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generate_NERF_transforms.py
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generate_NERF_transforms.py
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import os
import math
import numpy as np
import json
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--dataset-config", type=str, required=True, help="dataset config file (i.e.: the 'lightfield.cfg' file).")
parser.add_argument("--subdir", type=str, required=False, help="path prefix to include in the filename paths")
parser.add_argument("--output-transforms", type=str, required=True, help="path to the transforms.json to produce.")
parser.add_argument("--scene", type=str,
choices=["garden", "barbershop", "lone_monk", "kitchen"],
help="A specific scene name that will be used to pick sane scaling/aabb defaults.")
parser.add_argument("--scale", type=float,
help="A scale for NeRF to fit the unit cube.")
parser.add_argument("--extra-offset", type=float, nargs=3,
help="A 3-vector that defines extra offset in NeRF coordinates")
parser.add_argument("--render-aabb", type=float, nargs=6,
help="Two 3-vectors that define the AABB min and max in NeRF coordinates")
args = parser.parse_args()
names = []
positions = [] # camera positions in 3-tuples
rotations = [] # camera orientations in 3-tuples (degrees)
if args.dataset_config.endswith(".cfg"):
# Load the stupid format.
with open(args.dataset_config, "r") as file:
def nextline():
return file.readline().split(",")
file.readline()
resolution = [int(x) for x in nextline()]
proj0 = [float(x) for x in nextline()]
proj1 = [float(x) for x in nextline()]
proj2 = [float(x) for x in nextline()]
proj3 = [float(x) for x in nextline()]
camera_lines = file.readlines()
for cl in camera_lines:
p = cl.split(",")
names.append(p[0])
positions.append([float(x) for x in p[1:4]])
rotations.append([float(x) for x in p[4:]])
print("proj matrix")
print(proj0)
print(proj1)
print(proj2)
print(proj3)
camera_angle_x = 2.0 * np.arctan(1.0 / proj0[0])
camera_angle_y = 2.0 * np.arctan(1.0 / proj1[1])
elif args.dataset_config.endswith(".json"):
with open(args.dataset_config, "r") as file:
cfg = json.load(file)
cam_type = cfg['camera']['type']
resolution = cfg['resolution']
if cam_type == 'PERSP':
proj = cfg['camera']['projection_matrix']
camera_angle_x = 2.0 * np.arctan(1.0 / proj[0][0])
camera_angle_y = 2.0 * np.arctan(1.0 / proj[1][1])
else:
# Not supported by Nerf
raise NotImplementedError("Not supported by NerF")
for fr in cfg['frames']:
names.append(fr['name'])
positions.append(fr['position'])
rotations.append(fr['rotation'])
else:
parser.print_usage()
print("Not a valid config file given. Either .json (preferred) or .cfg (deprecated)")
exit(1)
focal = [float(x) for x in resolution]
principal_point = [x * 0.5 for x in focal]
print("camera_angle_x", camera_angle_x, "rad =", camera_angle_x / np.pi * 180, "degrees")
print("camera_angle_y", camera_angle_y, "rad =", camera_angle_y / np.pi * 180, "degrees")
average_position = np.mean(positions, axis=0)
print("average_position", average_position)
def generate_transform_matrix(pos, rot):
def Rx(theta):
return np.matrix([[ 1, 0 , 0 ],
[ 0, np.cos(theta),-np.sin(theta)],
[ 0, np.sin(theta), np.cos(theta)]])
def Ry(theta):
return np.matrix([[ np.cos(theta), 0, np.sin(theta)],
[ 0 , 1, 0 ],
[-np.sin(theta), 0, np.cos(theta)]])
def Rz(theta):
return np.matrix([[ np.cos(theta), -np.sin(theta), 0 ],
[ np.sin(theta), np.cos(theta) , 0 ],
[ 0 , 0 , 1 ]])
R = Rz(rot[2]) * Ry(rot[1]) * Rx(rot[0])
xf_rot = np.eye(4)
xf_rot[:3,:3] = R
xf_pos = np.eye(4)
xf_pos[:3,3] = pos # - average_position
# barbershop_mirros_hd_dense:
# - camera plane is y+z plane, meaning: constant x-values
# - cameras look to +x
# Don't ask me...
extra_xf = np.matrix([
[-1, 0, 0, 0],
[ 0, 0, 1, 0],
[ 0, 1, 0, 0],
[ 0, 0, 0, 1]])
# NerF will cycle forward, so lets cycle backward.
shift_coords = np.matrix([
[0, 0, 1, 0],
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 1]])
xf = shift_coords @ extra_xf @ xf_pos
assert np.abs(np.linalg.det(xf) - 1.0) < 1e-4
xf = xf @ xf_rot
return xf
average_position_transformed = np.transpose(np.mean([generate_transform_matrix(positions[i], [0,0,0])[:3,3] for i in range(len(names))], axis=0))
print("Average position transformed: ", average_position_transformed)
frames = [{
"file_path": names[i] if args.subdir is None else os.path.join(args.subdir, names[i]),
"transform_matrix": generate_transform_matrix(positions[i], rotations[i]).tolist(),
} for i in range(len(names))]
transforms_config = {
"camera_angle_x": camera_angle_x,
"scale": 0.2,
"offset": [0.5, 0.5, 0.5],
}
if args.scene == "barbershop":
transforms_config.update({
"scale": 0.1,
"offset": [0.5, 0.75, 0.5],
})
elif args.scene == "garden":
transforms_config.update({
"scale": 0.1,
"offset": [0.5, 0.5, 0.2],
})
elif args.scene == "lone_monk":
transforms_config.update({
"scale": 0.03,
"offset": [0.35, 0.5, 0.2]
})
elif args.scene == "kitchen":
transforms_config.update({
"scale": 0.2,
"offset": [0.2, 0.25, 0.5],
})
if args.scale is not None:
print("Overriding scale:", args.scale)
transforms_config["scale"] = args.scale
offset = np.array(transforms_config["offset"]).squeeze()
offset -= transforms_config["scale"] * average_position_transformed.squeeze()
if args.extra_offset is not None:
print("Apply extra offset:", args.extra_offset)
offset += np.array(args.extra_offset)
transforms_config["offset"] = offset.tolist()
if args.render_aabb:
transforms_config["render_aabb"] = [args.render_aabb[:3], args.render_aabb[3:]]
print()
print("Generating config:")
print(transforms_config)
transforms_config.update({
"frames": frames,
})
with open(args.output_transforms, "w") as outfile:
json.dump(transforms_config, outfile, indent=4)