def matches_from_sift(im1, im2):
"""
Computes a list of sift matches between two images.
Args:
im1, im2: paths to the two images (usually jp2 or tif)
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.
"""
if cfg['subsampling_factor_registration'] != 1:
im1 = common.image_safe_zoom_fft(im1, subsampling_factor_registration)
im2 = common.image_safe_zoom_fft(im2, subsampling_factor_registration)
# apply sift, then transport keypoints coordinates in the big images frame
p1 = common.image_sift_keypoints(im1)
p2 = common.image_sift_keypoints(im2)
matches = common.sift_keypoints_match(p1, p2, 'relative',
cfg['sift_match_thresh'])
# compensate coordinates for the crop and the zoom
return matches * subsampling_factor_registration
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