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Running
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Zero
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from PIL import Image
from typing import Any
import rembg
import numpy as np
from torchvision import transforms
from plyfile import PlyData, PlyElement
import os
import torch
from .camera_utils import get_loop_cameras
from .graphics_utils import getProjectionMatrix
from .general_utils import matrix_to_quaternion
def remove_background(image, rembg_session):
do_remove = True
if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
do_remove = False
if do_remove:
image = rembg.remove(image, session=rembg_session)
return image
def set_white_background(image):
image = np.array(image).astype(np.float32) / 255.0
mask = image[:, :, 3:4]
image = image[:, :, :3] * mask + (1 - mask)
image = Image.fromarray((image * 255.0).astype(np.uint8))
return image
def resize_foreground(image, ratio):
image = np.array(image)
assert image.shape[-1] == 4
alpha = np.where(image[..., 3] > 0)
# modify so that cropping doesn't change the world center
y1, y2, x1, x2 = (
alpha[0].min(),
alpha[0].max(),
alpha[1].min(),
alpha[1].max(),
)
# crop the foreground
fg = image[y1: y2,
x1: x2]
# pad to square
size = max(fg.shape[0], fg.shape[1])
ph0, pw0 = (size - fg.shape[0]) // 2, (size - fg.shape[1]) // 2
ph1, pw1 = size - fg.shape[0] - ph0, size - fg.shape[1] - pw0
new_image = np.pad(
fg,
((ph0, ph1), (pw0, pw1), (0, 0)),
mode="constant",
constant_values=((255, 255), (255, 255), (0, 0)),
)
# compute padding according to the ratio
new_size = int(new_image.shape[0] / ratio)
# pad to size, double side
ph0, pw0 = (new_size - size) // 2, (new_size - size) // 2
ph1, pw1 = new_size - size - ph0, new_size - size - pw0
new_image = np.pad(
new_image,
((ph0, ph1), (pw0, pw1), (0, 0)),
mode="constant",
constant_values=((255, 255), (255, 255), (0, 0)),
)
new_image = Image.fromarray(new_image)
return new_image
def resize_to_128(img):
img = transforms.functional.resize(img, 128,
interpolation=transforms.InterpolationMode.LANCZOS)
return img
def to_tensor(img):
img = torch.tensor(img).permute(2, 0, 1) / 255.0
return img
def get_source_camera_v2w_rmo_and_quats(num_imgs_in_loop=200):
source_camera = get_loop_cameras(num_imgs_in_loop=num_imgs_in_loop)[0]
source_camera = torch.from_numpy(source_camera).transpose(0, 1).unsqueeze(0)
qs = []
for c_idx in range(source_camera.shape[0]):
qs.append(matrix_to_quaternion(source_camera[c_idx, :3, :3].transpose(0, 1)))
return source_camera.unsqueeze(0), torch.stack(qs, dim=0).unsqueeze(0)
def get_target_cameras(num_imgs_in_loop=200):
"""
Returns camera parameters for rendering a loop around the object:
world_to_view_transforms,
full_proj_transforms,
camera_centers
"""
projection_matrix = getProjectionMatrix(
znear=0.8, zfar=3.2,
fovX=49.134342641202636 * 2 * np.pi / 360,
fovY=49.134342641202636 * 2 * np.pi / 360).transpose(0,1)
target_cameras = get_loop_cameras(num_imgs_in_loop=num_imgs_in_loop,
max_elevation=np.pi/4,
elevation_freq=1.5)
world_view_transforms = []
view_world_transforms = []
camera_centers = []
for loop_camera_c2w_cmo in target_cameras:
view_world_transform = torch.from_numpy(loop_camera_c2w_cmo).transpose(0, 1)
world_view_transform = torch.from_numpy(loop_camera_c2w_cmo).inverse().transpose(0, 1)
camera_center = view_world_transform[3, :3].clone()
world_view_transforms.append(world_view_transform)
view_world_transforms.append(view_world_transform)
camera_centers.append(camera_center)
world_view_transforms = torch.stack(world_view_transforms)
view_world_transforms = torch.stack(view_world_transforms)
camera_centers = torch.stack(camera_centers)
full_proj_transforms = world_view_transforms.bmm(projection_matrix.unsqueeze(0).expand(
world_view_transforms.shape[0], 4, 4))
return world_view_transforms, full_proj_transforms, camera_centers
def construct_list_of_attributes():
# taken from gaussian splatting repo.
l = ['x', 'y', 'z', 'nx', 'ny', 'nz']
# All channels except the 3 DC
# 3 channels for DC
for i in range(3):
l.append('f_dc_{}'.format(i))
# 9 channels for SH order 1
for i in range(9):
l.append('f_rest_{}'.format(i))
l.append('opacity')
for i in range(3):
l.append('scale_{}'.format(i))
for i in range(4):
l.append('rot_{}'.format(i))
return l
def export_to_obj(reconstruction, ply_out_path):
"""
Args:
reconstruction: dict with xyz, opacity, features dc, etc with leading batch size
ply_out_path: file path where to save the output
"""
os.makedirs(os.path.dirname(ply_out_path), exist_ok=True)
for k, v in reconstruction.items():
# check dimensions
if k not in ["features_dc", "features_rest"]:
assert len(v.shape) == 3, "Unexpected size for {}".format(k)
else:
assert len(v.shape) == 4, "Unexpected size for {}".format(k)
assert v.shape[0] == 1, "Expected batch size to be 0"
reconstruction[k] = v[0]
xyz = reconstruction["xyz"].detach().cpu().numpy()
normals = np.zeros_like(xyz)
f_dc = reconstruction["features_dc"].detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
f_rest = reconstruction["features_rest"].detach().transpose(1, 2).flatten(start_dim=1).contiguous().cpu().numpy()
opacities = reconstruction["opacity"].detach().cpu().numpy()
scale = reconstruction["scaling"].detach().cpu().numpy()
rotation = reconstruction["rotation"].detach().cpu().numpy()
dtype_full = [(attribute, 'f4') for attribute in construct_list_of_attributes()]
elements = np.empty(xyz.shape[0], dtype=dtype_full)
attributes = np.concatenate((xyz, normals, f_dc, f_rest, opacities, scale, rotation), axis=1)
elements[:] = list(map(tuple, attributes))
el = PlyElement.describe(elements, 'vertex')
PlyData([el]).write(ply_out_path)
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