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connecting_the_dots
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Reformat $EVERYTHING

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CptCaptain 3 years ago
parent 56f2aa7d5d
commit 43df77fb9b
32 changed files with 4429 additions and 4007 deletions
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  1. 1
      co/__init__.py
  2. 3
      co/args.py
  3. 9
      co/cmap.py
  4. 54
      co/geometry.py
  5. 6
      co/gtimer.py
  6. 7
      co/io3d.py
  7. 12
      co/metric.py
  8. 5
      co/plt.py
  9. 7
      co/plt2d.py
  10. 22
      co/plt3d.py
  11. 24
      co/table.py
  12. 6
      co/utils.py
  13. 8
      data/commons.py
  14. 10
      data/create_syn_data.py
  15. 8
      data/dataset.py
  16. 321
      data/lcn/lcn.html
  17. 7
      hyperdepth/hyperparam_search.py
  18. 5
      hyperdepth/setup.py
  19. 13
      hyperdepth/vis_eval.py
  20. 71
      model/exp_synph.py
  21. 61
      model/exp_synphge.py
  22. 47
      model/networks.py
  23. 64
      readme.md
  24. 4
      torchext/dataset.py
  25. 11
      torchext/functions.py
  26. 4
      torchext/modules.py
  27. 3
      torchext/setup.py
  28. 20
      torchext/worker.py
  29. 5
      train_val.py

1
co/__init__.py
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@ -7,6 +7,7 @@
# set matplotlib backend depending on env # set matplotlib backend depending on env
import os import os
import matplotlib import matplotlib
if os.name == 'posix' and "DISPLAY" not in os.environ: if os.name == 'posix' and "DISPLAY" not in os.environ:
matplotlib.use('Agg') matplotlib.use('Agg')

3
co/args.py
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@ -66,6 +66,3 @@ def parse_args():
def get_exp_name(args): def get_exp_name(args):
name = f"exp_{args.data_type}" name = f"exp_{args.data_type}"
return name return name

9
co/cmap.py
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@ -14,6 +14,7 @@ _color_map_errors = np.array([
[38, 0, 165] # inf: log2(x) = inf [38, 0, 165] # inf: log2(x) = inf
]).astype(float) ]).astype(float)
def color_error_image(errors, scale=1, mask=None, BGR=True): def color_error_image(errors, scale=1, mask=None, BGR=True):
""" """
Color an input error map. Color an input error map.
@ -32,7 +33,8 @@ def color_error_image(errors, scale=1, mask=None, BGR=True):
errors_color_indices = np.clip(np.log2(errors_flat / scale + 1e-5) + 5, 0, 9) errors_color_indices = np.clip(np.log2(errors_flat / scale + 1e-5) + 5, 0, 9)
i0 = np.floor(errors_color_indices).astype(int) i0 = np.floor(errors_color_indices).astype(int)
f1 = errors_color_indices - i0.astype(float) f1 = errors_color_indices - i0.astype(float)
colored_errors_flat = _color_map_errors[i0, :] * (1-f1).reshape(-1,1) + _color_map_errors[i0+1, :] * f1.reshape(-1,1) colored_errors_flat = _color_map_errors[i0, :] * (1 - f1).reshape(-1, 1) + _color_map_errors[i0 + 1,
:] * f1.reshape(-1, 1)
if mask is not None: if mask is not None:
colored_errors_flat[mask.flatten() == 0] = 255 colored_errors_flat[mask.flatten() == 0] = 255
@ -42,6 +44,7 @@ def color_error_image(errors, scale=1, mask=None, BGR=True):
return colored_errors_flat.reshape(errors.shape[0], errors.shape[1], 3).astype(np.int) return colored_errors_flat.reshape(errors.shape[0], errors.shape[1], 3).astype(np.int)
_color_map_depths = np.array([ _color_map_depths = np.array([
[0, 0, 0], # 0.000 [0, 0, 0], # 0.000
[0, 0, 255], # 0.114 [0, 0, 255], # 0.114
@ -65,6 +68,7 @@ _color_map_bincenters = np.array([
2.000, # doesn't make a difference, just strictly higher than 1 2.000, # doesn't make a difference, just strictly higher than 1
]) ])
def color_depth_map(depths, scale=None): def color_depth_map(depths, scale=None):
""" """
Color an input depth map. Color an input depth map.
@ -86,7 +90,8 @@ def color_depth_map(depths, scale=None):
lower_bin_value = _color_map_bincenters[lower_bin] lower_bin_value = _color_map_bincenters[lower_bin]
higher_bin_value = _color_map_bincenters[lower_bin + 1] higher_bin_value = _color_map_bincenters[lower_bin + 1]
alphas = (values - lower_bin_value) / (higher_bin_value - lower_bin_value) alphas = (values - lower_bin_value) / (higher_bin_value - lower_bin_value)
colors = _color_map_depths[lower_bin] * (1-alphas).reshape(-1,1) + _color_map_depths[lower_bin + 1] * alphas.reshape(-1,1) colors = _color_map_depths[lower_bin] * (1 - alphas).reshape(-1, 1) + _color_map_depths[
lower_bin + 1] * alphas.reshape(-1, 1)
return colors.reshape(depths.shape[0], depths.shape[1], 3).astype(np.uint8) return colors.reshape(depths.shape[0], depths.shape[1], 3).astype(np.uint8)
# from utils.debug import save_color_numpy # from utils.debug import save_color_numpy

54
co/geometry.py
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@ -2,6 +2,7 @@ import numpy as np
import scipy.spatial import scipy.spatial
import scipy.linalg import scipy.linalg
def nullspace(A, atol=1e-13, rtol=0): def nullspace(A, atol=1e-13, rtol=0):
u, s, vh = np.linalg.svd(A) u, s, vh = np.linalg.svd(A)
tol = max(atol, rtol * s[0]) tol = max(atol, rtol * s[0])
@ -9,10 +10,12 @@ def nullspace(A, atol=1e-13, rtol=0):
ns = vh[nnz:].conj().T ns = vh[nnz:].conj().T
return ns return ns
def nearest_orthogonal_matrix(R): def nearest_orthogonal_matrix(R):
U, S, Vt = np.linalg.svd(R) U, S, Vt = np.linalg.svd(R)
return U @ np.eye(3, dtype=R.dtype) @ Vt return U @ np.eye(3, dtype=R.dtype) @ Vt
def power_iters(A, n_iters=10): def power_iters(A, n_iters=10):
b = np.random.uniform(-1, 1, size=(A.shape[0], A.shape[1], 1)) b = np.random.uniform(-1, 1, size=(A.shape[0], A.shape[1], 1))
for iter in range(n_iters): for iter in range(n_iters):
@ -20,6 +23,7 @@ def power_iters(A, n_iters=10):
b = b / np.linalg.norm(b, axis=1, keepdims=True) b = b / np.linalg.norm(b, axis=1, keepdims=True)
return b return b
def rayleigh_quotient(A, b): def rayleigh_quotient(A, b):
return (b.transpose(0, 2, 1) @ A @ b) / (b.transpose(0, 2, 1) @ b) return (b.transpose(0, 2, 1) @ A @ b) / (b.transpose(0, 2, 1) @ b)
@ -38,9 +42,11 @@ def cross_prod_mat(x):
X[:, 2, 2] = 0 X[:, 2, 2] = 0
return X.squeeze() return X.squeeze()
def hat_operator(x): def hat_operator(x):
return cross_prod_mat(x) return cross_prod_mat(x)
def vee_operator(X): def vee_operator(X):
X = X.reshape(-1, 3, 3) X = X.reshape(-1, 3, 3)
x = np.empty((X.shape[0], 3), dtype=X.dtype) x = np.empty((X.shape[0], 3), dtype=X.dtype)
@ -61,6 +67,7 @@ def rot_x(x, dtype=np.float32):
R[:, 2, 2] = np.cos(x).ravel() R[:, 2, 2] = np.cos(x).ravel()
return R.squeeze() return R.squeeze()
def rot_y(y, dtype=np.float32): def rot_y(y, dtype=np.float32):
y = np.array(y, copy=False) y = np.array(y, copy=False)
y = y.reshape(-1, 1) y = y.reshape(-1, 1)
@ -72,6 +79,7 @@ def rot_y(y, dtype=np.float32):
R[:, 2, 2] = np.cos(y).ravel() R[:, 2, 2] = np.cos(y).ravel()
return R.squeeze() return R.squeeze()
def rot_z(z, dtype=np.float32): def rot_z(z, dtype=np.float32):
z = np.array(z, copy=False) z = np.array(z, copy=False)
z = z.reshape(-1, 1) z = z.reshape(-1, 1)
@ -83,6 +91,7 @@ def rot_z(z, dtype=np.float32):
R[:, 2, 2] = 1 R[:, 2, 2] = 1
return R.squeeze() return R.squeeze()
def xyz_from_rotm(R): def xyz_from_rotm(R):
R = R.reshape(-1, 3, 3) R = R.reshape(-1, 3, 3)
xyz = np.empty((R.shape[0], 3), dtype=R.dtype) xyz = np.empty((R.shape[0], 3), dtype=R.dtype)
@ -102,6 +111,7 @@ def xyz_from_rotm(R):
xyz[bidx, 2] = 0 xyz[bidx, 2] = 0
return xyz.squeeze() return xyz.squeeze()
def zyx_from_rotm(R): def zyx_from_rotm(R):
R = R.reshape(-1, 3, 3) R = R.reshape(-1, 3, 3)
zyx = np.empty((R.shape[0], 3), dtype=R.dtype) zyx = np.empty((R.shape[0], 3), dtype=R.dtype)
@ -121,14 +131,17 @@ def zyx_from_rotm(R):
zyx[bidx, 2] = 0 zyx[bidx, 2] = 0
return zyx.squeeze() return zyx.squeeze()
def rotm_from_xyz(xyz): def rotm_from_xyz(xyz):
xyz = np.array(xyz, copy=False).reshape(-1, 3) xyz = np.array(xyz, copy=False).reshape(-1, 3)
return (rot_x(xyz[:, 0]) @ rot_y(xyz[:, 1]) @ rot_z(xyz[:, 2])).squeeze() return (rot_x(xyz[:, 0]) @ rot_y(xyz[:, 1]) @ rot_z(xyz[:, 2])).squeeze()
def rotm_from_zyx(zyx): def rotm_from_zyx(zyx):
zyx = np.array(zyx, copy=False).reshape(-1, 3) zyx = np.array(zyx, copy=False).reshape(-1, 3)
return (rot_z(zyx[:, 0]) @ rot_y(zyx[:, 1]) @ rot_x(zyx[:, 2])).squeeze() return (rot_z(zyx[:, 0]) @ rot_y(zyx[:, 1]) @ rot_x(zyx[:, 2])).squeeze()
def rotm_from_quat(q): def rotm_from_quat(q):
q = q.reshape(-1, 4) q = q.reshape(-1, 4)
w, x, y, z = q[:, 0], q[:, 1], q[:, 2], q[:, 3] w, x, y, z = q[:, 0], q[:, 1], q[:, 2], q[:, 3]
@ -140,6 +153,7 @@ def rotm_from_quat(q):
R = R.transpose((2, 0, 1)) R = R.transpose((2, 0, 1))
return R.squeeze() return R.squeeze()
def rotm_from_axisangle(a): def rotm_from_axisangle(a):
# exponential # exponential
a = a.reshape(-1, 3) a = a.reshape(-1, 3)
@ -150,6 +164,7 @@ def rotm_from_axisangle(a):
R = np.eye(3, dtype=a.dtype) + np.sin(phi) * A + (1 - np.cos(phi)) * A @ A R = np.eye(3, dtype=a.dtype) + np.sin(phi) * A + (1 - np.cos(phi)) * A @ A
return R.squeeze() return R.squeeze()
def rotm_from_lookat(dir, up=None): def rotm_from_lookat(dir, up=None):
dir = dir.reshape(-1, 3) dir = dir.reshape(-1, 3)
if up is None: if up is None:
@ -175,6 +190,7 @@ def rotm_from_lookat(dir, up=None):
R[:, 2, 2] = dir[:, 2] R[:, 2, 2] = dir[:, 2]
return R.transpose(0, 2, 1).squeeze() return R.transpose(0, 2, 1).squeeze()
def rotm_distance_identity(R0, R1): def rotm_distance_identity(R0, R1):
# https://link.springer.com/article/10.1007%2Fs10851-009-0161-2 # https://link.springer.com/article/10.1007%2Fs10851-009-0161-2
# in [0, 2*sqrt(2)] # in [0, 2*sqrt(2)]
@ -183,6 +199,7 @@ def rotm_distance_identity(R0, R1):
dists = np.linalg.norm(np.eye(3, dtype=R0.dtype) - R0 @ R1.transpose(0, 2, 1), axis=(1, 2)) dists = np.linalg.norm(np.eye(3, dtype=R0.dtype) - R0 @ R1.transpose(0, 2, 1), axis=(1, 2))
return dists.squeeze() return dists.squeeze()
def rotm_distance_geodesic(R0, R1): def rotm_distance_geodesic(R0, R1):
# https://link.springer.com/article/10.1007%2Fs10851-009-0161-2 # https://link.springer.com/article/10.1007%2Fs10851-009-0161-2
# in [0, pi) # in [0, pi)
@ -195,7 +212,6 @@ def rotm_distance_geodesic(R0, R1):
return dists.squeeze() return dists.squeeze()
def axisangle_from_rotm(R): def axisangle_from_rotm(R):
# logarithm of rotation matrix # logarithm of rotation matrix
# R = R.reshape(-1,3,3) # R = R.reshape(-1,3,3)
@ -219,6 +235,7 @@ def axisangle_from_rotm(R):
np.divide(omega, r, out=aa, where=r != 0) np.divide(omega, r, out=aa, where=r != 0)
return aa.squeeze() return aa.squeeze()
def axisangle_from_quat(q): def axisangle_from_quat(q):
q = q.reshape(-1, 4) q = q.reshape(-1, 4)
phi = 2 * np.arccos(q[:, 0]) phi = 2 * np.arccos(q[:, 0])
@ -231,6 +248,7 @@ def axisangle_from_quat(q):
aa = a.astype(q.dtype) aa = a.astype(q.dtype)
return aa.squeeze() return aa.squeeze()
def axisangle_apply(aa, x): def axisangle_apply(aa, x):
# working only with single aa and single x at the moment # working only with single aa and single x at the moment
xshape = x.shape xshape = x.shape
@ -247,10 +265,12 @@ def exp_so3(R):
w = axisangle_from_rotm(R) w = axisangle_from_rotm(R)
return w return w
def log_so3(w): def log_so3(w):
R = rotm_from_axisangle(w) R = rotm_from_axisangle(w)
return R return R
def exp_se3(R, t): def exp_se3(R, t):
R = R.reshape(-1, 3, 3) R = R.reshape(-1, 3, 3)
t = t.reshape(-1, 3) t = t.reshape(-1, 3)
@ -269,6 +289,7 @@ def exp_se3(R, t):
return v.squeeze() return v.squeeze()
def log_se3(v): def log_se3(v):
# v = (u, w) # v = (u, w)
v = v.reshape(-1, 6) v = v.reshape(-1, 6)
@ -298,6 +319,7 @@ def quat_from_rotm(R):
q /= np.linalg.norm(q, axis=1, keepdims=True) q /= np.linalg.norm(q, axis=1, keepdims=True)
return q.squeeze() return q.squeeze()
def quat_from_axisangle(a): def quat_from_axisangle(a):
a = a.reshape(-1, 3) a = a.reshape(-1, 3)
phi = np.linalg.norm(a, axis=1) phi = np.linalg.norm(a, axis=1)
@ -311,17 +333,20 @@ def quat_from_axisangle(a):
q /= np.linalg.norm(q, axis=1).reshape(-1, 1) q /= np.linalg.norm(q, axis=1).reshape(-1, 1)
return q.squeeze() return q.squeeze()
def quat_identity(n=1, dtype=np.float32): def quat_identity(n=1, dtype=np.float32):
q = np.zeros((n, 4), dtype=dtype) q = np.zeros((n, 4), dtype=dtype)
q[:, 0] = 1 q[:, 0] = 1
return q.squeeze() return q.squeeze()
def quat_conjugate(q): def quat_conjugate(q):
shape = q.shape shape = q.shape
q = q.reshape(-1, 4).copy() q = q.reshape(-1, 4).copy()
q[:, 1:] *= -1 q[:, 1:] *= -1
return q.reshape(shape) return q.reshape(shape)
def quat_product(q1, q2): def quat_product(q1, q2):
# q1 . q2 is equivalent to R(q1) @ R(q2) # q1 . q2 is equivalent to R(q1) @ R(q2)
shape = q1.shape shape = q1.shape
@ -335,6 +360,7 @@ def quat_product(q1, q2):
q[:, 3] = a1 * d2 + b1 * c2 - c1 * b2 + d1 * a2 q[:, 3] = a1 * d2 + b1 * c2 - c1 * b2 + d1 * a2
return q.squeeze() return q.squeeze()
def quat_apply(q, x): def quat_apply(q, x):
xshape = x.shape xshape = x.shape
x = x.reshape(-1, 3) x = x.reshape(-1, 3)
@ -367,6 +393,7 @@ def quat_random(rng=None, n=1):
q /= np.linalg.norm(q, axis=1, keepdims=True) q /= np.linalg.norm(q, axis=1, keepdims=True)
return q.squeeze() return q.squeeze()
def quat_distance_angle(q0, q1): def quat_distance_angle(q0, q1):
# https://math.stackexchange.com/questions/90081/quaternion-distance # https://math.stackexchange.com/questions/90081/quaternion-distance
# https://link.springer.com/article/10.1007%2Fs10851-009-0161-2 # https://link.springer.com/article/10.1007%2Fs10851-009-0161-2
@ -375,6 +402,7 @@ def quat_distance_angle(q0, q1):
dists = np.arccos(np.clip(2 * np.sum(q0 * q1, axis=1) ** 2 - 1, -1, 1)) dists = np.arccos(np.clip(2 * np.sum(q0 * q1, axis=1) ** 2 - 1, -1, 1))
return dists return dists
def quat_distance_normdiff(q0, q1): def quat_distance_normdiff(q0, q1):
# https://link.springer.com/article/10.1007%2Fs10851-009-0161-2 # https://link.springer.com/article/10.1007%2Fs10851-009-0161-2
# \phi_4 # \phi_4
@ -383,6 +411,7 @@ def quat_distance_normdiff(q0, q1):
q1 = q1.reshape(-1, 4) q1 = q1.reshape(-1, 4)
return 1 - np.sum(q0 * q1, axis=1) ** 2 return 1 - np.sum(q0 * q1, axis=1) ** 2
def quat_distance_mineucl(q0, q1): def quat_distance_mineucl(q0, q1):
# https://link.springer.com/article/10.1007%2Fs10851-009-0161-2 # https://link.springer.com/article/10.1007%2Fs10851-009-0161-2
# http://users.cecs.anu.edu.au/~trumpf/pubs/Hartley_Trumpf_Dai_Li.pdf # http://users.cecs.anu.edu.au/~trumpf/pubs/Hartley_Trumpf_Dai_Li.pdf
@ -392,6 +421,7 @@ def quat_distance_mineucl(q0, q1):
diff1 = ((q0 + q1) ** 2).sum(axis=1) diff1 = ((q0 + q1) ** 2).sum(axis=1)
return np.minimum(diff0, diff1) return np.minimum(diff0, diff1)
def quat_slerp_space(q0, q1, num=100, endpoint=True): def quat_slerp_space(q0, q1, num=100, endpoint=True):
q0 = q0.ravel() q0 = q0.ravel()
q1 = q1.ravel() q1 = q1.ravel()
@ -411,6 +441,7 @@ def quat_slerp_space(q0, q1, num=100, endpoint=True):
s1 = np.sin(theta) / np.sin(theta0) s1 = np.sin(theta) / np.sin(theta0)
return (s0 * q0) + (s1 * q1) return (s0 * q0) + (s1 * q1)
def cart_to_spherical(x): def cart_to_spherical(x):
shape = x.shape shape = x.shape
x = x.reshape(-1, 3) x = x.reshape(-1, 3)
@ -420,6 +451,7 @@ def cart_to_spherical(x):
y[:, 2] = np.arctan2(x[:, 1], x[:, 0]) # phi y[:, 2] = np.arctan2(x[:, 1], x[:, 0]) # phi
return y.reshape(shape) return y.reshape(shape)
def spherical_to_cart(x): def spherical_to_cart(x):
shape = x.shape shape = x.shape
x = x.reshape(-1, 3) x = x.reshape(-1, 3)
@ -429,6 +461,7 @@ def spherical_to_cart(x):
y[:, 2] = x[:, 0] * np.cos(x[:, 1]) y[:, 2] = x[:, 0] * np.cos(x[:, 1])
return y.reshape(shape) return y.reshape(shape)
def spherical_random(r=1, n=1): def spherical_random(r=1, n=1):
# http://mathworld.wolfram.com/SpherePointPicking.html # http://mathworld.wolfram.com/SpherePointPicking.html
# https://math.stackexchange.com/questions/1585975/how-to-generate-random-points-on-a-sphere # https://math.stackexchange.com/questions/1585975/how-to-generate-random-points-on-a-sphere
@ -438,6 +471,7 @@ def spherical_random(r=1, n=1):
x[:, 2] = np.arccos(2 * np.random.uniform(0, 1, size=(n,)) - 1) x[:, 2] = np.arccos(2 * np.random.uniform(0, 1, size=(n,)) - 1)
return x.squeeze() return x.squeeze()
def color_pcl(pcl, K, im, color_axis=0, as_int=True, invalid_color=[0, 0, 0]): def color_pcl(pcl, K, im, color_axis=0, as_int=True, invalid_color=[0, 0, 0]):
uvd = K @ pcl.T uvd = K @ pcl.T
uvd /= uvd[2] uvd /= uvd[2]
@ -461,6 +495,7 @@ def color_pcl(pcl, K, im, color_axis=0, as_int=True, invalid_color=[0,0,0]):
color = (255.0 * color).astype(np.int32) color = (255.0 * color).astype(np.int32)
return color return color
def center_pcl(pcl, robust=False, copy=False, axis=1): def center_pcl(pcl, robust=False, copy=False, axis=1):
if copy: if copy:
pcl = pcl.copy() pcl = pcl.copy()
@ -470,13 +505,16 @@ def center_pcl(pcl, robust=False, copy=False, axis=1):
mu = np.mean(pcl, axis=axis, keepdims=True) mu = np.mean(pcl, axis=axis, keepdims=True)
return pcl - mu return pcl - mu
def to_homogeneous(x): def to_homogeneous(x):
# return np.hstack((x, np.ones((x.shape[0],1),dtype=x.dtype))) # return np.hstack((x, np.ones((x.shape[0],1),dtype=x.dtype)))
return np.concatenate((x, np.ones((*x.shape[:-1], 1), dtype=x.dtype)), axis=-1) return np.concatenate((x, np.ones((*x.shape[:-1], 1), dtype=x.dtype)), axis=-1)
def from_homogeneous(x): def from_homogeneous(x):
return x[:, :-1] / x[:, -1] return x[:, :-1] / x[:, -1]
def project_uvn(uv, Ki=None): def project_uvn(uv, Ki=None):
if uv.shape[1] == 2: if uv.shape[1] == 2:
uvn = to_homogeneous(uv) uvn = to_homogeneous(uv)
@ -489,6 +527,7 @@ def project_uvn(uv, Ki=None):
else: else:
return uvn @ Ki.T return uvn @ Ki.T
def project_uvd(uv, depth, K=np.eye(3), R=np.eye(3), t=np.zeros((3, 1)), ignore_negative_depth=True, return_uvn=False): def project_uvd(uv, depth, K=np.eye(3), R=np.eye(3), t=np.zeros((3, 1)), ignore_negative_depth=True, return_uvn=False):
Ki = np.linalg.inv(K) Ki = np.linalg.inv(K)
@ -510,6 +549,7 @@ def project_uvd(uv, depth, K=np.eye(3), R=np.eye(3), t=np.zeros((3,1)), ignore_n
else: else:
return xyz return xyz
def project_depth(depth, K, R=np.eye(3, 3), t=np.zeros((3, 1)), ignore_negative_depth=True, return_uvn=False): def project_depth(depth, K, R=np.eye(3, 3), t=np.zeros((3, 1)), ignore_negative_depth=True, return_uvn=False):
u, v = np.meshgrid(range(depth.shape[1]), range(depth.shape[0])) u, v = np.meshgrid(range(depth.shape[1]), range(depth.shape[0]))
uv = np.hstack((u.reshape(-1, 1), v.reshape(-1, 1))) uv = np.hstack((u.reshape(-1, 1), v.reshape(-1, 1)))
@ -540,12 +580,14 @@ def translation_to_cameracenter(R, t):
C = -R.transpose(0, 2, 1) @ t C = -R.transpose(0, 2, 1) @ t
return C.squeeze() return C.squeeze()
def cameracenter_to_translation(R, C): def cameracenter_to_translation(R, C):
C = C.reshape(-1, 3, 1) C = C.reshape(-1, 3, 1)
R = R.reshape(-1, 3, 3) R = R.reshape(-1, 3, 3)
t = -R @ C t = -R @ C
return t.squeeze() return t.squeeze()
def decompose_projection_matrix(P, return_t=True): def decompose_projection_matrix(P, return_t=True):
if P.shape[0] != 3 or P.shape[1] != 4: if P.shape[0] != 3 or P.shape[1] != 4:
raise Exception('P has to be 3x4') raise Exception('P has to be 3x4')
@ -575,11 +617,11 @@ def compose_projection_matrix(K=np.eye(3), R=np.eye(3,3), t=np.zeros((3,1))):
return K @ np.hstack((R, t.reshape((3, 1)))) return K @ np.hstack((R, t.reshape((3, 1))))
def point_plane_distance(pts, plane): def point_plane_distance(pts, plane):
pts = pts.reshape(-1, 3) pts = pts.reshape(-1, 3)
return np.abs(np.sum(plane[:3] * pts, axis=1) + plane[3]) / np.linalg.norm(plane[:3]) return np.abs(np.sum(plane[:3] * pts, axis=1) + plane[3]) / np.linalg.norm(plane[:3])
def fit_plane(pts): def fit_plane(pts):
pts = pts.reshape(-1, 3) pts = pts.reshape(-1, 3)
center = np.mean(pts, axis=0) center = np.mean(pts, axis=0)
@ -590,6 +632,7 @@ def fit_plane(pts):
plane = np.array([*vh[2], -vh[2].dot(center)]) plane = np.array([*vh[2], -vh[2].dot(center)])
return plane return plane
def tetrahedron(dtype=np.float32): def tetrahedron(dtype=np.float32):
verts = np.array([ verts = np.array([
(np.sqrt(8 / 9), 0, -1 / 3), (-np.sqrt(2 / 9), np.sqrt(2 / 3), -1 / 3), (np.sqrt(8 / 9), 0, -1 / 3), (-np.sqrt(2 / 9), np.sqrt(2 / 3), -1 / 3),
@ -599,6 +642,7 @@ def tetrahedron(dtype=np.float32):
normals /= np.linalg.norm(normals, axis=1).reshape(-1, 1) normals /= np.linalg.norm(normals, axis=1).reshape(-1, 1)
return verts, faces, normals return verts, faces, normals
def cube(dtype=np.float32): def cube(dtype=np.float32):
verts = np.array([ verts = np.array([
[-0.5, -0.5, -0.5], [-0.5, 0.5, -0.5], [0.5, 0.5, -0.5], [0.5, -0.5, -0.5], [-0.5, -0.5, -0.5], [-0.5, 0.5, -0.5], [0.5, 0.5, -0.5], [0.5, -0.5, -0.5],
@ -611,6 +655,7 @@ def cube(dtype=np.float32):
normals /= np.linalg.norm(normals, axis=1).reshape(-1, 1) normals /= np.linalg.norm(normals, axis=1).reshape(-1, 1)
return verts, faces, normals return verts, faces, normals
def octahedron(dtype=np.float32): def octahedron(dtype=np.float32):
verts = np.array([ verts = np.array([
(+1, 0, 0), (0, +1, 0), (0, 0, +1), (+1, 0, 0), (0, +1, 0), (0, 0, +1),
@ -622,6 +667,7 @@ def octahedron(dtype=np.float32):
normals /= np.linalg.norm(normals, axis=1).reshape(-1, 1) normals /= np.linalg.norm(normals, axis=1).reshape(-1, 1)
return verts, faces, normals return verts, faces, normals
def icosahedron(dtype=np.float32): def icosahedron(dtype=np.float32):
p = (1 + np.sqrt(5)) / 2 p = (1 + np.sqrt(5)) / 2
verts = np.array([ verts = np.array([
@ -638,6 +684,7 @@ def icosahedron(dtype=np.float32):
normals /= np.linalg.norm(normals, axis=1).reshape(-1, 1) normals /= np.linalg.norm(normals, axis=1).reshape(-1, 1)
return verts, faces, normals return verts, faces, normals
def xyplane(dtype=np.float32, z=0, interleaved=False): def xyplane(dtype=np.float32, z=0, interleaved=False):
if interleaved: if interleaved:
eps = 1e-6 eps = 1e-6
@ -652,6 +699,7 @@ def xyplane(dtype=np.float32, z=0, interleaved=False):
normals[:, 2] = -1 normals[:, 2] = -1
return verts, faces, normals return verts, faces, normals
def mesh_independent_verts(verts, faces, normals=None): def mesh_independent_verts(verts, faces, normals=None):
new_verts = [] new_verts = []
new_normals = [] new_normals = []
@ -682,6 +730,7 @@ def stack_mesh(verts, faces):
faces = np.vstack(mfaces) faces = np.vstack(mfaces)
return verts, faces return verts, faces
def normalize_mesh(verts): def normalize_mesh(verts):
# all the verts have unit distance to the center (0,0,0) # all the verts have unit distance to the center (0,0,0)
return verts / np.linalg.norm(verts, axis=1, keepdims=True) return verts / np.linalg.norm(verts, axis=1, keepdims=True)
@ -700,6 +749,7 @@ def mesh_triangle_areas(verts, faces):
t[:, 2] = (x[:, 0] * y[:, 1] - x[:, 1] * y[:, 0]); t[:, 2] = (x[:, 0] * y[:, 1] - x[:, 1] * y[:, 0]);
return np.linalg.norm(t, axis=1) / 2 return np.linalg.norm(t, axis=1) / 2
def subdivde_mesh(verts_in, faces_in, n=1): def subdivde_mesh(verts_in, faces_in, n=1):
for iter in range(n): for iter in range(n):
verts = [] verts = []

6
co/gtimer.py
Unescape View File

@ -2,6 +2,7 @@ import numpy as np
from . import utils from . import utils
class StopWatch(utils.StopWatch): class StopWatch(utils.StopWatch):
def __del__(self): def __del__(self):
print('=' * 80) print('=' * 80)
@ -14,13 +15,18 @@ class StopWatch(utils.StopWatch):
print(f' [median] {median}') print(f' [median] {median}')
print('=' * 80) print('=' * 80)
GTIMER = StopWatch() GTIMER = StopWatch()
def start(name): def start(name):
GTIMER.start(name) GTIMER.start(name)
def stop(name): def stop(name):
GTIMER.stop(name) GTIMER.stop(name)
class Ctx(object): class Ctx(object):
def __init__(self, name): def __init__(self, name):
self.name = name self.name = name

7
co/io3d.py
Unescape View File

@ -2,6 +2,7 @@ import struct
import numpy as np import numpy as np
import collections import collections
def _write_ply_point(fp, x, y, z, color=None, normal=None, binary=False): def _write_ply_point(fp, x, y, z, color=None, normal=None, binary=False):
args = [x, y, z] args = [x, y, z]
if color is not None: if color is not None:
@ -24,18 +25,21 @@ def _write_ply_point(fp, x,y,z, color=None, normal=None, binary=False):
fmt += '\n' fmt += '\n'
fp.write(fmt % tuple(args)) fp.write(fmt % tuple(args))
def _write_ply_triangle(fp, i0, i1, i2, binary): def _write_ply_triangle(fp, i0, i1, i2, binary):
if binary: if binary:
fp.write(struct.pack('<Biii', 3, i0, i1, i2)) fp.write(struct.pack('<Biii', 3, i0, i1, i2))
else: else:
fp.write('3 %d %d %d\n' % (i0, i1, i2)) fp.write('3 %d %d %d\n' % (i0, i1, i2))
def _write_ply_header_line(fp, str, binary): def _write_ply_header_line(fp, str, binary):
if binary: if binary:
fp.write(str.encode()) fp.write(str.encode())
else: else:
fp.write(str) fp.write(str)
def write_ply(path, verts, trias=None, color=None, normals=None, binary=False): def write_ply(path, verts, trias=None, color=None, normals=None, binary=False):
if verts.shape[1] != 3: if verts.shape[1] != 3:
raise Exception('verts has to be of shape Nx3') raise Exception('verts has to be of shape Nx3')
@ -88,6 +92,7 @@ def write_ply(path, verts, trias=None, color=None, normals=None, binary=False):
for t in trias: for t in trias:
_write_ply_triangle(fp, t[0], t[1], t[2], binary) _write_ply_triangle(fp, t[0], t[1], t[2], binary)
def faces_to_triangles(faces): def faces_to_triangles(faces):
new_faces = [] new_faces = []
for f in faces: for f in faces:
@ -100,6 +105,7 @@ def faces_to_triangles(faces):
raise Exception('unknown face count %d', f[0]) raise Exception('unknown face count %d', f[0])
return new_faces return new_faces
def read_ply(path): def read_ply(path):
with open(path, 'rb') as f: with open(path, 'rb') as f:
# parse header # parse header
@ -204,6 +210,7 @@ def _read_obj_split_f(s):
nidx = -1 nidx = -1
return vidx, tidx, nidx return vidx, tidx, nidx
def read_obj(path): def read_obj(path):
with open(path, 'r') as fp: with open(path, 'r') as fp:
lines = fp.readlines() lines = fp.readlines()

12
co/metric.py
Unescape View File

@ -1,6 +1,7 @@
import numpy as np import numpy as np
from . import geometry from . import geometry
def _process_inputs(estimate, target, mask): def _process_inputs(estimate, target, mask):
if estimate.shape != target.shape: if estimate.shape != target.shape:
raise Exception('estimate and target have to be same shape') raise Exception('estimate and target have to be same shape')
@ -12,19 +13,23 @@ def _process_inputs(estimate, target, mask):
raise Exception('estimate and mask have to be same shape') raise Exception('estimate and mask have to be same shape')
return estimate, target, mask return estimate, target, mask
def mse(estimate, target, mask=None): def mse(estimate, target, mask=None):
estimate, target, mask = _process_inputs(estimate, target, mask) estimate, target, mask = _process_inputs(estimate, target, mask)
m = np.sum((estimate[mask] - target[mask]) ** 2) / mask.sum() m = np.sum((estimate[mask] - target[mask]) ** 2) / mask.sum()
return m return m
def rmse(estimate, target, mask=None): def rmse(estimate, target, mask=None):
return np.sqrt(mse(estimate, target, mask)) return np.sqrt(mse(estimate, target, mask))
def mae(estimate, target, mask=None): def mae(estimate, target, mask=None):
estimate, target, mask = _process_inputs(estimate, target, mask) estimate, target, mask = _process_inputs(estimate, target, mask)
m = np.abs(estimate[mask] - target[mask]).sum() / mask.sum() m = np.abs(estimate[mask] - target[mask]).sum() / mask.sum()
return m return m
def outlier_fraction(estimate, target, mask=None, threshold=0): def outlier_fraction(estimate, target, mask=None, threshold=0):
estimate, target, mask = _process_inputs(estimate, target, mask) estimate, target, mask = _process_inputs(estimate, target, mask)
diff = np.abs(estimate[mask] - target[mask]) diff = np.abs(estimate[mask] - target[mask])
@ -52,6 +57,7 @@ class Metric(object):
def __str__(self): def __str__(self):
return ', '.join([f'{self.str_prefix}{key}={value:.5f}' for key, value in self.get().items()]) return ', '.join([f'{self.str_prefix}{key}={value:.5f}' for key, value in self.get().items()])
class MultipleMetric(Metric): class MultipleMetric(Metric):
def __init__(self, *metrics, **kwargs): def __init__(self, *metrics, **kwargs):
self.metrics = [*metrics] self.metrics = [*metrics]
@ -76,6 +82,7 @@ class MultipleMetric(Metric):
def __str__(self): def __str__(self):
return '\n'.join([str(m) for m in self.metrics]) return '\n'.join([str(m) for m in self.metrics])
class BaseDistanceMetric(Metric): class BaseDistanceMetric(Metric):
def __init__(self, name='', **kwargs): def __init__(self, name='', **kwargs):
super().__init__(**kwargs) super().__init__(**kwargs)
@ -99,6 +106,7 @@ class BaseDistanceMetric(Metric):
f'dist{self.name}_max': float(np.max(dists)), f'dist{self.name}_max': float(np.max(dists)),
} }
class DistanceMetric(BaseDistanceMetric): class DistanceMetric(BaseDistanceMetric):
def __init__(self, vec_length, p=2, **kwargs): def __init__(self, vec_length, p=2, **kwargs):
super().__init__(name=f'{p}', **kwargs) super().__init__(name=f'{p}', **kwargs)
@ -115,6 +123,7 @@ class DistanceMetric(BaseDistanceMetric):
dist = np.linalg.norm(es - ta, ord=self.p, axis=1) dist = np.linalg.norm(es - ta, ord=self.p, axis=1)
self.dists.append(dist) self.dists.append(dist)
class OutlierFractionMetric(DistanceMetric): class OutlierFractionMetric(DistanceMetric):
def __init__(self, thresholds, *args, **kwargs): def __init__(self, thresholds, *args, **kwargs):
super().__init__(*args, **kwargs) super().__init__(*args, **kwargs)
@ -128,6 +137,7 @@ class OutlierFractionMetric(DistanceMetric):
ret[f'of{t}'] = float(ma.sum() / ma.size) ret[f'of{t}'] = float(ma.sum() / ma.size)
return ret return ret
class RelativeDistanceMetric(BaseDistanceMetric): class RelativeDistanceMetric(BaseDistanceMetric):
def __init__(self, vec_length, p=2, **kwargs): def __init__(self, vec_length, p=2, **kwargs):
super().__init__(name=f'rel{p}', **kwargs) super().__init__(name=f'rel{p}', **kwargs)
@ -144,6 +154,7 @@ class RelativeDistanceMetric(BaseDistanceMetric):
dist = dist[ma != 0] dist = dist[ma != 0]
self.dists.append(dist) self.dists.append(dist)
class RotmDistanceMetric(BaseDistanceMetric): class RotmDistanceMetric(BaseDistanceMetric):
def __init__(self, type='identity', **kwargs): def __init__(self, type='identity', **kwargs):
super().__init__(name=type, **kwargs) super().__init__(name=type, **kwargs)
@ -162,6 +173,7 @@ class RotmDistanceMetric(BaseDistanceMetric):
else: else:
raise Exception('invalid distance type') raise Exception('invalid distance type')
class QuaternionDistanceMetric(BaseDistanceMetric): class QuaternionDistanceMetric(BaseDistanceMetric):
def __init__(self, type='angle', **kwargs): def __init__(self, type='angle', **kwargs):
super().__init__(name=type, **kwargs) super().__init__(name=type, **kwargs)

5
co/plt.py
Unescape View File

@ -6,6 +6,7 @@ import matplotlib.pyplot as plt
import os import os
import time import time
def save(path, remove_axis=False, dpi=300, fig=None): def save(path, remove_axis=False, dpi=300, fig=None):
if fig is None: if fig is None:
fig = plt.gcf() fig = plt.gcf()
@ -22,6 +23,7 @@ def save(path, remove_axis=False, dpi=300, fig=None):
ax.yaxis.set_major_locator(plt.NullLocator()) ax.yaxis.set_major_locator(plt.NullLocator())
fig.savefig(path, dpi=dpi, bbox_inches='tight', pad_inches=0) fig.savefig(path, dpi=dpi, bbox_inches='tight', pad_inches=0)
def color_map(im_, cmap='viridis', vmin=None, vmax=None): def color_map(im_, cmap='viridis', vmin=None, vmax=None):
cm = plt.get_cmap(cmap) cm = plt.get_cmap(cmap)
im = im_.copy() im = im_.copy()
@ -38,6 +40,7 @@ def color_map(im_, cmap='viridis', vmin=None, vmax=None):
im[mask, c] = 1 im[mask, c] = 1
return im return im
def interactive_legend(leg=None, fig=None, all_axes=True): def interactive_legend(leg=None, fig=None, all_axes=True):
if leg is None: if leg is None:
leg = plt.legend() leg = plt.legend()
@ -76,6 +79,7 @@ def interactive_legend(leg=None, fig=None, all_axes=True):
fig.canvas.mpl_connect('pick_event', onpick) fig.canvas.mpl_connect('pick_event', onpick)
def non_annoying_pause(interval, focus_figure=False): def non_annoying_pause(interval, focus_figure=False):
# https://github.com/matplotlib/matplotlib/issues/11131 # https://github.com/matplotlib/matplotlib/issues/11131
backend = mpl.rcParams['backend'] backend = mpl.rcParams['backend']
@ -91,6 +95,7 @@ def non_annoying_pause(interval, focus_figure=False):
return return
time.sleep(interval) time.sleep(interval)
def remove_all_ticks(fig=None): def remove_all_ticks(fig=None):
if fig is None: if fig is None:
fig = plt.gcf() fig = plt.gcf()

7
co/plt2d.py
Unescape View File

@ -3,6 +3,7 @@ import matplotlib.pyplot as plt
from . import geometry from . import geometry
def image_matrix(ims, bgval=0): def image_matrix(ims, bgval=0):
n = ims.shape[0] n = ims.shape[0]
m = int(np.ceil(np.sqrt(n))) m = int(np.ceil(np.sqrt(n)))
@ -18,6 +19,7 @@ def image_matrix(ims, bgval=0):
idx += 1 idx += 1
return mat return mat
def image_cat(ims, vertical=False): def image_cat(ims, vertical=False):
offx = [0] offx = [0]
offy = [0] offy = [0]
@ -40,15 +42,18 @@ def image_cat(ims, vertical=False):
return im, offx, offy return im, offx, offy
def line(li, h, w, ax=None, *args, **kwargs): def line(li, h, w, ax=None, *args, **kwargs):
if ax is None: if ax is None:
ax = plt.gca() ax = plt.gca()
xs = (-li[2] - li[1] * np.array((0, h - 1))) / li[0] xs = (-li[2] - li[1] * np.array((0, h - 1))) / li[0]
ys = (-li[2] - li[0] * np.array((0, w - 1))) / li[1] ys = (-li[2] - li[0] * np.array((0, w - 1))) / li[1]
pts = np.array([(0, ys[0]), (w - 1, ys[1]), (xs[0], 0), (xs[1], h - 1)]) pts = np.array([(0, ys[0]), (w - 1, ys[1]), (xs[0], 0), (xs[1], h - 1)])
pts = pts[np.logical_and(np.logical_and(pts[:,0] >= 0, pts[:,0] < w), np.logical_and(pts[:,1] >= 0, pts[:,1] < h))] pts = pts[
np.logical_and(np.logical_and(pts[:, 0] >= 0, pts[:, 0] < w), np.logical_and(pts[:, 1] >= 0, pts[:, 1] < h))]
ax.plot(pts[:, 0], pts[:, 1], *args, **kwargs) ax.plot(pts[:, 0], pts[:, 1], *args, **kwargs)
def depthshow(depth, *args, ax=None, **kwargs): def depthshow(depth, *args, ax=None, **kwargs):
if ax is None: if ax is None:
ax = plt.gca() ax = plt.gca()

22
co/plt3d.py
Unescape View File

@ -4,12 +4,15 @@ from mpl_toolkits.mplot3d import Axes3D
from . import geometry from . import geometry
def ax3d(fig=None): def ax3d(fig=None):
if fig is None: if fig is None:
fig = plt.gcf() fig = plt.gcf()
return fig.add_subplot(111, projection='3d') return fig.add_subplot(111, projection='3d')
def plot_camera(ax=None, R=np.eye(3), t=np.zeros((3,)), size=25, marker_C='.', color='b', linestyle='-', linewidth=0.1, label=None, **kwargs):
def plot_camera(ax=None, R=np.eye(3), t=np.zeros((3,)), size=25, marker_C='.', color='b', linestyle='-', linewidth=0.1,
label=None, **kwargs):
if ax is None: if ax is None:
ax = plt.gca() ax = plt.gca()
C0 = geometry.translation_to_cameracenter(R, t).ravel() C0 = geometry.translation_to_cameracenter(R, t).ravel()
@ -20,11 +23,18 @@ def plot_camera(ax=None, R=np.eye(3), t=np.zeros((3,)), size=25, marker_C='.', c
if marker_C != '': if marker_C != '':
ax.plot([C0[0]], [C0[1]], [C0[2]], marker=marker_C, color=color, label=label, **kwargs) ax.plot([C0[0]], [C0[1]], [C0[2]], marker=marker_C, color=color, label=label, **kwargs)
ax.plot([C0[0], C1[0]], [C0[1], C1[1]], [C0[2], C1[2]], color=color, label='_nolegend_', linestyle=linestyle, linewidth=linewidth, **kwargs) ax.plot([C0[0], C1[0]], [C0[1], C1[1]], [C0[2], C1[2]], color=color, label='_nolegend_', linestyle=linestyle,
ax.plot([C0[0], C2[0]], [C0[1], C2[1]], [C0[2], C2[2]], color=color, label='_nolegend_', linestyle=linestyle, linewidth=linewidth, **kwargs) linewidth=linewidth, **kwargs)
ax.plot([C0[0], C3[0]], [C0[1], C3[1]], [C0[2], C3[2]], color=color, label='_nolegend_', linestyle=linestyle, linewidth=linewidth, **kwargs) ax.plot([C0[0], C2[0]], [C0[1], C2[1]], [C0[2], C2[2]], color=color, label='_nolegend_', linestyle=linestyle,
ax.plot([C0[0], C4[0]], [C0[1], C4[1]], [C0[2], C4[2]], color=color, label='_nolegend_', linestyle=linestyle, linewidth=linewidth, **kwargs) linewidth=linewidth, **kwargs)
ax.plot([C1[0], C2[0], C3[0], C4[0], C1[0]], [C1[1], C2[1], C3[1], C4[1], C1[1]], [C1[2], C2[2], C3[2], C4[2], C1[2]], color=color, label='_nolegend_', linestyle=linestyle, linewidth=linewidth, **kwargs) ax.plot([C0[0], C3[0]], [C0[1], C3[1]], [C0[2], C3[2]], color=color, label='_nolegend_', linestyle=linestyle,
linewidth=linewidth, **kwargs)
ax.plot([C0[0], C4[0]], [C0[1], C4[1]], [C0[2], C4[2]], color=color, label='_nolegend_', linestyle=linestyle,
linewidth=linewidth, **kwargs)
ax.plot([C1[0], C2[0], C3[0], C4[0], C1[0]], [C1[1], C2[1], C3[1], C4[1], C1[1]],
[C1[2], C2[2], C3[2], C4[2], C1[2]], color=color, label='_nolegend_', linestyle=linestyle,
linewidth=linewidth, **kwargs)
def axis_equal(ax=None): def axis_equal(ax=None):
if ax is None: if ax is None:

24
co/table.py
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@ -3,6 +3,7 @@ import pandas as pd
import enum import enum
import itertools import itertools
class Table(object): class Table(object):
def __init__(self, n_cols): def __init__(self, n_cols):
self.n_cols = n_cols self.n_cols = n_cols
@ -44,6 +45,7 @@ class Table(object):
for c in range(len(cols)): for c in range(len(cols)):
self.rows[row + r].cells[col + c] = Cell(data[r][c], fmt) self.rows[row + r].cells[col + c] = Cell(data[r][c], fmt)
class Row(object): class Row(object):
def __init__(self, cells, pre_separator=None, post_separator=None): def __init__(self, cells, pre_separator=None, post_separator=None):
self.cells = cells self.cells = cells
@ -61,7 +63,6 @@ class Row(object):
return sum([c.span for c in self.cells]) return sum([c.span for c in self.cells])
class Color(object): class Color(object):
def __init__(self, color=(0, 0, 0), fmt='rgb'): def __init__(self, color=(0, 0, 0), fmt='rgb'):
if fmt == 'rgb': if fmt == 'rgb':
@ -93,6 +94,7 @@ class CellFormat(object):
self.bgcolor = bgcolor self.bgcolor = bgcolor
self.bold = bold self.bold = bold
class Cell(object): class Cell(object):
def __init__(self, data=None, fmt=None, span=1, align=None): def __init__(self, data=None, fmt=None, span=1, align=None):
self.data = data self.data = data
@ -105,6 +107,7 @@ class Cell(object):
def __str__(self): def __str__(self):
return self.fmt.fmt % self.data return self.fmt.fmt % self.data
class Separator(enum.Enum): class Separator(enum.Enum):
HEAD = 1 HEAD = 1
BOTTOM = 2 BOTTOM = 2
@ -143,6 +146,7 @@ class Renderer(object):
with open(path, 'w') as fp: with open(path, 'w') as fp:
fp.write(txt) fp.write(txt)
class TerminalRenderer(Renderer): class TerminalRenderer(Renderer):
def __init__(self, col_sep=' '): def __init__(self, col_sep=' '):
super().__init__() super().__init__()
@ -207,6 +211,7 @@ class TerminalRenderer(Renderer):
lines.append(sepline) lines.append(sepline)
return '\n'.join(lines) return '\n'.join(lines)
class MarkdownRenderer(TerminalRenderer): class MarkdownRenderer(TerminalRenderer):
def __init__(self): def __init__(self):
super().__init__(col_sep='|') super().__init__(col_sep='|')
@ -313,6 +318,7 @@ class LatexRenderer(Renderer):
lines.append('\\end{tabular}') lines.append('\\end{tabular}')
return '\n'.join(lines) return '\n'.join(lines)
class HtmlRenderer(Renderer): class HtmlRenderer(Renderer):
def __init__(self, html_class='result_table'): def __init__(self, html_class='result_table'):
super().__init__() super().__init__()
@ -331,10 +337,14 @@ class HtmlRenderer(Renderer):
color = cell.fmt.bgcolor.as_RGB() color = cell.fmt.bgcolor.as_RGB()
styles.append(f'background-color: rgb({color[0]},{color[1]},{color[2]});') styles.append(f'background-color: rgb({color[0]},{color[1]},{color[2]});')
align = table.get_cell_align(row, col) align = table.get_cell_align(row, col)
if align == 'l': align = 'left' if align == 'l':
elif align == 'r': align = 'right' align = 'left'
elif align == 'c': align = 'center' elif align == 'r':
else: raise Exception('invalid align') align = 'right'
elif align == 'c':
align = 'center'
else:
raise Exception('invalid align')
styles.append(f'text-align: {align};') styles.append(f'text-align: {align};')
row = table.rows[row] row = table.rows[row]
if row.pre_separator is not None: if row.pre_separator is not None:
@ -365,7 +375,8 @@ class HtmlRenderer(Renderer):
return '\n'.join(lines) return '\n'.join(lines)
def pandas_to_table(rowname, colname, valname, data, val_cell_fmt=CellFormat(fmt='%.4f'), best_val_cell_fmt=CellFormat(fmt='%.4f', bold=True), best_is_max=[]): def pandas_to_table(rowname, colname, valname, data, val_cell_fmt=CellFormat(fmt='%.4f'),
best_val_cell_fmt=CellFormat(fmt='%.4f', bold=True), best_is_max=[]):
rnames = data[rowname].unique() rnames = data[rowname].unique()
cnames = data[colname].unique() cnames = data[colname].unique()
tab = Table(1 + len(cnames)) tab = Table(1 + len(cnames))
@ -395,7 +406,6 @@ def pandas_to_table(rowname, colname, valname, data, val_cell_fmt=CellFormat(fmt
return tab return tab
if __name__ == '__main__': if __name__ == '__main__':
# df = pd.read_pickle('full.df') # df = pd.read_pickle('full.df')
# best_is_max = ['movF0.5', 'movF1.0'] # best_is_max = ['movF0.5', 'movF1.0']

6
co/utils.py
Unescape View File

@ -8,6 +8,7 @@ import re
import pickle import pickle
import subprocess import subprocess
def str2bool(v): def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'): if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True return True
@ -16,6 +17,7 @@ def str2bool(v):
else: else:
raise argparse.ArgumentTypeError('Boolean value expected.') raise argparse.ArgumentTypeError('Boolean value expected.')
class StopWatch(object): class StopWatch(object):
def __init__(self): def __init__(self):
self.timings = OrderedDict() self.timings = OrderedDict()
@ -40,9 +42,11 @@ class StopWatch(object):
def __repr__(self): def __repr__(self):
return ', '.join(['%s: %f[s]' % (k, v) for k, v in self.get().items()]) return ', '.join(['%s: %f[s]' % (k, v) for k, v in self.get().items()])
def __str__(self): def __str__(self):
return ', '.join(['%s: %f[s]' % (k, v) for k, v in self.get().items()]) return ', '.join(['%s: %f[s]' % (k, v) for k, v in self.get().items()])
class ETA(object): class ETA(object):
def __init__(self, length): def __init__(self, length):
self.length = length self.length = length
@ -76,6 +80,7 @@ class ETA(object):
def get_remaining_time_str(self): def get_remaining_time_str(self):
return self.format_time(self.get_remaining_time()) return self.format_time(self.get_remaining_time())
def git_hash(cwd=None): def git_hash(cwd=None):
ret = subprocess.run(['git', 'describe', '--always'], cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) ret = subprocess.run(['git', 'describe', '--always'], cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)
hash = ret.stdout hash = ret.stdout
@ -83,4 +88,3 @@ def git_hash(cwd=None):
return hash.decode().strip() return hash.decode().strip()
else: else:
return None return None

8
data/commons.py
Unescape View File

@ -30,6 +30,7 @@ def get_patterns(path='syn', imsizes=[], crop=True):
return patterns return patterns
def get_rotation_matrix(v0, v1): def get_rotation_matrix(v0, v1):
v0 = v0 / np.linalg.norm(v0) v0 = v0 / np.linalg.norm(v0)
v1 = v1 / np.linalg.norm(v1) v1 = v1 / np.linalg.norm(v1)
@ -44,7 +45,6 @@ def get_rotation_matrix(v0, v1):
def augment_image(img, rng, disp=None, grad=None, max_shift=64, max_blur=1.5, max_noise=10.0, max_sp_noise=0.001): def augment_image(img, rng, disp=None, grad=None, max_shift=64, max_blur=1.5, max_noise=10.0, max_sp_noise=0.001):
# get min/max values of image # get min/max values of image
min_val = np.min(img) min_val = np.min(img)
max_val = np.max(img) max_val = np.max(img)
@ -64,8 +64,10 @@ def augment_image(img,rng,disp=None,grad=None,max_shift=64,max_blur=1.5,max_nois
shear = 0 shear = 0
shift = 0 shift = 0
shear_correction = 0 shear_correction = 0
if rng.uniform(0,1)<0.75: shear = rng.uniform(-max_shift,max_shift) # shear with 75% probability if rng.uniform(0, 1) < 0.75:
else: shift = rng.uniform(0,max_shift) # shift with 25% probability shear = rng.uniform(-max_shift, max_shift) # shear with 75% probability
else:
shift = rng.uniform(0, max_shift) # shift with 25% probability
if shear < 0: shear_correction = -shear if shear < 0: shear_correction = -shear
# affine transformation # affine transformation

10
data/create_syn_data.py
Unescape View File

@ -10,14 +10,15 @@ import cv2
import os import os
import collections import collections
import sys import sys
sys.path.append('../') sys.path.append('../')
import renderer import renderer
import co import co
from commons import get_patterns, get_rotation_matrix from commons import get_patterns, get_rotation_matrix
from lcn import lcn from lcn import lcn
def get_objs(shapenet_dir, obj_classes, num_perclass=100):
def get_objs(shapenet_dir, obj_classes, num_perclass=100):
shapenet = {'chair': '03001627', shapenet = {'chair': '03001627',
'airplane': '02691156', 'airplane': '02691156',
'car': '02958343', 'car': '02958343',
@ -88,7 +89,6 @@ def get_mesh(rng, min_z=0):
def create_data(out_root, idx, n_samples, imsize, patterns, K, baseline, blend_im, noise, track_length=4): def create_data(out_root, idx, n_samples, imsize, patterns, K, baseline, blend_im, noise, track_length=4):
tic = time.time() tic = time.time()
rng = np.random.RandomState() rng = np.random.RandomState()
@ -98,7 +98,6 @@ def create_data(out_root, idx, n_samples, imsize, patterns, K, baseline, blend_i
data = renderer.PyRenderInput(verts=verts.copy(), colors=colors.copy(), normals=normals.copy(), faces=faces.copy()) data = renderer.PyRenderInput(verts=verts.copy(), colors=colors.copy(), normals=normals.copy(), faces=faces.copy())
print(f'loading mesh for sample {idx + 1}/{n_samples} took {time.time() - tic}[s]') print(f'loading mesh for sample {idx + 1}/{n_samples} took {time.time() - tic}[s]')
# let the camera point to the center # let the camera point to the center
center = np.array([0, 0, 3], dtype=np.float32) center = np.array([0, 0, 3], dtype=np.float32)
@ -148,7 +147,6 @@ def create_data(out_root, idx, n_samples, imsize, patterns, K, baseline, blend_i
cams.append(renderer.PyCamera(fx, fy, px, py, Rcam, tcam, im_width, im_height)) cams.append(renderer.PyCamera(fx, fy, px, py, Rcam, tcam, im_width, im_height))
projs.append(renderer.PyCamera(fx, fy, px, py, Rproj, tproj, im_width, im_height)) projs.append(renderer.PyCamera(fx, fy, px, py, Rproj, tproj, im_width, im_height))
for s, cam, proj, pattern in zip(itertools.count(), cams, projs, patterns): for s, cam, proj, pattern in zip(itertools.count(), cams, projs, patterns):
fl = K[0, 0] / (2 ** s) fl = K[0, 0] / (2 ** s)
@ -203,7 +201,6 @@ def create_data(out_root, idx, n_samples, imsize, patterns, K, baseline, blend_i
print(f'create sample {idx + 1}/{n_samples} took {time.time() - tic}[s]') print(f'create sample {idx + 1}/{n_samples} took {time.time() - tic}[s]')
if __name__ == '__main__': if __name__ == '__main__':
np.random.seed(42) np.random.seed(42)
@ -236,7 +233,8 @@ if __name__=='__main__':
imsize = (488, 648) imsize = (488, 648)
imsizes = [(imsize[0] // (2 ** s), imsize[1] // (2 ** s)) for s in range(4)] imsizes = [(imsize[0] // (2 ** s), imsize[1] // (2 ** s)) for s in range(4)]
# K = np.array([[567.6, 0, 324.7], [0, 570.2, 250.1], [0 ,0, 1]], dtype=np.float32) # K = np.array([[567.6, 0, 324.7], [0, 570.2, 250.1], [0 ,0, 1]], dtype=np.float32)
K = np.array([[1929.5936336276382, 0, 113.66561071478046], [0, 1911.2517985448746, 473.70108079885887], [0 ,0, 1]], dtype=np.float32) K = np.array([[1929.5936336276382, 0, 113.66561071478046], [0, 1911.2517985448746, 473.70108079885887], [0, 0, 1]],
dtype=np.float32)
focal_lengths = [K[0, 0] / (2 ** s) for s in range(4)] focal_lengths = [K[0, 0] / (2 ** s) for s in range(4)]
baseline = 0.075 baseline = 0.075
blend_im = 0.6 blend_im = 0.6

8
data/dataset.py
Unescape View File

@ -21,11 +21,13 @@ from .commons import get_patterns, augment_image
from mpl_toolkits.mplot3d import Axes3D from mpl_toolkits.mplot3d import Axes3D
class TrackSynDataset(torchext.BaseDataset): class TrackSynDataset(torchext.BaseDataset):
''' '''
Load locally saved synthetic dataset Load locally saved synthetic dataset
Please run ./create_syn_data.sh to generate the dataset Please run ./create_syn_data.sh to generate the dataset
''' '''
def __init__(self, settings_path, sample_paths, track_length=2, train=True, data_aug=False): def __init__(self, settings_path, sample_paths, track_length=2, train=True, data_aug=False):
super().__init__(train=train) super().__init__(train=train)
@ -111,7 +113,8 @@ class TrackSynDataset(torchext.BaseDataset):
img_aug_, disp_aug_, grad_aug_ = augment_image(img[i, 0], rng, img_aug_, disp_aug_, grad_aug_ = augment_image(img[i, 0], rng,
disp=disp[i, 0], grad=grad[i, 0], disp=disp[i, 0], grad=grad[i, 0],
max_shift=self.max_shift, max_blur=self.max_blur, max_shift=self.max_shift, max_blur=self.max_blur,
max_noise=self.max_noise, max_sp_noise=self.max_sp_noise) max_noise=self.max_noise,
max_sp_noise=self.max_sp_noise)
img_aug[i] = img_aug_[None].astype(np.float32) img_aug[i] = img_aug_[None].astype(np.float32)
disp_aug[i] = disp_aug_[None].astype(np.float32) disp_aug[i] = disp_aug_[None].astype(np.float32)
grad_aug[i] = grad_aug_[None].astype(np.float32) grad_aug[i] = grad_aug_[None].astype(np.float32)
@ -133,7 +136,6 @@ class TrackSynDataset(torchext.BaseDataset):
if key != 'blend_im' and key != 'id': if key != 'blend_im' and key != 'id':
ret[key] = val[0] ret[key] = val[0]
return ret return ret
def getK(self, sidx=0): def getK(self, sidx=0):
@ -142,7 +144,5 @@ class TrackSynDataset(torchext.BaseDataset):
return K return K
if __name__ == '__main__': if __name__ == '__main__':
pass pass

321
data/lcn/lcn.html
Unescape View File

@ -355,6 +355,7 @@ body.cython { font-family: courier; font-size: 12; }
.cython .vi { color: #19177C } /* Name.Variable.Instance */ .cython .vi { color: #19177C } /* Name.Variable.Instance */
.cython .vm { color: #19177C } /* Name.Variable.Magic */ .cython .vm { color: #19177C } /* Name.Variable.Magic */
.cython .il { color: #666666 } /* Literal.Number.Integer.Long */ .cython .il { color: #666666 } /* Literal.Number.Integer.Long */
</style> </style>
</head> </head>
<body class="cython"> <body class="cython">
@ -364,8 +365,13 @@ body.cython { font-family: courier; font-size: 12; }
Click on a line that starts with a "<code>+</code>" to see the C code that Cython generated for it. Click on a line that starts with a "<code>+</code>" to see the C code that Cython generated for it.
</p> </p>
<p>Raw output: <a href="lcn.c">lcn.c</a></p> <p>Raw output: <a href="lcn.c">lcn.c</a></p>
<div class="cython"><pre class="cython line score-16" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">01</span>: <span class="k">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span></pre> <div class="cython">
<pre class='cython code score-16 '> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_Import</span>(__pyx_n_s_numpy, 0, -1);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error)</span> <pre class="cython line score-16"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">01</span>: <span class="k">import</span> <span class="nn">numpy</span> <span
class="k">as</span> <span class="nn">np</span></pre>
<pre class='cython code score-16 '> __pyx_t_1 = <span class='pyx_c_api'>__Pyx_Import</span>(__pyx_n_s_numpy, 0, -1);<span
class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error)</span>
<span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1); <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);
if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_np, __pyx_t_1) &lt; 0) <span class='error_goto'>__PYX_ERR(0, 1, __pyx_L1_error)</span> if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_np, __pyx_t_1) &lt; 0) <span class='error_goto'>__PYX_ERR(0, 1, __pyx_L1_error)</span>
<span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0; <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
@ -374,21 +380,38 @@ body.cython { font-family: courier; font-size: 12; }
<span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1); <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);
if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_test, __pyx_t_1) &lt; 0) <span class='error_goto'>__PYX_ERR(0, 1, __pyx_L1_error)</span> if (<span class='py_c_api'>PyDict_SetItem</span>(__pyx_d, __pyx_n_s_test, __pyx_t_1) &lt; 0) <span class='error_goto'>__PYX_ERR(0, 1, __pyx_L1_error)</span>
<span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0; <span class='pyx_macro_api'>__Pyx_DECREF</span>(__pyx_t_1); __pyx_t_1 = 0;
</pre><pre class="cython line score-0">&#xA0;<span class="">02</span>: <span class="k">cimport</span> <span class="nn">cython</span></pre> </pre>
<pre class="cython line score-0">&#xA0;<span class="">02</span>: <span class="k">cimport</span> <span class="nn">cython</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">03</span>: </pre> <pre class="cython line score-0">&#xA0;<span class="">03</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">04</span>: <span class="c"># use c square root function</span></pre> <pre class="cython line score-0">&#xA0;<span class="">04</span>: <span class="c"># use c square root function</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">05</span>: <span class="k">cdef</span> <span class="kr">extern</span> <span class="k">from</span> <span class="s">&quot;math.h&quot;</span><span class="p">:</span></pre> <pre class="cython line score-0">&#xA0;<span class="">05</span>: <span class="k">cdef</span> <span
<pre class="cython line score-0">&#xA0;<span class="">06</span>: <span class="nb">float</span> <span class="n">sqrt</span><span class="p">(</span><span class="nb">float</span> <span class="n">x</span><span class="p">)</span></pre> class="kr">extern</span> <span class="k">from</span> <span class="s">&quot;math.h&quot;</span><span
class="p">:</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">06</span>: <span class="nb">float</span> <span class="n">sqrt</span><span
class="p">(</span><span class="nb">float</span> <span class="n">x</span><span class="p">)</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">07</span>: </pre> <pre class="cython line score-0">&#xA0;<span class="">07</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">08</span>: <span class="nd">@cython</span><span class="o">.</span><span class="n">boundscheck</span><span class="p">(</span><span class="bp">False</span><span class="p">)</span></pre> <pre class="cython line score-0">&#xA0;<span class="">08</span>: <span class="nd">@cython</span><span
<pre class="cython line score-0">&#xA0;<span class="">09</span>: <span class="nd">@cython</span><span class="o">.</span><span class="n">wraparound</span><span class="p">(</span><span class="bp">False</span><span class="p">)</span></pre> class="o">.</span><span class="n">boundscheck</span><span class="p">(</span><span
<pre class="cython line score-0">&#xA0;<span class="">10</span>: <span class="nd">@cython</span><span class="o">.</span><span class="n">cdivision</span><span class="p">(</span><span class="bp">True</span><span class="p">)</span></pre> class="bp">False</span><span class="p">)</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">09</span>: <span class="nd">@cython</span><span
class="o">.</span><span class="n">wraparound</span><span class="p">(</span><span
class="bp">False</span><span class="p">)</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">10</span>: <span class="nd">@cython</span><span
class="o">.</span><span class="n">cdivision</span><span class="p">(</span><span class="bp">True</span><span
class="p">)</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">11</span>: </pre> <pre class="cython line score-0">&#xA0;<span class="">11</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">12</span>: <span class="c"># 3 parameters:</span></pre> <pre class="cython line score-0">&#xA0;<span class="">12</span>: <span class="c"># 3 parameters:</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">13</span>: <span class="c"># - float image</span></pre> <pre class="cython line score-0">&#xA0;<span class="">13</span>: <span class="c"># - float image</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">14</span>: <span class="c"># - kernel size (actually this is the radius, kernel is 2*k+1)</span></pre> <pre class="cython line score-0">&#xA0;<span class="">14</span>: <span class="c"># - kernel size (actually this is the radius, kernel is 2*k+1)</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">15</span>: <span class="c"># - small constant epsilon that is used to avoid division by zero</span></pre> <pre class="cython line score-0">&#xA0;<span class="">15</span>: <span class="c"># - small constant epsilon that is used to avoid division by zero</span></pre>
<pre class="cython line score-67" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">16</span>: <span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="nb">float</span><span class="p">[:,</span> <span class="p">:]</span> <span class="n">img</span><span class="p">,</span> <span class="nb">int</span> <span class="n">kernel_size</span> <span class="o">=</span> <span class="mf">4</span><span class="p">,</span> <span class="nb">float</span> <span class="n">epsilon</span> <span class="o">=</span> <span class="mf">0.01</span><span class="p">):</span></pre> <pre class="cython line score-67"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">16</span>: <span class="k">def</span> <span class="nf">normalize</span><span
class="p">(</span><span class="nb">float</span><span class="p">[:,</span> <span class="p">:]</span> <span
class="n">img</span><span class="p">,</span> <span class="nb">int</span> <span class="n">kernel_size</span> <span
class="o">=</span> <span class="mf">4</span><span class="p">,</span> <span class="nb">float</span> <span
class="n">epsilon</span> <span class="o">=</span> <span class="mf">0.01</span><span
class="p">):</span></pre>
<pre class='cython code score-67 '>/* Python wrapper */ <pre class='cython code score-67 '>/* Python wrapper */
static PyObject *__pyx_pw_3lcn_1normalize(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ static PyObject *__pyx_pw_3lcn_1normalize(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
static PyMethodDef __pyx_mdef_3lcn_1normalize = {"normalize", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_3lcn_1normalize, METH_VARARGS|METH_KEYWORDS, 0}; static PyMethodDef __pyx_mdef_3lcn_1normalize = {"normalize", (PyCFunction)(void*)(PyCFunctionWithKeywords)__pyx_pw_3lcn_1normalize, METH_VARARGS|METH_KEYWORDS, 0};
@ -434,7 +457,8 @@ static PyObject *__pyx_pw_3lcn_1normalize(PyObject *__pyx_self, PyObject *__pyx_
} }
} }
if (unlikely(kw_args &gt; 0)) { if (unlikely(kw_args &gt; 0)) {
if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "normalize") &lt; 0)) <span class='error_goto'>__PYX_ERR(0, 16, __pyx_L3_error)</span> if (unlikely(<span class='pyx_c_api'>__Pyx_ParseOptionalKeywords</span>(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "normalize") &lt; 0)) <span
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switch (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)) { switch (<span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)) {
@ -447,21 +471,27 @@ static PyObject *__pyx_pw_3lcn_1normalize(PyObject *__pyx_self, PyObject *__pyx_
default: goto __pyx_L5_argtuple_error; default: goto __pyx_L5_argtuple_error;
} }
} }
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goto __pyx_L4_argument_unpacking_done; goto __pyx_L4_argument_unpacking_done;
__pyx_L5_argtuple_error:; __pyx_L5_argtuple_error:;
<span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>("normalize", 0, 1, 3, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); <span class='error_goto'>__PYX_ERR(0, 16, __pyx_L3_error)</span> <span class='pyx_c_api'>__Pyx_RaiseArgtupleInvalid</span>("normalize", 0, 1, 3, <span class='py_macro_api'>PyTuple_GET_SIZE</span>(__pyx_args)); <span
class='error_goto'>__PYX_ERR(0, 16, __pyx_L3_error)</span>
__pyx_L3_error:; __pyx_L3_error:;
<span class='pyx_c_api'>__Pyx_AddTraceback</span>("lcn.normalize", __pyx_clineno, __pyx_lineno, __pyx_filename); <span class='pyx_c_api'>__Pyx_AddTraceback</span>("lcn.normalize", __pyx_clineno, __pyx_lineno, __pyx_filename);
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@ -515,27 +545,49 @@ static PyObject *__pyx_pf_3lcn_normalize(CYTHON_UNUSED PyObject *__pyx_self, __P
return __pyx_r; return __pyx_r;
} }
/* … */ /* … */
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</pre><pre class="cython line score-0">&#xA0;<span class="">17</span>: </pre> class='error_goto'> if (unlikely(!__pyx_codeobj__20)) __PYX_ERR(0, 16, __pyx_L1_error)</span>
</pre>
<pre class="cython line score-0">&#xA0;<span class="">17</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">18</span>: <span class="c"># image dimensions</span></pre> <pre class="cython line score-0">&#xA0;<span class="">18</span>: <span class="c"># image dimensions</span></pre>
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">19</span>: <span class="k">cdef</span> <span class="kt">Py_ssize_t</span> <span class="nf">M</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mf">0</span><span class="p">]</span></pre> <pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">19</span>: <span class="k">cdef</span> <span class="kt">Py_ssize_t</span> <span
class="nf">M</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span
class="n">shape</span><span class="p">[</span><span class="mf">0</span><span class="p">]</span></pre>
<pre class='cython code score-0 '> __pyx_v_M = (__pyx_v_img.shape[0]); <pre class='cython code score-0 '> __pyx_v_M = (__pyx_v_img.shape[0]);
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">20</span>: <span class="k">cdef</span> <span class="kt">Py_ssize_t</span> <span class="nf">N</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mf">1</span><span class="p">]</span></pre> </pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">20</span>: <span class="k">cdef</span> <span class="kt">Py_ssize_t</span> <span
class="nf">N</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span
class="n">shape</span><span class="p">[</span><span class="mf">1</span><span class="p">]</span></pre>
<pre class='cython code score-0 '> __pyx_v_N = (__pyx_v_img.shape[1]); <pre class='cython code score-0 '> __pyx_v_N = (__pyx_v_img.shape[1]);
</pre><pre class="cython line score-0">&#xA0;<span class="">21</span>: </pre> </pre>
<pre class="cython line score-0">&#xA0;<span class="">21</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">22</span>: <span class="c"># create outputs and output views</span></pre> <pre class="cython line score-0">&#xA0;<span class="">22</span>: <span class="c"># create outputs and output views</span></pre>
<pre class="cython line score-46" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">23</span>: <span class="n">img_lcn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span></pre> <pre class="cython line score-46"
<pre class='cython code score-46 '> <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_t_1, __pyx_n_s_np);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 23, __pyx_L1_error)</span> onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">23</span>: <span class="n">img_lcn</span> <span class="o">=</span> <span
class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span
class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">),</span> <span
class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span
class="n">float32</span><span class="p">)</span></pre>
<pre class='cython code score-46 '> <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_t_1, __pyx_n_s_np);<span
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<pre class='cython code score-46 '> <span class='pyx_c_api'>__Pyx_GetModuleGlobalName</span>(__pyx_t_5, __pyx_n_s_np);<span class='error_goto'> if (unlikely(!__pyx_t_5)) __PYX_ERR(0, 24, __pyx_L1_error)</span> <pre class="cython line score-46"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">24</span>: <span class="n">img_std</span> <span class="o">=</span> <span
class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span
class="n">M</span><span class="p">,</span> <span class="n">N</span><span class="p">),</span> <span
class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span
class="n">float32</span><span class="p">)</span></pre>
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</pre><pre class="cython line score-2" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">25</span>: <span class="k">cdef</span> <span class="kt">float</span>[<span class="p">:,</span> <span class="p">:]</span> <span class="n">img_lcn_view</span> <span class="o">=</span> <span class="n">img_lcn</span></pre> </pre>
<pre class='cython code score-2 '> __pyx_t_6 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dsds_float</span>(__pyx_v_img_lcn, PyBUF_WRITABLE);<span class='error_goto'> if (unlikely(!__pyx_t_6.memview)) __PYX_ERR(0, 25, __pyx_L1_error)</span> <pre class="cython line score-2"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">25</span>: <span class="k">cdef</span> <span class="kt">float</span>[<span class="p">:,</span> <span
class="p">:]</span> <span class="n">img_lcn_view</span> <span class="o">=</span> <span
class="n">img_lcn</span></pre>
<pre class='cython code score-2 '> __pyx_t_6 = <span
class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dsds_float</span>(__pyx_v_img_lcn, PyBUF_WRITABLE);<span
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__pyx_t_6.data = NULL; __pyx_t_6.data = NULL;
</pre><pre class="cython line score-2" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">26</span>: <span class="k">cdef</span> <span class="kt">float</span>[<span class="p">:,</span> <span class="p">:]</span> <span class="n">img_std_view</span> <span class="o">=</span> <span class="n">img_std</span></pre> </pre>
<pre class='cython code score-2 '> __pyx_t_6 = <span class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dsds_float</span>(__pyx_v_img_std, PyBUF_WRITABLE);<span class='error_goto'> if (unlikely(!__pyx_t_6.memview)) __PYX_ERR(0, 26, __pyx_L1_error)</span> <pre class="cython line score-2"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">26</span>: <span class="k">cdef</span> <span class="kt">float</span>[<span class="p">:,</span> <span
class="p">:]</span> <span class="n">img_std_view</span> <span class="o">=</span> <span
class="n">img_std</span></pre>
<pre class='cython code score-2 '> __pyx_t_6 = <span
class='pyx_c_api'>__Pyx_PyObject_to_MemoryviewSlice_dsds_float</span>(__pyx_v_img_std, PyBUF_WRITABLE);<span
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</pre><pre class="cython line score-0">&#xA0;<span class="">27</span>: </pre> </pre>
<pre class="cython line score-0">&#xA0;<span class="">27</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">28</span>: <span class="c"># temporary c variables</span></pre> <pre class="cython line score-0">&#xA0;<span class="">28</span>: <span class="c"># temporary c variables</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">29</span>: <span class="k">cdef</span> <span class="kt">float</span> <span class="nf">tmp</span><span class="p">,</span> <span class="nf">mean</span><span class="p">,</span> <span class="nf">stddev</span></pre> <pre class="cython line score-0">&#xA0;<span class="">29</span>: <span class="k">cdef</span> <span class="kt">float</span> <span
<pre class="cython line score-0">&#xA0;<span class="">30</span>: <span class="k">cdef</span> <span class="kt">Py_ssize_t</span> <span class="nf">m</span><span class="p">,</span> <span class="nf">n</span><span class="p">,</span> <span class="nf">i</span><span class="p">,</span> <span class="nf">j</span></pre> class="nf">tmp</span><span class="p">,</span> <span class="nf">mean</span><span class="p">,</span> <span
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">31</span>: <span class="k">cdef</span> <span class="kt">Py_ssize_t</span> <span class="nf">ks</span> <span class="o">=</span> <span class="n">kernel_size</span></pre> class="nf">stddev</span></pre>
<pre class="cython line score-0">&#xA0;<span class="">30</span>: <span class="k">cdef</span> <span class="kt">Py_ssize_t</span> <span
class="nf">m</span><span class="p">,</span> <span class="nf">n</span><span class="p">,</span> <span
class="nf">i</span><span class="p">,</span> <span class="nf">j</span></pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">31</span>: <span class="k">cdef</span> <span class="kt">Py_ssize_t</span> <span
class="nf">ks</span> <span class="o">=</span> <span class="n">kernel_size</span></pre>
<pre class='cython code score-0 '> __pyx_v_ks = __pyx_v_kernel_size; <pre class='cython code score-0 '> __pyx_v_ks = __pyx_v_kernel_size;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">32</span>: <span class="k">cdef</span> <span class="kt">float</span> <span class="nf">eps</span> <span class="o">=</span> <span class="n">epsilon</span></pre> </pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">32</span>: <span class="k">cdef</span> <span class="kt">float</span> <span
class="nf">eps</span> <span class="o">=</span> <span class="n">epsilon</span></pre>
<pre class='cython code score-0 '> __pyx_v_eps = __pyx_v_epsilon; <pre class='cython code score-0 '> __pyx_v_eps = __pyx_v_epsilon;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">33</span>: <span class="k">cdef</span> <span class="kt">float</span> <span class="nf">num</span> <span class="o">=</span> <span class="p">(</span><span class="n">ks</span><span class="o">*</span><span class="mf">2</span><span class="o">+</span><span class="mf">1</span><span class="p">)</span><span class="o">**</span><span class="mf">2</span></pre> </pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">33</span>: <span class="k">cdef</span> <span class="kt">float</span> <span
class="nf">num</span> <span class="o">=</span> <span class="p">(</span><span class="n">ks</span><span
class="o">*</span><span class="mf">2</span><span class="o">+</span><span class="mf">1</span><span class="p">)</span><span
class="o">**</span><span class="mf">2</span></pre>
<pre class='cython code score-0 '> __pyx_v_num = __Pyx_pow_Py_ssize_t(((__pyx_v_ks * 2) + 1), 2); <pre class='cython code score-0 '> __pyx_v_num = __Pyx_pow_Py_ssize_t(((__pyx_v_ks * 2) + 1), 2);
</pre><pre class="cython line score-0">&#xA0;<span class="">34</span>: </pre> </pre>
<pre class="cython line score-0">&#xA0;<span class="">35</span>: <span class="c"># for all pixels do</span></pre> <pre class="cython line score-0">&#xA0;<span class="">34</span>: </pre>
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">36</span>: <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ks</span><span class="p">,</span><span class="n">M</span><span class="o">-</span><span class="n">ks</span><span class="p">):</span></pre> <pre class="cython line score-0">&#xA0;<span class="">35</span>: <span
class="c"># for all pixels do</span></pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">36</span>: <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span
class="nb">range</span><span class="p">(</span><span class="n">ks</span><span class="p">,</span><span
class="n">M</span><span class="o">-</span><span class="n">ks</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_7 = (__pyx_v_M - __pyx_v_ks); <pre class='cython code score-0 '> __pyx_t_7 = (__pyx_v_M - __pyx_v_ks);
__pyx_t_8 = __pyx_t_7; __pyx_t_8 = __pyx_t_7;
for (__pyx_t_9 = __pyx_v_ks; __pyx_t_9 &lt; __pyx_t_8; __pyx_t_9+=1) { for (__pyx_t_9 = __pyx_v_ks; __pyx_t_9 &lt; __pyx_t_8; __pyx_t_9+=1) {
__pyx_v_m = __pyx_t_9; __pyx_v_m = __pyx_t_9;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">37</span>: <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ks</span><span class="p">,</span><span class="n">N</span><span class="o">-</span><span class="n">ks</span><span class="p">):</span></pre> </pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">37</span>: <span class="k">for</span> <span class="n">n</span> <span
class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">ks</span><span
class="p">,</span><span class="n">N</span><span class="o">-</span><span class="n">ks</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_10 = (__pyx_v_N - __pyx_v_ks); <pre class='cython code score-0 '> __pyx_t_10 = (__pyx_v_N - __pyx_v_ks);
__pyx_t_11 = __pyx_t_10; __pyx_t_11 = __pyx_t_10;
for (__pyx_t_12 = __pyx_v_ks; __pyx_t_12 &lt; __pyx_t_11; __pyx_t_12+=1) { for (__pyx_t_12 = __pyx_v_ks; __pyx_t_12 &lt; __pyx_t_11; __pyx_t_12+=1) {
__pyx_v_n = __pyx_t_12; __pyx_v_n = __pyx_t_12;
</pre><pre class="cython line score-0">&#xA0;<span class="">38</span>: </pre> </pre>
<pre class="cython line score-0">&#xA0;<span class="">38</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">39</span>: <span class="c"># calculate mean</span></pre> <pre class="cython line score-0">&#xA0;<span class="">39</span>: <span class="c"># calculate mean</span></pre>
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">40</span>: <span class="n">mean</span> <span class="o">=</span> <span class="mf">0</span><span class="p">;</span></pre> <pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">40</span>: <span class="n">mean</span> <span class="o">=</span> <span
class="mf">0</span><span class="p">;</span></pre>
<pre class='cython code score-0 '> __pyx_v_mean = 0.0; <pre class='cython code score-0 '> __pyx_v_mean = 0.0;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">41</span>: <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span class="mf">1</span><span class="p">):</span></pre> </pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">41</span>: <span class="k">for</span> <span class="n">i</span> <span
class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span
class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span
class="mf">1</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_13 = (__pyx_v_ks + 1); <pre class='cython code score-0 '> __pyx_t_13 = (__pyx_v_ks + 1);
__pyx_t_14 = __pyx_t_13; __pyx_t_14 = __pyx_t_13;
for (__pyx_t_15 = (-__pyx_v_ks); __pyx_t_15 &lt; __pyx_t_14; __pyx_t_15+=1) { for (__pyx_t_15 = (-__pyx_v_ks); __pyx_t_15 &lt; __pyx_t_14; __pyx_t_15+=1) {
__pyx_v_i = __pyx_t_15; __pyx_v_i = __pyx_t_15;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">42</span>: <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span class="mf">1</span><span class="p">):</span></pre> </pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">42</span>: <span class="k">for</span> <span class="n">j</span> <span
class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span
class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span
class="mf">1</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_16 = (__pyx_v_ks + 1); <pre class='cython code score-0 '> __pyx_t_16 = (__pyx_v_ks + 1);
__pyx_t_17 = __pyx_t_16; __pyx_t_17 = __pyx_t_16;
for (__pyx_t_18 = (-__pyx_v_ks); __pyx_t_18 &lt; __pyx_t_17; __pyx_t_18+=1) { for (__pyx_t_18 = (-__pyx_v_ks); __pyx_t_18 &lt; __pyx_t_17; __pyx_t_18+=1) {
__pyx_v_j = __pyx_t_18; __pyx_v_j = __pyx_t_18;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">43</span>: <span class="n">mean</span> <span class="o">+=</span> <span class="n">img</span><span class="p">[</span><span class="n">m</span><span class="o">+</span><span class="n">i</span><span class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="n">j</span><span class="p">]</span></pre> </pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">43</span>: <span class="n">mean</span> <span class="o">+=</span> <span
class="n">img</span><span class="p">[</span><span class="n">m</span><span class="o">+</span><span class="n">i</span><span
class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="n">j</span><span
class="p">]</span></pre>
<pre class='cython code score-0 '> __pyx_t_19 = (__pyx_v_m + __pyx_v_i); <pre class='cython code score-0 '> __pyx_t_19 = (__pyx_v_m + __pyx_v_i);
__pyx_t_20 = (__pyx_v_n + __pyx_v_j); __pyx_t_20 = (__pyx_v_n + __pyx_v_j);
__pyx_v_mean = (__pyx_v_mean + (*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img.data + __pyx_t_19 * __pyx_v_img.strides[0]) ) + __pyx_t_20 * __pyx_v_img.strides[1]) )))); __pyx_v_mean = (__pyx_v_mean + (*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img.data + __pyx_t_19 * __pyx_v_img.strides[0]) ) + __pyx_t_20 * __pyx_v_img.strides[1]) ))));
} }
} }
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">44</span>: <span class="n">mean</span> <span class="o">=</span> <span class="n">mean</span><span class="o">/</span><span class="n">num</span></pre> </pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">44</span>: <span class="n">mean</span> <span class="o">=</span> <span
class="n">mean</span><span class="o">/</span><span class="n">num</span></pre>
<pre class='cython code score-0 '> __pyx_v_mean = (__pyx_v_mean / __pyx_v_num); <pre class='cython code score-0 '> __pyx_v_mean = (__pyx_v_mean / __pyx_v_num);
</pre><pre class="cython line score-0">&#xA0;<span class="">45</span>: </pre> </pre>
<pre class="cython line score-0">&#xA0;<span class="">46</span>: <span class="c"># calculate std dev</span></pre> <pre class="cython line score-0">&#xA0;<span class="">45</span>: </pre>
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">47</span>: <span class="n">stddev</span> <span class="o">=</span> <span class="mf">0</span><span class="p">;</span></pre> <pre class="cython line score-0">&#xA0;<span class="">46</span>: <span
class="c"># calculate std dev</span></pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">47</span>: <span class="n">stddev</span> <span class="o">=</span> <span
class="mf">0</span><span class="p">;</span></pre>
<pre class='cython code score-0 '> __pyx_v_stddev = 0.0; <pre class='cython code score-0 '> __pyx_v_stddev = 0.0;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">48</span>: <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span class="mf">1</span><span class="p">):</span></pre> </pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">48</span>: <span class="k">for</span> <span class="n">i</span> <span
class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span
class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span
class="mf">1</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_13 = (__pyx_v_ks + 1); <pre class='cython code score-0 '> __pyx_t_13 = (__pyx_v_ks + 1);
__pyx_t_14 = __pyx_t_13; __pyx_t_14 = __pyx_t_13;
for (__pyx_t_15 = (-__pyx_v_ks); __pyx_t_15 &lt; __pyx_t_14; __pyx_t_15+=1) { for (__pyx_t_15 = (-__pyx_v_ks); __pyx_t_15 &lt; __pyx_t_14; __pyx_t_15+=1) {
__pyx_v_i = __pyx_t_15; __pyx_v_i = __pyx_t_15;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">49</span>: <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span class="mf">1</span><span class="p">):</span></pre> </pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">49</span>: <span class="k">for</span> <span class="n">j</span> <span
class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span
class="n">ks</span><span class="p">,</span><span class="n">ks</span><span class="o">+</span><span
class="mf">1</span><span class="p">):</span></pre>
<pre class='cython code score-0 '> __pyx_t_16 = (__pyx_v_ks + 1); <pre class='cython code score-0 '> __pyx_t_16 = (__pyx_v_ks + 1);
__pyx_t_17 = __pyx_t_16; __pyx_t_17 = __pyx_t_16;
for (__pyx_t_18 = (-__pyx_v_ks); __pyx_t_18 &lt; __pyx_t_17; __pyx_t_18+=1) { for (__pyx_t_18 = (-__pyx_v_ks); __pyx_t_18 &lt; __pyx_t_17; __pyx_t_18+=1) {
__pyx_v_j = __pyx_t_18; __pyx_v_j = __pyx_t_18;
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">50</span>: <span class="n">stddev</span> <span class="o">=</span> <span class="n">stddev</span> <span class="o">+</span> <span class="p">(</span><span class="n">img</span><span class="p">[</span><span class="n">m</span><span class="o">+</span><span class="n">i</span><span class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="n">j</span><span class="p">]</span><span class="o">-</span><span class="n">mean</span><span class="p">)</span><span class="o">*</span><span class="p">(</span><span class="n">img</span><span class="p">[</span><span class="n">m</span><span class="o">+</span><span class="n">i</span><span class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="n">j</span><span class="p">]</span><span class="o">-</span><span class="n">mean</span><span class="p">)</span></pre> </pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">50</span>: <span class="n">stddev</span> <span class="o">=</span> <span
class="n">stddev</span> <span class="o">+</span> <span class="p">(</span><span class="n">img</span><span
class="p">[</span><span class="n">m</span><span class="o">+</span><span class="n">i</span><span
class="p">,</span> <span class="n">n</span><span class="o">+</span><span class="n">j</span><span
class="p">]</span><span class="o">-</span><span class="n">mean</span><span class="p">)</span><span
class="o">*</span><span class="p">(</span><span class="n">img</span><span class="p">[</span><span class="n">m</span><span
class="o">+</span><span class="n">i</span><span class="p">,</span> <span class="n">n</span><span
class="o">+</span><span class="n">j</span><span class="p">]</span><span class="o">-</span><span class="n">mean</span><span
class="p">)</span></pre>
<pre class='cython code score-0 '> __pyx_t_21 = (__pyx_v_m + __pyx_v_i); <pre class='cython code score-0 '> __pyx_t_21 = (__pyx_v_m + __pyx_v_i);
__pyx_t_22 = (__pyx_v_n + __pyx_v_j); __pyx_t_22 = (__pyx_v_n + __pyx_v_j);
__pyx_t_23 = (__pyx_v_m + __pyx_v_i); __pyx_t_23 = (__pyx_v_m + __pyx_v_i);
@ -686,25 +850,47 @@ static PyObject *__pyx_pf_3lcn_normalize(CYTHON_UNUSED PyObject *__pyx_self, __P
__pyx_v_stddev = (__pyx_v_stddev + (((*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img.data + __pyx_t_21 * __pyx_v_img.strides[0]) ) + __pyx_t_22 * __pyx_v_img.strides[1]) ))) - __pyx_v_mean) * ((*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img.data + __pyx_t_23 * __pyx_v_img.strides[0]) ) + __pyx_t_24 * __pyx_v_img.strides[1]) ))) - __pyx_v_mean))); __pyx_v_stddev = (__pyx_v_stddev + (((*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img.data + __pyx_t_21 * __pyx_v_img.strides[0]) ) + __pyx_t_22 * __pyx_v_img.strides[1]) ))) - __pyx_v_mean) * ((*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img.data + __pyx_t_23 * __pyx_v_img.strides[0]) ) + __pyx_t_24 * __pyx_v_img.strides[1]) ))) - __pyx_v_mean)));
} }
} }
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">51</span>: <span class="n">stddev</span> <span class="o">=</span> <span class="n">sqrt</span><span class="p">(</span><span class="n">stddev</span><span class="o">/</span><span class="n">num</span><span class="p">)</span></pre> </pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">51</span>: <span class="n">stddev</span> <span class="o">=</span> <span
class="n">sqrt</span><span class="p">(</span><span class="n">stddev</span><span class="o">/</span><span
class="n">num</span><span class="p">)</span></pre>
<pre class='cython code score-0 '> __pyx_v_stddev = sqrt((__pyx_v_stddev / __pyx_v_num)); <pre class='cython code score-0 '> __pyx_v_stddev = sqrt((__pyx_v_stddev / __pyx_v_num));
</pre><pre class="cython line score-0">&#xA0;<span class="">52</span>: </pre> </pre>
<pre class="cython line score-0">&#xA0;<span class="">52</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">53</span>: <span class="c"># compute normalized image (add epsilon) and std dev image</span></pre> <pre class="cython line score-0">&#xA0;<span class="">53</span>: <span class="c"># compute normalized image (add epsilon) and std dev image</span></pre>
<pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">54</span>: <span class="n">img_lcn_view</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">img</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span><span class="o">-</span><span class="n">mean</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">stddev</span><span class="o">+</span><span class="n">eps</span><span class="p">)</span></pre> <pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">54</span>: <span class="n">img_lcn_view</span><span class="p">[</span><span
class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span
class="p">(</span><span class="n">img</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span
class="n">n</span><span class="p">]</span><span class="o">-</span><span class="n">mean</span><span
class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">stddev</span><span
class="o">+</span><span class="n">eps</span><span class="p">)</span></pre>
<pre class='cython code score-0 '> __pyx_t_25 = __pyx_v_m; <pre class='cython code score-0 '> __pyx_t_25 = __pyx_v_m;
__pyx_t_26 = __pyx_v_n; __pyx_t_26 = __pyx_v_n;
__pyx_t_27 = __pyx_v_m; __pyx_t_27 = __pyx_v_m;
__pyx_t_28 = __pyx_v_n; __pyx_t_28 = __pyx_v_n;
*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img_lcn_view.data + __pyx_t_27 * __pyx_v_img_lcn_view.strides[0]) ) + __pyx_t_28 * __pyx_v_img_lcn_view.strides[1]) )) = (((*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img.data + __pyx_t_25 * __pyx_v_img.strides[0]) ) + __pyx_t_26 * __pyx_v_img.strides[1]) ))) - __pyx_v_mean) / (__pyx_v_stddev + __pyx_v_eps)); *((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img_lcn_view.data + __pyx_t_27 * __pyx_v_img_lcn_view.strides[0]) ) + __pyx_t_28 * __pyx_v_img_lcn_view.strides[1]) )) = (((*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img.data + __pyx_t_25 * __pyx_v_img.strides[0]) ) + __pyx_t_26 * __pyx_v_img.strides[1]) ))) - __pyx_v_mean) / (__pyx_v_stddev + __pyx_v_eps));
</pre><pre class="cython line score-0" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">55</span>: <span class="n">img_std_view</span><span class="p">[</span><span class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span class="n">stddev</span></pre> </pre>
<pre class="cython line score-0"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">55</span>: <span class="n">img_std_view</span><span class="p">[</span><span
class="n">m</span><span class="p">,</span> <span class="n">n</span><span class="p">]</span> <span class="o">=</span> <span
class="n">stddev</span></pre>
<pre class='cython code score-0 '> __pyx_t_29 = __pyx_v_m; <pre class='cython code score-0 '> __pyx_t_29 = __pyx_v_m;
__pyx_t_30 = __pyx_v_n; __pyx_t_30 = __pyx_v_n;
*((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img_std_view.data + __pyx_t_29 * __pyx_v_img_std_view.strides[0]) ) + __pyx_t_30 * __pyx_v_img_std_view.strides[1]) )) = __pyx_v_stddev; *((float *) ( /* dim=1 */ (( /* dim=0 */ (__pyx_v_img_std_view.data + __pyx_t_29 * __pyx_v_img_std_view.strides[0]) ) + __pyx_t_30 * __pyx_v_img_std_view.strides[1]) )) = __pyx_v_stddev;
} }
} }
</pre><pre class="cython line score-0">&#xA0;<span class="">56</span>: </pre> </pre>
<pre class="cython line score-0">&#xA0;<span class="">56</span>: </pre>
<pre class="cython line score-0">&#xA0;<span class="">57</span>: <span class="c"># return both</span></pre> <pre class="cython line score-0">&#xA0;<span class="">57</span>: <span class="c"># return both</span></pre>
<pre class="cython line score-10" onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span class="">58</span>: <span class="k">return</span> <span class="n">img_lcn</span><span class="p">,</span> <span class="n">img_std</span></pre> <pre class="cython line score-10"
onclick="(function(s){s.display=s.display==='block'?'none':'block'})(this.nextElementSibling.style)">+<span
class="">58</span>: <span class="k">return</span> <span class="n">img_lcn</span><span class="p">,</span> <span
class="n">img_std</span></pre>
<pre class='cython code score-10 '> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r); <pre class='cython code score-10 '> <span class='pyx_macro_api'>__Pyx_XDECREF</span>(__pyx_r);
__pyx_t_1 = <span class='py_c_api'>PyTuple_New</span>(2);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 58, __pyx_L1_error)</span> __pyx_t_1 = <span class='py_c_api'>PyTuple_New</span>(2);<span class='error_goto'> if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 58, __pyx_L1_error)</span>
<span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1); <span class='refnanny'>__Pyx_GOTREF</span>(__pyx_t_1);
@ -717,4 +903,7 @@ static PyObject *__pyx_pf_3lcn_normalize(CYTHON_UNUSED PyObject *__pyx_self, __P
__pyx_r = __pyx_t_1; __pyx_r = __pyx_t_1;
__pyx_t_1 = 0; __pyx_t_1 = 0;
goto __pyx_L0; goto __pyx_L0;
</pre></div></body></html> </pre>
</div>
</body>
</html>

7
hyperdepth/hyperparam_search.py
Unescape View File

@ -24,7 +24,6 @@ def get_data(n, row_from, row_to, train):
return ims, disps return ims, disps
params = hd.TrainParams( params = hd.TrainParams(
n_trees=4, n_trees=4,
max_tree_depth=, max_tree_depth=,
@ -52,9 +51,11 @@ for tree_depth in [8,10,12,14,16]:
prefix = Path(f'./forests/{prefix}/') prefix = Path(f'./forests/{prefix}/')
prefix.mkdir(parents=True, exist_ok=True) prefix.mkdir(parents=True, exist_ok=True)
hd.train_forest(params, train_ims, train_disps, n_disp_bins=n_disp_bins, depth_switch=depth_switch, forest_prefix=str(prefix / 'fr')) hd.train_forest(params, train_ims, train_disps, n_disp_bins=n_disp_bins, depth_switch=depth_switch,
forest_prefix=str(prefix / 'fr'))
es = hd.eval_forest(test_ims, test_disps, n_disp_bins=n_disp_bins, depth_switch=depth_switch, forest_prefix=str(prefix / 'fr')) es = hd.eval_forest(test_ims, test_disps, n_disp_bins=n_disp_bins, depth_switch=depth_switch,
forest_prefix=str(prefix / 'fr'))
np.save(str(prefix / 'ta.npy'), test_disps) np.save(str(prefix / 'ta.npy'), test_disps)
np.save(str(prefix / 'es.npy'), es) np.save(str(prefix / 'es.npy'), es)

5
hyperdepth/setup.py
Unescape View File

@ -8,7 +8,6 @@ import os
this_dir = os.path.dirname(__file__) this_dir = os.path.dirname(__file__)
extra_compile_args = ['-O3', '-std=c++11'] extra_compile_args = ['-O3', '-std=c++11']
extra_link_args = [] extra_link_args = []
@ -39,7 +38,3 @@ setup(
) )
] ]
) )

13
hyperdepth/vis_eval.py
Unescape View File

@ -6,10 +6,13 @@ orig = cv2.imread('disp_orig.png', cv2.IMREAD_ANYDEPTH).astype(np.float32)
ta = cv2.imread('disp_ta.png', cv2.IMREAD_ANYDEPTH).astype(np.float32) ta = cv2.imread('disp_ta.png', cv2.IMREAD_ANYDEPTH).astype(np.float32)
es = cv2.imread('disp_es.png', cv2.IMREAD_ANYDEPTH).astype(np.float32) es = cv2.imread('disp_es.png', cv2.IMREAD_ANYDEPTH).astype(np.float32)
plt.figure() plt.figure()
plt.subplot(2,2,1); plt.imshow(orig / 16, vmin=0, vmax=4, cmap='magma') plt.subplot(2, 2, 1);
plt.subplot(2,2,2); plt.imshow(ta / 16, vmin=0, vmax=4, cmap='magma') plt.imshow(orig / 16, vmin=0, vmax=4, cmap='magma')
plt.subplot(2,2,3); plt.imshow(es / 16, vmin=0, vmax=4, cmap='magma') plt.subplot(2, 2, 2);
plt.subplot(2,2,4); plt.imshow(np.abs(es - ta) / 16, vmin=0, vmax=1, cmap='magma') plt.imshow(ta / 16, vmin=0, vmax=4, cmap='magma')
plt.subplot(2, 2, 3);
plt.imshow(es / 16, vmin=0, vmax=4, cmap='magma')
plt.subplot(2, 2, 4);
plt.imshow(np.abs(es - ta) / 16, vmin=0, vmax=1, cmap='magma')
plt.show() plt.show()

71
model/exp_synph.py
Unescape View File

@ -12,9 +12,12 @@ import torchext
from model import networks from model import networks
from data import dataset from data import dataset
class Worker(torchext.Worker): class Worker(torchext.Worker):
def __init__(self, args, num_workers=18, train_batch_size=8, test_batch_size=8, save_frequency=1, **kwargs): def __init__(self, args, num_workers=18, train_batch_size=8, test_batch_size=8, save_frequency=1, **kwargs):
super().__init__(args.output_dir, args.exp_name, epochs=args.epochs, num_workers=num_workers, train_batch_size=train_batch_size, test_batch_size=test_batch_size, save_frequency=save_frequency, **kwargs) super().__init__(args.output_dir, args.exp_name, epochs=args.epochs, num_workers=num_workers,
train_batch_size=train_batch_size, test_batch_size=test_batch_size,
save_frequency=save_frequency, **kwargs)
self.ms = args.ms self.ms = args.ms
self.pattern_path = args.pattern_path self.pattern_path = args.pattern_path
@ -22,7 +25,7 @@ class Worker(torchext.Worker):
self.dp_weight = args.dp_weight self.dp_weight = args.dp_weight
self.data_type = args.data_type self.data_type = args.data_type
self.imsizes = [(480,640)] self.imsizes = [(488, 648)]
for iter in range(3): for iter in range(3):
self.imsizes.append((int(self.imsizes[-1][0] / 2), int(self.imsizes[-1][1] / 2))) self.imsizes.append((int(self.imsizes[-1][0] / 2), int(self.imsizes[-1][1] / 2)))
@ -50,13 +53,15 @@ class Worker(torchext.Worker):
self.eval_w = self.imsizes[0][1] - 13 - 140 self.eval_w = self.imsizes[0][1] - 13 - 140
def get_train_set(self): def get_train_set(self):
train_set = dataset.TrackSynDataset(self.settings_path, self.train_paths, train=True, data_aug=True, track_length=1) train_set = dataset.TrackSynDataset(self.settings_path, self.train_paths, train=True, data_aug=True,
track_length=1)
return train_set return train_set
def get_test_sets(self): def get_test_sets(self):
test_sets = torchext.TestSets() test_sets = torchext.TestSets()
test_set = dataset.TrackSynDataset(self.settings_path, self.test_paths, train=False, data_aug=True, track_length=1) test_set = dataset.TrackSynDataset(self.settings_path, self.test_paths, train=False, data_aug=True,
track_length=1)
test_sets.append('simple', test_set, test_frequency=1) test_sets.append('simple', test_set, test_frequency=1)
# initialize photometric loss modules according to image sizes # initialize photometric loss modules according to image sizes
@ -161,43 +166,76 @@ class Worker(torchext.Worker):
im_orig = self.data['im0'].detach().to('cpu').numpy()[0, 0] im_orig = self.data['im0'].detach().to('cpu').numpy()[0, 0]
pattern_diff = np.abs(im_orig - pattern_proj) pattern_diff = np.abs(im_orig - pattern_proj)
fig = plt.figure(figsize=(16, 16)) fig = plt.figure(figsize=(16, 16))
es_ = co.cmap.color_depth_map(es, scale=vmax) es_ = co.cmap.color_depth_map(es, scale=vmax)
gt_ = co.cmap.color_depth_map(gt, scale=vmax) gt_ = co.cmap.color_depth_map(gt, scale=vmax)
diff_ = co.cmap.color_error_image(diff, BGR=True) diff_ = co.cmap.color_error_image(diff, BGR=True)
# plot disparities, ground truth disparity is shown only for reference # plot disparities, ground truth disparity is shown only for reference
ax = plt.subplot(3,3,1); plt.imshow(es_[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'Disparity Est. {es.min():.4f}/{es.max():.4f}') ax = plt.subplot(3, 3, 1)
ax = plt.subplot(3,3,2); plt.imshow(gt_[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'Disparity GT {np.nanmin(gt):.4f}/{np.nanmax(gt):.4f}') plt.imshow(es_[..., [2, 1, 0]])
ax = plt.subplot(3,3,3); plt.imshow(diff_[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'Disparity Err. {diff.mean():.5f}') plt.xticks([])
plt.yticks([])
ax.set_title(f'Disparity Est. {es.min():.4f}/{es.max():.4f}')
ax = plt.subplot(3, 3, 2)
plt.imshow(gt_[..., [2, 1, 0]])
plt.xticks([])
plt.yticks([])
ax.set_title(f'Disparity GT {np.nanmin(gt):.4f}/{np.nanmax(gt):.4f}')
ax = plt.subplot(3, 3, 3)
plt.imshow(diff_[..., [2, 1, 0]])
plt.xticks([])
plt.yticks([])
ax.set_title(f'Disparity Err. {diff.mean():.5f}')
# plot edges # plot edges
edge = self.edge.to('cpu').numpy()[0, 0] edge = self.edge.to('cpu').numpy()[0, 0]
edge_gt = self.edge_gt.to('cpu').numpy()[0, 0] edge_gt = self.edge_gt.to('cpu').numpy()[0, 0]
edge_err = np.abs(edge - edge_gt) edge_err = np.abs(edge - edge_gt)
ax = plt.subplot(3,3,4); plt.imshow(edge, cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'Edge Est. {edge.min():.5f}/{edge.max():.5f}') ax = plt.subplot(3, 3, 4);
ax = plt.subplot(3,3,5); plt.imshow(edge_gt, cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'Edge GT {edge_gt.min():.5f}/{edge_gt.max():.5f}') plt.imshow(edge, cmap='gray');
ax = plt.subplot(3,3,6); plt.imshow(edge_err, cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'Edge Err. {edge_err.mean():.5f}') plt.xticks([]);
plt.yticks([]);
ax.set_title(f'Edge Est. {edge.min():.5f}/{edge.max():.5f}')
ax = plt.subplot(3, 3, 5);
plt.imshow(edge_gt, cmap='gray');
plt.xticks([]);
plt.yticks([]);
ax.set_title(f'Edge GT {edge_gt.min():.5f}/{edge_gt.max():.5f}')
ax = plt.subplot(3, 3, 6);
plt.imshow(edge_err, cmap='gray');
plt.xticks([]);
plt.yticks([]);
ax.set_title(f'Edge Err. {edge_err.mean():.5f}')
# plot normalized IR input and warped pattern # plot normalized IR input and warped pattern
ax = plt.subplot(3,3,7); plt.imshow(im, vmin=im.min(), vmax=im.max(), cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'IR input {im.mean():.5f}/{im.std():.5f}') ax = plt.subplot(3, 3, 7);
ax = plt.subplot(3,3,8); plt.imshow(pattern_proj, vmin=im.min(), vmax=im.max(), cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'Warped Pattern {pattern_proj.mean():.5f}/{pattern_proj.std():.5f}') plt.imshow(im, vmin=im.min(), vmax=im.max(), cmap='gray');
plt.xticks([]);
plt.yticks([]);
ax.set_title(f'IR input {im.mean():.5f}/{im.std():.5f}')
ax = plt.subplot(3, 3, 8);
plt.imshow(pattern_proj, vmin=im.min(), vmax=im.max(), cmap='gray');
plt.xticks([]);
plt.yticks([]);
ax.set_title(f'Warped Pattern {pattern_proj.mean():.5f}/{pattern_proj.std():.5f}')
im_std = self.data['std0'].to('cpu').numpy()[0, 0] im_std = self.data['std0'].to('cpu').numpy()[0, 0]
ax = plt.subplot(3,3,9); plt.imshow(im_std, cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'IR std {im_std.min():.5f}/{im_std.max():.5f}') ax = plt.subplot(3, 3, 9);
plt.imshow(im_std, cmap='gray');
plt.xticks([]);
plt.yticks([]);
ax.set_title(f'IR std {im_std.min():.5f}/{im_std.max():.5f}')
plt.tight_layout() plt.tight_layout()
plt.savefig(str(out_path)) plt.savefig(str(out_path))
plt.close(fig) plt.close(fig)
def callback_train_post_backward(self, net, errs, output, epoch, batch_idx, masks=[]): def callback_train_post_backward(self, net, errs, output, epoch, batch_idx, masks=[]):
if batch_idx % 512 == 0: if batch_idx % 512 == 0:
out_path = self.exp_out_root / f'train_{epoch:03d}_{batch_idx:04d}.png' out_path = self.exp_out_root / f'train_{epoch:03d}_{batch_idx:04d}.png'
es, gt, im, ma = self.numpy_in_out(output) es, gt, im, ma = self.numpy_in_out(output)
self.write_img(out_path, es[0, 0], gt[0, 0], im[0, 0], ma[0, 0]) self.write_img(out_path, es[0, 0], gt[0, 0], im[0, 0], ma[0, 0])
def callback_test_start(self, epoch, set_idx): def callback_test_start(self, epoch, set_idx):
self.metric = co.metric.MultipleMetric( self.metric = co.metric.MultipleMetric(
co.metric.DistanceMetric(vec_length=1), co.metric.DistanceMetric(vec_length=1),
@ -232,6 +270,5 @@ class Worker(torchext.Worker):
return es, gt, im, ma return es, gt, im, ma
if __name__ == '__main__': if __name__ == '__main__':
pass pass

61
model/exp_synphge.py
Unescape View File

@ -12,9 +12,12 @@ import torchext
from model import networks from model import networks
from data import dataset from data import dataset
class Worker(torchext.Worker): class Worker(torchext.Worker):
def __init__(self, args, num_workers=18, train_batch_size=8, test_batch_size=8, save_frequency=1, **kwargs): def __init__(self, args, num_workers=18, train_batch_size=8, test_batch_size=8, save_frequency=1, **kwargs):
super().__init__(args.output_dir, args.exp_name, epochs=args.epochs, num_workers=num_workers, train_batch_size=train_batch_size, test_batch_size=test_batch_size, save_frequency=save_frequency, **kwargs) super().__init__(args.output_dir, args.exp_name, epochs=args.epochs, num_workers=num_workers,
train_batch_size=train_batch_size, test_batch_size=test_batch_size,
save_frequency=save_frequency, **kwargs)
self.ms = args.ms self.ms = args.ms
self.pattern_path = args.pattern_path self.pattern_path = args.pattern_path
@ -52,14 +55,15 @@ class Worker(torchext.Worker):
self.eval_h = self.imsizes[0][0] - 2 * 13 self.eval_h = self.imsizes[0][0] - 2 * 13
self.eval_w = self.imsizes[0][1] - 13 - 140 self.eval_w = self.imsizes[0][1] - 13 - 140
def get_train_set(self): def get_train_set(self):
train_set = dataset.TrackSynDataset(self.settings_path, self.train_paths, train=True, data_aug=True, track_length=self.track_length) train_set = dataset.TrackSynDataset(self.settings_path, self.train_paths, train=True, data_aug=True,
track_length=self.track_length)
return train_set return train_set
def get_test_sets(self): def get_test_sets(self):
test_sets = torchext.TestSets() test_sets = torchext.TestSets()
test_set = dataset.TrackSynDataset(self.settings_path, self.test_paths, train=False, data_aug=True, track_length=1) test_set = dataset.TrackSynDataset(self.settings_path, self.test_paths, train=False, data_aug=True,
track_length=1)
test_sets.append('simple', test_set, test_frequency=1) test_sets.append('simple', test_set, test_frequency=1)
self.ph_losses = [] self.ph_losses = []
@ -231,23 +235,55 @@ class Worker(torchext.Worker):
diff0 = co.cmap.color_error_image(diff[0], BGR=True) diff0 = co.cmap.color_error_image(diff[0], BGR=True)
# plot disparities, ground truth disparity is shown only for reference # plot disparities, ground truth disparity is shown only for reference
ax = plt.subplot(3,3,1); plt.imshow(es0[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'F0 Disparity Est. {es0.min():.4f}/{es0.max():.4f}') ax = plt.subplot(3, 3, 1);
ax = plt.subplot(3,3,2); plt.imshow(gt0[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'F0 Disparity GT {np.nanmin(gt0):.4f}/{np.nanmax(gt0):.4f}') plt.imshow(es0[..., [2, 1, 0]]);
ax = plt.subplot(3,3,3); plt.imshow(diff0[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'F0 Disparity Err. {diff0.mean():.5f}') plt.xticks([]);
plt.yticks([]);
ax.set_title(f'F0 Disparity Est. {es0.min():.4f}/{es0.max():.4f}')
ax = plt.subplot(3, 3, 2);
plt.imshow(gt0[..., [2, 1, 0]]);
plt.xticks([]);
plt.yticks([]);
ax.set_title(f'F0 Disparity GT {np.nanmin(gt0):.4f}/{np.nanmax(gt0):.4f}')
ax = plt.subplot(3, 3, 3);
plt.imshow(diff0[..., [2, 1, 0]]);
plt.xticks([]);
plt.yticks([]);
ax.set_title(f'F0 Disparity Err. {diff0.mean():.5f}')
# plot disparities of the second frame in the track if exists # plot disparities of the second frame in the track if exists
if es.shape[0] >= 2: if es.shape[0] >= 2:
es1 = co.cmap.color_depth_map(es[1], scale=vmax) es1 = co.cmap.color_depth_map(es[1], scale=vmax)
gt1 = co.cmap.color_depth_map(gt[1], scale=vmax) gt1 = co.cmap.color_depth_map(gt[1], scale=vmax)
diff1 = co.cmap.color_error_image(diff[1], BGR=True) diff1 = co.cmap.color_error_image(diff[1], BGR=True)
ax = plt.subplot(3,3,4); plt.imshow(es1[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'F1 Disparity Est. {es1.min():.4f}/{es1.max():.4f}') ax = plt.subplot(3, 3, 4);
ax = plt.subplot(3,3,5); plt.imshow(gt1[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'F1 Disparity GT {np.nanmin(gt1):.4f}/{np.nanmax(gt1):.4f}') plt.imshow(es1[..., [2, 1, 0]]);
ax = plt.subplot(3,3,6); plt.imshow(diff1[...,[2,1,0]]); plt.xticks([]); plt.yticks([]); ax.set_title(f'F1 Disparity Err. {diff1.mean():.5f}') plt.xticks([]);
plt.yticks([]);
ax.set_title(f'F1 Disparity Est. {es1.min():.4f}/{es1.max():.4f}')
ax = plt.subplot(3, 3, 5);
plt.imshow(gt1[..., [2, 1, 0]]);
plt.xticks([]);
plt.yticks([]);
ax.set_title(f'F1 Disparity GT {np.nanmin(gt1):.4f}/{np.nanmax(gt1):.4f}')
ax = plt.subplot(3, 3, 6);
plt.imshow(diff1[..., [2, 1, 0]]);
plt.xticks([]);
plt.yticks([]);
ax.set_title(f'F1 Disparity Err. {diff1.mean():.5f}')
# plot normalized IR inputs # plot normalized IR inputs
ax = plt.subplot(3,3,7); plt.imshow(im[0], vmin=im.min(), vmax=im.max(), cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'F0 IR input {im[0].mean():.5f}/{im[0].std():.5f}') ax = plt.subplot(3, 3, 7);
plt.imshow(im[0], vmin=im.min(), vmax=im.max(), cmap='gray');
plt.xticks([]);
plt.yticks([]);
ax.set_title(f'F0 IR input {im[0].mean():.5f}/{im[0].std():.5f}')
if es.shape[0] >= 2: if es.shape[0] >= 2:
ax = plt.subplot(3,3,8); plt.imshow(im[1], vmin=im.min(), vmax=im.max(), cmap='gray'); plt.xticks([]); plt.yticks([]); ax.set_title(f'F1 IR input {im[1].mean():.5f}/{im[1].std():.5f}') ax = plt.subplot(3, 3, 8);
plt.imshow(im[1], vmin=im.min(), vmax=im.max(), cmap='gray');
plt.xticks([]);
plt.yticks([]);
ax.set_title(f'F1 IR input {im[1].mean():.5f}/{im[1].std():.5f}')
plt.tight_layout() plt.tight_layout()
plt.savefig(str(out_path)) plt.savefig(str(out_path))
@ -294,5 +330,6 @@ class Worker(torchext.Worker):
ma = np.reshape(ma[..., self.eval_mask], [tl * bs, 1, self.eval_h, self.eval_w]) ma = np.reshape(ma[..., self.eval_mask], [tl * bs, 1, self.eval_h, self.eval_w])
return es, gt, im, ma return es, gt, im, ma
if __name__ == '__main__': if __name__ == '__main__':
pass pass

47
model/networks.py
Unescape View File

@ -61,6 +61,7 @@ class OutputLayerFactory(object):
pos: estimate the absolute location pos: estimate the absolute location
pos_row: independently estimate the absolute location per row pos_row: independently estimate the absolute location per row
''' '''
def __init__(self, type='disp', params={}): def __init__(self, type='disp', params={}):
self.type = type self.type = type
self.params = params self.params = params
@ -118,12 +119,13 @@ class MultiLinear(TimedModule):
return y return y
class DispNetS(TimedModule): class DispNetS(TimedModule):
''' '''
Disparity Decoder based on DispNetS Disparity Decoder based on DispNetS
''' '''
def __init__(self, channels_in, imsizes, output_facs, output_ms=True, coordconv=False, weight_init=False, channel_multiplier=1):
def __init__(self, channels_in, imsizes, output_facs, output_ms=True, coordconv=False, weight_init=False,
channel_multiplier=1):
super(DispNetS, self).__init__(mod_name='DispNetS') super(DispNetS, self).__init__(mod_name='DispNetS')
self.output_ms = output_ms self.output_ms = output_ms
@ -166,7 +168,6 @@ class DispNetS(TimedModule):
self.predict_disp2 = output_facs(upconv_planes[5], imsizes[1]) self.predict_disp2 = output_facs(upconv_planes[5], imsizes[1])
self.predict_disp1 = output_facs(upconv_planes[6], imsizes[0]) self.predict_disp1 = output_facs(upconv_planes[6], imsizes[0])
def init_weights(self): def init_weights(self):
for m in self.modules(): for m in self.modules():
if isinstance(m, torch.nn.Conv2d) or isinstance(m, torch.nn.ConvTranspose2d): if isinstance(m, torch.nn.Conv2d) or isinstance(m, torch.nn.ConvTranspose2d):
@ -176,9 +177,11 @@ class DispNetS(TimedModule):
def downsample_conv(self, in_planes, out_planes, kernel_size=3): def downsample_conv(self, in_planes, out_planes, kernel_size=3):
if self.coordconv: if self.coordconv:
conv = torchext.CoordConv2d(in_planes, out_planes, kernel_size=kernel_size, stride=2, padding=(kernel_size-1)//2) conv = torchext.CoordConv2d(in_planes, out_planes, kernel_size=kernel_size, stride=2,
padding=(kernel_size - 1) // 2)
else: else:
conv = torch.nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=2, padding=(kernel_size-1)//2) conv = torch.nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=2,
padding=(kernel_size - 1) // 2)
return torch.nn.Sequential( return torch.nn.Sequential(
conv, conv,
torch.nn.ReLU(inplace=True), torch.nn.ReLU(inplace=True),
@ -229,19 +232,22 @@ class DispNetS(TimedModule):
disp4 = self.predict_disp4(out_iconv4) disp4 = self.predict_disp4(out_iconv4)
out_upconv3 = self.crop_like(self.upconv3(out_iconv4), out_conv2) out_upconv3 = self.crop_like(self.upconv3(out_iconv4), out_conv2)
disp4_up = self.crop_like(torch.nn.functional.interpolate(disp4, scale_factor=2, mode='bilinear', align_corners=False), out_conv2) disp4_up = self.crop_like(
torch.nn.functional.interpolate(disp4, scale_factor=2, mode='bilinear', align_corners=False), out_conv2)
concat3 = torch.cat((out_upconv3, out_conv2, disp4_up), 1) concat3 = torch.cat((out_upconv3, out_conv2, disp4_up), 1)
out_iconv3 = self.iconv3(concat3) out_iconv3 = self.iconv3(concat3)
disp3 = self.predict_disp3(out_iconv3) disp3 = self.predict_disp3(out_iconv3)
out_upconv2 = self.crop_like(self.upconv2(out_iconv3), out_conv1) out_upconv2 = self.crop_like(self.upconv2(out_iconv3), out_conv1)
disp3_up = self.crop_like(torch.nn.functional.interpolate(disp3, scale_factor=2, mode='bilinear', align_corners=False), out_conv1) disp3_up = self.crop_like(
torch.nn.functional.interpolate(disp3, scale_factor=2, mode='bilinear', align_corners=False), out_conv1)
concat2 = torch.cat((out_upconv2, out_conv1, disp3_up), 1) concat2 = torch.cat((out_upconv2, out_conv1, disp3_up), 1)
out_iconv2 = self.iconv2(concat2) out_iconv2 = self.iconv2(concat2)
disp2 = self.predict_disp2(out_iconv2) disp2 = self.predict_disp2(out_iconv2)
out_upconv1 = self.crop_like(self.upconv1(out_iconv2), x) out_upconv1 = self.crop_like(self.upconv1(out_iconv2), x)
disp2_up = self.crop_like(torch.nn.functional.interpolate(disp2, scale_factor=2, mode='bilinear', align_corners=False), x) disp2_up = self.crop_like(
torch.nn.functional.interpolate(disp2, scale_factor=2, mode='bilinear', align_corners=False), x)
concat1 = torch.cat((out_upconv1, disp2_up), 1) concat1 = torch.cat((out_upconv1, disp2_up), 1)
out_iconv1 = self.iconv1(concat1) out_iconv1 = self.iconv1(concat1)
disp1 = self.predict_disp1(out_iconv1) disp1 = self.predict_disp1(out_iconv1)
@ -256,6 +262,7 @@ class DispNetShallow(DispNetS):
''' '''
Edge Decoder based on DispNetS with fewer layers Edge Decoder based on DispNetS with fewer layers
''' '''
def __init__(self, channels_in, imsizes, output_facs, output_ms=True, coordconv=False, weight_init=False): def __init__(self, channels_in, imsizes, output_facs, output_ms=True, coordconv=False, weight_init=False):
super(DispNetShallow, self).__init__(channels_in, imsizes, output_facs, output_ms, coordconv, weight_init) super(DispNetShallow, self).__init__(channels_in, imsizes, output_facs, output_ms, coordconv, weight_init)
self.mod_name = 'DispNetShallow' self.mod_name = 'DispNetShallow'
@ -274,13 +281,15 @@ class DispNetShallow(DispNetS):
disp3 = self.predict_disp3(out_iconv3) disp3 = self.predict_disp3(out_iconv3)
out_upconv2 = self.crop_like(self.upconv2(out_iconv3), out_conv1) out_upconv2 = self.crop_like(self.upconv2(out_iconv3), out_conv1)
disp3_up = self.crop_like(torch.nn.functional.interpolate(disp3, scale_factor=2, mode='bilinear', align_corners=False), out_conv1) disp3_up = self.crop_like(
torch.nn.functional.interpolate(disp3, scale_factor=2, mode='bilinear', align_corners=False), out_conv1)
concat2 = torch.cat((out_upconv2, out_conv1, disp3_up), 1) concat2 = torch.cat((out_upconv2, out_conv1, disp3_up), 1)
out_iconv2 = self.iconv2(concat2) out_iconv2 = self.iconv2(concat2)
disp2 = self.predict_disp2(out_iconv2) disp2 = self.predict_disp2(out_iconv2)
out_upconv1 = self.crop_like(self.upconv1(out_iconv2), x) out_upconv1 = self.crop_like(self.upconv1(out_iconv2), x)
disp2_up = self.crop_like(torch.nn.functional.interpolate(disp2, scale_factor=2, mode='bilinear', align_corners=False), x) disp2_up = self.crop_like(
torch.nn.functional.interpolate(disp2, scale_factor=2, mode='bilinear', align_corners=False), x)
concat1 = torch.cat((out_upconv1, disp2_up), 1) concat1 = torch.cat((out_upconv1, disp2_up), 1)
out_iconv1 = self.iconv1(concat1) out_iconv1 = self.iconv1(concat1)
disp1 = self.predict_disp1(out_iconv1) disp1 = self.predict_disp1(out_iconv1)
@ -295,10 +304,13 @@ class DispEdgeDecoders(TimedModule):
''' '''
Disparity Decoder and Edge Decoder Disparity Decoder and Edge Decoder
''' '''
def __init__(self, *args, max_disp=128, **kwargs): def __init__(self, *args, max_disp=128, **kwargs):
super(DispEdgeDecoders, self).__init__(mod_name='DispEdgeDecoders') super(DispEdgeDecoders, self).__init__(mod_name='DispEdgeDecoders')
output_facs = [OutputLayerFactory( type='disp', params={ 'alpha': max_disp/(2**s), 'beta': 0, 'gamma': 1, 'offset': 3}) for s in range(4)] output_facs = [
OutputLayerFactory(type='disp', params={'alpha': max_disp / (2 ** s), 'beta': 0, 'gamma': 1, 'offset': 3})
for s in range(4)]
self.disp_decoder = DispNetS(*args, output_facs=output_facs, **kwargs) self.disp_decoder = DispNetS(*args, output_facs=output_facs, **kwargs)
output_facs = [OutputLayerFactory(type='linear') for s in range(4)] output_facs = [OutputLayerFactory(type='linear') for s in range(4)]
@ -336,11 +348,11 @@ class PosToDepth(DispToDepth):
return super().forward(disp) return super().forward(disp)
class RectifiedPatternSimilarityLoss(TimedModule): class RectifiedPatternSimilarityLoss(TimedModule):
''' '''
Photometric Loss Photometric Loss
''' '''
def __init__(self, im_height, im_width, pattern, loss_type='census_sad', loss_eps=0.5): def __init__(self, im_height, im_width, pattern, loss_type='census_sad', loss_eps=0.5):
super().__init__(mod_name='RectifiedPatternSimilarityLoss') super().__init__(mod_name='RectifiedPatternSimilarityLoss')
self.im_height = im_height self.im_height = im_height
@ -377,10 +389,12 @@ class RectifiedPatternSimilarityLoss(TimedModule):
val = (mask * diff).sum() / mask.sum() val = (mask * diff).sum() / mask.sum()
return val, pattern_proj return val, pattern_proj
class DisparityLoss(TimedModule): class DisparityLoss(TimedModule):
''' '''
Disparity Loss Disparity Loss
''' '''
def __init__(self): def __init__(self):
super().__init__(mod_name='DisparityLoss') super().__init__(mod_name='DisparityLoss')
self.sobel = SobelFilter(norm=False) self.sobel = SobelFilter(norm=False)
@ -412,11 +426,11 @@ class DisparityLoss(TimedModule):
return val return val
class ProjectionBaseLoss(TimedModule): class ProjectionBaseLoss(TimedModule):
''' '''
Base module of the Geometric Loss Base module of the Geometric Loss
''' '''
def __init__(self, K, Ki, im_height, im_width): def __init__(self, K, Ki, im_height, im_width):
super().__init__(mod_name='ProjectionBaseLoss') super().__init__(mod_name='ProjectionBaseLoss')
@ -465,7 +479,6 @@ class ProjectionBaseLoss(TimedModule):
uv = uv[:, :, :2] / (torch.nn.functional.relu(d) + 1e-12) uv = uv[:, :, :2] / (torch.nn.functional.relu(d) + 1e-12)
return uv, d return uv, d
def tforward(self, depth0, R0, t0, R1, t1): def tforward(self, depth0, R0, t0, R1, t1):
xyz = self.unproject(depth0, R0, t0) xyz = self.unproject(depth0, R0, t0)
return self.project(xyz, R1, t1) return self.project(xyz, R1, t1)
@ -475,6 +488,7 @@ class ProjectionDepthSimilarityLoss(ProjectionBaseLoss):
''' '''
Geometric Loss Geometric Loss
''' '''
def __init__(self, *args, clamp=-1): def __init__(self, *args, clamp=-1):
super().__init__(*args) super().__init__(*args)
self.mod_name = 'ProjectionDepthSimilarityLoss' self.mod_name = 'ProjectionDepthSimilarityLoss'
@ -503,11 +517,11 @@ class ProjectionDepthSimilarityLoss(ProjectionBaseLoss):
return l0 + l1 return l0 + l1
class LCN(TimedModule): class LCN(TimedModule):
''' '''
Local Contract Normalization Local Contract Normalization
''' '''
def __init__(self, radius, epsilon): def __init__(self, radius, epsilon):
super().__init__(mod_name='LCN') super().__init__(mod_name='LCN')
self.box_conv = torch.nn.Sequential( self.box_conv = torch.nn.Sequential(
@ -533,11 +547,11 @@ class LCN(TimedModule):
return (data - avgs) / stds, stds return (data - avgs) / stds, stds
class SobelFilter(TimedModule): class SobelFilter(TimedModule):
''' '''
Sobel Filter Sobel Filter
''' '''
def __init__(self, norm=False): def __init__(self, norm=False):
super(SobelFilter, self).__init__(mod_name='SobelFilter') super(SobelFilter, self).__init__(mod_name='SobelFilter')
kx = np.array([[-5, -4, 0, 4, 5], kx = np.array([[-5, -4, 0, 4, 5],
@ -563,4 +577,3 @@ class SobelFilter(TimedModule):
return torch.sqrt(gx ** 2 + gy ** 2 + 1e-8) return torch.sqrt(gx ** 2 + gy ** 2 + 1e-8)
else: else:
return torch.cat((gx, gy), dim=1) return torch.cat((gx, gy), dim=1)

64
readme.md
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@ -6,7 +6,9 @@ This repository contains the code for the paper
**[Connecting the Dots: Learning Representations for Active Monocular Depth Estimation](http://www.cvlibs.net/publications/Riegler2019CVPR.pdf)** **[Connecting the Dots: Learning Representations for Active Monocular Depth Estimation](http://www.cvlibs.net/publications/Riegler2019CVPR.pdf)**
<br> <br>
[Gernot Riegler](https://griegler.github.io/), [Yiyi Liao](https://yiyiliao.github.io/), [Simon Donne](https://avg.is.tuebingen.mpg.de/person/sdonne), [Vladlen Koltun](http://vladlen.info/), and [Andreas Geiger](http://www.cvlibs.net/) [Gernot Riegler](https://griegler.github.io/), [Yiyi Liao](https://yiyiliao.github.io/)
, [Simon Donne](https://avg.is.tuebingen.mpg.de/person/sdonne), [Vladlen Koltun](http://vladlen.info/),
and [Andreas Geiger](http://www.cvlibs.net/)
<br> <br>
[CVPR 2019](http://cvpr2019.thecvf.com/) [CVPR 2019](http://cvpr2019.thecvf.com/)
@ -24,40 +26,45 @@ If you find this code useful for your research, please cite
} }
``` ```
## Dependencies ## Dependencies
The network training/evaluation code is based on `Pytorch`. The network training/evaluation code is based on `Pytorch`.
``` ```
PyTorch>=1.1 PyTorch>=1.1
Cuda>=10.0 Cuda>=10.0
``` ```
Updated on 07.06.2021: The code is now compatible with the latest Pytorch version (1.8). Updated on 07.06.2021: The code is now compatible with the latest Pytorch version (1.8).
The other python packages can be installed with `anaconda`: The other python packages can be installed with `anaconda`:
``` ```
conda install --file requirements.txt conda install --file requirements.txt
``` ```
### Structured Light Renderer ### Structured Light Renderer
To train and evaluate our method in a controlled setting, we implemented an structured light renderer.
It can be used to render a virtual scene (arbitrary triangle mesh) with the structured light pattern projected from a customizable projector location. To train and evaluate our method in a controlled setting, we implemented an structured light renderer. It can be used to
To build it, first make sure the correct `CUDA_LIBRARY_PATH` is set in `config.json`. render a virtual scene (arbitrary triangle mesh) with the structured light pattern projected from a customizable
Afterwards, the renderer can be build by running `make` within the `renderer` directory. projector location. To build it, first make sure the correct `CUDA_LIBRARY_PATH` is set in `config.json`. Afterwards,
the renderer can be build by running `make` within the `renderer` directory.
### PyTorch Extensions ### PyTorch Extensions
The network training/evaluation code is based on `PyTorch`.
We implemented some custom layers that need to be built in the `torchext` directory. The network training/evaluation code is based on `PyTorch`. We implemented some custom layers that need to be built in
Simply change into this directory and run the `torchext` directory. Simply change into this directory and run
``` ```
python setup.py build_ext --inplace python setup.py build_ext --inplace
``` ```
### Baseline HyperDepth ### Baseline HyperDepth
As baseline we partially re-implemented the random forest based method [HyperDepth](http://openaccess.thecvf.com/content_cvpr_2016/papers/Fanello_HyperDepth_Learning_Depth_CVPR_2016_paper.pdf).
The code resided in the `hyperdepth` directory and is implemented in `C++11` with a Python wrapper written in `Cython`. As baseline we partially re-implemented the random forest based
To build it change into the directory and run method [HyperDepth](http://openaccess.thecvf.com/content_cvpr_2016/papers/Fanello_HyperDepth_Learning_Depth_CVPR_2016_paper.pdf)
. The code resided in the `hyperdepth` directory and is implemented in `C++11` with a Python wrapper written in `Cython`
. To build it change into the directory and run
``` ```
python setup.py build_ext --inplace python setup.py build_ext --inplace
@ -65,42 +72,59 @@ python setup.py build_ext --inplace
## Running ## Running
### Creating Synthetic Data ### Creating Synthetic Data
To create synthetic data and save it locally, download [ShapeNet V2](https://www.shapenet.org/) and correct `SHAPENET_ROOT` in `config.json`. Then the data can be generated and saved to `DATA_ROOT` in `config.json` by running
To create synthetic data and save it locally, download [ShapeNet V2](https://www.shapenet.org/) and
correct `SHAPENET_ROOT` in `config.json`. Then the data can be generated and saved to `DATA_ROOT` in `config.json` by
running
``` ```
./create_syn_data.sh ./create_syn_data.sh
``` ```
If you are only interested in evaluating our pre-trained model, [here (3.7G)](https://s3.eu-central-1.amazonaws.com/avg-projects/connecting_the_dots/val_data.zip) is a validation set that contains a small amount of images.
If you are only interested in evaluating our pre-trained
model, [here (3.7G)](https://s3.eu-central-1.amazonaws.com/avg-projects/connecting_the_dots/val_data.zip) is a
validation set that contains a small amount of images.
### Training Network ### Training Network
As a first stage, it is recommended to train the disparity decoder and edge decoder without the geometric loss. To train the network on synthetic data for the first stage run As a first stage, it is recommended to train the disparity decoder and edge decoder without the geometric loss. To train
the network on synthetic data for the first stage run
``` ```
python train_val.py python train_val.py
``` ```
After the model is pretrained without the geometric loss, the full model can be trained from the initialized weights by running After the model is pretrained without the geometric loss, the full model can be trained from the initialized weights by
running
``` ```
python train_val.py --loss phge python train_val.py --loss phge
``` ```
### Evaluating Network ### Evaluating Network
To evaluate a specific checkpoint, e.g. the 50th epoch, one can run To evaluate a specific checkpoint, e.g. the 50th epoch, one can run
``` ```
python train_val.py --cmd retest --epoch 50 python train_val.py --cmd retest --epoch 50
``` ```
### Evaluating a Pre-trained Model ### Evaluating a Pre-trained Model
We provide a model pre-trained using the photometric loss. Once you have prepared the synthetic dataset and changed `DATA_ROOT` in `config.json`, the pre-trained model can be evaluated on the validation set by running:
We provide a model pre-trained using the photometric loss. Once you have prepared the synthetic dataset and
changed `DATA_ROOT` in `config.json`, the pre-trained model can be evaluated on the validation set by running:
``` ```
mkdir -p output mkdir -p output
mkdir -p output/exp_syn mkdir -p output/exp_syn
wget -O output/exp_syn/net_0099.params https://s3.eu-central-1.amazonaws.com/avg-projects/connecting_the_dots/net_0099.params wget -O output/exp_syn/net_0099.params https://s3.eu-central-1.amazonaws.com/avg-projects/connecting_the_dots/net_0099.params
python train_val.py --cmd retest --epoch 99 python train_val.py --cmd retest --epoch 99
``` ```
You can also download our validation set from [here (3.7G)](https://s3.eu-central-1.amazonaws.com/avg-projects/connecting_the_dots/val_data.zip).
You can also download our validation set
from [here (3.7G)](https://s3.eu-central-1.amazonaws.com/avg-projects/connecting_the_dots/val_data.zip).
## Acknowledgement ## Acknowledgement
This work was supported by the Intel Network on Intelligent Systems. This work was supported by the Intel Network on Intelligent Systems.

4
torchext/dataset.py
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@ -2,18 +2,19 @@ import torch
import torch.utils.data import torch.utils.data
import numpy as np import numpy as np
class TestSet(object): class TestSet(object):
def __init__(self, name, dset, test_frequency=1): def __init__(self, name, dset, test_frequency=1):
self.name = name self.name = name
self.dset = dset self.dset = dset
self.test_frequency = test_frequency self.test_frequency = test_frequency
class TestSets(list): class TestSets(list):
def append(self, name, dset, test_frequency=1): def append(self, name, dset, test_frequency=1):
super().append(TestSet(name, dset, test_frequency)) super().append(TestSet(name, dset, test_frequency))
class MultiDataset(torch.utils.data.Dataset): class MultiDataset(torch.utils.data.Dataset):
def __init__(self, *datasets): def __init__(self, *datasets):
self.current_epoch = 0 self.current_epoch = 0
@ -46,7 +47,6 @@ class MultiDataset(torch.utils.data.Dataset):
return self.datasets[didx][sidx] return self.datasets[didx][sidx]
class BaseDataset(torch.utils.data.Dataset): class BaseDataset(torch.utils.data.Dataset):
def __init__(self, train=True, fix_seed_per_epoch=False): def __init__(self, train=True, fix_seed_per_epoch=False):
self.current_epoch = 0 self.current_epoch = 0

11
torchext/functions.py
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@ -2,6 +2,7 @@ import torch
from . import ext_cpu from . import ext_cpu
from . import ext_cuda from . import ext_cuda
class NNFunction(torch.autograd.Function): class NNFunction(torch.autograd.Function):
@staticmethod @staticmethod
def forward(ctx, in0, in1): def forward(ctx, in0, in1):
@ -16,6 +17,7 @@ class NNFunction(torch.autograd.Function):
def backward(ctx, grad_out): def backward(ctx, grad_out):
return None, None return None, None
def nn(in0, in1): def nn(in0, in1):
return NNFunction.apply(in0, in1) return NNFunction.apply(in0, in1)
@ -34,9 +36,11 @@ class CrossCheckFunction(torch.autograd.Function):
def backward(ctx, grad_out): def backward(ctx, grad_out):
return None, None return None, None
def crosscheck(in0, in1): def crosscheck(in0, in1):
return CrossCheckFunction.apply(in0, in1) return CrossCheckFunction.apply(in0, in1)
class ProjNNFunction(torch.autograd.Function): class ProjNNFunction(torch.autograd.Function):
@staticmethod @staticmethod
def forward(ctx, xyz0, xyz1, K, patch_size): def forward(ctx, xyz0, xyz1, K, patch_size):
@ -51,11 +55,11 @@ class ProjNNFunction(torch.autograd.Function):
def backward(ctx, grad_out): def backward(ctx, grad_out):
return None, None, None, None return None, None, None, None
def proj_nn(xyz0, xyz1, K, patch_size): def proj_nn(xyz0, xyz1, K, patch_size):
return ProjNNFunction.apply(xyz0, xyz1, K, patch_size) return ProjNNFunction.apply(xyz0, xyz1, K, patch_size)
class XCorrVolFunction(torch.autograd.Function): class XCorrVolFunction(torch.autograd.Function):
@staticmethod @staticmethod
def forward(ctx, in0, in1, n_disps, block_size): def forward(ctx, in0, in1, n_disps, block_size):
@ -70,12 +74,11 @@ class XCorrVolFunction(torch.autograd.Function):
def backward(ctx, grad_out): def backward(ctx, grad_out):
return None, None, None, None return None, None, None, None
def xcorrvol(in0, in1, n_disps, block_size): def xcorrvol(in0, in1, n_disps, block_size):
return XCorrVolFunction.apply(in0, in1, n_disps, block_size) return XCorrVolFunction.apply(in0, in1, n_disps, block_size)
class PhotometricLossFunction(torch.autograd.Function): class PhotometricLossFunction(torch.autograd.Function):
@staticmethod @staticmethod
def forward(ctx, es, ta, block_size, type, eps): def forward(ctx, es, ta, block_size, type, eps):
@ -103,6 +106,7 @@ class PhotometricLossFunction(torch.autograd.Function):
grad_es = ext_cpu.photometric_loss_backward(*args) grad_es = ext_cpu.photometric_loss_backward(*args)
return grad_es, None, None, None, None return grad_es, None, None, None, None
def photometric_loss(es, ta, block_size, type='mse', eps=0.1): def photometric_loss(es, ta, block_size, type='mse', eps=0.1):
type = type.lower() type = type.lower()
if type == 'mse': if type == 'mse':
@ -117,6 +121,7 @@ def photometric_loss(es, ta, block_size, type='mse', eps=0.1):
raise Exception('invalid loss type') raise Exception('invalid loss type')
return PhotometricLossFunction.apply(es, ta, block_size, type, eps) return PhotometricLossFunction.apply(es, ta, block_size, type, eps)
def photometric_loss_pytorch(es, ta, block_size, type='mse', eps=0.1): def photometric_loss_pytorch(es, ta, block_size, type='mse', eps=0.1):
type = type.lower() type = type.lower()
p = block_size // 2 p = block_size // 2

4
torchext/modules.py
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@ -4,11 +4,13 @@ import numpy as np
from .functions import * from .functions import *
class CoordConv2d(torch.nn.Module): class CoordConv2d(torch.nn.Module):
def __init__(self, channels_in, channels_out, kernel_size, stride, padding): def __init__(self, channels_in, channels_out, kernel_size, stride, padding):
super().__init__() super().__init__()
self.conv = torch.nn.Conv2d(channels_in+2, channels_out, kernel_size=kernel_size, padding=padding, stride=stride) self.conv = torch.nn.Conv2d(channels_in + 2, channels_out, kernel_size=kernel_size, padding=padding,
stride=stride)
self.uv = None self.uv = None

3
torchext/setup.py
Unescape View File

@ -14,7 +14,8 @@ setup(
name='ext', name='ext',
ext_modules=[ ext_modules=[
CppExtension('ext_cpu', ['ext/ext_cpu.cpp']), CppExtension('ext_cpu', ['ext/ext_cpu.cpp']),
CUDAExtension('ext_cuda', ['ext/ext_cuda.cpp', 'ext/ext_kernel.cu'], extra_compile_args={'cxx': [], 'nvcc': nvcc_args}), CUDAExtension('ext_cuda', ['ext/ext_cuda.cpp', 'ext/ext_kernel.cu'],
extra_compile_args={'cxx': [], 'nvcc': nvcc_args}),
], ],
cmdclass={'build_ext': BuildExtension}, cmdclass={'build_ext': BuildExtension},
include_dirs=include_dirs include_dirs=include_dirs

20
torchext/worker.py
Unescape View File

@ -40,6 +40,7 @@ class StopWatch(object):
def __repr__(self): def __repr__(self):
return ', '.join(['%s: %f[s]' % (k, v) for k, v in self.get().items()]) return ', '.join(['%s: %f[s]' % (k, v) for k, v in self.get().items()])
def __str__(self): def __str__(self):
return ', '.join(['%s: %f[s]' % (k, v) for k, v in self.get().items()]) return ', '.join(['%s: %f[s]' % (k, v) for k, v in self.get().items()])
@ -77,8 +78,10 @@ class ETA(object):
def get_remaining_time_str(self): def get_remaining_time_str(self):
return self.format_time(self.get_remaining_time()) return self.format_time(self.get_remaining_time())
class Worker(object): class Worker(object):
def __init__(self, out_root, experiment_name, epochs=10, seed=42, train_batch_size=8, test_batch_size=16, num_workers=16, save_frequency=1, train_device='cuda:0', test_device='cuda:0', max_train_iter=-1): def __init__(self, out_root, experiment_name, epochs=10, seed=42, train_batch_size=8, test_batch_size=16,
num_workers=16, save_frequency=1, train_device='cuda:0', test_device='cuda:0', max_train_iter=-1):
self.out_root = Path(out_root) self.out_root = Path(out_root)
self.experiment_name = experiment_name self.experiment_name = experiment_name
self.epochs = epochs self.epochs = epochs
@ -237,7 +240,6 @@ class Worker(object):
plt.savefig(str(err_img_path)) plt.savefig(str(err_img_path))
plt.close(fig) plt.close(fig)
def callback_train_new_epoch(self, epoch, net, optimizer): def callback_train_new_epoch(self, epoch, net, optimizer):
pass pass
@ -269,7 +271,6 @@ class Worker(object):
curr_state.update(state['state_dict']) curr_state.update(state['state_dict'])
net.load_state_dict(curr_state) net.load_state_dict(curr_state)
try: try:
optimizer.load_state_dict(state['optimizer']) optimizer.load_state_dict(state['optimizer'])
except: except:
@ -367,7 +368,8 @@ class Worker(object):
logging.info('Train epoch %d' % epoch) logging.info('Train epoch %d' % epoch)
dset.current_epoch = epoch dset.current_epoch = epoch
train_loader = torch.utils.data.DataLoader(dset, batch_size=self.train_batch_size, shuffle=True, num_workers=self.num_workers, drop_last=True, pin_memory=False) train_loader = torch.utils.data.DataLoader(dset, batch_size=self.train_batch_size, shuffle=True,
num_workers=self.num_workers, drop_last=True, pin_memory=False)
net = net.to(self.train_device) net = net.to(self.train_device)
net.train() net.train()
@ -418,10 +420,10 @@ class Worker(object):
bar.update(batch_idx) bar.update(batch_idx)
if (epoch <= 1 and batch_idx < 128) or batch_idx % 16 == 0: if (epoch <= 1 and batch_idx < 128) or batch_idx % 16 == 0:
err_str = self.format_err_str(errs) err_str = self.format_err_str(errs)
logging.info(f'train e{epoch}: {batch_idx+1}/{len(train_loader)}: loss={err_str} | {bar.get_elapsed_time_str()} / {bar.get_remaining_time_str()}') logging.info(
f'train e{epoch}: {batch_idx + 1}/{len(train_loader)}: loss={err_str} | {bar.get_elapsed_time_str()} / {bar.get_remaining_time_str()}')
# self.write_err_img() # self.write_err_img()
if mean_loss is None: if mean_loss is None:
mean_loss = [0 for e in errs] mean_loss = [0 for e in errs]
for erridx, err in enumerate(errs): for erridx, err in enumerate(errs):
@ -465,7 +467,8 @@ class Worker(object):
logging.info('-' * 80) logging.info('-' * 80)
logging.info('Test epoch %d' % epoch) logging.info('Test epoch %d' % epoch)
dset.current_epoch = epoch dset.current_epoch = epoch
test_loader = torch.utils.data.DataLoader(dset, batch_size=self.test_batch_size, shuffle=False, num_workers=self.num_workers, drop_last=False, pin_memory=False) test_loader = torch.utils.data.DataLoader(dset, batch_size=self.test_batch_size, shuffle=False,
num_workers=self.num_workers, drop_last=False, pin_memory=False)
net = net.to(self.test_device) net = net.to(self.test_device)
net.eval() net.eval()
@ -502,7 +505,8 @@ class Worker(object):
bar.update(batch_idx) bar.update(batch_idx)
if batch_idx % 25 == 0: if batch_idx % 25 == 0:
err_str = self.format_err_str(errs) err_str = self.format_err_str(errs)
logging.info(f'test e{epoch}: {batch_idx+1}/{len(test_loader)}: loss={err_str} | {bar.get_elapsed_time_str()} / {bar.get_remaining_time_str()}') logging.info(
f'test e{epoch}: {batch_idx + 1}/{len(test_loader)}: loss={err_str} | {bar.get_elapsed_time_str()} / {bar.get_remaining_time_str()}')
if mean_loss is None: if mean_loss is None:
mean_loss = [0 for e in errs] mean_loss = [0 for e in errs]

5
train_val.py
Unescape View File

@ -5,7 +5,6 @@ from model import exp_synphge
from model import networks from model import networks
from co.args import parse_args from co.args import parse_args
# parse args # parse args
args = parse_args() args = parse_args()
@ -19,11 +18,11 @@ elif args.loss=='phge':
channels_in = 2 channels_in = 2
# set up network # set up network
net = networks.DispEdgeDecoders(channels_in=channels_in, max_disp=args.max_disp, imsizes=worker.imsizes, output_ms=worker.ms) net = networks.DispEdgeDecoders(channels_in=channels_in, max_disp=args.max_disp, imsizes=worker.imsizes,
output_ms=worker.ms)
# optimizer # optimizer
optimizer = torch.optim.Adam(net.parameters(), lr=1e-4) optimizer = torch.optim.Adam(net.parameters(), lr=1e-4)
# start the work # start the work
worker.do(net, optimizer) worker.do(net, optimizer)

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