{"push_id":736671332,"size":2,"distinct_size":2,"ref":"refs/heads/master","head":"560bc1ac248a58bf47e71f2e50b0ef0967fff914","before":"c56e7b1ae9f440db51bd08fb67899cbab4198ae5","commits":[{"sha":"6bc4a68de038d44670efaf4dfbb2e0b0ced800c1","author":{"email":"0d364e3d9dcf9ffa02add366e96242200140565b@live.com","name":"jhollowed"},"message":"The algorithim can now handle multi-layered images (tests were done with RGB).\n\n-CalculateScalarNorm() that gave insensityDiff has been replaced with CalcPixelvalueVectorNorm(). It gives the difference of two pixels with any arbitrary number of components using the vecotr norm.\n\n-Added getters to ImageData() to get the image bands/channels, and the file format\n\n-Changed the way new files are written so they will write in the format of the original and not just .tif\n\n-Adjusted SegentImage() and DivideImage() in numerous places to accomodate for any data type given by PIL's Image.getdata() (can handle more than ints now. Everywhere that a change was made is commented)\n\n-Multi-layered images take considerably longer than grayscale even at 32x32. A 10x10 image was used for testing.","distinct":true,"url":"https://api.github.com/repos/roehrig/ImageSegmentation/commits/6bc4a68de038d44670efaf4dfbb2e0b0ced800c1"},{"sha":"560bc1ac248a58bf47e71f2e50b0ef0967fff914","author":{"email":"0d364e3d9dcf9ffa02add366e96242200140565b@live.com","name":"jhollowed"},"message":"Merged all changed each of us made, and the program is working.","distinct":true,"url":"https://api.github.com/repos/roehrig/ImageSegmentation/commits/560bc1ac248a58bf47e71f2e50b0ef0967fff914"}]}