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import numpy as np
def deg2rad(deg):
return deg*np.pi/180
def inv_RT(RT):
# RT_h = np.concatenate([RT, np.array([[0,0,0,1]])], axis=0)
RT_inv = np.linalg.inv(RT)
return RT_inv[:3, :]
def camNormal2worldNormal(rot_c2w, camNormal):
H,W,_ = camNormal.shape
normal_img = np.matmul(rot_c2w[None, :, :], camNormal.reshape(-1,3)[:, :, None]).reshape([H, W, 3])
return normal_img
def worldNormal2camNormal(rot_w2c, normal_map_world):
H,W,_ = normal_map_world.shape
# normal_img = np.matmul(rot_w2c[None, :, :], worldNormal.reshape(-1,3)[:, :, None]).reshape([H, W, 3])
# faster version
# Reshape the normal map into a 2D array where each row represents a normal vector
normal_map_flat = normal_map_world.reshape(-1, 3)
# Transform the normal vectors using the transformation matrix
normal_map_camera_flat = np.dot(normal_map_flat, rot_w2c.T)
# Reshape the transformed normal map back to its original shape
normal_map_camera = normal_map_camera_flat.reshape(normal_map_world.shape)
return normal_map_camera
def trans_normal(normal, RT_w2c, RT_w2c_target):
# normal_world = camNormal2worldNormal(np.linalg.inv(RT_w2c[:3,:3]), normal)
# normal_target_cam = worldNormal2camNormal(RT_w2c_target[:3,:3], normal_world)
relative_RT = np.matmul(RT_w2c_target[:3,:3], np.linalg.inv(RT_w2c[:3,:3]))
return worldNormal2camNormal(relative_RT[:3,:3], normal)
def trans_normal_complex(normal, RT_w2c, RT_w2c_rela_to_cond):
# camview -> world -> condview
normal_world = camNormal2worldNormal(np.linalg.inv(RT_w2c[:3,:3]), normal)
# debug_normal_world = normal2img(normal_world)
# relative_RT = np.matmul(RT_w2c_rela_to_cond[:3,:3], np.linalg.inv(RT_w2c[:3,:3]))
normal_target_cam = worldNormal2camNormal(RT_w2c_rela_to_cond[:3,:3], normal_world)
# normal_condview = normal2img(normal_target_cam)
return normal_target_cam
def img2normal(img):
return (img/255.)*2-1
def normal2img(normal):
return np.uint8((normal*0.5+0.5)*255)
def norm_normalize(normal, dim=-1):
normal = normal/(np.linalg.norm(normal, axis=dim, keepdims=True)+1e-6)
return normal
def plot_grid_images(images, row, col, path=None):
import cv2
"""
Args:
images: np.array [B, H, W, 3]
row:
col:
save_path:
Returns:
"""
images = images.detach().cpu().numpy()
assert row * col == images.shape[0]
images = np.vstack([np.hstack(images[r * col:(r + 1) * col]) for r in range(row)])
if path:
cv2.imwrite(path, images[:,:,::-1] * 255)
return images |