Spaces:
Runtime error
Runtime error
File size: 6,083 Bytes
5b765fe |
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 |
import numpy as np
class WarpMLS:
def __init__(self, src, src_pts, dst_pts, dst_w, dst_h, trans_ratio=1.0):
self.src = src
self.src_pts = src_pts
self.dst_pts = dst_pts
self.pt_count = len(self.dst_pts)
self.dst_w = dst_w
self.dst_h = dst_h
self.trans_ratio = trans_ratio
self.grid_size = 100
self.rdx = np.zeros((self.dst_h, self.dst_w))
self.rdy = np.zeros((self.dst_h, self.dst_w))
@staticmethod
def __bilinear_interp(x, y, v11, v12, v21, v22):
return (v11 * (1 - y) + v12 * y) * (1 - x) + (v21 * (1 - y) + v22 * y) * x
def generate(self):
self.calc_delta()
return self.gen_img()
def calc_delta(self):
w = np.zeros(self.pt_count, dtype=np.float32)
if self.pt_count < 2:
return
i = 0
while 1:
if self.dst_w <= i < self.dst_w + self.grid_size - 1:
i = self.dst_w - 1
elif i >= self.dst_w:
break
j = 0
while 1:
if self.dst_h <= j < self.dst_h + self.grid_size - 1:
j = self.dst_h - 1
elif j >= self.dst_h:
break
sw = 0
swp = np.zeros(2, dtype=np.float32)
swq = np.zeros(2, dtype=np.float32)
new_pt = np.zeros(2, dtype=np.float32)
cur_pt = np.array([i, j], dtype=np.float32)
k = 0
for k in range(self.pt_count):
if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]:
break
w[k] = 1.0 / (
(i - self.dst_pts[k][0]) * (i - self.dst_pts[k][0])
+ (j - self.dst_pts[k][1]) * (j - self.dst_pts[k][1])
)
sw += w[k]
swp = swp + w[k] * np.array(self.dst_pts[k])
swq = swq + w[k] * np.array(self.src_pts[k])
if k == self.pt_count - 1:
pstar = 1 / sw * swp
qstar = 1 / sw * swq
miu_s = 0
for k in range(self.pt_count):
if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]:
continue
pt_i = self.dst_pts[k] - pstar
miu_s += w[k] * np.sum(pt_i * pt_i)
cur_pt -= pstar
cur_pt_j = np.array([-cur_pt[1], cur_pt[0]])
for k in range(self.pt_count):
if i == self.dst_pts[k][0] and j == self.dst_pts[k][1]:
continue
pt_i = self.dst_pts[k] - pstar
pt_j = np.array([-pt_i[1], pt_i[0]])
tmp_pt = np.zeros(2, dtype=np.float32)
tmp_pt[0] = (
np.sum(pt_i * cur_pt) * self.src_pts[k][0]
- np.sum(pt_j * cur_pt) * self.src_pts[k][1]
)
tmp_pt[1] = (
-np.sum(pt_i * cur_pt_j) * self.src_pts[k][0]
+ np.sum(pt_j * cur_pt_j) * self.src_pts[k][1]
)
tmp_pt *= w[k] / miu_s
new_pt += tmp_pt
new_pt += qstar
else:
new_pt = self.src_pts[k]
self.rdx[j, i] = new_pt[0] - i
self.rdy[j, i] = new_pt[1] - j
j += self.grid_size
i += self.grid_size
def gen_img(self):
src_h, src_w = self.src.shape[:2]
dst = np.zeros_like(self.src, dtype=np.float32)
for i in np.arange(0, self.dst_h, self.grid_size):
for j in np.arange(0, self.dst_w, self.grid_size):
ni = i + self.grid_size
nj = j + self.grid_size
w = h = self.grid_size
if ni >= self.dst_h:
ni = self.dst_h - 1
h = ni - i + 1
if nj >= self.dst_w:
nj = self.dst_w - 1
w = nj - j + 1
di = np.reshape(np.arange(h), (-1, 1))
dj = np.reshape(np.arange(w), (1, -1))
delta_x = self.__bilinear_interp(
di / h,
dj / w,
self.rdx[i, j],
self.rdx[i, nj],
self.rdx[ni, j],
self.rdx[ni, nj],
)
delta_y = self.__bilinear_interp(
di / h,
dj / w,
self.rdy[i, j],
self.rdy[i, nj],
self.rdy[ni, j],
self.rdy[ni, nj],
)
nx = j + dj + delta_x * self.trans_ratio
ny = i + di + delta_y * self.trans_ratio
nx = np.clip(nx, 0, src_w - 1)
ny = np.clip(ny, 0, src_h - 1)
nxi = np.array(np.floor(nx), dtype=np.int32)
nyi = np.array(np.floor(ny), dtype=np.int32)
nxi1 = np.array(np.ceil(nx), dtype=np.int32)
nyi1 = np.array(np.ceil(ny), dtype=np.int32)
if len(self.src.shape) == 3:
x = np.tile(np.expand_dims(ny - nyi, axis=-1), (1, 1, 3))
y = np.tile(np.expand_dims(nx - nxi, axis=-1), (1, 1, 3))
else:
x = ny - nyi
y = nx - nxi
dst[i : i + h, j : j + w] = self.__bilinear_interp(
x,
y,
self.src[nyi, nxi],
self.src[nyi, nxi1],
self.src[nyi1, nxi],
self.src[nyi1, nxi1],
)
dst = np.clip(dst, 0, 255)
dst = np.array(dst, dtype=np.uint8)
return dst
|