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 GeoTIFF image files rpc1, rpc2: two instances of the rpcm.RPCModel class 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 """ # 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:]) elif method == 'sift': matches = sift_matches else: raise Exception( "Unknown value {} for argument 'method'".format(method)) if matches is None or len(matches) < 4: raise NoRectificationMatchesError( "No or not enough matches found to rectify image pair") # 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.projection(lon, lat, alt)[:2] x2, y2 = rpc2.projection(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) < 1: warnings.warn( "Need at least one sift match for the horizontal registration", category=NoHorizontalRegistrationWarning, ) 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) # recompute hmargin and homographies hmargin = int(np.ceil(max([hmargin, np.fabs(disp_m), np.fabs(disp_M)]))) T = common.matrix_translation(hmargin, vmargin) H1, H2 = np.dot(T, H1), 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) 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. This function is called by s2p.main only if there are two input images (not three). Args: tile: dictionary containing the information needed to process a tile. """ out_dir = 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]['rpcm'] rpc2 = cfg['images'][1]['rpcm'] print('triangulating tile {} {}...'.format(x, y)) 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') extra = os.path.join(out_dir, 'pair_1', 'rectified_disp_confidence.tif') if not os.path.exists(extra): # confidence file not always generated extra = '' mask_rect = os.path.join(out_dir, 'pair_1', 'rectified_mask.png') mask_orig = os.path.join(out_dir, '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) # we want rectified_ref.png and rectified_ref.tif to have the same size with rasterio.open(os.path.join(out_dir, 'pair_1', 'rectified_ref.tif')) as f: ww, hh = f.width, f.height common.image_apply_homography(colors, cfg['images'][0]['clr'], hom, ww, hh) else: common.image_qauto( os.path.join(out_dir, 'pair_1', 'rectified_ref.tif'), colors) # compute the point cloud with rasterio.open(disp, 'r') as f: disp_img = f.read().squeeze() with rasterio.open(mask_rect, 'r') as f: mask_rect_img = f.read().squeeze() pyproj_out_crs = geographiclib.pyproj_crs(cfg['out_crs']) proj_com = "CRS {}".format(cfg['out_crs']) xyz_array, err = triangulation.disp_to_xyz(rpc1, rpc2, np.loadtxt(H_ref), np.loadtxt(H_sec), disp_img, mask_rect_img, pyproj_out_crs, img_bbx=(x, x + w, y, y + h), A=np.loadtxt(pointing)) triangulation.filter_xyz_and_write_to_ply(ply_file, xyz_array, cfg['3d_filtering_r'], cfg['3d_filtering_n'], cfg['gsd'], colors, proj_com, confidence=extra) # 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) 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) # recompute hmargin and homographies hmargin = int(np.ceil(max([hmargin, np.fabs(disp_m), np.fabs(disp_M)]))) T = common.matrix_translation(hmargin, vmargin) H1, H2 = np.dot(T, H1), np.dot(T, H2) # 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]['rpcm'] rpc2 = cfg['images'][1]['rpcm'] 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 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') extra = os.path.join(out_dir, 'pair_1', 'rectified_disp_confidence.tif') if not os.path.exists(extra): extra = '' mask_rect = os.path.join(out_dir, 'pair_1', 'rectified_mask.png') mask_orig = os.path.join(out_dir, '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) # We want rectified_ref.png and rectified_ref.tif to have the same size with rasterio.open(os.path.join(out_dir, 'pair_1', 'rectified_ref.tif')) as f: ww, hh = f.width, f.height common.image_apply_homography(colors, cfg['images'][0]['clr'], hom, ww, hh) else: common.image_qauto(os.path.join(out_dir, 'pair_1', 'rectified_ref.tif'), colors) # compute the point cloud with rasterio.open(disp, 'r') as f: disp_img = f.read().squeeze() with rasterio.open(mask_rect, 'r') as f: mask_rect_img = f.read().squeeze() xyz_array, err = triangulation.disp_to_xyz(rpc1, rpc2, np.loadtxt(H_ref), np.loadtxt(H_sec), disp_img, mask_rect_img, int(cfg['utm_zone'][:-1]), img_bbx=(x, x+w, y, y+h), A=np.loadtxt(pointing)) # 3D filtering if cfg['3d_filtering_r'] and cfg['3d_filtering_n']: triangulation.filter_xyz(xyz_array, cfg['3d_filtering_r'], cfg['3d_filtering_n'], cfg['gsd']) # flatten the xyz array into a list and remove nan points xyz_list = xyz_array.reshape(-1, 3) valid = np.all(np.isfinite(xyz_list), axis=1) # write the point cloud to a ply file with rasterio.open(colors, 'r') as f: img = f.read() colors_list = img.transpose(1, 2, 0).reshape(-1, img.shape[0]) ply.write_3d_point_cloud_to_ply(ply_file, xyz_list[valid], colors=colors_list[valid], extra_properties=None, extra_properties_names=None, comments=["created by S2P", "projection: UTM {}".format(cfg['utm_zone'])]) # 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 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 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') extra = os.path.join(out_dir, 'pair_1', 'rectified_disp_confidence.tif') if not os.path.exists(extra): extra = '' 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, extra, 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'))