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70 lines
2.2 KiB
70 lines
2.2 KiB
import base64
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import io
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from typing import Union
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import ipywidgets as widgets
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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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from shap_e.models.nn.camera import DifferentiableCameraBatch, DifferentiableProjectiveCamera
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from shap_e.models.transmitter.base import Transmitter, VectorDecoder
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from shap_e.util.collections import AttrDict
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def create_pan_cameras(size: int, device: torch.device) -> DifferentiableCameraBatch:
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origins = []
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xs = []
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ys = []
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zs = []
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for theta in np.linspace(0, 2 * np.pi, num=20):
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z = np.array([np.sin(theta), np.cos(theta), -0.5])
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z /= np.sqrt(np.sum(z**2))
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origin = -z * 4
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x = np.array([np.cos(theta), -np.sin(theta), 0.0])
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y = np.cross(z, x)
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origins.append(origin)
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xs.append(x)
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ys.append(y)
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zs.append(z)
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return DifferentiableCameraBatch(
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shape=(1, len(xs)),
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flat_camera=DifferentiableProjectiveCamera(
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origin=torch.from_numpy(np.stack(origins, axis=0)).float().to(device),
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x=torch.from_numpy(np.stack(xs, axis=0)).float().to(device),
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y=torch.from_numpy(np.stack(ys, axis=0)).float().to(device),
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z=torch.from_numpy(np.stack(zs, axis=0)).float().to(device),
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width=size,
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height=size,
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x_fov=0.7,
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y_fov=0.7,
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),
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)
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@torch.no_grad()
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def decode_latent_images(
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xm: Union[Transmitter, VectorDecoder],
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latent: torch.Tensor,
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cameras: DifferentiableCameraBatch,
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rendering_mode: str = "stf",
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):
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decoded = xm.renderer.render_views(
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AttrDict(cameras=cameras),
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params=(xm.encoder if isinstance(xm, Transmitter) else xm).bottleneck_to_params(
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latent[None]
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),
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options=AttrDict(rendering_mode=rendering_mode, render_with_direction=False),
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)
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arr = decoded.channels.clamp(0, 255).to(torch.uint8)[0].cpu().numpy()
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return [Image.fromarray(x) for x in arr]
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def gif_widget(images):
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writer = io.BytesIO()
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images[0].save(
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writer, format="GIF", save_all=True, append_images=images[1:], duration=100, loop=0
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)
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writer.seek(0)
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data = base64.b64encode(writer.read()).decode("ascii")
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return widgets.HTML(f'<img src="data:image/gif;base64,{data}" />')
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