92 lines
2.4 KiB
Python
92 lines
2.4 KiB
Python
from typing import Any
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import numpy as np
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import torch
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from PIL import Image
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import StableFast3D.sf3d.models.utils as sf3d_utils
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def create_intrinsic_from_fov_deg(fov_deg: float, cond_height: int, cond_width: int):
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intrinsic = sf3d_utils.get_intrinsic_from_fov(
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np.deg2rad(fov_deg),
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H=cond_height,
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W=cond_width,
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)
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intrinsic_normed_cond = intrinsic.clone()
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intrinsic_normed_cond[..., 0, 2] /= cond_width
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intrinsic_normed_cond[..., 1, 2] /= cond_height
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intrinsic_normed_cond[..., 0, 0] /= cond_width
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intrinsic_normed_cond[..., 1, 1] /= cond_height
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return intrinsic, intrinsic_normed_cond
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def default_cond_c2w(distance: float):
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c2w_cond = torch.as_tensor(
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[
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[0, 0, 1, distance],
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[1, 0, 0, 0],
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[0, 1, 0, 0],
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[0, 0, 0, 1],
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]
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).float()
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return c2w_cond
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def remove_background(
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image: Image,
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rembg_session: Any = None,
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force: bool = False,
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**rembg_kwargs,
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) -> Image:
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import rembg
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do_remove = True
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if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
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do_remove = False
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do_remove = do_remove or force
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if do_remove:
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image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
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return image
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def resize_foreground(
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image: Image,
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ratio: float,
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) -> Image:
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image = np.array(image)
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assert image.shape[-1] == 4
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alpha = np.where(image[..., 3] > 0)
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y1, y2, x1, x2 = (
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alpha[0].min(),
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alpha[0].max(),
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alpha[1].min(),
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alpha[1].max(),
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)
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# crop the foreground
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fg = image[y1:y2, x1:x2]
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# pad to square
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size = max(fg.shape[0], fg.shape[1])
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ph0, pw0 = (size - fg.shape[0]) // 2, (size - fg.shape[1]) // 2
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ph1, pw1 = size - fg.shape[0] - ph0, size - fg.shape[1] - pw0
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new_image = np.pad(
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fg,
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((ph0, ph1), (pw0, pw1), (0, 0)),
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mode="constant",
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constant_values=((0, 0), (0, 0), (0, 0)),
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)
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# compute padding according to the ratio
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new_size = int(new_image.shape[0] / ratio)
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# pad to size, double side
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ph0, pw0 = (new_size - size) // 2, (new_size - size) // 2
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ph1, pw1 = new_size - size - ph0, new_size - size - pw0
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new_image = np.pad(
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new_image,
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((ph0, ph1), (pw0, pw1), (0, 0)),
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mode="constant",
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constant_values=((0, 0), (0, 0), (0, 0)),
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)
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new_image = Image.fromarray(new_image, mode="RGBA")
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return new_image
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