def ten_frame_profiling():
n_imgs = 1000
cam_intr = np.loadtxt("../data/camera-intrinsics.txt", delimiter=' ')
vol_bnds = np.zeros((3, 2))
for i in range(n_imgs):
depth_im = cv2.imread("../data/frame-%06d.depth.png" % (i),
-1).astype(float)
depth_im /= 1000.
depth_im[depth_im == 65.535] = 0
cam_pose = np.loadtxt("../data/frame-%06d.pose.txt" % (i))
view_frust_pts = grid_fusion.get_view_frustum(depth_im, cam_intr,
cam_pose)
vol_bnds[:, 0] = np.minimum(vol_bnds[:, 0],
np.amin(view_frust_pts, axis=1))
vol_bnds[:, 1] = np.maximum(vol_bnds[:, 1],
np.amax(view_frust_pts, axis=1))
hash_table = hash_fusion.HashTable(vol_bnds, voxel_size=0.02)
total_time = 0
# Loop through the first 10 RGB-D images and fuse them together
for i in range(10):
tic = time.perf_counter()
color_image = cv2.cvtColor(
cv2.imread("../data/frame-%06d.color.jpg" % (i)),
cv2.COLOR_BGR2RGB)
depth_im = cv2.imread("../data/frame-%06d.depth.png" % (i),
-1).astype(float)
depth_im /= 1000.
depth_im[depth_im == 65.535] = 0
cam_pose = np.loadtxt("../data/frame-%06d.pose.txt" % (i))
hash_table.integrate(color_image,
depth_im,
cam_intr,
cam_pose,
obs_weight=1.)
toc = time.perf_counter()
tictoc = round(toc - tic, 2)
total_time += tictoc
avg_time = round(total_time / (i + 1), 2)
print("Integrate frame {} in {} seconds. Avg: {}s/frame".format(
i + 1, tictoc, avg_time))
"""
print("Frame {}: num occupied buckets: {}; load factor: {}; collisions: {}; collision rate: {}".format(
i + 1,
hash_table.get_num_non_empty_bucket(),
hash_table.get_load_factor(),
hash_table.get_num_collisions(),
hash_table.get_num_collisions() / hash_table.get_num_non_empty_bucket()
))
"""
verts, faces, norms, colors = hash_table.get_mesh()
grid_fusion.meshwrite("mesh_hash_demo1.ply", verts, faces, norms, colors)
# Get point cloud from voxel volume and save to disk (can be viewed with Meshlab)
print("Saving point cloud to pc.ply...")
point_cloud = hash_table.get_point_cloud()
grid_fusion.pcwrite("pc_hash_demo1.ply", point_cloud)
def one_frame_profiling():
n_imgs = 1000
cam_intr = np.loadtxt("../data/camera-intrinsics.txt", delimiter=' ')
vol_bnds = np.zeros((3, 2))
for i in range(n_imgs):
depth_im = cv2.imread("../data/frame-%06d.depth.png" % (i),
-1).astype(float)
depth_im /= 1000.
depth_im[depth_im == 65.535] = 0
cam_pose = np.loadtxt("../data/frame-%06d.pose.txt" % (i))
view_frust_pts = grid_fusion.get_view_frustum(depth_im, cam_intr,
cam_pose)
vol_bnds[:, 0] = np.minimum(vol_bnds[:, 0],
np.amin(view_frust_pts, axis=1))
vol_bnds[:, 1] = np.maximum(vol_bnds[:, 1],
np.amax(view_frust_pts, axis=1))
hash_table = hash_fusion.HashTable(vol_bnds, voxel_size=0.02)
# fuse frame 0 for testing
color_image = cv2.cvtColor(cv2.imread("../data/frame-%06d.color.jpg" % 0),
cv2.COLOR_BGR2RGB)
depth_im = cv2.imread("../data/frame-%06d.depth.png" % 0, -1).astype(float)
depth_im /= 1000.
depth_im[depth_im == 65.535] = 0
cam_pose = np.loadtxt("../data/frame-%06d.pose.txt" % 0)
hash_table.integrate(color_image,
depth_im,
cam_intr,
cam_pose,
obs_weight=1.)
def ten_frame_profiling():
n_imgs = 1000
cam_intr = np.loadtxt("../data/camera-intrinsics.txt", delimiter=' ')
vol_bnds = np.zeros((3, 2))
for i in range(n_imgs):
# Read depth image and camera pose
depth_im = cv2.imread("../data/frame-%06d.depth.png" % (i),
-1).astype(float)
depth_im /= 1000.
depth_im[depth_im == 65.535] = 0
cam_pose = np.loadtxt("../data/frame-%06d.pose.txt" % (i))
view_frust_pts = grid_fusion.get_view_frustum(depth_im, cam_intr,
cam_pose)
vol_bnds[:, 0] = np.minimum(vol_bnds[:, 0],
np.amin(view_frust_pts, axis=1))
vol_bnds[:, 1] = np.maximum(vol_bnds[:, 1],
np.amax(view_frust_pts, axis=1))
tsdf_vol = grid_fusion.TSDFVolume(vol_bnds, voxel_size=0.02)
total_time = 0
for i in range(10):
tic = time.perf_counter()
color_image = cv2.cvtColor(
cv2.imread("../data/frame-%06d.color.jpg" % (i)),
cv2.COLOR_BGR2RGB)
depth_im = cv2.imread("../data/frame-%06d.depth.png" % (i),
-1).astype(float)
depth_im /= 1000.
depth_im[depth_im == 65.535] = 0
cam_pose = np.loadtxt("../data/frame-%06d.pose.txt" % (i))
tsdf_vol.integrate(color_image,
depth_im,
cam_intr,
cam_pose,
obs_weight=1.)
toc = time.perf_counter()
tictoc = round(toc - tic, 2)
total_time += tictoc
avg_time = round(total_time / (i + 1), 2)
print("Integrate frame {} in {} seconds. Avg: {}s/frame".format(
i + 1, tictoc, avg_time))
def main():
# ======================================================================================================== #
# (Optional) This is an example of how to compute the 3D bounds
# in world coordinates of the convex hull of all camera view
# frustums in the dataset
# ======================================================================================================== #
print("Estimating voxel volume bounds...")
n_imgs = 1000
cam_intr = np.loadtxt("../data/camera-intrinsics.txt", delimiter=' ')
vol_bnds = np.zeros((3, 2))
for i in range(n_imgs):
# Read depth image and camera pose
depth_im = cv2.imread("../data/frame-%06d.depth.png" % (i),
-1).astype(float)
depth_im /= 1000. # depth is saved in 16-bit PNG in millimeters
depth_im[
depth_im ==
65.535] = 0 # set invalid depth to 0 (specific to 7-scenes dataset)
cam_pose = np.loadtxt("../data/frame-%06d.pose.txt" %
(i)) # 4x4 rigid transformation matrix
# Compute camera view frustum and extend convex hull
view_frust_pts = grid_fusion.get_view_frustum(depth_im, cam_intr,
cam_pose)
vol_bnds[:, 0] = np.minimum(vol_bnds[:, 0],
np.amin(view_frust_pts, axis=1))
vol_bnds[:, 1] = np.maximum(vol_bnds[:, 1],
np.amax(view_frust_pts, axis=1))
# ======================================================================================================== #
# ======================================================================================================== #
# Integrate
# ======================================================================================================== #
# Initialize voxel volume
print("Initializing voxel volume...")
tsdf_vol = grid_fusion.TSDFVolume(vol_bnds, voxel_size=0.02)
# Loop through RGB-D images and fuse them together
t0_elapse = time.time()
for i in range(n_imgs):
print("Fusing frame %d/%d" % (i + 1, n_imgs))
# Read RGB-D image and camera pose
color_image = cv2.cvtColor(
cv2.imread("../data/frame-%06d.color.jpg" % (i)),
cv2.COLOR_BGR2RGB)
depth_im = cv2.imread("../data/frame-%06d.depth.png" % (i),
-1).astype(float)
depth_im /= 1000.
depth_im[depth_im == 65.535] = 0
cam_pose = np.loadtxt("../data/frame-%06d.pose.txt" % (i))
# Integrate observation into voxel volume (assume color aligned with depth)
tsdf_vol.integrate(color_image,
depth_im,
cam_intr,
cam_pose,
obs_weight=1.)
fps = n_imgs / (time.time() - t0_elapse)
print("Average FPS: {:.2f}".format(fps))
# Get mesh from voxel volume and save to disk (can be viewed with Meshlab)
print("Saving mesh to mesh.ply...")
verts, faces, norms, colors = tsdf_vol.get_mesh()
grid_fusion.meshwrite("mesh.ply", verts, faces, norms, colors)
# Get point cloud from voxel volume and save to disk (can be viewed with Meshlab)
print("Saving point cloud to pc.ply...")
point_cloud = tsdf_vol.get_point_cloud()
grid_fusion.pcwrite("pc.ply", point_cloud)