def compute_rectification_homographies_sift(im1, im2, rpc1, rpc2, x, y, w, h):
"""
Computes rectifying homographies for a ROI in a pair of Pleiades images.
Args:
im1, im2: paths to the two Pleiades images (usually jp2 or tif)
rpc1, rpc2: two instances of the rpc_model.RPCModel class
x, y, w, h: four integers definig the rectangular ROI in the first
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle.
Returns:
H1, H2: Two 3x3 matrices representing the rectifying homographies to be
applied to the two images.
disp_min, disp_max: horizontal disparity range, computed on a set of
sift matches
"""
# in brief: use ransac to estimate F from a set of sift matches, then use
# loop-zhang to estimate rectifying homographies.
matches = matches_from_sift_rpc_roi(im1, im2, rpc1, rpc2, x, y, w, h)
p1 = matches[:, 0:2]
p2 = matches[:, 2:4]
# the matching points are translated to be centered in 0, in order to deal
# with coordinates ranging from -1000 to 1000, and decrease imprecision
# effects of the loop-zhang rectification. These effects may become very
# important (~ 10 pixels error) when using coordinates around 20000.
pp1, T1 = center_2d_points(p1)
pp2, T2 = center_2d_points(p2)
F = estimation.fundamental_matrix_ransac(np.hstack([pp1, pp2]))
H1, H2 = estimation.loop_zhang(F, w, h)
# compose with previous translations to get H1, H2 in the big images frame
H1 = np.dot(H1, T1)
H2 = np.dot(H2, T2)
# for debug
print "max, min, mean rectification error on sift matches ----------------"
tmp = common.points_apply_homography(H1, p1)
y1 = tmp[:, 1]
tmp = common.points_apply_homography(H2, p2)
y2 = tmp[:, 1]
err = np.abs(y1 - y2)
print np.max(err), np.min(err), np.mean(err)
# pull back top-left corner of the ROI in the origin
roi = [[x, y], [x+w, y], [x+w, y+h], [x, y+h]]
pts = common.points_apply_homography(H1, roi)
x0, y0 = common.bounding_box2D(pts)[0:2]
T = common.matrix_translation(-x0, -y0)
H1 = np.dot(T, H1)
H2 = np.dot(T, H2)
# add an horizontal translation to H2 to center the disparity range around
H2 = register_horizontally(matches, H1, H2)
disp_m, disp_M = update_disp_range(matches, H1, H2, w, h)
return H1, H2, disp_m, disp_M
def corresponding_roi(rpc1, rpc2, x, y, w, h):
"""
Uses RPC functions to determine the region of im2 associated to the
specified ROI of im1.
Args:
rpc1, rpc2: two instances of the rpc_model.RPCModel class, or paths to
the xml files
x, y, w, h: four integers defining a rectangular region of interest
(ROI) in the first view. (x, y) is the top-left corner, and (w, h)
are the dimensions of the rectangle.
Returns:
four integers defining a ROI in the second view. This ROI is supposed
to contain the projections of the 3D points that are visible in the
input ROI.
"""
# read rpc files
if not isinstance(rpc1, rpc_model.RPCModel):
rpc1 = rpc_model.RPCModel(rpc1)
if not isinstance(rpc2, rpc_model.RPCModel):
rpc2 = rpc_model.RPCModel(rpc2)
m, M = altitude_range(rpc1, x, y, w, h, 0, 0)
# build an array with vertices of the 3D ROI, obtained as {2D ROI} x [m, M]
a = np.array([x, x, x, x, x+w, x+w, x+w, x+w])
b = np.array([y, y, y+h, y+h, y, y, y+h, y+h])
c = np.array([m, M, m, M, m, M, m, M])
# corresponding points in im2
xx, yy = find_corresponding_point(rpc1, rpc2, a, b, c)[0:2]
# return coordinates of the bounding box in im2
out = common.bounding_box2D(np.vstack([xx, yy]).T)
return np.round(out)
def compute_rectification_homographies_sift(im1, im2, rpc1, rpc2, x, y, w, h):
"""
Computes rectifying homographies for a ROI in a pair of Pleiades images.
Args:
im1, im2: paths to the two Pleiades images (usually jp2 or tif)
rpc1, rpc2: two instances of the rpc_model.RPCModel class
x, y, w, h: four integers definig the rectangular ROI in the first
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle.
Returns:
H1, H2: Two 3x3 matrices representing the rectifying homographies to be
applied to the two images.
disp_min, disp_max: horizontal disparity range, computed on a set of
sift matches
"""
# in brief: use ransac to estimate F from a set of sift matches, then use
# loop-zhang to estimate rectifying homographies.
matches = sift.matches_on_rpc_roi(im1, im2, rpc1, rpc2, x, y, w, h)
p1 = matches[:, 0:2]
p2 = matches[:, 2:4]
# the matching points are translated to be centered in 0, in order to deal
# with coordinates ranging from -1000 to 1000, and decrease imprecision
# effects of the loop-zhang rectification. These effects may become very
# important (~ 10 pixels error) when using coordinates around 20000.
pp1, T1 = center_2d_points(p1)
pp2, T2 = center_2d_points(p2)
F = estimation.fundamental_matrix_ransac(np.hstack([pp1, pp2]))
H1, H2 = estimation.loop_zhang(F, w, h)
# compose with previous translations to get H1, H2 in the big images frame
H1 = np.dot(H1, T1)
H2 = np.dot(H2, T2)
# for debug
print "max, min, mean rectification error on sift matches ----------------"
tmp = common.points_apply_homography(H1, p1)
y1 = tmp[:, 1]
tmp = common.points_apply_homography(H2, p2)
y2 = tmp[:, 1]
err = np.abs(y1 - y2)
print np.max(err), np.min(err), np.mean(err)
# pull back top-left corner of the ROI in the origin
roi = [[x, y], [x + w, y], [x + w, y + h], [x, y + h]]
pts = common.points_apply_homography(H1, roi)
x0, y0 = common.bounding_box2D(pts)[0:2]
T = common.matrix_translation(-x0, -y0)
H1 = np.dot(T, H1)
H2 = np.dot(T, H2)
# add an horizontal translation to H2 to center the disparity range around
H2 = register_horizontally(matches, H1, H2)
disp_m, disp_M = update_disp_range(matches, H1, H2, w, h)
return H1, H2, disp_m, disp_M
def rectification_homographies(matches, x, y, w, h):
"""
Computes rectifying homographies from point matches for a given ROI.
The affine fundamental matrix F is estimated with the gold-standard
algorithm, then two rectifying similarities (rotation, zoom, translation)
are computed directly from F.
Args:
matches: numpy array of shape (n, 4) containing a list of 2D point
correspondences between the two images.
x, y, w, h: four integers definig the rectangular ROI in the first
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle.
Returns:
S1, S2, F: three numpy arrays of shape (3, 3) representing the
two rectifying similarities to be applied to the two images and the
corresponding affine fundamental matrix.
"""
# estimate the affine fundamental matrix with the Gold standard algorithm
F = estimation.affine_fundamental_matrix(matches)
# compute rectifying similarities
S1, S2 = estimation.rectifying_similarities_from_affine_fundamental_matrix(F, True)
if cfg["debug"]:
y1 = common.points_apply_homography(S1, matches[:, :2])[:, 1]
y2 = common.points_apply_homography(S2, matches[:, 2:])[:, 1]
err = np.abs(y1 - y2)
print "max, min, mean rectification error on point matches: ",
print np.max(err), np.min(err), np.mean(err)
# pull back top-left corner of the ROI to the origin
pts = common.points_apply_homography(S1, [[x, y], [x + w, y], [x + w, y + h], [x, y + h]])
x0, y0 = common.bounding_box2D(pts)[:2]
T = common.matrix_translation(-x0, -y0)
return np.dot(T, S1), np.dot(T, S2), F
def cost_function_linear(v, rpc1, rpc2, matches):
"""
Objective function to minimize in order to correct the pointing error.
Arguments:
v: vector of size 4, containing the 4 parameters of the euclidean
transformation we are looking for.
rpc1, rpc2: two instances of the rpc_model.RPCModel class
matches: 2D numpy array containing a list of matches. Each line
contains one pair of points, ordered as x1 y1 x2 y2.
The coordinate system is the one of the big images.
alpha: relative weight of the error terms: e + alpha*(h-h0)^2. See
paper for more explanations.
Returns:
The sum of pointing errors and altitude differences, as written in the
paper formula (1).
"""
print_params(v)
# verify that parameters are in the bounding box
if (np.abs(v[0]) > 200*np.pi or
np.abs(v[1]) > 10000 or
np.abs(v[2]) > 10000 or
np.abs(v[3]) > 20000):
print 'warning: cost_function is going too far'
print v
x, y, w, h = common.bounding_box2D(matches[:, 0:2])
matches_rpc = rpc_utils.matches_from_rpc(rpc1, rpc2, x, y, w, h, 5)
F = estimation.fundamental_matrix(matches_rpc)
# transform the coordinates of points in the second image according to
# matrix A, built from vector v
A = euclidean_transform_matrix(v)
p2 = common.points_apply_homography(A, matches[:, 2:4])
return evaluation.fundamental_matrix_L1(F, np.hstack([matches[:, 0:2], p2]))
def rectification_homographies(matches, x, y, w, h):
"""
Computes rectifying homographies from point matches for a given ROI.
The affine fundamental matrix F is estimated with the gold-standard
algorithm, then two rectifying similarities (rotation, zoom, translation)
are computed directly from F.
Args:
matches: numpy array of shape (n, 4) containing a list of 2D point
correspondences between the two images.
x, y, w, h: four integers definig the rectangular ROI in the first
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle.
Returns:
S1, S2, F: three numpy arrays of shape (3, 3) representing the
two rectifying similarities to be applied to the two images and the
corresponding affine fundamental matrix.
"""
# estimate the affine fundamental matrix with the Gold standard algorithm
F = estimation.affine_fundamental_matrix(matches)
# compute rectifying similarities
S1, S2 = estimation.rectifying_similarities_from_affine_fundamental_matrix(F, True)
if cfg['debug']:
y1 = common.points_apply_homography(S1, matches[:, :2])[:, 1]
y2 = common.points_apply_homography(S2, matches[:, 2:])[:, 1]
err = np.abs(y1 - y2)
print "max, min, mean rectification error on point matches: ",
print np.max(err), np.min(err), np.mean(err)
# pull back top-left corner of the ROI to the origin
pts = common.points_apply_homography(S1, [[x, y], [x+w, y], [x+w, y+h], [x, y+h]])
x0, y0 = common.bounding_box2D(pts)[:2]
T = common.matrix_translation(-x0, -y0)
return np.dot(T, S1), np.dot(T, S2), F
def rectify_pair(im1,
im2,
rpc1,
rpc2,
x,
y,
w,
h,
out1,
out2,
A=None,
m=None,
flag='rpc'):
"""
Rectify a ROI in a pair of Pleiades images.
Args:
im1, im2: paths to the two Pleiades images (usually jp2 or tif)
rpc1, rpc2: paths to the two xml files containing RPC data
x, y, w, h: four integers defining the rectangular ROI in the first
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle.
out1, out2: paths to the output crops
A (optional): 3x3 numpy array containing the pointing error correction
for im2. This matrix is usually estimated with the pointing_accuracy
module.
m (optional): Nx4 numpy array containing a list of sift matches, in the
full image coordinates frame
flag (default: 'rpc'): option to decide wether to use rpc of sift
matches for the fundamental matrix estimation.
This function uses the parameter subsampling_factor from the
config module. If the factor z > 1 then the output images will
be subsampled by a factor z. The output matrices H1, H2, and the
ranges are also updated accordingly:
Hi = Z*Hi with Z = diag(1/z,1/z,1) and
disp_min = disp_min/z (resp _max)
Returns:
H1, H2: Two 3x3 matrices representing the rectifying homographies that
have been applied to the two (big) images.
disp_min, disp_max: horizontal disparity range
"""
# read RPC data
rpc1 = rpc_model.RPCModel(rpc1)
rpc2 = rpc_model.RPCModel(rpc2)
# compute rectifying homographies
if flag == 'rpc':
H1, H2, disp_min, disp_max = compute_rectification_homographies(
im1, im2, rpc1, rpc2, x, y, w, h, A, m)
else:
H1, H2, disp_min, disp_max = compute_rectification_homographies_sift(
im1, im2, rpc1, rpc2, x, y, w, h)
# compute output images size
roi = [[x, y], [x + w, y], [x + w, y + h], [x, y + h]]
pts1 = common.points_apply_homography(H1, roi)
x0, y0, w0, h0 = common.bounding_box2D(pts1)
# check that the first homography maps the ROI in the positive quadrant
np.testing.assert_allclose(np.round([x0, y0]), 0, atol=.01)
# apply homographies and do the crops TODO XXX FIXME cleanup here
#homography_cropper.crop_and_apply_homography(out1, im1, H1, w0, h0, cfg['subsampling_factor'], True)
#homography_cropper.crop_and_apply_homography(out2, im2, H2, w0, h0, cfg['subsampling_factor'], True)
common.image_apply_homography(out1, im1, H1, w0, h0)
common.image_apply_homography(out2, im2, H2, w0, h0)
# If subsampling_factor'] the homographies are altered to reflect the zoom
if cfg['subsampling_factor'] != 1:
from math import floor, ceil
# update the H1 and H2 to reflect the zoom
Z = np.eye(3)
Z[0, 0] = Z[1, 1] = 1.0 / cfg['subsampling_factor']
H1 = np.dot(Z, H1)
H2 = np.dot(Z, H2)
disp_min = floor(disp_min / cfg['subsampling_factor'])
disp_max = ceil(disp_max / cfg['subsampling_factor'])
return H1, H2, disp_min, disp_max
def compute_rectification_homographies(im1,
im2,
rpc1,
rpc2,
x,
y,
w,
h,
A=None,
m=None):
"""
Computes rectifying homographies for a ROI in a pair of Pleiades images.
Args:
im1, im2: paths to the two Pleiades images (usually jp2 or tif)
rpc1, rpc2: two instances of the rpc_model.RPCModel class
x, y, w, h: four integers definig the rectangular ROI in the first
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle.
A (optional): 3x3 numpy array containing the pointing error correction
for im2. This matrix is usually estimated with the pointing_accuracy
module.
m (optional): Nx4 numpy array containing a list of matches.
Returns:
H1, H2: Two 3x3 matrices representing the rectifying homographies to be
applied to the two images.
disp_min, disp_max: horizontal disparity range, computed on a set of
sift matches
"""
# in brief: use 8-pts normalized algo to estimate F, then use loop-zhang to
# estimate rectifying homographies.
print "step 1: find virtual matches, and center them ----------------------"
n = cfg['n_gcp_per_axis']
rpc_matches = rpc_utils.matches_from_rpc(rpc1, rpc2, x, y, w, h, n)
p1 = rpc_matches[:, 0:2]
p2 = rpc_matches[:, 2:4]
if A is not None:
print "applying pointing error correction"
# correct coordinates of points in im2, according to A
p2 = common.points_apply_homography(np.linalg.inv(A), p2)
# the matching points are translated to be centered in 0, in order to deal
# with coordinates ranging from -1000 to 1000, and decrease imprecision
# effects of the loop-zhang rectification. These effects may become very
# important (~ 10 pixels error) when using coordinates around 20000.
pp1, T1 = center_2d_points(p1)
pp2, T2 = center_2d_points(p2)
print "step 2: estimate F (Gold standard algorithm) -----------------------"
F = estimation.affine_fundamental_matrix(np.hstack([pp1, pp2]))
print "step 3: compute rectifying homographies (loop-zhang algorithm) -----"
H1, H2 = estimation.loop_zhang(F, w, h)
S1, S2 = estimation.rectifying_similarities_from_affine_fundamental_matrix(
F, True)
print "F\n", F, "\n"
print "H1\n", H1, "\n"
print "S1\n", S1, "\n"
print "H2\n", H2, "\n"
print "S2\n", S2, "\n"
# compose with previous translations to get H1, H2 in the big images frame
H1 = np.dot(H1, T1)
H2 = np.dot(H2, T2)
# for debug
print "max, min, mean rectification error on rpc matches ------------------"
tmp = common.points_apply_homography(H1, p1)
y1 = tmp[:, 1]
tmp = common.points_apply_homography(H2, p2)
y2 = tmp[:, 1]
err = np.abs(y1 - y2)
print np.max(err), np.min(err), np.mean(err)
print "step 4: pull back top-left corner of the ROI in the origin ---------"
roi = [[x, y], [x + w, y], [x + w, y + h], [x, y + h]]
pts = common.points_apply_homography(H1, roi)
x0, y0 = common.bounding_box2D(pts)[0:2]
T = common.matrix_translation(-x0, -y0)
H1 = np.dot(T, H1)
H2 = np.dot(T, H2)
# add an horizontal translation to H2 to center the disparity range around
# the origin, if sift matches are available
if m is not None:
print "step 5: horizontal registration --------------------------------"
# filter sift matches with the known fundamental matrix
# but first convert F for big images coordinate frame
F = np.dot(T2.T, np.dot(F, T1))
print '%d sift matches before epipolar constraint filering' % len(m)
m = filter_matches_epipolar_constraint(F, m, cfg['epipolar_thresh'])
print '%d sift matches after epipolar constraint filering' % len(m)
if len(m) < 2:
# 0 or 1 sift match
print 'rectification.compute_rectification_homographies: less than'
print '2 sift matches after filtering by the epipolar constraint.'
print 'This may be due to the pointing error, or to strong'
print 'illumination changes between the input images.'
print 'No registration will be performed.'
else:
H2 = register_horizontally(m, H1, H2)
disp_m, disp_M = update_disp_range(m, H1, H2, w, h)
print "SIFT disparity range: [%f,%f]" % (disp_m, disp_M)
# expand disparity range with srtm according to cfg params
print cfg['disp_range_method']
if (cfg['disp_range_method'] == "srtm") or (m is None) or (len(m) < 2):
disp_m, disp_M = rpc_utils.srtm_disp_range_estimation(
rpc1, rpc2, x, y, w, h, H1, H2, A,
cfg['disp_range_srtm_high_margin'],
cfg['disp_range_srtm_low_margin'])
print "SRTM disparity range: [%f,%f]" % (disp_m, disp_M)
if ((cfg['disp_range_method'] == "wider_sift_srtm") and (m is not None)
and (len(m) >= 2)):
d_m, d_M = rpc_utils.srtm_disp_range_estimation(
rpc1, rpc2, x, y, w, h, H1, H2, A,
cfg['disp_range_srtm_high_margin'],
cfg['disp_range_srtm_low_margin'])
print "SRTM disparity range: [%f,%f]" % (d_m, d_M)
disp_m = min(disp_m, d_m)
disp_M = max(disp_M, d_M)
print "Final disparity range: [%s, %s]" % (disp_m, disp_M)
return H1, H2, disp_m, disp_M
def matches_from_projection_matrices_roi(im1, im2, rpc1, rpc2, x, y, w, h):
"""
Computes a list of sift matches between two Pleiades images.
Args:
im1, im2: paths to the two Pleiades images (usually jp2 or tif)
rpc1, rpc2: two instances of the rpc_model.RPCModel class
x, y, w, h: four integers definig the rectangular ROI in the first image.
(x, y) is the top-left corner, and (w, h) are the dimensions of the
rectangle.
This function uses the parameter subsampling_factor_registration
from the config module. If factor > 1 then the registration
is performed over subsampled images, but the resulting keypoints
are then scaled back to conceal the subsampling
Returns:
matches: 2D numpy array containing a list of matches. Each line
contains one pair of points, ordered as x1 y1 x2 y2.
The coordinate system is that of the big images.
If no sift matches are found, then an exception is raised.
"""
#m, M = rpc_utils.altitude_range(rpc1, x, y, w, h)
m=5
M=20
# build an array with vertices of the 3D ROI, obtained as {2D ROI} x [m, M]
# also include the midpoints because the 8 corners of the frustum alone don't seem to work
a = np.array([x, x, x, x, x+w, x+w, x+w, x+w,x+w/2,x+w/2,x+w/2,x+w/2,x+w/2,x+w/2,x ,x ,x+w ,x+w ])
b = np.array([y, y, y+h, y+h, y, y, y+h, y+h,y ,y ,y+h/2,y+h/2,y+h ,y+h ,y+h/2,y+h/2,y+h/2,y+h/2])
c = np.array([m, M, m, M, m, M, m, M,m ,M ,m ,M ,m ,M ,m ,M ,m ,M ])
xx = np.zeros(len(a))
yy = np.zeros(len(a))
# corresponding points in im2
P1 = np.loadtxt(rpc1)
P2 = np.loadtxt(rpc2)
M = P1[:,:3]
p4 = P1[:,3]
m3 = M[2,:]
inv_M = np.linalg.inv(M)
v = np.vstack((a,b,c*0+1))
for i in range(len(a)):
v = np.array([a[i],b[i],1])
mu = c[i] / np.sign ( np.linalg.det(M) )
X3D = inv_M.dot (mu * v - p4 )
# backproject
newpoints = P2.dot(np.hstack([X3D,1]))
xx[i] = newpoints[0] / newpoints[2]
yy[i] = newpoints[1] / newpoints[2]
print xx
print yy
matches = np.vstack([a, b,xx,yy]).T
return matches
##### xx, yy = rpc_utils.find_corresponding_point(rpc1, rpc2, a, b, c)[0:2]
# bounding box in im2
x2, y2, w2, h2 = common.bounding_box2D(np.vstack([xx, yy]).T) ## GF NOT USED
x1, y1, w1, h1 = x, y, w, h
x2, y2, w2, h2 = x, y, w, h
# do crops, to apply sift on reasonably sized images
crop1 = common.image_crop_LARGE(im1, x1, y1, w1, h1)
crop2 = common.image_crop_LARGE(im2, x2, y2, w2, h2)
T1 = common.matrix_translation(x1, y1)
T2 = common.matrix_translation(x2, y2)
# call sift matches for the images
matches = matches_from_sift(crop1, crop2)
if matches.size:
# compensate coordinates for the crop and the zoom
pts1 = common.points_apply_homography(T1, matches[:, 0:2])
pts2 = common.points_apply_homography(T2, matches[:, 2:4])
return np.hstack([pts1, pts2])
else:
raise Exception("no sift matches")
def rectify_pair(im1, im2, rpc1, rpc2, x, y, w, h, out1, out2, A=None):
"""
Rectify a ROI in a pair of Pleiades images.
Args:
im1, im2: paths to the two Pleiades images (usually jp2 or tif)
rpc1, rpc2: paths to the two xml files containing RPC data
x, y, w, h: four integers defining the rectangular ROI in the first image.
(x, y) is the top-left corner, and (w, h) are the dimensions of the
rectangle.
out1, out2: paths to the output crops
A (optional): 3x3 numpy array containing the pointing error correction
for im2. This matrix is usually estimated with the pointing_accuracy
module.
This function uses the parameter subsampling_factor from the config module.
If the factor z > 1 then the output images will be subsampled by a factor z.
The output matrices H1, H2, and the ranges are also updated accordingly:
Hi = Z*Hi with Z = diag(1/z,1/z,1) and
disp_min = disp_min/z (resp _max)
Returns:
H1, H2: Two 3x3 matrices representing the rectifying homographies that
have been applied to the two (big) images.
disp_min, disp_max: horizontal disparity range
"""
# compute rectifying homographies
H1, H2, disp_min, disp_max = compute_rectification_homographies(im1, im2,
rpc1, rpc2, x, y, w, h, A)
## compute output images size
roi = [[x, y], [x+w, y], [x+w, y+h], [x, y+h]]
pts1 = common.points_apply_homography(H1, roi)
x0, y0, w0, h0 = common.bounding_box2D(pts1)
#x0,y0,w0,h0 = x,y,w,h
# check that the first homography maps the ROI in the positive quadrant
assert (round(x0) == 0)
assert (round(y0) == 0)
z = cfg['subsampling_factor']
# apply homographies and do the crops
# THIS STEP IS HERE TO PRODUCE THE MASKS WHERE THE IMAGE IS KNOWN
# SURE THIS IS A CRAPPY WAY TO DO THIS, WE SHOULD DEFINITIVELY DO IT
# SIMULTANEOUSLY WITH THE HOMOGRAPHIC TRANSFORMATION
msk1 = common.tmpfile('.png')
msk2 = common.tmpfile('.png')
common.run('plambda %s "x 255" -o %s' % (im1, msk1))
common.run('plambda %s "x 255" -o %s' % (im2, msk2))
homography_cropper.crop_and_apply_homography(msk1, msk1, H1, w0, h0, z)
homography_cropper.crop_and_apply_homography(msk2, msk2, H2, w0, h0, z)
# FINALLY : apply homographies and do the crops of the images
homography_cropper.crop_and_apply_homography(out1, im1, H1, w0, h0, z)
homography_cropper.crop_and_apply_homography(out2, im2, H2, w0, h0, z)
# COMBINE THE MASK TO REMOVE THE POINTS THAT FALL OUTSIDE THE IMAGE
common.run('plambda %s %s "x 200 > y nan if" -o %s' % (msk1, out1, out1))
common.run('plambda %s %s "x 200 > y nan if" -o %s' % (msk2, out2, out2))
# This also does the job but when z != 1 it fails (segfault: homography)
# TODO: FIX homography, maybe code a new one
# common.image_apply_homography(out1, im1, H1, w0, h0)
# common.image_apply_homography(out2, im2, H2, w0, h0)
# If subsampling_factor the homographies are altered to reflect the zoom
if z != 1:
from math import floor, ceil
# update the H1 and H2 to reflect the zoom
Z = np.eye(3);
Z[0,0] = Z[1,1] = 1.0 / z
H1 = np.dot(Z, H1)
H2 = np.dot(Z, H2)
disp_min = floor(disp_min / z)
disp_max = ceil(disp_max / z)
w0 = w0 / subsampling_factor
h0 = h0 / subsampling_factor
return H1, H2, disp_min, disp_max
def compute_rectification_homographies(im1, im2, rpc1, rpc2, x, y, w, h, A=None):
"""
Computes rectifying homographies for a ROI in a pair of Pleiades images.
Args:
im1, im2: paths to the two Pleiades images (usually jp2 or tif)
rpc1, rpc2: two instances of the rpc_model.RPCModel class
x, y, w, h: four integers definig the rectangular ROI in the first image.
(x, y) is the top-left corner, and (w, h) are the dimensions of the
rectangle.
A (optional): 3x3 numpy array containing the pointing error correction
for im2. This matrix is usually estimated with the pointing_accuracy
module.
Returns:
H1, H2: Two 3x3 matrices representing the rectifying homographies to be applied
to the two images.
disp_min, disp_max: horizontal disparity range, computed on a set of
sift matches
"""
# in brief: use 8-pts normalized algo to estimate F, then use loop-zhang to
# estimate rectifying homographies.
print "step 1: find matches, and center them ------------------------------"
sift_matches = matches_from_projection_matrices_roi(im1, im2, rpc1, rpc2, x+w/4, y+h/4, w*2/4, h*2/4)
#sift_matches2 = matches_from_sift(im1, im2)
#sift_matches = sift_matches2
# import visualisation
# print visualisation.plot_matches(im1,im2,sift_matches)
p1 = sift_matches[:, 0:2]
p2 = sift_matches[:, 2:4]
# the matching points are translated to be centered in 0, in order to deal
# with coordinates ranging from -1000 to 1000, and decrease imprecision
# effects of the loop-zhang rectification. These effects may become very
# important (~ 10 pixels error) when using coordinates around 20000.
pp1, T1 = center_2d_points(p1)
pp2, T2 = center_2d_points(p2)
print "step 2: estimate F (8-points algorithm) ----------------------------"
F = estimation.fundamental_matrix(np.hstack([pp1, pp2]))
F = np.dot(T2.T, np.dot(F, T1)) # convert F for big images coordinate frame
print "step 3: compute rectifying homographies (loop-zhang algorithm) -----"
H1, H2 = estimation.loop_zhang(F, w, h)
#### ATTENTION: LOOP-ZHANG IMPLICITLY ASSUMES THAT F IS IN THE FINAL (CROPPED)
# IMAGE GEOMETRY. THUS 0,0 IS THE UPPER LEFT CORNER OF THE IMAGE AND W,H ARE
# USED TO ESTIMATE THE DISTORTION WITHIN THE REGION. BY CENTERING THE COORDINATES
# OF THE PIXELS WE ARE CONSTRUCTING A RECTIFICATION DOES NOT TAKE INTO ACCOUNT THE
# CORRECT IMAGE PORTION.
# compose with previous translations to get H1, H2 in the big images frame
#H1 = np.dot(H1, T1)
#H2 = np.dot(H2, T2)
# for debug
print "min, max, mean rectification error on rpc matches ------------------"
tmp = common.points_apply_homography(H1, p1)
y1 = tmp[:, 1]
tmp = common.points_apply_homography(H2, p2)
y2 = tmp[:, 1]
err = np.abs(y1 - y2)
print np.min(err), np.max(err), np.mean(err)
# print "step 4: pull back top-left corner of the ROI in the origin ---------"
roi = [[x, y], [x+w, y], [x+w, y+h], [x, y+h]]
pts = common.points_apply_homography(H1, roi)
x0, y0 = common.bounding_box2D(pts)[0:2]
T = common.matrix_translation(-x0, -y0)
H1 = np.dot(T, H1)
H2 = np.dot(T, H2)
# add an horizontal translation to H2 to center the disparity range around
# the origin, if sift matches are available
print "step 5: horizontal registration ------------------------------------"
sift_matches2 = matches_from_sift(im1, im2)
# filter sift matches with the known fundamental matrix
sift_matches2 = filter_matches_epipolar_constraint(F, sift_matches2,
cfg['epipolar_thresh'])
if not len(sift_matches2):
print """all the sift matches have been discarded by the epipolar
constraint. This is probably due to the pointing error. Try with a
bigger value for epipolar_thresh."""
sys.exit()
H2, disp_m, disp_M = register_horizontally(sift_matches2, H1, H2, do_scale_horizontally=True)
disp_m, disp_M = update_minmax_range_extrapolating_registration_affinity(sift_matches2,
H1, H2, w, h)
return H1, H2, disp_m, disp_M
def compute_rectification_homographies(im1, im2, rpc1, rpc2, x, y, w, h, A=None,
m=None):
"""
Computes rectifying homographies for a ROI in a pair of Pleiades images.
Args:
im1, im2: paths to the two Pleiades images (usually jp2 or tif)
rpc1, rpc2: two instances of the rpc_model.RPCModel class
x, y, w, h: four integers definig the rectangular ROI in the first
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle.
A (optional): 3x3 numpy array containing the pointing error correction
for im2. This matrix is usually estimated with the pointing_accuracy
module.
m (optional): Nx4 numpy array containing a list of matches.
Returns:
H1, H2: Two 3x3 matrices representing the rectifying homographies to be
applied to the two images.
disp_min, disp_max: horizontal disparity range, computed on a set of
sift matches
"""
# in brief: use 8-pts normalized algo to estimate F, then use loop-zhang to
# estimate rectifying homographies.
print "step 1: find virtual matches, and center them ----------------------"
n = cfg['n_gcp_per_axis']
rpc_matches = rpc_utils.matches_from_rpc(rpc1, rpc2, x, y, w, h, n)
p1 = rpc_matches[:, 0:2]
p2 = rpc_matches[:, 2:4]
if A is not None:
print "applying pointing error correction"
# correct coordinates of points in im2, according to A
p2 = common.points_apply_homography(np.linalg.inv(A), p2)
# the matching points are translated to be centered in 0, in order to deal
# with coordinates ranging from -1000 to 1000, and decrease imprecision
# effects of the loop-zhang rectification. These effects may become very
# important (~ 10 pixels error) when using coordinates around 20000.
pp1, T1 = center_2d_points(p1)
pp2, T2 = center_2d_points(p2)
print "step 2: estimate F (Gold standard algorithm) -----------------------"
F = estimation.affine_fundamental_matrix(np.hstack([pp1, pp2]))
print "step 3: compute rectifying homographies (loop-zhang algorithm) -----"
H1, H2 = estimation.loop_zhang(F, w, h)
S1, S2 = estimation.rectifying_similarities_from_affine_fundamental_matrix(
F, True)
print "F\n", F, "\n"
print "H1\n", H1, "\n"
print "S1\n", S1, "\n"
print "H2\n", H2, "\n"
print "S2\n", S2, "\n"
# compose with previous translations to get H1, H2 in the big images frame
H1 = np.dot(H1, T1)
H2 = np.dot(H2, T2)
# for debug
print "max, min, mean rectification error on rpc matches ------------------"
tmp = common.points_apply_homography(H1, p1)
y1 = tmp[:, 1]
tmp = common.points_apply_homography(H2, p2)
y2 = tmp[:, 1]
err = np.abs(y1 - y2)
print np.max(err), np.min(err), np.mean(err)
print "step 4: pull back top-left corner of the ROI in the origin ---------"
roi = [[x, y], [x+w, y], [x+w, y+h], [x, y+h]]
pts = common.points_apply_homography(H1, roi)
x0, y0 = common.bounding_box2D(pts)[0:2]
T = common.matrix_translation(-x0, -y0)
H1 = np.dot(T, H1)
H2 = np.dot(T, H2)
# add an horizontal translation to H2 to center the disparity range around
# the origin, if sift matches are available
if m is not None:
print "step 5: horizontal registration --------------------------------"
# filter sift matches with the known fundamental matrix
# but first convert F for big images coordinate frame
F = np.dot(T2.T, np.dot(F, T1))
print '%d sift matches before epipolar constraint filering' % len(m)
m = filter_matches_epipolar_constraint(F, m, cfg['epipolar_thresh'])
print '%d sift matches after epipolar constraint filering' % len(m)
if len(m) < 2:
# 0 or 1 sift match
print 'rectification.compute_rectification_homographies: less than'
print '2 sift matches after filtering by the epipolar constraint.'
print 'This may be due to the pointing error, or to strong'
print 'illumination changes between the input images.'
print 'No registration will be performed.'
else:
H2 = register_horizontally(m, H1, H2)
disp_m, disp_M = update_disp_range(m, H1, H2, w, h)
print "SIFT disparity range: [%f,%f]"%(disp_m,disp_M)
# expand disparity range with srtm according to cfg params
print cfg['disp_range_method']
if (cfg['disp_range_method'] == "srtm") or (m is None) or (len(m) < 2):
disp_m, disp_M = rpc_utils.srtm_disp_range_estimation(
rpc1, rpc2, x, y, w, h, H1, H2, A,
cfg['disp_range_srtm_high_margin'],
cfg['disp_range_srtm_low_margin'])
print "SRTM disparity range: [%f,%f]"%(disp_m,disp_M)
if ((cfg['disp_range_method'] == "wider_sift_srtm") and (m is not None) and
(len(m) >= 2)):
d_m, d_M = rpc_utils.srtm_disp_range_estimation(
rpc1, rpc2, x, y, w, h, H1, H2, A,
cfg['disp_range_srtm_high_margin'],
cfg['disp_range_srtm_low_margin'])
print "SRTM disparity range: [%f,%f]"%(d_m,d_M)
disp_m = min(disp_m, d_m)
disp_M = max(disp_M, d_M)
print "Final disparity range: [%s, %s]" % (disp_m, disp_M)
return H1, H2, disp_m, disp_M
def rectify_pair(im1, im2, rpc1, rpc2, x, y, w, h, out1, out2, A=None, sift_matches=None, method="rpc"):
"""
Rectify a ROI in a pair of images.
Args:
im1, im2: paths to two image files
rpc1, rpc2: paths to the two xml files containing RPC data
x, y, w, h: four integers defining the rectangular ROI in the first
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle.
out1, out2: paths to the output rectified crops
A (optional): 3x3 numpy array containing the pointing error correction
for im2. This matrix is usually estimated with the pointing_accuracy
module.
sift_matches (optional): Nx4 numpy array containing a list of sift
matches, in the full image coordinates frame
method (default: 'rpc'): option to decide wether to use rpc of sift
matches for the fundamental matrix estimation.
This function uses the parameter subsampling_factor from the
config module. If the factor z > 1 then the output images will
be subsampled by a factor z. The output matrices H1, H2, and the
ranges are also updated accordingly:
Hi = Z * Hi with Z = diag(1/z, 1/z, 1) and
disp_min = disp_min / z (resp _max)
Returns:
H1, H2: Two 3x3 matrices representing the rectifying homographies that
have been applied to the two original (large) images.
disp_min, disp_max: horizontal disparity range
"""
# read RPC data
rpc1 = rpc_model.RPCModel(rpc1)
rpc2 = rpc_model.RPCModel(rpc2)
# compute real or virtual matches
if method == "rpc":
# find virtual matches from RPC camera models
matches = rpc_utils.matches_from_rpc(rpc1, rpc2, x, y, w, h, cfg["n_gcp_per_axis"])
# correct second image coordinates with the pointing correction matrix
if A is not None:
matches[:, 2:] = common.points_apply_homography(np.linalg.inv(A), matches[:, 2:])
else:
matches = sift_matches
# compute rectifying homographies
H1, H2, F = rectification_homographies(matches, x, y, w, h)
if cfg["register_with_shear"]:
# compose H2 with a horizontal shear to reduce the disparity range
a = np.mean(rpc_utils.altitude_range(rpc1, x, y, w, h))
lon, lat, alt = rpc_utils.ground_control_points(rpc1, x, y, w, h, a, a, 4)
x1, y1 = rpc1.inverse_estimate(lon, lat, alt)[:2]
x2, y2 = rpc2.inverse_estimate(lon, lat, alt)[:2]
m = np.vstack([x1, y1, x2, y2]).T
m = np.vstack({tuple(row) for row in m}) # remove duplicates due to no alt range
H2 = register_horizontally_shear(m, H1, H2)
# compose H2 with a horizontal translation to center disp range around 0
if sift_matches is not None:
sift_matches = filter_matches_epipolar_constraint(F, sift_matches, cfg["epipolar_thresh"])
if len(sift_matches) < 10:
print "WARNING: no registration with less than 10 matches"
else:
H2 = register_horizontally_translation(sift_matches, H1, H2)
# compute disparity range
disp_m, disp_M = disparity_range(rpc1, rpc2, x, y, w, h, H1, H2, sift_matches, A)
# compute output images size
roi = [[x, y], [x + w, y], [x + w, y + h], [x, y + h]]
pts1 = common.points_apply_homography(H1, roi)
x0, y0, w0, h0 = common.bounding_box2D(pts1)
# check that the first homography maps the ROI in the positive quadrant
np.testing.assert_allclose(np.round([x0, y0]), 0, atol=0.01)
# apply homographies and do the crops TODO XXX FIXME cleanup here
# homography_cropper.crop_and_apply_homography(out1, im1, H1, w0, h0, cfg['subsampling_factor'], True)
# homography_cropper.crop_and_apply_homography(out2, im2, H2, w0, h0, cfg['subsampling_factor'], True)
common.image_apply_homography(out1, im1, H1, w0, h0)
common.image_apply_homography(out2, im2, H2, w0, h0)
# if subsampling_factor'] the homographies are altered to reflect the zoom
if cfg["subsampling_factor"] != 1:
Z = np.eye(3)
Z[0, 0] = Z[1, 1] = 1.0 / cfg["subsampling_factor"]
H1 = np.dot(Z, H1)
H2 = np.dot(Z, H2)
disp_m = np.floor(disp_m / cfg["subsampling_factor"])
disp_M = np.ceil(disp_M / cfg["subsampling_factor"])
return H1, H2, disp_m, disp_M
def rectify_pair(im1, im2, rpc1, rpc2, x, y, w, h, out1, out2, A=None,
sift_matches=None, method='rpc'):
"""
Rectify a ROI in a pair of images.
Args:
im1, im2: paths to two image files
rpc1, rpc2: paths to the two xml files containing RPC data
x, y, w, h: four integers defining the rectangular ROI in the first
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle.
out1, out2: paths to the output rectified crops
A (optional): 3x3 numpy array containing the pointing error correction
for im2. This matrix is usually estimated with the pointing_accuracy
module.
sift_matches (optional): Nx4 numpy array containing a list of sift
matches, in the full image coordinates frame
method (default: 'rpc'): option to decide wether to use rpc of sift
matches for the fundamental matrix estimation.
This function uses the parameter subsampling_factor from the
config module. If the factor z > 1 then the output images will
be subsampled by a factor z. The output matrices H1, H2, and the
ranges are also updated accordingly:
Hi = Z * Hi with Z = diag(1/z, 1/z, 1) and
disp_min = disp_min / z (resp _max)
Returns:
H1, H2: Two 3x3 matrices representing the rectifying homographies that
have been applied to the two original (large) images.
disp_min, disp_max: horizontal disparity range
"""
# read RPC data
rpc1 = rpc_model.RPCModel(rpc1)
rpc2 = rpc_model.RPCModel(rpc2)
# compute real or virtual matches
if method == 'rpc':
# find virtual matches from RPC camera models
matches = rpc_utils.matches_from_rpc(rpc1, rpc2, x, y, w, h,
cfg['n_gcp_per_axis'])
# correct second image coordinates with the pointing correction matrix
if A is not None:
matches[:, 2:] = common.points_apply_homography(np.linalg.inv(A),
matches[:, 2:])
else:
matches = sift_matches
# compute rectifying homographies
H1, H2, F = rectification_homographies(matches, x, y, w, h)
# compose H2 with a horizontal translation to center disp range around 0
if sift_matches is not None:
sift_matches = filter_matches_epipolar_constraint(F, sift_matches,
cfg['epipolar_thresh'])
if len(sift_matches) < 10:
print 'WARNING: no registration with less than 10 matches'
else:
H2 = register_horizontally(sift_matches, H1, H2)
# compute disparity range
disp_m, disp_M = disparity_range(rpc1, rpc2, x, y, w, h, H1, H2,
sift_matches, A)
# compute output images size
roi = [[x, y], [x+w, y], [x+w, y+h], [x, y+h]]
pts1 = common.points_apply_homography(H1, roi)
x0, y0, w0, h0 = common.bounding_box2D(pts1)
# check that the first homography maps the ROI in the positive quadrant
np.testing.assert_allclose(np.round([x0, y0]), 0, atol=.01)
# apply homographies and do the crops TODO XXX FIXME cleanup here
#homography_cropper.crop_and_apply_homography(out1, im1, H1, w0, h0, cfg['subsampling_factor'], True)
#homography_cropper.crop_and_apply_homography(out2, im2, H2, w0, h0, cfg['subsampling_factor'], True)
common.image_apply_homography(out1, im1, H1, w0, h0)
common.image_apply_homography(out2, im2, H2, w0, h0)
# if subsampling_factor'] the homographies are altered to reflect the zoom
if cfg['subsampling_factor'] != 1:
Z = np.eye(3)
Z[0, 0] = Z[1, 1] = 1.0 / cfg['subsampling_factor']
H1 = np.dot(Z, H1)
H2 = np.dot(Z, H2)
disp_m = np.floor(disp_m / cfg['subsampling_factor'])
disp_M = np.ceil(disp_M / cfg['subsampling_factor'])
return H1, H2, disp_m, disp_M
def crop_and_apply_homography(im_out, im_in, H, w, h, subsampling_factor=1,
convert_to_gray=False):
"""
Warps a piece of a Pleiades (panchro or ms) image with a homography.
Args:
im_out: path to the output image
im_in: path to the input (tif) full Pleiades image
H: numpy array containing the 3x3 homography matrix
w, h: size of the output image
subsampling_factor (optional, default=1): when set to z>1,
will result in the application of the homography Z*H where Z =
diag(1/z, 1/z, 1), so the output will be zoomed out by a factor z.
The output image will be (w/z, h/z)
convert_to_gray (optional, default False): it set to True, and if the
input image has 4 channels, it is converted to gray before applying
zoom and homographies.
Returns:
nothing
The homography has to be used as: coord_out = H coord_in. The produced
output image corresponds to coord_out in [0, w] x [0, h]. The warp is made
by Pascal Monasse's binary named 'homography'.
"""
# crop a piece of the big input image, to which the homography will be
# applied
# warning: as the crop uses integer coordinates, be careful to round off
# (x0, y0) before modifying the homograpy. You want the crop and the
# translation representing it do exactly the same thing.
pts = [[0, 0], [w, 0], [w, h], [0, h]]
inv_H_pts = common.points_apply_homography(np.linalg.inv(H), pts)
x0, y0, w0, h0 = common.bounding_box2D(inv_H_pts)
x0, y0 = np.floor([x0, y0])
w0, h0 = np.ceil([w0, h0])
crop_fullres = common.image_crop_LARGE(im_in, x0, y0, w0, h0)
# This filter is needed (for panchro images) because the original PLEAIDES
# SENSOR PERFECT images are aliased
if (common.image_pix_dim(crop_fullres) == 1 and subsampling_factor == 1 and
cfg['use_pleiades_unsharpening']):
tmp = image_apply_pleiades_unsharpening_filter(crop_fullres)
common.run('rm -f %s' % crop_fullres)
crop_fullres = tmp
# convert to gray
if common.image_pix_dim(crop_fullres) == 4:
if convert_to_gray:
crop_fullres = common.pansharpened_to_panchro(crop_fullres)
# compensate the homography with the translation induced by the preliminary
# crop, then apply the homography and crop.
H = np.dot(H, common.matrix_translation(x0, y0))
# Since the objective is to compute a zoomed out homographic transformation
# of the input image, to save computations we zoom out the image before
# applying the homography. If Z is the matrix representing the zoom out and
# H the homography matrix, this trick consists in applying Z*H*Z^{-1} to
# the zoomed image Z*Im instead of applying Z*H to the original image Im.
if subsampling_factor == 1:
common.image_apply_homography(im_out, crop_fullres, H, w, h)
return
else:
assert(subsampling_factor >= 1)
# H becomes Z*H*Z^{-1}
Z = np.eye(3);
Z[0,0] = Z[1,1] = 1 / float(subsampling_factor)
H = np.dot(Z, H)
H = np.dot(H, np.linalg.inv(Z))
# w, and h are updated accordingly
w = int(w / subsampling_factor)
h = int(h / subsampling_factor)
# the DCT zoom is NOT SAFE when the input image size is not a multiple
# of the zoom factor
tmpw, tmph = common.image_size(crop_fullres)
tmpw, tmph = int(tmpw / subsampling_factor), int(tmph / subsampling_factor)
crop_fullres_safe = common.image_crop_tif(crop_fullres, 0, 0, tmpw *
subsampling_factor, tmph * subsampling_factor)
common.run('rm -f %s' % crop_fullres)
# zoom out the input image (crop_fullres)
crop_zoom_out = common.image_safe_zoom_fft(crop_fullres_safe,
subsampling_factor)
common.run('rm -f %s' % crop_fullres_safe)
# apply the homography to the zoomed out crop
common.image_apply_homography(im_out, crop_zoom_out, H, w, h)
return
def crop_and_apply_homography(im_out,
im_in,
H,
w,
h,
subsampling_factor=1,
convert_to_gray=False):
"""
Warps a piece of a Pleiades (panchro or ms) image with a homography.
Args:
im_out: path to the output image
im_in: path to the input (tif) full Pleiades image
H: numpy array containing the 3x3 homography matrix
w, h: size of the output image
subsampling_factor (optional, default=1): when set to z>1,
will result in the application of the homography Z*H where Z =
diag(1/z, 1/z, 1), so the output will be zoomed out by a factor z.
The output image will be (w/z, h/z)
convert_to_gray (optional, default False): it set to True, and if the
input image has 4 channels, it is converted to gray before applying
zoom and homographies.
Returns:
nothing
The homography has to be used as: coord_out = H coord_in. The produced
output image corresponds to coord_out in [0, w] x [0, h]. The warp is made
by Pascal Monasse's binary named 'homography'.
"""
# crop a piece of the big input image, to which the homography will be
# applied
# warning: as the crop uses integer coordinates, be careful to round off
# (x0, y0) before modifying the homograpy. You want the crop and the
# translation representing it do exactly the same thing.
pts = [[0, 0], [w, 0], [w, h], [0, h]]
inv_H_pts = common.points_apply_homography(np.linalg.inv(H), pts)
x0, y0, w0, h0 = common.bounding_box2D(inv_H_pts)
x0, y0 = np.floor([x0, y0])
w0, h0 = np.ceil([w0, h0])
crop_fullres = common.image_crop_LARGE(im_in, x0, y0, w0, h0)
# This filter is needed (for panchro images) because the original PLEAIDES
# SENSOR PERFECT images are aliased
if (common.image_pix_dim(crop_fullres) == 1 and subsampling_factor == 1
and cfg['use_pleiades_unsharpening']):
tmp = image_apply_pleiades_unsharpening_filter(crop_fullres)
common.run('rm -f %s' % crop_fullres)
crop_fullres = tmp
# convert to gray
if common.image_pix_dim(crop_fullres) == 4:
if convert_to_gray:
crop_fullres = common.pansharpened_to_panchro(crop_fullres)
# compensate the homography with the translation induced by the preliminary
# crop, then apply the homography and crop.
H = np.dot(H, common.matrix_translation(x0, y0))
# Since the objective is to compute a zoomed out homographic transformation
# of the input image, to save computations we zoom out the image before
# applying the homography. If Z is the matrix representing the zoom out and
# H the homography matrix, this trick consists in applying Z*H*Z^{-1} to
# the zoomed image Z*Im instead of applying Z*H to the original image Im.
if subsampling_factor == 1:
common.image_apply_homography(im_out, crop_fullres, H, w, h)
return
else:
assert (subsampling_factor >= 1)
# H becomes Z*H*Z^{-1}
Z = np.eye(3)
Z[0, 0] = Z[1, 1] = 1 / float(subsampling_factor)
H = np.dot(Z, H)
H = np.dot(H, np.linalg.inv(Z))
# w, and h are updated accordingly
w = int(w / subsampling_factor)
h = int(h / subsampling_factor)
# the DCT zoom is NOT SAFE when the input image size is not a multiple
# of the zoom factor
tmpw, tmph = common.image_size(crop_fullres)
tmpw, tmph = int(tmpw / subsampling_factor), int(tmph /
subsampling_factor)
crop_fullres_safe = common.image_crop_tif(crop_fullres, 0, 0,
tmpw * subsampling_factor,
tmph * subsampling_factor)
common.run('rm -f %s' % crop_fullres)
# zoom out the input image (crop_fullres)
crop_zoom_out = common.image_safe_zoom_fft(crop_fullres_safe,
subsampling_factor)
common.run('rm -f %s' % crop_fullres_safe)
# apply the homography to the zoomed out crop
common.image_apply_homography(im_out, crop_zoom_out, H, w, h)
return
def rectify_pair(im1, im2, rpc1, rpc2, x, y, w, h, out1, out2, A=None, m=None,
flag='rpc'):
"""
Rectify a ROI in a pair of Pleiades images.
Args:
im1, im2: paths to the two Pleiades images (usually jp2 or tif)
rpc1, rpc2: paths to the two xml files containing RPC data
x, y, w, h: four integers defining the rectangular ROI in the first
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle.
out1, out2: paths to the output crops
A (optional): 3x3 numpy array containing the pointing error correction
for im2. This matrix is usually estimated with the pointing_accuracy
module.
m (optional): Nx4 numpy array containing a list of sift matches, in the
full image coordinates frame
flag (default: 'rpc'): option to decide wether to use rpc of sift
matches for the fundamental matrix estimation.
This function uses the parameter subsampling_factor from the
config module. If the factor z > 1 then the output images will
be subsampled by a factor z. The output matrices H1, H2, and the
ranges are also updated accordingly:
Hi = Z*Hi with Z = diag(1/z,1/z,1) and
disp_min = disp_min/z (resp _max)
Returns:
H1, H2: Two 3x3 matrices representing the rectifying homographies that
have been applied to the two (big) images.
disp_min, disp_max: horizontal disparity range
"""
# read RPC data
rpc1 = rpc_model.RPCModel(rpc1)
rpc2 = rpc_model.RPCModel(rpc2)
# compute rectifying homographies
if flag == 'rpc':
H1, H2, disp_min, disp_max = compute_rectification_homographies(
im1, im2, rpc1, rpc2, x, y, w, h, A, m)
else:
H1, H2, disp_min, disp_max = compute_rectification_homographies_sift(
im1, im2, rpc1, rpc2, x, y, w, h)
# compute output images size
roi = [[x, y], [x+w, y], [x+w, y+h], [x, y+h]]
pts1 = common.points_apply_homography(H1, roi)
x0, y0, w0, h0 = common.bounding_box2D(pts1)
# check that the first homography maps the ROI in the positive quadrant
np.testing.assert_allclose(np.round([x0, y0]), 0, atol=.01)
# apply homographies and do the crops
homography_cropper.crop_and_apply_homography(out1, im1, H1, w0, h0,
cfg['subsampling_factor'],
True)
homography_cropper.crop_and_apply_homography(out2, im2, H2, w0, h0,
cfg['subsampling_factor'],
True)
# If subsampling_factor'] the homographies are altered to reflect the zoom
if cfg['subsampling_factor'] != 1:
from math import floor, ceil
# update the H1 and H2 to reflect the zoom
Z = np.eye(3)
Z[0, 0] = Z[1, 1] = 1.0 / cfg['subsampling_factor']
H1 = np.dot(Z, H1)
H2 = np.dot(Z, H2)
disp_min = floor(disp_min / cfg['subsampling_factor'])
disp_max = ceil(disp_max / cfg['subsampling_factor'])
return H1, H2, disp_min, disp_max