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Diffstat (limited to '')
| -rw-r--r-- | TODO | 1 | ||||
| -rw-r--r-- | src/aux.py | 76 |
2 files changed, 76 insertions, 1 deletions
@@ -11,7 +11,6 @@ Medium priority: connections are not need all the time. Low priority: - Raw ramp applying functions with precalcuated polynomial interpolation Add support for temporarily closing DRM connection so that multiple users can run in DRM Future stuff: @@ -63,6 +63,7 @@ def linearly_interpolate_ramp(r, g, b): # TODO test, demo and document this R, G, B = C(r), C(g), C(b) for small, large in curves(R, G, B): small_, large_ = len(small) - 1, len(large) - 1 + # Only interpolate if scaling up if large_ > small_: for i in range(len(large)): # Scaling @@ -74,6 +75,81 @@ def linearly_interpolate_ramp(r, g, b): # TODO test, demo and document this return (R, G, B) +def polynomially_interpolate_ramp(r, g, b): # TODO test, demo and document this + ''' + Polynomially interpolate ramps to the size of the output axes. + + This function will replace parts of the result with linear interpolation + where local monotonicity have been broken. That is, there is a local + maximum or local minimum generated between two reference points, linear + interpolation will be used instead between those two points. + + @param r:list<float> The red colour curves + @param g:list<float> The green colour curves + @param b:list<float> The blue colour curves + @return :(r:list<float>, g:list<float>, b:list<float>) The input parameters extended to sizes of `o_size`, + or their original size, whatever is larger. + ''' + C = lambda c : c[:] if len(c) >= o_size else ([None] * o_size) + R, G, B, linear = C(r), C(g), C(b), None + for small, (ci, large) in curves(*(enumerate((R, G, B)))): + small_, large_ = len(small) - 1, len(large) - 1 + # Only interpolate if scaling up + if large_ > small_: + n = len(small) + 1 + ## Construct interpolation matrix + M = [[small[y] ** i for i in n] for y in range(n)] + A = [x / small_ for x in range(n)] + ## Eliminate interpolation matrix + # (XXX this can be done faster by utilising the fact that we have a Vandermonde matrix) + # Eliminiate lower left + for k in range(n - 1): + for i in range(k + 1, n): + m = M[i][k] / M[k][k] + M[i][k + 1:] -= [M[i][j] - M[k][j] * m for j in range(k + 1, n)] + A[i] -= A[k] * m + # Eliminiate upper right + for k in reversed(range(n)): + A[:k] = [A[i] - A[k] * M[i][k] / M[k][k] for i in range(k]] + # Eliminiate diagonal + A = [A[k] / M[k][k] for k in range(n)] + ## Construct interpolation function + f = lambda x : sum(A[i] * x ** i for i in range(n)) + ## Apply interpolation + large[:] = [f(x / large_) for x in range(len(large))] + + ## Check local monotonicity + for i in range(small_): + # Small curve + x1, x2, y1, y2 = i, i + 1, small[i], small[i + 1] + # Scaled up curve + X1, X2 = int(x1 * large_ / small_), int(x2 * large_ / small_) + Y1, Y2 = large[X1], large[X2] + monotone = True + if y2 == y1: + # Flat part, just make sure it is flat in the interpolation + # without doing a check before. + for x in range(X1, X2 + 1): + large[x] = y1 + elif y2 > y1: + # Increasing + monotone = all(map(lambda x : large[x + 1] >= large[x], range(X1, X2))) and (Y2 > Y1) + elif y2 < y1: + # Decreasing + monotone = all(map(lambda x : large[x + 1] <= large[x], range(X1, X2))) and (Y2 < Y1) + # If the monotonicity has been broken, + if not passed: + # replace the partition with linear interpolation. + # If linear interpolation has not yet been calculated, + if linear is None: + # then calculate it. + linear = linearly_interpolate_ramp(r, g, b) + # Extract the linear interpolation for the current colour curve, + # and replace the local partition with the linear interpolation. + large[X1 : X2 + 1] = linear[ci][X1 : X2 + 1] + return (R, G, B) + + def functionise(rgb): ''' Convert a three colour curves to a function that applies those adjustments |