def tsdf_voxel_volume(fx, fy, cx, cy):
print("Estimating voxel volume bounds....")
cam_intrinsics = cam.camera_intrinsics(fx, fy, cx, cy)
vol_bnds = np.zeros((3, 2))
depth_image = np.load(
"/home/aditya/PycharmProjects/OpenCV-python/Project_2/TDCV-Project-2/trial1.npz"
)['depth']
camera_pose = np.identity(4)
view_frust_pts = fusion.get_view_frustum(depth_image, cam_intrinsics,
camera_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))
print("Initializing voxel volume...")
tsdf_vol = fusion.TSDFVolume(vol_bnds, voxel_size=0.01)
tsdf_vol.integrate(depth_image,
depth_image,
cam_intrinsics,
camera_pose,
obs_weight=1.0)
print("Saving mesh to mesh.ply...")
verts, faces, norms, colors = tsdf_vol.get_mesh()
fusion.meshwrite("mesh.ply", verts, faces, norms, colors)
"""
#print "Cam poses shape",cam_poses.shape
vol_bnds = np.zeros((3, 2))
file = open('data_table/associate.txt')
lines = file.read().split("\n")
print "Number of lines in associate", len(lines)
for i in range(len(lines) - 1):
# Read depth image and camera pose
depth_file = base_dir + '/' + lines[i].split(" ")[2]
depth_im = cv2.imread(depth_file,
cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)
depth_im = depth_im.astype(np.float)
depth_im /= 5000. # depth is saved in 16-bit PNG in millimeters
cam_pose = cam_poses[4 * i:4 * (i + 1), :]
view_frust_pts = 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))
break
print "Volume Bounds:", vol_bnds
file.close()
# ======================================================================================================== #
# Integrate
# ======================================================================================================== #
# Initialize voxel volume
print("Initializing voxel volume...")
tsdf_vol = fusion.TSDFVolume(vol_bnds, voxel_size=0.005)
def main(args):
if args.example == 'cpp':
print("Using PyTorch CPP.")
from cpp.integrate import integrate
elif args.example == 'jit':
print("Using PyTorch JIT.")
from jit.integrate import integrate
elif args.example == 'py':
print("Using vanilla PyTorch.")
from python.integrate import integrate
else:
pass
# ======================================================================================================== #
# (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 = 15
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 = 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))
# ======================================================================================================== #
# Initialize voxel volume
print("Initializing voxel volume...")
tsdf_vol = fusion.TSDFVolume(vol_bnds, 0.02, integrate)
# ======================================================================================================== #
# Integrate
# ======================================================================================================== #
# Loop through RGB-D images and fuse them together
t0_elapse = time.time()
times = []
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)
tic = time.time()
tsdf_vol.integrate(color_image,
depth_im,
cam_intr,
cam_pose,
obs_weight=1.)
toc = time.time()
times.append(toc - tic)
fps = n_imgs / (time.time() - t0_elapse)
print("Average FPS: {:.2f}".format(fps))
times = [t * TIME_SCALES[args.scale] for t in times]
print("Average integration time: {:.3f} {}".format(np.mean(times),
args.scale))
# Extract pointcloud
point_cloud = tsdf_vol.extract_point_cloud()
fusion.pcwrite("pc.ply", point_cloud)
# Get mesh from voxel volume and save to disk (can be viewed with Meshlab)
print("Saving to mesh.ply...")
verts, faces, norms, colors = tsdf_vol.extract_triangle_mesh()
fusion.meshwrite("mesh.ply", verts, faces, norms, colors)