Bilinear interpolation

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Bilinear interpolation is linear interpolation in 2 dimensions, and is typically used for image scaling and for 2D finite element analysis.

C

<lang c>#include <stdint.h> typedef struct {

   uint32_t *pixels;
   unsigned int w;
   unsigned int h;

} image_t;

define getByte(value, n) (value >> (n*8) & 0xFF)

uint32_t getpixel(image_t *image, unsigned int x, unsigned int y){

   return image->pixels[(y*image->w)+x];

} float lerp(float s, float e, float t){return s+(e-s)*t;} float blerp(float c00, float c10, float c01, float c11, float tx, float ty){

   return lerp(lerp(c00, c10, tx), lerp(c01, c11, tx), ty);

} void putpixel(image_t *image, unsigned int x, unsigned int y, uint32_t color){

   image->pixels[(y*image->w) + x] = color;

} void scale(image_t *src, image_t *dst, float scalex, float scaley){

   int newWidth = (int)src->w*scalex;
   int newHeight= (int)src->h*scaley;
   int x, y;
   for(x= 0, y=0; y < newHeight; x++){
       if(x > newWidth){
           x = 0; y++;
       }
       float gx = x / (float)(newWidth) * (src->w-1);
       float gy = y / (float)(newHeight) * (src->h-1);
       int gxi = (int)gx;
       int gyi = (int)gy;
       uint32_t result=0;
       uint32_t c00 = getpixel(src, gxi, gyi);
       uint32_t c10 = getpixel(src, gxi+1, gyi);
       uint32_t c01 = getpixel(src, gxi, gyi+1);
       uint32_t c11 = getpixel(src, gxi+1, gyi+1);
       uint8_t i;
       for(i = 0; i < 3; i++){
           //((uint8_t*)&result)[i] = blerp( ((uint8_t*)&c00)[i], ((uint8_t*)&c10)[i], ((uint8_t*)&c01)[i], ((uint8_t*)&c11)[i], gxi - gx, gyi - gy); // this is shady
           result |= (uint8_t)blerp(getByte(c00, i), getByte(c10, i), getByte(c01, i), getByte(c11, i), gx - gxi, gy -gyi) << (8*i);
       }
       putpixel(dst,x, y, result);
   }

}</lang>

D

This uses the module from the Grayscale Image task.

Translation of: C

<lang d>import grayscale_image;

/// Currently this accepts only a Grayscale image, for simplicity. Image!Gray rescaleGray(in Image!Gray src, in float scaleX, in float scaleY) pure nothrow @safe in {

   assert(src !is null, "Input Image is null.");
   assert(src.nx > 1 && src.ny > 1, "Minimal input image size is 2x2.");
   assert(cast(uint)(src.nx * scaleX) > 0, "Output image width must be > 0.");
   assert(cast(uint)(src.ny * scaleY) > 0, "Output image height must be > 0.");

} body {

   alias FP = float;
   static FP lerp(in FP s, in FP e, in FP t) pure nothrow @safe @nogc {
       return s + (e - s) * t;
   }

   static FP blerp(in FP c00, in FP c10, in FP c01, in FP c11,
                   in FP tx, in FP ty) pure nothrow @safe @nogc {
       return lerp(lerp(c00, c10, tx), lerp(c01, c11, tx), ty);
   }

   immutable newWidth = cast(uint)(src.nx * scaleX);
   immutable newHeight = cast(uint)(src.ny * scaleY);
   auto result = new Image!Gray(newWidth, newHeight, true);

   foreach (immutable y; 0 .. newHeight)
       foreach (immutable x; 0 .. newWidth) {
           immutable FP gx = x / FP(newWidth) * (src.nx - 1);
           immutable FP gy = y / FP(newHeight) * (src.ny - 1);
           immutable gxi = cast(uint)gx;
           immutable gyi = cast(uint)gy;

           immutable c00 = src[gxi,     gyi    ];
           immutable c10 = src[gxi + 1, gyi    ];
           immutable c01 = src[gxi,     gyi + 1];
           immutable c11 = src[gxi + 1, gyi + 1];

           immutable pixel = blerp(c00, c10, c01, c11, gx - gxi, gy - gyi);
           result[x, y] = Gray(cast(ubyte)pixel);
       }

   return result;

}

void main() {

   const im = loadPGM!Gray(null, "lena.pgm");
   im.rescaleGray(0.3, 0.1).savePGM("lena_smaller.pgm");
   im.rescaleGray(1.3, 1.8).savePGM("lena_larger.pgm");

}</lang>

J

<lang J> Note 'FEA'

  The interpolant is the dot product of the four function values with the values at the nodes.
  Sum of four linear functions of two variables (xi, eta) is one at each of 4 nodes.
  Let the base element have nodal coordinates (corners) (x,y) with at +/-1.

   2               3 (1,1)
  +---------------+
  |               |
  |               |
  |        (0,0)  |
  |       *       |
  |               |
  |               |
  |               |
  +---------------+
   0               1

  determine f0(xi,eta), ..., f3(xi,eta).
  f0(-1,-1) = 1, f0(all other corners) is 0.
  f1( 1,-1) = 1, f1(all other corners) is 0.
  ...

)

f0 =: 1r4 * [: */ 1 1&- NB. f0(xi,eta) = (1-xi)(1-eta)/4 f1 =: _1r4 * [: */ _1 1&- NB. f1(xi,eta) = (1+xi)(1-eta)/4 f2 =: _1r4 * [: */ 1 _1&- NB. ... f3 =: 1r4 * [: */ _1 _1&-

functions =: f0`f1`f2`f3`:0

CORNERS =: 21 A. -.+:#:i.4 NB. prove the function values at the corners are correct. assert (=i.4) -: functions"1 CORNERS NB. 4 by 4 identity matrix

interpolate =: (+/ .*) functions </lang>

Note. 'demonstrate the interpolant with a saddle'
   lower left has value 1,
   lower right: 2
   upper left: 2.2
   upper right: 0.7
)

GRID =: |.,~"0/~(%~i:)100
SADDLE =: 1 2 2.2 0.7 interpolate"_ 1 GRID
viewmat SADDLE

File:J bilinear interpolant.jpg

Racket

This mimics the Wikipedia example. <lang racket>#lang racket (require images/flomap)

(define fm

 (draw-flomap
  (λ (dc)
    (define (pixel x y color)
      (send dc set-pen color 1 'solid)
      (send dc draw-point (+ x .5) (+ y 0.5)))  
    (send dc set-alpha 1)
    (pixel 0 0 "blue")
    (pixel 0 1 "red")
    (pixel 1 0 "red")
    (pixel 1 1 "green"))
  2 2))

(flomap->bitmap

(build-flomap
 4 250 250
 (λ (k x y)
   (flomap-bilinear-ref 
    fm k (+ 1/2 (/ x 250)) (+ 1/2 (/ y 250))))))</lang>