def merge_ims(srcs, pts_or_rects, bg, opt = None):
""" Makes a new image where each image in patches is copied at a
corresponding pixel location. Overlapping images are averaged
together. """
dst = bg.copy()
layer = np.zeros(dst.shape)
counts = np.zeros(dst.shape[:2], 'l')
for src, r in itl.izip(srcs, pts_or_rects):
r = ut.int_tuple(r)
x, y = r[:2]
# rescale if we're given a rectangle, and it has a different size
if len(r) > 2:
assert len(r) == 4
assert opt != 'center'
if src.shape[:2] != (r[3], r[2]):
src = resize(src, (r[3], r[2]))
elif opt == 'center':
x -= src.shape[1]/2
y -= src.shape[0]/2
# crop intersecting
dx, dy, dw, dh = ut.crop_rect_to_img((x, y, src.shape[1], src.shape[0]), dst)
sx = dx - x
sy = dy - y
layer[dy : dy + dh, dx : dx + dw] += src[sy : sy + dh, sx : sx + dw]
counts[dy : dy + dh, dx : dx + dw] += 1
dst[counts > 0] = layer[counts > 0] / counts[counts > 0][:, np.newaxis]
return dst
def merge_ims(srcs, pts_or_rects, bg, opt=None):
""" Makes a new image where each image in patches is copied at a
corresponding pixel location. Overlapping images are averaged
together. """
dst = rgb_from_gray(bg)
layer = np.zeros(dst.shape)
#counts = np.zeros(dst.shape[:2], 'l')
counts = np.zeros(dst.shape[:2], 'd')
for src, r in itl.izip(srcs, pts_or_rects):
r = ut.int_tuple(r)
x, y = r[:2]
# rescale if we're given a rectangle, and it has a different size
if len(r) > 2:
assert len(r) == 4
assert opt != 'center'
if src.shape[:2] != (r[3], r[2]):
src = resize(src, (r[3], r[2]))
elif opt == 'center':
x -= src.shape[1] / 2
y -= src.shape[0] / 2
# crop intersecting
dx, dy, dw, dh = ut.crop_rect_to_img(
(x, y, src.shape[1], src.shape[0]), dst)
sx = dx - x
sy = dy - y
layer[dy:dy + dh, dx:dx + dw] += src[sy:sy + dh, sx:sx + dw, :3]
if np.ndim(src) == 3 and src.shape[2] == 4:
counts[dy:dy + dh, dx:dx +
dw] += np.array(src[sy:sy + dh, sx:sx + dw, 3], 'd') / 255.
else:
counts[dy:dy + dh, dx:dx + dw] += 1
dst[counts > 0] = layer[counts > 0] / counts[counts > 0][:, np.newaxis]
return dst
def weighted_add(src, dst, x, y, src_weight, dst_weight, opt = None):
if opt == 'center':
x -= src.shape[1]/2
y -= src.shape[0]/2
# crop intersecting
dx, dy, dw, dh = ut.crop_rect_to_img((x, y, src.shape[1], src.shape[0]), dst)
sx = dx - x
sy = dy - y
dst[dy : dy + dh, dx : dx + dw] = dst[dy : dy + dh, dx : dx + dw]*dst_weight + src[sy : sy + dh, sx : sx + dw]*src_weight
def weighted_add(src, dst, x, y, src_weight, dst_weight, opt = None):
if opt == 'center':
x -= src.shape[1]/2
y -= src.shape[0]/2
# crop intersecting
dx, dy, dw, dh = ut.crop_rect_to_img((x, y, src.shape[1], src.shape[0]), dst)
sx = dx - x
sy = dy - y
dst[dy : dy + dh, dx : dx + dw] = dst[dy : dy + dh, dx : dx + dw]*dst_weight + src[sy : sy + dh, sx : sx + dw]*src_weight
vals.append(v.reshape(im.shape[:2] + (1,)))
return np.concatenate(vals, axis = 2)
def stack_meshgrid(xs, ys, dtype = 'l'):
x, y = np.meshgrid(xs, ys)
return np.array(np.concatenate([x[..., np.newaxis], y[..., np.newaxis]], axis = 2), dtype = dtype)
def sub_img_pad(im, (x, y, w, h), oob = 0):
if len(im.shape) == 2:
dst = np.zeros((h, w))
else:
dst = np.zeros((h, w, im.shape[2]))
dst[:] = oob
sx, sy, sw, sh = ut.crop_rect_to_img((x, y, w, h), im)
dst[(sy - y) : (sy - y) + sh,
(sx - x) : (sx - x) + sw] = im[sy : sy + sh, sx : sx + sw]
return dst
def compress(im, format = 'png'):
out = StringIO()
im = to_pil(im)
im.save(out, format = format)
c = out.getvalue()
out.close()
return c
def uncompress(s):
return from_pil(Image.open(StringIO(s)))
def stack_meshgrid(xs, ys, dtype='l'):
x, y = np.meshgrid(xs, ys)
return np.array(np.concatenate([x[..., np.newaxis], y[..., np.newaxis]],
axis=2),
dtype=dtype)
def sub_img_pad(im, (x, y, w, h), oob=0):
if len(im.shape) == 2:
dst = np.zeros((h, w))
else:
dst = np.zeros((h, w, im.shape[2]))
dst[:] = oob
sx, sy, sw, sh = ut.crop_rect_to_img((x, y, w, h), im)
dst[(sy - y):(sy - y) + sh, (sx - x):(sx - x) + sw] = im[sy:sy + sh,
sx:sx + sw]
return dst
def sub_img_reflect(im, (x, y, w, h)):
x, y, w, h = map(ut.iround, [x, y, w, h])
yy, xx = np.mgrid[y:y + h, x:x + w]
vals = np.uint8(
lookup_bilinear(im,
xx.flatten(),
yy.flatten(),
order=0,
mode='reflect'))
return vals.reshape((h, w, im.shape[2]))
