def fundamental_error(rpc1, rpc2, x, y, w, h, n_learn, n_test):
"""
Measure the error made when imposing a fundamental matrix fitting on
Pleiades data.
Args:
rpc1, rpc2: two instances of the rpc_model.RPCModel class
x, y, w, h: four integers definig 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. In the first view, the keypoints
used for estimation and evaluation are located in that ROI.
n_learn: number of points sampled in each of the 3 directions to
generate image correspondences constraints for fundamental matrix
estimation.
n_test: number of points sampled in each of the 3 directions to
generate correspondences used to test the accuracy of the fundamental
matrix.
Returns:
A float value measuring the accuracy of the fundamental matrix. Smaller
is better.
"""
# step 1: estimate the fundamental matrix
matches = rpc_utils.matches_from_rpc(rpc1, rpc2, x, y, w, h, n_learn)
F = estimation.fundamental_matrix(matches)
# step 2: compute the residual error
matches = rpc_utils.matches_from_rpc(rpc1, rpc2, x, y, w, h, n_test)
err = evaluation.fundamental_matrix(F, matches)
return err
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 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
