interpolation

问题 I am trying to understand image interpolation algorithms in computer vision. I realize that there are a ton of interpolation techniques like linear, bicubic, nearest neighbor, etc. for image rotation. It seems that nearest neighbor technique is the simplest algorithm in this area.. I understand the basic concepts like when we rotate an image with a rotation matrix, the new image rows and columns go to floating point values because of cosine and sine operations. Thus we have to truncate the

问题 Is there a way to achieve this (OpenGL 2.1)? If I draw lines like this glShadeModel(GL_FLAT); glBegin(GL_LINES); glColor3f(1.0, 1.0, 0.0); glVertex3fv(bottomLeft); glVertex3fv(topRight); glColor3f(1.0, 0.0, 0.0); glVertex3fv(topRight); glVertex3fv(topLeft); . . (draw a square) . . glEnd(); I get the desired result (a different colour for each edge) but I want to be able to calculate the fragment values in a shader. If I do the same after setting up my shader program I always get interpolated

问题 ggtern output I am trying to plot the following data > foo Resp A B C 1 1.629 0.3333333 0.3333333 0.3333333 2 1.734 0.1666667 0.6666667 0.1666667 3 1.957 0.0000000 1.0000000 0.0000000 4 1.778 1.0000000 0.0000000 0.0000000 5 1.682 0.6666667 0.1666667 0.1666667 6 1.407 0.1666667 0.1666667 0.6666667 7 1.589 0.0000000 0.5000000 0.5000000 8 1.251 0.0000000 0.0000000 1.0000000 9 1.774 0.5000000 0.5000000 0.0000000 10 1.940 0.5000000 0.0000000 0.5000000 > Using ggtern() + geom_interpolate_tern( data

问题 I have some data which when plotted looks like the one shown in left on the picture attached. It has some kinks which I wish to delete and smooth-en to get a nice curve [shown on right]. Presently I manually delete the kinks and interpolate the deleted part by polynomial of high order [say 9]. Then I repopulate the deleted fragment and re-draw the curve. This takes a long time and I have quite a number of files to process. Could you folks suggest an efficient way to do this ? [in MATLAB or

问题 I have data in a csv file which appears as: DateTime Temp 10/1/2016 0:00 20.35491156 10/1/2016 1:00 19.75320845 10/1/2016 4:00 17.62411292 10/1/2016 5:00 18.30190001 10/1/2016 6:00 19.37101638 I am reading this file from csv file as: import numpy as np import pandas as pd d2 = pd.Series.from_csv(r'C:\PowerCurve.csv') d3 = d2.interpolate(method='time') My goal is to fill the missing hours 2 and 3 with interpolation based on nearby values. i.e. every time there is are missing data it should do

问题 I have two ellipsoids in R3 described in terms of their centre points (P), their axes lengths (a,b,c), and their rotation vector (R). I wish to interpolate a tubular structure between these two ellipsoids along a given centre line. This is done by creating an ellipsoid centred at each point along the centre line. Its axes lengths are interpolated linearly between those at the two endpoints, and the rotation is obtained as a quaternion using spherical linear interpolation, or SLERP. I

问题 I am converting an MATLAB program to a Python program using NumPy and SciPy and I am still new to it. In part of the program, I have the following MATLAB code: tImg(:,:,1) = interp2(x,y,Img(:,:,1),Tx,Ty,'cubic'); All parameters in the interp2 method are 298x142 double. So I tried to convert it to the following Python code: tImg[:, :, 0] = (scipy.interpolate.interp2d(x, y, img[:, :, 0], kind='cubic'))(Tx, Ty) I am given MemoryError in the interp2d method. The MATLAB code runs fine. I have read

问题 I have the following data frame ref_dat k Intensity Slope 1 0.021467214 33 16 2 0.012444759 33 8 3 0.006079156 33 4 4 0.003792025 33 2 5 0.022765086 47 16 6 0.017335375 47 8 7 0.007279113 47 4 8 0.003396341 47 2 9 0.034101951 75 16 10 0.023045525 75 8 11 0.009163758 75 4 12 0.003253822 75 2 13 0.039832629 110 16 14 0.029826826 110 8 15 0.010529787 110 4 16 0.002945170 110 2 17 0.042140630 155 16 18 0.032338534 155 8 19 0.013758171 155 4 20 0.002605399 155 2 I am trying to get the interpolated

问题 I'm currently trying to interpolate curves through multiple dimensions (which just involves using interpolation methods on each dimension individually), and I'm a bit confused about the PCHIP (piecewise cubic hermite interpolation polynomial). Does the PCHIP only apply to monotonic data? The data I am interested in is necessarily non-monotonic, so I used a Catmull-Rom spline, but the PCHIP seems to fit my data better, but the off the shelf implementation of the PCHIP im using only works for

问题 How can I interpolate between n colors. Simple case of 2 colors Consider a more simple case first, where we want to find the mid-point of 2 colors. Color1 is RGB ( 255, 0, 0 ) // Red Color2 is RGB ( 128, 128, 128 ) // Grey The solution being the mid-point between each R, G, B considered separately. RGB ( 128 + 64, 128 / 2, 128 / 2 ) = RGB ( 192, 64, 64 ) Since the mid-point is exactly in between the two and there is a linear relationship to the interpolation, then its possiable to interpolate