Track bundled vendor runtime sources

vendor/ComfyUI/comfy_extras/mediapipe/face_geometry.py

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+"""Pure-numpy port of MediaPipe's face_geometry (FACE_LANDMARK_PIPELINE mode)
++ weighted Procrustes solver. Computes the 4x4 facial transformation matrix.
+"""
+
+
+import math
+import numpy as np
+
+
+def _solve_weighted_orthogonal_problem(src: np.ndarray, tgt: np.ndarray, weights: np.ndarray) -> np.ndarray:
+    """Weighted orthogonal Procrustes (similarity). Returns 4x4 M with
+    `target ≈ M @ homogeneous(source)` in the weighted LS sense. fp64 for
+    SVD stability. Port of procrustes_solver.cc."""
+    sqrt_w = np.sqrt(weights.astype(np.float64))
+    w_total = float((sqrt_w ** 2).sum())
+    ws = src.astype(np.float64) * sqrt_w
+    wt = tgt.astype(np.float64) * sqrt_w
+
+    c_w = (ws @ sqrt_w) / w_total
+    centered = ws - np.outer(c_w, sqrt_w)
+    U, _S, Vt = np.linalg.svd(wt @ centered.T, full_matrices=True)
+    # Disallow reflection: flip the least-significant axis when det(U)·det(V)<0.
+    post, pre = U.copy(), Vt.T.copy()
+    if np.linalg.det(post) * np.linalg.det(pre) < 0:
+        post[:, 2] *= -1.0
+    R = post @ pre.T
+
+    denom = float((centered * ws).sum())
+    if denom < 1e-12:
+        raise ValueError("Procrustes denominator collapsed (degenerate source).")
+    scale = float((R @ centered * wt).sum()) / denom
+    translation = ((wt - scale * (R @ ws)) @ sqrt_w) / w_total
+
+    M = np.eye(4, dtype=np.float64)
+    M[:3, :3] = scale * R
+    M[:3, 3] = translation
+    return M
+
+
+def _estimate_scale(canonical: np.ndarray, runtime: np.ndarray, weights: np.ndarray) -> float:
+    """scale = ‖first column of M[:3]‖ per geometry_pipeline.cc::EstimateScale."""
+    return float(np.linalg.norm(_solve_weighted_orthogonal_problem(canonical, runtime, weights)[:3, 0]))
+
+
+def solve_facial_transformation_matrix(
+    landmarks_normalized: np.ndarray,
+    canonical_vertices: np.ndarray,
+    procrustes_indices: np.ndarray,
+    procrustes_weights: np.ndarray,
+    image_width: int,
+    image_height: int,
+    # face_geometry_calculator_options.pbtxt defaults
+    vertical_fov_degrees: float = 63.0,
+    near: float = 1.0,
+) -> np.ndarray:
+    """4x4 facial transformation matrix via two-pass scale recovery
+    `landmarks_normalized` is (N, 3) in MediaPipe normalized convention: x, y
+    in [0,1] with TOP-LEFT origin, z in width-scaled units.
+    """
+
+    h_near = 2.0 * near * math.tan(0.5 * math.radians(vertical_fov_degrees))
+    w_near = image_width * h_near / image_height
+
+    sub = procrustes_indices.astype(np.int64)
+    screen = landmarks_normalized[sub].T.astype(np.float64).copy()
+    canon = canonical_vertices[sub].T.astype(np.float64).copy()
+    weights = procrustes_weights.astype(np.float64)
+
+    # ProjectXY (TOP_LEFT y-flip, then scale all 3 axes; z uses x-scale).
+    screen[1] = 1.0 - screen[1]
+    screen[0] = screen[0] * w_near - 0.5 * w_near
+    screen[1] = screen[1] * h_near - 0.5 * h_near
+    screen[2] = screen[2] * w_near
+    depth_offset = float(screen[2].mean())
+
+    def _unproject(s: np.ndarray, scale: float) -> np.ndarray:
+        s = s.copy()
+        s[2] = (s[2] - depth_offset + near) / scale
+        s[0] *= s[2] / near
+        s[1] *= s[2] / near
+        s[2] *= -1.0
+        return s
+
+    first = screen.copy()
+    first[2] *= -1.0
+    s1 = _estimate_scale(canon, first, weights) # 1st pass: Procrustes on projected XY
+    s2 = _estimate_scale(canon, _unproject(screen, s1), weights) # 2nd pass: rescale z by s1, un-project XY
+    return _solve_weighted_orthogonal_problem(canon, _unproject(screen, s1 * s2), weights).astype(np.float32)
+
+
+def transformation_matrix_from_detection(face_dict: dict, image_width: int, image_height: int, canonical_data: dict) -> np.ndarray:
+    """Adapt a FaceLandmarker face dict to MP's normalized convention and solve.
+    FaceMesh emits (x, y, z) in 192-canonical units; MP's geometry expects
+    z_norm = z_canonical * scale_x / image_width"""
+
+    lmks_xy, lmks_3d = face_dict["landmarks_xy"], face_dict["landmarks_3d"]
+    aug = np.concatenate([lmks_3d[:, :2].astype(np.float64), np.ones((lmks_xy.shape[0], 1))], axis=1)
+    M, *_ = np.linalg.lstsq(aug, lmks_xy.astype(np.float64), rcond=None)
+    scale_x = float(np.linalg.norm(M[0]))
+    z_scale = scale_x / image_width if scale_x > 1e-6 else 1.0 / image_width
+
+    normalized = np.empty((lmks_xy.shape[0], 3), dtype=np.float32)
+    normalized[:, 0] = lmks_xy[:, 0] / image_width
+    normalized[:, 1] = lmks_xy[:, 1] / image_height
+    normalized[:, 2] = lmks_3d[:, 2] * z_scale
+    return solve_facial_transformation_matrix(
+        normalized, canonical_data["canonical_vertices"],
+        canonical_data["procrustes_indices"], canonical_data["procrustes_weights"],
+        image_width=image_width, image_height=image_height,
+    )