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up_scale_algs.py
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import numpy as np
import cv2
def three_or_more_equal(a, b, c, d):
return np.logical_or(
np.logical_and(
np.all(a == b, axis=2),
np.logical_or(np.all(a == c, axis=2), np.all(a == d, axis=2)),
),
np.logical_and(np.all(a == c, axis=2), np.all(a == d, axis=2)),
np.logical_and(np.all(b == c, axis=2), np.all(b == d, axis=2)),
)
def nearest_neighbor(img, scale_factor):
img = img.repeat(scale_factor, 1).repeat(scale_factor, 0)
return img
def EPX(img, Iterations=1):
for _ in range(Iterations):
img_scaled = img.repeat(2, 1).repeat(2, 0)
a = img[0:-2, 1:-1]
b = img[1:-1, 2:]
c = img[1:-1, 0:-2]
d = img[2:, 1:-1]
three_or_more_mask = np.logical_not(three_or_more_equal(a, b, c, d))
a_mask = np.logical_and(three_or_more_mask, np.all(a == c, axis=2))
b_mask = np.logical_and(three_or_more_mask, np.all(a == b, axis=2))
c_mask = np.logical_and(three_or_more_mask, np.all(d == c, axis=2))
d_mask = np.logical_and(three_or_more_mask, np.all(b == d, axis=2))
img_scaled[2:-2:2, 2:-2:2][a_mask] = a[a_mask]
img_scaled[2:-2:2, 3:-2:2][b_mask] = b[b_mask]
img_scaled[3:-2:2, 2:-2:2][c_mask] = c[c_mask]
img_scaled[3:-2:2, 3:-2:2][d_mask] = d[d_mask]
img = np.array(img_scaled, dtype=np.uint8)
return img
def scale_2x(img, Iterations=1):
for _ in range(Iterations):
img_scaled = img.repeat(2, 1).repeat(2, 0)
a = img[0:-2, 1:-1]
b = img[1:-1, 2:]
c = img[1:-1, 0:-2]
d = img[2:, 1:-1]
a_mask = np.logical_and(
np.all(a == c, axis=2),
np.logical_not(np.all(c == d, axis=2)),
np.logical_not(np.all(a == b, axis=2)),
)
b_mask = np.logical_and(
np.all(a == b, axis=2),
np.logical_not(np.all(a == c, axis=2)),
np.logical_not(np.all(b == d, axis=2)),
)
c_mask = np.logical_and(
np.all(d == c, axis=2),
np.logical_not(np.all(d == b, axis=2)),
np.logical_not(np.all(a == c, axis=2)),
)
d_mask = np.logical_and(
np.all(b == d, axis=2),
np.logical_not(np.all(b == a, axis=2)),
np.logical_not(np.all(d == c, axis=2)),
)
img_scaled[2:-2:2, 2:-2:2][a_mask] = a[a_mask]
img_scaled[2:-2:2, 3:-2:2][b_mask] = b[b_mask]
img_scaled[3:-2:2, 2:-2:2][c_mask] = c[c_mask]
img_scaled[3:-2:2, 3:-2:2][d_mask] = d[d_mask]
img = np.array(img_scaled, dtype=np.uint8)
return img
def eagle_2x(img, Iterations=1):
for _ in range(Iterations):
img_scaled = img.repeat(2, 1).repeat(2, 0)
s = img[:-2, :-2]
t = img[0:-2, 1:-1]
u = img[:-2, 2:]
v = img[1:-1, 0:-2]
w = img[1:-1, 2:]
x = img[2:, :-2]
y = img[2:, 1:-1]
z = img[2:, 2:]
top_left_mask = np.logical_and(np.all(v == s, axis=2), np.all(s == t, axis=2))
top_right_mask = np.logical_and(np.all(t == u, axis=2), np.all(u == w, axis=2))
bottom_left_mask = np.logical_and(
np.all(v == x, axis=2), np.all(x == y, axis=2)
)
bottom_right_mask = np.logical_and(
np.all(w == z, axis=2), np.all(z == y, axis=2)
)
img_scaled[2:-2:2, 2:-2:2][top_left_mask] = s[top_left_mask]
img_scaled[2:-2:2, 3:-2:2][top_right_mask] = u[top_right_mask]
img_scaled[3:-2:2, 2:-2:2][bottom_left_mask] = x[bottom_left_mask]
img_scaled[3:-2:2, 3:-2:2][bottom_right_mask] = z[bottom_right_mask]
img = np.array(img_scaled, dtype=np.uint8)
return img
def bilinear(
img, scaleFactor
): # There is a vectorized version of this that runs faster
height, width = img.shape[:2]
height_scaled = height * scaleFactor
width_scaled = width * scaleFactor
img_scaled = np.zeros((height_scaled, width_scaled, 3))
x_ratio = float(width - 1) / (width_scaled - 1)
y_ratio = float(height - 1) / (height_scaled - 1)
x1 = np.array([np.uint32(np.floor(x_ratio * np.arange(width_scaled)))]).repeat(
height_scaled, axis=0
)
y1 = np.uint32(np.floor(y_ratio * np.arange(height_scaled)[:, np.newaxis])).repeat(
width_scaled, axis=1
)
xh = np.array([np.uint32(np.ceil(x_ratio * np.arange(width_scaled)))]).repeat(
height_scaled, axis=0
)
yh = np.uint32(np.ceil(y_ratio * np.arange(height_scaled)[:, np.newaxis])).repeat(
width_scaled, axis=1
)
a = img[np.ix_(y1[:, 0], x1[0])]
b = img[np.ix_(y1[:, 0], xh[0])]
c = img[np.ix_(yh[:, 0], x1[0])]
d = img[np.ix_(yh[:, 0], xh[0])]
x_weight = (
np.array([(x_ratio * np.arange(width_scaled))]).repeat(height_scaled, axis=0)
- x1
)
y_weight = (y_ratio * np.arange(height_scaled)[:, np.newaxis]).repeat(
width_scaled, axis=1
) - y1
img_scaled[:, :, 0] = (
a[:, :, 0] * (1 - x_weight) * (1 - y_weight)
+ b[:, :, 0] * x_weight * (1 - y_weight)
+ c[:, :, 0] * y_weight * (1 - x_weight)
+ d[:, :, 0] * x_weight * y_weight
)
img_scaled[:, :, 1] = (
a[:, :, 1] * (1 - x_weight) * (1 - y_weight)
+ b[:, :, 1] * x_weight * (1 - y_weight)
+ c[:, :, 1] * y_weight * (1 - x_weight)
+ d[:, :, 1] * x_weight * y_weight
)
img_scaled[:, :, 2] = (
a[:, :, 2] * (1 - x_weight) * (1 - y_weight)
+ b[:, :, 2] * x_weight * (1 - y_weight)
+ c[:, :, 2] * y_weight * (1 - x_weight)
+ d[:, :, 2] * x_weight * y_weight
)
img = np.array(img_scaled, dtype=np.uint8)
return img
# Interpolation kernel
def _u(s, a):
temp = np.zeros(s.shape, dtype=np.float32)
mod1 = s[np.logical_and(np.abs(s) >= 0, np.abs(s) <= 1)]
temp[np.logical_and(np.abs(s) >= 0, np.abs(s) <= 1)] = (
(a + 2) * (np.abs(mod1) ** 3) - (a + 3) * (np.abs(mod1) ** 2) + 1
)
mod2 = s[np.logical_and(np.abs(s) > 1, np.abs(s) <= 2)]
temp[np.logical_and(np.abs(s) > 1, np.abs(s) <= 2)] = (
a * (np.abs(mod2) ** 3)
- (5 * a) * (np.abs(mod2) ** 2)
+ (8 * a) * np.abs(mod2)
- 4 * a
)
return temp
# Padding
def __padding(img, H, W, C, add):
zimg = np.zeros((H + (2 * add), W + (2 * add), C))
zimg[add : H + add, add : W + add, :C] = img
# Pad the first/last two col and row
zimg[add : H + add, 0:2, :C] = img[:, 0:1, :C]
zimg[H + add : H + (2 * add), add : W + add, :] = img[H - 1 : H, :, :]
zimg[add : H + add, W + add : W + (2 * add), :] = img[:, W - 1 : W, :]
zimg[0:add, add : W + add, :C] = img[0:1, :, :C]
# Pad the missing eight points
zimg[0:add, 0:add, :C] = img[0, 0, :C]
zimg[H + add : H + (2 * add), 0:add, :C] = img[H - 1, 0, :C]
zimg[H + add : H + (2 * add), W + add : W + (2 * add), :C] = img[H - 1, W - 1, :C]
zimg[0:add, W + add : W + (2 * add), :C] = img[0, W - 1, :C]
return zimg
# Bicubic operation, the math for this operation can be found here:https://link.springer.com/article/10.1007/s11554-022-01254-8
def bicubic(img, ratio):
a = -0.5
# Get image size
H, W, C = img.shape
# Here H = Height, W = weight,
# C = Number of channels if the
# image is coloured.
img = __padding(img, H, W, C, 2) / 255.0
# Create new image
dH = np.uint32(np.floor(H * ratio))
dW = np.uint32(np.floor(W * ratio))
dst = np.zeros((dH, dW, 3))
h = 1 / ratio
x, y = (
np.array([np.arange(dW)]).repeat(dH, axis=0) * h + 2,
np.arange(dH)[:, np.newaxis].repeat(dW, axis=1) * h + 2,
)
x1 = 1 + x - np.floor(x)
x2 = x - np.floor(x)
x3 = np.floor(x) + 1 - x
x4 = np.floor(x) + 2 - x
y1 = 1 + y - np.floor(y)
y2 = y - np.floor(y)
y3 = np.floor(y) + 1 - y
y4 = np.floor(y) + 2 - y
manip_x1 = _u(x1, a)
manip_x2 = _u(x2, a)
manip_x3 = _u(x3, a)
manip_x4 = _u(x4, a)
manip_y1 = _u(y1, a)
manip_y2 = _u(y2, a)
manip_y3 = _u(y3, a)
manip_y4 = _u(y4, a)
pixel_pos1 = img[np.ix_((y - y1).astype(int)[:, 0], (x - x1).astype(int)[0])]
pixel_pos2 = img[np.ix_((y - y2).astype(int)[:, 0], (x - x1).astype(int)[0])]
pixel_pos3 = img[np.ix_((y + y3).astype(int)[:, 0], (x - x1).astype(int)[0])]
pixel_pos4 = img[np.ix_((y + y4).astype(int)[:, 0], (x - x1).astype(int)[0])]
pixel_pos5 = img[np.ix_((y - y1).astype(int)[:, 0], (x - x2).astype(int)[0])]
pixel_pos6 = img[np.ix_((y - y2).astype(int)[:, 0], (x - x2).astype(int)[0])]
pixel_pos7 = img[np.ix_((y + y3).astype(int)[:, 0], (x - x2).astype(int)[0])]
pixel_pos8 = img[np.ix_((y + y4).astype(int)[:, 0], (x - x2).astype(int)[0])]
pixel_pos9 = img[np.ix_((y - y1).astype(int)[:, 0], (x + x3).astype(int)[0])]
pixel_pos10 = img[np.ix_((y - y2).astype(int)[:, 0], (x + x3).astype(int)[0])]
pixel_pos11 = img[np.ix_((y + y3).astype(int)[:, 0], (x + x3).astype(int)[0])]
pixel_pos12 = img[np.ix_((y + y4).astype(int)[:, 0], (x + x3).astype(int)[0])]
pixel_pos13 = img[np.ix_((y - y1).astype(int)[:, 0], (x + x4).astype(int)[0])]
pixel_pos14 = img[np.ix_((y - y2).astype(int)[:, 0], (x + x4).astype(int)[0])]
pixel_pos15 = img[np.ix_((y + y3).astype(int)[:, 0], (x + x4).astype(int)[0])]
pixel_pos16 = img[np.ix_((y + y4).astype(int)[:, 0], (x + x4).astype(int)[0])]
for c in range(C):
dst[:, :, c] = (
(
manip_y1 * pixel_pos1[:, :, c]
+ manip_y2 * pixel_pos2[:, :, c]
+ manip_y3 * pixel_pos3[:, :, c]
+ manip_y4 * pixel_pos4[:, :, c]
)
* manip_x1
+ (
manip_y1 * pixel_pos5[:, :, c]
+ manip_y2 * pixel_pos6[:, :, c]
+ manip_y3 * pixel_pos7[:, :, c]
+ manip_y4 * pixel_pos8[:, :, c]
)
* manip_x2
+ (
manip_y1 * pixel_pos9[:, :, c]
+ manip_y2 * pixel_pos10[:, :, c]
+ manip_y3 * pixel_pos11[:, :, c]
+ manip_y4 * pixel_pos12[:, :, c]
)
* manip_x3
+ (
manip_y1 * pixel_pos13[:, :, c]
+ manip_y2 * pixel_pos14[:, :, c]
+ manip_y3 * pixel_pos15[:, :, c]
+ manip_y4 * pixel_pos16[:, :, c]
)
* manip_x4
)
return dst
# __wd calculates the distance between the colors of two pixels.
def __wd(p1, p2):
y, u, v = (
np.abs(np.subtract(p1[:, :, 0], p2[:, :, 0])),
np.abs(np.subtract(p1[:, :, 1], p2[:, :, 1])),
np.abs(np.subtract(p1[:, :, 2], p2[:, :, 2])),
)
return np.add(np.multiply(48, y), np.multiply(7, u), np.multiply(6, v))
# Color Interpolation for Vectorized xBR
def __blendColors(edge, opposite, e, c1, c2):
edr = edge < opposite
mask = __wd(e, c1) <= __wd(e, c2)
spots = np.logical_and(edr, mask)
inverse = np.logical_and(edr, np.logical_not(spots))
e[spots] = np.add(np.multiply(0.5, e[spots]), np.multiply(0.5, c1[spots]))
e[inverse] = np.add(np.multiply(0.5, e[inverse]), np.multiply(0.5, c2[inverse]))
# No Blend (BROKEN)
# e[spots] = c1[spots]
# e[inverse] = c2[inverse]
return e
"""
description of the algorithm can be found here: https://forums.libretro.com/t/xbr-algorithm-tutorial/123
Pixels are represented in the following format
|A1|B1|C1|
|A0|A |B |C |C4|
|D0|D |E |F |F4|
|G0|G |H |I |I4|
|G5|H5|I5|
Consider E as the central pixel."""
def xBR(img, Iterations=1):
cv2.cvtColor(img, cv2.COLOR_BGR2YUV)
for _ in range(Iterations):
imgScaled = np.zeros((len(img) * 2, len(img[1]) * 2, 3), dtype=np.uint8)
img = __padding(img, len(img), len(img[1]), 3, 2)
a1, b1, c1 = img[:-4, 1:-3], img[:-4, 2:-2], img[:-4, 3:-1]
a0, a, b, c, c4 = (
img[1:-3, :-4],
img[1:-3, 1:-3],
img[1:-3, 2:-2],
img[1:-3, 3:-1],
img[1:-3, 4:],
)
d0, d, e, f, f4 = (
img[2:-2, :-4],
img[2:-2, 1:-3],
img[2:-2, 2:-2],
img[2:-2, 3:-1],
img[2:-2, 4:],
)
g0, g, h, i, i4 = (
img[3:-1, :-4],
img[3:-1, 1:-3],
img[3:-1, 2:-2],
img[3:-1, 3:-1],
img[3:-1, 4:],
)
g5, h5, i5 = img[4:, 1:-3], img[4:, 2:-2], img[4:, 3:-1]
# setting the weighted distances
ec, eg, if4, ih5, hf = (
__wd(e, c),
__wd(e, g),
__wd(i, f4),
__wd(i, h5),
__wd(h, f),
)
hd, hi5, fi4, fb, ei = (
__wd(h, d),
__wd(h, i5),
__wd(f, i4),
__wd(f, b),
__wd(e, i),
)
ea, gd0, gh5 = __wd(e, a), __wd(g, d0), __wd(g, h5)
bd, dg0, hg5 = __wd(b, d), __wd(d, g0), __wd(h, g5)
d0a, ab1 = __wd(d0, a), __wd(a, b1)
a0d, a1b = __wd(a0, d), __wd(a1, b)
b1c, cf4 = __wd(b1, c), __wd(c, f4)
bc1, fc4 = __wd(b, c1), __wd(f, c4)
# Top Right Edge Detection Rule
edge = ei + ea + b1c + cf4 + (4 * fb)
opposite = bd + bc1 + hf + fc4 + (4 * ec)
e = __blendColors(edge, opposite, e, b, f)
# Top Left Edge Detection Rule
edge = ec + eg + d0a + ab1 + (4 * bd)
opposite = hd + fb + a0d + a1b + (4 * ea)
e = __blendColors(edge, opposite, e, d, b)
# Bottom Left Edge Detection Rule
edge = ea + ei + gd0 + gh5 + (4 * hd)
opposite = bd + dg0 + hf + hg5 + (4 * eg)
e = __blendColors(edge, opposite, e, d, h)
# Bottom Right Edge Detection Rule
edge = ec + eg + if4 + ih5 + (4 * hf)
opposite = hd + hi5 + fi4 + fb + (4 * ei)
e = __blendColors(edge, opposite, e, f, h)
imgScaled[1::2, 1::2] = e
imgScaled[:-1:2, 1::2] = e
imgScaled[1::2, :-1:2] = e
imgScaled[:-1:2, :-1:2] = e
img = np.array(imgScaled, dtype=np.uint8)
cv2.cvtColor(img, cv2.COLOR_YUV2BGR)
return img
# Helper functions for rotsprite
# distance in color between 2 pixels (its like prior implemented _wd() but without the constant multiplication)
def dist(p1, p2):
y, u, v = (
np.abs(np.subtract(p1[:, :, 0], p2[:, :, 0])),
np.abs(np.subtract(p1[:, :, 1], p2[:, :, 1])),
np.abs(np.subtract(p1[:, :, 2], p2[:, :, 2])),
)
return y + u + v
# determines if pixels are more similar to each other than the base input
def similar(p1, p2, input):
return np.logical_or(
np.all(p1 == p2, axis=2),
np.logical_and(
dist(p1, p2) <= dist(input, p2), dist(p1, p2) <= dist(input, p1)
),
)
# return (p1 == p2 or (dist(p1, p2) <= dist(input, p2) and dist(p1, p2) <= dist(input, p1)))
def different(p1, p2, input):
return np.logical_not(similar(p1, p2, input))
def rotsprite(img, Iterations=1):
img = np.array(img)
for _ in range(Iterations):
img_scaled = img.repeat(2, 1).repeat(2, 0)
"""
From here: https://godotshaders.com/shader/rotsprite-like-algorithm-for-cleaner-pixel-art-rotation/
// suppose we are looking at input pixel cE which is surrounded by 8 other
// pixels:
// a b c
// d e f
// g h i
// and for that 1 input pixel cE we want to output 4 pixels oA, oB, oC, and oD:
// oA oB
// oC oD
"""
a, b, c = img[:-2, :-2], img[:-2, 1:-1], img[:-2, 2:]
d, e, f = img[1:-1, :-2], img[1:-1, 1:-1], img[1:-1, 2:]
g, h, i = img[2:, :-2], img[2:, 1:-1], img[2:, 2:]
background = np.ones(e.shape, dtype=np.uint8) * 255
# these huge boolean statements suck but whatever
different_mask = np.logical_and(
different(d, f, e),
np.logical_and(
different(h, b, e),
np.logical_or(
similar(e, d, e),
np.logical_or(
similar(e, b, e),
np.logical_or(
similar(e, f, e),
np.logical_or(
similar(e, h, e),
np.logical_and(
np.logical_or(
np.logical_or(
different(a, i, e), similar(e, g, e)
),
similar(e, c, e),
),
np.logical_or(
np.logical_or(
different(g, c, e), similar(e, a, e)
),
similar(e, i, e),
),
),
),
),
),
),
),
)
oAmask = np.logical_and(
different_mask,
np.logical_and(
similar(b, d, e),
np.logical_and(
np.logical_or(different(e, a, e), different(b, background, e)),
np.logical_or(
np.logical_or(different(e, a, e), different(e, i, e)),
np.logical_or(different(b, c, e), different(d, g, e)),
),
),
),
)
oBmask = np.logical_and(
different_mask,
np.logical_and(
similar(f, b, e),
np.logical_and(
np.logical_or(different(e, c, e), different(f, background, e)),
np.logical_or(
np.logical_or(different(e, c, e), different(e, g, e)),
np.logical_or(different(f, i, e), different(b, a, e)),
),
),
),
)
oCmask = np.logical_and(
different_mask,
np.logical_and(
similar(d, h, e),
np.logical_and(
np.logical_or(different(e, g, e), different(d, background, e)),
np.logical_or(
np.logical_or(different(e, g, e), different(e, c, e)),
np.logical_or(different(d, a, e), different(h, i, e)),
),
),
),
)
oDmask = np.logical_and(
different_mask,
np.logical_and(
similar(h, f, e),
np.logical_and(
np.logical_or(different(e, i, e), different(h, background, e)),
np.logical_or(
np.logical_or(different(e, i, e), different(e, a, e)),
np.logical_or(different(h, g, e), different(f, c, e)),
),
),
),
)
img_scaled[2:-2:2, 2:-2:2][oAmask] = b[oAmask]
img_scaled[2:-2:2, 3:-2:2][oBmask] = f[oBmask]
img_scaled[3:-2:2, 2:-2:2][oCmask] = d[oCmask]
img_scaled[3:-2:2, 3:-2:2][oDmask] = h[oDmask]
img = np.array(img_scaled, dtype=np.uint8)
return img