init

primitive_anything/michelangelo/models/tsal/inference_utils.py

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+# -*- coding: utf-8 -*-
+
+import torch
+from tqdm import tqdm
+from einops import repeat
+import numpy as np
+from typing import Callable, Tuple, List, Union, Optional
+from skimage import measure
+
+from ...graphics.primitives import generate_dense_grid_points
+
+
+@torch.no_grad()
+def extract_geometry(geometric_func: Callable,
+                     device: torch.device,
+                     batch_size: int = 1,
+                     bounds: Union[Tuple[float], List[float], float] = (-1.25, -1.25, -1.25, 1.25, 1.25, 1.25),
+                     octree_depth: int = 7,
+                     num_chunks: int = 10000,
+                     disable: bool = True):
+    """
+
+    Args:
+        geometric_func:
+        device:
+        bounds:
+        octree_depth:
+        batch_size:
+        num_chunks:
+        disable:
+
+    Returns:
+
+    """
+
+    if isinstance(bounds, float):
+        bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds]
+
+    bbox_min = np.array(bounds[0:3])
+    bbox_max = np.array(bounds[3:6])
+    bbox_size = bbox_max - bbox_min
+
+    xyz_samples, grid_size, length = generate_dense_grid_points(
+        bbox_min=bbox_min,
+        bbox_max=bbox_max,
+        octree_depth=octree_depth,
+        indexing="ij"
+    )
+    xyz_samples = torch.FloatTensor(xyz_samples)
+
+    batch_logits = []
+    for start in tqdm(range(0, xyz_samples.shape[0], num_chunks),
+                      desc="Implicit Function:", disable=disable, leave=False):
+        queries = xyz_samples[start: start + num_chunks, :].to(device)
+        batch_queries = repeat(queries, "p c -> b p c", b=batch_size)
+
+        logits = geometric_func(batch_queries)
+        batch_logits.append(logits.cpu())
+
+    grid_logits = torch.cat(batch_logits, dim=1).view((batch_size, grid_size[0], grid_size[1], grid_size[2])).numpy()
+
+    mesh_v_f = []
+    has_surface = np.zeros((batch_size,), dtype=np.bool_)
+    for i in range(batch_size):
+        try:
+            vertices, faces, normals, _ = measure.marching_cubes(grid_logits[i], 0, method="lewiner")
+            vertices = vertices / grid_size * bbox_size + bbox_min
+            # vertices[:, [0, 1]] = vertices[:, [1, 0]]
+            mesh_v_f.append((vertices.astype(np.float32), np.ascontiguousarray(faces)))
+            has_surface[i] = True
+
+        except ValueError:
+            mesh_v_f.append((None, None))
+            has_surface[i] = False
+
+        except RuntimeError:
+            mesh_v_f.append((None, None))
+            has_surface[i] = False
+
+    return mesh_v_f, has_surface