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Running
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Zero
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import argparse
import json
import os
import numpy as np
import torch
import torch.nn as nn
from PIL import Image
from unik3d.models import UniK3D
from unik3d.utils.camera import (MEI, OPENCV, BatchCamera, Fisheye624, Pinhole,
Spherical)
from unik3d.utils.visualization import colorize, save_file_ply
SAVE = False
BASE_PATH = os.path.join(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "assets", "demo"
)
def infer(model, rgb_path, camera_path, rays=None):
rgb = np.array(Image.open(rgb_path))
rgb_torch = torch.from_numpy(rgb).permute(2, 0, 1)
camera = None
if camera_path is not None:
with open(camera_path, "r") as f:
camera_dict = json.load(f)
params = torch.tensor(camera_dict["params"])
name = camera_dict["name"]
assert name in ["Fisheye624", "Spherical", "OPENCV", "Pinhole", "MEI"]
camera = eval(name)(params=params)
outputs = model.infer(rgb=rgb_torch, camera=camera, normalize=True, rays=rays)
return rgb_torch, outputs
def infer_equirectangular(model, rgb_path):
rgb = np.array(Image.open(rgb_path))
rgb_torch = torch.from_numpy(rgb).permute(2, 0, 1)
# assuming full equirectangular image horizontally
H, W = rgb.shape[:2]
hfov_half = np.pi
vfov_half = np.pi * H / W
assert vfov_half <= np.pi / 2
params = [W, H, hfov_half, vfov_half]
camera = Spherical(params=torch.tensor([1.0] * 4 + params))
outputs = model.infer(rgb=rgb_torch, camera=camera, normalize=True)
return rgb_torch, outputs
def save(rgb, outputs, name, base_path, save_pointcloud=False):
depth = outputs["depth"]
rays = outputs["rays"]
points = outputs["points"]
depth = depth.cpu().numpy()
rays = ((rays + 1) * 127.5).clip(0, 255)
Image.fromarray(colorize(depth.squeeze())).save(
os.path.join(base_path, f"{name}_depth.png")
)
Image.fromarray(rgb.squeeze().permute(1, 2, 0).cpu().numpy()).save(
os.path.join(base_path, f"{name}_rgb.png")
)
Image.fromarray(rays.squeeze().permute(1, 2, 0).byte().cpu().numpy()).save(
os.path.join(base_path, f"{name}_rays.png")
)
if save_pointcloud:
predictions_3d = points.permute(0, 2, 3, 1).reshape(-1, 3).cpu().numpy()
rgb = rgb.permute(1, 2, 0).reshape(-1, 3).cpu().numpy()
save_file_ply(predictions_3d, rgb, os.path.join(base_path, f"{name}.ply"))
def demo(model):
# RGB + CAMERA
rgb, outputs = infer(
model,
os.path.join(BASE_PATH, f"scannet.png"),
os.path.join(BASE_PATH, "scannet.json"),
)
if SAVE:
save(rgb, outputs, name="scannet", base_path=BASE_PATH)
# get GT and pred
pts_pred = outputs["points"].squeeze().cpu().permute(1, 2, 0).numpy()
pts_gt = np.load("./assets/demo/scannet.npy").astype(float)
mask = np.linalg.norm(pts_gt, axis=-1) > 0
error = np.linalg.norm(pts_pred - pts_gt, axis=-1)
error = np.mean(error[mask] ** 2) ** 0.5
# Trade-off between speed and resolution
model.resolution_level = 1
rgb, outputs = infer(
model,
os.path.join(BASE_PATH, f"scannet.png"),
os.path.join(BASE_PATH, "scannet.json"),
)
if SAVE:
save(rgb, outputs, name="scannet_lowres", base_path=BASE_PATH)
# RGB
rgb, outputs = infer(model, os.path.join(BASE_PATH, f"poorthings.jpg"), None)
if SAVE:
save(rgb, outputs, name="poorthings", base_path=BASE_PATH)
# RGB + CAMERA
rgb, outputs = infer(
model,
os.path.join(BASE_PATH, f"dl3dv.png"),
os.path.join(BASE_PATH, "dl3dv.json"),
)
if SAVE:
save(rgb, outputs, name="dl3dv", base_path=BASE_PATH)
# EQUIRECTANGULAR
rgb, outputs = infer_equirectangular(
model, os.path.join(BASE_PATH, f"equirectangular.jpg")
)
if SAVE:
save(rgb, outputs, name="equirectangular", base_path=BASE_PATH)
print("Output keys are", outputs.keys())
if SAVE:
print("Done! Results saved in", BASE_PATH)
print(f"RMSE on 3D clouds for ScanNet++ sample: {100*error:.1f}cm")
if __name__ == "__main__":
print("Torch version:", torch.__version__)
type_ = "l" # available types: s, b, l
name = f"unik3d-vit{type_}"
model = UniK3D.from_pretrained(f"lpiccinelli/{name}")
# set resolution level in [0,10) and output interpolation
model.resolution_level = 9
model.interpolation_mode = "bilinear"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device).eval()
demo(model)
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