def process_frame(img):
img = cv2.resize(img, (W,H))
frame = Frame(mapp, img, K)
if frame.id == 0:
return
print("\n*** frame %d ***" % (frame.id,))
f1 = mapp.frames[-1]
f2 = mapp.frames[-2]
idx1, idx2, Rt = match_frames(f1, f2)
f1.pose = np.dot(Rt, f2.pose)
for i,idx in enumerate(idx2):
if f2.pts[idx] is not None:
f2.pts[idx].add_observation(f1, idx1[i])
good_pts4d = np.array([f1.pts[i] is None for i in idx1])
# locally in front of camera
# reject pts without enough "parallax" (this right?)
pts_tri_local = triangulate(Rt, np.eye(4), f1.kps[idx1], f2.kps[idx2])
good_pts4d &= np.abs(pts_tri_local[:, 3]) > 0.005
# homogeneous 3-D coords
# reject points behind the camera
pts_tri_local /= pts_tri_local[:, 3:]
good_pts4d &= pts_tri_local[:, 2] > 0
# project into world
pts4d = np.dot(np.linalg.inv(f1.pose), pts_tri_local.T).T
print("Adding: %d points" % np.sum(good_pts4d))
for i,p in enumerate(pts4d):
if not good_pts4d[i]:
continue
u,v = int(round(f1.kpus[idx1[i],0])), int(round(f1.kpus[idx1[i],1]))
pt = Point(mapp, p, img[v,u])
pt.add_observation(f1, idx1[i])
pt.add_observation(f2, idx2[i])
for pt1, pt2 in zip(f1.kps[idx1], f2.kps[idx2]):
u1, v1 = denormalize(K, pt1)
u2, v2 = denormalize(K, pt2)
cv2.circle(img, (u1, v1), color=(0,255,0), radius=3)
cv2.line(img, (u1, v1), (u2, v2), color=(255,0,0))
# 2-D display
if disp is not None:
disp.paint(img)
# optimize the map
if frame.id >= 4:
err = mapp.optimize()
print("Optimize: %f units of error" % err)
# 3-D display
mapp.display()
def process_frame(img):
img = cv2.resize(img, (W, H))
frame = Frame(mapp, img, K)
if frame.id == 0:
return
print("\n*** frame %d ***" % (frame.id, ))
f1 = mapp.frames[-1]
f2 = mapp.frames[-2]
idx1, idx2, Rt = match_frames(f1, f2)
#print("=============================", idx1, idx2, Rt,f1.pts,f2.pts)
f1.pose = np.dot(Rt, f2.pose)
for i, idx in enumerate(idx2):
if f2.pts[idx] is not None:
f2.pts[idx].add_observation(f1, idx1[i])
# homogeneous 3-D coords
pts4d = triangulate(f1.pose, f2.pose, f1.kps[idx1], f2.kps[idx2])
pts4d /= pts4d[:, 3:]
#print(pts4d)
# reject pts without enough "parallax" (this right?)
# reject points behind the camera
unmatched_points = np.array([f1.pts[i] is None for i in idx1])
print("Adding: %d points" % np.sum(unmatched_points))
good_pts4d = (np.abs(pts4d[:, 3]) > 0.005) & (pts4d[:, 2] >
0) & unmatched_points
#print(sum(good_pts4d), len(good_pts4d))
#print(good_pts4d)
for i, p in enumerate(pts4d):
if not good_pts4d[i]:
continue
pt = Point(mapp, p)
pt.add_observation(f1, idx1[i])
pt.add_observation(f2, idx2[i])
for pt1, pt2 in zip(f1.kps[idx1], f2.kps[idx2]):
u1, v1 = denormalize(K, pt1)
u2, v2 = denormalize(K, pt2)
cv2.circle(img, (u1, v1), color=(0, 255, 0), radius=3)
cv2.line(img, (u1, v1), (u2, v2), color=(255, 0, 0))
# 2-D display
if disp is not None:
disp.paint(img)
# optimize the map
if frame.id >= 4:
mapp.optimize()
# 3-D display
mapp.display()
def process_frame(img):
img = cv2.resize(img, (W, H))
frame = Frame(img, mapp, K)
if frame.id == 0:
return
f1 = mapp.frames[-1]
f2 = mapp.frames[-2]
idx1, idx2, Rt = match_frames(f1, f2)
f1.pose = np.dot(Rt, f2.pose)
pts4d = triangulate(f1.pose, f2.pose, f1.pts[idx1], f2.pts[idx2])
#homogeneous 3-Dcoords
pts4d /= pts4d[:, 3:]
#rejecting pts without good parallax
#reject pts behind cam
good_pts4d = (np.abs(pts4d[:, 3]) > 0.005) & (pts4d[:, 2] > 0)
#print(sum(good_pts4d), len(good_pts4d))
for i, p in enumerate(pts4d):
if not good_pts4d[i]:
continue
pt = Point(mapp, p)
pt.add_observation(f1, idx1[i])
pt.add_observation(f2, idx2[i])
#print(f1.pose)
#print(pts4d)
for pt1, pt2 in zip(f1.pts[idx1], f2.pts[idx2]):
#u1, v1 = map(lambda x: int(round(x)), pt1)
#u2, v2 = map(lambda x: int(round(x)), pt2)
u1, v1 = denormalize(K, pt1)
u2, v2 = denormalize(K, pt2)
cv2.circle(img, (u1, v1), color=(0, 255, 0), radius=3)
cv2.line(img, (u1, v1), (u2, v2), color=(255, 0, 0))
#print(img.shape)
# 2-D display
if disp is not None: disp.paint(img)
#3-D display
mapp.display()
def process_frame(img):
img = cv2.resize(img, (W, H))
frame = Frame(mapp, img, K)
if frame.id == 0:
return
f1 = mapp.frames[-1]
f2 = mapp.frames[-2]
idx1, idx2, Rt = match_frames(f1, f2)
f1.pose = np.dot(Rt, f2.pose)
# homogeneous 3-D coords
pts4d = triangulate(f1.pose, f2.pose, f1.pts[idx1], f2.pts[idx2])
pts4d /= pts4d[:, 3:]
# reject pts without enough "parallax" (this right?)
# reject points behind the camera
good_pts4d = (np.abs(pts4d[:, 3]) > 0.005) & (pts4d[:, 2] > 0)
for i, p in enumerate(pts4d):
if not good_pts4d[i]:
continue
pt = Point(mapp, p)
pt.add_observation(f1, idx1[i])
pt.add_observation(f2, idx2[i])
for pt1, pt2 in zip(f1.pts[idx1], f2.pts[idx2]):
u1, v1 = denormalize(K, pt1)
u2, v2 = denormalize(K, pt2)
cv2.circle(img, (u1, v1), color=(0, 255, 0), radius=3)
cv2.line(img, (u1, v1), (u2, v2), color=(255, 0, 0))
# 2-D display
if disp is not None:
disp.paint(img)
# 3-D display
mapp.display()
def process_frame(img):
img = cv2.resize(img, (W, H))
frame = Frame(mapp, img, K)
if frame.id == 0:
return
f1 = mapp.frames[-1]
f2 = mapp.frames[-2]
idx1, idx2, Rt = match_frames(f1, f2)
f1.pose = np.dot(Rt, f2.pose)
pts4d = triangulate(f1.pose, f2.pose, f1.pts[idx1], f2.pts[idx2])
pts4d /= pts4d[:, 3:]
# Reject points without enough "Parallax" and points behind the camera
good_pts4d = (np.abs(pts4d[:, 3]) > 0.005) & (pts4d[:, 2] > 0)
for i, p in enumerate(pts4d):
if not good_pts4d[i]:
continue
pt = Point(mapp, p)
pt.add_observation(f1, i)
pt.add_observation(f2, i)
for pt1, pt2 in zip(f1.pts[idx1], f2.pts[idx2]):
u1, v1 = denormalize(K, pt1)
u2, v2 = denormalize(K, pt2)
cv2.circle(img, (u1, v1), 3, (0, 255, 0))
cv2.line(img, (u1, v1), (u2, v2), (255, 0, 0))
# 2-D display
display.paint(img)
# 3-D display
mapp.display()
def process_frame(self, img, pose=None):
start_time = time.time()
assert img.shape[0:2] == (self.H, self.W)
frame = Frame(self.mapp, img, self.K)
if frame.id == 0:
return
f1 = self.mapp.frames[-1]
f2 = self.mapp.frames[-2]
idx1, idx2, Rt = match_frames(f1, f2)
# add new observations if the point is already observed in the previous frame
# TODO: consider tradeoff doing this before/after search by projection
for i, idx in enumerate(idx2):
if f2.pts[idx] is not None and f1.pts[idx1[i]] is None:
f2.pts[idx].add_observation(f1, idx1[i])
if frame.id < 5 or True:
# get initial positions from fundamental matrix
f1.pose = np.dot(Rt, f2.pose)
else:
# kinematic model (not used)
velocity = np.dot(f2.pose,
np.linalg.inv(self.mapp.frames[-3].pose))
f1.pose = np.dot(velocity, f2.pose)
# pose optimization
if pose is None:
#print(f1.pose)
pose_opt = self.mapp.optimize(local_window=1, fix_points=True)
print("Pose: %f" % pose_opt)
#print(f1.pose)
else:
# have ground truth for pose
f1.pose = pose
sbp_pts_count = 0
# search by projection
if len(self.mapp.points) > 0:
# project *all* the map points into the current frame
map_points = np.array([p.homogeneous() for p in self.mapp.points])
projs = np.dot(np.dot(K, f1.pose[:3]), map_points.T).T
projs = projs[:, 0:2] / projs[:, 2:]
# only the points that fit in the frame
good_pts = (projs[:, 0] > 0) & (projs[:, 0] < self.W) & \
(projs[:, 1] > 0) & (projs[:, 1] < self.H)
for i, p in enumerate(self.mapp.points):
if not good_pts[i]:
# point not visible in frame
continue
if f1 in p.frames:
# we already matched this map point to this frame
# TODO: understand this better
continue
for m_idx in f1.kd.query_ball_point(projs[i], 2):
# if point unmatched
if f1.pts[m_idx] is None:
b_dist = p.orb_distance(f1.des[m_idx])
# if any descriptors within 64
if b_dist < 64.0:
p.add_observation(f1, m_idx)
sbp_pts_count += 1
break
# triangulate the points we don't have matches for
good_pts4d = np.array([f1.pts[i] is None for i in idx1])
# do triangulation in global frame
pts4d = triangulate(f1.pose, f2.pose, f1.kps[idx1], f2.kps[idx2])
good_pts4d &= np.abs(pts4d[:, 3]) != 0
pts4d /= pts4d[:, 3:] # homogeneous 3-D coords
# adding new points to the map from pairwise matches
new_pts_count = 0
for i, p in enumerate(pts4d):
if not good_pts4d[i]:
continue
# check parallax is large enough
# TODO: learn what parallax means
"""
r1 = np.dot(f1.pose[:3, :3], add_ones(f1.kps[idx1[i]]))
r2 = np.dot(f2.pose[:3, :3], add_ones(f2.kps[idx2[i]]))
parallax = r1.dot(r2) / (np.linalg.norm(r1) * np.linalg.norm(r2))
if parallax >= 0.9998:
continue
"""
# check points are in front of both cameras
pl1 = np.dot(f1.pose, p)
pl2 = np.dot(f2.pose, p)
if pl1[2] < 0 or pl2[2] < 0:
continue
# reproject
pp1 = np.dot(K, pl1[:3])
pp2 = np.dot(K, pl2[:3])
# check reprojection error
pp1 = (pp1[0:2] / pp1[2]) - f1.kpus[idx1[i]]
pp2 = (pp2[0:2] / pp2[2]) - f2.kpus[idx2[i]]
pp1 = np.sum(pp1**2)
pp2 = np.sum(pp2**2)
if pp1 > 2 or pp2 > 2:
continue
# add the point
color = img[int(round(f1.kpus[idx1[i], 1])),
int(round(f1.kpus[idx1[i], 0]))]
pt = Point(self.mapp, p[0:3], color)
pt.add_observation(f2, idx2[i])
pt.add_observation(f1, idx1[i])
new_pts_count += 1
print("Adding: %d new points, %d search by projection" %
(new_pts_count, sbp_pts_count))
# optimize the map
if frame.id >= 4 and frame.id % 5 == 0:
err = self.mapp.optimize() #verbose=True)
print("Optimize: %f units of error" % err)
print("Map: %d points, %d frames" %
(len(self.mapp.points), len(self.mapp.frames)))
print("Time: %.2f ms" % ((time.time() - start_time) * 1000.0))
print(np.linalg.inv(f1.pose))
def process_frame(img):
start_time = time.time()
img = cv2.resize(img, (W,H))
frame = Frame(mapp, img, K)
if frame.id == 0:
return
f1 = mapp.frames[-1]
f2 = mapp.frames[-2]
idx1, idx2, Rt = match_frames(f1, f2)
if frame.id < 5:
# get initial positions from fundamental matrix
f1.pose = np.dot(Rt, f2.pose)
else:
# kinematic model
velocity = np.dot(f2.pose, np.linalg.inv(mapp.frames[-3].pose))
f1.pose = np.dot(velocity, f2.pose)
for i,idx in enumerate(idx2):
if f2.pts[idx] is not None:
f2.pts[idx].add_observation(f1, idx1[i])
# pose optimization
#print(f1.pose)
pose_opt = mapp.optimize(local_window=1, fix_points=True)
print("Pose: %f" % pose_opt)
#print(f1.pose)
# search by projection
sbp_pts_count = 0
if len(mapp.points) > 0:
map_points = np.array([p.homogeneous() for p in mapp.points])
projs = np.dot(np.dot(K, f1.pose[:3]), map_points.T).T
projs = projs[:, 0:2] / projs[:, 2:]
good_pts = (projs[:, 0] > 0) & (projs[:, 0] < W) & \
(projs[:, 1] > 0) & (projs[:, 1] < H)
for i, p in enumerate(mapp.points):
if not good_pts[i]:
continue
q = f1.kd.query_ball_point(projs[i], 5)
for m_idx in q:
if f1.pts[m_idx] is None:
# if any descriptors within 32
for o in p.orb():
o_dist = hamming_distance(o, f1.des[m_idx])
if o_dist < 32.0:
p.add_observation(f1, m_idx)
sbp_pts_count += 1
break
good_pts4d = np.array([f1.pts[i] is None for i in idx1])
# reject pts without enough "parallax" (this right?)
pts4d = triangulate(f1.pose, f2.pose, f1.kps[idx1], f2.kps[idx2])
good_pts4d &= np.abs(pts4d[:, 3]) > 0.005
# homogeneous 3-D coords
pts4d /= pts4d[:, 3:]
# locally in front of camera
# NOTE: This check is broken and maybe unneeded
#pts_tri_local = np.dot(f1.pose, pts4d.T).T
#good_pts4d &= pts_tri_local[:, 2] > 0
print("Adding: %d new points, %d search by projection" % (np.sum(good_pts4d), sbp_pts_count))
for i,p in enumerate(pts4d):
if not good_pts4d[i]:
continue
u,v = int(round(f1.kpus[idx1[i],0])), int(round(f1.kpus[idx1[i],1]))
pt = Point(mapp, p[0:3], img[v,u])
pt.add_observation(f1, idx1[i])
pt.add_observation(f2, idx2[i])
for i1, i2 in zip(idx1, idx2):
pt1 = f1.kps[i1]
pt2 = f2.kps[i2]
u1, v1 = denormalize(K, pt1)
u2, v2 = denormalize(K, pt2)
if f1.pts[i1] is not None:
if len(f1.pts[i1].frames) >= 5:
cv2.circle(img, (u1, v1), color=(0,255,0), radius=3)
else:
cv2.circle(img, (u1, v1), color=(0,128,0), radius=3)
else:
cv2.circle(img, (u1, v1), color=(0,0,0), radius=3)
cv2.line(img, (u1, v1), (u2, v2), color=(255,0,0))
# 2-D display
if disp is not None:
disp.paint(img)
# optimize the map
if frame.id >= 4 and frame.id%5 == 0:
err = mapp.optimize()
print("Optimize: %f units of error" % err)
# 3-D display
mapp.display()
print("Map: %d points, %d frames" % (len(mapp.points), len(mapp.frames)))
print("Time: %.2f ms" % ((time.time()-start_time)*1000.0))
def process_frame(img, pose=None):
start_time = time.time()
img = cv2.resize(img, (W,H))
frame = Frame(mapp, img, K)
if frame.id == 0:
return
f1 = mapp.frames[-1]
f2 = mapp.frames[-2]
idx1, idx2, Rt = match_frames(f1, f2)
# add new observations if the point is already observed in the previous frame
# TODO: consider tradeoff doing this before/after search by projection
for i,idx in enumerate(idx2):
if f2.pts[idx] is not None and f1.pts[idx1[i]] is None:
f2.pts[idx].add_observation(f1, idx1[i])
if frame.id < 5:
# get initial positions from fundamental matrix
f1.pose = np.dot(Rt, f2.pose)
else:
# kinematic model
velocity = np.dot(f2.pose, np.linalg.inv(mapp.frames[-3].pose))
f1.pose = np.dot(velocity, f2.pose)
# pose optimization
if pose is None:
#print(f1.pose)
pose_opt = mapp.optimize(local_window=1, fix_points=True)
print("Pose: %f" % pose_opt)
#print(f1.pose)
else:
# have ground truth for pose
f1.pose = pose
# search by projection
sbp_pts_count = 0
if len(mapp.points) > 0:
map_points = np.array([p.homogeneous() for p in mapp.points])
projs = np.dot(np.dot(K, f1.pose[:3]), map_points.T).T
projs = projs[:, 0:2] / projs[:, 2:]
good_pts = (projs[:, 0] > 0) & (projs[:, 0] < W) & \
(projs[:, 1] > 0) & (projs[:, 1] < H)
for i, p in enumerate(mapp.points):
if not good_pts[i]:
continue
q = f1.kd.query_ball_point(projs[i], 5)
for m_idx in q:
if f1.pts[m_idx] is None:
# if any descriptors within 32
for o in p.orb():
o_dist = hamming_distance(o, f1.des[m_idx])
if o_dist < 32.0:
p.add_observation(f1, m_idx)
sbp_pts_count += 1
break
# triangulate the points we don't have matches for
good_pts4d = np.array([f1.pts[i] is None for i in idx1])
# do triangulation in local frame
lpose = np.dot(f1.pose, np.linalg.inv(f2.pose))
pts_local = triangulate(lpose, np.eye(4), f1.kps[idx1], f2.kps[idx2])
good_pts4d &= np.abs(pts_local[:, 3]) > 0.01
pts_local /= pts_local[:, 3:] # homogeneous 3-D coords
good_pts4d &= pts_local[:, 2] > 0 # locally in front of camera
pts4d = np.dot(np.linalg.inv(f2.pose), pts_local.T).T
print("Adding: %d new points, %d search by projection" % (np.sum(good_pts4d), sbp_pts_count))
# adding new points to the map from pairwise matches
for i,p in enumerate(pts4d):
if not good_pts4d[i]:
continue
u,v = int(round(f1.kpus[idx1[i],0])), int(round(f1.kpus[idx1[i],1]))
pt = Point(mapp, p[0:3], img[v,u])
pt.add_observation(f1, idx1[i])
pt.add_observation(f2, idx2[i])
# 2-D display
if disp2d is not None:
# paint annotations on the image
for i1, i2 in zip(idx1, idx2):
u1, v1 = int(round(f1.kpus[i1][0])), int(round(f1.kpus[i1][1]))
u2, v2 = int(round(f2.kpus[i2][0])), int(round(f2.kpus[i2][1]))
if f1.pts[i1] is not None:
if len(f1.pts[i1].frames) >= 5:
cv2.circle(img, (u1, v1), color=(0,255,0), radius=3)
else:
cv2.circle(img, (u1, v1), color=(0,128,0), radius=3)
else:
cv2.circle(img, (u1, v1), color=(0,0,0), radius=3)
cv2.line(img, (u1, v1), (u2, v2), color=(255,0,0))
disp2d.paint(img)
# optimize the map
if frame.id >= 4 and frame.id%5 == 0:
err = mapp.optimize()
print("Optimize: %f units of error" % err)
# 3-D display
if disp3d is not None:
disp3d.paint(mapp)
print("Map: %d points, %d frames" % (len(mapp.points), len(mapp.frames)))
print("Time: %.2f ms" % ((time.time()-start_time)*1000.0))
print(np.linalg.inv(f1.pose))