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demo.py

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+"""
+单文件：参考深度反投影到 ECEF → 投影到 query 相机 → 并排可视化对应点。
+运行: python demo.py
+"""
+
+from pathlib import Path
+
+import cv2
+import numpy as np
+import pyproj
+from scipy.spatial.transform import Rotation as R
+
+# 与 poses 里帧名一致（如 888.jpg）
+REF_STEM = "888"
+QUERY_STEM = "1047"
+NUM_SAMPLES = 100
+# 内参 fx, fy, cx, cy — 需与渲染一致
+FX, FY, CX, CY = 1931.7, 1931.7, 800.0, 600.0
+W, H = 1600, 1200
+
+ROOT = Path(__file__).resolve().parent
+ASSETS = ROOT / "assets"
+OUT = ROOT / "outputs"
+
+
+def wgs84_to_ecef(lon, lat, h):
+    t = pyproj.Transformer.from_crs(
+        "EPSG:4326",
+        {"proj": "geocent", "ellps": "WGS84", "datum": "WGS84"},
+        always_xy=True,
+    )
+    x, y, z = t.transform(lon, lat, h, radians=False)
+    return np.array([x, y, z], np.float64)
+
+
+def enu_to_ecef_rot(lon, lat):
+    lat_r, lon_r = np.radians(lat), np.radians(lon)
+    up = np.array(
+        [
+            np.cos(lon_r) * np.cos(lat_r),
+            np.sin(lon_r) * np.cos(lat_r),
+            np.sin(lat_r),
+        ]
+    )
+    east = np.array([-np.sin(lon_r), np.cos(lon_r), 0.0])
+    north = np.cross(up, east)
+    m = np.zeros((3, 3))
+    m[:, 0], m[:, 1], m[:, 2] = east, north, up
+    return m
+
+
+def pose_to_c2w_ecef(lon, lat, alt, roll, pitch, yaw):
+    r_pose = R.from_euler("xyz", [pitch, roll, yaw], degrees=True).as_matrix()
+    r_enu = enu_to_ecef_rot(lon, lat)
+    r_c2w = r_enu @ r_pose
+    t = wgs84_to_ecef(lon, lat, alt)
+    T = np.eye(4, dtype=np.float64)
+    T[:3, :3] = r_c2w
+    T[:3, 3] = t
+    T[:3, 1] *= -1
+    T[:3, 2] *= -1
+    return T
+
+
+def load_poses(path):
+    d = {}
+    for line in open(path, encoding="utf-8"):
+        p = line.split()
+        if p:
+            d[p[0]] = list(map(float, p[1:]))
+    return d
+
+
+def unproject_xy_depth(xy, z, T_c2w, fx, fy, cx, cy):
+    """像素 (x,y) + 深度 z → ECEF。"""
+    x, y = xy[:, 0], xy[:, 1]
+    z = z.astype(np.float64)
+    xc = z * (x - cx) / fx
+    yc = z * (y - cy) / fy
+    pc = np.stack([xc, yc, z], axis=1)
+    Rm, t = T_c2w[:3, :3], T_c2w[:3, 3]
+    return (Rm @ pc.T).T + t
+
+
+def project_ecef(pts, T_c2w, fx, fy, cx, cy):
+    """ECEF → 像素 (x,y)。"""
+    Rm, t = T_c2w[:3, :3], T_c2w[:3, 3]
+    Rinv = Rm.T
+    pc = (Rinv @ (pts - t).T).T
+    z = pc[:, 2]
+    u = fx * pc[:, 0] / z + cx
+    v = fy * pc[:, 1] / z + cy
+    return np.stack([u, v], axis=1)
+
+
+def vis_side_by_side(img_l, img_r, pl, pr, out_path):
+    h1, w1 = img_l.shape[:2]
+    h2, w2 = img_r.shape[:2]
+    pl = np.asarray(pl, dtype=np.float64)
+    pr = np.asarray(pr, dtype=np.float64)
+    in_l = (pl[:, 0] >= 0) & (pl[:, 0] < w1) & (pl[:, 1] >= 0) & (pl[:, 1] < h1)
+    in_r = (pr[:, 0] >= 0) & (pr[:, 0] < w2) & (pr[:, 1] >= 0) & (pr[:, 1] < h2)
+    m = in_l & in_r
+    pl, pr = pl[m], pr[m]
+
+    h = max(h1, h2)
+    w = w1 + w2
+    vis = np.zeros((h, w, 3), np.uint8)
+    vis[:h1, :w1] = img_l
+    vis[:h2, w1 : w1 + w2] = img_r
+    for i in range(len(pl)):
+        c = (int(37 * i % 255), int(91 * i % 255), int(17 * i % 255))
+        x1, y1 = int(pl[i, 0]), int(pl[i, 1])
+        x2, y2 = int(pr[i, 0]), int(pr[i, 1])
+        cv2.circle(vis, (x1, y1), 4, c, -1)
+        cv2.circle(vis, (x2 + w1, y2), 4, c, -1)
+        cv2.line(vis, (x1, y1), (x2 + w1, y2), c, 2)
+    OUT.mkdir(parents=True, exist_ok=True)
+    cv2.imwrite(str(out_path), vis)
+    print("Wrote", out_path)
+
+
+def load_bgr(rgb_path, depth_path):
+    im = cv2.imread(str(rgb_path))
+    if im is not None:
+        return im
+    d = cv2.imread(str(depth_path), cv2.IMREAD_UNCHANGED)
+    d = np.flipud(d[:, :, 0] if d.ndim == 3 else d).astype(np.float32)
+    v = d[d > 0]
+    lo, hi = (np.percentile(v, [2, 98]) if v.size else (0.0, 1.0))
+    g = (np.clip((d - lo) / (hi - lo + 1e-6), 0, 1) * 255).astype(np.uint8)
+    return cv2.applyColorMap(g, cv2.COLORMAP_VIRIDIS)
+
+
+def main():
+    pose_txt = ASSETS / "HongKong_seq2@500@30_60@cloudy.txt"
+    ref_d = ASSETS / f"{REF_STEM}_1.png"
+    q_rgb = ASSETS / f"{QUERY_STEM}_0.png"
+    q_d = ASSETS / f"{QUERY_STEM}_1.png"
+
+    poses = load_poses(pose_txt)
+    T_ref = pose_to_c2w_ecef(*poses[f"{REF_STEM}.jpg"])
+    T_q = pose_to_c2w_ecef(*poses[f"{QUERY_STEM}.jpg"])
+
+    depth = cv2.imread(str(ref_d), cv2.IMREAD_UNCHANGED)
+    depth = np.ascontiguousarray(np.flipud(depth))
+
+    rng = np.random.default_rng(0)
+    ex = rng.integers(0, W, size=NUM_SAMPLES)
+    ey = rng.integers(0, H, size=NUM_SAMPLES)
+    xy = np.column_stack([ex.astype(np.float64), ey.astype(np.float64)])
+    z = depth[ey, ex].astype(np.float64)
+
+    m = z > 0
+    xy, z = xy[m], z[m]
+
+    pts_ecef = unproject_xy_depth(xy, z, T_ref, FX, FY, CX, CY)
+    uv_q = project_ecef(pts_ecef, T_q, FX, FY, CX, CY)
+
+    ref_img = load_bgr(ASSETS / f"{REF_STEM}_0.png", ref_d)
+    q_img = load_bgr(q_rgb, q_d)
+
+    vis_side_by_side(q_img, ref_img, uv_q, xy, OUT / "reprojection_matches.png")
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+numpy>=1.20
+scipy>=1.7
+torch>=1.10
+opencv-python>=4.5
+pyproj>=3.3