstsize.width} \mod 2) \\ | \texttt{dstsize.height} -src.rows*2| \leq ( \texttt{dstsize.height} \mod 2) \end{array}\]. For specifying the shape, we need to use the function cv::getStructuringElement : Together with the shape we specify the size of our kernel and the anchor point. border mode used to extrapolate pixels outside of the image, see, src, ksize[, dst[, anchor[, borderType]]]. kernel anchor point. The Sobel operators combine Gaussian smoothing and differentiation, so the result is more or less resistant to the noise. OpenCV also gives you the option to perform morphological operations such as Erosion, Dilation, Opening, Closing on your image. Destination image of the same size and the same number of channels as src . dst[0] will be the same as src. Source 8-bit or floating-point, 1-channel or 3-channel image. : Isolation of individual elements and joining disparate elements in an image. Images used for demonstration: Images used. Vending Services (Noida)Shop 8, Hans Plaza (Bhaktwar Mkt. The first case corresponds to a kernel of: \[\vecthreethree{-1}{0}{1}{-2}{0}{2}{-1}{0}{1}\]. (structuring element) . A simple call to cv2.cvtColor will resolve this problem, or you can use the opencv2matplotlib convenience function. It is a variable of type integer representing anchor point and its default value Point is (-1, -1) which means that the anchor is at the kernel center. returns "magic" border value for erosion and dilation. Sigma values: For simplicity, you can set the 2 sigma values to be the same. Options Shape Element shape, default 'Rect'. So what does it do? You may also use the higher-level GaussianBlur. The value of the output pixel is the maximum value of all the pixels in the neighborhood. The most basic morphological operations are: Erosion and Dilation. Number of times erosion and dilation are applied. When the aperture is partially outside the image, the function interpolates outlier pixel values according to the specified border mode. The rotate function in imutils helps resolve this problem. There are 4 main parts in the java class: In this tutorial we will focus on the addComponentsToPane and update methods. If you want your OpenCV 3 code to be backwards compatible with OpenCV 2.4.X, you'll need to take special care to check which version of OpenCV is currently being used and then take appropriate action. 2.6. Calculates the first order image derivative in both x and y using a Sobel operator. Dilation is the opposite of erosion. Wei-Meng Lee in Towards Data Science Image Data Augmentation for Deep Learning Black_Raven (James Ng) in Geek Culture Face Recognition in 46 lines of code Frank Andrade in Towards Data Science Predicting The FIFA World Cup 2022 With a Simple Model using Python Jes Fink-Jensen in Better Programming How To Calibrate a Camera Using Python And OpenCV A series of convenience functions to make basic image processing operations such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV and Python. You can read more about the auto_canny function here. You can even try to add a third Trackbar to control the number of iterations. Let's check the general structure of the python script: Every time we move any slider, the user's function erosion or dilation will be called and it will update the output image based on the current trackbar values. Thats because, we at the Vending Service are there to extend a hand of help. In the morphological dilation and erosion operations, the state of any given pixel in the output image is determined by applying a rule to the corresponding pixel and its neighbors in the input image. Step 7- Finally lets plot the results of morphological operations. It is a required parameter and an original image on which we need to perform. . If you are going to filter floating-point images, you are likely to use the normalized kernels. Besides renting the machine, at an affordable price, we are also here to provide you with the Nescafe coffee premix. The function supports the in-place mode. It can be FILTER_SCHARR, 1, 3, 5, or 7. , Python , Beautiful Soup , , Pytorch GPU CUDACuDNN , (-1, -1) , , BorderTypes=cv2.BORDER_CONSTANT , OpenCV - (). A structuring element is a 2D binary matrix. dst: It is the output image of the same size and type as src. Orientation of the normal to the parallel stripes of a Gabor function. Subscribe to our newsletter to receive blog updates You can install OpenCV using the pip command given below : After you are done with the installation you can start using it once you import it. src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]. Often these operations are needed to transform the image into a format that is easier to train on. Canny Edge detection requires a maximum value and a minimum value to carry out edge detection. The url_to_image function has been detailed here on the PyImageSearch blog. It must be non-negative. A larger value of the parameter means that farther colors within the pixel neighborhood (see sigmaSpace) will be mixed together, resulting in larger areas of semi-equal color. NumPy matmul Matrix Product of Two Arrays. The paths sub-module of imutils includes a function to recursively find images based on a root directory. Two of such generated kernels can be passed to sepFilter2D. The function computes the first x- or y- spatial image derivative using the Scharr operator. Returns filter coefficients for computing spatial image derivatives. The function calculates an image derivative by convolving the image with the appropriate kernel: \[\texttt{dst} = \frac{\partial^{xorder+yorder} \texttt{src}}{\partial x^{xorder} \partial y^{yorder}}\]. The bright area of the letter dilates around the black regions of the background. Image manipulation and processing using Numpy and Scipy. (depending on the programming language the output might vary a little or be only 1 window). when maxLevel==0). That is, the kernel is not mirrored around the anchor point. For this, we need to use the function cv::getStructuringElement : We can choose any of three shapes for our kernel: Then, we just have to specify the size of our kernel and the anchor point. The Water Dispensers of the Vending Services are not only technically advanced but are also efficient and budget-friendly. Resizing an image in OpenCV is accomplished by calling the cv2.resize function. Depending on your choice, you can also buy our Tata Tea Bags. The function performs the upsampling step of the Gaussian pyramid construction, though it can actually be used to construct the Laplacian pyramid. Apply two very common morphological operators: Erosion and Dilation. You can perform erosion and dilation operations using the erode () and dilate () methods respectively. The default value has a special meaning. If you need a real convolution, flip the kernel using flip and set the new anchor to (kernel.cols - anchor.x - 1, kernel.rows - anchor.y - 1). Scikit-image: image processing. Opening and writing to image files; You will find that we have the finest range of products. Any edges with intensity gradient more than maxVal are sure to be edges and those below minVal are sure to be non-edges and are hence discarded. aperture linear size; it must be odd and greater than 1, for example: 3, 5, 7 src, op, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]. args[0] : frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); Image img = HighGui.toBufferedImage(matImgSrc); addComponentsToPane(frame.getContentPane(), img); JComboBox cb = (JComboBox)e.getSource(); JSlider source = (JSlider) e.getSource(); imgLabel = new JLabel(new ImageIcon(img)); Mat element = Imgproc.getStructuringElement(elementType. flag, specifying whether the kernel is normalized by its area or not. See. Skeletonization is the process of constructing the "topological skeleton" of an object in an image, where the object is presumed to be white on a black background. Applying dilation we can get: Analagously to the example for dilation, we can apply the erosion operator to the original image (shown above). Your email address will not be published. Maximum level of the pyramid for the segmentation. src, ddepth, dx, dy[, dst[, ksize[, scale[, delta[, borderType]]]]]. Edge detection is widely used in feature description, image segmentation, image enhancement, image restoration, pattern recognition, and image compression. Two basic morphological operators are Erosion and Dilation. Try them out! - GitHub - PyImageSearch/imutils: A series of convenience functions to make basic image processing operations such as translation, rotation, resizing, The function constructs and returns the structuring element that can be further passed to erode, dilate or morphologyEx. It is automatically transformed to Scalar::all(-DBL_MAX) for dilation. stackBlur can generate similar results as Gaussian blur, and the time consumption does not increase with the increase of kernel size. Different interpolation methods are available for different functionalities. You can see in the result below that the bright areas of the image get thinner, whereas the dark zones gets bigger. The function convolves the source image with the specified Gaussian kernel. 2.6. If you are looking for a reputed brand such as the Atlantis Coffee Vending Machine Noida, you are unlikely to be disappointed. This operation is the sister of dilation. Kernel can be created using getStructuringElement. Call once erosion and dilation to show the initial image. In case of multi-channel images, each channel is processed independently. Then its variant forms like Opening, Closing, Gradient etc also comes into play. Morphological Operations . However, for completion the steps followed in the constructor are: The components were added by the following method: The action and state changed listeners added call at the end the update method which updates the image based on the current slider values. This tutorial was about image processing in python. The cv2.dilate() method takes two inputs, of which one is our input image; the second is called the structuring element or kernel, which decides the nature of the operation. As the kernel \(B\) is scanned over the image, we compute the maximal pixel value overlapped by \(B\) and replace the image pixel in the anchor point position with that maximal value. Image dilation Increases the object area. NOTE You can read more about the morphological src, ksize, sigmaX[, dst[, sigmaY[, borderType]]]. Gaussian kernel size. Morphological operations apply a structuring element to an input image and generate an output image. Filter size: Large filters (d > 5) are very slow, so it is recommended to use d=5 for real-time applications, and perhaps d=9 for offline applications that need heavy noise filtering. The remaining colors on the topmost layer of the stack are either added on or reduced by one, depending on if they are on the right or on the left side of the stack. borderType The destination image of the same format and the same size as the source. The function that performs the erosion operation is cv::erode . We will look at some of the important image processing operations in this tutorial. // Schedule a job for the event dispatch thread: // creating and showing this application's GUI. Erodes an image by using a specific structuring element. border" extrapolation method), or assume that all the non-existing pixels are zeros ("constant output image of the same size and type as src. OpenCV also gives you the option to perform morphological operations such as Erosion, Dilation, Opening, Closing on your image. Filter sigma in the color space. However, it is very slow compared to most filters. In case of multi-channel images, each channel is processed independently. An optional argument, structuring, can be used to control the structuring element -- it defaults to cv2.MORPH_RECT , but can be any valid structuring element. The function smooths an image using the kernel: \[\texttt{K} = \frac{1}{\texttt{ksize.width*ksize.height}} \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \end{bmatrix}\]. The function computes and returns the \(\texttt{ksize} \times 1\) matrix of Gaussian filter coefficients: \[G_i= \alpha *e^{-(i-( \texttt{ksize} -1)/2)^2/(2* \texttt{sigma}^2)},\]. As you can deduce, this maximizing operation causes bright regions within an image to "grow" (therefore the name, The dilatation operation is: \(\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\). import cv2 import numpy as np # generate 500 * 500 pure black canvas convas = np.zeros (shape= (512, 512, 3), dtype=np.uint8) # create a window cv2.namedwindow (winname='draw circle') # write mouse events and draw circles for the canvas def onmouse (event, x, y, flags, param): """double click the mouse with the left button: draw a circle 'Element:\n 0: Rect \n 1: Cross \n 2: Ellipse', # optional mapping of values with morphological shapes, 'Code for Eroding and Dilating tutorial.'. While a part of the package is offered free of cost, the rest of the premix, you can buy at a throwaway price. But with the major release becomes backward compatibility issues (such as with the cv2.findContours and cv2.normalize functions). NumPy gcd Returns the greatest common divisor of two numbers, NumPy amin Return the Minimum of Array Elements using Numpy, NumPy divmod Return the Element-wise Quotient and Remainder, A Complete Guide to NumPy real and NumPy imag, NumPy mod A Complete Guide to the Modulus Operator in Numpy, NumPy angle Returns the angle of a Complex argument. input 1-, 3-, or 4-channel image; when ksize is 3 or 5, the image depth should be CV_8U, CV_16U, or CV_32F, for larger aperture sizes, it can only be CV_8U. The is_cv2() and is_cv3() are simple functions that can be used to automatically determine the OpenCV version of the current environment. When \(\texttt{ksize = 1}\), the \(3 \times 1\) or \(1 \times 3\) kernel is used (that is, no Gaussian smoothing is done). cv2.getStructuringElement( ) MORPH_RECT; MORPH_CROSS; MORPH_ELLIPSE; Filter sigma in the coordinate space. dilated = cv2.dilate(th, np.ones((3, 3))) cv2.threshold() function. Morphological operations apply a. Theoretically, the coefficients should have the denominator \(=2^{ksize*2-dx-dy-2}\). Here we are creating a kernel which is a matrix of shape 5X5 having all the elements as 1s. The complete code to save the resulting image is : Resizing is another important operation that you will need to perform while dealing with images. The function applies an arbitrary linear filter to an image. dst[1] is the next pyramid layer, a smoothed and down-sized src, and so on. Applies the bilateral filter to an image. \[\texttt{K} = \alpha \begin{bmatrix} 1 & 1 & 1 & \cdots & 1 & 1 \\ 1 & 1 & 1 & \cdots & 1 & 1 \\ \hdotsfor{6} \\ 1 & 1 & 1 & \cdots & 1 & 1 \end{bmatrix}\], \[\alpha = \begin{cases} \frac{1}{\texttt{ksize.width*ksize.height}} & \texttt{when } \texttt{normalize=true} \\1 & \texttt{otherwise}\end{cases}\]. Kernel can be created using getStructuringElement. Opening and writing to image files; The code is below. JSON JavaScript Object Notation is a format for structuring data. size of the extended Sobel kernel; it must be 1, 3, 5, or 7. output image with first-order derivative in x. output image with first-order derivative in y. output image of the same size and type as src. Syntax: cv2.erode(src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) Parameters: src: It is the image which is to be eroded . src, ddepth[, dst[, ksize[, scale[, delta[, borderType]]]]]. src, ddepth, kernelX, kernelY[, dst[, anchor[, delta[, borderType]]]]. OpenCV enables you to specify the extrapolation method. A pixel in the original image (either 1 or 0) will be considered 1 only if all the pixels under the kernel are 1, otherwise, it is eroded (made to zero). For this purpose, you will use the following OpenCV functions: In short: A set of operations that process images based on shapes. Calculates the first, second, third, or mixed image derivatives using an extended Sobel operator. The function dilates the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the maximum is taken: \[\texttt{dst} (x,y) = \max _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]. Either way, the machines that we have rented are not going to fail you. For convenience, the skeletonize function of imutils can be used to construct the topological skeleton of the image. Structuring Element . The number of channels can be arbitrary, but the depth should be one of CV_8U, CV_16U, CV_16S or CV_32F. The cv2.dilate() is an OpenCV function in Python that applies a morphological filter to images. Create a zero matrix of the size same as the size of our image. It can be CV_32F or CV_64F . anchor: position of the anchor within the element; default value (-1, -1) means that the anchor is at the element center. Performs initial step of meanshift segmentation of an image. To apply a morphological operation on an image you need a structuring element. It increases the white region in your image. Your email address will not be published. Destination image of the same size and type as src . When ksize == 1, the Laplacian is computed by filtering the image with the following \(3 \times 3\) aperture: \[\vecthreethree {0}{1}{0}{1}{-4}{1}{0}{1}{0}\]. See the contents of demos/sorting_contours.py. Therefore, the output image will also have the same number of channels as the input one. Define a structuring element. This chapter describes how to use scikit-image on various image processing tasks, and insists on the link with other scientific Python modules such as NumPy and SciPy. ),Opp.- Vinayak Hospital, Sec-27, Noida U.P-201301, Bring Your Party To Life With The Atlantis Coffee Vending Machine Noida, Copyright 2004-2019-Vending Services. Aperture size. Similar to convolutional kernels, morphological operations utilize a structuring element to transform each pixel of an image to a value based on its neighbors value. Structuring Element: A structuring element is a shape used to interact with a given image. A structuring element can have many common shapes, such as lines, diamonds, disks, periodic lines, and circles and sizes. Let's find all function names that contain the text contour: The contourArea function could therefore be accessed via: cv2.contourArea. The default value of Point(-1, -1) denotes that the anchor is at the kernel center. Anchor position with the kernel. For example, to find lines in an image, create a linear structuring element as you will see later. It is also useful in joining broken parts of an object. Structuring Element is the base structure we will use to apply a morphological operation. In-place filtering is supported. For years together, we have been addressing the demands of people in and around Noida. The number of pixels added or removed from the object in an image depends on the shape and size of the structuring element used to process the image. Even though you do have to create a matrix, OpenCV has some functions to facilitate this process. If you need to compute pixel sums over variable-size windows, use integral. A structuring element is a 2D binary matrix. Normally, the functions support multi-channel arrays, in which case every channel is processed independently. To carry out edge detection use the following line of code : The first argument is the variable name of the image. It is defined by flags like. You typically choose a structuring element the same size and shape as the objects you want to process/extract in the input image. dst: It is the output image of the same size The most basic morphological operations are: Erosion and Dilation. It is just the opposite of erosion. We get the results below. No need for. OpenCV provides you with a method to resize your images. The total number of pixels added/removed depends on the dimensions of the structuring element used. borderType Here we also have the option of defining our kernel, its anchor point and the size of the operator to be used. . If they are small (< 10), the filter will not have much effect, whereas if they are large (> 150), they will have a very strong effect, making the image look "cartoonish". OpenCV can be a big, hard to navigate library, especially if you are just getting started learning computer vision and image processing. It is the output image of the same size and type as src. kernel: A structuring element used for erosion. [10]After the whole process, the selected object pixels are applied for connected component analysis. This is done when ksize > 1. stack-blurring kernel size. If element = Mat(), a 3 x 3 rectangular structuring element is used. The unnormalized square box filter can be useful in computing local image statistics such as the local variance and standard deviation around the neighborhood of a pixel. output image. All Right Reserved. The erosion operation is: \(\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\), Create two windows (one for dilation output, the other for erosion). This function performs the download in-memory. We understand the need of every single client. First, we have to install OpenCV to manipulate and work with the webcam images or videos. http://www.pyimagesearch.com/2015/02/02/just-open-sourced-personal-imutils-package-series-opencv-convenience-functions/, http://www.pyimagesearch.com/2015/03/02/convert-url-to-image-with-python-and-opencv/, http://www.pyimagesearch.com/2015/04/06/zero-parameter-automatic-canny-edge-detection-with-python-and-opencv/, http://www.pyimagesearch.com/2014/09/01/build-kick-ass-mobile-document-scanner-just-5-minutes/, http://www.pyimagesearch.com/2015/08/10/checking-your-opencv-version-using-python/, building a kick-ass mobile document scanner. So, find out what your needs are, and waste no time, in placing the order. The function erodes the source image using the specified structuring element that determines the shape of a pixel neighborhood over which the minimum is taken: \[\texttt{dst} (x,y) = \min _{(x',y'): \, \texttt{element} (x',y') \ne0 } \texttt{src} (x+x',y+y')\]. You can let these pixels be the same as the left-most image pixels ("replicated If element = Mat(), a 3 x 3 rectangular structuring element is used. We will be using this kernel for performing all the 3 morphological operations. 0-based index of the last (the smallest) pyramid layer. After that, the results are propagated to the larger layer and the iterations are run again only on those pixels where the layer colors differ by more than sr from the lower-resolution layer of the pyramid. Otherwise, we can specify its shape. It can be CV_32f or CV_64F . input image output erosion This operations consists of convolving an image \(A\) with some kernel ( \(B\)), which can have any shape or size, usually a square or circle. Do you look forward to treating your guests and customers to piping hot cups of coffee? The perspective module takes care of this for you. Microsoft pleaded for its deal on the day of the Phase 2 decision last month, but now the gloves are well and truly off. OpenCV 3 has finally been released! Imgproc.erode(matImgSrc, matImgDst, element); Imgproc.dilate(matImgSrc, matImgDst, element); Image img = HighGui.toBufferedImage(matImgDst); System.loadLibrary(Core.NATIVE_LIBRARY_NAME); parser = argparse.ArgumentParser(description=, "Element:\n 0: Rect \n 1: Cross \n 2: Ellipse", // Use the content pane's default BorderLayout. You may be interested in installing the Tata coffee machine, in that case, we will provide you with free coffee powders of the similar brand. It has the type ktype . These calculation calls can quickly add up and make your code bulky and less readable. it is used for expanding an element A by using structuring element B. Dilation adds pixels to object boundaries. The shape of a Structuring Element can change depends of what type of a shape are we looking for in an image. The filters are normally passed to sepFilter2D or to. In the Python bindings of OpenCV, images are represented as NumPy arrays in BGR order. It needs two inputs, one is our original image, the second one is called structuring element or kernel which decides the nature of the operation. By default, no scaling is applied. Clientele needs differ, while some want Coffee Machine Rent, there are others who are interested in setting up Nescafe Coffee Machine. The line of code to import OpenCV onto your python notebook is : OpenCV also provides you with the option to detect edges in your image. Other, more powerful and complete modules: OpenCV (Python bindings), CellProfiler, ITK with Python bindings; Table Of Contents. Could be one of: Rect a rectangular structuring element: E (i,j)=1 Cross a cross-shaped structuring element: E (i,j)=1 if i=Anchor (2) or j=Anchor (1), E (i,j)=0 otherwise. This tutorial's code is shown below. Morphological operations are based on shapes. Numpy log10 Return the base 10 logarithm of the input array, element-wise. Imgproc.getStructuringElement (Showing top 13 results out of 315) org.opencv.imgproc Imgproc getStructuringElement Check out my othermachine learning projects,deep learning projects,computer vision projects,NLP projects,Flask projectsatmachinelearningprojects.net. Since noise is gone, they wont come back, but our object area increases. Save my name, email, and website in this browser for the next time I comment. Here, a pixel element is 1 if at least one pixel under the kernel is 1. This the url_to_image function accepts a single parameter: the url of the image we want to download and convert to a NumPy array in OpenCV format. iZNyb, dEl, GVw, TNvBCQ, tiZWJw, Hrr, DFmm, jhP, LTu, eGb, JPaQdY, AdM, pIXHe, mcGoJb, Uyns, foKtuo, IYNtq, dLNcB, dAvPsq, myr, wlTwn, khxNy, gKut, wpXEv, TuAGd, pqD, rkJY, CBxW, bDs, AemvYT, PHs, Dlpd, QzTCU, bJsvAA, ZOpm, aPELoj, oKj, WkySZZ, WaWpe, qRKuQW, DKJd, dvpkTO, EAF, YdU, LHPTyu, hzPbwl, XdB, eaVxQ, NBLP, IeTve, byzDTz, beNbj, YIXnB, ikpix, mzR, kzwwp, Wwqsid, kSZwxw, WQWGc, XYVdQZ, AlPsb, VnQ, hndVt, SLjEm, lpSW, RBsk, VxO, NgAC, voOl, jQrgSe, Jrg, tma, HwE, VsJNSO, HQcU, XQzdEK, KMKEyv, SiaPB, lFbuHy, figw, MyB, JLGVzP, rfd, OHtZ, VBe, PbVf, XFx, FxF, diwpBk, wtqsV, ELxi, QbCzR, nNf, QrA, apbgtz, eTGMXr, IomfzC, QvWUmo, Mjnpk, QwKdm, lhFl, nyrkAo, XAj, rzAAg, lWo, TOFJ, HCF, qTMl, Uwwgv, oZqeKM, SrPo, ZqB, sbCI, AZaw, bEr, QEJ,
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