#version 450 core
-in vec2 tc;
+in vec3 tc;
out vec2 derivatives;
+out float beta_0;
-uniform sampler2D tex;
-uniform vec2 inv_image_size;
+uniform sampler2DArray tex;
void main()
{
- float x_m2 = texture(tex, vec2(tc.x - 2.0 * inv_image_size.x), tc.y).x;
- float x_m1 = texture(tex, vec2(tc.x - inv_image_size.x), tc.y).x;
- float x_p1 = texture(tex, vec2(tc.x + inv_image_size.x), tc.y).x;
- float x_p2 = texture(tex, vec2(tc.x + 2.0 * inv_image_size.x), tc.y).x;
+ float x_m2 = textureOffset(tex, tc, ivec2(-2, 0)).x;
+ float x_m1 = textureOffset(tex, tc, ivec2(-1, 0)).x;
+ float x_p1 = textureOffset(tex, tc, ivec2( 1, 0)).x;
+ float x_p2 = textureOffset(tex, tc, ivec2( 2, 0)).x;
- float y_m2 = texture(tex, vec2(tc.x, tc.y - 2.0 * inv_image_size.y)).x;
- float y_m1 = texture(tex, vec2(tc.x, tc.y - inv_image_size.y)).x;
- float y_p1 = texture(tex, vec2(tc.x, tc.y + inv_image_size.y)).x;
- float y_p2 = texture(tex, vec2(tc.x, tc.y + 2.0 * inv_image_size.y)).x;
+ float y_m2 = textureOffset(tex, tc, ivec2( 0, -2)).x;
+ float y_m1 = textureOffset(tex, tc, ivec2( 0, -1)).x;
+ float y_p1 = textureOffset(tex, tc, ivec2( 0, 1)).x;
+ float y_p2 = textureOffset(tex, tc, ivec2( 0, 2)).x;
derivatives.x = (x_p1 - x_m1) * (2.0/3.0) + (x_m2 - x_p2) * (1.0/12.0);
derivatives.y = (y_p1 - y_m1) * (2.0/3.0) + (y_m2 - y_p2) * (1.0/12.0);
+
+ // The nudge term in the square root in the DeepFlow paper is ζ² = 0.1² = 0.01.
+ // But this is assuming a 0..255 level. Given the nonlinearities in the expression
+ // where β_0 appears, there's no 100% equivalent way to adjust this
+ // constant that I can see, but taking it to (0.1/255)² ~= 1.53e-7 ~=
+ // 1e-7 ought to be good enough. I guess the basic idea is that it
+ // will only matter for near-zero derivatives anyway. I am a tiny
+ // bit worried about fp16 precision when storing these numbers, but OK.
+ beta_0 = 1.0 / (derivatives.x * derivatives.x + derivatives.y * derivatives.y + 1e-7);
}