Created
February 18, 2024 05:15
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taichi interpolation
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@ti.func | |
def _bilateral_interp_coeffs(self, | |
uv_grid:tm.vec2, | |
probe_elevation:float, | |
grid_height:int, | |
grid_width:int, | |
probe_height:int, | |
probe_width:int, | |
cascade:int) -> (tm.ivec4, tm.vec4): | |
"""Returns pixel indices and weights for interpolation""" | |
pos = tm.vec2(grid_width * uv_grid.x - 0.5, grid_height * uv_grid.y - 0.5) | |
x0, y0, x1, y1 = int(tm.floor(pos.x)), int(tm.floor(pos.y)), 0, 0 | |
dx = (pos.x + 1.) - (float(x0) + 1.) | |
dy = (pos.y + 1.) - (float(y0) + 1.) | |
if self.wrap_mode == WrapMode.REPEAT: | |
x0, y0 = x0%grid_width, y0%grid_height | |
x1, y1 = (x0+1)%grid_width, (y0+1)%grid_height | |
elif self.wrap_mode == WrapMode.CLAMP: | |
x0, y0 = tm.clamp(x0, 0, grid_width-1), tm.clamp(y0, 0, grid_height-1) | |
x1, y1 = tm.min(x0+1, grid_width-1), tm.min(y0+1, grid_height-1) | |
q00 = int((y0 * grid_width) + x0) | |
q01 = int((y1 * grid_width) + x0) | |
q10 = int((y0 * grid_width) + x1) | |
q11 = int((y1 * grid_width) + x1) | |
indices = tm.ivec4(q00, q01, q10, q11) | |
depth_bias = self.depth_bias | |
canvas_shape = tm.vec2(float(self.shape[1]), float(self.shape[0])) | |
uv00 = tm.vec2(x0 * probe_width + probe_width * 0.5, y0 * probe_height + probe_height * 0.5)/canvas_shape.xy + self.eps | |
uv01 = tm.vec2(x0 * probe_width + probe_width * 0.5, y1 * probe_height + probe_height * 0.5)/canvas_shape.xy + self.eps | |
uv10 = tm.vec2(x1 * probe_width + probe_width * 0.5, y0 * probe_height + probe_height * 0.5)/canvas_shape.xy + self.eps | |
uv11 = tm.vec2(x1 * probe_width + probe_width * 0.5, y1 * probe_height + probe_height * 0.5)/canvas_shape.xy + self.eps | |
d00 = self.positions.sample_bilinear(uv00).z | |
d01 = self.positions.sample_bilinear(uv01).z | |
d10 = self.positions.sample_bilinear(uv10).z | |
d11 = self.positions.sample_bilinear(uv11).z | |
weights = tm.vec4(0.); | |
weights[0] = (1. - dx) * (1. - dy) | |
weights[1] = (1. - dx) * dy | |
weights[2] = dx * (1. - dy) | |
weights[3] = dx * dy | |
weights[0] *= tm.exp(-ti.abs(probe_elevation - d00) * depth_bias) | |
weights[1] *= tm.exp(-ti.abs(probe_elevation - d01) * depth_bias) | |
weights[2] *= tm.exp(-ti.abs(probe_elevation - d10) * depth_bias) | |
weights[3] *= tm.exp(-ti.abs(probe_elevation - d11) * depth_bias) | |
weights /= tm.dot(tm.vec4(1.), weights) | |
return indices, weights |
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