def check_triangulation_input(base_img, new_img, imgp0, imgp1, rvec_keyfr,
tvec_keyfr, rvec, tvec, cameraMatrix,
distCoeffs):
img = cvh.drawKeypointsAndMotion(base_img, imgp1, imgp0, rgb(0, 0, 255))
draw_axis_system(img, rvec_keyfr, tvec_keyfr, cameraMatrix, distCoeffs)
print "base_img check"
cv2.imshow("img", img)
cv2.waitKey()
img = cvh.drawKeypointsAndMotion(new_img, imgp0, imgp1, rgb(0, 0, 255))
draw_axis_system(img, rvec, tvec, cameraMatrix, distCoeffs)
print "new_img check"
cv2.imshow("img", img)
cv2.waitKey()
def check_triangulation_input(base_img, new_img,
imgp0, imgp1,
rvec_keyfr, tvec_keyfr, rvec, tvec,
cameraMatrix, distCoeffs):
img = cvh.drawKeypointsAndMotion(base_img, imgp1, imgp0, rgb(0,0,255))
draw_axis_system(img, rvec_keyfr, tvec_keyfr, cameraMatrix, distCoeffs)
print "base_img check"
cv2.imshow("img", img)
cv2.waitKey()
img = cvh.drawKeypointsAndMotion(new_img, imgp0, imgp1, rgb(0,0,255))
draw_axis_system(img, rvec, tvec, cameraMatrix, distCoeffs)
print "new_img check"
cv2.imshow("img", img)
cv2.waitKey()
def handle_new_frame(
base_imgp, # includes 2D points of both triangulated as not-yet triangl points of last keyframe
prev_imgp, # includes 2D points of last frame
prev_img,
prev_img_gray,
new_img,
new_img_gray,
triangl_idxs,
nontriangl_idxs, # indices of 2D points in base_imgp
imgp_to_objp_idxs, # indices from 2D points in base_imgp to 3D points in objp
all_idxs_tmp, # list of idxs of 2D points in base_imgp, matches prev_imgp to base_imgp
objp, # triangulated 3D points
objp_groups,
group_id, # corresponding group ids of triangulated 3D points, and current group id
rvec_keyfr,
tvec_keyfr, # rvec and tvec of last keyframe
base_img): # used for debug
# Save initial indexing state
triangl_idxs_old = set(triangl_idxs)
nontriangl_idxs_old = set(nontriangl_idxs)
all_idxs_tmp_old = np.array(all_idxs_tmp)
# Calculate OF (Optical Flow), and filter outliers based on OF error
new_imgp, status_OF, err_OF = cv2.calcOpticalFlowPyrLK(
prev_img_gray, new_img_gray, prev_imgp
) # WARNING: OpenCV can output corrupted values in 'status_OF': "RuntimeWarning: invalid value encountered in less"
new_to_prev_idxs = np.where(
np.logical_and((status_OF.reshape(-1) == 1),
(err_OF.reshape(-1) < max_OF_error)))[0]
# If there is too much OF error in the entire image, simply reject the frame
lost_tracks_ratio = (len(prev_imgp) - len(new_to_prev_idxs)) / float(
len(prev_imgp))
print "# points lost because of excessive OF error / # points before: ", len(
prev_imgp) - len(new_to_prev_idxs), "/", len(
prev_imgp), "=", lost_tracks_ratio
if lost_tracks_ratio > max_lost_tracks_ratio: # reject frame
print "REJECTED: I lost track of all points!\n"
return False, base_imgp, prev_imgp, triangl_idxs_old, nontriangl_idxs_old, imgp_to_objp_idxs, all_idxs_tmp_old, objp, objp_groups, group_id, None, None, rvec_keyfr, tvec_keyfr, base_img
# Save matches by idxs
preserve_idxs = set(all_idxs_tmp[new_to_prev_idxs])
triangl_idxs, nontriangl_idxs, all_idxs_tmp = idxs_update_by_idxs(
preserve_idxs, triangl_idxs, nontriangl_idxs, all_idxs_tmp)
if len(triangl_idxs) < 8: # solvePnP uses 8-point algorithm
print "REJECTED: I lost track of too many already-triangulated points, so we can't do solvePnP() anymore...\n"
return False, base_imgp, prev_imgp, triangl_idxs_old, nontriangl_idxs_old, imgp_to_objp_idxs, all_idxs_tmp_old, objp, objp_groups, group_id, None, None, rvec_keyfr, tvec_keyfr, base_img
new_imgp = new_imgp[new_to_prev_idxs]
#cv2.cornerSubPix( # TODO: activate this secret weapon <-- hmm, actually seems to make it worse
#new_img_gray, new_imgp,
#(corner_min_dist,corner_min_dist), # window
#(-1,-1), # deadzone
#(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) ) # termination criteria
##corners = corners.reshape(-1, 2)
# Do solvePnPRansac() on current frame's (new_imgp) already triangulated points, ...
triangl_idxs_array = np.array(
sorted(triangl_idxs)) # select already-triangulated point-indices
filtered_triangl_imgp = idxs_get_new_imgp_by_idxs(
triangl_idxs, new_imgp,
all_idxs_tmp) # collect corresponding image-points
filtered_triangl_objp = objp[imgp_to_objp_idxs[
triangl_idxs_array]] # collect corresponding object-points
print "Doing solvePnP() on", filtered_triangl_objp.shape[0], "points"
rvec_, tvec_, inliers = cv2.solvePnPRansac( # perform solvePnPRansac() to identify outliers, force to obey max_solvePnP_outlier_ratio
filtered_triangl_objp,
filtered_triangl_imgp,
cameraMatrix,
distCoeffs,
minInliersCount=int(
ceil((1 - max_solvePnP_outlier_ratio) * len(triangl_idxs))),
reprojectionError=max_solvePnP_reproj_error)
# ... if ratio of 'inliers' vs input is too low, reject frame, ...
if inliers == None: # inliers is empty => reject frame
print "REJECTED: No inliers based on solvePnP()!\n"
return False, base_imgp, prev_imgp, triangl_idxs_old, nontriangl_idxs_old, imgp_to_objp_idxs, all_idxs_tmp_old, objp, objp_groups, group_id, None, None, rvec_keyfr, tvec_keyfr, base_img
inliers = inliers.reshape(-1)
solvePnP_outlier_ratio = (len(triangl_idxs) - len(inliers)) / float(
len(triangl_idxs))
print "solvePnP_outlier_ratio:", solvePnP_outlier_ratio
if solvePnP_outlier_ratio > max_solvePnP_outlier_ratio or len(
inliers) < 8: # reject frame
if solvePnP_outlier_ratio > max_solvePnP_outlier_ratio:
print "REJECTED: Not enough inliers (ratio) based on solvePnP()!\n"
else:
print "REJECTED: Not enough inliers (absolute) based on solvePnP() to perform (non-RANSAC) solvePnP()!\n"
return False, base_imgp, prev_imgp, triangl_idxs_old, nontriangl_idxs_old, imgp_to_objp_idxs, all_idxs_tmp_old, objp, objp_groups, group_id, None, None, rvec_keyfr, tvec_keyfr, base_img
# <DEBUG: visualize reprojection error> TODO: remove
reproj_error, imgp_reproj1 = reprojection_error(filtered_triangl_objp,
filtered_triangl_imgp,
rvec_, tvec_, cameraMatrix,
distCoeffs)
print "solvePnP reproj_error:", reproj_error
i3 = np.array(new_img)
try:
for imgppr, imgppp in zip(filtered_triangl_imgp, imgp_reproj1):
cvh.line(i3, imgppr.T, imgppp.T, rgb(255, 0, 0))
except OverflowError:
print "WARNING: OverflowError!"
cv2.imshow("img", i3)
cv2.waitKey()
# </DEBUG>
# ... then do solvePnP() on inliers, to get the current frame's pose estimation, ...
triangl_idxs_array = triangl_idxs_array[
inliers] # select inliers among all already-triangulated point-indices
filtered_triangl_imgp, filtered_triangl_objp = filtered_triangl_imgp[
inliers], filtered_triangl_objp[inliers]
preserve_idxs = set(triangl_idxs_array) | nontriangl_idxs
new_imgp = idxs_get_new_imgp_by_idxs(preserve_idxs, new_imgp, all_idxs_tmp)
triangl_idxs, nontriangl_idxs, all_idxs_tmp = idxs_update_by_idxs( # update indices to only preserve inliers
preserve_idxs, triangl_idxs, nontriangl_idxs, all_idxs_tmp)
ret, rvec, tvec = cv2.solvePnP( # perform solvePnP() to estimate the pose
filtered_triangl_objp, filtered_triangl_imgp, cameraMatrix, distCoeffs)
# .. finally do a check on the average reprojection error, and reject frame if too high.
reproj_error, imgp_reproj = reprojection_error(filtered_triangl_objp,
filtered_triangl_imgp, rvec,
tvec, cameraMatrix,
distCoeffs)
print "solvePnP refined reproj_error:", reproj_error
if reproj_error > max_solvePnP_reproj_error: # reject frame
print "REJECTED: Too high reprojection error based on pose estimate of solvePnP()!\n"
return False, base_imgp, prev_imgp, triangl_idxs_old, nontriangl_idxs_old, imgp_to_objp_idxs, all_idxs_tmp_old, objp, objp_groups, group_id, None, None, rvec_keyfr, tvec_keyfr, base_img
# <DEBUG: verify poses by reprojection error> TODO: remove
i0 = draw_axis_system(np.array(new_img), rvec, tvec, cameraMatrix,
distCoeffs)
try:
for imgppp in imgp_reproj1:
cvh.circle(i0, imgppp.T, 2, rgb(255, 0, 0), thickness=-1)
except OverflowError:
print "WARNING: OverflowError!"
for imgppp in imgp_reproj:
cvh.circle(i0, imgppp.T, 2, rgb(0, 255, 255), thickness=-1)
print "cur img check"
cv2.imshow("img", i0)
cv2.waitKey()
# </DEBUG>
# <DEBUG: verify OpticalFlow motion on preserved inliers> TODO: remove
cv2.imshow(
"img",
cvh.drawKeypointsAndMotion(new_img, base_imgp[all_idxs_tmp], new_imgp,
rgb(0, 0, 255)))
cv2.waitKey()
# </DEBUG>
# Check whether we got a new keyframe
is_keyframe = keyframe_test(base_imgp[all_idxs_tmp], new_imgp)
print "is_keyframe:", is_keyframe
if is_keyframe:
# If some points are not yet triangulated, do it now:
if nontriangl_idxs:
# First do triangulation of not-yet triangulated points using initial pose estimation, ...
nontriangl_idxs_array = np.array(sorted(
nontriangl_idxs)) # select not-yet-triangulated point-indices
imgp0 = base_imgp[
nontriangl_idxs_array] # collect corresponding image-points of last keyframe
imgp1 = idxs_get_new_imgp_by_idxs(
nontriangl_idxs, new_imgp, all_idxs_tmp
) # collect corresponding image-points of current frame
# <DEBUG: check sanity of input to triangulation function> TODO: remove
check_triangulation_input(base_img, new_img, imgp0, imgp1,
rvec_keyfr, tvec_keyfr, rvec, tvec,
cameraMatrix, distCoeffs)
# </DEBUG>
imgpnrm0 = cv2.undistortPoints(np.array(
[imgp0]), cameraMatrix, distCoeffs)[
0] # undistort and normalize to homogenous coordinates
imgpnrm1 = cv2.undistortPoints(np.array([imgp1]), cameraMatrix,
distCoeffs)[0]
objp_done = linear_LS_triangulation( # triangulate
imgpnrm0.T,
trfm.P_from_R_and_t(cvh.Rodrigues(rvec_keyfr),
tvec_keyfr), # data from last keyframe
imgpnrm1.T,
trfm.P_from_R_and_t(cvh.Rodrigues(rvec),
tvec)) # data from current frame
objp_done = objp_done.T
# <DEBUG: check reprojection error of the new freshly triangulated points, based on both pose estimates of keyframe and current cam> TODO: remove
print "triangl_reproj_error 0:", reprojection_error(
objp_done, imgp0, rvec_keyfr, tvec_keyfr, cameraMatrix,
distCoeffs)[0]
print "triangl_reproj_error 1:", reprojection_error(
objp_done, imgp1, rvec, tvec, cameraMatrix, distCoeffs)[0]
# </DEBUG>
filtered_triangl_objp = np.concatenate(
(filtered_triangl_objp,
objp_done)) # collect all desired object-points
filtered_triangl_imgp = np.concatenate(
(filtered_triangl_imgp,
imgp1)) # collect corresponding image-points of current frame
# ... then do solvePnP() on all preserved points ('inliers') to refine pose estimation, ...
ret, rvec, tvec = cv2.solvePnP( # perform solvePnP(), we start from the initial pose estimation
filtered_triangl_objp,
filtered_triangl_imgp,
cameraMatrix,
distCoeffs,
rvec,
tvec,
useExtrinsicGuess=True)
print "total triangl_reproj_error 1 refined:", reprojection_error(
filtered_triangl_objp, filtered_triangl_imgp, rvec, tvec,
cameraMatrix, distCoeffs)[0] # TODO: remove
# ... then do re-triangulation of 'inliers_objp_done' using refined pose estimation.
objp_done = linear_LS_triangulation( # triangulate
imgpnrm0.T,
trfm.P_from_R_and_t(cvh.Rodrigues(rvec_keyfr),
tvec_keyfr), # data from last keyframe
imgpnrm1.T,
trfm.P_from_R_and_t(cvh.Rodrigues(rvec),
tvec)) # data from current frame
objp_done = objp_done.T
# <DEBUG: check reprojection error of the new freshly (refined) triangulated points, based on both pose estimates of keyframe and current cam> TODO: remove
print "triangl_reproj_error 0 refined:", reprojection_error(
objp_done, imgp0, rvec_keyfr, tvec_keyfr, cameraMatrix,
distCoeffs)[0]
print "triangl_reproj_error 1 refined:", reprojection_error(
objp_done, imgp1, rvec, tvec, cameraMatrix, distCoeffs)[0]
# </DEBUG>
# Update image-points and indices, and store the newly triangulated object-points and assign them the current group id
new_imgp = filtered_triangl_imgp
triangl_idxs, nontriangl_idxs, all_idxs_tmp = idxs_update_by_idxs(
preserve_idxs, triangl_idxs, nontriangl_idxs, all_idxs_tmp)
objp_groups_done = np.empty((len(objp_done)), dtype=int)
objp_groups_done.fill(group_id) # assign to current 'group_id'
(
objp, objp_groups
), imgp_to_objp_idxs, triangl_idxs, nontriangl_idxs = idxs_add_objp(
(objp_done, objp_groups_done), preserve_idxs - triangl_idxs,
(objp, objp_groups), imgp_to_objp_idxs, triangl_idxs,
nontriangl_idxs, all_idxs_tmp)
## <DEBUG: check intermediate outlier filtering> TODO: remove
#i4 = np.array(new_img)
#objp_test = objp[imgp_to_objp_idxs[np.array(sorted(triangl_idxs))]]
#imgp_test = idxs_get_new_imgp_by_idxs(triangl_idxs, new_imgp, all_idxs_tmp)
##print imgp_test - filtered_triangl_imgp
#reproj_error, imgp_reproj4 = reprojection_error(objp_test, imgp_test, rvec, tvec, cameraMatrix, distCoeffs)
#print "checking both 2", reproj_error
#for imgppr, imgppp in zip(imgp_test, imgp_reproj4): cvh.line(i4, imgppr.T, imgppp.T, rgb(255,0,0))
#cv2.imshow("img", i4)
#cv2.waitKey()
## </DEBUG>
# Check whether we should add new image-points
mask_img = keypoint_mask(
new_imgp
) # generate mask that covers all image-points (with a certain radius)
print "coverage:", 1 - cv2.countNonZero(mask_img) / float(
mask_img.size) # TODO: remove: unused
to_add = target_amount_keypoints - len(
new_imgp) # limit the amount of to-be-added image-points
# Add new image-points
if to_add > 0:
print "to_add:", to_add
imgp_extra = cv2.goodFeaturesToTrack(new_img_gray, to_add,
corner_quality_level,
corner_min_dist, None,
mask_img).reshape((-1, 2))
print "added:", len(imgp_extra)
group_id += 1 # create a new group to assign the new batch of points to, later on
else:
imgp_extra = zeros((0, 2))
print "adding zero new points"
base_imgp, new_imgp, imgp_to_objp_idxs, triangl_idxs, nontriangl_idxs, all_idxs_tmp = idxs_rebase_and_add_imgp( # update indices to include new image-points
imgp_extra, base_imgp, new_imgp, imgp_to_objp_idxs, triangl_idxs,
nontriangl_idxs, all_idxs_tmp)
# <DEBUG: visualize newly added points> TODO: remove
cv2.imshow(
"img",
cv2.drawKeypoints(
new_img, [cv2.KeyPoint(p[0], p[1], 7.) for p in imgp_extra],
color=rgb(0, 0, 255)))
cv2.waitKey()
# </DEBUG>
# Now this frame becomes the base (= keyframe)
rvec_keyfr = rvec
tvec_keyfr = tvec
base_img = new_img
# Successfully return
return True + int(
is_keyframe
), base_imgp, new_imgp, triangl_idxs, nontriangl_idxs, imgp_to_objp_idxs, all_idxs_tmp, objp, objp_groups, group_id, rvec, tvec, rvec_keyfr, tvec_keyfr, base_img
def handle_new_frame(base_imgp, # includes 2D points of both triangulated as not-yet triangl points of last keyframe
prev_imgp, # includes 2D points of last frame
prev_img, prev_img_gray,
new_img, new_img_gray,
triangl_idxs, nontriangl_idxs, # indices of 2D points in base_imgp
imgp_to_objp_idxs, # indices from 2D points in base_imgp to 3D points in objp
all_idxs_tmp, # list of idxs of 2D points in base_imgp, matches prev_imgp to base_imgp
objp, # triangulated 3D points
objp_groups, group_id, # corresponding group ids of triangulated 3D points, and current group id
rvec_keyfr, tvec_keyfr, # rvec and tvec of last keyframe
base_img): # used for debug
# Save initial indexing state
triangl_idxs_old = set(triangl_idxs)
nontriangl_idxs_old = set(nontriangl_idxs)
all_idxs_tmp_old = np.array(all_idxs_tmp)
# Calculate OF (Optical Flow), and filter outliers based on OF error
new_imgp, status_OF, err_OF = cv2.calcOpticalFlowPyrLK(prev_img_gray, new_img_gray, prev_imgp) # WARNING: OpenCV can output corrupted values in 'status_OF': "RuntimeWarning: invalid value encountered in less"
new_to_prev_idxs = np.where(np.logical_and((status_OF.reshape(-1) == 1), (err_OF.reshape(-1) < max_OF_error)))[0]
# If there is too much OF error in the entire image, simply reject the frame
lost_tracks_ratio = (len(prev_imgp) - len(new_to_prev_idxs)) / float(len(prev_imgp))
print "# points lost because of excessive OF error / # points before: ", len(prev_imgp) - len(new_to_prev_idxs), "/", len(prev_imgp), "=", lost_tracks_ratio
if lost_tracks_ratio > max_lost_tracks_ratio: # reject frame
print "REJECTED: I lost track of all points!\n"
return False, base_imgp, prev_imgp, triangl_idxs_old, nontriangl_idxs_old, imgp_to_objp_idxs, all_idxs_tmp_old, objp, objp_groups, group_id, None, None, rvec_keyfr, tvec_keyfr, base_img
# Save matches by idxs
preserve_idxs = set(all_idxs_tmp[new_to_prev_idxs])
triangl_idxs, nontriangl_idxs, all_idxs_tmp = idxs_update_by_idxs(
preserve_idxs, triangl_idxs, nontriangl_idxs, all_idxs_tmp )
if len(triangl_idxs) < 8: # solvePnP uses 8-point algorithm
print "REJECTED: I lost track of too many already-triangulated points, so we can't do solvePnP() anymore...\n"
return False, base_imgp, prev_imgp, triangl_idxs_old, nontriangl_idxs_old, imgp_to_objp_idxs, all_idxs_tmp_old, objp, objp_groups, group_id, None, None, rvec_keyfr, tvec_keyfr, base_img
new_imgp = new_imgp[new_to_prev_idxs]
#cv2.cornerSubPix( # TODO: activate this secret weapon <-- hmm, actually seems to make it worse
#new_img_gray, new_imgp,
#(corner_min_dist,corner_min_dist), # window
#(-1,-1), # deadzone
#(cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) ) # termination criteria
##corners = corners.reshape(-1, 2)
# Do solvePnPRansac() on current frame's (new_imgp) already triangulated points, ...
triangl_idxs_array = np.array(sorted(triangl_idxs)) # select already-triangulated point-indices
filtered_triangl_imgp = idxs_get_new_imgp_by_idxs(triangl_idxs, new_imgp, all_idxs_tmp) # collect corresponding image-points
filtered_triangl_objp = objp[imgp_to_objp_idxs[triangl_idxs_array]] # collect corresponding object-points
print "Doing solvePnP() on", filtered_triangl_objp.shape[0], "points"
rvec_, tvec_, inliers = cv2.solvePnPRansac( # perform solvePnPRansac() to identify outliers, force to obey max_solvePnP_outlier_ratio
filtered_triangl_objp, filtered_triangl_imgp, cameraMatrix, distCoeffs, minInliersCount=int(ceil((1 - max_solvePnP_outlier_ratio) * len(triangl_idxs))), reprojectionError=max_solvePnP_reproj_error )
# ... if ratio of 'inliers' vs input is too low, reject frame, ...
if inliers == None: # inliers is empty => reject frame
print "REJECTED: No inliers based on solvePnP()!\n"
return False, base_imgp, prev_imgp, triangl_idxs_old, nontriangl_idxs_old, imgp_to_objp_idxs, all_idxs_tmp_old, objp, objp_groups, group_id, None, None, rvec_keyfr, tvec_keyfr, base_img
inliers = inliers.reshape(-1)
solvePnP_outlier_ratio = (len(triangl_idxs) - len(inliers)) / float(len(triangl_idxs))
print "solvePnP_outlier_ratio:", solvePnP_outlier_ratio
if solvePnP_outlier_ratio > max_solvePnP_outlier_ratio or len(inliers) < 8: # reject frame
if solvePnP_outlier_ratio > max_solvePnP_outlier_ratio:
print "REJECTED: Not enough inliers (ratio) based on solvePnP()!\n"
else:
print "REJECTED: Not enough inliers (absolute) based on solvePnP() to perform (non-RANSAC) solvePnP()!\n"
return False, base_imgp, prev_imgp, triangl_idxs_old, nontriangl_idxs_old, imgp_to_objp_idxs, all_idxs_tmp_old, objp, objp_groups, group_id, None, None, rvec_keyfr, tvec_keyfr, base_img
# <DEBUG: visualize reprojection error> TODO: remove
reproj_error, imgp_reproj1 = reprojection_error(filtered_triangl_objp, filtered_triangl_imgp, rvec_, tvec_, cameraMatrix, distCoeffs)
print "solvePnP reproj_error:", reproj_error
i3 = np.array(new_img)
try:
for imgppr, imgppp in zip(filtered_triangl_imgp, imgp_reproj1): cvh.line(i3, imgppr.T, imgppp.T, rgb(255,0,0))
except OverflowError: print "WARNING: OverflowError!"
cv2.imshow("img", i3)
cv2.waitKey()
# </DEBUG>
# ... then do solvePnP() on inliers, to get the current frame's pose estimation, ...
triangl_idxs_array = triangl_idxs_array[inliers] # select inliers among all already-triangulated point-indices
filtered_triangl_imgp, filtered_triangl_objp = filtered_triangl_imgp[inliers], filtered_triangl_objp[inliers]
preserve_idxs = set(triangl_idxs_array) | nontriangl_idxs
new_imgp = idxs_get_new_imgp_by_idxs(preserve_idxs, new_imgp, all_idxs_tmp)
triangl_idxs, nontriangl_idxs, all_idxs_tmp = idxs_update_by_idxs( # update indices to only preserve inliers
preserve_idxs, triangl_idxs, nontriangl_idxs, all_idxs_tmp )
ret, rvec, tvec = cv2.solvePnP( # perform solvePnP() to estimate the pose
filtered_triangl_objp, filtered_triangl_imgp, cameraMatrix, distCoeffs )
# .. finally do a check on the average reprojection error, and reject frame if too high.
reproj_error, imgp_reproj = reprojection_error(filtered_triangl_objp, filtered_triangl_imgp, rvec, tvec, cameraMatrix, distCoeffs)
print "solvePnP refined reproj_error:", reproj_error
if reproj_error > max_solvePnP_reproj_error: # reject frame
print "REJECTED: Too high reprojection error based on pose estimate of solvePnP()!\n"
return False, base_imgp, prev_imgp, triangl_idxs_old, nontriangl_idxs_old, imgp_to_objp_idxs, all_idxs_tmp_old, objp, objp_groups, group_id, None, None, rvec_keyfr, tvec_keyfr, base_img
# <DEBUG: verify poses by reprojection error> TODO: remove
i0 = draw_axis_system(np.array(new_img), rvec, tvec, cameraMatrix, distCoeffs)
try:
for imgppp in imgp_reproj1: cvh.circle(i0, imgppp.T, 2, rgb(255,0,0), thickness=-1)
except OverflowError: print "WARNING: OverflowError!"
for imgppp in imgp_reproj : cvh.circle(i0, imgppp.T, 2, rgb(0,255,255), thickness=-1)
print "cur img check"
cv2.imshow("img", i0)
cv2.waitKey()
# </DEBUG>
# <DEBUG: verify OpticalFlow motion on preserved inliers> TODO: remove
cv2.imshow("img", cvh.drawKeypointsAndMotion(new_img, base_imgp[all_idxs_tmp], new_imgp, rgb(0,0,255)))
cv2.waitKey()
# </DEBUG>
# Check whether we got a new keyframe
is_keyframe = keyframe_test(base_imgp[all_idxs_tmp], new_imgp)
print "is_keyframe:", is_keyframe
if is_keyframe:
# If some points are not yet triangulated, do it now:
if nontriangl_idxs:
# First do triangulation of not-yet triangulated points using initial pose estimation, ...
nontriangl_idxs_array = np.array(sorted(nontriangl_idxs)) # select not-yet-triangulated point-indices
imgp0 = base_imgp[nontriangl_idxs_array] # collect corresponding image-points of last keyframe
imgp1 = idxs_get_new_imgp_by_idxs(nontriangl_idxs, new_imgp, all_idxs_tmp) # collect corresponding image-points of current frame
# <DEBUG: check sanity of input to triangulation function> TODO: remove
check_triangulation_input(base_img, new_img, imgp0, imgp1, rvec_keyfr, tvec_keyfr, rvec, tvec, cameraMatrix, distCoeffs)
# </DEBUG>
imgpnrm0 = cv2.undistortPoints(np.array([imgp0]), cameraMatrix, distCoeffs)[0] # undistort and normalize to homogenous coordinates
imgpnrm1 = cv2.undistortPoints(np.array([imgp1]), cameraMatrix, distCoeffs)[0]
objp_done = linear_LS_triangulation( # triangulate
imgpnrm0.T, trfm.P_from_R_and_t(cvh.Rodrigues(rvec_keyfr), tvec_keyfr), # data from last keyframe
imgpnrm1.T, trfm.P_from_R_and_t(cvh.Rodrigues(rvec), tvec) ) # data from current frame
objp_done = objp_done.T
# <DEBUG: check reprojection error of the new freshly triangulated points, based on both pose estimates of keyframe and current cam> TODO: remove
print "triangl_reproj_error 0:", reprojection_error(objp_done, imgp0, rvec_keyfr, tvec_keyfr, cameraMatrix, distCoeffs)[0]
print "triangl_reproj_error 1:", reprojection_error(objp_done, imgp1, rvec, tvec, cameraMatrix, distCoeffs)[0]
# </DEBUG>
filtered_triangl_objp = np.concatenate((filtered_triangl_objp, objp_done)) # collect all desired object-points
filtered_triangl_imgp = np.concatenate((filtered_triangl_imgp, imgp1)) # collect corresponding image-points of current frame
# ... then do solvePnP() on all preserved points ('inliers') to refine pose estimation, ...
ret, rvec, tvec = cv2.solvePnP( # perform solvePnP(), we start from the initial pose estimation
filtered_triangl_objp, filtered_triangl_imgp, cameraMatrix, distCoeffs, rvec, tvec, useExtrinsicGuess=True )
print "total triangl_reproj_error 1 refined:", reprojection_error(filtered_triangl_objp, filtered_triangl_imgp, rvec, tvec, cameraMatrix, distCoeffs)[0] # TODO: remove
# ... then do re-triangulation of 'inliers_objp_done' using refined pose estimation.
objp_done = linear_LS_triangulation( # triangulate
imgpnrm0.T, trfm.P_from_R_and_t(cvh.Rodrigues(rvec_keyfr), tvec_keyfr), # data from last keyframe
imgpnrm1.T, trfm.P_from_R_and_t(cvh.Rodrigues(rvec), tvec) ) # data from current frame
objp_done = objp_done.T
# <DEBUG: check reprojection error of the new freshly (refined) triangulated points, based on both pose estimates of keyframe and current cam> TODO: remove
print "triangl_reproj_error 0 refined:", reprojection_error(objp_done, imgp0, rvec_keyfr, tvec_keyfr, cameraMatrix, distCoeffs)[0]
print "triangl_reproj_error 1 refined:", reprojection_error(objp_done, imgp1, rvec, tvec, cameraMatrix, distCoeffs)[0]
# </DEBUG>
# Update image-points and indices, and store the newly triangulated object-points and assign them the current group id
new_imgp = filtered_triangl_imgp
triangl_idxs, nontriangl_idxs, all_idxs_tmp = idxs_update_by_idxs(
preserve_idxs, triangl_idxs, nontriangl_idxs, all_idxs_tmp )
objp_groups_done = np.empty((len(objp_done)), dtype=int); objp_groups_done.fill(group_id) # assign to current 'group_id'
(objp, objp_groups), imgp_to_objp_idxs, triangl_idxs, nontriangl_idxs = idxs_add_objp(
(objp_done, objp_groups_done), preserve_idxs - triangl_idxs, (objp, objp_groups), imgp_to_objp_idxs, triangl_idxs, nontriangl_idxs, all_idxs_tmp )
## <DEBUG: check intermediate outlier filtering> TODO: remove
#i4 = np.array(new_img)
#objp_test = objp[imgp_to_objp_idxs[np.array(sorted(triangl_idxs))]]
#imgp_test = idxs_get_new_imgp_by_idxs(triangl_idxs, new_imgp, all_idxs_tmp)
##print imgp_test - filtered_triangl_imgp
#reproj_error, imgp_reproj4 = reprojection_error(objp_test, imgp_test, rvec, tvec, cameraMatrix, distCoeffs)
#print "checking both 2", reproj_error
#for imgppr, imgppp in zip(imgp_test, imgp_reproj4): cvh.line(i4, imgppr.T, imgppp.T, rgb(255,0,0))
#cv2.imshow("img", i4)
#cv2.waitKey()
## </DEBUG>
# Check whether we should add new image-points
mask_img = keypoint_mask(new_imgp) # generate mask that covers all image-points (with a certain radius)
print "coverage:", 1 - cv2.countNonZero(mask_img)/float(mask_img.size) # TODO: remove: unused
to_add = target_amount_keypoints - len(new_imgp) # limit the amount of to-be-added image-points
# Add new image-points
if to_add > 0:
print "to_add:", to_add
imgp_extra = cv2.goodFeaturesToTrack(new_img_gray, to_add, corner_quality_level, corner_min_dist, None, mask_img).reshape((-1, 2))
print "added:", len(imgp_extra)
group_id += 1 # create a new group to assign the new batch of points to, later on
else:
imgp_extra = zeros((0, 2))
print "adding zero new points"
base_imgp, new_imgp, imgp_to_objp_idxs, triangl_idxs, nontriangl_idxs, all_idxs_tmp = idxs_rebase_and_add_imgp( # update indices to include new image-points
imgp_extra, base_imgp, new_imgp, imgp_to_objp_idxs, triangl_idxs, nontriangl_idxs, all_idxs_tmp )
# <DEBUG: visualize newly added points> TODO: remove
cv2.imshow("img", cv2.drawKeypoints(new_img, [cv2.KeyPoint(p[0],p[1], 7.) for p in imgp_extra], color=rgb(0,0,255)))
cv2.waitKey()
# </DEBUG>
# Now this frame becomes the base (= keyframe)
rvec_keyfr = rvec
tvec_keyfr = tvec
base_img = new_img
# Successfully return
return True + int(is_keyframe), base_imgp, new_imgp, triangl_idxs, nontriangl_idxs, imgp_to_objp_idxs, all_idxs_tmp, objp, objp_groups, group_id, rvec, tvec, rvec_keyfr, tvec_keyfr, base_img
