I have a requirement where in I have to determine whether a photo is corrupted and accordingly tag it as such. You can try some things, but with certain file formats (example: BMP, JPEG to some extent) only a human can ultimately decide if the file is OK or corrupted. The simplest test is to … Read more
The technique is called texture mapping. This is a code example from surface function (R2011b): load clown surface(peaks,flipud(X),… ‘FaceColor’,’texturemap’,… ‘EdgeColor’,’none’,… ‘CDataMapping’,’direct’) colormap(map) view(-35,45) This example loads RGB image from “peppers.png” and maps it onto cylinder: imgRGB = imread(‘peppers.png’); [imgInd,map] = rgb2ind(imgRGB,256); [imgIndRows,imgIndCols] = size(imgInd); [X,Y,Z] = cylinder(imgIndRows,imgIndCols); surface(X,Y,Z,flipud(imgInd),… ‘FaceColor’,’texturemap’,… ‘EdgeColor’,’none’,… ‘CDataMapping’,’direct’) colormap(map) view(-35,45) Things are … Read more
You need to set the PorterDuff mode on a Paint object: mPaint.setXfermode(new PorterDuffXfermode(PorterDuff.Mode.SRC)) and draw with Color.TRANSPARENT Also see this thread in the Android developer group: http://groups.google.com/group/android-developers/browse_thread/thread/5b0a498664b17aa0/de4aab6fb7e97e38?lnk=gst&q=erase+transparent#
Ok, here is a way involving several randomization steps needed to get a “natural” non symmetrical appearance. I am posting the actual code in Mathematica, just in case someone cares translating it to Matlab. (* A preparatory step: get your image and clean it*) i = Import@”http://i.stack.imgur.com/YENhB.png”; i1 = Image@Replace[ImageData[i], {0., 0., 0.} -> {1, … Read more
The best approach for this situation may be to create a new image in memory with the combined dimensions you desire, then copy or resample the existing images to the new image, and then save the new image to disk. For example: function merge($filename_x, $filename_y, $filename_result) { // Get dimensions for specified images list($width_x, $height_x) … Read more
OK, here we go. Implemented as a small, self-contained example where you can drag around the corners with the mouse: +1 to… tucuxi who already posted the basic approach in his answer MvG at math.stackexchange.com who described how to compute a projective transformation from 4 points Here’s the code: import java.awt.Color; import java.awt.Graphics; import java.awt.GridLayout; … Read more
This can be done faster, by reshaping and swapping axes, and then repeating over all kernel elements, like this: im = np.arange(81).reshape(9,9) print np.swapaxes(im.reshape(3,3,3,-1),1,2) This gives you an array of 3*3 tiles which tessalates across the surface: [[[[ 0 1 2] [[ 3 4 5] [[ 6 7 8] [ 9 10 11] [12 13 … Read more
Use bwconncomp instead since it returns the coordinate indexes for region in a separate cell, where the size of each is easily discerned: >> BW = [1 0 0; 0 0 0; 0 1 1]; % two regions >> CC = bwconncomp(BW) CC = Connectivity: 8 ImageSize: [3 3] NumObjects: 2 PixelIdxList: {[1] [2×1 double]} … Read more
Below is a self contained sketch. The mosaicing algorithm is mid-way through the algorithms implemented in an excellent reference. It works well enough for two hours of work, I think. I tried for the code to be reasonably correct, with two caveats, left, as they say, as an exercise to the reader. I’m not tracking … Read more
Here is a simple and effective linear filtering strategy to remove the horizontal line artifact: Outline: Estimate the frequency of the distortion by looking for a peak in the image’s power spectrum in the vertical dimension. The function scipy.signal.welch is useful for this. Design two filters: a highpass filter with cutoff just below the distortion … Read more