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 get_coordinates_with_config(tile, m, M):
tile_cfg = s2p.read_config_file(os.path.join(tile, "config.json"))
x = tile_cfg['roi']['x']
y = tile_cfg['roi']['y']
w = tile_cfg['roi']['w']
h = tile_cfg['roi']['h']
rpcfile = tile_cfg['images'][0]['rpc']
rpc = rpc_model.RPCModel(rpcfile)
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])
lon, lat, __ = rpc.direct_estimate(a, b, c)
out = list(common.bounding_box2D(np.vstack([lon, lat]).T))
out[2] += out[0]
out[3] += out[1]
latlon = [[out[0], out[3], 0], [out[2], out[3], 0], [out[2], out[1], 0],
[out[0], out[1], 0], [out[0], out[3], 0]]
return latlon
def get_coordinates_with_config(tile, m, M):
tile_cfg = s2p.read_config_file(os.path.join(tile, "config.json"))
x = tile_cfg['roi']['x']
y = tile_cfg['roi']['y']
w = tile_cfg['roi']['w']
h = tile_cfg['roi']['h']
rpcfile = tile_cfg['images'][0]['rpc']
rpc = rpc_model.RPCModel(rpcfile)
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])
lon, lat, __ = rpc.direct_estimate(a, b, c)
out = list(common.bounding_box2D(np.vstack([lon, lat]).T))
out[2] += out[0]
out[3] += out[1]
latlon = [[out[0], out[3], 0],
[out[2], out[3], 0],
[out[2], out[1], 0],
[out[0], out[1], 0],
[out[0], out[3], 0]]
return latlon
def kml_roi_process(rpc, kml):
"""
"""
# extract lon lat from kml
with open(kml, 'r') as f:
a = bs4.BeautifulSoup(f, "lxml").find_all('coordinates')[0].text.split()
ll_bbx = np.array([list(map(float, x.split(','))) for x in a])[:4, :2]
# save lon lat bounding box to cfg dictionary
lon_min = min(ll_bbx[:, 0])
lon_max = max(ll_bbx[:, 0])
lat_min = min(ll_bbx[:, 1])
lat_max = max(ll_bbx[:, 1])
cfg['ll_bbx'] = (lon_min, lon_max, lat_min, lat_max)
# convert lon lat bbox to utm
z = utm.conversion.latlon_to_zone_number((lat_min + lat_max) * .5,
(lon_min + lon_max) * .5)
utm_bbx = np.array([utm.from_latlon(p[1], p[0], force_zone_number=z)[:2] for
p in ll_bbx])
east_min = min(utm_bbx[:, 0])
east_max = max(utm_bbx[:, 0])
nort_min = min(utm_bbx[:, 1])
nort_max = max(utm_bbx[:, 1])
cfg['utm_bbx'] = (east_min, east_max, nort_min, nort_max)
# project lon lat vertices into the image
if not isinstance(rpc, rpc_model.RPCModel):
rpc = rpc_model.RPCModel(rpc)
img_pts = [rpc.inverse_estimate(p[0], p[1], rpc.altOff)[:2] for p in ll_bbx]
# return image roi
x, y, w, h = common.bounding_box2D(img_pts)
return {'x': x, 'y': y, 'w': w, 'h': h}
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 kml_roi_process(rpc, kml):
"""
"""
# extract lon lat from kml
with open(kml, 'r') as f:
a = bs4.BeautifulSoup(f, "lxml").find_all('coordinates')[0].text.split()
ll_bbx = np.array([list(map(float, x.split(','))) for x in a])[:4, :2]
# save lon lat bounding box to cfg dictionary
lon_min = min(ll_bbx[:, 0])
lon_max = max(ll_bbx[:, 0])
lat_min = min(ll_bbx[:, 1])
lat_max = max(ll_bbx[:, 1])
cfg['ll_bbx'] = (lon_min, lon_max, lat_min, lat_max)
# convert lon lat bbox to utm
z = utm.conversion.latlon_to_zone_number((lat_min + lat_max) * .5,
(lon_min + lon_max) * .5)
utm_bbx = np.array([utm.from_latlon(p[1], p[0], force_zone_number=z)[:2] for
p in ll_bbx])
east_min = min(utm_bbx[:, 0])
east_max = max(utm_bbx[:, 0])
nort_min = min(utm_bbx[:, 1])
nort_max = max(utm_bbx[:, 1])
cfg['utm_bbx'] = (east_min, east_max, nort_min, nort_max)
# project lon lat vertices into the image
if not isinstance(rpc, rpc_model.RPCModel):
rpc = rpc_model.RPCModel(rpc)
img_pts = [rpc.inverse_estimate(p[0], p[1], rpc.altOff)[:2] for p in ll_bbx]
# return image roi
x, y, w, h = common.bounding_box2D(img_pts)
return {'x': x, 'y': y, 'w': w, 'h': h}
def rectification_homographies(matches, x, y, w, h, hmargin=0, vmargin=0):
"""
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 defining the rectangular ROI in the first
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle.
{h,v}margin: translations added to the rectifying similarities to extend the
horizontal and vertical footprint of the rectified images
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, cfg['debug'])
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: ", end=' ')
print(np.max(err), np.min(err), np.mean(err))
# pull back top-left corner of the ROI to the origin (plus margin)
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 + hmargin, -y0 + vmargin)
return np.dot(T, S1), np.dot(T, S2), F
def utm_roi_to_img_roi(rpc, roi):
"""
"""
# define utm rectangular box
x, y, w, h = [roi[k] for k in ['x', 'y', 'w', 'h']]
box = [(x, y), (x+w, y), (x+w, y+h), (x, y+h)]
# convert utm to lon/lat
utm_z = roi['utm_band']
north = roi['hemisphere'] == 'N'
box_latlon = [utm.to_latlon(p[0], p[1], utm_z, northern=north) for p in box]
# project lon/lat vertices into the image
if not isinstance(rpc, rpc_model.RPCModel):
rpc = rpc_model.RPCModel(rpc)
img_pts = [rpc.inverse_estimate(p[1], p[0], rpc.altOff)[:2] for p in
box_latlon]
# return image roi
x, y, w, h = common.bounding_box2D(img_pts)
return {'x': x, 'y': y, 'w': w, 'h': h}
def utm_roi_to_img_roi(rpc, roi):
"""
"""
# define utm rectangular box
x, y, w, h = [roi[k] for k in ['x', 'y', 'w', 'h']]
box = [(x, y), (x+w, y), (x+w, y+h), (x, y+h)]
# convert utm to lon/lat
utm_z = roi['utm_band']
north = roi['hemisphere'] == 'N'
box_latlon = [utm.to_latlon(p[0], p[1], utm_z, northern=north) for p in box]
# project lon/lat vertices into the image
if not isinstance(rpc, rpc_model.RPCModel):
rpc = rpc_model.RPCModel(rpc)
img_pts = [rpc.inverse_estimate(p[1], p[0], rpc.altOff)[:2] for p in
box_latlon]
# return image roi
x, y, w, h = common.bounding_box2D(img_pts)
return {'x': x, 'y': y, 'w': w, 'h': h}
def rectification_homographies(matches, x, y, w, h, hmargin=0, vmargin=0):
"""
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 defining the rectangular ROI in the first
image. (x, y) is the top-left corner, and (w, h) are the dimensions
of the rectangle.
{h,v}margin: translations added to the rectifying similarities to extend the
horizontal and vertical footprint of the rectified images
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, cfg['debug'])
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: ", end=' ')
print(np.max(err), np.min(err), np.mean(err))
# pull back top-left corner of the ROI to the origin (plus margin)
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 + hmargin, -y0 + vmargin)
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 disparity_to_ply(tile):
"""
Compute a point cloud from the disparity map of a pair of image tiles.
Args:
tile: dictionary containing the information needed to process a tile.
"""
out_dir = os.path.join(tile['dir'])
ply_file = os.path.join(out_dir, 'cloud.ply')
plyextrema = os.path.join(out_dir, 'plyextrema.txt')
x, y, w, h = tile['coordinates']
rpc1 = cfg['images'][0]['rpc']
rpc2 = cfg['images'][1]['rpc']
if os.path.exists(os.path.join(out_dir, 'stderr.log')):
print('triangulation: stderr.log exists')
print('pair_1 not processed on tile {} {}'.format(x, y))
return
if cfg['skip_existing'] and os.path.isfile(ply_file):
print('triangulation done on tile {} {}'.format(x, y))
return
print('triangulating tile {} {}...'.format(x, y))
# This function is only called when there is a single pair (pair_1)
H_ref = os.path.join(out_dir, 'pair_1', 'H_ref.txt')
H_sec = os.path.join(out_dir, 'pair_1', 'H_sec.txt')
pointing = os.path.join(cfg['out_dir'], 'global_pointing_pair_1.txt')
disp = os.path.join(out_dir, 'pair_1', 'rectified_disp.tif')
mask_rect = os.path.join(out_dir, 'pair_1', 'rectified_mask.png')
mask_orig = os.path.join(out_dir, 'cloud_water_image_domain_mask.png')
# prepare the image needed to colorize point cloud
colors = os.path.join(out_dir, 'rectified_ref.png')
if cfg['images'][0]['clr']:
hom = np.loadtxt(H_ref)
roi = [[x, y], [x + w, y], [x + w, y + h], [x, y + h]]
ww, hh = common.bounding_box2D(common.points_apply_homography(
hom, roi))[2:]
tmp = common.tmpfile('.tif')
common.image_apply_homography(tmp, cfg['images'][0]['clr'], hom,
ww + 2 * cfg['horizontal_margin'],
hh + 2 * cfg['vertical_margin'])
common.image_qauto(tmp, colors)
else:
common.image_qauto(
os.path.join(out_dir, 'pair_1', 'rectified_ref.tif'), colors)
# compute the point cloud
triangulation.disp_map_to_point_cloud(ply_file,
disp,
mask_rect,
rpc1,
rpc2,
H_ref,
H_sec,
pointing,
colors,
utm_zone=cfg['utm_zone'],
llbbx=tuple(cfg['ll_bbx']),
xybbx=(x, x + w, y, y + h),
xymsk=mask_orig)
# compute the point cloud extrema (xmin, xmax, xmin, ymax)
common.run("plyextrema %s %s" % (ply_file, plyextrema))
if cfg['clean_intermediate']:
common.remove(H_ref)
common.remove(H_sec)
common.remove(disp)
common.remove(mask_rect)
common.remove(mask_orig)
common.remove(colors)
common.remove(os.path.join(out_dir, 'pair_1', 'rectified_ref.tif'))
def rectify_pair(im1,
im2,
rpc1,
rpc2,
x,
y,
w,
h,
out1,
out2,
A=None,
sift_matches=None,
method='rpc',
hmargin=0,
vmargin=0):
"""
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.
{h,v}margin (optional): horizontal and vertical margins added on the
sides of the rectified images
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, hmargin,
vmargin)
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
if cfg['debug']:
out_dir = os.path.dirname(out1)
np.savetxt(os.path.join(out_dir, 'sift_matches_disp.txt'),
sift_matches,
fmt='%9.3f')
visualisation.plot_matches(
im1, im2, rpc1, rpc2, sift_matches, x, y, w, h,
os.path.join(out_dir, 'sift_matches_disp.png'))
disp_m, disp_M = disparity_range(rpc1, rpc2, x, y, w, h, H1, H2,
sift_matches, A)
# compute rectifying homographies for non-epipolar mode (rectify the secondary tile only)
if block_matching.rectify_secondary_tile_only(cfg['matching_algorithm']):
H1_inv = np.linalg.inv(H1)
H1 = np.eye(
3
) # H1 is replaced by 2-D array with ones on the diagonal and zeros elsewhere
H2 = np.dot(H1_inv, H2)
T = common.matrix_translation(-x + hmargin, -y + vmargin)
H1 = np.dot(T, H1)
H2 = np.dot(T, H2)
# 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]), [hmargin, vmargin],
atol=.01)
# apply homographies and do the crops
common.image_apply_homography(out1, im1, H1, w0 + 2 * hmargin,
h0 + 2 * vmargin)
common.image_apply_homography(out2, im2, H2, w0 + 2 * hmargin,
h0 + 2 * vmargin)
if block_matching.rectify_secondary_tile_only(cfg['matching_algorithm']):
pts_in = [[0, 0], [disp_m, 0], [disp_M, 0]]
pts_out = common.points_apply_homography(H1_inv, pts_in)
disp_m = pts_out[1, :] - pts_out[0, :]
disp_M = pts_out[2, :] - pts_out[0, :]
return H1, H2, disp_m, disp_M
def disparity_to_ply(tile):
"""
Compute a point cloud from the disparity map of a pair of image tiles.
Args:
tile: dictionary containing the information needed to process a tile.
"""
out_dir = os.path.join(tile['dir'])
ply_file = os.path.join(out_dir, 'cloud.ply')
plyextrema = os.path.join(out_dir, 'plyextrema.txt')
x, y, w, h = tile['coordinates']
rpc1 = cfg['images'][0]['rpc']
rpc2 = cfg['images'][1]['rpc']
if os.path.exists(os.path.join(out_dir, 'stderr.log')):
print('triangulation: stderr.log exists')
print('pair_1 not processed on tile {} {}'.format(x, y))
return
if cfg['skip_existing'] and os.path.isfile(ply_file):
print('triangulation done on tile {} {}'.format(x, y))
return
print('triangulating tile {} {}...'.format(x, y))
# This function is only called when there is a single pair (pair_1)
H_ref = os.path.join(out_dir, 'pair_1', 'H_ref.txt')
H_sec = os.path.join(out_dir, 'pair_1', 'H_sec.txt')
pointing = os.path.join(cfg['out_dir'], 'global_pointing_pair_1.txt')
disp = os.path.join(out_dir, 'pair_1', 'rectified_disp.tif')
mask_rect = os.path.join(out_dir, 'pair_1', 'rectified_mask.png')
mask_orig = os.path.join(out_dir, 'cloud_water_image_domain_mask.png')
# prepare the image needed to colorize point cloud
colors = os.path.join(out_dir, 'rectified_ref.png')
if cfg['images'][0]['clr']:
hom = np.loadtxt(H_ref)
roi = [[x, y], [x+w, y], [x+w, y+h], [x, y+h]]
ww, hh = common.bounding_box2D(common.points_apply_homography(hom, roi))[2:]
tmp = common.tmpfile('.tif')
common.image_apply_homography(tmp, cfg['images'][0]['clr'], hom,
ww + 2*cfg['horizontal_margin'],
hh + 2*cfg['vertical_margin'])
common.image_qauto(tmp, colors)
else:
common.image_qauto(os.path.join(out_dir, 'pair_1', 'rectified_ref.tif'), colors)
# compute the point cloud
triangulation.disp_map_to_point_cloud(ply_file, disp, mask_rect, rpc1, rpc2,
H_ref, H_sec, pointing, colors,
utm_zone=cfg['utm_zone'],
llbbx=tuple(cfg['ll_bbx']),
xybbx=(x, x+w, y, y+h),
xymsk=mask_orig)
# compute the point cloud extrema (xmin, xmax, xmin, ymax)
common.run("plyextrema %s %s" % (ply_file, plyextrema))
if cfg['clean_intermediate']:
common.remove(H_ref)
common.remove(H_sec)
common.remove(disp)
common.remove(mask_rect)
common.remove(mask_orig)
common.remove(colors)
common.remove(os.path.join(out_dir, 'pair_1', 'rectified_ref.tif'))
def rectify_pair(im1, im2, rpc1, rpc2, x, y, w, h, out1, out2, A=None,
sift_matches=None, method='rpc', hmargin=0, vmargin=0):
"""
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.
{h,v}margin (optional): horizontal and vertical margins added on the
sides of the rectified images
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, hmargin, vmargin)
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
if cfg['debug']:
out_dir = os.path.dirname(out1)
np.savetxt(os.path.join(out_dir, 'sift_matches_disp.txt'),
sift_matches, fmt='%9.3f')
visualisation.plot_matches(im1, im2, rpc1, rpc2, sift_matches, x, y, w, h,
os.path.join(out_dir, 'sift_matches_disp.png'))
disp_m, disp_M = disparity_range(rpc1, rpc2, x, y, w, h, H1, H2,
sift_matches, A)
# impose a minimal disparity range (TODO this is valid only with the
# 'center' flag for register_horizontally_translation)
disp_m = min(-3, disp_m)
disp_M = max(3, disp_M)
# compute rectifying homographies for non-epipolar mode (rectify the secondary tile only)
if block_matching.rectify_secondary_tile_only(cfg['matching_algorithm']):
H1_inv = np.linalg.inv(H1)
H1 = np.eye(3) # H1 is replaced by 2-D array with ones on the diagonal and zeros elsewhere
H2 = np.dot(H1_inv,H2)
T = common.matrix_translation(-x + hmargin, -y + vmargin)
H1 = np.dot(T, H1)
H2 = np.dot(T, H2)
# if subsampling_factor'] the homographies are altered to reflect the zoom
z = cfg['subsampling_factor']
if z != 1:
Z = np.diag((1/z, 1/z, 1))
H1 = np.dot(Z, H1)
H2 = np.dot(Z, H2)
disp_m = np.floor(disp_m / z)
disp_M = np.ceil(disp_M / z)
hmargin = int(np.floor(hmargin / z))
vmargin = int(np.floor(vmargin / z))
# 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]), [hmargin, vmargin], atol=.01)
# apply homographies and do the crops
common.image_apply_homography(out1, im1, H1, w0 + 2*hmargin, h0 + 2*vmargin)
common.image_apply_homography(out2, im2, H2, w0 + 2*hmargin, h0 + 2*vmargin)
if cfg['disp_min'] is not None: disp_m = cfg['disp_min']
if cfg['disp_max'] is not None: disp_M = cfg['disp_max']
if block_matching.rectify_secondary_tile_only(cfg['matching_algorithm']):
pts_in = [[0, 0], [disp_m, 0], [disp_M, 0]]
pts_out = common.points_apply_homography(H1_inv,
pts_in)
disp_m = pts_out[1,:] - pts_out[0,:]
disp_M = pts_out[2,:] - pts_out[0,:]
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',
hmargin=0,
vmargin=0):
"""
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.
{h,v}margin (optional): horizontal and vertical margins added on the
sides of the rectified images
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, hmargin,
vmargin)
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
if cfg['debug']:
out_dir = os.path.dirname(out1)
np.savetxt(os.path.join(out_dir, 'sift_matches_disp.txt'),
sift_matches,
fmt='%9.3f')
visualisation.plot_matches(
im1, im2, rpc1, rpc2, sift_matches, x, y, w, h,
os.path.join(out_dir, 'sift_matches_disp.png'))
disp_m, disp_M = disparity_range(rpc1, rpc2, x, y, w, h, H1, H2,
sift_matches, A)
# impose a minimal disparity range (TODO this is valid only with the
# 'center' flag for register_horizontally_translation)
disp_m = min(-3, disp_m)
disp_M = max(3, disp_M)
# if subsampling_factor'] the homographies are altered to reflect the zoom
z = cfg['subsampling_factor']
if z != 1:
Z = np.diag((1 / z, 1 / z, 1))
H1 = np.dot(Z, H1)
H2 = np.dot(Z, H2)
disp_m = np.floor(disp_m / z)
disp_M = np.ceil(disp_M / z)
hmargin = int(np.floor(hmargin / z))
vmargin = int(np.floor(vmargin / z))
# 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]), [hmargin, vmargin],
atol=.01)
# apply homographies and do the crops
common.image_apply_homography(out1, im1, H1, w0 + 2 * hmargin,
h0 + 2 * vmargin)
common.image_apply_homography(out2, im2, H2, w0 + 2 * hmargin,
h0 + 2 * vmargin)
return H1, H2, disp_m, disp_M
