Spaces:
Running
Running
File size: 7,152 Bytes
4a582ec |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 |
import numpy as np
from numba import njit, prange
# The foreground estimation refer to pymatting [https://github.com/pymatting/pymatting/blob/master/pymatting/foreground/estimate_foreground_ml.py]
@njit("void(f4[:, :, :], f4[:, :, :])", cache=True, nogil=True, parallel=True)
def _resize_nearest_multichannel(dst, src):
"""
Internal method.
Resize image src to dst using nearest neighbors filtering.
Images must have multiple color channels, i.e. :code:`len(shape) == 3`.
Parameters
----------
dst: numpy.ndarray of type np.float32
output image
src: numpy.ndarray of type np.float32
input image
"""
h_src, w_src, depth = src.shape
h_dst, w_dst, depth = dst.shape
for y_dst in prange(h_dst):
for x_dst in range(w_dst):
x_src = max(0, min(w_src - 1, x_dst * w_src // w_dst))
y_src = max(0, min(h_src - 1, y_dst * h_src // h_dst))
for c in range(depth):
dst[y_dst, x_dst, c] = src[y_src, x_src, c]
@njit("void(f4[:, :], f4[:, :])", cache=True, nogil=True, parallel=True)
def _resize_nearest(dst, src):
"""
Internal method.
Resize image src to dst using nearest neighbors filtering.
Images must be grayscale, i.e. :code:`len(shape) == 3`.
Parameters
----------
dst: numpy.ndarray of type np.float32
output image
src: numpy.ndarray of type np.float32
input image
"""
h_src, w_src = src.shape
h_dst, w_dst = dst.shape
for y_dst in prange(h_dst):
for x_dst in range(w_dst):
x_src = max(0, min(w_src - 1, x_dst * w_src // w_dst))
y_src = max(0, min(h_src - 1, y_dst * h_src // h_dst))
dst[y_dst, x_dst] = src[y_src, x_src]
# TODO
# There should be an option to switch @njit(parallel=True) on or off.
# parallel=True would be faster, but might cause race conditions.
# User should have the option to turn it on or off.
@njit(
"Tuple((f4[:, :, :], f4[:, :, :]))(f4[:, :, :], f4[:, :], f4, i4, i4, i4, f4)",
cache=True,
nogil=True)
def _estimate_fb_ml(
input_image,
input_alpha,
regularization,
n_small_iterations,
n_big_iterations,
small_size,
gradient_weight, ):
h0, w0, depth = input_image.shape
dtype = np.float32
w_prev = 1
h_prev = 1
F_prev = np.empty((h_prev, w_prev, depth), dtype=dtype)
B_prev = np.empty((h_prev, w_prev, depth), dtype=dtype)
n_levels = int(np.ceil(np.log2(max(w0, h0))))
for i_level in range(n_levels + 1):
w = round(w0**(i_level / n_levels))
h = round(h0**(i_level / n_levels))
image = np.empty((h, w, depth), dtype=dtype)
alpha = np.empty((h, w), dtype=dtype)
_resize_nearest_multichannel(image, input_image)
_resize_nearest(alpha, input_alpha)
F = np.empty((h, w, depth), dtype=dtype)
B = np.empty((h, w, depth), dtype=dtype)
_resize_nearest_multichannel(F, F_prev)
_resize_nearest_multichannel(B, B_prev)
if w <= small_size and h <= small_size:
n_iter = n_small_iterations
else:
n_iter = n_big_iterations
b = np.zeros((2, depth), dtype=dtype)
dx = [-1, 1, 0, 0]
dy = [0, 0, -1, 1]
for i_iter in range(n_iter):
for y in prange(h):
for x in range(w):
a0 = alpha[y, x]
a1 = 1.0 - a0
a00 = a0 * a0
a01 = a0 * a1
# a10 = a01 can be omitted due to symmetry of matrix
a11 = a1 * a1
for c in range(depth):
b[0, c] = a0 * image[y, x, c]
b[1, c] = a1 * image[y, x, c]
for d in range(4):
x2 = max(0, min(w - 1, x + dx[d]))
y2 = max(0, min(h - 1, y + dy[d]))
gradient = abs(a0 - alpha[y2, x2])
da = regularization + gradient_weight * gradient
a00 += da
a11 += da
for c in range(depth):
b[0, c] += da * F[y2, x2, c]
b[1, c] += da * B[y2, x2, c]
determinant = a00 * a11 - a01 * a01
inv_det = 1.0 / determinant
b00 = inv_det * a11
b01 = inv_det * -a01
b11 = inv_det * a00
for c in range(depth):
F_c = b00 * b[0, c] + b01 * b[1, c]
B_c = b01 * b[0, c] + b11 * b[1, c]
F_c = max(0.0, min(1.0, F_c))
B_c = max(0.0, min(1.0, B_c))
F[y, x, c] = F_c
B[y, x, c] = B_c
F_prev = F
B_prev = B
w_prev = w
h_prev = h
return F, B
def estimate_foreground_ml(
image,
alpha,
regularization=1e-5,
n_small_iterations=10,
n_big_iterations=2,
small_size=32,
return_background=False,
gradient_weight=1.0, ):
"""Estimates the foreground of an image given its alpha matte.
See :cite:`germer2020multilevel` for reference.
Parameters
----------
image: numpy.ndarray
Input image with shape :math:`h \\times w \\times d`
alpha: numpy.ndarray
Input alpha matte shape :math:`h \\times w`
regularization: float
Regularization strength :math:`\\epsilon`, defaults to :math:`10^{-5}`.
Higher regularization results in smoother colors.
n_small_iterations: int
Number of iterations performed on small scale, defaults to :math:`10`
n_big_iterations: int
Number of iterations performed on large scale, defaults to :math:`2`
small_size: int
Threshold that determines at which size `n_small_iterations` should be used
return_background: bool
Whether to return the estimated background in addition to the foreground
gradient_weight: float
Larger values enforce smoother foregrounds, defaults to :math:`1`
Returns
-------
F: numpy.ndarray
Extracted foreground
B: numpy.ndarray
Extracted background
Example
-------
>>> from pymatting import *
>>> image = load_image("data/lemur/lemur.png", "RGB")
>>> alpha = load_image("data/lemur/lemur_alpha.png", "GRAY")
>>> F = estimate_foreground_ml(image, alpha, return_background=False)
>>> F, B = estimate_foreground_ml(image, alpha, return_background=True)
See Also
----
stack_images: This function can be used to place the foreground on a new background.
"""
foreground, background = _estimate_fb_ml(
image.astype(np.float32),
alpha.astype(np.float32),
regularization,
n_small_iterations,
n_big_iterations,
small_size,
gradient_weight, )
if return_background:
return foreground, background
return foreground
|