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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+1_19_Idle/1_19_Idle_view2.mp4 filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/process-checkpoint.py

@@ -0,0 +1,126 @@
+import numpy as np
+import torch
+import os
+import re
+from packaging import version as pver
+
+def custom_meshgrid(*args):
+    # ref: https://pytorch.org/docs/stable/generated/torch.meshgrid.html?highlight=meshgrid#torch.meshgrid
+    if pver.parse(torch.__version__) < pver.parse('1.10'):
+        return torch.meshgrid(*args)
+    else:
+        return torch.meshgrid(*args, indexing='ij')
+
+
+def ray_condition(K, c2w, H, W, device, flip_flag=None):
+    # c2w: B, V, 4, 4
+    # K: B, V, 4
+
+    B, V = K.shape[:2]
+
+    j, i = custom_meshgrid(
+        torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
+        torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype),
+    )
+    i = i.reshape([1, 1, H * W]).expand([B, V, H * W]) + 0.5          # [B, V, HxW]
+    j = j.reshape([1, 1, H * W]).expand([B, V, H * W]) + 0.5          # [B, V, HxW]
+
+    n_flip = torch.sum(flip_flag).item() if flip_flag is not None else 0
+    if n_flip > 0:
+        j_flip, i_flip = custom_meshgrid(
+            torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
+            torch.linspace(W - 1, 0, W, device=device, dtype=c2w.dtype)
+        )
+        i_flip = i_flip.reshape([1, 1, H * W]).expand(B, 1, H * W) + 0.5
+        j_flip = j_flip.reshape([1, 1, H * W]).expand(B, 1, H * W) + 0.5
+        i[:, flip_flag, ...] = i_flip
+        j[:, flip_flag, ...] = j_flip
+
+    fx, fy, cx, cy = K.chunk(4, dim=-1)     # B,V, 1
+
+    zs = torch.ones_like(i)                 # [B, V, HxW]
+    xs = (i - cx) / fx * zs
+    ys = (j - cy) / fy * zs
+    zs = zs.expand_as(ys)
+
+    directions = torch.stack((xs, ys, zs), dim=-1)              # B, V, HW, 3
+    directions = directions / directions.norm(dim=-1, keepdim=True)             # B, V, HW, 3
+
+    rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2)        # B, V, HW, 3
+    rays_o = c2w[..., :3, 3]                                        # B, V, 3
+    rays_o = rays_o[:, :, None].expand_as(rays_d)                   # B, V, HW, 3
+    # c2w @ dirctions
+    rays_dxo = torch.cross(rays_o, rays_d)                          # B, V, HW, 3
+    plucker = torch.cat([rays_dxo, rays_d], dim=-1)
+    plucker = plucker.reshape(B, c2w.shape[1], H, W, 6)             # B, V, H, W, 6
+    # plucker = plucker.permute(0, 1, 4, 2, 3)
+    return plucker
+
+def plucker_from_camera_params(
+    K, R, t, W0, H0, cw_np,
+    H, W,
+    device='cpu',
+    normalize_intrinsics=True
+):
+    """
+    K, R, t, W0, H0 同 get_camera_params 返回，
+    cw_np 是 camera.matrix_world 转成的 numpy.ndarray (4×4)。
+    H, W 是你要的光线网格尺寸。
+    """
+    # 1) 归一化 intrinsics
+    if normalize_intrinsics:
+        fx = K[0,0] / W0
+        fy = K[1,1] / H0
+        cx = K[0,2] / W0
+        cy = K[1,2] / H0
+    else:
+        fx, fy = K[0,0], K[1,1]
+        cx, cy = K[0,2], K[1,2]
+
+    intrinsics = torch.tensor(
+        [[[fx, fy, cx, cy]]],
+        dtype=torch.float32,
+        device=device
+    )  # [1,1,4]
+
+    # 2) cw_np 就是 c2w
+    #    直接转成张量，不要再调用 Matrix 方法
+    c2w = torch.tensor(cw_np, dtype=torch.float32, device=device)
+    c2w = c2w.unsqueeze(0).unsqueeze(0)  # [1,1,4,4]
+
+    # 3) 调用 ray_condition
+    flip_flag = torch.zeros(1, dtype=torch.bool, device=device)
+    plucker = ray_condition(
+        intrinsics,
+        c2w,
+        H, W,
+        device=device,
+        flip_flag=flip_flag
+    )  # [1,1,H,W,6]
+
+    return plucker[0,0]  # [6, H, W]
+
+
+dir_path = "1_19_Idle"
+dict_files = sorted([os.path.join(dir_path, file) for file in os.listdir(dir_path) if file.endswith(".npy") and "dict" in file])
+
+
+for dict_file in dict_files:
+    params = np.load(dict_file, allow_pickle=True).item()
+    match_view = re.search(r'view(\d+)', str(dict_file))
+    if match_view:
+        view = int(match_view.group(1))
+    else:
+        view = None
+
+    plucker_embedding = plucker_from_camera_params(
+        params['K'], params['R'], params['t'], params['W0'], params['H0'], params['cw'],
+        480, 720,
+        device='cpu',
+        normalize_intrinsics=True
+    )
+    plucker_embedding = torch.tensor(plucker_embedding).permute(2,0,1)
+    o = params['cw'][:3, 3] 
+    r = np.linalg.norm(o)
+    plucker_embedding[:3] = plucker_embedding[:3] / r  
+    torch.save(plucker_embedding, f"{view}.pt")

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